From 9d5b20af91bfd2e69cc0348e3b7c7a3de19682ca Mon Sep 17 00:00:00 2001 From: David Brochart Date: Wed, 22 Mar 2017 17:49:24 +0100 Subject: [PATCH 01/53] Added live_traceplot function --- docs/source/notebooks/live_sample_plots.ipynb | 911 ++++++++++++++++++ pymc3/plots/__init__.py | 2 +- pymc3/plots/traceplot.py | 134 +++ pymc3/sampling.py | 2 +- 4 files changed, 1047 insertions(+), 2 deletions(-) create mode 100644 docs/source/notebooks/live_sample_plots.ipynb diff --git a/docs/source/notebooks/live_sample_plots.ipynb b/docs/source/notebooks/live_sample_plots.ipynb new file mode 100644 index 0000000000..a91c436d02 --- /dev/null +++ b/docs/source/notebooks/live_sample_plots.ipynb @@ -0,0 +1,911 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Live sample plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook illustrates how we can have live sample plots using the `live_traceplot` function with an `iter_sample` generator. It is based on the \"Coal mining disasters\" case study in the [Getting started notebook](https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/getting_started.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pymc3 as pm\n", + "from pymc3 import Model, Exponential, DiscreteUniform, Poisson\n", + "from pymc3.math import switch\n", + "\n", + "%matplotlib notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "disaster_data = np.ma.masked_values([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,\n", + " 3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,\n", + " 2, 2, 3, 4, 2, 1, 3, -999, 2, 1, 1, 1, 1, 3, 0, 0,\n", + " 1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,\n", + " 0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n", + " 3, 3, 1, -999, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,\n", + " 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1], value=-999)\n", + "year = np.arange(1851, 1962)\n", + "\n", + "if False:\n", + " plt.plot(year, disaster_data, 'o', markersize=8);\n", + " plt.ylabel(\"Disaster count\")\n", + " plt.xlabel(\"Year\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "with Model() as disaster_model:\n", + "\n", + " switchpoint = DiscreteUniform('switchpoint', lower=year.min(), upper=year.max(), testval=1900)\n", + "\n", + " # Priors for pre- and post-switch rates number of disasters\n", + " early_rate = Exponential('early_rate', 1)\n", + " late_rate = Exponential('late_rate', 1)\n", + "\n", + " # Allocate appropriate Poisson rates to years before and after current\n", + " rate = switch(switchpoint >= year, early_rate, late_rate)\n", + "\n", + " disasters = Poisson('disasters', rate, observed=disaster_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Assigned Metropolis to switchpoint\n", + "Assigned NUTS to early_rate_log_\n", + "Assigned NUTS to late_rate_log_\n", + "Assigned Metropolis to disasters_missing\n" + ] + }, + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
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');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width);\n", + " canvas.attr('height', height);\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "with disaster_model:\n", - " step = pm.assign_step_methods(disaster_model)\n", - " iter_sample = pm.iter_sample(10000, step)\n", - " trace = pm.live_traceplot(iter_sample, skip_first=100, refresh_every=200, roll_over=1000)" + " trace = sample(10000, live_plot=True, skip_first=100, refresh_every=300, roll_over=1000)" ] } ], diff --git a/pymc3/plots/__init__.py b/pymc3/plots/__init__.py index 419da00218..00a93b89b1 100644 --- a/pymc3/plots/__init__.py +++ b/pymc3/plots/__init__.py @@ -3,4 +3,4 @@ from .forestplot import forestplot from .kdeplot import kdeplot, kde2plot from .posteriorplot import plot_posterior -from .traceplot import traceplot, live_traceplot +from .traceplot import traceplot diff --git a/pymc3/plots/traceplot.py b/pymc3/plots/traceplot.py index 8f0752615a..463466be08 100644 --- a/pymc3/plots/traceplot.py +++ b/pymc3/plots/traceplot.py @@ -7,7 +7,7 @@ def traceplot(trace, varnames=None, transform=identity_transform, figsize=None, lines=None, combined=False, plot_transformed=False, grid=False, alpha=0.35, priors=None, - prior_alpha=1, prior_style='--', ax=None): + prior_alpha=1, prior_style='--', ax=None, live_plot=False, skip_first=0, refresh_every=100, roll_over=1000): """Plot samples histograms and values. Parameters @@ -45,6 +45,16 @@ def traceplot(trace, varnames=None, transform=identity_transform, figsize=None, Line style for prior plot. Defaults to '--' (dashed line). ax : axes Matplotlib axes. Accepts an array of axes, e.g.: + live_plot: bool + Flag for updating the current figure while sampling + skip_first : int + Number of first samples not shown in plots (burn-in). This affects + frequency and stream plots. + refresh_every : int + Period of plot updates (in sample number) + roll_over : int + Width of the sliding window for the sample stream plots: last roll_over + samples are shown (no effect on frequency plots). >>> fig, axs = plt.subplots(3, 2) # 3 RVs >>> pymc3.traceplot(trace, ax=axs) @@ -57,6 +67,8 @@ def traceplot(trace, varnames=None, transform=identity_transform, figsize=None, ax : matplotlib axes """ + trace = trace[skip_first:] + if varnames is None: varnames = get_default_varnames(trace, plot_transformed) @@ -70,9 +82,23 @@ def traceplot(trace, varnames=None, transform=identity_transform, figsize=None, prior = priors[i] else: prior = None + first_time = True for d in trace.get_values(v, combine=combined, squeeze=False): d = np.squeeze(transform(d)) d = make_2d(d) + d_stream = d + x0 = 0 + if live_plot: + x0 = skip_first + if first_time: + ax[i, 0].cla() + ax[i, 1].cla() + first_time = False + if roll_over is not None: + if len(d) >= roll_over: + x0 = len(d) - roll_over + skip_first + d_stream = d[-roll_over:] + width = len(d_stream) if d.dtype.kind == 'i': hist_objs = histplot_op(ax[i, 0], d, alpha=alpha) colors = [h[-1][0].get_facecolor() for h in hist_objs] @@ -82,7 +108,7 @@ def traceplot(trace, varnames=None, transform=identity_transform, figsize=None, ax[i, 0].set_title(str(v)) ax[i, 0].grid(grid) ax[i, 1].set_title(str(v)) - ax[i, 1].plot(d, alpha=alpha) + ax[i, 1].plot(range(x0, x0 + width), d_stream, alpha=alpha) ax[i, 0].set_ylabel("Frequency") ax[i, 1].set_ylabel("Sample value") @@ -103,140 +129,13 @@ def traceplot(trace, varnames=None, transform=identity_transform, figsize=None, lw=1.5, alpha=alpha) except KeyError: pass + if live_plot: + for j in [0, 1]: + ax[i, j].relim() + ax[i, j].autoscale_view(True, True, True) + ax[i, 1].set_xlim(x0, x0 + width) ax[i, 0].set_ylim(ymin=0) + if live_plot: + ax[0, 0].figure.canvas.draw() plt.tight_layout() return ax - - -def live_traceplot(iter_sample, skip_first=0, refresh_every=100, roll_over=None, - varnames=None, transform=identity_transform, figsize=None, - lines=None, combined=False, plot_transformed=False, - grid=False, alpha=0.35, priors=None, prior_alpha=1, - prior_style='--', ax=None): - """Plot samples histograms and values. - - Parameters - ---------- - - iter_sample : trace generator - skip_first : int - Number of first samples not shown in plots (burn-in). This affects - frequency and stream plots. - refresh_every : int - Period of plot updates (in sample number) - roll_over : int - Width of the sliding window for the sample stream plots: last roll_over - samples are shown (no effect on frequency plots). - varnames : list of variable names - Variables to be plotted, if None all variable are plotted - transform : callable - Function to transform data (defaults to identity) - figsize : figure size tuple - If None, size is (12, num of variables * 2) inch - lines : dict - Dictionary of variable name / value to be overplotted as vertical - lines to the posteriors and horizontal lines on sample values - e.g. mean of posteriors, true values of a simulation. - If an array of values, line colors are matched to posterior colors. - Otherwise, a default red line - combined : bool - Flag for combining multiple chains into a single chain. If False - (default), chains will be plotted separately. - plot_transformed : bool - Flag for plotting automatically transformed variables in addition to - original variables (defaults to False). - grid : bool - Flag for adding gridlines to histogram. Defaults to True. - alpha : float - Alpha value for plot line. Defaults to 0.35. - priors : iterable of PyMC distributions - PyMC prior distribution(s) to be plotted alongside posterior. Defaults - to None (no prior plots). - prior_alpha : float - Alpha value for prior plot. Defaults to 1. - prior_style : str - Line style for prior plot. Defaults to '--' (dashed line). - ax : axes - Matplotlib axes. Accepts an array of axes, e.g.: - - >>> fig, axs = plt.subplots(3, 2) # 3 RVs - >>> pymc3.traceplot(trace, ax=axs) - - Creates own axes by default. - - Returns - ------- - - MultiTrace object with access to sampling values - - """ - for it, trace in enumerate(iter_sample): - if it > skip_first: - if it == skip_first + 1: - ax = traceplot(trace[skip_first:], varnames, transform, figsize, - lines, combined, plot_transformed, grid, - alpha, priors, prior_alpha, prior_style, - ax) - if varnames is None: - varnames = get_default_varnames(trace, plot_transformed) - x0 = skip_first - elif (it - skip_first) % refresh_every == 0: - for i, v in enumerate(varnames): - if priors is not None: - prior = priors[i] - else: - prior = None - first_time = True - for d in trace[skip_first:].get_values(v, combine=combined, squeeze=False): - if first_time: - ax[i, 0].cla() - ax[i, 1].cla() - first_time = False - d = np.squeeze(transform(d)) - d = make_2d(d) - if roll_over is None: - d_stream = d - else: - d_stream = d[-roll_over:] - width = len(d_stream) - if d.dtype.kind == 'i': - hist_objs = histplot_op(ax[i, 0], d, alpha=alpha) - colors = [h[-1][0].get_facecolor() for h in hist_objs] - else: - artists = kdeplot_op(ax[i, 0], d, prior, prior_alpha, prior_style)[0] - colors = [a[0].get_color() for a in artists] - ax[i, 0].set_title(str(v)) - ax[i, 0].grid(grid) - ax[i, 1].set_title(str(v)) - ax[i, 1].plot(range(x0, x0 + width), d_stream, alpha=alpha) - - ax[i, 0].set_ylabel("Frequency") - ax[i, 1].set_ylabel("Sample value") - - if lines: - try: - if isinstance(lines[v], (float, int)): - line_values, colors = [lines[v]], ['r'] - else: - line_values = np.atleast_1d(lines[v]).ravel() - if len(colors) != len(line_values): - raise AssertionError("An incorrect number of lines was specified for " - "'{}'. Expected an iterable of length {} or to " - " a scalar".format(v, len(colors))) - for c, l in zip(colors, line_values): - ax[i, 0].axvline(x=l, color=c, lw=1.5, alpha=0.75) - ax[i, 1].axhline(y=l, color=c, - lw=1.5, alpha=alpha) - except KeyError: - pass - - for j in [0, 1]: - ax[i, j].relim() - ax[i, j].autoscale_view(True, True, True) - ax[i, 1].set_xlim(x0, x0 + width) - ax[i, 0].set_ylim(ymin=0) - ax[0, 0].figure.canvas.draw() - if roll_over is not None: - if width >= roll_over: - x0 += refresh_every - return trace diff --git a/pymc3/sampling.py b/pymc3/sampling.py index 60caa4eacf..e6d540f081 100644 --- a/pymc3/sampling.py +++ b/pymc3/sampling.py @@ -11,6 +11,8 @@ from .step_methods import (NUTS, HamiltonianMC, Metropolis, BinaryMetropolis, BinaryGibbsMetropolis, CategoricalGibbsMetropolis, Slice, CompoundStep) +from .plots.utils import identity_transform +from .plots.traceplot import traceplot from tqdm import tqdm import warnings @@ -18,7 +20,7 @@ import sys sys.setrecursionlimit(10000) -__all__ = ['assign_step_methods', 'sample', 'iter_sample', 'sample_ppc', 'init_nuts'] +__all__ = ['sample', 'iter_sample', 'sample_ppc', 'init_nuts'] def assign_step_methods(model, step=None, methods=(NUTS, HamiltonianMC, Metropolis, @@ -85,7 +87,7 @@ def assign_step_methods(model, step=None, methods=(NUTS, HamiltonianMC, Metropol def sample(draws, step=None, init='ADVI', n_init=200000, start=None, trace=None, chain=0, njobs=1, tune=None, progressbar=True, - model=None, random_seed=-1): + model=None, random_seed=-1, live_plot=False, **kwargs): """ Draw a number of samples using the given step method. Multiple step methods supported via compound step method @@ -142,6 +144,8 @@ def sample(draws, step=None, init='ADVI', n_init=200000, start=None, model : Model (optional if in `with` context) random_seed : int or list of ints A list is accepted if more if `njobs` is greater than one. + live_plot: bool + Flag for live plotting the trace while sampling Returns ------- @@ -175,7 +179,9 @@ def sample(draws, step=None, init='ADVI', n_init=200000, start=None, 'tune': tune, 'progressbar': progressbar, 'model': model, - 'random_seed': random_seed} + 'random_seed': random_seed, + 'live_plot': live_plot, + **kwargs} if njobs > 1: sample_func = _mp_sample @@ -187,15 +193,27 @@ def sample(draws, step=None, init='ADVI', n_init=200000, start=None, def _sample(draws, step=None, start=None, trace=None, chain=0, tune=None, - progressbar=True, model=None, random_seed=-1): + progressbar=True, model=None, random_seed=-1, live_plot=False, + **kwargs): + live_plot_args = {'skip_first': 0, 'refresh_every': 100} + live_plot_args = {arg: kwargs[arg] if arg in kwargs else live_plot_args[arg] for arg in live_plot_args} + skip_first = live_plot_args['skip_first'] + refresh_every = live_plot_args['refresh_every'] + sampling = _iter_sample(draws, step, start, trace, chain, tune, model, random_seed) if progressbar: sampling = tqdm(sampling, total=draws) try: strace = None - for strace in sampling: - pass + for it, strace in enumerate(sampling): + if live_plot: + if it >= skip_first: + trace = MultiTrace([strace]) + if it == skip_first: + ax = traceplot(trace, live_plot=False, **kwargs) + elif (it - skip_first) % refresh_every == 0 or it == draws - 1: + traceplot(trace, ax=ax, live_plot=True, **kwargs) except KeyboardInterrupt: pass finally: From bdd25afa0b50942730261fcea756828433c3fefa Mon Sep 17 00:00:00 2001 From: Junpeng Lao Date: Tue, 7 Mar 2017 16:15:03 +0100 Subject: [PATCH 04/53] Add tutorial to detect sampling problems (#1866) * Expand sampler-stats.ipynb example include model diagnose from case study example in Stan http://mc-stan.org/documentation/case-studies/divergences_and_bias.html * Sampler Diagnose for NUTS * descriptive annotation and axis labels * Fix typos * PEP8 styling * minor updates 1, add example to examples.rst 2, original content in Markdown code block --- docs/source/examples.rst | 1 + ...ng_biased_Inference_with_Divergences.ipynb | 1060 +++++++++++++++++ docs/source/notebooks/sampler-stats.ipynb | 86 +- 3 files changed, 1104 insertions(+), 43 deletions(-) create mode 100644 docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 42051dc854..6c0a8a125e 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -9,6 +9,7 @@ Howto .. toctree:: notebooks/sampler-stats.ipynb + notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb notebooks/posterior_predictive.ipynb notebooks/howto_debugging.ipynb notebooks/LKJ.ipynb diff --git a/docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb b/docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb new file mode 100644 index 0000000000..c4159cba15 --- /dev/null +++ b/docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb @@ -0,0 +1,1060 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Diagnosing Biased Inference with Divergences\n", + "\n", + "#### PyMC3 port of [Michael Betancourt's post on ms-stan](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html). For detailed explanation of the underlying mechanism please check [the original post](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html) and Betancourt's [excellent paper](https://arxiv.org/abs/1701.02434). \n", + " \n", + "Bayesian statistics is all about building your model and estimating the parameters in the model. However, due to limitations in our current mathematical understanding and computation capacity, naive or direct parameterization of our probability model often ran into problem ([you can check out Thomas Wiecki's blog post on the same issue in PyMC3](http://twiecki.github.io/blog/2017/02/08/bayesian-hierchical-non-centered/)). Suboptimal parameterization is often lead to slow sampling, and more problematic, biased MCMC estimators. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### More formally, as explained in [the original post](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html) (in markdown block, same below): \n", + "> \n", + "Markov chain Monte Carlo (MCMC) approximates expectations with respect to a given target distribution, $$ \\mathbb{E}{\\pi} [ f ] = \\int \\mathrm{d}q \\, \\pi (q) \\, f(q), $$ using the states of a Markov chain, ${q{0}, \\ldots, q_{N} }$, $$ \\mathbb{E}{\\pi} [ f ] \\approx \\hat{f}{N} = \\frac{1}{N + 1} \\sum_{n = 0}^{N} f(q_{n}). $$ These estimators, however, are guaranteed to be accurate only asymptotically as the chain grows to be infinitely long, $$ \\lim_{N \\rightarrow \\infty} \\hat{f}{N} = \\mathbb{E}{\\pi} [ f ]. $$\n", + "> \n", + "To be useful in applied analyses, we need MCMC estimators to converge to the true expectation values sufficiently quickly that they are reasonably accurate before we exhaust our finite computational resources. This fast convergence requires strong ergodicity conditions to hold, in particular geometric ergodicity between a Markov transition and a target distribution. Geometric ergodicity is usually the necessary condition for MCMC estimators to follow a central limit theorem, which ensures not only that they are unbiased even after only a finite number of iterations but also that we can empirically quantify their precision using the MCMC standard error.\n", + "> \n", + "Unfortunately, proving geometric ergodicity theoretically is infeasible for any nontrivial problem. Instead we must rely on empirical diagnostics that identify obstructions to geometric ergodicity, and hence well-behaved MCMC estimators. For a general Markov transition and target distribution, the best known diagnostic is the split $\\hat{R}$ statistic over an ensemble of Markov chains initialized from diffuse points in parameter space; to do any better we need to exploit the particular structure of a given transition or target distribution.\n", + "> \n", + "Hamiltonian Monte Carlo, for example, is especially powerful in this regard as its failures to be geometrically ergodic with respect to any target distribution manifest in distinct behaviors that have been developed into sensitive diagnostics. One of these behaviors is the appearance of divergences that indicate the Hamiltonian Markov chain has encountered regions of high curvature in the target distribution which it cannot adequately explore.\n", + "\n", + "In this notebook we aim to replicated the identification of divergences sample and the underlying pathologies in `PyMC3` similar to [the original post](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sb\n", + "import pandas as pd\n", + "import pymc3 as pm \n", + "\n", + "%config InlineBackend.figure_format = 'retina'\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Eight Schools Model\n", + "> \n", + "The hierarchical model of the the Eight Schools dataset (Rubin 1981) as seen in `Stan`:\n", + "$$\\mu \\sim \\mathcal{N}(0, 5)$$\n", + "$$\\tau \\sim \\text{Half-Cauchy}(0, 5)$$\n", + "$$\\theta_{n} \\sim \\mathcal{N}(\\mu, \\tau)$$\n", + "$$y_{n} \\sim \\mathcal{N}(\\theta_{n}, \\sigma_{n}),$$ \n", + "> \n", + "where $n \\in \\{1, \\ldots, 8 \\}$ and the $\\{ y_{n}, \\sigma_{n} \\}$ are given as data. \n", + "> \n", + "Inferring the hierarchical hyperparameters, $\\mu$ and $\\sigma$, together with the group-level parameters, $\\theta_{1}, \\ldots, \\theta_{8}$, allows the model to pool data across the groups and reduce their posterior variance. Unfortunately, the direct *centered* parameterization also squeezes the posterior distribution into a particularly challenging geometry that obstructs geometric ergodicity and hence biases MCMC estimation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Data of the Eight Schools Model\n", + "J = 8\n", + "y = np.asarray([28, 8, -3, 7, -1, 1, 18, 12], dtype=float)\n", + "sigma = np.asarray([15, 10, 16, 11, 9, 11, 10, 18], dtype=float)\n", + "# tau = 25." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A Centered Eight Schools Implementation \n", + "\n", + "`Stan` model:\n", + "\n", + "```C\n", + "data {\n", + " int J;\n", + " real y[J];\n", + " real sigma[J];\n", + "}\n", + "\n", + "parameters {\n", + " real mu;\n", + " real tau;\n", + " real theta[J];\n", + "}\n", + "\n", + "model {\n", + " mu ~ normal(0, 5);\n", + " tau ~ cauchy(0, 5);\n", + " theta ~ normal(mu, tau);\n", + " y ~ normal(theta, sigma);\n", + "}\n", + "```\n", + "Similarly, we can easily implemented it in `PyMC3`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "with pm.Model() as Centered_eight:\n", + " mu = pm.Normal('mu', mu=0, sd=5)\n", + " tau = pm.HalfCauchy('tau', beta=5)\n", + " theta = pm.Normal('theta', mu=mu, sd=tau, shape=J)\n", + " obs = pm.Normal('obs', mu=theta, sd=sigma, observed=y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Unfortunately, this direct implementation of the model exhibits a pathological geometry that frustrates geometric ergodicity. Even more worrisome, the resulting bias is subtle and may not be obvious upon inspection of the Markov chain alone. To understand this bias, let's consider first a short Markov chain, commonly used when computational expediency is a motivating factor, and only afterwards a longer Markov chain." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### A Dangerously-Short Markov Chain" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Assigned NUTS to mu\n", + "Assigned NUTS to tau_log_\n", + "Assigned NUTS to theta\n", + "100%|██████████| 600/600 [00:01<00:00, 354.50it/s]\n" + ] + } + ], + "source": [ + "with Centered_eight:\n", + " short_trace = pm.sample(600, init=None, njobs=2, tune=500)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the [the original post](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html) a single chain of 1200 sample is applied. However, since split $\\hat{R}$ is not implemented in `PyMC3` we fit 2 chains with 600 sample each instead. \n", + "The Gelman-Rubin diagnostic $\\hat{R}$ doesn’t indicate any problems (value close to 1) and the effective sample size per iteration is reasonable" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'theta': array([ 1.01466455, 1.00153203, 1.00351269, 1.009334 , 0.99977168,\n", + " 1.00057635, 1.0159455 , 1.00228008]), 'tau_log_': 1.0439591442751275, 'mu': 1.0081977447110899, 'tau': 1.0164411689259558}\n", + "\n", + "{'theta': array([ 274., 384., 311., 358., 282., 343., 263., 409.]), 'tau_log_': 59.0, 'mu': 182.0, 'tau': 88.0}\n" + ] + } + ], + "source": [ + "print(pm.diagnostics.gelman_rubin(short_trace))\n", + "print('')\n", + "print(pm.diagnostics.effective_n(short_trace))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Moreover, the trace plots all look fine. Let's consider, for example, the hierarchical standard deviation $\\tau$, or more specifically, its logarithm, $log(\\tau)$. Because $\\tau$ is constrained to be positive, its logarithm will allow us to better resolve behavior for small values. Indeed the chains seems to be exploring both small and large values reasonably well," + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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tnommOHUuwo7NPYSCyqVYa84GOHaXB7LKj4MFfgGu8oyr8kCWMrJanwJZIiLS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bE/l696vNKsQvV5hab2tBsN8RTs6eIi7FScmddv/SqMCe3HZMz/uB0wbR77elIylEUcI0/RNZ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\n2lNFz/vnuGP8WeDTQE/h+zhwq7X2qXX7oTa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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 135, + "width": 857 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the trace of log(tau)\n", + "pm.traceplot(short_trace, varnames=['tau_log_'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Unfortunately, the resulting estimate for the mean of $log(\\tau)$ is strongly biased away from the true value, here shown in grey." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SJEmSJEmSJKlzSnIEX0TsD+zfYnd1RJxT8PimlNImYA6QgP1audT0fDu/tX5S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\nNWxqD1MTcC3H9IWYE2OyDUmxrKMbJvFknp7hNL0jE/SOZJjIFZnIlBb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2WvE4bTTUenA5\nbJimic1qIRJysaElRE1AqguJmTnsVja0htjQGroyNj5RoHt4gp7JfpTdwxOMTxRWcJY3D90w6RlJ\nL7oXo82qXClnenXfQfc1/QjdThvFko5hmNgmF1dc+X6lZKkyZRGFRVEmAyrOZckKXc10o5IVfr6v\nkhF7aahyrpTKxrLOw6IoOB0WrJZKSVyUyrnttFmwWi3ki/qCe3JWQ7FkMDyWZXgO718CXgeRoIua\ngAvfZB/OgNdBwGvHNCsBVN0wSaaLOGwWtm+IXgnwiaVRrQDfXcA3ZwnuAaBpmqaq6reBD1fpmELM\nycgM/feiIXnzLK5vXUuQh7Y1su/Y4JTxnpE0f/39k3z+6W1zLkujGwb/47snZqyhXRtw8q9+cTuN\ntd6qzFsIIYQQQgix/KIhN0/c38GH7mtnIJ7lXG+CWCJHOlcinSsxkSthURQiQdfkV6WPVyToJux3\nygLUOaoLe3jvrjbeu6sNqJRM7YtluNCf5MLA4nqYLVQ05KK+xsP65iBb19TSVOvFYbfIRXuxZMKT\nWTaX+w4CpDJFuocrAb+B0QyxRI5coTz5pVMo3bh3qN9jp8bvwuWwEvQ5aKjxYLe9E3zIFSrZw7lC\nmWyhTGYyk7hYWt5gyXIo6yYT2UpvwqXkddnwexykcyUuV9mzWS0EvJWejUGvg4DPQchbKRUbDbmI\nhNw4F5BFdbsyTZNcQWciW2QkkWNoLMvAaIbekUpPyuV4fPrcdppqPTTUemms9dBY66G+xlPpU2u3\nTgnyGqaJAtNeIwzDZHyiwEgix8h4dvJ7jmSmSKGoky9WzuVqla1eqFSmSCpT5OJAak7bf++1Lr78\n+Ydpbwws8cxWr2oF+PzAzB1cp+oBQjfcSogqis0Y4JOVkOLGfuHRdRy9EJ9W/uLohTjff7WLjz60\nZk73862XLnDi4ti0ca/LJsE9IYQQQgghbiOKUikP1xyR9/jLwW6z0tkYoLMxwLsnx5LpAgPxLImJ\nAr0jabqHJ+gemiA7z+yIoNdBW72fgMdOSTfIF3UsikI44CTsc9Ic8bK2OUjA66j+f0yIeQp4Hdyx\nppY71tTOeLtuGFf6X+YLOrph4nRY0XUDt9NGwOvAZl1Y7zrDMCnpBqVy5ausG5WMNquFbKGMAlgs\nCqWygdfnoljSGU9m0XWTXKGMy2FFuap8Zlk3KJYMRsZz9I9mGJvIUyzppDKlOQUqbyWVIOn056Zk\npkjvdfYLeB1EQ65K0C/oJhpyUxd2U1/jIeC5tbICTdMkX6wEriayJcYnS2fmi5U+mUAlU3vyh5Ju\nVvqmpip98cZShWUvqem0W1HbQmxfH+HOtZF5tfKZLVnAYlGoDbqoDbrY1B6edf90rkRiovJ/Niaz\n5YzJL9000XWTbKFEOltiPF0gNp5jJJFjeDxHobj850+uoPPc/kv8xse2LfuxV4tqBfjigDqH7dZO\nbivEspkpwCelTsRc+Nx2fvvpbXzp629PW/Hz/7N352GWV/Wd+N/VXb1U7w10A4JsLgdFjeCCgkaC\nRk1i3M0yoII4Y6KZaNA46EggShKdMCZRxmgUNS75JcYVR6MmIUhUouLKGD2K7AjYdNN7V/VS9/fH\nvYVFLd1VdW91eeu+Xs9Tz7frnPP9nnPL+j72028+51zx5Zuyb7iRZz3h+En/Ejy0e18+8Pmaa743\n/r9/WNS/IK98wS8I9wAAADpo9YolWb2i+Y+tj2+1NRqNbNwymJvv2pYbfrI13/3xxmzYsivLlvRn\n7colWbNiSda0rkevW57jjliVNSsWd9U/ksP+LFywIMuWLsiypYs6/uwFC/qyZMHCCavKxsaN7Z7F\nNVJJuLcVut+xcWdu27D93jB/y/beOJ9wpIrqx7ePr6IaWNKfIw5Z1vw6dFkOXzuQ1csXZ/nSRVk+\nsCjLW+cSzqbhRiM7B/dm287d91ZCbtvV/PP21p+379yTrTt2N4Onn9PgdvGiBTl01dLm1+qlOXzt\nsjzo6NU59oiVMw7E2zVyduR0NRqN3L1lMDeP2tr37s27cs+2oeye5S1L+/vn5mfVKzoV8H0pyXNL\nKWfUWq+aaEAp5Ywkv5Hk0x2aE6bkp/eo4GPmjj9yVV726yfl7R+/blzfZ665OV/7/l35r884KQ88\nevV9+oaHG3nbx76b7998z7j7+vqS33nmSXngUavH9QEAANBZfX19OWzNQA5bM5BHlfV5wS89cK6X\nBMzAyLl3I445fGVOzeH3fr9l+1BuvqsZ9m3csiu79w5nz57h7N47nOFGI4sWLsjChX33Vozt2LUn\nu4b2pq+vLwsX9t1bVXiwK8I6adfQ3tx4x9bceMfkWygu6l+Q5Uv7W4Hfonv/fOiqpVnfqgZct3Yg\nK6dwRuDefcO5bcP23HjHttx4x9bcdMfW3LFx58/1z3Bx/4KsWdmsxl6zckkOXzuQE+63OuvWLM3a\nlUuyeNHCKR/L0y36+vpa27wO5NEnrr+3vdFoZMfg3mzaOph7tg1l07ahbNo6mLu3DObuLc2qv33D\nzSrLzduGpn3u7YqBRfn1J0xtBzRmplMB35+lebbev5RSrkxyTZKfJulLsj7J6UmelGR3kj/t0Jww\nJSr4aNfJD16XZ55+XK748k3j+jZsHsybP/zN/LdnPjSPfcjP/lL5z9feOmG4lyQvOOOBOfnB62Zr\nuQAAAAA9Z/WKJXnEiiV5xAMm3qp0KhqNRnbvHf7ZuYOjzjIcOYdwcPfee7c77e9fkP6Ffdm7r5E9\ne/dl775G9u4dvnfb0r37hu8TijSGG9k5tDcbNg9m7765Ob9wz97hbN6+O5sPUPG4dPHCrF87kCMP\nXZ6j1zV3oNo33KzO275rT+7ctDO33LV9zj7HVCxf2p8HHLU6DzxqdR509OocvX5Fli3pV6Hd0tfX\nd29V4DGHr9zv2L37hrNp21A2bt6Vzdt3N8/i3LUnG7cONrexXtCXBX3NbUgHlvTnfoctz2Mesj7r\nD1l2kD5Nb+pIwFdr/VYp5XlJ3pvkKUmePKp75G25Pcm5tdbvdGJOmIrhRvOA0rEOXb10DlZDN3vm\nE47PLXdtz7evv3tc33CjkXd+6ntJknLM2uwa2puPX33DhM85/eFH5GmPvf+srhUAAACA6evr68uS\nRc0tR9esmPrZatM1PNzIxq2D2TW09z5nIA7u3putO/Zky46hbNmxO1u2786mrYPZsHlXNmwezKZt\ng/eeTTfbBnfvyy13bc8td23PVw/OlDOyeNGCrBxYlDUrluSIQ5Zl/SHLcvS65bn/+hU5dNVSYV6H\n9C9c0KzwVDjzc6VTFXyptX6mlHJMkqcneXSSdWmegfnTJF9P8rla6/RONIY2bd2xe1xJ+LIx5fww\nFQv6+vLSZzw0f/3J6/K9myauzBsJ+SbzvCedkF953LH+YgEAAADQwxYs6JvwCKEVA4ty2OrJA5S9\n+4azcVTgt2Hzrmy4Z1fuumdn7rpnV/bM8nlqs6F/4YKsXNasIlu9YnHWrFhy7/ago/8Jra8v6Utf\nVi1fnENWLskhq5Zm/doB/85LT+vob3+tdSjJp1pfMOc2bh0c13bIqtn7r2+Y35Yt7c/5v/nIfPv6\nu/OJq2/MbRu2T/nepzzq6Pza44+bvcUBAAAAMK/1L1yQw9cuy+Frx297ONxoZNOWwdx5z87cuXFn\n7ty0Mxu3DGZHayvFHYPN68E4H2/p4oVZuWxRVi5bnBUDi+7988qBRVkx6s+HrFqaNSsW+4/hYYbE\n28xr92wdvz3nIatsz8nM9fX15eQHrcuJx6zNm/722ty5aecB71kxsCjP+UUHygIAAAAwOxb09eWw\nNQM5bM1AHnb8xOcQNhqNDO3Zlx27mufo7RxsBn9bd+7Ohs278tN7Wl+bp14NuHLZohx/5Kocd8TK\nHH/kqhx/5KqsWr64kx8NmISAj3lt4go+AR/tG1jSn1c852F528e+mw2bx/+ejfaMxx9ruwAAAAAA\n5lRfX1+WLu7P0sX9OXT15P9GOtxoZMv23blz447ceOe2bN2xOwsX9GXhwr4MLO7P8lZVnnPuYG75\nF2fmtU0TVfCttEUnnXHUuhW5+NzH5t+/85N858cbs3HrYLbt3JNdQz87bvSBR6/OL51y1ByuEgAA\nAACmbkFfX9auXJK1K5fkIccdMtfLASYh4GNe2zRBBd+hKvjooIEl/XnqY4/JUx97zL1tt/50e75/\n8z0ZWLwwj3nI+izqXziHKwQAAAAAYL4R8DGvbdo20RadKviYXfdfvyL3X79irpcBAAAAAMA8tWCu\nFwCz6Y6NO8e1rVXBBwAAAAAAdDEBH/PWt6+/O4O7992nrS/J2hUq+AAAAAAAgO7VsS06SymvTPLi\nJA9OMrCfoY1aq61BmVV3b9mVt330u+PaVy1fnEX9cm0AAAAAAKB7dSRoK6X8jyR/mmaB1IFMZQy0\n5Zs/vHvC9mOPWHmQVwIAAAAAANBZnaqke2mSwSTnJPlCrXVLh54LM3LHxh0Ttv/66ccd3IUAAAAA\nAAB0WKcCvmOSvKfW+o8det6UlFIWJ7kkyWuSXF1rPWMa956W5MIkj0tzS9EfJnl3kstqrY0xYx+a\n5I1JnpRkVZKbk3woyZtrrbvb/yR02p0bd45r+80zH5gH3G/1HKwGAAAAAACgczoV8G1McluHnjUl\npZSS5O/SPPNvWtt+llLOTPJPSW5NcnGSTUmeleRtSR6Q5FWjxp6U5CtJdiW5NM3PeUbrvlOSPLud\nz8HsuGPT+IDvEQ84dA5WAgAAAAAA0FmdCviuTHJah551QKWUtUm+meRHSR6d5AfTfMQ70txS9Im1\n1jtabR8spXwyye+XUt5Xa/1Oq/2tSVYkeUKt9bpW24dLKTuSvLKU8sxa6xXtfB46a+eaxU9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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 277, + "width": 892 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the estimate for the mean of log(τ) cumulating mean\n", + "logtau = short_trace['tau_log_']\n", + "mlogtau = [np.mean(logtau[:i]) for i in np.arange(1, len(logtau))]\n", + "plt.figure(figsize=(15, 4))\n", + "plt.axhline(0.7657852, lw=2.5, color='gray')\n", + "plt.plot(mlogtau, lw=2.5)\n", + "plt.ylim(0, 2)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('MCMC mean of log(tau)')\n", + "plt.title('MCMC estimation of log(tau)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Hamiltonian Monte Carlo, however, is not so oblivious to these issues as 2% of the iterations in our lone Markov chain ended with a divergence." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of Divergent 14\n", + "Percentage of Divergent 0.02333\n" + ] + } + ], + "source": [ + "# display the total number and percentage of divergent\n", + "divergent = short_trace['diverging']\n", + "print('Number of Divergent %d' % divergent.nonzero()[0].size)\n", + "divperc = divergent.nonzero()[0].size/len(short_trace)\n", + "print('Percentage of Divergent %.5f' % divperc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Even with a single short chain these divergences are able to identity the bias and advise skepticism of any resulting MCMC estimators.\n", + "\n", + "> Additionally, because the divergent transitions, here shown here in green, tend to be located near the pathologies we can use them to identify the location of the problematic neighborhoods in parameter space." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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i0nIUFBMRERERERERkZajoJiIiIiIiIiIiLQcBcVERERERERERKTlKCgmIiIi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\nAACAzhGKAQAAANA5QjEAAAAAO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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 610 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot between log(tau) and theta[0]\n", + "# for the identifcation of the problematic neighborhoods in parameter space\n", + "theta_trace = short_trace['theta']\n", + "theta0 = theta_trace[:, 0]\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(theta0[divergent == 0], logtau[divergent == 0], color='r')\n", + "plt.scatter(theta0[divergent == 1], logtau[divergent == 1], color='g')\n", + "plt.axis([-20, 50, -6, 4])\n", + "plt.ylabel('log(tau)')\n", + "plt.xlabel('theta[0]')\n", + "plt.title('scatter plot between log(tau) and theta[1]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the current example, the pathological samples from the trace is not necessary concentrated at the funnel (unlike in `Stan`), the follow figure is from the [the original post](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html) as comparison.\n", + "\n", + " \n", + "\n", + "In `Stan` the divergences are clustering at small values of $\\tau$ where the hierarchical distribution, and hence all of the group-level $\\theta_{n}$, are squeezed together. Eventually this squeezing would yield the funnel geometry infamous to hierarchical models, but here it appears that the Hamiltonian Markov chain is diverging before it can fully explore the neck of the funnel.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# A small wrapper function for displaying the MCMC sampler diagnostics as above\n", + "def report_trace(trace):\n", + " # plot the trace of log(tau)\n", + " pm.traceplot(trace, varnames=['tau_log_'])\n", + " \n", + " # plot the estimate for the mean of log(τ) cumulating mean\n", + " logtau = trace['tau_log_']\n", + " mlogtau = [np.mean(logtau[:i]) for i in np.arange(1, len(logtau))]\n", + " plt.figure(figsize=(15, 4))\n", + " plt.axhline(0.7657852, lw=2.5, color='gray')\n", + " plt.plot(mlogtau, lw=2.5)\n", + " plt.ylim(0, 2)\n", + " plt.xlabel('Iteration')\n", + " plt.ylabel('MCMC mean of log(tau)')\n", + " plt.title('MCMC estimation of log(tau)')\n", + " plt.show()\n", + " \n", + " # display the total number and percentage of divergent\n", + " divergent = trace['diverging']\n", + " print('Number of Divergent %d' % divergent.nonzero()[0].size)\n", + " divperc = divergent.nonzero()[0].size/len(trace)\n", + " print('Percentage of Divergent %.5f' % divperc)\n", + " \n", + " # scatter plot between log(tau) and theta[0]\n", + " # for the identifcation of the problematic neighborhoods in parameter space\n", + " theta_trace = trace['theta']\n", + " theta0 = theta_trace[:, 0]\n", + " plt.figure(figsize=(10, 6))\n", + " plt.scatter(theta0[divergent == 0], logtau[divergent == 0], color='r')\n", + " plt.scatter(theta0[divergent == 1], logtau[divergent == 1], color='g')\n", + " plt.axis([-20, 50, -6, 4])\n", + " plt.ylabel('log(tau)')\n", + " plt.xlabel('theta[0]')\n", + " plt.title('scatter plot between log(tau) and theta[1]')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### A Safer, Longer Markov Chain \n", + "> \n", + "Given the potential insensitivity of split $\\hat{R}$ on single short chains, `Stan` recommend always running multiple chains as long as possible to have the best chance to observe any obstructions to geometric ergodicity. Because it is not always possible to run long chains for complex models, however, divergences are an incredibly powerful diagnostic for biased MCMC estimation. " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Assigned NUTS to mu\n", + "Assigned NUTS to tau_log_\n", + "Assigned NUTS to theta\n", + "100%|██████████| 5000/5000 [00:11<00:00, 444.46it/s]\n" + ] + }, + { + "data": { + "image/png": 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ZQCW1bnvIr6EldWYsMbF42XFtUla64vLFC4GXibkkjpu/fHkswt3G+gYi5+Nb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\npFTo66jhyKWl0J8nqSXpcPsodv8aVc7v2UiaQDRFR3yWU7f22yThAk1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wNhsnlqxdMV80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\nTbNDRHQCFwL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p0+tVGSG4a7J0dXx4lDbvBy4EPgx8lVL1eDNwCPCVzBytryRJkiRJkiRJktRT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oUQPh5COKucder4VlXhCD5JkiRJkiRJknR8KWvAN1LMey+wGPgN4JfAHxa2Ulk01GUmPG7AJ0mS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\nLuAruAGoA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ERJqOgmIiIiIiIiIiItJ0FBQTEREREREREZGmo6CYiIiIiIiIiIg0nZZ6N0BE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\nQOMIxQAAAABoHKEYAAAAAI0jFAMAAACgc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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 610 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "with Centered_eight:\n", + " longer_trace = pm.sample(5000, init=None, njobs=2, tune=1000)\n", + " \n", + "report_trace(longer_trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'theta': array([ 1.00037593, 0.99990022, 1.00114791, 0.9999347 , 1.00259776,\n", + " 1.00022565, 1.00152861, 0.99992059]), 'tau_log_': 1.0398125400857179, 'mu': 1.0006934326362604, 'tau': 1.0208315582727976}\n", + "\n", + "{'theta': array([ 1953., 2127., 2155., 2106., 1382., 2054., 988., 2195.]), 'tau_log_': 72.0, 'mu': 1024.0, 'tau': 101.0}\n" + ] + } + ], + "source": [ + "print(pm.diagnostics.gelman_rubin(longer_trace))\n", + "print('')\n", + "print(pm.diagnostics.effective_n(longer_trace))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> \n", + "Similar to the result in `Stan`, $\\hat{R}$ does not indicate any serious issues. However, the effective sample size per iteration has drastically fallen, indicating that we are exploring less efficiently the longer we run. This odd behavior is a clear sign that something problematic is afoot. As shown in the trace plot, the chain occasionally \"sticking\" as it approaches small values of $\\tau$, exactly where we saw the divergences concentrating. This is a clear indication of the underlying pathologies. These sticky intervals induce severe oscillations in the MCMC estimators early on, until they seem to finally settle into biased values. \n", + "> \n", + "In fact the sticky intervals are the Markov chain trying to correct the biased exploration. If we ran the chain even longer then it would eventually get stuck again and drag the MCMC estimator down towards the true value. Given an infinite number of iterations this delicate balance asymptotes to the true expectation as we’d expect given the consistency guarantee of MCMC. Stopping the after any finite number of iterations, however, destroys this balance and leaves us with a significant bias. \n", + "\n", + "More details can be found in Betancourt's [recent paper](https://arxiv.org/abs/1701.02434). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mitigating Divergences by Adjusting PyMC3's Adaptation Routine\n", + "> \n", + "Divergences in Hamiltonian Monte Carlo arise when the Hamiltonian transition encounters regions of extremely large curvature, such as the opening of the hierarchical funnel. Unable to accurate resolve these regions, the transition malfunctions and flies off towards infinity. With the transitions unable to completely explore these regions of extreme curvature, we lose geometric ergodicity and our MCMC estimators become biased.\n", + "> \n", + "Algorithm implemented in `Stan` uses a heuristic to quickly identify these misbehaving trajectories, and hence label divergences, without having to wait for them to run all the way to infinity. This heuristic can be a bit aggressive, however, and sometimes label transitions as divergent even when we have not lost geometric ergodicity.\n", + "> \n", + "To resolve this potential ambiguity we can adjust the step size, $\\epsilon$, of the Hamiltonian transition. The smaller the step size the more accurate the trajectory and the less likely it will be mislabeled as a divergence. In other words, if we have geometric ergodicity between the Hamiltonian transition and the target distribution then decreasing the step size will reduce and then ultimately remove the divergences entirely. If we do not have geometric ergodicity, however, then decreasing the step size will not completely remove the divergences.\n", + "\n", + "Like `Stan`, the step size in `PyMC3` is tuned automatically during warm up, but we can coerce smaller step sizes by tweaking the configuration of `PyMC3`'s adaptation routine. In particular, we can increase the `target_accept` parameter from its default value of 0.8 closer to its maximum value of 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adjusting Adaptation Routine" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5000/5000 [00:11<00:00, 450.42it/s] \n", + "100%|██████████| 5000/5000 [00:15<00:00, 325.33it/s]\n", + "100%|██████████| 5000/5000 [00:19<00:00, 258.28it/s]\n", + "100%|██████████| 5000/5000 [00:36<00:00, 138.24it/s]\n" + ] + } + ], + "source": [ + "with Centered_eight:\n", + " step = pm.NUTS(target_accept=.85)\n", + " fit_cp85 = pm.sample(5000, step=step, init=None, njobs=2, tune=1000)\n", + "with Centered_eight:\n", + " step = pm.NUTS(target_accept=.90)\n", + " fit_cp90 = pm.sample(5000, step=step, init=None, njobs=2, tune=1000)\n", + "with Centered_eight:\n", + " step = pm.NUTS(target_accept=.95)\n", + " fit_cp95 = pm.sample(5000, step=step, init=None, njobs=2, tune=1000)\n", + "with Centered_eight:\n", + " step = pm.NUTS(target_accept=.99)\n", + " fit_cp99 = pm.sample(5000, step=step, init=None, njobs=2, tune=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Step_size Divergent delta\n", + "0 0.206308 39 .80\n", + "1 0.193720 70 .85\n", + "2 0.186060 57 .90\n", + "3 0.136067 10 .95\n", + "4 0.060498 4 .99\n" + ] + } + ], + "source": [ + "df = pd.DataFrame([longer_trace['step_size'].mean(),\n", + " fit_cp85['step_size'].mean(),\n", + " fit_cp90['step_size'].mean(),\n", + " fit_cp95['step_size'].mean(),\n", + " fit_cp99['step_size'].mean()], columns=['Step_size'])\n", + "df['Divergent'] = pd.Series([longer_trace['diverging'].sum(),\n", + " fit_cp85['diverging'].sum(),\n", + " fit_cp90['diverging'].sum(),\n", + " fit_cp95['diverging'].sum(),\n", + " fit_cp99['diverging'].sum()])\n", + "df['delta'] = pd.Series(['.80', '.85', '.90', '.95', '.99'])\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Interestingly, unlike in `Stan`, the number of divergent transitions decrease since we increased the adapt_delta and decreased the step size. \n", + "> \n", + "This behavior also has a nice geometric intuition. The more we decrease the step size the more the Hamiltonian Markov chain can explore the neck of the funnel. Consequently, the marginal posterior distribution for $log (\\tau)$ stretches further and further towards negative values with the decreasing step size. \n", + "\n", + "Since in `PyMC3` after tuning we have a smaller step size than `Stan`, the geometery is better explored.\n", + "> \n", + "However, the Hamiltonian transition is still not geometrically ergodic with respect to the centered implementation of the Eight Schools model. Indeed, this is expected given the observed bias." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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X3Uu4dg3RylUMXXMthOHstlVE5Dil4ZMiIiIiIiLNpFik/Us3EmyxsGABdHaO\nfryzE7q6CLZY2r70RSgWZ6edIiLHOQXFREREREREmkjrD27FO3gA2jvGf2J7B/7B/bT+4NaZaZiI\nyByjoJiIiIiIiEiz6Osj2LRx4oBYWXsHweObXO0xERFpiIJiIiIiIiIiTSJcuwY8r7GFPM8tJyIi\nDVFQTEREREREpEnktm4eW0NsIh0dbjkREWmIgmIiIiIiIiLNolia2eVEROYxBcVERERERESaRZib\n2eVEROYxBcVERERERESaROmcFdDf39hC+Tylc1dMT4NEROYwBcVERERERESaRPGKKyFJGlsoSShe\ncdX0NEhEZA5TUExERERERKRZdHURrVwFA/n6nj+Qd89vtDi/iIgoKCYiIiIiItJMhq65lmTJ0okD\nYwN54iXLGHrjm2amYSIic4yqMYqIiIiIiEy1vj7Ce39ObtsWNzNkmKN0zgo3PLKra/xlw5CB91xP\n6w9uhZ3b3H1eOPJ4Pg9JQrRylQuIhWH11xERkXEpKCYiIvNHsY9w98/JHd4CcQn8HKWFKyiefiWE\nE3RQRERE6lEs0vr9Wwge3wSeNzKssTBEuO5ewrVrXDDrmmvHD2aFIUNvfiu0e3D33SQP/XIkuPac\ni+oLromIyLgUFBMRkbkvKtK6+RaCnk2AB2HaQYmGCLvvJexeQ7R4FUMrroVAV9tFRGSSikXav3Qj\n3sED1QNWaYAs2GJp+9IXGXzPeyfO8urqgle/moHLr5769oqIzHOqKSYiInNbVKT90RsJDlmXDRZW\nFCIOOyHsIjhkaXv0ixAVZ6edIiJy3Gv9wa0uINbeMf4T2zvwD+53wyNFRGTWKCgmIiJzWuuWW/EG\nDkBugg5KrgN/YD+tW9RBERGRSejrI9i0ceKAWFl7hxti2dc3ve0SEZGaFBQTEZG5q9hHcHDjxAGx\nslwHwcFNUFQHRUREGhOuXeNqiDXC89xyIiIyKxQUExGROSvcvQaYRAdltzooIiLSmNzWzSNF9evV\n0eGWExGRWaGgmIiIzFm5w5vH1hCbcKEOt5yIiEgjiqWZXU5ERI6ZgmIiIjJ3xZPsaEx2ORERmb/C\n3MwuJyIix0xnYBERmbv8HERDk1tOZL4o9hHu/jm5w1tcQNjPUVq4guLpV7oZW0WkLqVzVhCuu7ex\nIZT5PKULL5q+RomIyLj0q19EROas0sIVhN33NjaEspSndJI6KDIPREVaN99C0LMJ8EY+J9EQ4Y67\naVv3LTgP0Js4AAAgAElEQVTcQql4HoStlM5ZQfGKK6FLgTKRaopXXNl40fwkoXjFVdPTIBERmZCC\nYiIi0ryOMYOlePqVhN2T6KCcpg6KzHFRkfZHb8QbODD6sxTH5Dasxzu4Hxcog5DDlPovJVx3L+Ha\nNUQrVzF0zbUQhrPWfJGm1NVFtHIVwRYL7XXMejyQJ1q5qvHi/CIiMmVUU0xERJpPVKR100103P8J\nwu61eMV+vGgIr9hP2H0vHfd/gtZNN0FUHP91wi6ixauglK9vvaU80ZJVjRfnFznOtG651QXEcpmO\nexwTPnQ/Xs8BaGmBlhC8EI9+gtwG13Hv6iLYYmn70hehOMHnT2QeGrrmWpIlS2Fggu+dgTzxkmUM\nvfFNM9MwERGpSkExERFpLmkGS3DIugyWygBV2AlhF8EhS9ujX5wwMDa04lqS9qUTB8ZKeeL2ZQyd\nqw6KzHHFPoKDG0cHxIDcxvWQz4/NAPNCfH8/UHB/t3fgH9xP6w9unZn2ihxPwpCB91xPtGIl9Pe7\nf1n5PPT3E61YyeB73quMSxGRWabhkyIi0lSqZrBUk+vAH9hP65ZbGVr51trPC0IGLrie1i23Ehzc\n5O7LBtpKeUgSoiWrXEAsUAdF5rZw9xrAG31noYB3YL/LEKsm8fD9HcTxue7v9g6CxzdBX59qjE1E\nExnMP2HI0JvfCn19hGvXkNu6GYolCHOUnnORavOJiDQRBcVERKR5lDNY6u0o5jpcoKvYN/4yQegC\nZ8U+wt1ryB3ePNI5XXaROqcyr+QObx6TgRns2smYQFmWF+IHPcRx9j6PcO0aii9/5bS087g33kQG\n3fcSdq8hWryKoRXXKhg/V3V1UXz5K/UZEanU10d478/JbdsyEjDWZC4ySxQUExGRplE1g2Uinke4\new3FZ9bR6Qi7KD7zlRRRB0Wa3HRmF8WlMXf5PQddDbFxRaP/7Oggt3WzOvzV1JrIoCwNkJWHgQ9e\n8F4FxkSktrmScVos0vr9W1ymseeNTDJRGNJkLjJrFBQTEZGmUS2DZeKFOsgd3qxAl8wNldlFQUhw\nZAf+YA8tT94Gj3ye0pIL6H/eh6F10eTW4ecgGhp936gUsFqCsXcVxwbY6jaHMwWmfBi4iMxPcynj\ntFik/Us34h08UP0cnwbIypO5qOaezBQFxUREpHlUyWCZ1uVEmkk2uyjXQe7geryB/YAHfgiemx8p\n9/T9nHDbmxlacS1DK3+z4Y5QaeEKwu57RwegfX9MItgoSZE4Wj72/nASPyXneqbAdA0DbwZzOJAp\n0nTmWMZp6w9udQGx9gkuFmQmcxl6sy4WyPTT7JMiItI8/Eleq5nsciJNZDi7KGgj3Hu/+7/f4gJi\nWWE7Xlyi5Ykf1zUDa6Xi6VcCyaj74sVLoDDO63gJcXzG6PvyeUrnrmho3eVMgWCLdUGUzorM0M5O\n6OoazhSg2Nh7awbHMgy8aRWLtN58Ex1//QnCX6zF6+/HKwzh9fcTrruXjr/+BK0333Rc7i+RZjWZ\njNOm1ddHsGnjxAGxsuxkLiLTTEExERFpGqWFK6DYP/ETRy2Up7SowY65SLMpZxelGWIU82ODYVl+\niD/Ui9/3VOMdobCLaPEqN/NqKnrGGVQGyoYlReJ4GVAxM2WSULziqoZWPZlMgePNsQwDb0rzIJAp\n0nQy3wl1yWacNqFw7RqXGdyIdDIXkemmS+siItI0iqdfSdjd4A+gJKF4WmMdc5FmM5xdFBfckEm/\nZcJl8Dz8/H6YxNC7oRXXjhqqSUsLyZJleD0HRg9ZTIokdBKVzhv9AgN5opWrxgZIxlPOFKh3mF02\nU+B4Gpo3x4aBTxzILOD7O/BP6IH8IOG3H2bw6msnVwBcwzNFgGmYeGiWC/Xntm5u7PsCNJmLzBgF\nxUREpHmkGSzBIVvf1dFSnmjJqsazMkSaTDm7KDi8lbo7Qn6IP9hDvOA01xE6/cr6Oz1ByMAF19O6\n5VaXXQCUzjuf8KH7IZ93vxC9hDhZlgbEMkX2B/LES5Yx9MY3NfQejyVTYFo7RVPdWaw2kUG9y820\niYJQ4wYyI4LcBnx/f/p3C7T4ePufItxxd2MFwOd6nTmRBk3ZxEPNUqh/spOyHMtkLiJ1UlBMRESa\nypgMllpKeeL2ZQyd21jHXOaR42kK+zRLyB88OP6wSYAowu/txRscIInB2x2T23w/4Vl3QdhSf6cn\nCN2Mh8U+wt1ryB3eTOG5LyC3YQNJd5G4eBp0ZGa4zOchSVxw4o1vajg40XSZAtPUWaw6kcGEC+Up\nnXRRY+0/FnUGoeKFC2sEMiNy4QN45BkzrNaDYM8BorPPqa8AuGakExlrKjJOm6lQf5iDwiQuFkxm\nMheRBukoExGR5lIlg2VU57KUdsyXrHIBsSaeaUlmSbNcGW9EObsoiWs/J0nw9+/DG0hrgQUBJB5+\ntB0OFfD2RSRLllE673w3myTU1+kJuyg+85Uj2QWX4TKI1q5xgaxyBtFzLjq2YWzNlCkwmc5incYd\nBh4XCI7swB/scfva84nblhC1L525YeANBKFa7ONEF1w45ilBboMLiHlVPj9hiN9zkOjsc0YVAB9a\nWX0WOc1Idww03HTumoKM08kU6q/1OT1WpXNWEK67t7ELI/k8pQtn8GKBzFsqtC8iIs0nzWDJX/Zh\niqe+gCTsJAlaScJOisuvIH/Zh90Pt2YJaEjzSIMdwSHrgh2V2TphJ4Rdw8GORmdunC7Dk0x4NX6a\nJQnBnqdcQCwIIAhIkggoun9BK7S04PUcIPfQAxBXBNcanZ2sq4viy1/JwLuvo/TaVfB8yC3dSPuW\nrxE++Z+TK+Y82Sv+05ApMK2zulWZyIAkJnfgUcKn1uAf3QVRwWV0RAX8w08QHNpC69Yfzsjx2EgQ\nyus7Sm7j+ooHCm7IZLWAWFn2+BuvALhmpJsczQY65x3zxENNVqi/eMWVkNSYzKWWSUzmIjIZyhQT\nEZGpM9VXrSszWOaCuXhlv4mGKc7KlfEp2Kfl7KK4bQn+0Z1jhlD6+/e5Dm4wUtvL8xIggcQjTtrd\nnWGIl+8nt3E9pfMvGL2SbKdnov0yDdl2TZMpUO4s1ntslrdboQ9aMsuMs99HDQMP2gj33p/OKFox\n1DAukrSdSHTSxTMzhKnRyQ5a2/AOHIBCAVpc231/x8TL+RXB3RoFwJu2zlwz03DTic2B79ljnXho\nygv1H6uuLqKVq9wstvUEwSczmYvIJAU33HDDbLfheHdDPl+Y7TbIPNTZ2QqAjj+ZaVWPvWKR1ltv\npvVH/0Kwexce4EURXrFI8MQ2wrVr8Pd0E61cPapTP6/MxW0UFWm1N9O69V8IjqbvKYnw4iJB7zaX\nFdPfTbR4NfhT857GPfcV+1y2Tb21nPwQv6+b4vLnQVDHbI9j1jeF+zRowe/vhlI/QX83eJnnRxF+\nz4FRr+GyxMDzfDdpZXwywwMAggDvaB/xqaeNXW9SgiQhXnRu7bak2Xb+0V1uW1Zum6DFtTf/NEHP\nRkonXVLX/o1PWe6CIC0NbOtCwQ2Va2SZCYQ7f0pwdFdj+zwp0RL6sMSQ7+2feL/v3cvgC9+GP7Sf\nll0/xRs8DLm2kdeLixBHxB3LiJZe6LafH+IVevEH9hItvaBmU45FePdPCXbvqn97DgzgHzyIFwQk\nixcDEOS2jh/HKhaJT14+/HzAfdaKRyktv3zUU1vvuK3Rbrsbntl3lNJll0/83Dkie95r/d538Xfv\nnDiwEIZ4R3rx9+4levb0HE9NZy59z6bfCX7+6YnrTMLwxEPRyZcC0Lp9Ep+tGp/TqepzRCtXk9u4\nAe9I7/iB2vJkLm9/V/PvJ5lW6bH3selej4Jix05BMZkVCorJtOjrI7zrTlrvuI1w3VrChx7AO3SI\n+JTlw52oMcdeetXa373LXdGr7Gy1tEBLC/7epwk2bqR00SXuR04d65ozJruNmtk0BU4mMt65b7LB\njgmDRNVMwz6NFq8md2gTXv9evNLAcGDM7z2MN+Rqy3iDg3iDeRgo4Q2VgIjEW0DCCaNfLI7xYHRg\nAmp2erJaN3/XZatNlG3XaBCnpQV/Tzf+3qfry1wZyBOZVUQXXzrxcxsw2c5iS9wPyy8j+exn69vv\nmx5n6OprCHrWQ9CGFxVc4oYXEnecQrTsApKuU0cPmT3WQO0EGg1CJV0LCJ7ajRdFxKed7poY7Eyz\nFGuIYkrPvmDs8e75lE59/qi7wnVr8aKogRZlXuvy50/8vDli+Ly3r4fWH/2wvuyZQgF/9y7C+36B\nv2sn4SMPz93vWDj2c3Kxj3DnnbRuv42wey3h3gfwBg8Rdy2fls9iPaLFq8n1bMAr9I4fGCtPPLT6\nXcPfs2H3Wrxkkp+tis/plPU5goDSRZfg792Lv6fbZT9n91M+D4UCkVnlAmLzLcNRxpipoJiGT4qI\nzJRmTuevcyayoWuuHbNow0WSb70ZfL++dc2RH0TTXkh6Fo6tZirgO7yqqZrCvg7Tsk/Lk0y0LKJt\n83fdcLuwA28gjzcwACUXVEmKLXC0Exb34uUjOJTHb99LfNJJI0PRssXOK403q9lkhxbWMyQTGLrm\n2pGhX+Ntu3KmwBunYXbZY5nV7TvfaWi/d37/UyTntBIvOpdxplAYERXwjzxJ588/RLzwnKkfitzo\npAUtLcRLl+EdPpy5MwBqdLaLRZKlS6ufu/0q3Q7NSNeQuoabRhHBxg34B/ano+c8ctu2Ep19zpz9\njoVjOCc388QsxzLx0BQU6p8WYei2+3RM5iIySfPzG0VEamvmwM3xqpGA02z8QG2wPgkf/ZORdjZa\nn6alldbv3eKyCE48ccJ1zYlaKI1uo2wh6YmWma1ja5oDJ422pVzPLNx1t8v6altMfMKZ9V/dbzRI\nMp37NAgZOu8dDJ39Gjof+BTB/keg9zBJMYZiGwy0kfgJXhDDYAvJQDv4Ht5AHr+7m/jUU0c6zZXF\n9svG6fRMfx2aIUqvOJu2Nf+Dt28PJAFx7hTi+EygxWUKJIk7bt/4pun5/E+2s1iK4LHHGisKv2cN\npbOfN/Fz44igZwN+fr/7u9hPccEzpr5jPokgVLT6PHKP/RIG8tDeQRwtJsjtBCo+X8UiSUcnpdXn\nj32RUp7SSWNrwzVNnbnjRG7r5vG3VRSRe+gBvHx+VAbOcIB8Ln7HwuTPyUcO0b7tm43NQjsLgbGh\nlW9Nv+vWuIs/5dqdyy6qGTAvLVxB2H1vYxeKanxOp0U6mcu8rQ0oTUVBMRFxmj1wc7w6DgriNnp1\nle98B975TqDxIsnBxg14gwMEB/YTVQuKVayr4YypJjRthaRn6tiqEihn+RFYXIJGXm6qC/hWubrv\nkUBcIDi6k+DoTlevafF5Ew/bbPDK+IwUB29bRP+Vf0XrzV/F2/YV/K48eK6oftLXQXzoRLxFvfiL\neyEKwA/wigX8/fuITzo5fV9VZrKcoNMzbdl2FfsrOs/Auc8k2LWToGcPQfwUsb+cwQveSPEFL5rW\nizCT7SzyVK7KNi3g+zvwgx5c9lRAHC0eCfL5McGundUz9sriiNy+B/CK+ZFAbpIJaE5hx3xSQaih\nIQbe+g783l6CxzcRe0sJFu4cebxYdPM9LF3qAmLVjrtMAfBRE3Oc0E+u/SFif/nINptIs8xIl30f\ng/34O3bCQY+4eAaEHdNzQXGCTL9g4wYXEKs810cVAfI59B0Lkz8nd9zxKTgtbKqM55oanHjoWAv1\ni8wnCoqJzHd9fYT33E37d74JfUehtY140WLiM84cucrYBIGb49W0D5s7VpO4usr69S7jhTquWmcV\nCm44R3t77WFdFeuqO7umUhPNhjhmGxUKBDt34B/qcR2VwCdevIToGWeMfOY6Osht3TxuAGXaj61x\nAuW5zevw/ALJkmWUzqvRCa40yWGKVaX1zCqv7g/P3Bi0uCFEe7aSe/Jx4vg08HNjtzNM6sp4Q8d9\nWR37dIy+PoLHnyAKVsOWnRCODhgkh06Exb0jd/iB6xBHEcQx8cnLx77mRJ2eYxlaWEuN/UVLC9HZ\n54ycC0p5gvYnKLa/YnJtqNNkO4t0t0FnAP1DQESQ24Dvp5ldw8GciCC3k4CdxPEy8FomPN8FPRtc\nQCxbM8ir8pmago558YorXQChEUlC8YUvdsd8OuQp2LoPL9qLF7QSn7x87OcqKy0Ajt9C66abRg9T\nC4BlCwl6niBocdssKp2He6CKZpiRLhvgjRNym5/EO7gf8KAlATYTDy3DW7dn6i8ojpfpV/6OrbYf\ngirH07F8xzaZyZ2Tc4T7HqR4Zp1BoOnMeJ4OYRfR4lUEh+zEQT8Y+Zw2elFEZA5QUExkvsp0eHN2\nE17fUciFrsO+eyfBrp3ES5cRrT5vpBDpHLuyOO2mc4jVFJns1VXuvhsuv7qh+jT+zh0jo7Iqr1qP\ns66GsmumsjbIVA0lLm+jOCa3YT3egf1uG5Y7SBH4u3bi79xBsjQTZBpv2073sTVRFlprAF4LXs8B\ncg89QOmS59YXGJtswKXSo9+pWs8sOuEM/CPb8fftxRvIA5D44CV7SKKT3XbetWN0MG8yV8Ybrcs0\nyeXKn8/4jDMJdu0c+4QoIO7rwOvK40UjQQS/t5e4awHRGWeOfn49nZ5pqEPTdPXnJttZjMp1tSJy\n4QN45Kme2eTu870DQB7i8Ya7FdyQyexQ37hI3FkloAnH3jHv6iJauYpgi61vGGhlEKo85OklLxkJ\ndI63DcsFwJ/1+uqBUaB03vmED+Uhn8fPHcALH6RUvJQxgbHew3hHjuAv2kf75/52dso7ZAO8QQfh\nI/e7Ib8VgSjfO4C3cIBS8dIpvaA4XqbfqO/YrGLRFdevptHv2GaVPbfWc+EJ8P0dENeYMCIq4B/Z\ngT+YyQBtW0zcsWxqM56n2dCKaxv7nJ47DTUcRY4DCoqJzEfZDm9LC96RI6N/0KXZCP7BA3gPP0jp\n4ktHBcbmypXF6TYjQ6yO0aSurnZ2wuOPu6BYA/Vp/EM9I5ku1a5aV9NIdk2tbJSyeocg1cyQOkrr\nQ/9Ex6P/F+9Ej/jkU4m6TqFwxssonvXS2h3UMAeDA4QP3u9q8lS7ip92lLyeA+QefIDSpc8dt5B0\n/cdWZmhX5yCd//4hCs97zYQZc6Oz0MYOD/O8Q0AnhCFevp/cxvWUzq9j9sGpKOBb6IN9j9X4gZ/D\n33sUr9g3vG89wPPyRFEELZnt/NADlJ5zPtHSSVwZn87i4NkaaVvvgYWRG4530iL8noN4J/fjdQ5k\nhlK2QylHkiu5wJgfQH8fyVnPHN0Br+z01Aj6ls54BmH+IbdN4gLBkR34ffvxDh/EGxggiTtI/EXE\ni08e6WSOl23XTPXnMkZ1FuPcSCc6jsHPdKL90sh2u+9rMDREkNvgAmLeBAEOL4SkFc8/UPMp/pEd\nVe+PTjiz6v3udY9tKPKUTHbQYAHwcQOjvk/xksvIbVyPd+AAntdLkNtAVErPKUePEtjH8TwomZV4\ng4Pu/lko75B9H7n1j7qAWLV1eiFe0u/eR/sF+Hu66bjhIySnnnZMF1nGy/Qb9R1bYUyAvGwyGazN\nKP2ebeTCkx/0gN82+nUqa/sFmQzQdFh+0L+X4hkvmfnaYpNxLIX6ReYRBcVk8ppoeJI0JtvhDbZt\nrd25DkO8/n6CjRuInp3p8M6VK4uTVWcG0YwNsToWk814KbnlGqpPE6WzlY131bqaOts4JdkoVTOk\nIoLcYwSBxW/PAwHkwds9gHdSL7mDG4g3fYvC2a9laOVvjvlRWTpnBe3/9GUXEMtN8IMzF+IN9BP8\nz8MMXvfbtZ824bFVZWhXi0/uYDfJRBlzw1lo7QS5R6sOD/O8AXx/H0mygDg8Ce/AASgUag+fgqkr\n4Lv97upDy4DcxvXE+RPwWwfxkiJ4LpifJOB5vSTJYvfEMMTL9+Jv3k3+V25ouAnTUhy8Wo20uAhe\niSC3A++iA/hHeqHXBVqcBG/xESAhyUVA4oI6uUyx88pOTwytt95Us34k64bIPWMLnNKGN7gf/8BB\nvPwgXmHIfYa9/eBtJ+kLyHV3knR1ES9bQvGkS6oGsiZduH/rT2BH2/RN+hKEDKx+N503f5Dc/vVp\nJzo9d0Tg796Gv2srpWXnM/iWP3Wfk5Ur4ed34rfsp67aVwClgGRhJ+T34w8dHpN54g/sG5MllrQv\nHT2UEkYClIM9kMTkeh4HaPw3V/r9RRwTbN+Od/Ag8aJFxOeuGPn81jvZQb0FwOsJjPq+C6wXCgS7\nduL3dFPyPfBbCXbtIjp3xexPzpJ9H4WCC76Md87zQnz24214GG/fISgV3TDUlpbJB/TGy/SLqswI\nWhpnNtDh9zVFGbyzqPTMZ9HxjzdCVKr/wlNxkPiU00aeU622X1Z6n5ffO3tF9ydjkoX6ReYTBcWk\ncc08dbFMrGLYld9zcPwfS2GIf3A/UbbDO1euLDaq0ckIZmiI1TGZbMZLzn19NFSfJgiGf7TXvGpd\nTZ3ZNVORjTK2TldELryfIHgCj4jhjnCAK2rec5j4pJPxi0dp3Xwr3mAPgxf+3qhzX/HCi+k4eLD+\nAEouxO85QPHCS8Z5v+MdI+MM7YrjCTPmXBZaPO7wsCRZCuTxyOP73cTesomLiU9VAd/9j0NLJxQr\njttMJzWOT8P396XtB88LgLwLiiVF8BLi4BTiHcthYAi6GvuumnRdplrFwatlOcYFPP8gXnIUz+vF\nIyZZmINcCfYFkHjpZyrN4o0SkqJH5J0GZy0hCdurBCfqmZyhDf/ILrzBPB45l0lcKpVT7iAsQhDh\nk0A8QNzTB0WPcNn9hHsfGPP933Dh/jgmt2kruf0PUhq4ZPomfSkWaf/Kl0kOLqbY/mJ8bwd+kglY\neacQJ2fCzhJtX/mKC7a86EVw79cqXihy+8cbAGLAJ0naSZITgQCSmHjhQlr2rCXJtYPXit/bizc4\nAMkW8I5A2Em0+AzwEpKwk9KSzOyNSUzu4Hq8gbRm1XCwLGnsN1eV769o1WoXhNq6mfD++4iXLKV0\n0cWUnnNRY8HHCQqANxQYLdeZO/NUSstW0rLuF/jP2I4fBoyZxCBrBso7ZN+HG85c5T1Fkdu/AwPu\nfDvQS5LvIA7OBM/D37mD+Jxz3XMnGdCrmemX+Y4FXECsvcZsoKPe2PHfHfT37YXBgYmHBKcXnnIb\n18Nqf9Rvkaq1/arwcq2zX3R/Mhos1C8ynxz/Z0GZWVM1PElmzZhhV+XaTkGEt/hwxbCcDlfIuVjx\nQw7mxJXFhkxqpr9pHGI1RSaV8dLfD5de6v7fQH2aeNFigu1PkJyyvP7O7ETZNalJZ6NkhyBVqdPl\nsq12uoCYV1HfJghc3aoogiDEi4uE3feSdJ406ody+MjDxIuX4Pcdre99F4vES5YS/s9DtQPP4xxb\n4w7tytb9qpExl9u6meDE7ZnXiPC8w2mnPwE8kqSDOG7D8wbxKOC3HoKertpBscpaVseSaVyjLtno\nTqpHHJ9MNmCRJAlJ0kIcnTLSqfbyk8t6Pda6TBVaH/sWwcYHCA73QVxy2XltEbQk+AO94JUAH88r\nwAkJcadPcnAp3sCgm6XQ80nau0hO6CApLmLQ/BbFS8a+p3omZwhyG0i8hfiHD+CVBiEoz7iYQGsB\nvATPRchIggQvKuBtO0p49BdwwonkoodpvevHDJz9djeLZCN15OKY8CFXoylpbQO/YnsNn2cfo/NL\na4kvX+G+ryaRqV65LeL4XOJqpQ7bW4aDLfz+78IpPt6+w3i5Ap53FM8rkiQ5oAPwKR9z0EtSaiE5\nsRU/9onbT8Lfdwg/3w146W8jDxIPb6iXXPcGotazKF5w5UgmZBIT7r0finnwK4PTQUNDwmt+f7W0\nEK0+nwhgIE/iBxRf/JIpzbZqPDAaERzeSth9P+wbdEFwIionMRhTkH+ayztk34ffc3B4ODYASYK/\nb5+b6MLDTXrR3welCNr6CDbvJOnoIGlvH/1bKm13QwG9MGTgPdfT+oNb3fsF6Ox037G7R86D484G\nWlb+jp2qGpqzoa+P4IkniE9ejn/wwMTHbi7Ee/ppSlddBBSAsHptv2rK9f6Ot6L7IjKu4IYbbpjt\nNhzvbsjnC7PdhhnTuvm7bmaviYYn+SFeoRd/YC/R0jrqzEjDOjvd8JlGj7/WO24bFToInt6Nd/Ie\nglMO4LUP4eHheeD5CV7nAP7iXugowaEc8anPGFmwrY3SZZdPwTs5PrR+77v4u3dO3AEOQ7wjvfh7\n9xKd9gyCJ7aNP7yiUvoDNT773ImfOwXiU5a7QGkDbWxJInjXu8gXXYHaaOVqchs34B3pHffHaBIE\n+D09rkZdvbXWCgXXSZigfa3bb2s0JAZ+iF88Smm5O47Du39KsHtXZl0Fcrn1BEHv2IBYWZLgAUl7\nO3gBXjFPgk/x1OcP/7huveM2ksWL8Q/sxxsaGqnPV02xSNLZSXTRJfj9fTU/Y96hQzWOrQK53Cbw\nqmyvYpH45OUkixeP3gZ93RSXP2+4veG6u8l568HL4Xn7CIIDeN4QnpeeG7wEzxvA84q4DkWA55VI\nkhOJTz9j7HrLtaxWvwuSmFZ7M61b/4Xg6C4XWkkivLhI0LuN8Kk1+P3dRItXu9pYVXT2PAxxkWJx\n9FCh3LYtVeKiPtBOkpxAkiylVHweSbKE4Y50GOL3HaW0+nzCu+6k9Y7bCNetJXzoAbxDh9ww3xrH\nXr3H/XBdpre/a+y+LxZpveWfaHvkW/hHB8FL8L3deMkA3lARrzCIFx91gak0EAU+eAXoyBEvOI3k\nhIUkJ5wA7S4zzKOX/GveD20VQYi+Plp/9MMJAuDp8RPn8Lftx/OL0B64wFNLCfwk/ei6YZpeAbz+\nEt7TA/gHe4gXLsTzc3hRP8GujeTueZzc0BaSpYvr+sznNjyW2Z4hcXx65VYnyK0n174Vf2gf/tEj\nJMuWNnT81L8tMsIQf88uWpbshN5fQP4gPr14w8HKCM9zM1JCCPgQJXgt/dDhkbQuJOjuJuk7kSQo\nbyztOaEAACAASURBVIvM8ev5QAcUErx9A8TLTwXPI3fwMbyh3ipDKV3HPGlLP8vZ31wnPotw5520\nbr+NsHst4c61tH3z78ndt57g6QMEe7rxBgdJurrGHo/Z769nT91vt7B7LV5SZWhfNekQNn+oF+9I\nH+TbK9rpMsY8+vCDA8TxctznPFUqQZKMDTxNgez7CHbvcgFpgCQh6H5q5Pzu+ZDELkDm+25/72vH\nKxSgP09kVo4NVIUh/p5uis99XtVzzpjffEFA9OwL3PMBv+8oyYITCHbvIj7tdErnP3v4OBrX4ADk\nQlr/48cEu9NzchThFYsET2wjXLsGf0830crV4393zaLyd3ey/NT6v2dbWxm66jUE7XsgaMHvfQK/\n0Dv+eQMgiSktu8D9JkjSY23RzPxem02T7XOIHKv02PvYdK9HQbFjN3+CYsU+Wrf+sP6rfVU6WzJ1\nJvsFFa5bizecXh+R63oUj8N4Sc5N05aV+O5fWwGvs59owSrAn/HAzaybVAeqm6FX/zrh/fc1FhSr\nMwg0ZVpa8Pd04+99ur7MgIE8LRc+By67bNSP89JFl+Dv3Yu/pxuKxdHtz+ehUHDZCKvPw9+/r+51\nRWYV0cWXTvjUhjpdWZ5P6dTnA2MDxr7/BEGwKw3+1LjS7vt4UUSy4IT0jhiISVoWDv9QDtetxUti\n4lOW4/X3u5leo2j0j/ZiEeKIeMkyogsuHO5YlS5/ftXVjgpmFgr4Tz5BsG0rwcCj+KWDUIzcY36C\n5x3C93tcTa2lrRANkrQsGPnxX/HDvvWhm/Hj/fj+3jQYFlR5/376z3PBgAQIEqLTVo48pZSHqOBq\nWa1+FwDtj96If3SX+x6p/F4IWlznJP80Qc9GSiddUrWD0un3wcGtFOPRbRrVSa2UFImj5SM1xYY3\nZEzusV+SW/9Y4x3Ceo97s8oFxCqP+TR7J7dvLX7bgHvv/j48hlyHy/fBL+DHQ3ilCHIB5aif50GS\npDXE6Br1mvHiRSTLl4/pqI0N+o7l+0/g+714Pb34vYdhMAdDrSRhgNeWZiZGMd5gAnkfBhPwcAHT\n/hivrx8vjkk6FuC1DBAHz8I7sp+gZzPx8meM30EvFAjsJte+qvvLDQv2vV4X9A1CvKN9xKee5vZN\nncdPvdtitIhc+CC5ob3QkRDHfXildHgcSRrU8lxwLBmCOEfS3o7XnuB5Cf7hgzDku4DYqEDtiSTJ\nQny/HxIfr9CH99RRck9swz/SQzC4k6S1c2wAJY7cBcfs+/MCWnb9lGD/owT93XhxTPjYI+Q2/A/h\ngcfxlx6FlkHobcU7dNhNLNDXR7xs2ej9MkFwZjLCvQ+4+nhZhQLBk0+Q27aVYPeu4WCdX9yFXzzq\nfkMeOkTiVakjlr5fj0E8v58kPnl0+/uOTstFu+z7CPZ0Q+y+c/z9+1wgJrM/vKHBdNixB5EH+1rd\nfoxi8DySk0+uePUCvr+NcP/PyQ1Zt67BQ8RdyyFoqf2br6WF+JxzKV12OaUrrnQFFIsFaKsoIF9N\nfz9BdzeQuN83lfu7pcX9Rtj7NMHGjZQuuqQpA2PD392+X//37EWX4A8WiFef4c4ZR3aMPT3FEd7Q\nIfzBHryhXrxCL0nYQbzgTLevKy6szWUKislsmamgmIZPSt2mZHiSzL7MsKsgt4FkYSv+UX/MzOdZ\nXhRAa2lkNqjxauPMhmlO+5/0LJKPPDylQ6ymS6MzkfGbvzn6/nT7+z0HSTq78J/aCYd6iE9/BnR2\njq5Pkx3GM9lZz6rxcxBNYqhqdjbEiiHBftCTBsQmunKcmdLdD/EL/eQObx6p21H+zKVX9qNCAX94\nunj3oz0++RTiM86smAV2nK/ori6ic1fQ8h8/wu894k7NYQt+ex9eEbyhw5B0QyfQ0QGxR9LRgZeZ\nQSvuWEa0+Dw3i1q2vUvA6+4Bv5gGxGrzvBxJ4pGUQqKFJ5OEnTUL+LY+/u3xJ0IoFIZnAEyiIcJ1\nDzN4zrVjP8dnvQie/NnY5X1/VPLN6IYmxHFFFlscEz54P0mxCKvOG7tMPfV+wtAFsfv6CNeucRMg\nlM9BE9RlKg/f80/ox9VHivC8dCKHcrO9IklLK97QEF6hQNJSLrDv43nxyKyaBC77oaOT6LyLR+9P\ngGIfLVt/RLBwD8M1s6rUZnIzjLbgHzrkznm+D0MlSHIwFOINFSFKXCwuDaaSeNAeuyBFYQgG8vh7\nuomXL8X3dxDnVhDk7x49Q2lUwC8XjU/bw+EhXFC5+v6qOizYY2wtu1oTadSa1bNafaoKQW6DOxf0\nHIUlC/FKR0k6F7hsuaEhKJUDPgH/n703i7EsO6/0vn/vc+4cEZmRY+RUE6tYA1lkcVSLppoNNTQY\nNtxCy5YMtWX0g224DRv2i1/dMGCjAdkwBFsG7LYMw+q2QEMm1aKklijOVSRFsqpIFitryqkyInKI\nOW7c+Qz798N/7hQROddAgrGAQGbEjbj3DPvsYe31r6Vlj9ZqEJUg6Vt5ZNomuEN23XZDHWxnCB17\njisO2gFJBElb+FYHrdYIx47bPQkpoXZsmlQellkOdnClDcLcY6MyVOn1sPIwjzS6yEPXCVdPGQk3\naTo+Sby9w2E62aEniK+/UCSaBksI3Cg80oYliDm4pSv40jJanSEcOYyGib5i0qvLkjNMoTuXk5Mw\ndQ/fKXuHXWXebvsibtAkn3/CSuKXFsE7pNPdVwWKc+ADrNlzq3mOzs7t8mmdCEUpARsd0kfP7vHp\nZf4/uis7knsZz921ZfJTp27ze0Xy8OwmdPvE/+Jl+p/77eky5Z+G0K3J+30v42yajVJoCX1Gmz8a\nkO4aLjNPSpwHzVFXAoT42vPj8fNeysMPcIAD/NTigBQ7wF3jnj0hYO9i6wDvO8YeUrFNwlzFFsu9\n7q1l4yFHy3M4t0be2yZ/8tn3nLjZF/dqfH+feJAUyd5//E/eHRLoncQt/ElG2JVENjO8lre4/nr6\nLHQ6SLtNfubstD/NPX7WHe9bMSF3m2/gm5fAVwmVecLsQ3dWqO5OQ9zj05UzWqTfDnsI03xqorzH\nt63Y2d/3nQtiSFZukp85R/UPfn9/gjdNcVub5inlZJxqKQooHO4iPocU2OkQDh22RTWMrovrrSOr\nL5Id/8TU8YYzJ3A3u/uXYE6iIAQkTUFzwtY5stVbkNG7UtuG5BchmNKo3TZu0XsoxVbGHZaJv/s1\n4m98FUEICwsQFA7V4WgKjR7UxiqS0SK1tKvNaErQY+wmPqLXXoWdHcIjj97+PO/G76fRIP3VX78z\niTAk8F9/ldJXvwLVKvLBVfTwDBI19/mDwsOtXLbSq+J6mTJJjRvIN9D88LR/0PB+TgTjROFK8Xfm\nEef9BeBvUZ0hS58ihEcYsYppMiZJFIhTpDcoVC7DQ9Pxv6oQxBafzlsIxdo2enzGvLr8Am7jJgwe\nx7cumHcPTDyjOb69BNWcEMrk+QeZvl9JkYK6q03GMW5zBY7oKJURcYTKEcgTeKwNrnybVM/b+FPt\n99lDZdgQzhkxQ3XqLyTvFYEOHun3ir/YZ2OlKLsj9ejhCPE5UkpRSrjBFhTqDBkSjSeOoaWGLcYn\nEG28ar5jURXX38Rd61lfGsdGIhVju+QeLaXIwhp6/cSU6fiIsIR3PEwnPfNZ4uvPT3nG7adCk7LN\nQ6TXxa30CfIwaNjj1TX6/Z0mrr1JnH6T9PQvj9vsg/py3iJQSmsncBvncb0VtH4YCLhma//94mEz\nEeBGZfTfMDcHIRQ+rY/uDTRJE/zFC+P+0TncoauQb8Ev/dd3Pva7HWMfeRSSFBoz+12Agqi7iWgT\nN2hBlsDiefyXzpPPP0ry1C9DpYLffnPqGr0voVv7eWzebpyd/Dsf03v2PyNa/SHSvmbtrbcGeToi\nw9AcjWqE2nGGN3s4fqan/+67dVYHOMAB3kMckGIHuHvc727IwS7KTxWGqWnOXR39LBw/XkzM01sS\nYzo3B2mCHGm9P8TNbtyX8f19TsweJEVy9wQ1TfDr68XuZUDzDD10mOTv/TKD3/nddy9K/k64V8XL\ng1z/B1DXjLBr0aL1E8j2WxCSvUqoW5G9u9IQ94YODMsGb1OWmedoffex2kQ6vvKXtns+2yGqvkRw\nC7dWpQzVE+trBcmm6IKVgexH8Jb/5PPI1ibZ3/kM0WuvIuvrxTkJzHSMEBsuypwz0mxI3g0VF/0e\nqhlu6QbpkY/DB82c2g1W0EoV6Sf7l8qoIp2O9RfFW2ocIbp1SzI6Xn4eghL95JVphUhQ3I3Ci0cc\nWqsRjg8VMUq08SJy0UGeobOHTNHSAVYjovASeqxG/szHwDnys+dwS1d3HWuKUi8IjyESnF7Cz7wC\nc4qeqxfKpNsohh7UwHsXgeyGpVdJgmzv4FpbcGIAjequBba1BRA0jk19RArBSlYl5Ih2yUtHkF4P\nf+Uy+dlz1oZ3B+O4EhKu4VyhfsDKMYUd4tKLhLCESHtviamAJP39CbHRdQaCImQoOiI2yK3UJs+e\nsfZx4cswN7M/Ya0ONEckK0IdCgUZTI1Xkx/q3CqiPWgNpny3XGsRt32J+nf/KaG+gAy2plUrI1Vh\nQRDLOhK/SJZ+gt3E2NRnOwf9JiGaQbIucqu+RYCsB1HFyAQpI/T3XDa3tlqMuxE0Z9CZjqV7xtjf\nUR5dGkn7sNkne/Zz031aSCyZ0pXs2d64gbzWLrysnD2r5dLIvF8KxVjuc0sujWLrPyYTpuGdDdOJ\nG+TzT1H6zp+OyLr9MFJKuhySGOl37RqFsH9f5DyCx7E6Vrz1+3cVzjKFKbX5gDj+PuFQmfDQ49PX\nxJcIjVNGiKTbyFwLVjr7jzECuIBuxpA5U4nVakWpr7dNjag3Vj8WRv0M+kZWTSjo/LUVWPkivHoZ\n/uHv3XmecBdjbPzt5/FXLu/zxzlR/D2cW8T1tmzTA2tLuIAfrOPWt4lf+C6hfJTkI/9gL+n1Hodu\n3Vdg0GSIj4/pP/HbxMtfJ1550ZRiRRCGxg1CeW6vr6iLraSyu/KOncce/DSo8A5wgJ8THHiKPTh+\nbjzF9vWEuBtElZ+Levv3Gvdd3194SMXN7yLDSaYIOjODpImpEVRHE2hTiZXRSg09epz848+Snf03\npt8zbU8b++7ywng3cF/G9/dpHBy/9INiYniPGIYReE/+5FO4xbeJXvkxbn3dJszeE44eI5w8iWs2\ncasr77+Z7aQ/yS/8ItmnfsEMiycWBvV6Gf7oj0gvXX6w638Xn7UvisX+lDeVeFzaRtK2fe88krZx\n/XVCbWHcnoco0hDzE2O/sr2hAz2cWy8MtG/hKaZKOH5ygnAaQNrB5T0kaY1EPa7dxPWW8KVlIx7C\n0fF79vuUvvLXuKVFpGteKFqtER56uFBO7fJ1eeKDlL/0r2wBIEI4foJw6nRhWr+Nm98CPJTKaL0O\nlQqSJmhjBre+httctzI3oVBkdQk354i+dd785WpXET/A9QpPnMmyKlWk1YJ8+HMjQGjUUK0T/DP7\netCUL32J0ss/MBP1UmnUxt3Nm0hzZ9T3SLeD7DTRmVncyioubaOdeWtHSR/XaROdOQ2lMoNsAde5\niV+/RDg6D+UartW2xDcXQHKCHiPPPmrXY2jSHr1O1F4yz59GHWbrOLeJ94t7781Um7lPA++CQHbL\nSyPfHn/p4pj7KuVIY4DEPetnSqUJ9WEoSnjBEigHEBUm3iqI80gcEOlD3kM2+/ily2h7FldawXWW\nrVw15ETbryD9bcRF0+cnDiFHJLeFudtEthVJ0rEKrJoiogVHp6bYG8IDbYGeWGMXKXzBFM09ofIE\n4JB4G6cbRvxpmF5khhTX2UG1SginEJIprygfXdwlyFScu2b+a66Ezh6avubiwcf4jfO47gph9pGp\nPkD6fWRra9zX3sqfavKz0xR/9gz4NrmUkKwLIUN29y3Da5r1UIkgyTDfN0F1wh8rz20sGPX3AkkJ\n7VTR5iwy20ErZcChOkPgJHQiwumzU2OEb15BBk3c+ro929stpO3s2Va1Z6rfNxVfbOS4uuL+dQuF\nWwh2aydDON7hMJ28fI7KN/8YKYW9BMPwCkgTIUcpETiFW1kxwjy6A6EiAitlXKdNOHT47n0505Ty\n5/8fyv/qiyNPwUh+hAubuO0OfukqrjXtu6aVo7j+ms2HqxVca8389Xa3A5dBS+GtGTQPEMfoiYmx\nwue4h1qmyB0a9fd66OzcXuLde3ypAjvLhB+ukz33qbubJ9xmjN3toTn6qOgVIv8mrrOFZMW9Gh5z\nsVlBVUFSJO/gVm6SL3xwf4uJ9yh0634Cg3b7t4bGAvHS1/Gdm2hlHi3N2ldUtc2b7W3c1iZuZwfX\nbiFZhpZKhJlTpKf+zjs7183TBw6keadx4Cl2gPcLB0b7Pzv4uSHFpL9VlCfdQ8dflCeFQz8nhuzv\nIR5kgMqffJryK3+KDHoTiwJB6w10ZnZkNq0h2MT42Y+SffhZSzJyfmRM/r4N3PdpfH+/xsG3Tvq7\nDSbDCIaL4ps30NNnCOceIpw5Szh9Bp0/ApXqz4SZ7RB1TeHznyct3YWRL7zjxs23SsEN1WP43qoR\nU+IhgNtcxS9dwC1vjVPXKp7QOGnm75PtclfogOoMzt/AuTb7kiR5jlZr6LD8RBXXuQZRjezkp0wl\nMjy2Y8fw6xswyBDftdS07ATRq+eJv/sCbmfHSOo8hyhC6zX80hLS3UZO7ODjS7jyDfzgAqVv/w3K\nDMQTJVveo/Pz6Lzia6tQro0WwKNj29pAhoqLCaJLyQnyMBqfxq3cJOpeQhqJEVNJE8lbBWGQWJ+R\n6egviWJLsRMI4WShtmIPGVr9qz9AdrbGCoeguJs3casrdkzD43SKVLu49DpS6kI9hyyCQQkkQtpt\n/MMPgfekaUDlFGFnDlHQU0cIR4/jVjcInVly/RgaTjFU+02atMvaBsQlwmiReodEu+Kc7sfAez8C\n3y0tIkPZ0KCEm28i5QzIkZBPPCfeCC8SIDMfsZBbqIT4glzwWPlegkR91NXJV05QWv064fSjIILf\neBWhj2t39pq2g5E4khD0CM6twEyCuAE0AkQKaY6UwzQZNjoZ4KY1Bx0ed7mMxqBbNcL8E1iq5RsQ\n5gjl40bO7myYGXamhMPnCHIaNrMiZMIj0iLkZwCP84tGyg0/chhIEEAbjaKEcRc0x/U3rFwy66C1\nMdmljQZ+6ep0Pzv6zOM4dxUfXcT5RZxbNGIyi/Af/zh0bpDnORo3IKRIvmsjCdCQgwa0VEcG5UK1\n6VCdHf2O294uyOmJv/M52pxF1+dRSuiRQ6geLu6v25e8ijbfxBdqS3UgOwrpBIkUFMkzJAQkM9JV\n1EGcodvF8XiPJAnh2HH8lcv411+DQUL09uU7prDeLeJvfAO5MkDKCSItJpWAdvIp4naAKiGchDzg\nNjbG5Pt+5OPoHB26cxjZ3ib9pc+Rf+rTdz6gfcjqqfTeQtElnTZufW2c5CiOUFtAsg6SdnB5B+1X\njUQOisaKOCV0G/CikddaqxkhNvHsyfEmHI0BPzbqLzY59ntGvTfv17Czg9xIHjgddDp0aYiEOH4B\n19spCLH9+gqQSo6ItyTivAPNiHBiYe/vJgn+6hLxa9/BfWeJ+KWXx+1JknduM/U+AoP2hPj4EqWr\nX8G1l8aqU1Xc2urURpJ1dIr0O0gzg46ihw4T5j94b8d8K+y36TeJewgUeSdxQIod4P3CASn2s4Of\nG1IsNBaIrz1/b4NVnjB46h8dpE++C3igAcp7nLuGa20hrbaVxQwXB86hUYTW6oRHHiH97OfQo0fH\nrw+Vf+/jwH3vyWE8UEz7g+5CvpeqtvcC9Re+AYuLpLdST+2He73+t1IflmcpX/7z/f0NRQj1U7hB\nC3/zCm5zDUkyRAZo3oA8QTZX8C8v4p7v4i9fIX75xalFX/7k00SvnTdFU1xBpIPIFkI6vUDIc3SK\nVAHp3rD58uEPEOqn9h7bwilcp420u0joEi2dh+UMt72Niqk6RounkkfObhDNLeL6K1CvI05NALP+\nBu7IDuI7e1RNPrqMSB8hmVbG9Lq2aNtNHmiOqVgqhHDGiJ/0Ej67jsu3oOSgXC7WAjmS903pUfJo\ndQ7KZSCg1FCdt/cYIlaije/i5Q3iy3+DK/WADEKMu3ED2dkxQsxZWQ6n+sjxAdIISJwb35PkSOxw\n8ztQTmCnjBcHR4+SpsWCLq7Aak7v3/4vyB75ewye+w3kRoK7sTJKhPTRq0aIZVh6YsiLxecudcNQ\nMRR2cBfbloy3NE7GC40ZS3i7W9yCwHc3ro8XpOqgnCDVARLn1h+XyuN2JTuYD5gOKylBpHg5Q9VB\nKOEGCQxaphZb20J0C+mnhGNHiDZfh8h8ySRJ9ll0K9DBuW1AkSiHviIoUrH7QQ3zqJs6EaALtIeX\nzxVjhSAlT7h+knD6IZxcJNq8hFvbQLa3kc0ebAdoCjQV2epbGV+3C1EEeY40t/DLV3FvbuDdMpKn\nRro5xbl1KxXu9xERXKs1pd7AOdxgG0KKVuaRpEVonBmPQd6PVYXDsS1PcYOrxINX8NsruNYOktl9\ncbSRRoarCFRmyDubVvIYN9DSbKGgK8oNxaGlGfKZMxBVkFyh10FllknvMbe5uae5iFPC9ROQ5OSH\nHrLEyEniaEhenR4/Z/HFH5jq0RtJwUY8begf+SIZ0Ug1JkhX3S6Ua6q41RUjqVs7SMjJn34GCeHO\nKaztNvHXvkL5r/+S+DvfNmX1PkSaKZMcGk4Y2akgMpy/xIT8JCE/iUgGREYapgkaG0ElebYvSWNE\n4gw0I7RcJv2lzxGeuDNBsS9ZXaSv7rnmg/5IheavXCa6fAnZTKDr0bSPliLC3Gk0eHS7SrhxEu0d\ngVIFnZmBuUPTSqo0hccyC0EZKgYF22SZ2c/jqyDF8ASXwbJ74E2m/RTwzr1F5C8j3cH+5DlAKYNY\nGNZSi0twgw1EuvjuTSTro75G9Npr+LdeR3Z2gGDq4mQGf/ktKi//cyoXv4DEXUuufQc2U6fH7tsQ\nY0P/1t/9x3vacrT+E3z3ZhHa40Zk8+6NJDRHpUzgFNLp46++SfLcb7wjm5m32vTbg/dIhTfEASl2\ngPcLB6TYzw5+bkgxfAnXuY7r3pzy7rgl9ilPOsA7hwcdoGSwjZTbhLOP2BorSWzSFtmOX/ahD493\nRoeYUP69nwP3WPaf4Nzl8Y6+vw70UJ1hj2Hyg8S0P8gu5HusansvUP/KXwGMSYm7wd1c/7RN/PZf\nU//+f0fl/B8aEZa0IaoiGvDNS5Tf/GN89yahvrB/uYYq/vWraNvb4tqZ8bwGh152yI8drJWQTgd3\n8yZ69Cj+yuXxou+ZD5F94lO4lRXcjevoYM6UDc7Kegi2S6zV2hQhRtbHJduEQ4+RHf/Y/sc2UeoY\nXV1C0g24LtDP0JlZI2lmZmzR/9B1I0lCjGS5JQ8WJTWytWNKj2q+R9Xk/CJQNTKPYhEZgpENTqA8\noe7TokwpnASEEM4COb50Hpc30VoFIdjCJI6RoUooisEr4iyVUClZil9+qvCjKsoU5VV8a4n4/KtI\n0jZigR4uW0MYIK3CAnk+gdMDUyINPbQEiNVOK0shq0NpgCtt436yAdtNWF425V+jYUqdvIePrlBe\n/GtkvgWPli19cwCRewOi2qhvc52OlfDt037c6jq+uQhX+7jSNVxjGVe+jk8u4xffQFuQf+i5u1oA\n3ZLA7/Vw25vj92jXkNoAqXctrRApzMJ3Co+tyXZki2MoiLFEkbRrBGIeQb8ELsUlKe7mDSSsQ9WB\ns80O1y384AYDpNdDBn1EdqyE0jlgFnQA3kESTDGSq9lvVTDRGkWTS4FV4DBwTGBWkXqOlgKhcwjt\nHrFNhRtfG/koClqoirHyvmSAa7eRVhMyI4nd5qYlXfoM3WqA9HHZCv7GCi5bRbI20i08vcplxuqN\nLq61guttINl2sW4XI7AQtHpkdBlNvbkK/YEp1/JFHAP7k26leL8eMmhDKRDOPIwPPcgH5GmfUXKt\nOIiqVmpVnjOSzEWExhnEmwecazet/HGCwHY7O1PebOpztF2DnRkIOfkzHy08p9rT5YYiluwLkCRE\nV3+ERA7VHNUassO055uIEbB5Ds7Z/8tlUG9KsZHZf2oKtBAIR46hpwpif3f59lDJvE/poeQ5kqb7\nEmnxN76Ov3gBf+kibukacn1AaM+T154myEOoHkF1Du8XAT8mDQXCqTNG6KaJ9cGTiivNCUvzhPnj\n5M99HNfr3nmcv8W4vLdMd/ghDv/2FTP8bxfG+looJwcR7soGuvQw2ZnPEOY/SDj9EOH0GcLDj+DX\n1yHvIsebuOObyOEmMrcDpyOQMm678HcslafHlF0wUgzyPBAGC/e9yTc6pX0U8FH8PVzatDay7/ga\nrHTSO0TSorRbbWzMYqTbwd+8SPzaC/ibV5C1rpFKuUJJCZwmqv8YF3WQToLbbE7PMx9kM9V7suc+\nPhq7hxsiI3S7kCTkH3zKCLF95nLxje8QaidwaRt/40pBNk/Yb2sOoii10biJ99AfwDX/4JuZaZvy\nxS/cfaiZi3Ht66QLn37XxQcHpNgB3i8ckGI/O/j5IcWAfP5pos3zSNK8PTGWdQnVY3vLkw7wjuFB\nB6iR8q9URefnbQI3Kumb33/BN1T+heT9GbiH6qE3vkDkv0cU/wjnNoCSVTVIfntvIHFkv/CLe9/v\nLuT797sL+V6r2t5VFIqAyp99Ea5cQZeXodez8sE7EQRJgrt8mejty3vVBF6sDPet/4/KlS8hSRNx\nkfkU9zfxrUUkbaP1U/idq0h/h+jaG7irG/jl5XFZZKNB9MZrxT0qA1VUZ9GkgXvrGu67A6TTM4Io\nKBBw3a4tMCcXfZ/4JPlHnjNiEoFmw4ilqAk1Rzh63DyMRCCktqsccsLMWSubvF2ZD5gyYHkRPTSH\naI76E6bgKhZ6cmrVCLF8WNrsTDExM2uL2k7HFDGzh/b4IDl/HZGA6gwiiX0NCj8hccVieJ+JbkEl\niwAAIABJREFUPTEhnMFHRZmdtCCeNcVaUR5mpZMZkmb2fiE14Vd4GBzkmS0IIv99/OYl/FoTSXNc\nuw2+hlT7SJLhun2IciinVh45lyGxFhWMOuR7jBvrx+ByRDqWiKl9xHVhs0QuDmlu45euEM1cItZX\noJGPy7glINUevvoWMufIHv+ElSt7v9dTKs9xW1v4t68g7R2k0cIdWreSURGzyiLH1XvE156n8oU/\nRN7uIjut25aW3cq3Rxsz+MXFKT8p3Z6B2TZUElPQlXNEBkXZoBTJk1IQJMUFSgMSAAcanJWZ1vrI\nTAdKOfiAy1tWDl+U+UqvbyqowcDeo5QiLhjpmWZIH7R+jHB0Dul0kSwxVdbQ972EKe56WDjrMYrq\nPsfQXJxITBV0tIpc2iFKL1jJZ9GGpyDO2nWaIttbtkETx0Xym8KbfdyNFnKyB0GQWtdKAYd/l+dG\nIEoH8b3iXHIkJFBqIPnA/AaTJvmhJ8bPZ0FSx6+9isiKqeGG5FO/YooqQEszyGyw63boEISUPOnZ\n+4iFarjBtqU+Jju4pAV5Snb8o4TGaVz3OpoKdOOpftK1WiPySr0Zy+vySUizESml4SjOG4k8OrYo\nGinF/NtXkJ0NcF2gjOpJI9H7vel+qBSbKigURHCkaOcwdKtWutfrWZhOFKH1OvmzH917nyaVzE8+\ntU/p4QQmibSf/AR/8S3K//rPcdtbxfOpSJ7jtjfxi4uWPnv0qG2iSdvKmHfadn2cR+dmrUx2pij3\nzHNraxEEv0D66OeMxCvCBabG+X1wq3F5d5kuMBEG0keiaLQ5MUIUmYn9pQS3sTFN8khAzm7i55Zx\nvmXnTYB6FZnt4nQT6TfRaM7KD3crVycwJsU8wT9y/5t8w9PaRwEfRT/E9bqwb68FoFBziEuwh18Q\nEQg5stFHuh2zAkhz69PjBNlIcJ0O0u7geteQWbVQiEkF3vFpH7/73kz1RkzZ2A2u3bLnoFIh+8hz\nDH77d8g//olbzlfMOzkjRIdxF1fs3o6CdjyqDUI4UWy8TlwjV35n1HuLX8W3lu5tnqzFfPHwuztf\nPCDFDvB+4b0ixQ7SJw9wbyiii8sXPo/fKKKeJ4mRrIh6PvIUg8d/611NmznAA6JIg/Jbb95Z7QUj\n5R9xnfjKv+bWk6ZbQIR4+XnSR+4j4n0yaVAV768geb/wgOkCi4RQQ/UYt00TG8a03yJu/bZR4ncb\nc/7kU5bOWRBn0cW37i0RCaBWI7r4Fumv3se1ejewKzmPzEgRSTL88iJ+aZFw9Bj5088YubB4tVCF\n2G6ztNuAQr1E/miK85uQ5MQ//CvKP/i/4JQjPPEoUevtImFpot/wJQg5fusCfuMNXKtpqWzqUemY\nSio3jyb39mXc1qYtSsAWM2urlqzWT0HKI+Nw2WnCTpOwuQmPPW6kVLWG21ij/Ceft9LXRoP0V399\nfB/SNvHVL1O6+jf4InEqrz1Mcu5XiJoXx0EkaYd45UVcd6UwFHeE2knSEx+HuI5fWsSMWWKk0kWZ\nMN/2Oa7RtVS4XZBm03zDqlUzuweQGCdr5CRQKLZ8tGj/DyeAHN++hDiHlkqo2sRewxyj50JTQr6A\nKS/XgApBZpB+hzB/DkKGX12Ebt9+HYHgIY0QMtzWFYKegGMeH/0Ev3oZGeTjhYcqqEOTGHF9O/Us\nQD1FS2bfM9WdhInvfYIMMNKlDtIWOBXgxlX8eoS2D8FHcyBBltrwyV3loXEdl3aRrEO08gOyE58E\nmUiqLBLfpNst0hIz5EjPVGoA3RSVfKS2UUqI90ipSeX8/85g6x/um7Y5fnb2SfBrt4l++BL+6hUY\nJIh3hHqDcHKB8PpjuIeX0UMdK9lFjZDKFZKihMcHU0AqkHojw5witQQpZYAYyeIVYgflzBRYKx4J\nmSmCZmaM6B30kSgp4g2B2KNx1QJWaqcIqYf+VSROzOtsS9EKsCrI0ULJp0Up1bCiqiuw7WEugpMp\nce/bVvpa9kjcQpMY+mUI06SL9I101ShCyxVTaW11oNcAH6PbJaTWnVB/GSlGliLJOpRjRpsgEoxA\n6/VG6bCSdohWXyQ7/onRZl305uuE2Tq+VEcHKZKmqArqvZEwc3OF3cCKhXisrsLJk+Ar4Eq4zjUk\nHxSfae+pwdpLfPNvCaU5krO/jD42T+Vb/y/0uuAiXO8GUt2CkNgxN2vkSwuQ5EZKPT1MS/Vk6Sfx\n0XmcrBlhNuHdJJs3UD8LmqFqSrQwN4dvNnc1uiJMZ5gamybo1pz1490uSqF+PXLUPvtWmxxFCmv5\nX/7fljx8J0uAUpnS1/7G3nvhFG5pcfr1uBivN9aRl18k+9gnyHkGiX+A+oBk+TQJNfROBIbJsnsS\nQ+N9ljZT6ZIZ8fe/S2jMEM49tIvEsECOSbi1VSMUoxjpWSqqSLNISDVGOpyso4dqyHaT6LVXyT70\nLEMfQ6FLOHrKrvXmJpLnhJMLiN+C+qBoi7m91R2REPLT9t8HTQdtNMiffAp/4c2J+1iQ7ftBQ+FT\n2Weqk85MQS1pgmpsxKsT29SIFT2SwTqmIitl6I1q4U8rRrTul3wKENVsnZG27z1pcffYfZfIDj1B\nfP0F/NINIEJ1fk9i7B4Mx08R4m8//0Dztmj7rbvfbB79UY1o+y1Sfkrmiwc4wM8oDkixA9w7fMzg\nyX9URAU/b534MCr42HMHUcE/Qxg88dtUX/kDpLd+e2JsqPx7/LeAd3Dg3jVRJY7IPvAE6Wc+O05f\nKrzLpLcOcYNo/RW0EiEjqxWbDIt0EblOGBprS4xoBx+dNwXLMH571/vtwe2ixO8i5nxPatT9Tlwf\ndML7TqEwI5aN9fG5HT0KV68CbryoWVvF/8UlU6M4sZ+HUJQx9JDH+3CuAj4zcgqgApG8gey00R+/\nhR6fnTKnRwPSXcNlXUBtQRcS22UmQ1gpOK4TpoTYbCH9Pu76dcLJBdzN61Zyk2eo2zXcFYs+6fUo\nfeNrJL/ya/azYtFH20yS9mufnc/83p77HL30P0KeUlr8Mq67auThyKw3x+9cxjcvEWrH0c3DUCpe\n8xT+SVZCw6k20h6glKFSHqs9nLcFPthCvd2auE6Cc1cJ4XFCeAjP5MLTQ6eGZjH52XNGZo1QLO5c\nGyXGR28i0kP1KKrHkHAT8gS/sgqpQFJl9wpBHcZDv1Imuv63uEeXjBCbVAgPVROtOlR7qJdCLTFR\nFTb5tsOfDReJ9YnXYzWC6HCKlBV5etle26qi2qX8F39G9vSH7FxHi6zclAdph2jjVbKjz0KphM4f\nxb/2qpE9zsheOdy3zxit+cTMyYcm8t4bIVUPuPImlf4fockJwte+Svy9b9L+p78Hhw+PzyWOIClI\nkzSl9JUv49ZW7ftyBZLUCKutLdzWlhEi4RHcM8ugMaSJna+TQkWikAs6cEgrBy9wKENcbgfsFYJY\nCWacQmRJg+JzpHsNdYfGSX7OoVVMiSiKDu+tpkgSiK5chvUM6XqQGjpITGUYC0QBTaRQVxX3qiuw\n4+z/MXA0w+siMmcbGJRM+SXVPlT7Ro6160aqaTCyxnskywhxjLocuSZ2r0KASw14vA9RZE0qSxEN\nUA22EB8EtFwxFaR6oIxk/WKRbuWjknbwm+dNfZIkyPoaUi5Ki6tVtFIi6Bw6M5HCCIRwHOdS6OxA\nfsya9WB7uu1KQYj5GK0cmfi5Y/DMf8Dg7K8x97/8O/iwDCjqI6TloRMjOiA69jp5epj0A7+5i5Ty\n5Nmz5CS49C3yR5+y0lYXkWfn0GQBH72Bk3XrL7xHKxWkuW0qOlUQQaPYPAtdgHVBfRXXXrXit1Nn\nyD77d61ZXLk83tTwnnB4fpo8ylJKX/8q+TMf4k7wr503BWDaJP3gk7jFq/v/YhwjnQ7+tfPkH36W\nLP0kIm08V9G53T6IKYgWybLPMEWIDcf5IXZv6BQbVNIf4Le3pjd0vJ/aVABGpKGFPyjUd/B+qJQp\nSFBNEBfDp/vIcgqvXoMzZ/FHlxC6ILG1bYXw2AfInv6Qtcc8Ib72PL6fQp7g3GqxkXF7hHCuuGa3\nWcLdzdwKGPy7vz0e36uWIIys7+XF1MrotVZD6I1/nmemDFWglhZq06HxIdYPVXO07KGfIQOF2IIF\nwoniXAX80iL5Yx/Yex4Pspl6H0jPfJb4+vO4zY3xGH0niNo9qZUefDMz3Oe8737/7gAHOMAIB6TY\nAe4fcYP0kV8/2J34acRdTojuW/n3oAP3LSaqJAPi77wwpbwoX/r8mLQLCdJbQ+fmodWeemsRj2qK\nyJqRJDCtolEl/cwvUb7w+TuTgABRDddbo3zh80YCT+JediEnF8X3gttNeO8VaZt4+VtE2xfGBPah\nJ+6KwC7/yedtwuwj/MULtlgiwLVrSLVuBI0Ibn3NPIryfFQKIetrZm7/0QFSytHc466vE04Ny0ty\nnBsAFSRbg/V18hNP2WsacJ3rkKdGCHXaRfkYjFPLAs5to5oTwinbxY9iJE3wVy5Z6UOSWnrexv7X\nU+IYmtujxRgAqtT/+/8WjaI7ts+RMkiV8qUvIll3/9KHgiBzvTXQa+Q8AeqQ3gD/9hWGSWccSiEV\nJPRh0LcUzHqdYsVt7xWCXeM0LXbuY5zfLCqjSoRwrFgg22dqniNB8TdvFuVIwOEEaoqKFqWW4Fyr\nML425WVwZ2BzG9LCXyqO7ZjEWRsQrFyvV0IW2vgjl8G34XAV0gh6FcjVlB6hIMpWYphP0IVsqgrQ\nbsbE98N1VTTx/fC2O+BoAheCGcKrovMdaB1CWju4q1dwS1fRI8fInvkQI/WHi+3ZHxGrE+1gMDCy\nqxLGZJxixMBojRdgrouUC6IhgMQ9QtrFH+7js29w6L/5NXqf/k8Z/Hu/A3FM9oEniL/zggVu/NkX\noWPBJpKl0O8Nm46VIFGoKV57g/DMaVhsIvOCViK79skAMm9pnElmxzNTeGBNKqQo0vqcebNZOVgP\nDQLSxgzAtAiR6IwuthFjDpe10DxHuzFE8yNSxfqy4vqcELhZRVULs+4c5gKctNJcDgWoJcjWANEI\nLUcT97YoEy6lMNdCmzNIf6KP9OCS61BX9OxhOK1ouw6XA/LGHNQ34VRivEUcoFI0oCzAQEFLUJ4r\n7lnfUhkrJUuLdDGuu0aeJyPFpqmNh6XKFCrK3RBCOA1uBTbXcFECISPMnAPNkd4GkvcJpQZaWyBU\njxJmHzJ/pMEm5df+iPI3/xIqdXL3YSPBez3o7SAhQ+MaVMqI5JQ2v0Ry7B/s7Ud6GekT/yaDT4/H\no+p3fh8ZdMgzU1dJ6ODWtqw8OC2UhuKK8uc+ZD00r5B3nib71KeJvvMCHDtO9pHn8G++Yf24AM7j\nhkT9hQvw4vcIx06S/OJncGtruO2tXXqqfZAk9n6lEoQ+Uecl5MMtpN8CidB2rVCrDZXcMW7jOnJj\n3fqvdAC9HLY66FylKGXzZsgfHmJEXE2iGOeB/Td0Rm1sYkNnQqUWmN5UkGaToQGezLWs7HSXX6kI\nhHDESKOzDj3mUIlx5W0IVhYfTizsIuoBXyLUjiGVJq6VIL7LuJPbq0YzQ7/H7Lx3k39D3MPcijje\no4DPs1P4eAnpF36UwzEnjtFavVCJeRiqWMVel6y4pi6DGSyhuOewBwpo5OZ12MuhJIUqt1ATxzFu\nc2N/Uuy9VkEVFRRR/rL5Ot4JmhKGFQpJgnv7bap/8Pu3n3ffDi4qTP7vEbs3/Q5wgAPcMw48xR4c\nP1eeYgf46cG+9f33aHwLgPPkR581vy8FlxYeDFGF7NhzDJ76HQtLmFB+mO/C7hiyu0BUITv68bv3\nInnth/iZq1CyCYVvXkGSHfDRvilqIg6RpIi9L36uAZI+2eO/SP7hJ99zL7T9zGzviGLCGx57QI+I\nPDWvrotfxLeWxl5Ld5vy1G5T/uKfEF25jH/zdaS1AxTeJv0+NJu41g5uY8PONbL7ojOFefPGOvJE\nH2mkRkKUK0ieWZlFvYHINiKW8iRugOQpeEFLDaS3imQDM9IOAel3CzLGyLSx0fhQFZIizYyhHYy0\nWlCtIv2+qWcuNExBsw/Mm0gIp87YzvSPf4jcvGGebrdtn2PT6eoP/hm+vYxoQLIekg/sS4OpZEYe\nMx5Jught5EYH3awgqxOlhgv94jTFvornl0oE9QFS7yHSJDx6DDdowYCREbAZ5TP2IdI+7sYK0u2Y\nEqYgs2R2B6GHDBJIHSE6W6iFmgU54yD0LW1wq4L6wpzcqxkKq4OkZMqvUobMdO24Ztq4ODfD/HKG\nVBJwOfmRh3A7LfN/ShKoZUijWFLva7jF9GvDfwv1luTFL5WLBZsK4hStlyCJEWelb9Jp49bXCMcP\n4/pbhUdVMG7GNfCvn7eyyNYO0m4j9UHxnkUpYFugWxBCAnIiQ+KCFNKJgx0MoOsgroK08Rs/ovxn\nf0z54peI13+A3/ox0Zs/xC1u4vqppehNnLiMWD9Q702xdqyNHj6Mzh1GYgdUIXFIYgtkyTKoZVCe\nNlTHB/D5+FER7L5LjkQBIwerBRmWIVIozLDnSAb5yLtMMg/RAHqxKY5ckSzZSCHOoR1buzoWrJRy\neCwzglQiqJXBDayEM/djbnHyxrrieJuF4mw+hfkMiRPIq0hattTVeg+Z3TDioBfghNo5lrCuYHgZ\nFCub7OdQqiAumLIoKNIR3E4L6bSQVgu32bU2KU17PDUvUlT3T/8DwUeHQGvkZfNd0sohcCXy2YfJ\nTv0i4eiHCDNnzdB/2Ke6mPKL/xJp923scc5UabOz6JGjRpCKGPmPBwa47k1C44nxR98iMW88vpQJ\n6XH84qs4WqbmK1XHBvvefN50uwQ/iSEPhJOnkCwlf+oZoh+9jCv8Mt3GhhFFg2FZaNGQmttEb72J\n296GRoNw9twtrlNx2lcu49pbyJkN/KktnG8RFk7ieh0kpEhjgJtvWmltu4yfuYybv4FjywIfyiWY\nqeF627j2Nux40vJnUDkO7DNeTQbcsH+65Oi6tdu4t9/GNbeRdgu3uWnqpTMPT4UbuM1Na60zHSRK\nLel2okR6GG5gTJBdJhdvElVfhYVZmI8JJ46QnzwHDnzzMtH2RXxrCd+5jsYWrCLNDRuHUJxr4dx6\n4Sc4HDoynMsBR9AW2p9l8O//7vT4VJCAdze3mghMmPDh0qROvPM93KAYb0tl25QpWxqukXQJImGs\nGg5AIjYODcNYPLYZlQoqgiYOen7sjxgV92+YhjwZHrHnZjmyU7f3iHsnkc8/TfnlLyF5l6mAi90o\nynfz5FmiV8/bHKnTQRcW7jzvvgWkv4VvXrq3+eZEANa7iQNPsQO8XzjwFDvAAQ5wb7jdriiMdgz9\nhTep/G//K/3/5J9M+9/cg/Jv6LtwTyWUxcA9Uh7dyYukWiPa+RHurR7Zhz8JgOtvjBQ34dhx/I1r\no5KbIUzU0CxS8IAMZDZh8Ju/Rbz8FfZfhd8GQ/n+sc/enfpuF9LPfNbMbO8Fk7vd94s7lYlqjF+8\nSvSjH1H+mz8l9Z8h+8BTU+cTf+vrxK/8yEokdk+wT5wwgiQZmPF7HI2NvId+NlFADicQIrRRtBXn\nrRwlz5F4QqGBkQ/S3YLqPC7tjhaV0u9jzt5DCc/wX2sLUnjLqYvspUJ1IoOBlQptxpDdxvze2aLP\nLV7F9Xvm+zOZ0rgfJv3H/q3PEW1fMJXYkFQaIu+b/52L0bhQfEVlJNkCPURon0Zr67Zz7rwRd36C\n6HACh/pIrQ+NOoTUSlicoscruM0NtAOanJz4WE+29RSl1S+jh3PCow/hl2/aS40OGmWICprG0Czh\n4pum3sMx9NMRnJUFRT0US5VU5nH9cnGsDuZ2kNpgrEgiFCKlgHgFn0NJcaU1tFa18s9ShMxk49s4\nfBxzxqqwIYavDX82PLxQlDfW1YgrMNVd1EejeuH7g5VkdTu45ZiRbVuhFPIvX8FdvzYqmSVJoZYU\npx9Qb6WA4sQ6lWPBmtvkMYMRMNUA7RS21pGjAVfbRisVdKVDOH4Cl3bwpWX0U87a4hulaf8gKfy8\nNCAK4fgJZLBij8TNnilU1JlyUQsFWCWDWhjfc4tyHF8vLQ7SBfsqPk8kgFg4if2x1T6qOiRR0GCe\ncbmzZ7U6gIczaBfXfeAtyTKIfd6J1NqrOtR5qMYwGyzApVq1lE+fo2WFbgnFIT4fPyPqTDEmAscS\niBRR0IFANtFv5R7ppfDBxNpaS6BmZZxD5Zk1GUG9gusjHWfvGw2gVBwv9v5+cwlZDGilCicFyIok\n1uPcEaGJVg6jUZ1s4dN3/v1+B9dZR0tH9/oTiZCfOm2+Vd1u0RxiPKukySbOXce5LfTsEfTZeeLl\nr4wVvu029HrEP3zJFLKrK5DnFgww34TZBPWAq5gf21IEiSA+Jz/3CPkHHkcXFoj+9rv22VFkKZRZ\nuu/iXeIYzXPc2gqapeOy1FvAba/jPrAOUQLtFMlSfOuCkYBZBiJoHCP1HPfkMrLhrN31lTBXIxyz\ne+Gvmw+euDVKa396CxVdQRr+plk80G7jX39t79gcAtH5V5HVFVxrZ2pc89eW4VvfIJw8jn6kirhe\nUTYYrI0GsfLcAqo5EBdepopzq4XqENA2OaetLHLnbaKVlwAlVI9NHbtrL1tJcjWGfmIJ2tpgXJqZ\nm3Baa4ClDLv8JvpQGSrT12A8t4pw7oJ5dxYbSCGfH6vrdntnDtFokP76b9A7v0P1hd/H9dr7+AIP\nrA/BNhhQbGgehmyMLk7xK9UA7Qi2IuszYodkqfliFnYAdiFuNz5Hd1/98E7Ax/Qe+w+pvPx/4mo7\nxQ8nrvVk+W7yFPGLL9mcQYSwsDD9Xnead+/CsHzznqBKevoB54sHOMABDpRi7wAOlGIHeF+we9fm\ndruiU5hMkLrP+OhRcmUO/sploksX8ctLUymAeybVecLg7G9Q/tKX7tp43pevIO024dRp8N5SeXS0\nukMbM2PFmGqx6++ADE2qkAf06FGy5z5Bdu6XKL+9fxLcHY6C0g++SfQXP7579d0kSiXcjeu4lZu3\nT60cYtdu9/2i/NYf41qLe8tEQyB69Sf4t15HdnZMXRe6SLaFXGxPnU/9f/hnZkgfTR+397bYzGp1\nZHvbynJUi8Q8S22T/gA53UMOC1rbldSkat/VkrGAShIjxTSApIV6RiDrIGnbkgRFip1zKXach2W8\nZVR7SLkHvo/EPSMwMoW+hzca058/iTxHa0aKuOVF3I3rRqpoIJx7+PY7u3GMu7FEpfN/4Pur4+Of\n+qxCZVEo9PAla6edNuQxeuMEWqsbeZBnUFFkLitUbQrHEygrkitan0FLZcKJk4WyJELrM1AXqNXJ\n9XHb2a5UcDst8tqj5Ic+Ak6QwQ6kfWSmb2V/wSFekWoGmuDWd5CQIHnbStni2HgWl04pL7Vex3U7\nUNtBGoUKL4sgjyyRLFL7cmrrupIDErRWRzoCtT6u2hsK/cZfjjFHI+x/u4y/sYqdlLFCKHdjzx8f\nQRajs7PDxoq0u4QTh23nH4e7fg251jIVYuHZJmmC1JKRd5f0BHaKw5BgCYtDIqsQblHFKppKamlr\ncxlSUiS1A5SlHdzmNrLTRJKA5AL1AMcUVvyYuBrByEX6PcQnuEoHoir4DPGZpQpGA6SUQEnHfPLk\ntRu9VUECFSq3cVO0Z0dCgDS39hrUSKdQEGJDtWUpNaGEU2xRqGbkP5ubSX6sMBNZImVVjaSqA+T2\nzIuDQR/Jg5G4TmHjMES5lb0OVSOiUBsYIQZoz8F6DJWJvksDrrqDlAveOS8aSVUZdSLD0/SmuJF8\nYF1EXkHLR4vSYLWLJYK0I0i7poqsVAl6+hYNbwzr+7bI61VCfQGtFJsveYJrXsZvX8S1F3Ht61aq\nWJohvvQDXL8JTotnaRdE0LolK9r4ksFMC1deRM+dJH/2I+jCSUQzU/gufZPS175A/Bd/i7+5ggxa\nuPhtXGMRN7eN1LaRdoDFGbhRgRuKRC1koQMLAzgj6NE6NJXskScoffPr1mesrSLJ0Ph9f4hz0O2i\nMw1kWMa9LxKi8jfx9XXEdXClHqJJUd5aEM2otelKC6lk5jkXaoRGw8b8QialMzP2fCYZaB/XWxmr\n6JrbuIsXcJ0OOj9vKt+tLfyFN/E3b0xv5oRA/OL3kVYTSmUkS5E0GfUBajcYyQOsxejCLK6/ilS7\niM+s3LJcLogqRbVWhBsIzl1jnBDqTCFZKoGv4Lo3rW/VHMkHaGliPBRv/Xi1juttohkgw3bvUJ0p\nfMZm8N5DmpJV6uTPfADXL5IZ223iv/xzqv/iD4n8T4jDj3Bh03w2Heybyh2XcTeu75uWmB95BqGD\nv3EByXtF11+ojKUzetw0A3Jn/Zr343NSHT9GAVj0SDdHVgMykxpPV6nYvZ2dgzQlnFiw1PPdGLRx\nrzeJ//zb9zf/uk+Ek6eJvvkGwT9a7NcN13kxIT9Jnj2LhlNE58+P50ghJ//Qs/sfx93Ou30J17mO\n695kKnDoVigCsPITDzZfvBscKMUO8H7hQCl2gAMc4O5xq13RW2HSTPy+dtnKyFvbRGvngXhsSDpM\nAZz083FuNHDH339pzwLm9rCSmpEJq+zaTRQhHDmK29pCmlujnXaiMmHhFPkjj9qELy6M3e/VCy0E\n4pe+j3YG0HjSfpYk+FG6YgDvcItXkZs36P/n/9W+xNduM9tbYvdu9/2iu0J8+c9xqSVlmf9RBuJN\nCZBkaLmBaiGfGXqv1Z8ESrar+T//T8j1a7cn8kTMBPvQYSu1ScblNjo3Cw8zFHNNY2QaXxA/gGqM\nFH4lknZMhZi2bCGxZ6EqqJaABDAfOZHYvIU0h0qGHALNEtipANvWdlyAQW6EQK6w6eFtTLVWLDiI\nzRRZ+gPibz8/ZcK8B0lC3P42/upFpFT4rEg6Up5Z2WQ0Vo9pjqQdNBOQmKIOcFotshzgpKU8Mm9+\naJIr6qTYiT619xmSiOBqRPyYjCdx6evE2SuEY0fMtyafJzn89yk3v2DlcuIKNZFCluBtKd49AAAg\nAElEQVTiDI4GU/p4M52XJIWmouUyUt1GKUooJRBOl/F0TBkG5rFTGhhB5rXgHQqlkaaIJki4SuBk\nkY7ozGNsNzE2fYsp3nx6gTUkpvrYDCa2y44UZFkpNZXR7vdqN9BGgr9xFdLMyHMN9tXv273PC+VV\n6mDdI5rb6/OjZgoNxm16eGwBOJ7b8SQKbVMs0chhu22qGCh8nUBLKTwp8FrRLxVEv0YxEnIjZa+V\n4ZHUXltzFiwwmxmBlBfXeZcocXSuAkOvn6Gf1LgkVYv+IEBaQbMSUhmYd5+qLXJLYkRYXpR1Cgg9\nVEqor9jFF0VODKzkMIpAffFc9a199jbRuDIOExBBfEBFodVAJUC1D7EphqSmcDNCOzGkAa1Om6tL\n2odDjIMBqwrLDm04ZCje0XHDEQ1oFKClhPikeT5N+DSpClpuwFKMXq4jH2rC0bsYm9IE5i1wIsyc\ns2TczfO47pq9PlIB5fjWon21L4NXhB7OWaMxQmUiARbM6H3+EM51gQbiS+SPF2b2IcHvXMV113HX\nFiEZEA4fJugC7iNb+NdvWmDEcIw8NYAza1bS2i/4x0RMCTgzR6SXiLoXKH3nuyjRtKH8neAjaxa7\nUwOTBLd4haj8GlLbwZVXkEzGJMmcIPMJ2vPm8ZgF0MwIscyhcYqWPDIY4K9dG8q+0WqVcOQoHAHX\nbOJ76yT5DvHay7h4m/DkLFTr+Px1Quch4u+8QPTDl2Du0HgOAkSvvWqKnmhCbT5UxjlvY0Cvi87P\nI+0e+kqdtP4x4tlvQVxCqw1TRGqDMJHe69wKQsqo1C7PzSog61mZaBi/JiHBdVcJtV1konNQ8zCI\nCDvFa94hzSaud9P6kMjByQWyj30EShX82quUX/rn+Dev4BYv484uW+hE4pF+B7/VQWs1wvHjWMQv\n06nct0pL9DG9j/2XhOpxyt/7E9z2IuIyS6nFoxpAfeEvWEHzokTZFaXdIUAmRbsL6EwGiyVYjtAz\nmZV+N5tWkhmsb8rPPbS3nYVA/OOXSTc+cn/VDw+CiWTOUH288OvchSRB1grPvDQlHDl2e5uMu5x3\n328A1s8cHsDr9gAHeDdwoBR7cBwoxQ7wvmBy1yb++lfxy0v35luVZTax/8A9+hAUZZrczHC1HcRP\nTAZh5FEx8vM5dphQO87g6X9M+ct/dU9KLeevG6+QJITTZ5Csjwy27PNUcWuruM112+2NY5tklcuo\neGgqrtsjHKqTnfgY4dDj9+yFFp3/ie0CxjVCdsoUVhPeWmiAPMe1dojefIPohy+R/v1f2buw8J7s\nuY/jVlYsjTHr4MrL+Ogizi/i8rch+//Ze7MfW7LrzO+39o44Y87DnerWTBZZRarIIimKkFqC1R7U\nsuUGDKsh2W4IkAHDht8MP/jJMGD4HzBgNdCGX/qh20250YaMtiDZLbdbFEVZnMmaq26x7lD33rw5\nZ54ppr38sHacE3ky71TFQSRzAYnMPEPEjh07Ivb69vd965jy+VfIfu8/+/ATu+gh1vvO/4Qf3kYQ\n/OgufnQPN9nD7d+2ibpvITLBuUMMWOpNV+5V1yFN8a9+H3fz5pmTN+8tyaiqYCwYkalpry4tEZ64\naoyi5f37Y6Ai6FIvepQ4wJu/mDhjGmmJqAEoUjUtnWsPMWMEGkJR2d+CybG0ipiUwkJA1ie4do4s\n5chysKqFmSCLOTxZIZ0C2caStE7HrotLl211dzDA7e7Y/7XEo6rwr71K8u6r+I3bsDSwpEECooKE\ngKCRNVZXf4vyklAZs0qWDIgZg7u0j6wdwRMBXekbs6ZTwLJ5pGmSQK+PlDmSH+IHd5HxAVINkb0t\n3K1D/Hu7eH2H9OB1/OF13GiEOz5C8jGyOCJt/wWydGwMO2dSVMkKM41vB/O1EUVSQbxHXECSEqly\nOBqg7U3Eb+P9DuIOzGjcGUVJkjL6VUXWj4uytRCmUkdxAWGElBiIEdROXfSEPwWOnWA3xc8okBnu\nSA4kYtvIJPavgHOoXz8JqniP5AXlM5/Bv/86SBcZ5nA8REYjG18pxsjqYAzDxQALsY1R9co6xooy\nv+9Z2/I4DCMJiRbR6w04OJ1NSQX0K/hBAQGT8PUXrJpiVYO2oP0SaZdIUUEnIB07T6SxXfdj1DWj\nCYjVUcZLJhO0ahmprKyQsbdrRqqYrEb2rTNKnkwMdNbKIUsBSWbHY2M9stcSA8ikNAN+qbDKlpU3\nhlkRG1+kkLVNniYCd61ypSYpYXMTGY+RLEMmY6R1bOy4MMcMy1v2+NHG2ENgEqWeI6HafDYC1Ubx\n09ClKl+k5HO4N++B7yJyBKstHmZY7VXhSpeivYb2LpLc+zouO4wM0Ln7vjjcZBc33kYkRyQB2nad\nyfjk/bcG8+Re9JKyAVZdeYlk9/v4vTeQ/MiA82yCJA7ntvD+Nm4wQg5KA6pqJrUKslHY/a8NHInJ\nyyPwav53CTK5hyyN4UYw78IHydjAmLWrfWTzEHkmwye3kN4e7gevkXztuySLr+PkCJEjpG/yadrx\nxzsoTb4p7QI5AlmOnwkgBKswPFZo1YCxIpOxmf4XBWFjDRaGJOFNWEiREHDHh7ijPfz4A7y+B/0K\nuVOab9z2ti0kFIUBEmljjlSz0ObY5rq0bPeM/X2qp5/D6xZh41mUlcj06zJFo6sxrrpuDNtqaD+h\nJCyvISGz+caJSrwOCTnaWjqxyOeyA6gmaHeR4pO/SnLrJv7uHSTLjZ3X7+MuX7Zhcv067ugYd/0a\nyWuv4n5wSKLfRlojuzcm3vbpnDHsRiN0cTEy7zzCBHFD1F/FDY4pv/il0+fZeaoLnyX7hb9Ltf4k\nDCvcZAe0Z+s73ctod2XqISZ5gCo1xnAGZNUMpE6A/69jQFk/wHJ8fntbHAtPPRPl+3PX2qvfQg9S\ntPXcg8fkD0H9cFZUn3yJ5PXIBDtjTubf/wFydAQhWNXglz/78OvnUebdzlNe+DxuvGWM05phPt3G\nCKrcCmC99PtnSFx/NPFDY4p9VK/b8/i5ix8XU+wcFPvocQ6KncdPJJoPqPaffghZYJref0L0gJjJ\nNBcI4TLihogcMzM/j6EFbucu7vVtwt5zpN/+Nu7116HXewya+xjn9owJc/VJtLWAP74OOPydD4yd\n5P2JiYhqhcoS+Gi0vbvF+G/9V5B2ZiamcF+py/QhnOf4t96AVAjVRZK/ftdkF2nrTNDLpHS3kb1d\nyi988czPVC+9iFu9Thq+h692TAaYePTSBcJLl3Brh7jx1oebDEQPMXd8k2RwCxDc8AOTELloSj8Z\nI04xak0Hm9jn5sfFMiIFIVwFIHn/B2ZW793Z8kkiKHZ8jNRGOS1g8xBe8sjGANe9Z54/ZUNaUYdz\nhMWNmBhaIitaoi0fsZXIKALbfghThovkgpDb61NJmABdcElcmQ+QCpKqJc1tYIQlYIsKawESTGrV\nwpg4tywZ017Pkoh43mQyMQPdixehqki++XXc0SFyaYBc3kU6WcSFw7Q9U58jZ6iPqkLSgbRnbCJy\nY7a5liU+kzEuH+PSI2iVsJEjLQMLcQ7SCXQykyhSIFWGGxzgxmPrm6WRHXPlcNsTS4a0gsUhPr2D\ntIKxxBIgaZmUqVsa42iKOUaUZepB5QyESSpksAu9JCbrg+hN5VBxUTHkI/AQQZgaVGlu2tlrUmk8\n9nospEz9sJqfPzXGrdtkGP8v43nNbL+aJiCeaump2Jbm9sSAn/0h4d2ncdfew1Uj67sKtAtSKbIa\n7NKof/pYscZaQTov+TwLQxE7beTA4RnvE/Nh72BXTCpcF1QQh+Tmv8QWcNkZILYajMGWRlCs2ZaH\nhcz9XWLyw0qR9xxymIIrzJS+ExqsMpneh1SxcZsFM+NfsoIFOIUQ71Xem6l+/d0oJ9Ya1Jyk1u6s\nfbJ9vTGCg4EV1dDNTaSqIhu3su0s5g1wNI6xloOjNrJgTCNyQekaQ6Uw7zBST1iYS7iloipfNqnS\nYGBS5lEb2dzBdQ8N0JFB7KjovVZVyPY9/NEObO/De9skW99Hwgg6/dPJsOr0/muM3RrMr8HaWvMa\n77+6AAQDnetFJudxfohkh+DMh87t7cRny9BAeNTkzu3SqvvFohuyVkTZNWjLQTeBImpPq2pqnM4k\nh6U2rtiD/faDk3ot4cUceQmkVyBpglQlbusDfLmF+8SB+QW2ArKRz66NepwmagC8RAlli1lxhnrM\neayabRl9LCUC3s4hRYZL7iKa4YYDZFeRPItJtd3+ZJLhBndwbgedrCN5bp8djQzA8MaGlY193IW9\n2WLEWg/ytoGyy6uQJlRr6+S/8Zv4hX1keHjyma4VfnANLzdxvphJ+1FbAChGUA3snn/qWR450skM\nuHeTPUQDobtO8v57aNVG19bQlRUryNDt4pPE5hHYgp3/4A7iCjju4dZvI6WYJDRWgaaV2v2kUdzG\nzodH5JhQXQVJKb/0ABN73yJsvkjx6d9C/IDqyovQTiEJaKdnxRmSxMZgXUG3qmzxpx7mI2c+igA7\ngj6RWMGSrl03unnBLAGaMTrEv32Tyn2R03TYMyLOv86Sg37omF/MLIqTHnRvvIaEirC+aYDYo8xr\nH3Xe/SEKYP2o44cCijXmqaT9096AvmX35dFd/N7rlBc+fw6Mnce5fPI8zuM8HiOKx5QFftjvnZJp\neqryZSpynLtuxq6hxG1tw1aJ7q3YyuHSCFolya0b8N67D5akNSKEWB69nqi7FtrdxN9885TBfh1G\nUImywAR00KP9R/+C7D/6+xRXvkTn+/8zLo9MrzOkLqG3SbX2KfzNG9TMF/fq4ITs4r6RpCTf+w7t\nf/yPCJeunDSFff45kpU3kHBE9fFPGw5RSzH3dq10vXO4hWuk3/zXVIcvQaGPbCjbfufLM7q9Btz4\n3knZRl1JDIdIheoQWEDEo2oSRNVG8lgFdHkJOTw0Jst9Qrs9ZHAAL0yQ1QJRpVxdABR2PbIwhm6G\n5qlVK0QgVOY7hyeEHiIja0foom2T20pVmzirMfLKWJ2uzrBF4688TsYFGJoxekei+S9MfZpSzBeq\nxDDBBFgWaAdkWdEJMBZ4JyM8/czJg0xT3O42VZ7j33rTZEYtj3v+BtLNor/ZrIqfdXj8FdTYJyIm\nO5EEkQNUHRrWICSYFjB+r3QR4BOTpCCwqBHMrLerUZJnrBlpZ5Y0em+S0RqYWxsZ09IpFGJm4wSU\naI4usb21EjVumkqMLeXD9H3RzBgHfUHcBBBUBZGSKRguEo3odcbsYrZdcWqnr8TAlGjQrKImN6yP\nbR7oqbc1Ao7rMRD3c4xlw+rAO1T7BjbNh4B/4ztwp8J/902YdGFQghf43ATpVUg9zJu3lV4cK2ap\nY6e5CUgFDDyrMHZYHQ9bvC9ANhS9rvBMjqxnCJX1/XYwWS8pfDOFXx1HWbDO7oWBWa54FjBWD8XG\nkITG35UaKywbQe5hCDzTYLWFyOwJ1QyEyltw4JGLEwNWalwnFdRFkEVzuyabO3OgPkG6FeSVjWtp\ndJBU6KSFriwb6H14YABhzZqr/eLAwM5QDwgDdHXsoV1Z8iTG1LTtOnQ+QdaCoJuQg7/xDjI+xi9f\ng6UMdtqEpWWcN/mySA4aZfmD6J/nF+DeRWRpz2Tet4F799Buz9gkk4kBmu3Krqu0jbqWeTMJdg8A\nYIJIgUiBeTXtotqOx9W1/u21zfMsegu5oxphDfF7zqR/qdr1ulbAXsu8ufoYK8+BBEF7AQ51WoFX\nJhMUMVDpSGElI0hhfnh1ldE6qsqu18/l6GobCR7t2jPW3duKYKhH2rkZq+sZ17812yLFgHcRe8bV\nn9P6/BoDm9HQGJR1LE9Min2cGRv8rPlDXcGzdYxvv0WlLyI7O0i7Ay2PXN7CLUSbhWpW5EU2BrBW\nUbWuUK0skRy9Tzq5TvLn/wPV01eQyR6IIq0KqHDZtt0XfU0PZSb3bBmLWUKBju5BeyUy82rA2CPl\n+OSwDCWa9nB7R5BnaLrMmaEBf+uWSaxFjAm8WlcNZnZ/KArk+NgWdxrFbaZ9poJz16nSz569n7PC\nJVBllOufJt36ayhG9vwfj2zsl/F5FaoIzlYGpN+Vads1aaHfbaEvVXCxQ1hfR/Z2ZzLccmT9uA3l\n+LPQfwxA5H5y0I8SaWrFCAbmt5q8+/Z0TlddfQq9fPkkCHeGvUZYW6d68qnZ5x5n3v0YBbB+GuLE\nPPVBkfRw423a73yZ7JN//8GfPY/z+CHFOSh2HufxsxBpMvNzetzvPc7Hv/qV6WrgyWgRwscJhbFo\ndLRkkyQHVMXUEyxcumyeY7s7yLe+Qfm5LzwEGGsRxkvohdkDtFx8AT/63snENcasPLpnWi7bvWKy\niaN9utf+0ZTBcMrENAJkbryD3PsGsmfV5kK+htvae7TVR+9J3n0Hf+sWxRe/BCsr9nqeWSWjZA9d\nv0z5yRdJ3nwD2d0GxFZ0VXH37uEHx6gLeP8GxebfgdyT/uVfkH71K1SffJHs7/3uaSp/McDvvj7z\nYdC4Ut1cYSuL2aQch0gREzRHXb0xhEYW0wrI2hB39RDtjsEl6KCH7i/TfHToyiI88wHSCwastDtT\nUEJZRcKW7SMt0OVjODQGVlheiedsE5HbaJUZ+7C/hB/egXJghtNVNUuWoudRrZQy0Iap9NOGZpQp\nJs4+NA8e1DlMhiWTYzcDNl5WuBWTslPjUnBvv4V/922kypBf20Z6k4hHeAwVmc8CMTBDvTHfQglV\nFhk0it4GySLYmqRzhRfvWXuXSztt9WVXlhHYi4iHqBm9SzTcWqzgggdfRMwsgk8SzLPKYW1x0Vet\n7peEWd0CsIQ6E7QbzdYXAhoOEelOz/90n9OiCDpLiBsFBmd9AUwqdAGT+QhYgi+zD9RAzlnRx4CG\noRqOCHATuAjaUmNRdJ86/b08h6M9OMjRu5fw2S1IEruOPjGEZyvr4yL21Sp2SmuVbh31/80kPjAD\nzRxQ57rC6eNvhgCXA/JrWJ/l8dg98AzwNHAk8CYG1h4Bi0AaGoypue2dtY95QKya+38F2IuAUgbs\nY9dCio1dMYIQux4OBNbiBvL4OSWCXIKoATzzbREwo3IfoO2gs49mKewvQaeAfoUuGHCkxxUc5QZ8\nJQlaFMYaqoGwJmBTKJLn6G4KlxR6yYxR6rzJjtMGK00LNHSR7wbS7a+AU9zHttG0jTsOMMhw14bo\nYh/dTHDJDuSRMdby6MG6ycLkNtIbWXXdibd2lMfQkrhPjJU5cZDnaOKZVg3lII73Il4/dQ9lJh+P\n7DF1Qli4eOI5JeNxPPYZoCIhWLVNPNIprZhBty4oUIMRaqDxQgmHCZSlyQXTNBqEm/RZLo7h9iIa\ngr1Xywn7C/BShiwJEgQ6A2QphTIHV0RPv9IWGBzx3GGvnxX1OK9ViJPGexHU1DRFiviMEgdiFSBl\nXFe9fMjcpd1Hlvdxd+4SVlZx3feQFwaQVFaYo0hg3JkxeisH/SG+/y7J5G10sAh5aUy4g21kYWjH\nc9yDVgFSmXw4qQykLyOL08dzD0BqF09+CJqjLoWkP1uMqCPK47S1hhvfQn2solxVyOHhtPALiYcs\ng9GYWipOViArA7RsI2HSWECM7MbhcMoQc4eHhNrMXlJcdZfs4y88uB8b0aw6Xlz8Isnuq4T1gN+a\noHSRYbB5qANE0XEKhx6OYpt8gvb66Moq+Sf/NnQ9rrqO07vIrduET3yKcvMVipXP0f/yf0dy5879\ngaWzotcjefftHy4oVsfCAsVv/OaJbXf/4H80uS/MqprubNu5qedotc/ujevoRvTZfcx5989MzM9T\nHxZJD7/7BhSDc4+x8/ixxLl88qPHuXzyPH4i0aQyy/4+/r1rj0cbH40oP/sK4flH9xR7mEzTv/aq\nrWY3QRvvp55gurCAv3EdWq2TkrQHhI4XCV98FqkG4FL89fdhv0SSimY1sWl59LAOEqxcdvlZwENZ\nkh79OdLN0MUn8eN7EZw4A1kTj1QTONpCwya8s4QcHT+cGh+CySezDF1ZsfL102pKOUnnHfAdZDAg\n/e63rN2tKMVUxd+OctAkRVyC4xjeHRMuPGFSl1YLt3UX//rrlK98/kR70ht/ZpU5I7DnD9/HZXsn\njk9Ogab1pNzOlWqFhhVCuIpPXsUt3sDJIdrpGAsgFMhCZhLPTg7DPlWlyBM7SHtskirvCRcvWTVH\nQLsLyOTQJB6YTxWuQJOVmTwRQfM22gH6Ga4czdpcZkyZQ06g8Oi4hbSjqe+8LAxmrDFX03jmPtME\nxuq31UOnayv8Cznc6kLTkyoEZHeX5L1rSJUjv76PrBdIUoNKEZy678VhSboBCOZ9psMEOWibL1Zv\njPTHSHdipue9IdLJkU4wQk0Z21rvxynRhm3my1WTEFSRSUAWA9LSGYsJTLboayRHT+Rl0/6oAap2\nMJP/ZMb6EDU2l/gibjTMOjXEbdYvBWZJcd0vBcgWSF2Ar75NSC2BbpyveXyxzvEF80iqreR2BQYC\nfU+1tgnS55SMe3eLqvMUXFvB7ezbdeYcdD3y+WMkVZNxLWFG+nXf1qrfun8b2N0J039pfE6YkQZH\nmHzSY2b968Aytp/L8fX9yGpssroqLNFeAl7MkM3KALFmGzh5mCf6eT7q16P13nTMgIEX+xhAuhI/\nW2IAWQbkYpVKtzxSlMhSwfSaFKbqQjN5b7x+1k8NarUXkdYEWR7CgiP0LuBiZ7owsCIZ/QTy1AAk\nVRuLndhJPkG9g3ECY9BOF3UrBkp0vLH/XIL5n/WRvSPc4ADuVshfZiYZbLeRK9s4d4jzA1jMTVa9\nqBAynI4M3xsDIYWg5mk5UAgObVdWNKIfTCLeIEtKgt2fvIJE0/HKIy4CBoQIiE07ZnryTYJXGbPO\nK9pemn7GHR3Fz4ynr039FkMLiYVkpH2aiSLOpNCyH2KFXwMXdXHJ2GIBkBy5JWYgXuRWwTVJkWqE\nu7SN646RhRHSCtDvIYMjJPWw4GE5t9frsdscm/Pjsv7fMQPiOxgr06kxIlOJhQEiwNAbm3dhlkcg\nLY1fuE+IobmycYi7UuDW95AOCM4wtrREupmBWnkCS8eIz5Hg7HvBzrVczJC23ffwwdB8LcyUH2Fa\nZTeNzMp4/1T1cf3NGaDlkli4ozQprHiTT4aK0NskLFzFb1+3Aid4ZGuM29k5IQ91GuDgAJlYtWda\ngt5r2YJUr233teaiZVMq6zxSVbOqvABVxfi3/5tHnjdOq457k7WG3kXC4lWrnjw6slvYcGL3450U\nxiaL13cXCD3zGq2efc7kmi1m6gIPLi/IX/p13F/dpv0v/sS8vFSpfVtlf98YWIMBYXPz5HGeOO/u\nwXLQH2JM591JMqtqej97jdpn9/YH5P/WbxAeA4z8mxIfVT45P099pNDowbb6mN7H5/EzFefyyfM4\nj/N45Ch+5VeNxfU4oUrxK7/2mDt6AO07z00CeNYEq4rZY6uFbmwiezsnJGn3nZSNR1Sf/DTZL/4O\n7Xe+jN99A7d3B1rtWKa8QthF3IQQFlC9TKg2COFpLFuL0UtI732D4mk73nqVU8Y79n6TNRaN+MUJ\nRfEy6d73bFLuK2TtAOmPqT1RpsypytsKYe05kaa4vV2rmIlN/upwB3vIaIQTR4iAoNu+Z99tMLvU\nO1z7Lv7112bmsd0ebneb9j/7slH6YyQHb5s/w3QnCacqdZ6KZkYSJ+8iJOnXEUbo4jrsD6HlCJev\nmMHzeGRJe28EVz+A6xu43jGUXfPLqQG+ZuWwzgYk24hmJtlKKnSS4W7dhKBop0N4+mnYbKMaCAuX\nSba/Y/07HsUMMxpzt4IlllJ7d51xWLUPUu1pMs+Wqb/jsYpnHQeuawwFdXAxR759hBIBzQh2UhTG\n0vosyGJlksRKZk/RmpF2VpuiZEoIKJUlRZM2LI1mCaxGClInxxFZNU3zdiEyTZgxLZqAg8Y3Agbs\naOO7LYyJVAU75UmzQxoRFS+04pcrmYEncfVftPYOrNkQNYOn0QeBWVLcZOkdAH3rDqkJZqmAq2ll\nDWCsCUA1t0Nj+yVwVdD9NmG/TzX8LPq84JNriAwxwDWlOlyiWn0Ft3Id/861WdLy1Bg2DPw74X90\nVvc0QLkpOc43vlOHJ7LZ4uuXmOXuNYjWx0CAgFWLG8buHMc+qoDEIeulbasVt9tk0dXg2aOoi5rM\ntoQZcFlhbJ2nMQDvzGGhMBIoIgPLx7a3mPnDP4jd1wzBfAPDPiRtkNSAhMKb3GswRrIijvUc1hN0\nsGKgKRUk90zeFwyA17wPqy3CykpjAaLC5Tdxo50p4KejBcL+Mu7OjnnjiQOfIS8emQQu9oV6jws5\n9HM0xdg/lY/JudrxHkkERKvZcacYc041FgJodojYa1PZa4H45FSHGfYXQD0qDtopotXJSoUis+t8\n/vyKoEQZaZg/kTVjLBjI5ICVEt0M0N6x+9okINugSTAW53hsC1Bpin6iwF0uDfip2zGuDLzrJuAL\nA+AdBgROxGSaTk+tS8zaG//uYv1a368KAS9ItzQgeFKhZceqlBb1vUFgkNh97r6hcFGRokL3JrAk\ns3EPU4aYpAW6uYvkCmLsNDRY7RmXm2+dOshTA86THPMADGivC5SNxbn6BuiAFNVyJi8PgrY7thhX\nDAiLT1ItPElYetpAgpCTvvvXpgzfHSHj9DS4Mo5MMBEkVGgekK0l2BjBQmfKrjvlCzfJbJGn6fFY\nTJC1lO7b/8ujV/5LF6jWXsTvvzWTv7kW1doLVGsvQJ7T+pf/F+KPkNYYyNHhMtVTzxM2LxCeehpa\nHp+8hnOxWms9TyvH9P70D2A0JqxcNulw1phvxoVW2dsh+cbXKb/wi6ePE36sLKx63j1f1fS+kaTI\neGSS45/DODVPfaQv9UgO3v6ZkY+ex9/sOAfFzuM8fhaiUT6a7kO0+hDBphenJa0fOR4g03Q3rt8/\nMWp4/JSf+jTpN/7aJhEI7sb1syvxjEeE9U2y3/4d8Kn5ChQD+t/4r/F6l5i9U4XOnJ4AACAASURB\nVFUvnwbB5tvmrp9MFMRRbryMlbm/gZvsxkzdEfqXqZaewk3exh1cg1AiV+7jQ7J2CGuHhKMO7v2R\nMRDK0gAf7/GR8u/6e9a+qkKGo+nkiLjKL8PRqQmwVB5Wctw7c8DhWWW9wxxYKYImPaQcMWWLJSnk\nE5AqshQsM1FNQVOUPiLboCsxWQVtt8283Anh4gai+7hs34BDPcYtHRBYRRc2cFtbyGCAHB4YKBWP\nR46P4SCFhQy6aglPZwKTPrrQRXst3PAtlAWKl/9d8CklQnrre2i5hugAmZo1KaRl9KDiJIAxHXuW\nWJ7sj9nX6/4BzGOrmq3uA3a8VwvICjtPEeyUtsATGfJSbgngtNpiYx+n2tKIEOWVkhD8E7jVuzCM\nLKEawGwVllBXGNOt9lGqAZH6GO4LCMb3W0SWhZ5M3Au1RD/RkwBePRwclsjWstNKY1XGxvalqR+c\nO+55NpVvbLvCmE9JPKxDIqgiM7kV1OjAye3T2Gb9WgosOTTz6EjR762SPvWX6HYbcGh7Ae31TCb2\njCNt/b/oCwGZHBqTSBU+NTCW2PTYOP13E1+o/07mXm/2Q90HYyypr/ug+bmazDZhVs3yCGORLWMA\nlVQzNlzNXKvb0DxvTX+z+dDGD41t5I3XasZbL/ZpXZmybmsB3A42Hi8yY5M187/7gcFntUcwnz1y\n22+ZkeweGkZRyvR+ZcrCApJd9KgHvT4qi0hriIZgMvmOPetkPJ6djuBgF3SQEPwl9E408K4q81VK\nEmR4BH97aOc+JLP36/OXYKxKSjOwPxZLwN1MgijdaibDdrFPE6J8MwIpIQIvTsFnqOsijBrjW+y5\nE6WhVrwiQRfWkeLYJGnFABk7K5iSjmAyASnjzgR1LkqpMeCgymvV6ywimCahggtiRUZa4Lwzr0Yf\nz/9TwG8NkdcFvm8eiPLJCn7ZPm+dbKCa+Aw6WJurZAbYO4zpNc9+PNEerC/raqJD4v0JA+1Ept5n\ntAPSP7Z7fhnAgU7c7P0zx1mAzoF5aipIOoyfF5PVNz+HIm0b3zIs4zzAGi1LFRpq+a1A1rKqwIna\neMkLtNW8GSiq9SqEUGvSRQQNBSSrkHQhFORX/425hawWGnrI5K5JNM8yFy9mFgiaCOwIHI3R/cTY\npL0+DI6jB9xMRillgdKdHpfTO9DNKK68Yr54AFVGevsvSG9/hWrtRbIXfpezqhpmL/wu3e/9wdm+\nUAnm27nmUNqoLlIWnyGEZ2OfVNNFt/n5mtvZJiwtQdrD6Q58fATfaVuhghP7SJHxkOT1Vyk/PVdp\nMqoffmyxsED17HOkX/uqFZB6WBQF4eJl/A/eOzl/+3mJ+Xnqj/p753Eejxnn8smPHufyyfP4icQ8\nlflh5aOnUYNNv/f7D5cEzsWDZJr+2rtn50VFQbh0ebaSL2LMo7oi1GhsK4h1jEaQ51SfeNHaeEKK\n2SL55nvo5AIhPEkIV1Fd52F0CZ+8iyQJ4Ymrcwfk0c4aYeEqYfFJwsJVtLNmry+u4G+9hU/vIp2x\ngVTqTn5fHagg2TbSH1j1wl4/VqISKEuj/Pe3YaGHOzgwGaO4aWLksmz22hmhBz1c/w5udQvnb+D8\nbdAhmncIH38RgHTr65YA1Mc7vI0mHVwxNPq5CKITpBo3fGzq0xGiv1gXkWzGkAIzqV5ZwYXbuGTP\nKh+2Uly3A50O2h0gXcEd3IMsITzzHFr7qFUxM3MeKXIo2qguETY20F5CtfAk2l0iyEXzGxp3cTu7\nhMtX0HSB9M2vIaOJJUplTDxaJVIDYtb4+HNmz3EqG6srmUkD5am8JXX1K+rQ9gqBp5DDQ/zeNvJi\nhjw7RJ4okcUwy3e8TBlUp/Yz3xrfQn0bWgsmHZYSbbWRykVwNEDbEigzOQ8nAY/5xLI5XGTu75jP\nTz9Xe2E5jO0x3zxhxjyr/y+c/XTmPu/q/+/DNpO53zWGNuIkSNbGQMHckmNNJXZbBKzuB0oFsWIK\nDvNPqgKSq/m7dSvwXasyORojeY4Mj3F+Dxfu4cOeyVJHIM9nuH48l2ddes32n9Vf9e95MA1qvN6A\nknlfpXVmbK0afIpADEVks7QxuWXttVSDVjXI0GxvQzp7X2Bqvn0a2+VjG2slLHE/0cOdEXAnvv4E\nxhADAzBqLKApLb0fOHdWW0oDfwWTGcpYI1unZmBFRkxL4RCkKIE+2i+tGmoRrAJiNwcf0O4KFBXu\n3haagnZW4AjcxQNk9RDX3jZ22G4Oz0+QJ0sDfTVEJrPa87CjxnZqAsXTc2OVBdkvDFhqPnZKDNzJ\nsAqctVw6BGNCOoUqsobSBSsGEMr4HLAbSnBmsiV5DpojxRjJR7jJAMlihcOqjIhhlLE6D0M37S9c\nAiNM2hnivdF7lNL6cimy/XxrJnkGAz0LZ+3aUOgG5KrCiiK1w0FN2q2Zi8DUK23+Oqiv26Lx+aZP\nXw3Ml1ixjHq8Ye2dVjJEbPEiKQ3AKx3smRyQ9lwVU1UYDZHJEFnJTe4dJLIa+7YwUZbT7WurDQve\nmLkEZBJmz+E0oC0gNOc6glDYNoOgQaElDSls8yBnHTF9v90HrdCkg6YLaHf9RPPl1l3c7j5Ky1jL\nc+HigqS6YL6Kr7aRskJDH3kGm4+0WhAqk9VGtrYdT0rotnG9Peh5woWLVBc/dxJ8e5TKf85TXvg8\nbrxlVbtDAS4h2f0+fu8NpDjCZWOUHqoXcO4A728gMsC5LZwccaLIBlhxislkJu0UD22QfA+Gi5wK\n55HBgHDliZNz2Dw3Bv0Pq/rkI4S7/QHJd79jxTUeNJ8uCrTftyqV8dycuRj8Nzg+snxybp76yJF0\nKC8/pFrnefxMx49LPnkOin30OAfFfl5jMCD9f/4l7T/9Y9K//CrpN7+O7O8TLl3+sTyUTz2gHlI+\n+oFg0yNGuHTZZJpnHJ+7ecNkJqe+FCh/4eWTEwYRwoWLhCtPoKFCRkOSd97B3b2NOkfxK3+L/O/8\neyd9neqvfgj/NBeuoZcuNiQ2jxDe4w/fgFGBm5T3Aa0UOT5Gxjl0QJc9HPWhsuqK2l8A73G9XWQ8\nNGlDnTmIgSFS3mcVTBTpj3CrY8QPYHHBACGpcOkR6dG3YVGp1l5CskP84bWpV4OUEyTbR9srSJVH\nJlxkQtSyRjUqj2oHo7QcIRIiyCg2iVvfQC5XSH6E5DGBcxOcjIEJqhlSTGzivdFHZclWqLtdKyO/\ntIyMx5ZoJSlSVpB7Qu8JyuKXCeEqEnZIBh/gDo6RnR2S99/D37huXmSpMnWZTyrER7MmmUvCzjgv\n9wVtmpQrBcoUQm0IFNBxirZXKZ78NSgm+CduIP3SWCzrYr453s/AMJUGUDQ9ebN2U7NfEpsMFy0k\nP7aEzYG2lqDVRlweDZoje8TrSQDkfkDMPGgkNDzIOG0E32SczZuu18lsAQxS6AQD0U4AcBGIPBOp\na7Sx3l7djmZiPGbma1WALgmSxDdVT4OMJ8JZoqwGnok4ZEVhuYrsup6RHRczJB3glg4QlyEhtWRW\nC3uvE2qyzf1BsbPG11n9Pv+deLo5xICkbvy9yIxxk8/tI5Uo19OZPDH2zxRErFl7aWM/D2orjWOr\nL4mCGaupBudSjL2WxP1kwCC2YRm4El+vsD7s8fhgWDOaY7BmFIrAXtxHqhH48XHbgpYBlscmddfE\nmJudlo2BVon0MnTRo9pCVjtov4fjCKkqxAmSj5ClArk0QK6WJ8Fmwa5XDTbm59mKCZAZq0nq8VmD\nQ3UIswquXgz0qUGnOM5EQEpnnlA4NO2Aa5uc2rnYyQ4pJogWCCUiScSgPVQlUhZIhVU9DJU9Q8bY\nc0QCmrehXEQ64xm46AQ6JSSCJGJAR6IG1iU6BcS17GK3oGCy3248NxvMxuP8eXeNEzo/BpqAbVPu\nXV8fYGPtALgZwa9auq3xvEcAS5LSKhiP1pBJhrbbJ+Vqqsjg2PphORj7C2DkIVWrbNoTxOcmxeyn\n5pXpCgNc6+djFResOsGaOw4zgMm56KcYD3fooe3Bta2giSoSKiQPSFVGvzdnTEEBWl3UtQgLV5Cq\nICw2FunKEe76LvoXbeiDdHJ7rqiNS5lMrGJlKNAdQb5lnapBIe2iF7vQyQ1Ma7UiYCiIBtQ5tN1B\nP7mKbiyiC31CbxNdaFSbPnHeUiQ/xI23qDZePuN9T9V5Dt4ek7z5fVq3/gw5vIsET7n5cdgCfE0p\ntfu6cIhPfoBOqaazkL09dGnZip9M95Egcgx3U8xCYS6CnZ/pnG48ovrEi1Sf+8LZx/Qjivb//Sfo\nhQvIcIjULL3mXLcozDdufdMAMe/NXmNwTPnFny6g56OCYjLZPzFPfaQoR5QXXiGs/HQBiOfxw41z\nT7HzOI+/qVEUtP+3f2oSNpGZBDHPHl4l8EceGeFLa7hnV3Dvv4PffYuqXCckz1N+5lcofuVXPxpl\n+0EyTe+ncsBplAW6sXF2P1QV/q03cYcHFK98nvLqU9O30m99k/Sb3zizHz+Uf1pwVE022iN9J0c3\nejBZgsN3zpQzyHA4k0BWDi459LaZDdfVFQF0tIBr7SHDAu01JBPaNDlpblhh5RAt0shQm/9MC0KC\n33+Lzvf+AZMXf4/09qxPqqWncMfXp9vWdCGu6BZIFk168aiuAomtLOsIqSCpvo1OWmi6QrW2ANWY\nsHkJN7yLjA5i8mXZjXhv7K22QjjADzN0v0MtRdV2GxkOZslLXRq+1Gm1Jr/0PSQN9p4Icm8LbbWQ\nSYVeKpFOzCh9zTxrJKsNFdIj+xohs+1InXw3vjxuE55YhwC+9w7aaRvLba0H6xk+34pgj+OEx9cJ\nYKDZxpjgqWJogk3mKUukqKD0VpjAVzNJkMjJhLLR9LMO58QuldM+PmeBaPXveWlfHWVlwFwTX4wA\nxekvzkUTMBJm3lc1KFNLBhNm4ENZJ57zINzcrpxGcCt2uveW5ArQLxG3hWRMq8FJ3VYdGrCJGNBT\nA4fJ3L7mwa4Hjan5897sJ4dJiQIzACxtfL7FDPASwCcGyCSYV1L9+ZqtBCcllPMMtub4PzUe4981\n0NVpvNeNbYwkJhRjqa1ggMWQGUOsRawwep99PCzmr9PmWPQKfYEbEWReAV2qoCfoWgZLzjzO8y5M\nuqhzhKsLSDJAl5cIq2vIzm18dwdlAUkS9NICbOfGOA0BGWXGlKpBykGjYc1rd35oazz2Ccbi7Mms\naMFZXnSjWm5Yb1dnx13L+ENAshxt11I7QFNkfADtfKoirP2ebB8OTVsmvadCnEMrh/ZBjsXuzVsZ\neEXT1KSfkoCUUfIYAbpET4J+oiYfbE/s76HAQuyH9blz3JRyN89r8/jr8V/LezNmzMSaKVZHzcRb\nBvYTdNiCfoG0S1QC2mqhskjwq7juDnIs9jyZn1OMhjPZYCefeZodCVwQxB/Z57otY+IVE7tvUJhf\naBn7ZtTwopyGxkrIcQGrrbNncog3t0zsWeKaF4YiZYmW8Thci9DdZHZTBMoRVBFkSDzuhR1wCeFw\nwYiAYRfK3KYKuy14LxiDt4oLUs6hTtA7m8jTt9FWYXMGiYtToUXo9wnPPYss7wOCpn2qtU/xwLhf\n5b+5+a9bPkRlDSSF7QK/vYVMChR/YuFS3AiruLqN6sUT2xPv7Tk7F7q+DlcmcNOfPt9N39am1caP\nOwpjHla/8DJVnuNuXMft703BsXDxUvRSa53+3s9ZFFd/9cQ89ZFCleKJx/Q+Po/z+JBxDoqdx3k8\nThQF3X/4B8juztngUgTI/Dtv0fmH/4DJf/5f/miBscGA9E/+mOTamyTlt3DJDmFljerpF9DnnqF8\n7hkohggHuLUd6J6eeDxuZH/vd2d90ADGwuoa/tYNq74DBoh1+5Qvffr0RqqK5JtfR46OCM8+d9rb\n7EH9+GH80555GctMH/1cuL23CZ11whdfQg4OrIhArNgFGPCT55a5eI8miSUaF0fozrpNzmtz/oUh\nrA6hHU2ks66BKvczw18Ygg/IQWTKKbi9vViaXUGEsLgIIcGNt2m//3+eNMB1LbS7iYzuma+YS8Al\nKF00XYThwHxoAkjYBTUJkqgHX0KrDZ0x6a2/QpM+2u8iWkK3b3lP7RF3tAcUSF4iIUPTEehmZF5V\nyPY9S0BaLbTft4ShN8KPr+MPb8FKCYsTyNox0Q62suoTNO0gt0ewWcGaItQMogYQ9TgxTcZr4MnZ\n8buAtjMYOQMAignyzj5O3sAtHlkinrYImxdwctukLWS2HRXuX3VSG0mioNojhBWc20adt0pxIsh4\nZFVYl5iBHmk4DV6dOIZGzINaNfBUD60mS6z+v8Zi50GV0PhNYOotdgL4CY8GhNRtmW9fTbTIMXBs\nCTu3QU+yUJpxwsbMGBvT7SYmTZO0QsWZb5LH2p5WBuJM4pdbOmO/zCf380BN87X7Hd/8Z+pct8nu\nqy2GcgxUqRlyNThQL7q7Enp6mkG2QoO9h7W/7pP5ds+PlTqa0sj67xpsrMkcNVhZv57Ftq4xGxc0\n2v+o/fQoUW+rSQxO1Rg2pTOJWi0FTEbQy9GyjepVtOgTNtZx+R4+20U0M7l23gLZQ1cSwsYy/o0A\nK5UxoOpz0sIqb2oNhNXg1dzNRZldR0dAP35ugPnk1bjWUvxsxFpI9fQ1UIO5IsYAKgo0Tcy/q9o3\nKbm4xr3OEEuR3Pwfi4mdgyAGrCWgGtBuG458BOsy2Ba4KJAWdn2UzMDQ0GjQdBxLrKKpM1bgUrzF\nBRc9FOG+99363nTi/XgcSeyPUmfXtzJjqi5j1+ZKBVkGBx30uIUmCeo3CBcMRFE3AQ7QjQ2Tq9XF\naWI/Tn20xM6hjh1cKNFeH6lvipHxRVmZ9FqidriFFQhwkVknpYFP036KHZVLPK/xIlWsIqaGmVch\n1ezeWTmoHFXxPOpX0fVV/NF7SDUhufvXSDGiai8Tlp5BXAULXXsmLOfIYIDuOri2PANYiwPri1bL\n+kqNrU4QwvUryOVtZGFkAF2rAJ/BRop0byCjjGr141Qbnzlzke9UiJDe+grFs9Hk/NT8N4+G+XHO\n14qVrBHc3g5hbX0KBomMEWkhMqKqIpBY2CJhaLdPjsnp8GmjL/TRUR+3ux3B6Rm4pJMJDIe2ePrb\nv/MTWITmpM9uq0X42McftFx08ns/b3FWoYYHRTmiWn/x8c35z+M8PmScyyc/epzLJ3+Oov2H/yvu\n1o2HgzFpihwd4ra2ZpUDHyceJs0sCvr/7J/CH/4h1Q+ukaZfx+k+BI8cHONvXscdx9LVSfvhPhGP\nE/eRaerCIv7GDZvchIBubFB+5hXOqhDkX3sVd3QIzp2WVjbjPv34YP+0HOfewyfv4sI1ZPmY7Nf+\nHfzwhpnc1hFy/OF7JAfv4o9v4oe3kXKC0sa/f530zneQvTb+7l3zrmilUBbUfkeSRUBscRHE2Sq9\nAh1P8E8hV7bxl3aQbobgTP6XlDifQ78AX6J+CfIMd3hkpddzM/iRlQnkbTP2PR7YampdTVEVqhIV\nh9u+hxtm0M2ZfPo/JTl4B8kPwaWE7ibJ3ptmpNusaqmVJTmhi8u2gcIAOvU2UUtTm3A7k2pKGNk2\nWgvTxMA5+63jCZRZlNwYu0mSCsbWz5JFaWWokMXo89KqkGjcLYnDLQyQ9hCpRrjtgZWkz3PzjSoq\nmCRoO0DqkLThIB3EkpgaQDgLpJgeNCcAqtnLPaQsDfALAbZ60O8g30/x2es4DpCsRFfX0E4HXIUk\nAdHItlMx76/5/TYSJpyLpIwWIVzFuW3ElwhVvFaM+SSpWlJVM+K0kUCeeRyN12sgpgY0HJY415K7\neeaVa/ye/7tilqzWQIDOfe9Ro/5O/XedLTSBmTo3uJ8Ub56VJMRqgPHNBnNQAtanU6JG3FEt3ZyX\nfyknvdTqNp3VjmbofT43D1oqJhWtGWk1CNXsEy+zNtSASi3rTBqfrdtccPKcNds834b58dIEwuYB\ntGZBRMFAsWMMqKq9n5QZiFcDQR9mTDTbVh97DRTuApciMFPjR12FsZh8sQpm1J8EZHQPl2/jdz/A\nb93FMbLCtC5AaQxbKSucO4CV3EC2NrMx3ZSklo32nVVIATt2uYsx+Vz83ZQC1t9P515verahkMT7\nhleUCpUKwRlz1EfwX8UAFQxkAEFCZmM/1FJjh3pHpPiivgUbXXu+9AsICYzb4EpkKZy8lmmcu1oS\n3QQ+o0xXIHqT6elr5axz2wSFq7gzpzOfvea1F+K2U2dMKwVJzFuRrAULS0iRo4smy9eiDWWKrvXR\n5TWkyO2ZOZ7Y87G+t/cLq5wbgJ4zyarLkKIwxlcNwDgHiUcI1Kb7ZEkcj2qg2CmWtqCusmMTM4+X\ndmnnjSoWqhBqY38Vhdwh2+DYwpUHaL9PWP0krhpByHHlGD+8g4x20c46bjRCDgdQBqSvsFrAdgvn\nPJQlWjPW0pYtYFUlEgK6sASDHnQzZOEYSXPoLxCe/RgScjRdQKoMKYdWFfphFapdiiuOp35O8/Nf\n597DuUNOebp6j7Y6SBa9UquAJMdRlazm61a2pnNEf/cu02rRp8aUUG18gXDFpKZSxBtQmlI99RSj\n//a/p/r8Fx7s5/UjjA9j51EXBAjP/3RJAj+qfBKgWnuJZO+16Tz1vlGOCN1Nspd+/6PlK+fxMxHn\nnmI/PXEOiv28xGBA+4/++aNXbExT3J3bFL/4S4/+wCwK2l/+J7T/6H/H37pp89WqQooC/9410q9+\nBXfrJq2v/Gtad2/DwgIheR0nh0zNS71V/pPhwCr6XL4SAYuH+EQ8TkS6ePGLvwSAGxybZ9RkTOj2\nqD7zymy/85HnJG+9AU7QjQ373IPirH48E5jz+ORVkuQNXLVrfh4b61SfeRk/vIXfews3vIN2N0n2\nXsPvvIo7vomb7CLZPpId4Leukdx6DTm+Z15b2jNA5+DAJp6IeWUtLNqEPElj3yrSGZnUZF1wl3ah\nN0by1syvKk/N1yUUdi50gvMjGCVx8mh9JYtj0ArupeY9U5UGvDUnBqoGkKapnefdbcKFi2Sf/I8b\nBrglrjiySXCVo6FERkNkVEDRRooDcBWE1NoGUV6TRO8ySxzElZaIlTm06slwBMWOjmLi46LfvRhz\nZ9izhDSbAAoXCwO0VNGijeyX0O1G+WluflquhE4FAwM9JARLgKsSFiromLzSEioxIK8IJw3l54Gx\neVBAYSpnLB2SGwClIYXQQrMEbpdw2yEvqPVvZZ53Mh5BLshCBV4QSktGa1ZJc5/TqF/sAA7n9oAI\nNtYoQ0uhGwzREYnJ1Vybz/q7eWnVQFj9dy1NfBDAM9/emsHV/K6f+3kU8OMsMKFqbK82aa//D432\nN9tab+cMoElk7r36/0ob+9cZ6NLcdvN/OAnsPAogBqf7rvn6/E8tD0sbfzfb4Oc+W4NDTZlZE6xI\n5947q501C6d5XHXUUtZ5ee7JInoWNbNNMYZUxkxmWbPOdG4792vTWdEcs/V3VjCwqiD6sEWQKAkN\n0LYyH6hujrQCbhyQpARnCzJTNlAlSGuCUM78Lut+rttd7zfFAL+aUXVW7IMM4mcvMMMCgr136pjq\n/pqIbVOZAbT1/T4WDrH7bWRn1gO8Suw+V6YGAoZ4oTRN2J2DJEVdgrQdQoaUCYQeHCr4rvVnrzh5\nWqYAqcz21+yjJpOyUjuOWv77oKjfzzl5Xus3S5ltu3RMC6WIWDGDCNBq19sx1CBK2kI3n+Dov/jH\ntF77GpLvoEt9dHEVd3iIaJj6o+kIZCywGmxREJAqVpSoj9E5u3UnqbHFqtqDzBYndOSRpGaEzUUQ\nZAy0PLQqm3oFWyyjbF4MagtQtGB1bM+S8cQYVEmK5McGDDiPFaQZ4rJdQrKBu3MHyTIkK8DlkE5w\nBx1btMpz1CewuGhsuouryBNj5MkR7rlbyNIIdX3K1U+S//LfJSw9Zb6i4uJ+BrjJDqF3+eHAmDjK\nK7985vzXJ++eOcWzN03CWfziLyHe47ItO80uQZf6FJ/99ekcUSYTZH//PsBWSghXDWhbWyc8cZVw\n9UnC6hrFv/lvE158iAz0RxwP8tm9b/wECgL8MKKfFvDun6Bv/h+kt79qxvmTfcLC5Uf3CTurUEPz\nu1FOXK2/aIDYGRVQz+PnL85BsZ+eOAfFfk4i/Vd/hr918/EeZKWZkz9SlZlITXe3btrEY34/rRa0\nWqR//q/wb79N8vGPgSsJ+mpcsZwL75FsghsOpvIDXIob3Ka4/EuPZ3Z5v4h08fKLX6L80i+T/+Zv\n4e98YD5S96Gyux+8h9vfRZd7hM8t4ZNrs6qKHCNylyT9OknybXzyOs6/D1qgeW9abbE+vikwpyXt\nwZ8icoQkPcLFJyg//QszYM630N4l/OEPSO9+DTfewWWHSChsTq6KO44Aki9ASkQz3GQPN7mLK3dx\n2TFaOnRxGb18BXfvLoLC0gjWSqRnsgtpl7bNJCDdzECiPGYTWRshB4kss5a3ZOeogpCBlmgSkJ3E\nGFMhRzcTWC6hO4F2DlKg6aKtnk/Pc4HfuUX+uf+QauNlO78KyfZ30LRHaK/g9obWDtc1wKo4hqFH\nJmqT9VLRtLLEy8fVcsq4iq5IVcA4t6qhkzHkOZpNYNKxZATqiu9WLbJIbRV/zRJXFbFEabdtgFdR\nQCiQVgYXgiXZCxiwWCnk0UstKCwq0q2rhkV2UKUzNsY8Q6EBhmkAKTgpDaywhH+iMPbguqjH/Fe2\nPoEuryAXRkjqDbCMIB2TzPpN1OSmacz6mvIyaew/+klJBojGAnFqY6D2FqoT84QoUVJ7r/YsavpI\nNaMJzEyTTE4CWPOAyPxrdRtrQCx6m0+ZZnW7HgYSnQWEnbXfZs4zDwzV4Nj9vt/4W5ptnweI5llU\nD2JVzb/+uPEgsLG5/bqNTWP/eSCsBinmAat5gG2e7QYN8OKM78y3dd6c3zVeq/u/amyz2dYy/u5T\n47yn+/Osfjgrmt+p95vHtrSY+aoljf12MOliH+iBdDDgoqjMpL3uC8WMMxG9FgAAIABJREFUzikM\nZG5eawUnGVw1g6/u07oIQXOsClYZ8WZs9gpWNKF+r2bvNR95JXaPiVUptR/bBQZkqcnqph5UNcA7\n9Sk0lpJWsSHiDNhzilYClVXrlJoNqR50GRnmaJUidyskiwe4mSFphRQGFJ7pyxgarzXHVt33NbD9\nMGZgfX+t2XHBoZLasQdlKlVVjDGXqJ2jFAN6kjaIN2P9ogdJC/p9il/+JfJX/n3ChU+Tv/JbcFOR\ne9uIZjAZQ9IhTNbgO4LcTOBTBdIzJNrYx8yYahqP14n5k4X4nKuly2Ngt2MS3voeL0A7oJ3I5K0S\ntFsgqdh3HcYOq+oHgEDl0dA14MzZYgpl3P6CBzc3B0ta+A9+gNu/iwx0uhAmAaRXIW+NoILiYx9H\nvEfKDHl+iHwKZDkgmwFdTJBWG1bacLGNK4eE7iZ+eMcW28D6t5oYY6x3kQdGrPx31vzX+RtWYOC+\nYyEgzpvvV78NrQpdWkF7PcLKM9OP6cIC/ub106CYFoTqMqpnFEj6mwIstVq4O7dxW3cfTb75EyoI\n8JGiKmi/9U9ov/PP4fAmRVEhWiGhwB9eI/3gK7jhbaq1lx6N1eX8iXmqK44NnE06lJuvkL34n1Bd\n/MI5Q+w8pnEOiv30xDko9nMS7T/948fPmx6jyswjSTPznOSdtxEN+PEILg2pwh6n6Ot1eI8cz5Wu\n1gjUrf4IqNs1g+vudZLdv8LL27jWBxHwGqMjj3/3XdyLE3gJnD+KC9UB72+TJK+RpO8hMrCqWxJw\nbohv3yY9+Bra71Ktf+rkw7LVIi2/DisQnvoY4YmrVpFofoIlDjfZwx9csypOvjWbdI6GJo10VpXR\nVRkShghmWiuiSFLgkgFusIMcluili8jSEXQFSVs2gfZjKx1fJ4qVt4qJrcLkIAgcBWTorXpWVSCa\noQcJ8oHAqwmypAgVsl7AekB6giTJtJ+ko7DqDFijZ9v0Ho6OyD77H9gk0bcIqx/Hje6ivUu4Gwdw\n5FC/joYl/PZtm1DXq9t1F0nMaIrIEgsTkzVE9kXNVJAQzEcjUytjXzhLcJwiiLEbJmZMIytjA7eG\nIHdAJhmiFaxlyJUclnWWmAuWuKxiQNmSxgTXgDFUGzKvucn4lNkgs2OqQEYYi6PxGqWzbXkx0sVk\nghwGdLKCHq7asa8c2yJ6WdmKfR7v82NnjLYWluhoE8lhBtY0//YBCSUi5VQmIrUf2Ykx2mhnxkkG\nVROoaH4eZomoMGP2PAjImt/ONCHnNHOpuZ/7xYOAoQd9fx5EmZcX3uf7ArPE+iwGWxMAOgu0kTN+\nPmzMM9nO2u78fuc/N/+Z5u//n703i7EkS+/7ft85EXfLm0vtXdXbDKe7p7unp4fN4XAVbUOARdHL\nmwmNYIKA5AcaEizDNuAHP41eDNh+MPxAAbL9JoHmwIQEQbAFGZBIcxaTMxwuw+m9p7trX7IyK5eb\nd4uI8/nhi3MjbuS9WVnd1d1VnPyArMq8N+JsEXHifP/z//5fE5xadC7MgxZ1a4IYi9pQ/12p6ZxR\namOVf7ewKSeGIC4qB+4/nvXz4vhFICUmYWgCs/GnyV508RytxiFanBPqgHVeKyutfR7LGlMBKPEV\nkgPvYEQuj2VjbDIom8zMBAPxPGgf2AYZi7GhBCpWaPkTGayz0MPEgLFpatk181IfL1PLiHggZRst\nAytFQHbLhAIuM2af84gG5FRhYe1JOT6JHn2N6vdRExSL16wJ7EKVOGIoMEwsm6DH2qTB5rSRs+M7\nMGPFlTpe4sQYxYmAJmi3Rzj7NPRWCE9dYvLSb9h723uKL/8M05/5VUJ2keQ7V2B7FXflAJWUcOES\n7qlde/cqpj8mYu8pX26mxBDVNIWSMSyhZMRdTmxjaOyhVyArRZkEI5QbG1gmy04wyYE82EZTLuVG\nToLmMYUs0MqM5ewDZG0YFOj5dasjWggk775t0gFJAftl6GY5Ppo6pJVYePHuLuGZp5BXBrYW8F0Q\nQVYO4PR5wrkL6Oo6uMRYYaNNtH0KmeyUNzAGjE33Cf2nloMPtcx/i9a/zt8oNdmWmPfIdGprslYf\nv3/Z6pd0PvOm97j9gW2mzmUpLyjyVzm0vn3EgKWj5TxqFhMC/Obf+czCPR/YiozuD38bt3+V1so6\n+BZZVrvm3tabH0mepVyn5hd/gfzSL5Ff/AXzSx7Ghv2J/ZWyk+yTJ/bZ22BA+u0/JPnxu5YpJU3I\nn3vh42cw/LTsYbf/o2aLOc55gwH+zTfu2y535XK54E7hzh2oL7yWmYC/esV26wCSHsnOO2T82rHa\n9UBjWGS03/td/LPvEy49jdzs4ra3IAR86xbyxAi3sY2GNSp2m+LcNUR2SlAmKRda+6iuEj0P0Qnt\nd76J7N2k2HmZ5P23cPl7+GQT17lGOH2e0DtPWHt28Us1TPH33oHWCoRASPtImBjgk+UgHdCpicar\ngji0lZsgvcosZEU6AZ99SNjbgm6BJBkipYB+2zKkIQF8AUmOFh6Zgq7sw702pCn5517A//g9ZOxM\nF2Snjd7tIhyATuCJwvRc1MEkNw0TEQuTWVmxBbAOEblBCJeIaEb6nW+R/Wp1XfONF0gv/z5yd9PA\nMlX8jeuQDu2c5ip35GElh2mGjPMZKFXdS6HMSlgKOo+dOTwiMHBop0UIzny8IMiqwI4i28w0VtAC\nLiqyUXNiY8hfkzGzqhZeeACagvgA4mDiQfL5tbLWftGy3KjnlGNMjzKSZeZAowbQIdaPe3chPG1O\nyKCHnNoxrZoYchOC0flvAqt5Ge5TgltNZlKBOYExFCuBmXh2jrEU6uAZtfMpzxtTMaiaDJYICtXX\n4L5Wxv2c3jqYE0GB+vfRtHHsUWU+6OfNdtTD8Y4C9OI5zbV3E0hqllEHC4/Tp+NYE8Ra9n3d5iOs\nDn8+98wtKG/RZ5EBs6zO5jU9Tt8Fuwc7GHMrAiORXdVkMX4UgDFei5wqCUKznc1r1gTg6s9egj3r\nsY31sVSMZVZ/jiIIGFljaxiIvkMVqnpAqasFbJTP+ggD4+I41NsdWZ5g4HkCTED3W8jp3OaBbilq\nL1RzCNRYcwHN24iMLbRP1ATeKdtJUSVGcEZVk1Ywba6sQNfFAPtOAYlaFU5NvyuO5aJrsQhwjZkj\ncyq2VRy7+H/5PCog01JWYATqxBhx4k0KoK/MNMY8FRgoVaXiC7SjyNjSg6orFgtu9/uz9136//4+\n6Wg4Yw7p/orpeNLYtB55qya1jQ3VYJs49/qoL5BsgLgyAcJMOF5N6wysL4k3qQFXXodE0EmLMOwg\nrfLCx7DENEfiQBUOnaaQZrjBkHCqitP1H/zYNubabRjlsBZgp7qpJANOB/AtZJrhkzfQ/inChecM\nUJRtxEM41WBVudQ0QcUh421L8FLutqhLcbs/Jpx+iYVWz/y3YB0bitP45ApHrkFn+m1l8p+DW4T1\ni4cOy7/0CukPvgfDoQFLmhH03OGyP8tMk8ssTRn91t+n/XvftKycMC+zMhyC6mebEOAjWvvdbyKj\nu/cXxk96lvTp3W8yefE3Pp3GndiJPWQ7AcVO7LA1Ui7PJvfphPS73yb9zrdscv/1rz+ak/sn1f56\nlpkHsWNkmUm/8y2WizNU5u5tV9kdAe7dhY21+xReS10dLdwHqHuQMZxMSuDsLdLi2+DH6OknKJ5+\nhuILz83V6+/+ENkdIMWkBHME5+4gslsCYnXvpsC8EQPfRDz+znU67/8TNPRnacIlH+D2d3H7+2jn\nKuHsFULvnKUcr+1Y+Z33LROjb5lMiPOE3lM2pqEAPzKdsDK7Y7l1bNm7prUxVwdJjmvvlDoiEc0x\nmoumZThhkSNFsN3hTgatlFCsoGtncFtb0G6jnVKM9lQB1zy6tl6GEg5KcK10GCZT6HZN32x/H01T\npN1GpUxxHk4T/DMk770zB4plT/0Kne/+E6Kz4e7egtY+9HNzTFzplEVR4qnCgSID0J4eIjLNNLkm\noXIuUHPaPLZwHpxFs4TwwZP402+UjIcyvGS9gDNlqGQcsrqGT/3yR4ZCi1lopHqgaCGa2ffNcEGk\nBNnUHLk9zHk9R5V5jbKfA1c6zeUY90DOT5H3bqEXLqH31qFzwxb17TaaZUhRoHGH96AESpOy71HY\nPgqSC/PZ/EowZxZiWug8kFEf4+jot8r2Q+WUxsx2BxiIsMo846VpywCQOshQZ1Qtc5bvZ816FjGo\nYF5nqKk5lFKBgHWwo15mHYBZBh4dB6B6EPAm9uVhAWl1q/djUR2LrsmisXZLvmPJ382xi2XUiR8J\nFs4cmWHUzrsPm+9Ia7YxMA+2xM9iufHYRXU2wV2w+WXC4XEribVzfYRqJRzvOwWu2XwjY2ATOK/G\nEHuCisEZQenmcxxZaDE0ucDmoH2BOw6kMIbPE0UJ0ovNwRmQC9oOSI7pGJ4OBmSF2gZFgoVw1uYS\nyYOBZnEu6I+RA18x8OJ8n8ZNAKl0tODwHKC1z+Pck3D0PRpAMrH3y65YEoBxYhICUo6/K/tRTxiB\nlmGHgDMAUHyBJgVS3KJ44qtMni9BkLhR98bruPfexe9sU6yukfzohzhV0+vy3ubm3VVIt9AOBkyF\nsg4FnRoVUYtVuNeydzUQijUYZEh7F86ZvAKZQ7PUsqD6gDpnzPC8HEcF0gLpjdHN09CZIq3M3t8S\n0Dw1SQEVJM1R5yyT9Kly/LLM9DXju8Wl6EqB7DRQbkf5PgI5NaE4dcE2VwCRYbkWaZgqbryN7F1G\nkw4SMgMoKZBsSnr7B+QhO7ReOpT5b8H6N4Rn8Vw5XGfdaomW8jOvkE52CN1zC4/LvvpzJG/8CNm6\nhYYuhdT0wh51YClNLZxzMCD9zrdI3nun2kj+ymuPD5mgbtkAv/XGbK19X0t6+K03IRsc/5wTO7FH\nyE5AsRObt0MplxtWgiP+3bfp/ON/xPi3/t6j9XL6BNufP/cC6Xe/fXyhfZhlmbmfJe+9c/9yp1Pc\nnTsW6hcCJB5uj2F15f5U7Ga6a3fEo3/cMXz7Lfr/+X9mgu9Jgl//MSJ7EFLk6hXc1cvomXPkX3rF\nFkbFFDfchO4asruNH30Auymyvm2LSOdRLzVw0CGS2U5uoTCZIInHtQdomFCEdcAjMiJmsZHxFHd7\nEy6A5H9Cfr7SJfC7PyaGDgiC7G3B9gjZ3THQpjexsMAaOCm5Q32kMtU8slQRP0FDUmqTTKk8icLq\nSRI0qXn/eW4JEe4WJirrnInmtttoyxOeegp8ge9NrE+eMtROkVBY1iZs918GA2RvD/UObe8TpkpI\nft5CN+uW9mEnhVRx3MR1b9uucqtkKURLtNLk8sA+yBUqhkg8tAAOotZXAO/M0fGFOTO5g+4Q3TuH\n27xjYF8rRdfHSKcUpY/hUWAOXEwI2gQ84jExBGqEAVdhXDm1EWyaOc5a/Z4CF6ict7qT3cHCYTJg\nO34ukAvuidsU+QWr4w5wGnPYWi3UKboBkkwN6KzXOWNt1Ma/HmLUdCLjbbWIGVM6mASMsUJ5HVrl\nd5HREqeuKBDerdWzCNxq2iL2zf0AlaPKut959f7Fn3qIZvn4MKQCYur6WfVjljGKHrY12TAf15YB\nlPXfF9W1CMSK9iAAVXOs6s8d2L21QsUKq/ffN46N12HZvbPMmu2PumEwry0Xxf9lyXnNz+LzF0Gq\nUe24HvPgVZzv6q/GCES3gYsgbwA/Al7B5owDDAx3akL79Wc1PpfNcY0C/i3rnKpatt57HroGjOuK\noXUxW684DEhaYZ7RVp8X42ex3UX5RzkPScDC+pwvmalU8yJaAjZU17U5b0TQKloE+SIYX39uA2Um\nYIGhh5UCVgIMPLrfQzYGVl/c4Ig2oZZ4IICff3+JBEQnhAvrtN/8HfgLxb/5Fsk7byP7e8TQwvSD\n93HXr5k26O1b6EqfcGoVOTeArRa4BO2XiVzK9mrWguGGAVDtNrJ5BySHm224vkJYfQaey3F+x+Z7\ndeBSQqtt7+6wAwnIeGLjrYKmBfSHsLeKDssX28buvJa9Bki7zNjHgL91Y67feAvz1HYb8ryULShB\nvU4H/akC8dbX8Oyzdk6RW/bJuqniDq4bEOa8Zd/OMXFz8eXaSHCju8id2nopZv57vmJjLV7/tgjh\nHE7uMkv0VLcsI1yoscKKMeMX/jZ4b+AJzLP/wpj8i1+gWPn3CbfOk/z4/ccPWCrZi/XNycfV0mvf\n4oFfeiKk175F9vnHv/8n9pNnJ6DYic1Z+/e+aWDIUbpWAN0ebmuT9u9903ZHHhE7dvt9QvIn32P1\nz/+U/JVXjxVamf3yrxij60FMleyX/537H3dUiGVR4N94HXd3E9nbtVA1MFDixhTf/QBtrxLOn2cp\n26y2Wxd1IpbZscYwBNLX/xL295DplOLLL+LcJpWGRglSbd8l+cH3yb/6Ndze5ZJlNEH2BhZq0eqA\nj9kNcyTPwfkybCH2ZYIMc7TbBV/Gk0mOyG4pwFrf7fZINsVt3SOcFfz267NMm5INQJzpVuQZqgLS\ntkVqkpnuSIHtvM5ARlcuoFPb+YVycVyYY+QDMEbwqLYRHZWL3Qzbm3bgOiYqrwFtAdvDkp2kpmU1\nHpftUOTULgRBx65ciNfGvEzaIDF1uYhl2JwGZG+f9M0/Ijz5lGXirAG9efYibfcXyPSe7VS3Sn0V\nlEOOtsdAoi4GKL0NfInKAZqWOicA3QJaUeTZxsnCQYe49V14qsw8eXZs2i6FmmNZB7tivU0mTpOt\n4GyIZg5ck6URy4m/ByptrTrzKjpw9WPOAnexsd1J4FSGfHjPAL7rPfTVIdLJjeXWDUirBdq2sMtu\nMWsfQuXY18c0fh//jk6hx0LTovB37FNsYwz9PCjbOaZyIuPljecNyp8OFdulySRatrZtggofx46q\np3mvLWojVHpPi8AbasdEa4bxPWxg7JMA2h60zjqwW79eTTBs0bnHKbt5/ePzUmfzRGClGab5cQHJ\nelkTqmvbBEubfx9VVpwn2lTPTGx3PWQ5Y/75q5ezBbwj8HNqgFpGCYaoAeX1dtSTfdRBtrru2BBY\nLZBtY1AJCiOBjYC0R2gSLIlAZJeVZCdj64Zq3mterzhfNDEJAXpqyUQmzhIRZJSMsQVjXB/bHJv/\ncqAdquse6xlRgVmhPFYDTBXNxSQHBLibQr+D5lOkd1DWIXOAkCX5iGsaLZlPghZY4pP1C9BaofMH\nv41MRpZV8kKB9nvwTobbH1uxaQqTia0vigL/R/voXy9R3EmOjOzCqGJaZxvr9g492EMmLdOMbIuB\nYiEga3dh6OBcgXbXF66tdGXVmN5x+BTojdHBioVkQvm+nL9hQ+sUTobVeQ0tLfUJaCmZkKZ2tg/I\nnS70enB6BzRBhoNaqY6wvjFXjxvdqTHDQPIxRf9J3PCOsebBNhTLEEu/+WcUp1+yzH/P/y3qmf+W\nrX+L/EtI+n1Eh4eBMYXimRK0G+7ibu2hW6chUzRdh4sTuJQaE9Il5OdeI3vqV2Yso4wT+ywt2Xnn\ncMjyfU96AHmWEzuxR8xOQLETq+yYulYz6/YsvG4weDR2b47T/hBIXv+RaSyVwIB87vMm1n6/0Mp+\nn+LFl/Dvvn1/0A1MDPTFl47HLFsWmlkUJD/4PjI0rQztr5qYZ1xA3e3DCxkyGuJu3CBcunR48dbc\nravrRDTtmPdA8saPYDSEThe3tYnqErZdmiLDAzv+zAH+zqaBNq0WFFNkZWQMI6Tm+BXIRNG2eTKi\nE8BDYswxY4cViAxLUKwR8+O9hZ3oWdxwk6KYmsaYKjLYN/aV1EWLxECuILaqVTXdrKSG2uz3UQmW\nATLNwRV2tgCyguY5Mh2XIr6VZy5aGJW88NDpQjsY+BZBSucMWLuhuJs34HNqx257C1uKu/Ngi+/Z\npS135T0wUuTWPu7WTTgY0P9v/gGDf/jfwymLzUjSt9DpBmR3oF8b72hNB+sMBsRQ/n4bA45amJ4K\nGBPAlc6NiDGpxgnaSdChg2FAVsdwYWrScaKV8HSdTbHM8W06/dEhK4Wc1ZVMiGb7FzE2mv9HRy6y\nXhzm5F6Pn4uxF6/1QBJ4vY/+u1uW/dKVBaeZMTmiJhC1MpuhWXXAoAkeRIZMHUxrMmMuYg517O+I\nSucpsql62PWRxk+zvKOsDnB8VHuQc5vHRtAiAhfx77qA/CIApgkSPCyA71Gzo8bgYZTdtAgswTyQ\nHK/NotDWZWUtszp7s1lm87lt2lH1xLZ47LmoPxux3giKNS2Gc58G/pZW9+AQ2FUYaQW4xzrq81n9\n+a/PYwmmIyYe2h20mFoilQ0FLaruOGaZPSWGkNdfV7HcJljcZGEVlEBZYSHeDpuv4jwqzOs4xn4W\nUoZWerRoI2EKWtugmYVQiumXoSg6W3ZIUsAOaN+hZ9Q2UvI+mhwYaCRSllXOnXHO1Nr7BLUqXA/p\n7tH+0b+w5DjZ2BhonQS3fhu9pDBK4L0evCugbcimUBQwBrk8hjOKFFKti5wH55C9PdM4S1K0t4J2\nU9hUA/iKAs576JRZkvN9dHW1KqMo0HbfupK2kOmEmeSCC9CZQGSKZYlt5mWlVoB63OAmsqG4fUHT\nFQ4laxGFLLXPI81MgFulzkDMHhozhmYZYeM8aBR9w+6pbDgfElneOKF3wb4fb4Nv23vNtyDpMHzt\nH0AzI2UZsuqvX0M2b0O7Qzh1mvDMs9BqkWdfwyev42SzuhmzDD17FsKE5C//HL0NxeSLyIqBmEyB\nvxjDn48ebTmWn2S7n8zKwz7vxE7sM7YTUOzEZnZcXas5Ezkk7v1Z2X3bHwLpn3zPwJyYxjnLcFcu\nE557/lihlZNf/3oVWngUMNYUAz1KsL4NXNwjeee70LZt5VCcNr2GN94yQKxsh66vw96ulVkU0N4g\nDMZIf2gMqc07hPONBU19t66pE/GgYwiWHWlzs5YKW/B778PpFWR327QyysWcdrvo+jpy9y4y3TZA\nzHt0ZQXZD+BGxngLNUqP2MJSsgxNLDxRu31sy7/WqXKVrtpFZJem6rbb2yWsr+L2LhNOPY8cZPay\nloiKtO38NEUYUa3646AVFsaQl9OkOhiW13x1gIV3OphM0DQ1fbAiB5lUbKqZQ6PINENbifW3PsYC\n3O4i+RSGY1jt2vm3HTxdmNg8VPpbEymd4wL2QDad9avIYW0d/9abrP7W32X6a/8h4XSP5OCPcNm+\nlZ+q9cnVHJLIDIiOWxShDljI3ps2VBrE9MFOlbprOcYImDoQb6GFRYJO1izFtvewLsYmi/o+0TFt\nOnks+HtRSFhkS0TnM3A4zKvOnljEpKF2fPy/DeyosRUzRVpbsLqDnBP0aUG6CgMPZxy0yrSQ0ZmL\nTnEcyyYbZZkzC5VWT90yqhCuyJiKWmVTKv2y+jgucvzrfV1kTbbNMvbNw7BlYFv9utWP8czfL0Xj\n7/r5i37/JPtSr+OzAN4+6TqbYFQ9nDFaPfS32Z4HHZf6sRH8roPj97unG9P2rK31+SxaU6esy+F7\nJcGet6g/drf8/TTwtFZgYWxn8x5stq0OIva1BGUUng9IIpW2YCzAlSdGVtZRz+8iwD8CY/X+5wq5\nVOPbDMHM62WotS/DNnqcoH1KQIsKUAxleTFpyRjLOkl8f4OeKmC8Bwc9Y075crL2JTAWGWVKBQz5\nohwDQVr7uHsDZL+NrvRNVmAjwJ4guRprfiVHv7wP5wVuAW+1DIAKAX4I+ose2jm4tjHE4rt3OrV3\npjjU5ZCsw4dq73BAe21ca88GNRTIwQHa7xsglrYI5y7gB1fRtXVka7MG6IG0MgufVIWtAs5NkHYw\nYf+s1C8LHre7BX4XOmMD46blBRdQXUfcgW3iJaDbqR1DOfa+ZNVlGdpbIX/5K6Q3v1PdipPdBTdN\nfd3h0dY62ZO/wiwxUT4kvf2nVehbQ1u2eP4F0t0dGB7gDwb4q1cIZ89RvPwlCl6lYIpzl3HhFvTW\nyF75Wdz33iPfeh66G4c9zkddjuUn3VwCxUfQUT5KnuXETuwRNv+Nb3zjs27D427fGA6n9z/qMbBF\nKZfva2mKG+yT/9wvfBJNeiC7X/uT1//SWFZJ7aVbhtuFJ2vpodMU2dvF3b5N8eVX5wvxnvy1r+Ju\n3zZmT2Q9RRsOYTql+OJLlnYZaH/zd2j/i3+Ov3bV1sxFgWQZ/v136Pzp/0bnvX+GXlzB37yOJA6R\nAue28XyA27kCkzVmixnnTHR9OsU5gSeeoNjvIqtDaAVkkqGraxUTqdytCxcvVToRL/8dlqVMPs49\n4D/8wHZZI1tNBO8u44YDZDqxPiqIKjIe4XZ3kckIxx502rNyJM9Bx5YGfQ68UAPJQmHsKudBurYg\nrnkBql1U14AWzu0yV4hzJoq+toEUU0L7PO6DH+OSARXdom/leY/kQ2SRJ+3EQiGi0L4GmA6RjSHi\nxBhlObaQEwHnbWPXYTu8WgJgYhgWRYJslcCflt7IbUWu5LYjfX6MbEzhiTFy3jJQUmIwMyenDUwU\nrmBi8YqFrWytoGfOmL7b1av4t9+gzb9E1gIyzhGfGStt5tCW3ohjXmQ5Ajwx/Ogs8BZwzYMvkL4i\nIwdTMUerHBcZCTo+hYxGxoZ7ZmKsgTp7IgJudYZY07Fjyd+142eYYgSl4iMdGRCO+bIXlVMHYwKm\n37OpJqR9XnFOEQrkTIGEgJxVpFUKYE9L9Cs6mPW6Yt31NkitLhrf1R35CG5Fh7g+TinmnKe1/iqH\nGWLHMW38H+uo//8w7UFBkibQ4Go/UCWqW9bvTwOs+qwAsQe5zg+z3niv1CUW66GrywDKo8qsWwS3\n68zARfNE3er3cZNdugigilYH9OrhoPWkHXGuqifoAAvjjnPYUVKezWcs4j/7XZsb0zKkPIgJ7TtX\nviuEWTbbRf1uPrPxpz5edUZhLKc+99Z/l0ZZQSyrbluhZ4jobB8plhezRqIQnDGxnBhANqV8Z9vg\n6UHPWLXqZpphkjkT4HcYABeo5r5ZHxxCsLlXJ4juIxpsXVEEY1Qi93a9AAAgAElEQVSLNdyyaoq9\nb88GuFbeRGkLudezpDEb5TgHe/dKnkEqaMvDdkq4+yzaX0fGIwPPLgWkP4ZWhrQKxE2s/e0W4ewT\nuL0BMtpDpkMDDKNuq4ptHo3ayGDf3utncnuXagsyX2qJ9hHJIeRIoZZx26tlqMxSmHRsLZqPYCdH\n/hRkmkEIljigP0F7a4TP/xT5V16DJLXMmdkAxJu4/tz9X6BpH01KBlvICL1zaP9SdYxLcdk++cVf\nmGnLumtXDbxqtUCEcPES7mCADEcgYlEKW3dNW3acoZNVsi/8dYa/+Q38dy4j14bQXT30eMzZUWvu\nE/vMTMb3TIu3BE1bLQO7sqxJh69ZKc8SNp7/NJp4Yj8htrLSBviHn3Q9J3DuiVV2lK7VJ3Hew7aj\n2nGI3VSzYsEEf1Ro6HGzzBwpWF+QbLyBMIRtSMbvoqfPIPe2y52ylrGuVkdI7wbh8iVbVAJ67hxy\n/Sp0OgZMFUq4fAm5uIl0B7j9LcLGGSRsIb2CYt2RXv9DsrNfYfzyb87pRDzQGJbmtreq3bwQDBw8\nNYV+d8HBpZbF/j4UI+ibEInsl2GMSdvSoLu8BAbKlbwrnYPpBE0iqy16AwHVFqqRqedR7dlYSs1L\n0cqL81evoGED1S2EDK2vvp0DSUFyc1LqoR1TgUHHyhoe2AJ3LUOiXkjAtD6Kg8qhCZhTE3e+48rU\nKxwIodvD7dwzB2Io8Gbbjn9lgvzUBDlF5YTWWVA5sF/+7jDw5nowZ+CWQ7IpqOJvXEcnE9yLY6Q9\nQkapLcLdGIkMNh+qcmC58xVBl5dB/kjhmsKGq4AYFVvAbwdQQfwO2mrB2VJvLIbqRAe6oDq3bnXH\n7LhMk+ig1ccoOrVNa+KdsfzIkPPAOqafpiADgdzDem7n9kqQdopdzx72e1qW0cz+WHe0631phodC\nxeaIY1R31DMqwKzuiGt5Tp3B86BAySKG1mcB9NRtEbBSvyc+DQbYic2Pc/0Zi8L1cW+jDux+lPuo\nDm7HsiIrsgnWLDu/zv5qlhuz09Y/j89XtPgsxbKaz3B8Nke1Mupsq+Z9WW9TLD+317dkAutTpFsY\newtsA2gMtLRqaxMQWzTGzT5Hi/NI/TVf3/RonidUzLeYATOtdagWwj8/34idl5XtdgopaFdg7JFh\ngCxH8hRVh2YJDHuI30NbGaLBWGFKlX0YDFRSX27ghOpeKBnXpremJctLiGLx0o3s9AJ5KcCPUlQc\nTHP08im4PIVnCsvmWeS22bTZgnsbyERLFmBB8cznyF99hXb4v6E3MjaaimUdbqVIGJHcfhM0RWUF\nJYfEm9ZnCJB7FGcRCaroBQeuZezqqQPn0d4KIKiuInKApoqMM9uES3PYXoeJAWq6k8J7G0haMtsm\nE+TDAl7IKb72NHQHJLe/T+icpth43jb4smHjYpWXr11qjoUMTVcs4+Shg2wNuFRb1jnT4Z1O8Vev\n2HpwPMH/+D3Gv/71av37uMuxnBjZU79CeuMj6Cgvk2c5sRN7xO0EFDuxypbpWh3nvEfBjmi/v3oF\nloUFLsvceL/Q0PtkmTlKsN4nr5cgTgopyPAAbXdMRLUMl5TRCKFlC8iLm+iNMiwyFBSf+wLJxips\nbcE0N72xGxcoktO4L1zFrQ/QlS7FE08TOmcJa89CyOj9yf9EcfolJi98fTE4dpx7oJbGXe6aRpju\nd5ALhe3eLhrKooB9jw6HiDoDGDymUdXKSm0msRCLUPNYihyZBrQMc5Eis3PH4O7to8U+iLPF8sYQ\nWg7a3WrX3UYbt30baQ0BQckRHMoIiwsUyFML5VStdtUFOAhI+y4MFYrWDBBRbSEyLp0N23me7RQ7\nVzodJcAWs0YiME7Kndwcbgu87iCZwn+Uw3pAYlaveEoETKKztgrsUTk958vyMwdPDPDdt2wXfKsD\nfUssQJ6hq+smrJuHioWwKDyxadE3agFfLEGuTYwer2rPTpqCD+jqmjExJZRZImGmO1ZgP+0FdRy6\nWeBI8KP+fT1kMX7nGscJh8usj2+93G7ZTlE4l1fl1zWEItjaDEFaBipGB7o+3vHYeqhX/Gni0pFF\n0czyF0HAennHsebY1sfrYdsiYHCRLbvmzf4ep6wTe/gWQ+ZSDutw1YGkuhbcg1ynCMosAq8f5N6J\nz1ydMRWPqf8Ph++nova7a/zuqET76+cuasMMwKF6/uNzPlbk1PRwX7UsO4Le9Y2D5vx2P1CsPufV\nr0tsz6KQ8ln5av2M7Y1M3Hq2V1drWJwzKZlhgjHAWwUkhUkk7O8iCNpztk6ZnoHxFGQXaecVIBZg\nNlFKXr1Po8Zb7G+itfB0LWUOvL1uTznkVgHnFNJQNdN7VFbgbopcGdqmnE/RvumEaciQrS3C88+T\nv/wSSfsHBDaQZBsk7uooMhygqbOXm2QII1RXUMSuWT61LJxjZ7pbawG3uo+O2uhmx1hsq758OdqE\nrtorgckxUJQE82243kZvbFjIpGCJhhKBLwzQ1Ry6KX7rBuHi0yAFfv8Kfv8KoXsGdR3cwU1bKzlv\nLLGkB1pAyAm9cwaILYoYcMnxAK1Wi+ILz1F84Tn7++BgLlHV4y7HcmJA2qc4/RL+3tuQHENH+T7y\nLCd2Yo+6PSJoxok9CrY45fJ9bDgk/+nlmQw/TTuq/XPsprplmdG+F1mvR/LeO2R//VdIr/0hyc67\ntovmEvKNF+ay5ADzumEHB6Tf/kPCExdnYqSVTeczNYKBYNtbZL/wSyTvvYPcvQt5BuIQFeT8FprY\nVrX2Vik2noBbKQzasLOH3Nsy5thPDwkr62Rf+w8Os+JKCrS/9zadH/4jxq/+vUPA2LHuAe9KkKPA\nDS2DUdg9hbC//BzFFs0HCunEBGyTHN1PbJe2E8pFvJgosNqOtWSCTobI4MCyYHUUDR7dC8jWju2+\nxgxNYQVWDpDpDnhPOH/JxPyLMU5vIC4QwmlE+iD7Fo6pQ5gEyBPLeNmaWPhjwJyUsVhYQz9Av0CL\nDtptIX5chgSWtIA5B6f0SmagSOmNTAr4cIjcceitVWRvjFDA35yYZkvUfCmYd1yioxQd0npGtfPA\nPZDt3MDE4gCeTOHFIXhQbVsTpxPodmxRLCXqIo2f5vWKdTvMKYmC7xSwnYBXE1Jujy0JQjJA264U\n1G9DMbaxaDp/x7Fl7Wk6txHYqpM9m87k/Zgm9eMiKDXE2GA95kGqethRXfdqkdUBseYx9XGvszBi\nG+q6QG0WX6fj+hvNMW+yWBbpQn2aVgcv73fciX02FrXvog9dD+39qCy+o86rC8Afx5aBXk0QqAku\n1etu6tg1j2uV7YoZMpvtbtZVB9l8ia140FY1BR+yGCq/yryeWL1fi7TMYv3L5vM6C7VpTZZbPKbM\nb3OIeRsZW3NzURPxL9+JrQCfP4ChR1TRicC4ja6eQ3amxupqFeVYiW0oFYUBWop93vRSIug6y1Sp\nBv6ICePjvbXv2QBXQg34UcKp07C2bl1I0/KaCfT7hDNnyV95FZ/8sNyw7BDCKby/DShSjIwVLr7c\nBFMQRcIuSoq6FupPw6pHNwNuMIFkBd3fQNM1wtPr1rZQILKLyBCRfWL6U3XnLCt3yEx24IkcbQ/h\nvRVbD4nCy/u2iSgtilNfxGU3cJu3COcvztZ3bryNpitkZ3+a5N6bSDFBfYfQf7LaIPULIiZgFvr2\nMACt5L13HsyXgGrNfQKKPTI2eeHrdH/428joLkfuakZ5luf/1qfWthM7sYdtJ6DYic1sWcrlI02V\n7JcfDarske0vwuLPUcLTzyz5riDJv0/ve7cBqXY/ignpjW+T3viWsa6e/pus/N7/iL/1l7YD2OrC\nnQmiE/y1K3NipHiPc5cXVyfgr1+bUdNb//b/QfSm0eZF0QtCaD+J296itfnHxoq6sArTM6huIOdv\nIDJC8o5pYiyzpIcbbdJ+95tMXvyN449haeH0GdzVK7bbGkexd4owKEzwfyFbTNG0BZs5PDu1cRoJ\nkhW2E+qcgV6zxSZlyErLwiwK8zA0KQwE2kyY6aYB5BmyP0BZRUcK6RD6Y1x6QLHyJCpnCHkfC7UM\niGzi3AEy3gUK6JQelPNoHpAJFto4S6fubfHcKZD+dJZtER9KwdtZN+2fWSouzBnZdeguyB8DXxD4\nWoBegZ7PYaV0mKJ3MqUSWW86aNFxazHvNDyjyLBANxWyVqk7pdDKIYilm/dqgCZSOTXLAKPmZymV\nxtgGcH4CE0FQUI+mCXhBehOkkxlAl6uxCJphSB8V3Fjm0NVZGQXzzukidlbz/Do4lZQHrsc2a6Up\ntIhZFz+vA2v175thS/V6m/XXv1vERPk4FtkqdeZKHdj4pOxBQY0TezRsEajSWvJ9fX6q39eLyqH2\n/bKwxzinfZR7p/lcHaX5Fc9dxsJaVEcch/gsNc9vMsPqz/K98jDFwiWT2jl5+ZkCa9g74EETZ8T6\nmsfWN1mOmutjmGJ9qVTP0Bt/moAYUMY0ViGhLUrGtyKi0CtglKArDlYDkt2pQtdFQS2skFBO4LEf\ntWSKh9tcHheZ2lqWhdUdLiZws1WK6ztIPeHpZyiefob0L/7MhPbnyhSaG5aqFwhhB5F9yyQN83Ib\nZZ2S55AFNHfkyc8hNzP04AB9qY2kzbWnR/UUIkNMHKxcVyagp05bdEGSw0SRjQz90j68vgqf27PM\n151Vimc/bxt/PG2MsGxsgJtLwaVIdoCgaNonnHqB/MwrDeByiZWhb93/63//+IDW4y7HcmJmPmX0\n6t+n/e43YfTj8sPaZnpuYcLFmZcMEDtKnuXETuwRtxNQ7MQq6/cpXnwJ/+7bR2dWjDYaUrz40oO/\nPD8pO6r9kd1UtywjnDm3WGeMgiT9vi1O0pcPf52uQChovfvP6P2b/4EwOg2dFWwVOcX7q/BiQRj0\n0JvncFt3kT/9E/Kf+Vlcus1spVcUyO7uLGOj27prHz/1FDwlsJeivuxLcLhrt0zXypWaWL0h7tkJ\n4doF3MYEMk8IgeQH3yf/6tfmgaO6JT381puQDebZbse4B4qnn8FduYyMhpAE9PPgnrwJLiArQzQI\nctAjaqBZfYkJwXYHMCyg5dDcVQK+B4npZXRCxZTJgU4OE29FTRx6V5BhQIocbblqQZwXCFMohtBx\n0G6hxT10uEb+wi/hp1dwV69Ay2g+qhcoplP8vTG0U+iObTy3+qZzImPEF1RZJMV+DQVaKEIKo2BZ\nreqaT9GJKIIxxMpQPC1ABiB/bQrnHazktq7vl+fUNaWgYiItcrrqQEw9vKUHcimgm1MikCdBwE2Q\nA4dqatpYTb2a41odBFoF+ooO1VLOp6ntgOelF1gPi1nUn+Mwg5p1LzuvyWqLTuBxyq8fE8d0GkFN\nrZhaUQ8tHtcEFOvti21aFkYWr1s8rw7gxfNsGrG6P+r1inXFNkVnPfYJ7Bn7uGDlif3Vt4967y06\nN4JAdTC5fuzHBWrrz9yDzgHHsbkNECq2Zfy9DohFkGlExdSKz+AyYrVQirIz/9wvYr7dz+rHH7Up\nAPPJO5oJROJ7unlOvRyC6YqVv1qWZG9zacsyE6MedteQZBPOl+CHlu/xImemwxk4HEZ+qD6oZ3y0\ncMvahWgl0OlY5u2ioHj6GUIZ6hc39uaiBxZuWDpU13EhinlyuPMCisC0heQ57saHqJ5Fzt/D57sm\ni4GFSaquAx7n7iBkzGmgAkzG6KlTqIptOg0Htnn28ggurMCTn7P16gw4EkJxjpBfgo0EN96yMfQt\nSFcYP/ur+IObxwPE6qFvDwPQetzlWE6sMp/aBvq6wIe/j374F1XUzLnXDkfNnNiJPaZ2Mvv8VbFs\ncLwQv/vY5Ne/XonDHwWMjYaEM+eY/CePFlV2WfsPLYKyDF1ZMfbWAvPJ60i+R3jipxZXFAqSO9/H\n37yM5FNce0AINXCwcBAw5tSzJpQvBwf4N16HnykgKG7zDjIclo5AuTjKC9zVKyQ3fwDdUIEygBzs\nQ9aZ14EIHloZ7gtXZ8fq6TPI8IDkjR8Z6yzadIq/chl3b9syGLmc7u3/mdHf+K/mtCPmxzCxFNt+\nm4jABHcavbCO9N+GcyArKxUTb7+PrO4jZ3ZMWHd3FUIgrK0jo21Ix6aJdc+Dd+gG1k8B9hxyG0Dh\nlNrOqQ/oXhu92oWrXdzde/C1Au0Gy/wJtoN7NkBPjbnkUtQVuGwMo0B6+Q/QlbM0hWdldwDDri0+\nE0MpdNyFAnTswO9BO5/hI0w82mkhKy1joikwkDIjoFZAnpVe6mmVQsEdterPYzvlSTAnZCZYz+KQ\nvPsxLmI2SjAQLlXkdI6qgyRBxlO0G1AvkBUwDtDVeWfruLYICOoVqAgymdoiPy1MMLp53DIA6aO0\noTk+dQAK7s8OOcpiIoCJVuXHepe9LZcBY/fr25QKkGoyXCIj5eMCBE0gNTq49VC4JrvkxE7so1id\nOdVkjS1injXvzXjsUfd8/V5dBvIc9f3HsSajLQLKEeSCKjNnvT1x7utiz/w94DQWor3o2Ys4Tdzw\naI7ncdq56Fos20xoWrw2TVBfqZKmNK+RwkyPM7YdSjCmrCgpIC/F7+91IYzgUjD5gjSYzFaGAYgd\nKmCw3q9YXLs8ZhLrFXABhikqimDrL+31ypDMfC4iIG7szayU0XB+i3lqWoFzE+SuN520nlRsNMUA\nvWAXWKcpDHr407dA7kC7hxRTLF2pIrIL7BJCp8ykffiFInmGdjqgjvDURcvcPRohF/ZNpH9/DzZO\nzZ+Uprh7u2TP/TwFz1Wf50NC/xlcyCz07ShNqGbo20MAtB53OZYTW2CtPrzwHzM69e991i05sRP7\nROwEFHvcrchov/O7+O03AVke4rdMWL1pacrot/4+7d/7pmWCgfmX2rCkyr74kgFii3S6Pktb0v7Z\nIijLACWcqcIZD1tJoc8T22VcYH77dWS8j4wm4FOEIXNqtOXupRR+TijfbW2iWRu3ed0YX96DBmOK\n5RmI4L3C2QGM20bxj1kNpxm4wwsMCQLntmFiWQZdesPEW+8Wdn6SkLz+I2RrExBopWXbIH3nu8hf\n7CKfF8IrF23B5xLyv/Esre+8TnL3bQPbJC6oCrz+GPfqbdguYH/N0o9HU4G9NdQF6I2gv2faZ1NB\np4rbwxZ3HgtD2AR13gCcaRRrAbbLcIhUYasLN1fRThf8AH4g8GIOp6ZI4kpR3RJ0coImDhVwBw7y\nCclbbxPO3DJALnQhiZSlkT0T3XE1luwgt8e1EIkaKCkBeWoEOxN0w6PtYDpgw/J6t7WWtUst9fqB\nQletlKeBePlSKs2YRaBRWUSjCYedI6mVo+UYdAo4APHDkqXgLMwzAGNvwNVHfWzr9YttdEsYlM5b\ny5gCTadpkZbNR3FYm05e/KkzQD8u4ylOBz2qMK6mQ9l0Jhc5nPW2Ns+NAFt0eGkcG49/WOytyECs\ni/I36/4k7CiH/MT+alnzPl70jDTnrXgfNss4yo4LPH9S91uzbyYHZRYZYXUwu2AWWThLmrJVfteh\nymhZL78+B9QBNzi6X822NRlzze/q5R01x0X2bQSt2ixm49bPCTATwY9aYUmBjlqES0/huI7bnBiz\n6FYXzmYgZdi9Bgsl7dTaVWcZx/dJp/zJ1MZRBfYctDxKjt7tEs6dXxwR0GqhZ88h23chsSwq4eln\ncNyZH1LZNQZaUaCTFmhqIYyuTFijYm2eJjBYQTb2oD01XdSQW/g9Bxgiaje7czsYoro2P4BFgSYp\nqgXcy/HbV5htmCalZMPOLuzs4NpdwvnzzLPkGpb0SPben4W++a1yPVwXQV8S+vYwAK3HXY7lxE7s\nxH7y7AQUe5ytyCoBxEVssPLld5Sw+kJLUyZ/+zdMOP473zLBzCyHNCH/ymtzGWYeSVvU/labcOlJ\ntMgJz72wJGTSzLnLkOXo2YuLQb9iihtu4gbD6jO1BZTqafuz27VMfM4jhUf6QwpfQCbIj24i58am\n/XAwmDGeANslTIfIZAKjqQE4E1AKmDbbotAfIMnUwjydQzvrBt7ILrS2kXf/AB32YThe0GfFde+S\ndv4MNnP0jzcs5BKl+8H/aqGQZ86jgz7u3j1beDkHGxNC70lkrY+7dRfulqJLdQZbrrDbhgKCrqHX\nL+HPv2NAUbQIHIYCnZSCJhIH09mfeUBXRmj3AjIaoqf68MTIdKumij6VQ0cM7MHB0CG3FJ6ZGEjV\nAUkyXDaxxe9oCOMO2u8jnaEln/QF5ImNuU4rFs1pMYZXXIxb8kooArIDuirGBgslgDcW+xGs7nZA\n1xRpYWXGMLwonF63Rc7OMnZAE2hwmAbWLvPOTgmSSVGgOAgO7QZklzJj2EewJmBX/yybGpi07JyH\nYYvGJDptdZAx2nEBmTooFdkIywToI5vlKIf8KOe0WV/983oZTTD041izTTE8E5oEyodnJ0DYT4Yt\nYobR+L2uH7bo3m4yR+937yx7Lj8NILYO6N0GIgk5Y57lFY+thyoPmH8PNEGxDodZnHUgaJktm2+a\n4xCExZpgVBsMTcBcyzbVs47Ws1lClVRg7lqWf6iWTHJBBuD8B7j+oNTHKuCJCdxqQz83FnMsf0q1\niRTLjfUUtbqjfuYdMZZ4twtkhNtPQ5EvjQjIv/QK6Z98D/b3COefKNdHUYugrFaGMJzaeiQtXzDZ\ngjWhgpzdRtqZdbktMMghtBCZlJ1JUV2xxD0ENAyQcVKt/zSgrTZufw/d6YOf32yUVgYTGxAZDXE3\nbhAuXapAx0UW8ir0LRuQXvsWyc479w19eyiA1uMux3JiJ3ZiP3F2Aoo9xtZ+95v3p0XDkcLqC60e\nink2h/MJ+cbLj1/ceL9P9qu/VhP+zKqwwIXqrWYu3EJXNshffmXx93uXAYzdFZlm4rH03PZnWF/H\n7+7OzlFATu2it9dxb47gvEP29suU2eWCJgRot5HWABN2x3Zai2BaWKMWaJliG4W1fXCZaWClgC8Q\n9k07ijb4Fm7zQzTtEdKnG71QnLtuCzHOQdqahVzqEyCaQdpDsh3oZ2Rf+JotDMOU9Pq3wLXQjTPo\n/h7a7yLb7VIXzUIZtN+H6RQZC647odgqoMjQdsdEaXXeS5JQrna9Ce5qmpo4fLkC1o1V3KXbls5e\nFTlQA3h9DsGjKXBQIDqF8xgQVWqZ4EDEwvzoOdgPyN5eyYATJJkiTGG/sOouUi7GtRIf7gBnqATu\nDwKyD9pKkHZRLdrniIdqkiExc2ST4RRvjKPChY7DhFAM5BLMcRmKZf06wJyMAKIBvV5YSveVsi+L\nQKTj2CKgrq5TVT/mo5R/P2uOY535VA/DepC67wdKRTt0jZccs6i9TUBxmfMe+/GwGVzN+mI200+T\n7HvCGvuracvu47rVMwkuex6O88wuey7r333S91mce3awfq2WnydUSUlgPkwUDEAbYRIC6zQxmIqZ\nWmeIRT3DB3lOlzHpnB5+d8R2xnknJhGI4NxsE0BKoCscBiDrc1acI+tguwAakLUdxCWW/MUDU4+u\nFcjFCewLXHOms9mp1VHXQnSxDZH9HGbd0G4PXb1IWOsRphu4D0aEtR7FV15bHBHgHNmXvoy7fs0y\nkB8cEFZP45MrQMvAqiRHZuuZyYIBBUuuVFhSmyh3UV47daajJpIBWZltUpFpDmECkxaIRQuQpKbT\nupfBMIVeUo55gDQz9pnbsTaME2RoEhzh1GnChSUZ1F3NxUv7ZJ//NTKOkdlxGaA1neKiBEdRmNj/\nqdOEc+coXnn1EKD1uMuxnNiJndhPlvlvfOMbn3UbHnf7xnA4vf9RD9uyAe33/tk8FfoocylucIPs\n4s8vT8dcZLTf/h3a7/1z/P5VW59qgYQMv/tj0uvfwh3coDj98jwr6HEx78lf+yru9m3czRu26Kmz\np4ZDA3IuTSm+8pWlu29+5z3bnNvbo7nyNyFVwDlkOkUy22UUdbYz+mFAxlOklyOtmPMcW9ylKbTa\nSHdc2zEXA8LGjuL883ZNigJWB7iVMXQVbSfgpNyBTBDJEJkg5MhBhnZbmPdbAZrO3QFGKOvMaEPe\nI/u7SG8ASYlwiEemQ/yN9/GX75Jc/0v8wRaSFyXAVeDyA0L3CXRtHV1bs59WC7e1Bd6jpfiu5HvQ\nsRBPRJDYSZFZxkltl5kzRayfKKoeeQokPUByB9PCMlJuqDGyChsTWQ8G+EwwgKaeXU8cIsbi0pZA\n0oegiCr4HNHcLkUUh48L+pXyJ64tpfy9iwnbA4xKz6FTqzM6F3Hzua7TUnf8jhJBPo7VnZ8YppNi\n2TNvStX2AmQnIHkwp6xX/iwCg45R58Js7XWnaFnGs4flqDYd6CaQ9EmyRJrWdMCbvy9ixhzFiFl0\n7Md18he1L4KJn7TI/idw/U9wtUfIFgFZTSZpnBNlwTn3ex6Oa/WNhk8CjC9NwObafeAyBm7tYf2L\nUXLxuYL5TJVRK7ANbGOhlKtU74uY6TYCSwX2PovLlGXviybQtWw863NJE7Rsss1iMoRxrT8qh7Uv\n66BY7C+UWlt2jPqATBwyFSS3DTCR8obwQC5IgumLbnnYU1uuRHY2lPqLrgSefAWQaQLjFvQSgq6g\nKxcYf/nvMvwv/mvEJ7hbt5au9YoXX2L03/53ZL/4y9bU/YCXDyHpEC5cRM+2cOMReIeMJ4tffG3L\nDi5SG3gFNIX1dSgc4iK1TZF8BEV5rBMLi3Se0OvAKEPueKQISD6FMzmyeoC0MiTNEadl8qfM2PI6\nQZN18ld/+jDwlw/Jz79G2Hj+cJuPYcWLL5O88XoZ8eDwr/+I5O03cfu7pW+gSFHg7t7BbW9TvPwl\nipcakiTHXHMXX3yJyW/+nUdPjuXE5mxlxfyCz8TnPbGfaCvvvX/4SddzAop9fPtMQLH0yr/B719d\nDnAtMs1BlXBqwUuyDMV0+1cNaGuW61vGPBrewm+/QX7+q48tMFZ8+VWyr/08AG6wD+Kg0yH/ymtM\nvv6f4lu3DSRZYm5wBVG1c7W+kvSors3+0pUV3PDAsiqV2ZbHhSQAACAASURBVH/kA9sWljvA2QJp\nBVsgeW+7kQjSmdYWswFVD/tT5FQBKxmsB6S3h2tZCm6drbbruewFiollUEz6iEzLttlxzt1FRAjh\nAnWqkrCNc1O0uwJqO5Hu3jZusouGPk62ERQZj3B7uwZgobbYqemduZ0d0wiLgKCbwrZDLqSQtk1P\nDWwRlCQGTsVubARYz2A1wFqANQ9Jq1xQZyVYBpyuxVD0tAp3i85El4YDIIgvvQ/nwHWMIUeBhKk5\nJNPyRzBnJWHeWYhhL9GJCQotqXbRc60cwDpAVnfUjgJQHsSajLMY3hKdzy1vjlu9X9FhSYCNBfXf\nrw1lP2SRUxvBsGXMj4ftpNbLbzqLnyZqUnc0F/W36bA2P1tWZv33h9Wfpnj2Y4ouPabN/smx5tyw\n6Kf5LHzcjYG6fYJAryi2sbCLsXEVA8ZOU+mG1fvUZItBBZz9f8B3sXdVl0pHaxd4B5vTTzMfyr1s\nPlgGlB1nHJrvOMHAsBiuGJlaoXy/xQQh9ePj77MypWyrmu7pqByYokCcoOXmmGoLKbQEloIBgKlW\nY5FhmqTOkvMQHFIEGCscCEoH7bTQlQ5BNsh3X4OVNYqf/hmKr7xWrfW2tnDvv0/y4ftoUPTSRYpn\nPke49CT0+4Tnnif/uV+GNUwz9OwFKCb4e9eAcpMuFPPAmAvGWJPqY1Us0VLSpfjcTyHTrAxVD0g+\nKTcvnd1Hoqi0YaWHDAW220hQ2wg9N0H6E2gHA9wEBFfeUwGSAkkyaEHxua8cBuyKKZOXfuPBfIS6\nRUDrxg1a//pf4XZ3oNOpQK8ss4RN5y9YVvXNO/g33iB/7auHgLH7rbmLr/7sEn3fE3uU7AQUO7HP\nyj4tUExUF60oTuwBTDc3l+XW/uSs+2f/C5IdPPB5mq4weu2/PPR5+61/it9+6/6hmGCpm0+/eLxQ\nzI9rDymr5oNY+sG/Ir3xbdB0LlsjzhFOn0E6txEC7t627aJ5D1oQdH2mKTYzrbJMau7hO+XipijT\nlz83tIyEvV4lWN8bIb0RqKASEBXLHNjpoSsriGwjMkbK7dugJhQ7vx0NMhmj3hZnIXTRsn12/jaq\nq6hemGuuc9cQLxQXL+FvXreFT61/IgfljmhpURw26aB7JUuuleKvXzd6fTAAK7T7FDsvkvb/GNRi\nEOXgoNLTCDlyaoqugDjL6KTOIeRomhj4N0mQa5nt2KrCU0UFOq1TLcgdtghd5TBjIUlAHEoLzfug\nguzdQ9pjA7v2sN3pFQzUak6PkQ0Gle6JlJ9F56DutDTZS8t29D8KEyjU/o8iz9Ey4C2qsJvvAk9i\nYZMeC+N5jgpHjTpnRwEwtX5I46ND7ZmF3FTnfKJIRvM6f1pheotCJePny9pxEkL4sW3h/Xdij5c9\nLFZnk+EER4ekfxwLFSimb1Jlcv0i9m6og0WL2hdDIeOmyi7w5xhj7EPgc8CzVBscHvgSxpqS2mew\neNxiXRHEKudhDY3DF4Wbxk2NvGzXENs46VC9R6LmWF07TWufx77ngDp7T5dsbsaJJdnJov5ph7Da\nhz1BtoH+EHFD25Cc1lDE1Fmm6pFH8zOWhbs3tRBLL2inS+g/gbKBapc8+/lZON74t/4eAO3/83ct\n8ZLIfIjfwQGzxFG//nXbpKtr9UpC641/ids/MPb/fil5IQ4kGDgVHLSnNVBMYdwhXHyScPpMOU4F\nbuce7tZ1C7Ns5aAt1CcEfRLVdfz1W2WCH0XSu7AxNWaYTw08Cw7n1daFKKiiImi7Q/75XyI/99NV\nvx7iGr39f/xT/I/+En93E7e9ZXIe3tbCxdPPzDO/RkOKF140Td8T+ytn585ZnPhn4fOe2E+2lffe\nJ75yPtEUe1wtLGcyPfB52QC/9cbxQaakZ5lsssEnpzH2sLNqPoBlF36B7r/9x8jWvtVdZmukAHf1\nCuK2kX5OOHMBv1fqhgloWD9cmAjh/AXIx+itNt5fB0B9gna66OhZuAlyahfpD23FPeiCK0wjzAM4\nE9zPMypF37h1HJGZFC1Bq9mqWynHJkOkBwxLUGwAdFA9t6D3Cgpu804FiAFRM61ipJXmvaURb7XJ\nfumv4a9ewW1v2XK2ZL/p+jrquhSrz+Ev/xDZmFpWzl4PRmNkMoTzGfiA5M7CQdspOEWnZRyJCuJG\npnF3NdgudFz4R52vutUzZNW6RgjmIQSBdIC4xMIQ4i0Ud/mbgFjMKJZQgV+OKtwylt/c7KxjlU37\nOCyxeE4sO2t8PsYcml3gDsZoeKd2zC+Wbb/AXNjlfes7ypaF9txPJPph2DIH8bOot/75g7LEPi5Q\nemIn9qjZcea/h1VHnBMf5rOjjf+nmGD+k1Tzf5fDDLF6u6JFhlh8ha5gYex9DAzbbhy/gc3dKdV7\nrs48WzS/xM/LzQkVb+BKMCBlbpwCVmChBmTlDkYe9QqFIu/k8AJVqH2Gbb5MgVWx9Uq93ub+XHwf\nFmI6l60Ak2CanHmOGx4Q/Cr6rEcmLTQDzUbQyZGpRwKwqzDw4FuWUTkPsOPBp4Sz5wm9C7WBKAe2\n28NtbdL+5u/gdu6ZptWi5FAlQObffZvOP/5HBqKl6VzGxuLUJWTvLUQ8urqKDAeQZ+g4RdJQhZUS\nyqFI0ZU+YeNU7bp7AwL7a+WaagjaRklrG6kFrAyRNIPVKUIJJmqBhtQSLXWmBsYpFqnRShEUd+9t\nOPMyuBbkQ/5/9t48xpIrO+/8nRsRb82lcqt94VKsKi5d7Gaz2a2maFkeWe22IFjW2pJlzQiS1eNu\nwD3ADAYWMIvGY/iPgQF7YLQ8lj2rrZEIEdLAgG3ZGllyk93uaS6tpsiiqopbLWQtuVQuL98WEffM\nHzciI17ke5lZexUZH/j43ou4ceNGvKy4537nnO/Y+pyrJnmzaLXw3joFk5PE9bpz8l5bgtg6ggwG\nibF6w5GPrda9XYyrRIkSJYagTJ+8edyd9MkrLyM23L5hEX6NaN/nBvu61amYN4u7mcoZhtT/2T/F\nLJ9DKv2kXHf+3B6YOiaadxFLlSoSdlFpojo+vE8AY4n0KeIHH8Gsr6OTk1CvJxoZBtp1dHkCXZ5E\nlyeRmWWk3kPUibBqteo0OIIIEeeVlMS61Q12qImznGOEyHkuvTT/T0ANSgPVOIkQy+rGiyxjzBIi\ny65aUthNCLXBlYVqA5EOA2yTMdCLiA88js7NYQ8cxLTX0WbTXaPE2HgfauaQSxHGXMF0rmFWWk53\nbTZGamz0KeAMP+uj1ToigvS6iFUkELSiyJp1i5AGmQ7LxiDJRMT9wj7SNA2XMilhkqqQWvapUHIe\nabrksJScdHv6ubg4K0aN3S6kBGA69lQf7UPgVTaThg/giLMpXGX4/D+fHSwmt2xSXBDebHrUjeBu\nnHM77CRariTEdoTy1txHuNPE9O34t59GvwKSFl9Jo4krZKnyxX/jW40t7bOPi+DdjyPG9uDmtRZZ\ninuPrJhK0flQjJTLnVOtoKaKxLgIn0hcVqOKmzNiARugUQVZFxehlep9GkVWxGl3+jhyLvW5iefG\nbTSLEKskLx+3zdfBOTHWLP2yAlpzYvkS9pH1dSeNYCMM1lV9bnuwWIWwAlHsHFr9EDwfHRsnfugo\nOjnBYNpggLUHk48BlT/+92ilAmNb2GZJW1ldwVy5QvyJk2A84tmThPs+i9ZnCC6+Ar02mArWn8J6\nB5BeFfHXk99EIRDUr4GMo40mWjinuba08du4YkgGtVWggTFXMf4i4kVQCZEgSdP0kr6tD9ZHNIDA\nQyse+InxIQbxweKhlXHimUfpPfaLt8RZHPzRH+JdOId/+jTe6beQtdXkAqxLg712zWVTtFrYuTk3\n5ihZGxy9DWuDEncVZfpkibuFO5U+eTuXaSVuI6Jdx+B60yejNtHUsU2b/eUzOxfs3zio4Y67DbiR\nqpq37NwvPI8sLhCbp1AaoMOIRw8r4444InbVHXtTQ9ol0BDbncTO7Kf9X/1tJ666FbwYiT0k8h05\nBFCtOi+lZwsEhyaCtWk+hkBcx5oZ4sZs5gkmwuo0Yf85VA+QWqvGXMHzzmPMitO6sAYIEdNDZCWJ\nKstyUjYKCQBZzt4KmDaVM3+At/w22L5LG+gn904Uaw87ozZS5A/W4FKIBgpV68R1Y01SVB1HyLrC\nJc8Ra8Zkhq8FxgzqAauydf6U4hYTqVc+9VxveMgB6WX3OE0dqZIJG+crNOYrc+XPkc8nTN9tbv9O\nyJDiuHeSF5bvN12MQCaQ7OEWVyfJnvQVnOf/EFlaTr6/W4V7Ib/tfmZO7uexlyhxq7DT54cUXrcD\naZB0h0xQv6ivtd3Y8tGiqRPjITI9spREmgE+gZNWSAm0Ntl8ls5D6Ssv5p8iBukpJuqADd0cu+rB\nxRos+TBvYNGgax6EARpUnW6XOvJKsI54iZL+I9x8MgGMx07z0yb3oU7mkEmvTcicTClplrTRBuDH\nsAbSCxEbI/2eqzhtxQnvN2LY3XP2UlBBm+NovYGdmiJ+4EEICokuGmLjmex7v48sLWHm57f4cXLI\nRzmlCMYIH/5Rlv/Tf08UfA7bm0J1DLwKdnoaO7ELO95E65OoVwXbRIMqdm735v4L+rPW7iG2RzDm\nPMIaeFV3P/2QjT8UNRAHUPdhUmBiEirTKJOo1lwhUL+CSoBEHdrP/KpLmbxF2RP+6bcITr2JLC24\naLCiEH4QQKWCLC3gv/Kys+EaDfy3b8/aoESJEiVuJ0pS7D5FePA5rnvFqUp44M9t3n4rUzFvFmkq\n5060zWAwlfNmkYaK1xuARxR+BquzZCrlGdTuAuOj6zG9J34EnZlzJFC/QKKFHbRjiNceRGxM/f/8\n38Ba5HKqH7EZMrXidCNWxtGu7zS1NE5Kjk84g0ezOuWqCYNjO07jq97A7juInT6I2rFES2yMODoO\nVLD9CWT5Cl7rFKZ1BVldh04/SS1MxKUktfiTMuIaoepWAu59LSHNuohYxNQwnTXM2nmCD15Em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aNNDKOFqbduXXAQ2mYKFP83t/F313LxgfOz0DRzXTAAFcRclpVKezTbsUc+UyGoY5Q2iQmTEr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/uta5dQak7wR8bvk4QwN4+83YLmBjI3gOle9BBDkdYgBrkQg2MFFUGKFJJoFCZSdc0MUZQr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\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8R4wf/xHPPvsi3br1cJ/TLL744gs2bdpMYuJZIiIuo3XrtgwceC8lS5ZM7efk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\nE7WhxICG2tlJeOMmtO5ulPb2aY/TJ5M6pu5PJXbTLcRuumVimduYJOHXX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6GUWG5zLPYF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\nQ83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Ppq8P1enEsWgxlddch2o7uZ/0V80W1AopESZOb1aLxp03LmRes5d7n9pJtsiDLY++\nvJ+93SE+9rbFJyQII05tum7QMzyaBffS5h52dgZJZ/IfjtJUhdULa7n6nFba6gv7DE9GOpOjZyhO\n30icoVCSrsHRQKLbYabCbcNlMzEUTrGnO8TBvkjRz/mZ4nFasJhUrBaNBS0VLJtbxZxGL9v2D/P8\nhi627h8py31URaGu0s5Z82u4cGkDdZWOsswrhBBCCCGEEEJIgE+cMsYK8I3Xg69YBp/zUICvnDT3\nxBtg1sbG0ufzeFFdLvRo9PCYkc2SGejHUt8wpTVOl57JEHn1lYJx59JlJ2A14i2+Sy7DXF1D8Nmn\niW14s+C4c+VZ1H/4L9AcTsyVldjb55yAVQohSqUoCpeubKKt3s0PHtrCULiwB+W2/SN86e7XuOtt\ni1nQVnECVilOJbFkhvWBAdbt6Gd3V4hUkV6PR8vpBq9s7eOVrX3MbvBw7sJarr2wHZ/LStdAlJ6h\nOKlMjlQmh6Yq5HSDcCxNR3+UYDSF02am2msjFEuzZd9wQfn0462hykFLrQuP04KuG1R5bcxv9tHe\n6CmaIbdqQS2rFtTSPxJn7cYeXtzUTThe+PPkeOxWE+ctrmPZnCrmt/iwWeRXLiGEEEIIIYQQ5acY\nJ0u9m0ny+/2VwJeAm4AGYBD4A/CFQCDQM851dwB3TzD984FA4NJD5+8H2sY5d2UgENhQ6rqP9bWv\nfc3ITbO83plg64Jebnw+yJyudMGxnzVfR49ttPylSo6zPJsPH+tLV9ORzC93WWMepM1e3n52Cw50\n0jw0PObxnKLw3IolGJMotXT2zj1URPPLfm6e1UJf5YnZzJ3d08ecnr68sZyi8MLShWRNsnF1MjBn\nsszu7cMbixO3WumsqSLkcp7oZQkhpiira+xNthHOesY4w6DR2kuDpQ+p5CeOldIt9KerGUhXoTPd\n7H8DBQPjqLLoJzOHGqPGMoTPFMKsTu/nbN1QCGY9DKSrieTGfqBLQcelxag0j1BpDqIpJzawKYQQ\nQgghhBDi1PGlL31pSjs7p+SuvN/vtwPPAQuA7wOvA/OAzwKX+/3+swOBwFh1dZ4FbhnjWDPw78DW\nY8YHgL8c45p9pa+8kAT3SmfOFg9Gh0yuw38/duMpqxd+ipuUbHkXBmQmCHBFHPZJBfcAInZ7QYCv\nMhI77gE+VddpGBopCO4BDPg8Etw7iWTMJna2FJZ3FUKcmkxqjnn2vfSk6+hO1QPHvo4odKcaiGRd\nzLYfwKKW//VNnFoMA6I5J/3pGkayXgo/Z6ZKwSjbXFNjVtLY1BQ2NYldS2JScqjoKIqBXU2iKjqJ\nnA2LmsaqTi7jbjyqYlBpDlFpDpHSLUSyTnKGhlVNY1Ez5AwVk5LFpqYk0C6EEEIIIYQQ4rgqy868\n3+93BgKB2DjHZwcCgWkFwo7xt8BS4JOBQOAHR91nI/AQ8AXg08UuDAQCB4ADY6zzYWAI+OIxh+KB\nQOA3ZVi3mAbTGH1Z4ib74b8bKBgGhzdYckbhE+uaUv6gato8cYBvsobdTloHBvPGKiMR8j7AGeZM\nJFi5ez+2TOFGmQEcrK0+LusQQogzlaJAo7UPpxZjX2IWWaPw9SaSc7Mt5me2/SBeU+QErFLMNMOA\nhG4nknOSylnJGhoJ3U7GMKOiH/rZxiBrmMgYk+vNqJHDpiUxKxkiORe5Ip9jx4tJyeBQE9i1JBYl\ng1VNYVVTWNRMSRlxbtOYv46UhVVNY7UUVpMQQgghhBBCCCFOhGn9Bu/3+62MZrzNAa4Z45zFwBt+\nv//bgUDgC9O531E+BMSAHx8z/gjQCdzm9/s/EwgESq4/6vf73wm8A7gzEAgMlWmdoozMJSYm6Kho\njG4CFdsInYkMvrRp/NJXMZt10nOOuF3okJeTaE9nsKfSJKYw31QsPNhVNLgHcKCuhrBTyj8KIcTx\n4DVFWeQMsDfRRjTnKjieNczsis+h3tJHo7UHVTKJTkmGASnDQlq3EM/ZSeh2UrqFpG4r+jPNkQtL\nv4dGDruWwK4m8ZmDeLTo4eeGdENhKFNBf7qGhD75h5OK3k/J4lTjo8ExNYVFTY8GInUz6UMBSpcp\njkuLYlEykgUnhBBCCCGEEEKUaLqP6P4/4E4g7Pf7rYFAIFXknCogBHze7/fHAoHAP0/nhn6/38No\nac4Xjr1fIBAw/H7/a8DNwGxgb4lzWoHvAq8BP5ngXAeQmEzwcDyapkmZzhKNVaLzWLqhoB3aHMoW\nyeAzzUAG30QlOuPWyQfkcppG2OnAF4vnjVdEo8clwOdIJgvu/ZaY1crehroZX4MQQogjLGoGv2M3\n3al6etJ1FCu/2JuuI5Jz0W7fX9YyhWLqMrpGOOshZVjIGdqhPyo6KhYlg0VNk9YtpAwL8Zxj/EDe\nFCnoVJuHqLKM4FTjYwbRVMWgxjJMjWWYRM7KcKaC4ayPlG4DRoN1DjWBRc2goGOgomBgUjKY1Sx2\nNUnW0EjrFjRFx64mcGgJVOXU7PkthBBCCCGEEEKczKa8g+D3+y9gNLi3HbhmjOAegUBgrd/vXwa8\nAHzV7/f/KhAI7J/qfYG2Q287xzh+8NDbdkoM8AEfBVqAD44RuLP7/f7vAR8EfEDS7/c/Afx9IBDY\nUeI9ivrHf/zH6Vx+xvifdb8k7Pw9vujEwblP/e1nqKkYfer87/7jecIdwbzjd915OwtmVZZ1fbH9\n+9nwqc+Mefyjn/u/2GprJz3vgV/eR+cD+dVhr1i6jPaP3TnpuSar44HfcHDbzsIDqsqaL3+Bqxct\nnPE1CCGEKG7jrgH+9ZfrGYkU/vgVyznZm1vB37xrBecvazwBqztzHOgJ86c/H2DDrgHiySw+lwWf\n20aNz47HaaFvOM7Lm7pJZycuLzkTKj02brxwNlef24bXNbWHgwzDIBxLk83pVHpsKJJiJ4QQQggh\nhBBCnBSm84jwHUAOeEcgEBgr2AZAIBDo9fv9NwNvAp8E/s807us+9LZ4atFo6c6jzxvXoey9vwfW\nBgKB58c4rRaYBdwFpIHLGP04LvX7/asDgUCRKIgop4tnncta1+PQl5+NELEW/jens0eCgJFYYZ8U\nt3NyvWlKYWtoAFUFvXADT7VYsFZPrVedc9asgrHYgYOFJ5aZYRgMPP9C0WMLPvdZPBLcE0KIE2r5\nvBq+95nL+Ld71/PmzoGC47FEhm/+bB1XrW7lozctxW49cX3VTmWpTI7Xt/XR0R/B0A0MYDCY4GBf\nhP7heEGAdTCYYLRwxYmjKrC4vZqrz23lguVNmE3qxBeNQ1GUKQcHhRBCCCGEEEIIMXOms9tzHvDH\nQCCwu5STA4HAZr/f/0fgeqYX4Cu3O4Am4K/HOH47kAsEAi8eNfaw3+/fDPwv8BXg/VO9+cBAZKqX\nnlGqqOOsukWk9qzPG9/SdFbBub19YSyHmtGEY4WZDelEekb+3S21daR7ewrGzXX1DA7FilwxsbSn\nMDAY3buf/v7wjD5BH9++jURnYdx+1je+jV5bK5+3QkxBTc3oAwny9SPK6ZPvXMLjrx7gobX70I3C\nIgRPvnaQTbsG+NjbFzO7wXMCVnjqSWVybNs3zOa9Q6zfOUAkfnKUOrWYVea3+GitdVPpsdJQ6aCu\n0kEmqxNPZUmlc7gcZqo8tsMB3eDI1H7+OJZ8/xJCnMrke5gQ4lQl37+EEKcq+f41eW/9m03WdAJ8\nDcCjk7xmE3DpNO4JED701jnGcdcx503kTmCIMT6WcbL6fsJoD8IrS7yPmCaP2cmxOQqKyQzHJM1l\ncqMD2ZxOIpVf0lMBHDOUxWBpbika4LM0TL08mrm2FsViwUgfyUTU4zGyIyOYK8tbZvRoQ797uGDM\nPt+PZQplRoUQQswcVVG44bxZ+Fsq+OHvtjAcLnywpW8kwTd+vp6bLprNdee2oapSYvFo8WSW9YF+\nugZjDIeTbNs/QjyVPWHrsZhVGqqc1Fc6aKtz01TjxOu00FDlwGwq7C0shBBCCCGEEEKIM9N0Ih0O\nSg+ivSUBTK9OEOwDDKB5jONv9ejbNdFEfr9/FrAKuCcQCEzq8exAIKD7/f5BRst3iuPA0Av776ma\nWhjgy+gYhsEv/hQoON9hM83Yxqa1uZno668VjLtXnzvlORVVxdLYRGr/vrzxdFfnjAX4Rp58gsSu\nwqqzviskli2EECeruc1evvzh1dz9h+28uWuw4HhON/jt83vZvHeYj964iCqv7QSscvIMw2BXZ4i9\n3WHiqSyqAl6XFbfdTGudC8OAA30ROgdi9AzFCMfSRBMZNFXBatFw2cw0VjvxOC14HBbcDjNNNS4q\n3FYMw+C5Dd385rndBQ8ElZvFrLJ6QR0NVQ5sVhM2i4ZhGPQMxUmkstitJpprXHidFtobPVjMEsgT\nQgghhBBCCCHE+KYT4BtgtC/dZPgPXTdlgUAg5vf7NwFn+f1+WyAQSB6e3O/XgPOBjkAgUEqjsmsO\nvX2m6GL9/nZG++39ORAIbDnmmIvR0p57pvBhiCkwivS3M5lNcExodl9vmG/f92bROZx280wsDQD3\nqtUMPfIQHFMizbl8xbTmtTY3FwT4Uh0HcS5dNql54ju2E9+xDUtdPa5Vq1HNhf8W8Z0BBn59f8G4\nuaYG14rCcqhCCCFOHi67mb+6eSlrN3Zz39O7SGcKXzd3dgT54k9e40PX+Dl3Ud0JWGWhUCzNzo4g\nvUMxbFYT1R4b0USGfb0RAgdH6Bkaq+1yaTbuGSoYa6x2oirQOTD9EpatdS4uXNrAvGYfmZzOwEiC\nUCxNMJrCZTczu9HDnEYPNov0QRRCCCGEEEIIIUT5TGen4TXger/fby4l+83v91cANzBGMG2Sfgx8\nD7gL+O5R47cxmlH3paPuuwBIBQKB/AjJqLMPvd1S5BhAHfAj4Cm/3391IBA4OnLz94xWfHxwSh+B\nmDQjV7hRaTYXfgr/+tmxY66uGQzwWerrqbv9w/T99CcAaB4Ps7/5L9PulWdtbikYS3UV9scbT2jt\n8/Tdc/eROZ/8Ey2f+zyq1Xp4zNB1+u/5KRQJpFbe+HYUTbIJhBDiZKcoCpesaGJ+i4//+f02DvQW\n1rtPpLL89++2smnPELdeNR+H7fgGnjLZHFv2DrN1/zC7O0N09Ecp7B44s7oHSw/saarColmVtNW7\nUFCwW000VjuorXDgcZhx2PJ/tpjb5C33coUQQgghhBBCCCEKTGdH57fAzcA/Af9Qwvk/hP/f3n3H\n+VXV+R9/fWfSe0ISCCUhQfgEQhcFEWkq6K4FRFddUerv57roT0V0bQiKurpiWWWtINiwrAVlxe4C\nUqQqRfBDb9JreiDJ9/fHvYNfZr5TvzOZuZnX8/HI42bOPfeec8d4+M687zmHaUDX6UH992XgDcCp\nEbEAuBJYAhwPXAec2lD3RiCBxU3us115vKNZI5l5aUScBRwJnB8RPwDWUMz8e3XZ1sdaexT1WZPg\nqVnA15N6fWh/hTh9n32Zvs++g3rPpgHfXX2ZoFpY+8TjPPj9sztdfydPXPwHZh7492U3V954Q9M9\nBKft/Xym7b1PP3osSRpu8zaZzAfe+GzO+cPt/OKPdzYN0C79y/385Y5Hed2Bz2LPHTbt8YWUh59Y\nxfW3P8rdDyxnTHsb2245nSULZzGxH/varq/Xueja+/jh+beyfFW/Vkbf4MaNaWOPxXPZ9Vmz2WHr\nWRs8BJUkSZIkSepNK7+t+D7FLLb3RMQM4EOZ2WX5zXIG3eeAFwNXZOYPWmgTgMx8KiIOAk4GDgPe\nCjxIMdvupMzs61pOM8tj19fb/+5Y4CLgOOBTFHsI3g58FPiPzOzpWg2iZkt0tvdzj5qtN5s2WN3Z\nYJoFfE/edy9r7r2X8Ztv3uv1j//ut9TXrOlSvvL6654R8C295KIudSYsWsSmbzqq5VmIkqQNb0x7\nG6/efxt2XDiLr/3PDTy2rOt/C5aueJKvnnsDf7j2Pl69/zYsnPf3/04+tmwNv7nybq655eEuy2T+\n5sq7GdNeY/GCmewRc5kzfQJLVz7FuDFtbDprEnNnTqStVuOuB5dxVT7Enfcv464Hl7N0xZND/twD\ntecOm7LN5tOYNnkcSxbOYvKEoZv1L0mSJEmS1KoBB3yZuT4iDgXOp1gq86iIuJRixtxyivBsd2BX\niqUsbwUObbXDDe0vpZixd3wv9bpNJjJzlz60s45iSdAz+ttHDa76unVdytrH9C/gO2D3LQarOxtM\n+5QpjJ+/gDV33fmM8pU3XM/4zTenvnYtD3zjTFb85TqmPW9vZh70EsZMn/F0vRXXX9f0vqtuvon6\n+vXU2tqor1vHimuv6VJnxgEvpDbGWQuSVGWLF8zkI8c8l2/8Mrnyrw82rXPjnY9xyjeuZOdtNuG5\n28/locdX86vL72L1k13/29th7bo619/2KNff9miXc221Gm1tRZ3BMH/TKey0aBPW1+s88sRqHnhs\nFUtXPElbDaZNHsc2m09nizmTmTNjIlMmjqVehzVPreOBR1fy6LI1LFv5JMtWPsXfHl7RZXnOreZO\n4ZAXLGS3becMSl8lSZIkSZI2hJZ+c5+Zt0XEbsAnKfa/26/802glRTj2QWe7qRXNZvCN6Uf4dNh+\ni9hyzpTB7NIGM+XZe3QJ+Fbffjv19eu588Mf4sn77gXgsV/9kmVXXMHWp3yctvHjWbdqFWvubr6c\n5/pVq3jyb/cwfqv5rL79NtavWtWk3ecM/sNIkja4yRPG8pZXLuGSbTbh27+5iTXdBHfX3voI1976\nSMvtra/XWd99NtjFprMmsWjeVJ5cu54HH1vF+HHtbLvFdLbZYjrbbD6N6VPG936TJrbbakaXsnvL\nkG/c2HY232QSs2dMHNC9JUmSJEmShlPLU3My8xHg2Ig4HngBsAiYCiyl2Pvu0sxc3mo7UrM9+IoZ\nfH2bHfAPey0Y5A5tOBMWLupStubuO1l22aVPh3sd1j76CEsvuZgZBxzI6ltvgR72HVx1+22M32o+\nK/5yfZdzk3fdjbZx41rvvCRpRKjVajx/p3ksnj+T7/7uZq6+qcvK6hvUHjGH52y/KdttOX3AAd5A\nbD57MpvPnrzB2pMkSZIkSRoKg7b2Xrlk5s8H635SZ02X6BzbDqzt0/VV3kduwvyu4eST997LExde\n0LT+8mv+xIwDDmTVTdnjfdfccQf1569j6cXY/Zc/AAAgAElEQVRd99+bvGTHgXVWkjSibTJ9Am99\n1U5cc8vDnP3bm3jo8dW9XlMDFmw2le22msHKNWu5/rZHeHx5//fTqwFLFs3ipc+dz/Zbz+p/5yVJ\nkiRJkgQMYsAnDbXmS3T2LeCbv2k1l+bs0D5lCmNmz2btww8/o3zVzTc1rb/y+ut48v77Wf7nP/V4\n39V33M7KG29g7aNdl2ObtIMBnyRtzHZ51myWLJzFJdffz7kX38EjS5sHfXst2ZTXHrgt0yf/fVZ3\nvV4n73qc8//8N+56YDn1ep3NZ09m1Zq13PfoSp4ow7/JE8Ywf9Op7LD1TDafPZkFm05l1rQJG+T5\nJEmSJEmSNmYDDvgi4usDvLSemccMtF2NXt0HfL3bb9ctBrs7G9yE+QtY3ing68mdp5xEfc2aHuus\n+ds9LL304i7lk7bfgXGbbtrvPkqSqmVMexv77rI5e++4GRdfdx8XXXsfDz2xms1mTmTnZ81mt21n\nM2+TrstZ1mo1Fi+YyeIFM5ved/WTa6nXYeJ43yWTJEmSJEkaCq381uVIis3P+rvuYR0w4FP/NQv4\nxo7t9bJjX7Y9e+84byh6tEFN2Hohy6++qs/1ewv3AFi3jmWX/bFL8fT9D+xP1yRJFTemvY39dt1i\n0F6ImTDOYE+SJEmSJGkoDcZvX64AfgqcCywdhPtJTdXXdQ34ij34utfeVuN5SzYbqi5tUJN33Z2H\nf/zDlu7RNnEi61et6rFObexYJu+4U0vtSJIkSZIkSZKkodNKwLcdcBRwOPBR4IPAOcDXM/O3g9A3\n6Rnq69d1KRvTS8A3cfwYarX+TjIdmcZvvjkzXnQQj//21wO6vn36DOa85p+4//Sv9lhv6nP2pG38\n+AG1IUmSJEmSJEmShl7bQC/MzFsy8wPA1sBLKMK9VwK/iog7IuKkiFgwON2Umu/BN3ZMzxn1pI1s\n75+5r/tn5r7pyKbn5rz29bRN7rpPUodJ223H5F12ozZuXLd12qdOY85rXttqNyVJkiRJkiRJ0hAa\ncMDXITPrmfnrzPxnYB7wr8B9wEnArRHxm4h4XUQ4JUgtabZE59he9viZOGHjCvgAZuy7P1u9/0Ro\n//vsxVkvfyUzXnQQM198cLfXTdwuaJ84kanP3avbOrMPPYz2qVMHtb+SJEmSJEmSJGlwDWr6kZlL\nga8AX4mIoFjC8w3A2cDjEXE2cGZmXjWY7WqUaDKDb8yYnpfo3Nhm8HWYuGgbtvvKGax/6ilq7e3U\n2oqsftZL/5FHfnZO0+/VpO13AGDGgS9k6UUXNr3vtBfsO3SdliRJkiRJkiRJg6LlGXzdycJ7gfnA\nC4HLKGb3XTZUbWrj1myJzkmTep4YurEGfB3axo59OtwDqLW386wvfIkxs2Y9o96UZ+/BuM3mATBh\n/gIm77xLl3vNfeORG81+hZIkSZIkSZIkbcyGLOADiIhdgM8C3wQOBlYB3xnKNrXxqq9b16Vs2pQJ\nPV6zMS7R2Zu28ePZ4u3HM2aTTRg7Zw7T9zuAzY465hl1Njv2/zJ5l12hrY0xm2zC3DceyfR99xum\nHkuSJEmSJEmSpP4Y9PQjIqZTLMt5DLArUAP+BHwc+E65jKfUb81m8NHWxvTJ43hixZNNr9nYZ/B1\nZ/wWW7Lok5/u9nz7pMls8bZ3FEt8jhnjzD1JkiRJkiRJkipk0NKPiHgRcDRwCDABeBz4EnBGZv5p\nsNrRKNYk4Ku11Zg9fYIB3wC1jR073F2QJEmSJEmSJEn91FL6ERHzgaOAIyn22qsBFwKnAz/MzNWt\ndlDqUF/fdYnOWls7E3sI8aZMMsCSJEmSJEmSJEkblwEHfBHxG+AAilDvZuATwJmZecsg9U16hu6W\n6Owp4Js7c+IQ9kiSJEmSJEmSJGnDa2UG3wuB9cDlwI3APOD9EdHbdfXMPKaFdjVK1df1P+DberNp\nQ9gjSZIkSZIkSZKkDa/VDcragD3LP31VBwz41H9N9+BrY9KE5v+MZ0wZx5SJLtEpSZIkSZIkSZI2\nLq0EfAsHrRdSH/R3ic7Z012eU5IkSZIkSZIkbXwGHPBl5p2D2RGpN80CvlpbG5O6CfieWtskEJQk\nSZIkSZIkSaq4tg3RSERM3RDtaONVr9ebLtFJWxvzNpnU9JqDnrPVEPdKkiRJkiRJkiRpw2s54IuI\n90TE93o4Px+4IyKObbUtjWLNwr1ajVqtxnZbzaCtVnvGqUnjx/C8HTfbQJ2TJEmSJEmSJEnacFrZ\ng4+I+DBwIrAqIsZn5pom1XYCJgFfiYg1mfmtVtrU6NTd/nsAY9rb+K/j9+XXl9/F1Tc/zAt2nscB\nu22xgXsoSZIkSZIkSZK0YQx4Bl9E7Ax8ELgP2LebcI/M/DmwB/AIRcg3d6BtahRrtv9ee/vTfx8/\ntp2XP38hJx35HA7cfUtqnWb0SZIkSZIkSZIkbSxaWaLzqPL4ysy8qqeKmfkX4NXABOBfWmhTo1TT\nGXy1DbKFpCRJkiRJkiRJ0ojSSkJyAHB+Zl7Zl8qZeSFwPnBoC21qtGo2g6/NWXqSJEmSJEmSJGn0\naSXg2xz4Uz+vuQrYpoU2NVr1sAefJEmSJEmSJEnSaNJKQjKNYl+9/ngcGNtCmxqlmi3RWTPgkyRJ\nkiRJkiRJo1ArCckjFLP4+mMh/Q8FJag7g0+SJEmSJEmSJAlaC/j+DLykr5UjYiLwMuDaFtrUKFVf\nX+9S5gw+SZIkSZIkSZI0GrWSkPwMWBQR/6+P9T8BzAF+3EKbGq2azeCr1TZ8PyRJkiRJkiRJkobZ\nmBauPRN4H/DpcnbepzNzbedKETET+CRwDHALcFYLbWqUarYHn0t0SpIkSZIkSZKk0WjAAV9mPhkR\nhwG/Bz4OvDMifgHcCCwHZgK7AwcDkyj23ntlsxBQ6lWzJTprBnySJEmSJEmSJGn0aWUGH5l5VUQ8\nG/gycCBwBNCYxNTKr88FjsvMe1ppT6NYsyU6ncEnSZIkSZIkSZJGoZYCPoDMvAV4UUQsBl4ILAKm\nAkuBBH6Xmbe12o5Gt2ZLdNbcg0+SJEmSJEmSJI1CLQd8HTLzr8BfB+t+0jO4B58kSZIkSZIkSRIA\nJiSqhnrXPfgM+CRJkiRJkiRJ0mg04Bl8EfGhAV5az8xTBtquRqemS3Qa8EmSJEmSJEmSpFGolSU6\nTwY6plX1ZzO0OtBywBcRs4CTgEOAecDDwHnAiZl5Xy/XNpkO9gwzM/Pxhvo7AB8B9gOmAXcC3wY+\nkZlPDvgh1HfNluh0Dz5JkiRJkiRJkjQKtboHXx24CvgpcCnQJIUZfBExETgfWAycBlwJbAucABwY\nEc/OzMd6uc0NFAFhMysa2loCXAKsAk4F7gH2pwg4d6cIGDXEnMEnSZIkSZIkSZJUaCXgezFwFHAo\nsAdwL/At4MzMvHkQ+taTdwA7Acdl5hc7CiPiGuAnwInA8b3c46HM/GEf2voMMAXYJzOvK8u+ExEr\ngLdHxCsy82f9fgL1j3vwSZIkSZIkSZIkATDghCQzf5eZhwObAW8B7gbeC/w1Ii6IiCMiYtIg9bOz\nN1HMsjujU/lPKWbYHR4RLa/fGBHzKILM3zeEex1OK49vbLUd9c4ZfJIkSZIkSZIkSYWWE5LMXJaZ\nX83MvYEA/gNYBJwJ3BcRX4uIvVptp0NETKNYmvPqzFzTqS914HJgDrCwj/erRcTkbk7vQbG/4KWd\nT2TmLcCjwJ59770GzD34JEmSJEmSJEmSgNb34HuGcmnO90XE+4GDgSOBNwBHR0QCXwe+lZkPtNDM\ngvJ4Tzfn7yqPi4DberjP7Ij4JvAqYHJELAPOAd6XmX8r62zdh7Z2jYgxmbm2L53vbM6cqQO5bNR5\n/L6JXf5HGDd+rN8/SZXjuCWpqhy/JFWZY5ikqnL8klRVjl9Db0jWOMzMemb+MjNfB8wDjgH+BnyS\nvwdwA9Xxr2JlN+dXdKrXnR3K4+HAayj27nsjcGlEzB7kttSqZjP4XKJTkiRJkiRJkiSNQoM6g6+z\nMig7HHg1xVKWNeCPQ9lmH70UeCgzr2oo+2FE3A18AHgX8L4N0ZGHHlq2IZqpvBWPLe9S9tTa9X7/\nJFVGx1tLjluSqsbxS1KVOYZJqirHL0lV5fjVfwOd7TjoAV9E1CgCtGOAfwTGAQ8AnwFOL5fxbMXS\n8tjdvnlTOtXrIjN/2c2pL1IEfC+iCPj62pb/UodYvekefM7gkyRJkiRJkiRJo8+gBXwR8SzgaOBN\nFMtyrgd+BZwBnDvQPeqauB2oA1t2c75jj76BBIkPlfeeVn7dsYdfT23dPojPpm6svuWWLmW1ttow\n9ESSJEmSJEmSJGl4tRTwRcRE4J8ogr19KJbgvAM4CTgzM//Wagc7y8wVEXEtsHtETMjM1Q39aQf2\nBu7OzKZ7/UXETmWdXzSps235DB3llwNrgec3uc+OwAzg3BYfSX3w6C9+3qWs/pS5qiRJkiRJkiRJ\nGn0GvMZhRHwNuB/4OrAr8D3goMxclJkfHYpwr8EZwCTgzZ3KDwfmAqc39HNxRCxsqLMj8GXgQ03u\n27Hv3o8BMvNh4GfA/hGxW6e67yqPp6OhV693KVp541+GoSOSJEmSJEmSJEnDq5UZfMdQLMN5OfB7\nYA2wd0Ts3ct19cw8pYV2oQjo3gCcGhELgCuBJcDxwHXAqQ11bwQSWFx+/d8UMw6PiYjZwHlAO/Aq\nir33fgt8reH6dwP7Ar+KiFOBe4GXlO2fkZkXtvgskiRJkiRJkiRJUp+1ugdfG7Bn+aev6kBLAV9m\nPhURBwEnA4cBbwUepJhNd1Jmruzh2rUR8fLymqMpwrr1wE0UYd5/Nu6pl5m3laHlx4D3AFOBW4ET\ngM+18hySJEmSJEmSJElSf7US8B0waL0YgMxcSjFj7/he6tWalK2mmOV3atcrmt7jZoq9BiVJkiRJ\nkiRJkqRhNeCALzMvGMyOSJIkSZIkSZIkSepd23B3QJIkSZIkSZIkSVLfGfCpsmrjJwx3FyRJkiRJ\nkiRJkjY4Az5V1hZvf+dwd0GSJEmSJEmSJGmDM+BTJbRNmtylbMzMmcPQE0mSJEmSJEmSpOFlwKdK\naJvYdTnOGrVh6IkkSZIkSZIkSdLwMuBTNdSblJnvSZIkSZIkSZKkUciATxXRJOGrmfBJkiRJkiRJ\nkqTRx4BP1dBsBp9T+CRJkiRJkiRJ0ihkwKeKaDaDb8P3QpIkSZIkSZIkabgZ8KkS6nU34ZMkSZIk\nSZIkSQIDPlVF03zPgE+SJEmSJEmSJI0+BnyqiK4JX82AT5IkSZIkSZIkjUIGfKqG9e7BJ0mSJEmS\nJEmSBAZ8qgz34JMkSZIkSZIkSQIDPlWFe/BJkiRJkiRJkiQBBnyqiHqzhM98T5IkSZIkSZIkjUIG\nfKqGeteAr2bCJ0mSJEmSJEmSRiEDPlWDS3RKkiRJkiRJkiQBBnyqjGYJnyRJkiRJkiRJ0uhjwKdq\naLJEJ23O4JMkSZIkSZIkSaOPAZ+qoVnA5x58kiRJkiRJkiRpFDLgUyU0z/cM+CRJkiRJkiRJ0uhj\nwKeK6Jrwme9JkiRJkiRJkqTRyIBP1eASnZIkSZIkSZIkSYABn6rMKXySJEmSJEmSJGkUMuBTNTSd\nwSdJkiRJkiRJkjT6GPCpGpoFfM7gkyRJkiRJkiRJo5ABnyqhvnZt10IDPkmSJEmSJEmSNAoZ8Kmy\nagZ8kiRJkiRJkiRpFDLg04i3buWK5icM+CRJkiRJkiRJ0ihkwKcRr23CRNqnTH1G2fit5lNrbx+m\nHkmSJEmSJEmSJA0fAz6NeLW2NjY9+ljaJkwAoH3KVOa+8cjh7ZQkSZIkSZIkSdIwGTPcHZD6YsrO\nu7Dnt89i9X33s3zcVGfvSZIkSZIkSZKkUcsZfKqMtrFjmTR/K8M9SZIkSZIkSZI0qhnwSZIkSZIk\nSZIkSRViwCdJkiRJkiRJkiRViAGfJEmSJEmSJEmSVCEGfJIkSZIkSZIkSVKFjBnuDgxURMwCTgIO\nAeYBDwPnASdm5n19uH6f8vrnAhOAu4EfAadk5vKGencAC3q41W6Z+eeBPYUkSZIkSZIkSZLUP5UM\n+CJiInA+sBg4DbgS2BY4ATgwIp6dmY/1cP0bgG8DSRHyLQVeBrwHeEFE7JOZ6xsueQj4125ud3tr\nTyNJkiRJkiRJkiT1XSUDPuAdwE7AcZn5xY7CiLgG+AlwInB8swsjYjzwJYoZe3tm5hPlqa9HxE8o\nZgS+hGI2YIeVmfnDQX8KSZIkSZIkSZIkqZ+qugffm4AVwBmdyn8K3AMcHhG1bq7dDPgx8O8N4V6H\njlBv58HqqCRJkiRJkiRJkjSYKjeDLyKmUSzN+YfMXNN4LjPrEXE58CpgIXBb5+sz807gyG5uP708\nLu2h/UnAqsys97/3kiRJkiRJkiRJUmsqF/ABC8rjPd2cv6s8LqJJwNediBgHHA2sBM7pdHpiRHwe\neCMwA1gdEb8C3puZf+1rG83MmTO1lctHJb9nkqrMMUxSVTl+SaoyxzBJVeX4JamqHL+GXhWX6Oz4\nV7Gym/MrOtXrVUS0AV8DtgdOzMx7O1WZC2wNvBk4FPgq8DLgjxGxXV/bkSRJkiRJkiRJklpVxRl8\ngyoiJgJnA4cA/5WZn+lU5QhgXWZe1FB2TkRcRxEKfhh4/UDbf+ihZQO9dNTpSPz9nkmqIscwSVXl\n+CWpyhzDJFWV45ekqnL86r+BznasYsDXsT/e5G7OT+lUr1sRMQf4GbAXcEpmfqhzncy8oJvLvw58\nAXhRb+1IkiRJkiRJkiRJg6WKS3TeDtSBLbs537FH38093SQiNgUuBvYAjmoW7vUkM9cDDwPT+nOd\nJEmSJEmSJEmS1IrKBXyZuQK4Ftg9IiY0nouIdmBv4O7MvKu7e0TENOCXwHzgFZl5Vjf1FkXEMRGx\nY5NzU4AtgG7bkSRJkiRJkiRJkgZb5QK+0hnAJODNncoPB+YCp3cURMTiiFjYqd5/ArsCr8/MX/TQ\nzqblvT4bEbVO594L1IAf97/7kiRJkiRJkiRJ0sBUcQ8+gC8DbwBOjYgFwJXAEuB44Drg1Ia6NwIJ\nLAaIiJ2BI4AbgPaIeHWT+z+UmRdk5qURcRZwJHB+RPwAWAMcDLy6bOtjg/50kiRJkiRJkiRJUjcq\nGfBl5lMRcRBwMnAY8FbgQYrZdidl5soeLt+dYubdDsB/d1PnAmD/8u/HAhcBxwGfopj1eDvwUeA/\nMnNZK88iSZIkSZIkSZIk9UetXq8Pdx9GtYceWub/AH00Z85UAB56yExVUvU4hkmqKscvSVXmGCap\nqhy/JFWV41f/zZkztfMWcX1S1T34JEmSJEmSJEmSpFHJgE+SJEmSJEmSJEmqEAM+SZIkSZIkSZIk\nqUIM+CRJkiRJkiRJkqQKMeCTJEmSJEmSJEmSKsSAT5IkSZIkSZIkSaoQAz5JkiRJkiRJkiSpQgz4\nJEmSJEmSJEmSpAox4JMkSZIkSZIkSZIqxIB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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 277, + "width": 892 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "theta0 = longer_trace['theta'][:, 0]\n", + "logtau0 = longer_trace['tau_log_']\n", + "divergent0 = longer_trace['diverging']\n", + "\n", + "theta1 = fit_cp99['theta'][:, 0]\n", + "logtau1 = fit_cp99['tau_log_']\n", + "divergent1 = fit_cp99['diverging']\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(theta1[divergent1 == 0], logtau1[divergent1 == 0],\n", + " color='r', alpha=.5, label='Centered, delta=0.99')\n", + "plt.scatter(theta0[divergent0 == 0], logtau0[divergent0 == 0],\n", + " color=[1, .5, 0], alpha=.5, label='Centered, delta=0.85')\n", + "plt.axis([-20, 50, -6, 4])\n", + "plt.ylabel('log(tau)')\n", + "plt.xlabel('theta[0]')\n", + "plt.title('scatter plot between log(tau) and theta[1]')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "logtau2 = fit_cp90['tau_log_']\n", + "\n", + "plt.figure(figsize=(15, 4))\n", + "plt.axhline(0.7657852, lw=2.5, color='gray')\n", + "mlogtau0 = [np.mean(logtau0[:i]) for i in np.arange(1, len(logtau0))]\n", + "plt.plot(mlogtau0, label='Centered, delta=0.85', lw=2.5)\n", + "mlogtau2 = [np.mean(logtau2[:i]) for i in np.arange(1, len(logtau2))]\n", + "plt.plot(mlogtau2, label='Centered, delta=0.90', lw=2.5)\n", + "mlogtau1 = [np.mean(logtau1[:i]) for i in np.arange(1, len(logtau1))]\n", + "plt.plot(mlogtau1, label='Centered, delta=0.99', lw=2.5)\n", + "plt.ylim(0, 2)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('MCMC mean of log(tau)')\n", + "plt.title('MCMC estimation of log(tau)')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A Non-Centered Eight Schools Implementation \n", + "> \n", + "Although reducing the step size improves exploration, ultimately it only reveals the true extent the pathology in the centered implementation. Fortunately, there is another way to implement hierarchical models that does not suffer from the same pathologies. \n", + "> \n", + "In a non-centered parameterization we do not try to fit the group-level parameters directly, rather we fit a latent Gaussian variable from which we can recover the group-level parameters with a scaling and a translation. \n", + "> \n", + "$$\\mu \\sim \\mathcal{N}(0, 5)$$\n", + "$$\\tau \\sim \\text{Half-Cauchy}(0, 5)$$\n", + "$$\\tilde{\\theta}_{n} \\sim \\mathcal{N}(0, 1)$$\n", + "$$\\theta_{n} = \\mu + \\tau \\cdot \\tilde{\\theta}_{n}.$$\n", + "\n", + "Stan model:\n", + "\n", + "```C\n", + "data {\n", + " int J;\n", + " real y[J];\n", + " real sigma[J];\n", + "}\n", + "\n", + "parameters {\n", + " real mu;\n", + " real tau;\n", + " real theta_tilde[J];\n", + "}\n", + "\n", + "transformed parameters {\n", + " real theta[J];\n", + " for (j in 1:J)\n", + " theta[j] = mu + tau * theta_tilde[j];\n", + "}\n", + "\n", + "model {\n", + " mu ~ normal(0, 5);\n", + " tau ~ cauchy(0, 5);\n", + " theta_tilde ~ normal(0, 1);\n", + " y ~ normal(theta, sigma);\n", + "}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "with pm.Model() as NonCentered_eight:\n", + " mu = pm.Normal('mu', mu=0, sd=5)\n", + " tau = pm.HalfCauchy('tau', beta=5)\n", + " theta_tilde = pm.Normal('theta_t', mu=0, sd=1, shape=J)\n", + " theta = pm.Deterministic('theta', mu + tau * theta_tilde)\n", + " obs = pm.Normal('obs', mu=theta, sd=sigma, observed=y)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5000/5000 [00:05<00:00, 854.95it/s]\n" + ] + } + ], + "source": [ + "with NonCentered_eight:\n", + " step = pm.NUTS(target_accept=.80)\n", + " fit_ncp80 = pm.sample(5000, step=step, init=None, njobs=2, tune=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'theta': array([ 0.9999017 , 0.99990126, 0.99994554, 0.99991853, 0.99992781,\n", + " 0.99990295, 0.99990909, 0.99991026]), 'tau_log_': 1.0005729430235684, 'tau': 0.99990735598654512, 'mu': 0.99990583189162452, 'theta_t': array([ 0.99990015, 1.00004939, 0.99990031, 0.99990172, 0.99990554,\n", + " 0.99990178, 1.00008856, 0.99991732])}\n", + "\n", + "{'theta': array([ 8024., 10000., 9338., 10000., 9299., 9330., 8541.,\n", + " 8856.]), 'tau_log_': 3741.0, 'tau': 4488.0, 'mu': 10000.0, 'theta_t': array([ 10000., 10000., 10000., 10000., 10000., 10000., 10000.,\n", + " 10000.])}\n" + ] + } + ], + "source": [ + "print(pm.diagnostics.gelman_rubin(fit_ncp80))\n", + "print('')\n", + "print(pm.diagnostics.effective_n(fit_ncp80))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> \n", + "As shown above, the effective sample size per iteration has drastically improved, and the trace plots no longer show any \"stickyness\". However, we do still see the rare divergence. These infrequent divergences do not seem concentrate anywhere in parameter space, which is indicative of the divergences being false positives." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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DDixKbo6UN0UYRr8hBn3XDpqL8kFw5AJZ0Anvbvctv9D++zA7BLIM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\nCwBj2qSp9+4HXgQ+3SJtWUrdtdvgUL31RcrSkNcBM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4YdvrNekR3LVZvjreN0abdwLnAe8BPkeperweOBT4bGaO1Vc1uv+hR7nkmr/z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\ne/n4Vx+hOicIeMGWfv7kN27AdWw+9K/38+nb9i64nn9850voKa6+kmT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IiIg0HAXFRERERERERESk4SgoJiIiIiIiIiIiDUdBMRERERERERERaTgKiomI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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 610 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "report_trace(fit_ncp80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> As expected of false positives, we can remove the divergences entirely by decreasing the step size," + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5000/5000 [00:06<00:00, 721.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of Divergent 0\n" + ] + } + ], + "source": [ + "with NonCentered_eight:\n", + " step = pm.NUTS(target_accept=.90)\n", + " fit_ncp90 = pm.sample(5000, step=step, init=None, njobs=2, tune=1000)\n", + " \n", + "# display the total number and percentage of divergent\n", + "divergent = fit_ncp90['diverging']\n", + "print('Number of Divergent %d' % divergent.nonzero()[0].size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> The more agreeable geometry of the non-centered implementation allows the Markov chain to explore deep into the neck of the funnel, capturing even the smallest values of $\\tau$ that are consistent with the measurements. Consequently, MCMC estimators from the non-centered chain rapidly converge towards their true expectation values." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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r7bfjxWtKVh1QvZi4gPwDuJvwA7igx05chg+4Ys9zK648OMWgzNG4G7a+VBrilxnGvtur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AAAAg\nAElEQVQXf3Y7K6/XI/rql1BbzYA8iODLFbB2l6Ogfv0MsjEGj/nwWpahr15BNnqI3wwLXxPjk2T3\nhNoPmGE7w+UU08dR6UZgvN0SXgn+0CH09esUScTS1QhdyvFW3TandaKo0mEttbz8Xc1HFgy6k+En\nVrFkDBkNOx0Xd7roHS2CO5rsXwPGsHa77NID3Q4D/T5DP52h0ZwhkQ6+u8n/+fKnmZnxHD/u6H3j\nFIfcdabTdRQWh2ZDzXCeI2R+mzGmvOW4/T4zbhUPzHbXaFT3k0jGgrvAxPI1srgObsgKGbQlkH4w\nPseJxric1JSpuBaVboPE9gAwTxRkSQXyEmXfoJw2aNoqM1GFa70HuHED9u3z9M04diaiPT3PieKz\no/M8kUHnpQdZuPY8MzOXSJIe4qsksokU6XZDQjLvus84h7S2qPz2/4r6cJPi+MPkH/vEbuaMs5iV\nbyF5F+nntLoRSvUwOsOYFLzglEJ7z9him6JVpijCPROlKHoJ1bGUwpURgXY7sHpqxRZtvYPh4xwL\n/grFuubgz14imUgxpQxt/Kj5yngqdHBKyChRVk28U0he4MUiWiORB58jzQw2NxnPKtyYfIjzsz/J\no60vEJ1ew4tCXIHkCn9WE7xXh+OpUJQU+hGLKoUL5SRCJSlEikm5gKnm+J6iuzqLRSPimHpwDVVJ\ncQXUDrbwmZDQI6oXeK3CbbCKKM4QBxgf7kGQNtudODIoSRNBsOBAsk4A0MTgcsu0vkRTDjGlrjA5\nc5nNbI5XeZKPb/wlpaJHMWUoLm6vfLTLyFKhme5HlQv0zTbMGTCD/uMtknf3XFx4l+Nqi6GMMgY/\nHRyeJenyxotWtw34iSCDn8sgHUulPr1eeUfxqmIqXoe64sCBqyjlcU7QPYveKgJoEzBMsrhKbJsI\njqJh0CaFqRSJcyTPoXBQA6JeOCCCQOmqItIAtYErTaPWb5J84fNwvxCdeCkwavfUOBiw1hp9THaD\nB2olpLtJ6gQrpdE9nDFbHK5uomoVnMwxLCN1bpFo8QSYFFwMKsHNz4O1SKMRNLy8Q8ZS/ON98vKn\nIC4TYfnML/wVS7ag1YkpfIxSoLHsM5eZTy6zmc6SpAn3LvrtW+cFpS4iKh24D8OIKeYKorVvIQsZ\nunwZH8+A7ChdL5XwtXrYaNlswowOmn0+Q+lV7P7FYKjQ7eJTg8Q5mBhUFyEDUqgm+KSGEcf8/Bbz\n81s4l2Dtg9xzZI/Fa6WCOXd217ztLee2A/bI7LkL/GcTOVLzTKfnWWk9tMt9t9pbvw0o0zq4ak/W\nwB46vLst1qLPnyP69ku4ShVz6QLS6wbK+tYmamsTf+0q7spl3Owc9vhAn+2tmPHvJKIofFe7TfTC\n85hzZyEvyKNPIgeWYdYHFGjn87sYuGtPHyO9/5fD+qTdRr96+u0z8cp7u+veSbyZhu4P+jvei3Pf\nzXGnTqE/knGXrcV/FOMDUOyDeNeRvP750AnfwIFoFG+2yH0f4m4Y9H7t10o0GkKSvPkDKkmEzU34\nuZ+DP/3T96lxP4SIIvjN38z5whfCfYNw36yOMDYbmuuNFu1v5K69/YXvbMj79V/P+dKXynx+33/D\nf7L2hxztngKgr7cfPCXbATwXKg/zNzOfIe9H/MZv9DB/cvbts8SGsWPCe2v/unpV7bmkuuaf4h71\nPAf8d7jKR0avex9Asb/5omFywlOJujSZ5Rv9X6X1dvP+1lKIiWtgUiQXJG1Ao4GvlvBzMUIXseth\nV98MSnFsF+yAFRYNV7QCbgCQKRU0V4pQXqSal8MOqKmg11+FvB0e8m9VkvHVL2POn6N4MmiRZT1L\n68XTVC+epsh7eBNjjCdJPKrZgGYDt7EB994fUNVyBbXeRM418Q8aiCvYe+/D3nOI6PrzSNZGpY1b\nADGLjyp7MkXs+D3YYn9YpOed8GJUDWVBSNCuUQVXTtbotzXjh5tvkhTCBFusu2kuLFe5f64xWMhd\npiju59SpYL9uTMbc/CWq1XWUOIpCsXFjlahlmJ6yIBFKQqGrB1otcFaQW/pO6qscG3uZ8V6brdWE\nub/6fZ7efI5IOQoZLpgsB+wlDnKJZT/LKz6Aix9x36Dku2SDRaV4SxR5qmOW+sxN4nqXWHVQaQFr\nFfqXyviB4LMfogAEnaqybVO2bSyKnBhij561JHmKsilWRWRJmXp1nbHKGqrqKLym15rGjR8mlwoL\ncpqYNhmhv8YxdJ/+BAv5V3ng/tepSQ98imQ5RANgwhGqZd0gX9WgXYVCMo1MbZD86Z8gm58ieuF5\n9JHL2OPHQCn0xqkAlogh67dIkuHOuuB9cIMzkiORQ0vOzNQNivUK/aiOihR5q4ottVBxTqtZJ+vC\nlF+m5reIfIYvhFRVMHSh46nf06Q0keIKwZQs3m+jRiNwDEcifZxXgMKaBOs9icnBCVQy6FXxY2Vc\nXkOnfbxo0nIdqXhM3qXS3wLjkSMOmfYBCLdgNwQ57pGKBytQARXleDEUElGyaSBU1hxxeQ2TGgSF\nSixTlT62byhPd1FmALL5AARjJWi5acL3qh2g2Ha32AYxRwCmI6Bo4QMiCixUVBPDZXRUYT2dZyJe\n4+PVv6TTHKNlpqhIk1ziEeC7Vd7HauUI+5zjXv01epsRrDVhdiJ8b9rYu6t6Cyoi3/+J0UvFw48Q\nnXgJ309Q0SrSU5DnoURXBK8UlIdlkyoImg9E/J0HazVRlJP1ElrUEfHU4jY106FQmk4vjIVKgRkv\nGBtvknVjbKNKFIXxtm/GSPJOAMHiHiQ6jMuFg6oFEwwYfBxqVIUcaOFcJbCnZAsZOw2veaSTQrcB\n0VvM+3SCrHag1IRSRNKH2ZmdN3Aw8PS6qBtLuH2zOGbR5nWICyQFN7ZzRyxoJ+7iCvoc7V/HFg9j\nom+ho2UOHj5AnsONm55OB8RZ6kWTuulybPYapt3Fdefw0UQAaMRgou/h3QywGxBSqytIGvQjdbSG\n035Qcrw9aLrZOVSRI7KOdIcsYj2QEHC4uQGgt7GOknWoFyFdTRlE4/1QawxAB8BVisEGUw+lrgEN\nwGEij7NT2Dy4a77V3PbvX3D8/D1/wEfvO4lSgnFj4dwRmAN97u9/mWZ3PzduPIL3GuX33lyUIuhw\n7ZooWIs58S2k3UZtrMPU1EAfMx7dLwDJMtT6WgB7v/Pt8IweAGO3lV6+F/FGm707ShB5gxJE4F1t\nZkbRp3cxoN9uRNEbVDnc4Xf8sM59t8bQKXR9Xd6sSpbXX1d87nMRv/mb+Y8kMHa3rMV/lOOD8sl3\nHz/e5ZNvw4FoV6gI1V4i3/fMOyrbeqfxTu2R38+4eBF+93fjPRlie0UcCzdvws/8TMbk5A+4cT+o\neBsirDtFR8MhQrm7yXzzHLOL0RsKiN4W3S7F40/g7r3zcoNaDU6c0NxYNlwaf5yT9Y8Cnqpt4hFS\nVeK12pP8x7n/klfrT9NLNfff76jXofEfXuD6ZX9nrpAAoiieevS2/nX+vEJLxoycZ1adZUquMC7X\nMaQs++PUZZmcCgpL4WOWlsKucUl1mJrIWPbH+WbxGziit5f3w3LEa1fD7CCGODkDnTwwFpTAWBdV\naqB6m0iWIjpHtAtlN4G/DZkP2zB6x+R/qMruHTJ0PB0sElx94HrmB2UitSO3tOP2TqyuX0PdvIGs\nbXBybR/Z10+gNjepZls4FQ3IakKaCtYLUayQLEWvrGCP3htWlFEEq+AfmEB6m+jLVzEXLqKaq0i/\njfheKMsalk2qGFdZ2D2Bdjm+PIOr7geX033mvydf/HjQCMtb6LNnAIOzC7S/FXPx2xUOPHiNCn1K\n9EnIUDicmB2sEMFgaalxejZhvtRATU8BGS998zDttuPw4VfYv/9VyuUGQhDo1toxXrpB5egKnXJM\nHNewhdDrC70u2OJ2QGw7BR0b0RyfuPgfmWpdpdpdR+vdHc2JxommTpt5tcK0X2fMN8mJRre3ptpM\nP9uhfnSNUrmJiISyIeWIp/qYIzm6bslXo+A8SIbgiXyG8TkKj8djMUT35vhxFRYzDsxUj2SihYly\njM+puUa4jbrDXOkKJdWi5ybwwJp/YNRuq2MmW5d4Yt/nMeUeEtuBlhMBEAvEubDuLYAU6OngEOgF\nrzzy7RS9vIzqdlG9s+gbV7D7FjDN10FFSOs6ttsZKIMJeI/JMrQUI7akKKDkUCsFSdaFNKPny/R7\nCT1dx2zmzPibUEDmI8R7TGwp06ZyswkOykd7+EJjygWmVGz3r+07iVegxQ10xATvdSDraBdIL1XB\njyX4coQplemt95lpnKfeWEFdcThjiB7poY47/IQKZhg6AqNQD1r0/oFuYeIRAwzKPwUfyjVF4TCo\n2GLKBTopwIMoTzLRR8cWZQbXQ8LriKCMH5HA0AMi1dD28taf6bmltHLwIaUoBsJnmS8jRcqYWSXy\nPWbiZSr1Fs4btvIZXtn6GMvJIVaSgzSjUCJcryuSpEVS6TERxbBvGpu2UP2NALiNzh+gZq8Tiqnj\n+OEYNrjmbm4efeYiihvQC/qKYoP2mVgHiQ1Aozaj3xbKkz1ZFof2pzHLzLM4tkSiU0o6xRaGXm97\nIeS8wviCctyjVO6iGzmRTRHvyEsJrhoRO0URKZzNkaTARRprykgU2iR5gVg7KPtzOHco5DR9xG4h\n3+sh+2/RRNsj1MoK0vZInEE1gVVz2waC89BPFWk/p7UivHLpo4zVXyMRUL0ubmHh9lK3nSEakRYi\nDZTdhDEN5RJaw/iYY94vMyerjJf6lMvBuELSLlJ0UCs9JMuQWhuRFM/07u+2NrhFaw3lPhJZfGyA\nHNixghbB1+ooNoMD8JCNNESlcwPO4w8cxN27D6EPUahvlTwffG7AkhaPp4JzCyi1hDZnEJWjtQY8\nzuUotYGOrmIrnt/708e4es3seizGWZsHLv4Nj1/7c346+W0m17/D2pIwcXiSm6vRSCqxVd5HrbNO\nRa9RHVun2djPWGvpNmBM2RxbqlD5xOO7nnX61ElUs4FsbSJ5Hsb2NL39fikVNsqsBW2QTgc/Pyj9\nfxeloHccb6MEESB57q/vsAiRkUTHyn3PvqFJ1BtFtwuPP+649953B069mUHVuz33P9TyyXfqFPoj\nFXfJWvydxgfuk3dP/FiDYm/Hgei2GC5yJ9+HB+Ag7oZB71/8i5hr1/RIbPqtQqlQbnPxInz607cI\nZL5fjj/vNN6Bo08cw333eZ5+2nH/T81z8PzfMbUQvX3wMssCrX7n78/bRFe+RHLpr4mWXiBa/hbS\n38TV9t2W0z/1U5YvftHQagnEMVfLD3By7Cf4/tjHOTn2E1wtP0ChYnq9UMZ2//2OpSXFkZWXUEWO\ntQNXyCuKdvstXCEBSiVkvrO7f3lHce0k8+pVytIIE1DxKHFUZYNJdYXCJ5wufpY1dz9bK236fSGn\nxNnWU1wa/zWuuY/sKq+EN8/7ka6GNkF7a+sltGpBo4n3DpnuIZFF0gIpBovuxCNmyHogLPAUAyCJ\n2yfMIgO2BxAluGQsaHBBeHjnLfTXLgz0PQxKXdit90IvuEydvwiiWL6SUb96Bq2gXLSJbDpy8xoC\nNdYKeS4kZRUcHkW2J+lZjnq1gV4+h+pcByze1xE6SNGFrBv0Y8rjuOoegFhUpZh9AmwPO30suGju\nmIyrF6/h8wW8neTKX59jMb9M+XB3BE4JnoiCEn0MlnzEzBKaMo7xljZVZqYdK+tCXvQ5duwrjI8v\nEcVB/6ywSRB0BirdLeKxHkb60F2n2k3JugWttESWK7I0bBwMrdv1gNSX+4RFzmOvxfRVBd/rM+U3\nRk6TO8OJpuI7HHCXaZvJEXEn0Sn1j7aoTXZxLsLk2WixYb3C2ILCxeiaJZrLyZZiDJ6y6wRQxLsR\nOcjGmuTZDKYFqeckiz102SKZRzwURIhzeOeouTZSFJiSp6K2SKlx2X1s1F5Dl0/X/lsmx66H676z\njnMIugzZYh7oDt7wHqyF3CIvW+j18BMTSOJRfgV949UACkuPrJdhCzAuMKDioovynkHtYTjvcOgu\ng1tXiLGUVYcbzXtoP19HrirWZYobZh8118JlQuNanfTlBLXiGP+JLeLY4jCYchFArtvGlZD0StwA\nmxNEAhPFe49zkGUGkRituuj0BhW7TtRs01sfJ272KP+jLdTBAlW1UAEbGYrM4JTGzBSo2EOZoGVd\nBCHzIPAeNJScaCSyqMiF3+1AjMOUbQDA2FEBqYb3YOf4IaNSwKFZY0i821JxB1i2jZwFAmJO5HuU\nTYdYZySkeCdoYxHJUSonigs2N2cZsoC0hnrds7k5y+LiMvVKHyr76C8+jG5dGwj6h0b7qIYrTeEq\n89jZx9mJNmd9R/srJ2g2NbrSwdoAkiiCm6fXBhJBtB+J64Vh0VPkGuuC8cZmOkWl1qVkUnIXU4va\nZFlMmm47AddoE1EQ+QJtXCiP7WqUKzCqjzhHqxhnmfuoyiZ6LKdQMcoWkAVzCT/QVPN98K0Cf34L\n3eng6xOo/gpyReEOziC092TJAjsAJYNPFV4qAVizfUCPSrd7mcN5T7ZVI32lSnVqiVKxRL7VR9I+\nOokgid8cGPM5St+EogqLYwxrTvWN6wGk0XrX8ZJmiLGQlpA8QyWb+KiKZ2x3Km1tIVkKopBqL9zr\nUhmRDO/HRnkSPixIkkJsGNiihhzQDjv/EMUjH8LNz6A3z+CTCXw8FrIzzyAv8FTxvoZz83hfQ6kl\nlGSIsng/idZhcmidg9zh5g9w9lJB0jjFSvRhPBplc5489fs8duZPmWxdZf++V6iUthCrSdobuAtX\nmNAtbto5lAnjQqO2SJx2qbpVymaTbmOKan8DrzTaZihvaSazFI8+zuTMjt+bZZgzr4LW6JVlGB8P\nZa1vFEoFV/PpOqpyHZnbREXXUKVVVGOJ/IlP/lAW6MvL8K/+VcTv/V7MH/+x4c/+zLDv7PPMThV3\nNl3OMtSFCxyx54le/Dr3rLxEubdBs7b/tnLUPQ7lV3/1Ds+3RywseF54Qd/R97zdc/9DBMXerVPo\nj0rcLWvxdxofaIp9EHdF7NJRedsHVTBbZ8l5fzSshk47b1saoAyvvabeczb3W8WpU2agGfb2o1QK\nx8HgIfVDcPy543iHjj67olbDPnQM/fqZtxZBBeh1sQ8d274eNic5+8fojVcB2c5hmxItfZ1o6Xns\n1DHSB35lVHNfqcC//bd9fuu3Ek6dUqPXRqfojYy3+PjHLWODufXK5IPce+15sqg6+hkbG8K3v635\n8If3cIWEEattV//yjmj5JWqqT+5uf/DZQclHSVo8Ff0R/6b1//D89z+9vXMcez6y5woyxJ55326j\nT53EXL6ErK2GSf/9LXAajEEqLaQYsMBkwBpzduAIlm9r/wzZHQyAr6Hg1c4QwPpw/K3sg34H/eoS\neuLy7XovWLS5guYKMrfF8ndnyQvFTG+ZjeQoUd7dE8QRCfer24VqTaHWV7FZBsYQnT6Jz3OKj30C\n6zNUEdw1rT2EUiWENXwm0CiwsaW31UTyNnhPJjW61QNM1proycWgUcJuvZdn/j6h5lscN3/D+BMN\nKtImOpjhM4XdMngrDOBFDDl136TFGAWCc+E6fjH9SRaLv6KStDh8+DpGZ2FRJI5yuUG53CDLysgN\niyn6CB7lBS8WKk30UsGcbdDVZfLJmHKlh4jDOUW3WyFtVWlWZ5nNlrgkx6iUPWu1wxxqXrrVaHMU\nFdehTA/lLTIQ4Z9+ZJXmxDST9uYexwkORUROVkToqqX6oQ75yTJiXRDExqOUQz/qMLMFMhWAEl21\niPEMRd997ija4QTG5fR8mZJ0KbdyWvV5DqoTo7Mqcn7V/FMm9dUB2GK383RnqEEqpkAF6NhRrko7\nJJFkKVw4h2tNwrMmgARbGdLroXwJ8aBcjnIF4tyIIeazAWaZCmgPZY+zkC8ZuCTMRKu8Vn6Sr7WP\n0X0t5Pqqm+OQu0RGzAGuoh/IUWWPODDsvVARwO0EnSVcz4DtbU8Bi1zY3IBx6VEtW+I6EHWxjYj4\nZxtB765QOG3QyhKP99HjBi8e5wUlClEha30MRR7R9yUMWTCpENCRG+HfYnZvCgjs0gqTwfX3Iw2x\nAHwOUmL3/fIEcEzd8rqEg73zKG8DDBOocngRiDNyIgobE8UF6+uzVCprHH/4m5w+9QxFoanVBvfL\nayrlD0N8BtaWYH4WX5nD2sF1d4Ht4yqz2KmHRzpjzsGpU4rSmZPU+j3ERDSa+6iUG/RxoMao2iax\nsYEl5IOmVhgjFUopsjxG4ejkVdZkhsPRZawPfb2bVQf9zVMq9ahHbZQ4vBeKXGMyjypbnHI4JTgr\naMkp+Q6xz8l9hSKNKfkmGIfzIM4juQ8lzT4gkb5akG80kcZpTN3AdIQtPoFE30J8l5Gz387cazTC\nsdpCVsKfPEz25BPEa1+CqEGro7CFkDfr9JfGqDXWGc+WUKrA5gnORLSYpnR9i2qzAZUKbnYu9Nlb\nz6W6iOviZh7AVyuo1hXU2kZA+vfaOTMGeiniN0JOpEUwC5jZ/WySXm9bM847iJJBPoBIA+9360d5\nX0FMDspg9y+GF5XB7rsPAL11bkeSa1xpCnQFudLEm7nR9yi1HMpXReO9DbpuzO04EfQXDnP9GyWk\nv8bExh/z3NXP8Ms3fodEr9GbqhHHGbXa6kgvzJuYdgaLbpUja9/k+oFn8ErhlWJp/jG0PcZs8wyr\nW/cx0bpOoSMa1X1sjh+m72I+dmT35qu6cjmMhY1QRuzGJ9Cd7u3XenQxPTzQQS+cC5uTrWgg2G8x\n7gKVl/7H2+ZdP8joduFf/sswnxPZns9lGZy5lHDl9YK5Oc+nPvUWkirOYU6dRNZW8VFM6cgRFiYV\nGxsZ93ae596rX+PmzHG+e/wzuD1+V68HDz3k7lhlY6+o1cJ3vf76W5MA3utz343xXjmF/rDjbliL\n3w3xAVPs3cePN1Ns6YXgInenIYpi/0+89w3aI4ZOO3eC6g/Y3Nx33/tXZ/+5z0U49/ZHZ6WGmjGe\nf/bP8j3K3PauEX3PHX/uMN6po8+tYR86jjl9KjgbvdmMpdfFTc+SfvbXw+8diFGq1tXwELl1Z0XH\noGNU9+Zu98N2m8rzX+Ln5S/4z+e/ygOb3yRub7JR2oepxDz7rOWppyxTU7uxvmZtP/de+btdO4VK\nhYnXnq6QMGK1RasvjfqXWX8FSRvkNqLXlzfcNPeiMT5ldcPw/ZXHB25VYQdxp2HUsLzi+Pm/5OjV\nr3HoxkuUOhtslPZz9KFwPaMvPUfpr/4C6baDRojWyGQDpQUSA5VOWIxaN1ioDhrlFJJsAwij/wph\nkQVBH2i0mh387UP5XDF7HF/abqy6eBnTO4uXNltbCZsbEY2m0G7JwDBPo5SGdJ0i6qCXCuI8sL+M\n22Ym7YrIER/poY/0MAdTZD5H8ibq6ibSaEEU4w4cBDTeT+PcAZw7hLXHsMVxRDUo0iVce43cCRl1\nWsxjXYzqrZIuX2NztUe9uMCpL36HF77Y5NSVRTyK46X/l4f1n6F0izQ3GGWJkgIzWWDGLSrx2M62\nM2QoPCvYYIpCIhpmgkfv+x7V6hZF4akmmyjxuEGJmEchzlFxG+hKRmdshkptA51YVGQpKOhtlSjN\n9hmfa1BPWuSSBFxTecrlHuPjDSwO1zGsbdzDwoKnUld0l9uMSXtPoHHSrQfXPqAvFSLTp/JwF1uZ\nJuv0iSsN1HiOrmVQLZDIU2RRcMUTFVg7YwVyxaOsI/I5WlnkWQfjQKGgBlpb5Nb5nxZU7MjTGMGT\nkmCdQvsCQ4GPFa8Uv0CzE7N47XN8qvp/U9ZtwCN6ANCoW/7BNjCmgd4gVQ3wioL1Ad3LaPAKoQlT\nfcSE8mFvfWBbuBzj8lH6SwSyTf6EXHBdRfFqRHE2ZsXOU8rbvN47xBEuc5hLLPrr5ERMyhZeR0zQ\noPpgBzNpB830UBJQfsC8klG3c6IDQ00NQETx4NVAXyxEr18JOm2+IC8UcezQZQtJhk8EKbYviVeC\nJCAlhx5z6LLHq6Ax5tBYZWgWY/SkQpVOIHpFDmUCKD8ynd0xNsjOcWIYg2Fh+N4QKNsFhu0ExNQt\nx+84bsgQ3f4X9AyVDiWPWZqwvj6PdZo4SqnV2qyvLzA/77F26GwsxPEBMPfQfewp7NYSreVNVrdK\nXG/u40L3cTpqkVpd0Br6ffjSlzQ3rhQsbp0mtTGichpbhwPQqx0mysglwheeSBXh3gzGRi8GfIQt\nICfibPsBxksNEtNHBApraLUqTCWb1ModKqaLkgA8anFEUYEqWbwRLJp8LcLnglaKdmWGZv0ApXqD\nUtZBCij6EbpvkUzwRbiYo2tvPL4dB4ZfL0WrlGL6KZzfj3MdWs0OW1uOrYYZjcul/ipKeXyzBK+U\nUKvr6MtXkWs9eidz7AWwN0v02xOMNW9ibIqIwiz26Fcmadb2k0Y1pNsl6zlsvyDd7NJRY8S3eJ0o\nVvFxQv7YJ/FJHb11EbW5efscx3uk04EsCxqCZlDWrzxywyPdHpLl+Eo1gN7NxjZhsZTia4PXJdRV\nB7bYzohRqhF65HBnTBlcLZTSmq3XuT0cnn1Ipztorw0upYMxdngupYLJhk1T7NQMf/v6Qa4vKXpZ\nzGxynbnXLzPbWaKRV2k2hZmZ80xMNna4DA9IiFqTuD7SadOpL2y/pzRZqcbl/R9l1T+AF0O7tkDu\n9J7O3vr8udC311ahUgnlo+3WbtbtqC96eKSF1C2SC8TB6GZ0jUyCO3j09nnXMN7j6oduF37jN0pc\nvaqoVG6fQk64DY7k59jqxly4oLj//jeQVHGO6NsvIa0GILjFA/ipKWZnPauril4R46OYsfYN9q2f\n5OrCh3e5evZ6MD3t+exni/dsOv7QQ47TpxXN5u3O7TvjTs/9D5Ep9oN063w/425Yi7+b+IAp9kHc\nHaEM2PSdHfc+xblz6p3onHPunHpfdwKMCWDcOzkOIPnC52933dsr3kvHnzdxAfvkHkIAACAASURB\nVNyTBfYuHH1uOyaK6P3mf0Xyhc+Hz8BuQfvuwFHooWOkv/jLo1nPHYtRvvqH8H21i31Xj+FTz6R8\nqvMV8F/GPnSM9Z/5FX77f6ve1swsrnFz5jjz66+R7xAlNiYI4GfZLXO6nay2Yf9yGdJbBRUzNuZp\nNLcf4oKlzBaRdEMZDkJfjbFfvsdYqU3PhgYdPBgmqsrmPHH6D1lYOw0iZIPdJWMzjneeJ/q3XyPp\nPED6S79C6d9/IWjKmB0zqyGoVU7xcYz0UkYMMGcHOjkecgmi+rfON9RgIuHZsTIOi3iRHF/0cdXF\n7c8XXdTaJdaajk47CYyTwWLIAo2m0GgIlYqnktcxyQale1vkp6pERRePjNg54fye+KEOemYwscuE\nrouRbhdtXsYsWOy4pr8xxtUz02x17mN8POLgwT6lUmCM4XOazQa2mEN5T2NmEVTw1Yxpg47waHTn\nJue/3KIdf4SHa8/zCF9lXK7TmZrCLGcjoLRDDb1lKY9bcIKqOJIDKem1ZHBphYicviRM7Nvknn0X\nqERdlBbSWhKE7wtPKWlirSHN6kGHq6owUUbdruCyCKNSnBVUbJn50Co2NeTtGOUdE2xgMeFaWWhL\nlbnKElv1GRYXc0BTKsHpyqNM2K8zf+Qm8UyGqFCSp7SjljcRDyXXQW0U9CVilWkmo5uUDvWIKPBO\ntrGQsQI1VuB6hmLNYGyBVg59pI89GxP7DD7koQK+0MG1sGfRYzvSykBY83kMBSXdo79VHlZPUThN\n1GvTiR5g6+Wv85VXn+GrP/8/DUTnBY0PQNVOkGaUK2yzHBUwCWwSwLELetgBQxlcpwPf8/ijIPUc\ncsG4Am1zZKjPEzPSzApg2OD/Sx4Ve6JHc/JzEdNujZicJ/y3uGTvGwxdlrq/zoxdx7tBRuvtrocn\nMDaVDHT7hhj0dvZ7u03oETwai+BxTqH6jogMh0LEk1qHKgxmvAgMRp+AS9GVAhX5sMj14Vx+oA3G\noDraOx+APwuFN0Q+R5ntZ+mILfZma5FBqeSIKeZlABYNIt/92R3DyG3xhltbg3PEJsdV2uxfvIL3\nQq9XIYoLxsZSnItucza2usq/+/uf4+KZT/Gf1v4H/FCg20LzKly+rMkyodWCfioczS/jveC8kKXw\n8ssP8NCxnH0Lmk57hlJ5Cxt1KXyZut6AbMBejBWUYkwpYelmlYlyg/216yhlUYUn60dE9Zxeq0yp\n1CeObLimgzHDD8ZqbRyq3qP/zYTSxzyFjnEqotpdxY1HaJtSeD3YQPDb+bITkBx1WkGspV+AOnmS\n78njbG4+zNzcOebnz1GvBCZvv1dl5WyVyqWcMddF9dbx1pE2czKrSbsSgLu8wWxnFRHoJ+NkUYU8\njmhWF0auuE19gKl8hXHfQWV9/PIKV5sLVCqe2bEeqtVExnqgNdGJb+GmppGuI6ClO1b73iPBWSQg\naloHkwHtg16gV5AXyEj0fz+jGmdn8VIdPL9GlpV7JJXGuQpiBvNil+Oq+3a04ZaFtMsDu/DAIyM3\nT4lbe3zv4Fx5jitX+WbvUVZXhTgC4zMO9c7wTPIcm515iBz6iGXm6BImskSxkOcV+r0JtNL0esK+\n/Ybi6io3+xlS2p6AZFJlTp/h68f/az5x4neIW6uU65W9nb2thSLH6wg/G1hsvlwOzLEhyuIcpCly\nfweURbqC1wMHieFcIs9x84NrdKsI+A+o+uG3fithY0MolfZ+/7vjn+TJ5t8FQmFP+PKXDT/907dP\n0M3pk9DrhjlSnuEOHgJCen34w5bTp4MBTk6FameVJ07/ESc+9Nnh9JSHHnL84i++t+Zeb2RQNYwf\n5LnvtvgH49Z5F6zF74b4gCn27uPHmikm/U104/yd1TEXXYq5J3AT708d84svGuw7AdBFePbZ9w8U\n+8Y3hEuX7kxTrN+HJ5/M+fQ/apD8+b9/+y6HUYS6sUT+kWfemcbYO9AEA4j+9svoa1fv7JxvJsKq\nNfaeo9DrYl49hTn3OmppCU8f//gYfGQcmesSrZ4IWmHJGMmFv7wDmrEmefGv8dcEqmOh3VmGvngB\nc/4c+uZN9PoacvUK1752mZfLzxIlt2+5Lc88zL61k5TSxi76fCiFE6YGJWG3stqG/Uu3ryNZE0SP\nWGZ5BmN6mZqsEUnQ7QlklwJQ1GWZxco5/n75U0xNC/v3B0DsEyd+h8nmFfK4epvOhdUxzsQcipbQ\nL38X872XkVtnTKZAV/tQ7oc1qHXbW/YDLRxE8NYgJbtbFHvIxhnGrpopM5j2lyBWuMo+EIXrt1l7\n+Qb9bhxkYXbMRZyHLIU0DTt3G+0SY7ZJXO1ilxJwQh5ViYseXhQilsrjG5h6H505yBzKZaiZnGgq\nRZkiuAPGDinnlHorzIy9jqpdYmVliZXVNmtrQpqtYHSPJE4pS5O41aG/Oc64XEP7nEzXcRLR7Wqw\nKdq16ccHmVevMS7XmMyuopoOZQMrx3nIfIkkSdGRRbyAARU7bEejcFilGD/WIJrIqSUdQCh8FFhm\nKidSGcoHlojRGYlqIyZAHyKOtBgj0j28A2UcSgUJ/6SSEkU5UZIRJQWRylHWYbyjLm2KDYMZg05n\nARHLY0+8gjncoFJrk0hKbaFNZb5DebpHMpmiSw5SoT9VovJgi8X5G9RUh9jleK9QODQuMJu8gJdA\nEKwp+v0q3pSJTB+1pBDjUA+HMi43SCSVedR+H5hWEaP8CuwfQSKPihxoyPOQ30o5Xjl7lI0s4Z8f\n+J+5Z+Ei2rvAnDN+m7k0ysk9/h6GB14WWBogUlozVK2WNEV6BRwhiMID4gdWCcmO3Lews9rRE8op\nndKoMsTXUzKfoLGsMcOkXuPA/VfY98x1ak81mXhkk8rhNqrqwCtUOZTLYQUpbV8PBlpeyjsUDiWD\n/iNsEzQFim6ESXOSSoqp5KA8KrIo59BRgXiPzgvUmAvi4EjIUcJv8koFLSxkxFQSPH0X6ncS30fF\nblsz7I1AyDcIP2CDycgRlACK7Rxbhtf2TcCxnUwxdn7EhxzpdOsoFcoQx+oNZmc30HofTzx5nigK\nGoaSX+YbKwt8beUpri7XUZ0lTP8mjWZMXgilBFbXhEZTaDWFcgUO9c8gImids7U1y8bGIisrc4yN\nrVCtZuRZjTQdp9WeZGxSINIQ1UFXcWN1jOuSZZ5IMkomGHEYZSlV+hhjSV1CuZMiadDfGzHkXMgz\naYe/49kcPZfjpxSlUpOy3SSWDgqLdaGMeS+YZ+CFgm+FyYl4T15ErL+W4A81WDx4hkqlRZGV0FsF\neisjyTpM6CWM7mCvFPjc0vMlMhKKoN2P8SmR6xMVPawPJWztyhyJbqOjLcr9FlXXxGBpmGl68Tgm\nEhLbJS2NETfXKZopydwYUs3wvgo2RjY3URdvIlEWgMWhnmS3EzZ5hoxmrQJbKfdIoWCiQCoZ1Dzi\nM6Rr8UkJ6XbwcYKbWUSp5o5kU3swxYA8gjET3D2BYvbREetLd5YCOg0j/Uk78zgojdu3H9Vpo/rX\nkCHbqt8PJZzdPmqlD9UqL5efZqsT02t77ut8n6PdU8z2blCK25Qmesw8sMx4bZ16bQtDQSGGUqlP\npdxAm5QsqzA+DtWyBQ9rbnq4nzXqRKc6P8n5qQ/z0PgNnl68irL57nlbt4u6fh2mpvCV8qi9Po6D\ns7MI0u0gvS6icngwDwwxCKY6gyTwU9PgHMWHHt1uwFAEfOZJyv/Xv3nPqx+Wl+Ff/+t4l/zFrVGo\nmJl0iZnsBt5EtFrCffe53U3IsgDWRXEA9qZn8fv3j94Wgbk5z/79YUDq25ix9hKr9z3DI08afuVX\nCp566gdj6rWXQZWIUCp5HnvMvaNz/ygyxdpt+MpXNM89Z3jxRcOJE4rNTWFhwb+tZcaJE+odAVyl\nkv+RYordDWvxdxMfCO3fPfFjDYq52j6i68/fWUe0Gemxf/K+CWreLYPehz5k+YM/iDDm7bVVKRm4\navaY/e57DDa9WbyLMs134+hTPP3s4PwDcfzzf0H5//tdSn/77yiuXOIbZ4/yzWsH6U5tkEeXKVYu\nk6Qt1PwU4gt04zylM3+M6tzAVxbYKYL8RmFOvYI0tyCO8XYSc/IV9JlXkVYzfMC7ICbcasLp1ziy\ndoIzR396Fz0eQlnC1YUPU+/cZLy9hLaBJaR1sE1fnAzlHPbBYwEQGwBRw/5lWpd3fV+l4jHd6yif\n4kXjdyBNgqfJfrr9EhNmmdnaOtGRpxClefLUHzDZvDxirGmdMT1zntm5s0xNXmF84jqlUo/JuUn0\nydOoq1dvZ+ilMXqmCXE6AMWKwaJ0sNo0Bh8nofQhKkZ6QjsauMffMqi4MCDT+EqMFB18XOf1M4Ja\nXUXvONAzEGjuCtZur3SzXGFsRsW3g2tbM6FTmaXc3yLOu5QeaqDGLGKDNpcmRx3wqMQHWSkPThTi\nhSKO8FrQcYdyvIWLPI3GBM56pqdWiIs+JuuD9cRRRmViA/GeqMgp9xvoImWrCGy/Ei36eo4FcxYr\nJWr9ZXompmgIGosdlE33OmV0zYVyPg8q8RRNTY8y8bGccpIS6xyjckQU7bxOVbWIfYr2FiUWIwWa\nDD1gk4h3g3wUUmvQUmAii9LBmVJ0KLcTT7hXkUAJVOzxW1DaSqmrJdbSB9l/4GVmZ7dYXIy4tlkn\nme+gkwLlBFzQH5LYk9diWps1FvbdpFRKSaIclToiiiAdRwD6hlEQkaoEPz1GQx2g2l2jvzyBuidD\nTTgKSuAcgStmYZEAOt0CbvgBQuJQeBHiOCfPDe1Oje5WQiOe5FOLX8EkBXGUocUielca7p2jQ7ZY\nAX4L5G8JKMM9Dv8gcNAicykkDq6Dvw9kOC1QBPm78MMDmCOBsSUm/PMKilaEyoHUo2oevywYlTP1\n+Aazn1yjfLSLLg8AJw1RrSCaLlDjQVvNZ+H6eyOIBNAzAJCWoUKdDAXqHfheoEk5q3G5Iqpk4ATf\nVkS6COWO2qFKHkxwKFXKhjJMCQCeN6CMRQ3F/f1wBBKUWHRmCa6pBToutoEweWs8bCeuJfkA4Bm+\nlgLF4N3hb7p1ON8Bju0EwkZv72CXBfASchsFl0evMJFiYeEmExPn2NiwNBqKThu21nPOqRnm+s+T\nZDf40s1f5mjtNBXdpN2LuXlTSPvhZEUexplD7jKRzuj3Kpw58ySgEFHcuLmfUrnF2HgLpSzWabyr\nUKl0gCy0sVrCVWapJJZ+J6eq2uhBPZ/SDueEUpyiSxbflQDuZAJpAJClAFUjmCBUwPZN+Kw2qLIl\njruoyELq9pQKFMKj0rc1vh+AYFuKyK4r1OGcylibVGrUWitMtK5RTreIbJ+k30YVFl3O0TM57ipk\nyRggRFmHkutgfIEZ5KjCY/IeY90bpJsaf0QQJyg8ie9Rt1tom5OOzZKbCnHepYgqeGuRZpNEdWDL\nQuagXEY6HaQtQAYlBd4i3f72Bo44UA5PDIWgqhniB068kUFii5gWvmogi3H79xNcLjOEPp6gwSh0\nUP0VVGsN1VtDFSso38Du24+4Pj6p40ozo/muFH2kuwIM9OcGgNgwKd3cPIotVGMTabeQImwseRlD\nzR3GOkjPXqFWNJjZOsd8/wpV22TcrlLb30J7i7Uxzhmieo5WBcblOFPCozA6o5R0iJMaog2T5ZSF\nj+wHhCwbdExTYuqxp/nlzzgOffpDFE8/E/Kt3QrJUCpRPPYExaOPIv1+mANtbYRNSq/obWWwvoHL\nHdYpOGJREw5xocN5bUZMPen1cIeOhOu7awAoiJ//GtxI37XUxq3xu78bcfGiekuG1IXKw9zXfYVa\n0SDzEf2+cOjQNmysL10MOnTO4atV7KOP72kGoTVMTXkWFz37ZzOeetJyzz++930Rat9pUPXss5an\nn3bcd9/bA4xujR8lUCzP4fOfN/z5nwcTNQjGY3ke3DdfeEFz44bw0ENvDvz9IN0638+4G9bi7yY+\nKJ/8IO6OiGrYqWPozTNvXY4GUHSx08fuXBDwXcR99zlefFHfUQnlcNB7P+Oee+DoUcfFi4okeWvo\nqN+HBx+Ew4fB/OXZt88SG0algjl3lvwf35nI4tsu09QG8+1vUn/5OxSPPAqRQZ0+jd/3DjQg8mK3\nOL7zRN8/hWt3OX8tIU3XmJ37O47e26LTHqOb12j3Yf2lTfRrJzj0S08RJVVU0YG8g9z8Br48g0q3\nArAlCleaxo4dAjVoW5YFcfm4jHJr6G+vB5r8Xm2PIjKTsG/1FT75rf+Fr37kv7tNUNXpiBMf+ixx\n1ubolb9jbvMM2uZYFZF/9NG9S04H/cssfwfZaYPeW2GsmtHtafIBoz+wMyyZr1JYjdIe8ZoHD6ww\nyR/xSvcXWFg7RRbXELHs23eSWi2UuoyEeJ1lfuEyUXwJqTfDZLzfg9IOxVaroVOB2rDEY7QaZmDr\nCJUIb/qIVQOkyYcSlR0fDREmkH5IBXEGki6qu4L01ujUHuHiWp0j5VWmty4Q512ivEuWehKvyKIE\nJiApp4g4ilzR7ZZJtxJKEz02L87jEbQNGjgy5yAXvAPtLTLv8XEAciQa9nePKzQ+E0rTXbKOQQph\n3DSwMzFZaqi4dihPHIB5LofY9BFxeK9RKse4JveqNdZYoJvXmCtO4CPodKDUU0RRj+XkIOPpOiZv\nkxCALa55mANXFbzSuAlNHGeUSn2cU2htcVaBhTl3E6Udfkif84B4tHGBjOALFAYpMrxSNBpjmIk+\nkQl6SiNcauCLQDg8gEXKk8cxXkFUpDw2/vucbz9Avx+jFCweWKZez2m3qrRzBtpPnkm/QbWSsn/+\nGnGcBz2exBPNZMGcAPC5UPQN3gcGWINxCm+omj6ldAOHQdkMNx/jsxxDhiWAaTJnA0hi2I2c7Eqr\ngHgYU5AkKZcu3YMRy4dr30awGJ+hdbHNmhpeuzeKIbNqcI3crxLEyHsgGzlYCaXCh4HDIJYAhPWA\nPtvi8PH/z96bBVtynHd+vy+zlrPetW8v6MZGNAgQDUCEQFAkIQ09kkO0xHDMg8cxQ3ssB0MPDGse\n/OA3Ofw6EXY4wlJ4NDH0xFgPEyOJMm1LGsfIDkojiiQoiRCGIoh97/V2993v2WrL/PyQVeece/t2\nNxqUNIDUX8SNe7aqyszKqsr85////7hh1FVjTESLJeyDdzHmuKfVmsDTkJwtwQq+nE22FKHyMXZU\nIUseErCmpNpPMKMAqonqVFp6QxPVCRPdyKDDwBxkJDVfswo+SxrALRTEhoyTkgN4TATGuimDq0FT\nxIDXRh6rqChWqwBSepl6t2ld71uBY1Kfk9r+LLRlzKyvjglebm1BIwdJYPz5yiKpC/3uNmy0qfWR\ngHqh1x5Sll1ASZMBReEoS08r3WWYr9EaXqca5Dwl1zGifKb9DZ5Z/Xf8r+/+z3zu9O/zYPdlnMC4\n6pPl1NK2MSaq2N0+xttvP3HA38mI5eWXnmI0zDh+4i3W1l6n1d5CJAcCSq9VhClHiK84sZLjhyVi\nNNx3aqaxwQfAcxWqLUtkQmIBFGQBNAZfX2+IwWiFQ8EbdGJh0RH3S/xgPq1n09fCde33o/qcGoqo\ni+s6TKvEZMLxwWvEVQZi6j7jsd4FxuGih9MQ3VPSubqB37a4ty3qDUhIQqEIkRYhsYYHX0Yk13KK\nE4JWtk64Aakb0x1cwklEJ9umSlKifo60K9RbpLuPTMZwcQfyMmSP2UnRogWdAlEzleJqkUKrQEdd\nxBs03kNwoVFFAIvGCSyl6EIE+wXEMaqKmMCuY6RIMYK0gkWPqKKVBdciuvwuvtWlfORTiCvxrRag\nVCufwFYT/PIjR09Gvcdcuwoi6OJS/aHDuzNgLTu7Fm8c960/jyknDFhATUS8ViCxJykKEgoqE1NN\nIpIFh3GOKB9RtXp4tbTSEpENVE+AD+ynhx7yPPQQUI0p73mSct5Uv9ej/MLP3ThWHA6JX/hz/H33\nYy5cYOO6MB4LC5mQzF1fuuRwkwA4WkPwjlDllpmlfIRd/3Oq9t+5+W/m41ZWG4fixRftLVliTTgT\n87VT/y0/u/kbfGz8MntXFJjpLeXqOqD41TXcY+fen0fvBxx3341ZBDJAzNaW3CpXF2++afjqV2O+\n8pXypgDos886nnvuzqh6qmG7D1V8BObiH4W4C4rdjR858o//Q9ov/trtfZqqMb69Ns3G9tcVH6Wb\n3r/+1xk/93Nt9va4JTCW58rqKvybfxOkdFNk5E7jTrc70hOswJjaa4ngicR7Q+RdDVkKEeSBByFJ\niC5dCJnajt3BIAIgllkfi3tEL72IH455892IVnubfn9Mp71HFJf0em0unH8I1RiJYmR/zMu/9Srn\n/otzxL7CZNtQDtDhJbQ7W500gwuYwXm0vUa1+jj24gWaCYK5vo5Olg56ax0KYwLwdXr9L3jmxV9n\nv38PaztvBODLxlxffoR37vs8RdLjtbNf5DW+SFIMOXf9j4jeeo3o1VeCJ9uZe0Ehunyx9mkT6Iyh\nayFuhRXvagwmAL3eK3kOVeVxGjMxayx2ldP3KOcvx2xJh5O8Qn6pEyQN4rjv/u+RJOMpGDYfC/1Q\nR2mP4CmF7yvaOpTG6NoJWNyDzjBIp1w1o2IoaJogZoLkoFUE4pFphr96VjydfykiEiaLopBEiC9A\nIkZvv8QDUUS7yFkaXELFklcBYeivDYjbOQiUmjKRLlGkLCzsUS3FmL0Rox+0WNlep7Ip8f1jpsbc\nqpAExpFJdMoWUUfIptmCpJVhYo8vhTKLSJMJx3rrwScKh6ssvoiwqSPqFJhISZIJ3huCibkQa84p\nPc8wXsJKxcXBx/G+znwHdHu7kEOSeOJ+TtwOzDpVoRxYInUkaQ6LineGqoiofEQUQ0vHQYLog/Rr\nyvOYZ+ZZXzMNZn5B1oZMdY0nlTQyugjEKYqipaBjIenlyCOeahyTLmScvnqBa+XHat+oETs7EUkC\nJ0/olHgpmw43KWmvOExEkDnWkyNfBUBHWkraKqCE8bCNxRFJiRHP6pldRt1lJFKixQyzrzP/KKtI\nNzCHsKHMByyDEDCBAZiminNBOmpthVo44y7SqcYk1MkXbkaNOSoEiAPIIALsgfZA+sBI4Rqzcm4A\njxCYYc28Lz1i302/0xqL70H8WBm2/2nQRQKQ5APrSwmG6U14jbBlABU0gbhXImOLHwjaqb30qrqN\nQrcMYFHNSItwsBykm2Qyk2zV16XHBPCvAbQTmQFNcxh3Y9gvNZ2rAVxjH1JsqghVGYdzzFz/1AZ0\nuUmbQ5BNAhrVYGP9GT2FStGxhSyGoaCnHFmV0j6+f0MC26NC5sphjBKbEnVK2h4hxk2v5a5s0Nu+\nis+VfRZJqFCvxKNdPhX/Ib9S/gzP/f7f5Wq0xKdOf5dTx64xkZQNTvDi4NN8besX+bHyRfSI550Y\nx9LSy5w5/SpRPEE1ApLay3GMGV0LF46xGG8wUYzzDmOCZ5ZoBdRM11iJFhw6ilDvQxul9fl3PsiK\nI48ODbZdok4wrkJzkNiH5BUj25zBEAZ0bAJYZWKKVovJXkp6dp8ocXTlOrTq3+c1+DkOx5F2nYDF\ngywodqfCnqmw91rcdUv1wxg8NZsxHLXCYrWieKlF0s0peq3a9B9ULNbl9LN1OCHYhQJE8XlEttWl\nvTZCejl0S3Tgkc0CXVhERhlkEaqLs4Zf3AdnkSpMg7SIIVY06UKaBil0WWI3NvELPcQNAtge+3Dh\nj/ehKqATxlDSPNdij7Y9w2GPvYsr5O++y/Uzj9Nai1j5+f+G3kJM+uq/CpNXbnz+Rq+8hOZ26v2n\n6lDt0HToycizMroY/DLFkPqM3HSw/Qq3E02fLZEv8dsGFgKQaV2JU49iSFODmDHOudkiYBOqlKdv\nAkSVQ+JL3wrJAnwFJkI+VuHeq3hncIJkuElkDS03ZpIukpaj4Kk456NYYom8R+MY7Xbxa8eR7S0O\nm6vaixdm9ND3GyLEz337toBTWd7y6wPhTMzvH/+vabshnx5/k5/vvjT1z3Vn7rtxkbcosBfOY3a2\ng6zeGvzKKu7e+2a/+6Dj9ZtW6MbzUi19nPLMT0H8Pv17P0Lx9a9HbG3JbbNrttuwtSV8/esRX/rS\n0W3+Nylb54d9Lv5RiLvyyR89/lbLJwEwlur405jJNczwSqBNzK+AVWNwBW71E+SPffmvJdXyfCQJ\nrK8L167dOhNLE5MJPPKI58d//K9fL95qwd//+xXf/Kbl+nWhquSAx1ieK1UV2G/f/KZlcTFQmeMX\nnkfu5Ek/d8CpLPF9xEFPMIeNXiKKXsWYvcBUUo+9ehFrtjHHx9BxsJ8iCLqygpQlsh9Wc83WZsgc\ndLM0ik2Mx8jH9pF2Hm70RYF9/WU29/fo9TaJ4wzwteTEkCQFS8vbqCqDQQdHRKca8OL2aT7e+x7i\nJoiJEVeiyQKz2bwN8ohyiJlsYK6Npswnc20LHa7cqpRUFYwzw/LoIqc2X8b4kE7dqCNyJWu7b/HQ\nhT8OnhLLH+epV36Dcy//n5xNL7C8pEhZEn//35P84TeIv/enyN4euryMVB72xtjJu5hihEiGaDkr\ntzriSIm7HZKVE/QXwmDBSsludYqt8SqxKTm19UPG4zXuueeHtNt7eHeIyeaDLLPXD+/N/ghihyQF\nOuwfADCttbC3iF/ZChIwXxFGvQo2wndATAWVhUkUwAwNzACqCJxBow6N7imM44MPFHGKJov4znF2\ntuH48HX6doNy0sa4isIJ3VMDojT4f6kaLI5YSyqb4HyQP7okZqe7inkvQ73BPhwm5CpgTjjM2eBL\nJcxJqUzNQKonFiby2FaFtR4TK4YglTQoSacg6RXYuAzfWY+IBq8u0RrcMljjiKwjNhmCZ+z6ROpI\nfcYC27TTMelahmlDRYQzEd5YklaJTT0SOaQCxGBjT6udk9gcGwWD6CD3IUggzcwfSwh1EqOIdUhc\nEac5SVIgCDgJlkxzWft0DN4ZbNtjeg5Jg2G6FQeRkEhBspjT7++ErucNYvwkDwAAIABJREFUZRWy\nqKoHO9rHFhmiDrNcH38O8BBqhlUtdMTUvmlxRKubE1lH2vLE/Ydpj7eIloboMuiiw3Qd9oTDdENX\noQFJmrFunUFORfBqyLIWqpYoqlhd3uLU6jr9/h7JYkHUdrPLfl4yeTuCbiCQhGPW+SVEQ/vTA+b8\nsbUPLIK0mfmJNUhDw2KaY1iJD7cgWmBWgFMgi00dZa6IekAu7QqLJIoVD16RUrF4pAjMrgaHRsOk\nlDHIKACL0qv3GYfjSkSQfdYJAYQAyBHVWGv9LJIpYjKrj0oA4Wqu0JQJlO+00NygE4O0fO09dqjt\n68rJoX2GCoZ2EstMfprZkIlU60yb3pAPUiY2AVuSdMuZVBRua+o/9eFyihlVpJ0cj6XIE9rFmMSN\nAcPOeJmUgpaOSTUj1oLEFSz1dnn43Vd4ePw62VaH/YsL+PcM6cWc1c0tkmLEw+41NpNTs/s2BI++\nJ/+Ee+99nSSpwkIOpibQTKAdI9UonAf1UANg4R7jIVzJB/OVGEVHivR8OL8N8F0nZ5BYp15x+LA6\noaVgY0ViRfM5vywBcYZiq0cR93CpJeoXRL0xdlGJOwVxt4QYTKzh/EaKWaww/XAv0rxpXMJ9cSiB\n1dVTzHGPXFas+unpzmhjUErTRi8b8sU2cb8E6zEYutUu0ckc7YJKjJskFFt91BtSiZGkCpkkEyDK\nkX2CHUGRT7MSaVQi7QL2FmadpIyRuAj3y/EkPNCrCqoyLCxEA2SSYaoKMg8b3cBOrO/7KKiNyFyP\n8bhDliVkxRK2yjFZzrWyzct/usHzFz/Jw88+Srr7MlLsgZl7DhcF9vVXIepizF4ANicOuWaCPHA4\nQLY2iItx/SwTjHf4BSVqV7grEdowNUUwPlgDkNaMTwGbRiRpzc6uKvzxB9EmLXXNHnEnPnXwAnEl\n6eu/QfrW/40d1B6y6hBfwkLJ7js/YJKCbFn6o+tEvkDF4GxCZVPsqTwwmkWoTEoZd5BHzobMkyLg\ng7x7Wo6iIH7hzzBbu5iX9rHrV5AsQ3u9Wy+kHrbauEn8zu9Ed2ypUpmEzZWH+Xv/46eoPvM5qk9/\nhui9d4I/GoD3N7XXkJ2dAJQNh/i1NWi372jcfdO4xXmxe28TX/42ZnQFt/LYwUyeHzA+DPLJ4RB+\n93ejO7FPZn1deOYZd1OByl9Vts6/9viQz8V/lLjrKfbRibugGICxuGNPUp76ibDqWtbeA1GLau0p\n8k/8l+FB+5dwY/4g8VG66bVa8KUvVfz8z5ecPy/s7IRBapLA009X/PN/nvFLv1SxsjJ7QMnODvad\nt+9MljgeU33yKfxD799TbOYJ5oji5zGyV1NBQmOZ69eRPA+zLDXQKpDFDDYS/D33or0e9sJ5SBIk\ny5DRCD1x4iZHKzDmHax9FbN8BZPvQDXBrG/C6C2yoqKqLKqGVmtCFAVgRlXqVXSh0xkzGPQR71lt\nn2d5LcfWgJJOdUuHlofEIi7D7G+i2kX2t+B6BNmtn8JJ5LHrV4hdTtZawpmEcXt1+r2zCc4mLA4u\n87kf/G+I90yiPuc+aUMWriatd6sV2mc4nAKHGq1iom100kL85kx2IAZN+vjOCUj6B2d+3tH52BNs\nbMVM8oSTkzcYDdY4efJV/CGGmPeBBHfypM58mqoqZJZcMvjJMWRSTgek1hrwFudr6kYRxG2IoN0F\nJLFAF2wngLW2DCBRFQVWh7QgNQTfnJCFSiRCkxbE3dD+xR7R/gblfpu4PcG1UqpdaK8MiFI3zaxW\nl3bqn+SIKMXw/ORznCwuk/YmXN26j9b9GVncRk46kpUcSZV51aFiwuTbBHaOiX14L2HqaZwGT6xY\nA3hQY3w1lnpo0i21j5Givgb+jCc2Abje11XWikuk/QwETOKIWo645YhSF/ygKgcqRKsVcc8RmwqM\nwcQeG+kUwGuOLcLMML4B+MxBMCMyc2CQr8GNBtgRIAk+ZjfsswWoohPBpEqrNyBNsyBBNB5XWdrF\nPpEvMa6Cbg04NkDQHPAkri5PTYgxbSVNsyBGNRA5Ra4qgofOBOlWkIJJ67o2wGVtsi++fg2UmqBq\nqKoY70NfbbUy2q0xYhUbKcaFTI/S+G/L3N/NQpiCM9P6jGdfhYsCSGv22Frd7jFTAG+awVIIoNoc\nSwlT/yaagZTTtm+HNjKiwSOKJmuknxa7yBNiG/za2KvL1jy/KmAXdA+4CKZdl6nNAa2ANHVICCBs\nzAxwoQal6j5/WGoqELzG/Hy9BBGlHAfkWQSKMsGkvpa3mnDN1Qju/CmYyhr9rD20Ah2C3zL4tG5Q\nMYiHKrbkrZjB9UWi1ZAw4DAYe+T5PQzuOdBIiFsVcVRh1BPHOZIKplPRisdEUYXPDaKQkhNTEZmS\nWCpkC1QMI+nhsFRqacuERYZYI5zO3+Faeu8UGDt79kXO3PsWcVRirA0E1kTDtZ6MET9E6gyhDZAc\nAHeZAu/TdpvrT9Kqs3/W/VWL2YkUrzCRgJSm4CTFqEOzcN+TSBEfgJ5ykuK22hhTEcmEpD1GBxHS\nq5A+tOwEEHxzg/HUwBhoJaHPJIIWYFoEVmVLMX1Fa/ah6TrkWgB6PZaKCARK2wKF7HqX7WsnUaDT\nK+ksb0Oi+GHCZHcZN55RMNMUyBOwDok82FrzrN0AqrSjaZZUyZOayd6cew/7Gvq95IgrawBfgvdV\nWgRG89DCxQrtdpDeGPExajr4dIH9cS8k95CQ9TfLF1AiWuWAvaUHWU6v8b0rn+WHr3Z54j95ijg/\nOHmdelSJw4y2kEGGbIVFRVEw6mF3l7jMUK9UPjhtRms5+aSNHdSS/qZvG4ER+J4NzFtRWotpfW8P\nF1X5yDOYwXns9iuYfBdtrSL5Lr53KkyoXUn7xV/DDC4GqdUhyWfhU/58/QFW46vEqxnpewOsC8k5\nEBPu423wq4Y8XmLSXmazcx/9JTNbD7UWKQr8qXsCsPTGq5ita+heD0bp0cDSzVBuMVSf+dzR39Xx\n5pvCG2/c3lNsPiYT+MxnHD/5k7PF8um4O4pm47c4uRG4szbUcTTEXLlM8R9/Af/wx9//wY+K25wX\nbAI2wYyvYrdfoTr+9I88//owgGJ/9EeWS5fuzAOstk/m7NmjPRKshaee8ly7JqyvB6/mQzklKIpA\nlviFX/iQZ+v8kM/FP2jc9RS7Gx/NiHuUD/4cJR8uvfxHMUXxgw/Cv/gXBQfSlN0kymd/ivi5b9/Z\nAVQpn32ffg3TAwVaho1eRhgz5fhDGLiMxwcGBOIsmpTIsfXwQZKgx9aQ7c2wqre1gTtEmw8MtJcx\nZiOkR1+xqDHgC+zgAnb3LSZeqar+dIs4Dpnt5qP5bO3Ydba21liOr3Bl9wz3L4xC2YyFaoxyBAPM\nxIgZB5bYZIIbnL5t00Q7G6SmJPcxziZ0s002uRFwPLb7Nt3xBstEtB95kjiG6KW5tN6zCiCjEfaV\nl3FPPIn3a5hoM2S2msT44zMw0TnY2xayTIJ9l5RI9wR9TfjU0yEtuLsE/f57B8ri6szoAChcuGAY\nDsFVQmyXOZXvY7sW+/garTdyzNZGGGwn9Yr79RVkaQuWKrSebGAyGLtaliJhdd4KOAWnSJqjtkC0\nqCksJpjzx8kceGLBe1KGlK0EMMTJmMvt05zp7Qb8rfb0OnDOfc6YFTZZ5c3tsyyU29y/lhG9NkG8\np3NshEnd9Mk3Je5YmZN+1Z81LCcFmzh8ZaYARsMMAKYy0HmMYxaKGI+4ity3MMazGG2yn60SdQps\nWgXGF0xBPmMg6hTQCswN8fUe4yCTnAqb5hlOhxlI85/F4bU4Qr0bIKxh3jR1YG6ecZgkW7PRorUK\nLSZgDCKeNMmxpqKfDKhKgxlXwe+okf5AyH7XzFtrJhRzoFtTjxiHqkc0gt46knoqY7ExSKWoldCP\nKsLNuiCAUG1qGV4ARj2GqpZEdbsjBI96Q5a3aMU5JiqmPl4H2vFw6Nz3noMntsYUoK5PXP9uDWRE\nAKUUdB9kpd52Xo2fMPPiOgzKzYOIzfsI6IbfS01CsCha7zSmgmHtMTUC3azb2gFbIL2afVbVZWsz\n7Rc31LnZrvmuIMiKm9GizJW7+Vez0DQX1AvOWGwSPMdM7Jhstcl2W+E+3gosS60ZMrGUWK2mDTRt\nipnyDjw4F1G8mZL4Akk8LjaoRjix5NoiT1tUJ1P2skV6foCx75/p3ficYSFplTQS5FYrgxp40krw\nkcEuupDcYOzRaxIyvlZgVj0dJvR0wKas4SSAXIWP6eiIsV0lcRmPDF/gtf6niaKC1WNXAyA8B860\n4griSQBIq1tZONQXed1govU9K5qdG62zT4LUbLmaWbcQtlMToTnoQAJQvA/eCe5yhKkcKRPQHL8V\nQV+RFSUtB3CfC9l8VfEmdAyPQQ3YOkkISTi2tDSA6mU4xSEJo2IWQZccrIJ73dQm/mExwdcXl4qh\n5cfsFatMLq7Qv+7QHzOwYXBRjLbmWXf1CxUY9FDjoZUhrX3YmKCk6PUufrCKuXe9Bszq8C4wyZIU\nuToGjdAFA6kLmUBtFRjORYLsJODHYHah10frC2M8mrMjq6Pd2mU8Dgtjy3vn2V++h3PdP+b7W1/k\n6/9Xmy996R/V0rdvE+2+gWzvoraNvL6FW78X8+A2JOVMNjzJMNbgS0HUE0tOFceYSpls9lBGxFqi\nUj8lPIgI5aWY5LQnXS5RcaGjuwppe+Jr3wnN1j1Ftfo44jLiK98hvvJt3MonQP0t5VgXLxowwsW1\nZ2gt73Hm08+z98OU7mSbuJpQxgtMBg/S711nFB0LiYE87O8pyytzN6CqIn7he2EwniRhTLAzJ3et\nB+WyvUn0589TfeqZo1UG8e2ntV/+cskf/MGdTX9V4Rd/8aAaoxl3R68cMX47KqI4qCSuX7ujYx8V\n6Ztfu71MDiDqYCYbpG9+jfzRf/QjH/c/dLz1lvkg9sm89ZbhC1+4+f00jgMZYTiE556zvPVWSNAW\nxyFb57PPuttZ1X244kM6F/+wx11Q7G78rYm/cTe9+ej1cI9+Avvm67c3wAeYjHGPfuLOzfnjCIpB\nAKwO+WHI3t6RE0xxFulPCKPzhOrc48R//r0wiEAwF87PZcAMDDRhHAzRO0uw1shGAGORWi/V6w0Y\nDvscdVBVIY5LsqxNuzNiyVtElf2hRVc7wZNL7BwlYS68Q4pdiDJs9Tq0DOaBiyG7Xj0b1mEnDNhc\nPZlxDjMc0TXBh0T2LqDGMmodY2fxfly9imddQW+8QWE7LJUbrJzNwsRhY+OmBv4NcOg4h8TPg5Fg\nJuwcaiwbG8J4EtrAWjBaktHh7euP469bjq0q5855NFlj/NJ5MpdMWU7VnM3C5qZQNGbrAkUZMfYp\nbhhz7bUB6+uf4Wd+aky6fh5GO3DsMvbYNegWgRXkNMyedRRmaKlBSwt58BTDOCQOqJLE1RwY4MFX\nSCkBGGsin6Aak/oRUZWhXrh3aYgnYmS7xL4g0trhHSHXmFxjSmPZLk7gSBDgGse5ct89uN2IRx/5\nIWJqb7PgbjSTvzR9Z9pxZ6CYCpiWR7Omf013cWNWu3ofU3ummo41KPsspvtEUrIi54m6VfDfUzmI\ntwg1+0lnUjMPpAQGxyFw5sBBDxdknrlT70c9M5P6uTmF1O+bydThfQkEv5pYahKCJ04caoIXlURg\nTImWszYRC5rWZ6iZ99uD+52vNyhEFWY5nCPjakmtpWYehNeKBFZPoUi73tYHBhUuMHiidokRxXvB\nuTDhLqqUOC1ubKujYh6kajzMmu0qkKW53ypT5hqd+nUFMqzbO2fq6zUFB+cBsfkwc/tsytEAYy3C\nuXFMmVxQly+rgZEdkD+Z218C8nlmfmfXgDNHHHe+LkkoPwYYzbHL6nNxZP8QAuCyG1hjhU0pnMXn\nEf6aoetG5FsJl6/ex4lPrtM+lZHaLMgtm04dyJGh6g2I60NCgOG1RXLToeMH2IslxYmEqFPWZv8V\nPhYWFq4TRWUwPPezch1oz6ZN59u2+cgTjNTrOkpjemZAUiU+VYRHkQSQWY8DO6Bjgc1wwfXZZ1F3\n2ZYAhniFcRmT7G/yLfuTPK4vouUOx89epJXsEFUT2q2MyFZ1ZlFB0zamyLmho07LqwcLr1OsO9S3\nCo/MOZvHAHpZreWM4RpSY0jaE3TRoAODvyr4ysK3Ax9RCNlDJakw/5FHS4tag4kd1h/06FGtEzY0\n5TA69dGT+vtGp+oxOLWBydCpkJ9V+H88lcYISjm/2IbHWqXTgfax7QBGH6LmqoeoNddW6mGUIXsV\nalL0QoXuHwtZkRdq70oIYJh4dAmEAqlG0PGQWxhGsGvRxUWww9m9uCwhihAzgVGM9np4D2UhBxJa\nq1riZAzjVZxJ6E422Vx5mOP2ddrtL/Laa6b2hJ+bvP5xRfQXzyNbE4hj/PkWcmoD6Y2nx/bNcysO\n92DZ8BRZimkJedQlcgNE3ZQxpggtU5JWC+ie4LtdRAdInOE7C/jFhw4mGIKp+bbdeolo4wdUp57l\nZrG9LTSP7SxaxJ+MuTj4iRv8Su+pfkC3tYk6izUwmQjLc/3bbG7gFxbCQF1LPMchq0HJ+YhiZDIi\neuWlkMBp3tO2zHDHPkb87u/f0k/rxAk4d87z2muGVuvInxyILAu/X1s79EWvh3vwY8R/8hzvy7m/\nLPEnTmHffed9JQS4+X6G2K1X3r9fWNTBbr0K5fAj7zF2p7LXO92u14MvfMHdEkC7G39z46588keP\nu/LJj1j8ZaYo/g8Zh6nM7tHHiF55GdnfC9kQC3j3XcPbbxsuXjSsrxuyTOjbEXJ8jfwXvvz+je7r\nkJ0d4kt/hElGHHYyNtvbR8+zvMP3u2iaoroKIvhT92BGQ2Q8RsYT/H33A2CjlzBuK3hOHTtG9WNP\nYUeXp3QmyXcxkwF5HiSS1lZY62i3g6zF2mBU7msAqyhaiECnM6KoUgZmkROn28GnRYN5sSYLoZzq\nkfF1bLaJuBysIOUwyMw6Y6STQ1whZYR0M8zKHqQFDNqY8+cxgz3EOaKlLt6Dc4K4itXBedJiwLBz\nnMWtd2nle7S6hhPHguG52d8PkombnQvvQRVdOYb3pzByHSM74JXLu32KXLAWIikQcYx0jcv6FMZa\nrA1+T7sbE1Y++5MsXvgeCytCv68MhjV7wMD2juCqoLZo/KgEyEnZM8t0bMGb7zzIO+djzv7EAq2H\nr8DqO0Eeaw2Su5DJLEkQW0/WagkrFpiYAIiJRVspYmrkpKEKCai6kLI9CXJW2d8jGilaeDDBsytd\nGBOZIGMSo4zoU0ibiaZUxKgJmcv+ZPenMZHhHn8J8RC3KnbKNdbOXMO2Q6WNBImNHLHY3EwuG1BM\nqIvZMKoaKdwh2eQ025YevBK8twyqBaw4jHEspIHuk5ftIIubSqOa9veBJVbPO7WRDdaFkzmmzoH/\nh0GWZu7sCVkQ6+3mGVrNNlPfpcPz7QOSOcFEgW1ijNabh/+RdZjYIx2mUrtmQixHgUDzdSAYnTd+\nbAfqIIKxc3U2BFaoCVlDTV0/rbFO7w02dtjYT9FJdULkFYPDxm5W9cNteBSweLgdG7AmYdoHGunj\ndPtGalknGRAzt217ru6HmWIH6n3Ea8tUKnpgm8YTzABXQS7O7e6hGsBrQK3luqwLc3U4ihk4rP8a\nD7XGfukwa476s5odRxJAQBGw5z12xzH6To/8UotyJ2b5zA7LZ3aIxwUYCed8fp81wKeAFkK1GUNl\nkMxjFit0EaJeieQw2ujh1CDWY3sFkSmJnMO7CIl83Z+4MQ63bd3/tSbRTKXHTSKHhg06L1lukkik\n9S4WgqyR655MW+zqIhbHCjusssWC7nPCX+GaP843yr/DZx76Dh+//03SdoZEGjIIdiOMqTBVgUwv\n/qPKPddZhBnbF4J3WBZYXFOpdCrBf3DqwSdUEmO0vhZUwAi+Z5Bth1ysfekICTnkIZDFcB251Qjb\nmXnDBXZmbZ8QN2w/CaCYmeFPSDivVLUYVIKXpHUOaWmQ314NstCR6YOY8BzFss8io5Fw7IHLweZT\nLBiDi1rTNup163vReBTGFS7I+UUFWlAt/RgyHCC7u3BaQwKbtQpZE4wtEOeCZNIK0vLQq9CWgO9A\nu5bjqgR5MkDPBVCt3SbLJTzvj+hrWVaznUTYXbgPEN7zP3lQ0lUOiS/8Aekbv0M0fhk5NoGogjyF\n/T66G8YoUo0oc0VL0CtQ/iCFK0LRbsExA95QmoQIR4Sr7QGEKBJsO8K32mi0CP0Yv7qCO/Y4funs\n7MF2KOzgEma8gbgJ2jna5uLiJXNgbTGthvQmmwyzg78fDtfo9a8TxXmdVAcWFrRppCBvXVgALVG6\nVPIM9sLFo8dFxiKjfeSBXaLk9Zmnratwj5/DDt69rZ/W5z/v+MY3IgaDg769N56/YKnyq7+aH6kg\nMVcuE/3gL5CqvPV4uizRbhf35CcDtV91bjH4ziK+8IfYwcWjs5feLDR0OL/8wY4JHw755AsvmA8E\njLVaYc53Nz6acVc+eTfuxoc9hkPi73yL6O03p9loqrMfp3z2pz74CtCPEnHM5Cv/mOhrX+Od33ud\nrW0ok970QW6zEdd24fs7j7P/6D/gP0O4Ddn7hiif/Sna3/9VjsqahHq8Qp5BVcl0kh1bsJ1VjN2m\n8STFmLDKVxTIxfPIpQvYjSvYB95B24u4B+/FPfhQoMfPGxNXE4gSRDNsXJGmGUURfISsrVCVGiir\n8D7MUr03pHHGdnksHF8E3z2NjC4jVYHdvxAKWuyHY9nar8uXqA0zTanCzFbiEr84ILvap6oMmDFx\n+zWigQseHUmMGEO35Wj1e3gTM5kI/WyT9vU/o93yLN8fYW2glpidGki8lV43Dr8LTWcpy89D8k12\n1kuyXkxkPZUaRnqSHf8A7tC5iWIoJwV/+rzhZ1cGyHiP4SQiSTqoLrG1acOC+Ty4A1hfcpUTvKxP\n8vD4deJ8hK+Ut956g0996gcEXZUEzzARNEkI05IYjAIZkni0cpBa1EeIcYjLbnzyqAYmUJnD9haV\nF8gUVxqkXWJiF1hnaqbdIUlylpNNRlUXzVLSJMO6ik414u+t/iajSZ9x0SXSkqijnD79JnFc0WpN\nQt9UNyUcTAf18xO4ufeB+cBUWta8PwAuNeHDCj4iwbeuZpq1dYQUnjgtiZKKKo9BhUIS4rgkMoHZ\nFA4fAB1fBkRKOnODuXkm0WFQ50CbMgMxaqZZM9m/oZ5zsp+plKzZhQRwwliFJguhl+m2kbgAnBk9\nADBKxcw763D5bsaSmm5c/8wG5s4B1o8AJkgip+/j+rdOMKIz8HIKZDi8AVcELygakO0mx+Vwf2jK\n27RpM/fxc983f1PJGrP2LkIZGwnkTet9VHnmy9Cw+xqgqom538gxDgBdslr/9iSBxSbMMhI2IFvD\nDJswBaUogcvA8fqvOY5yEERrzO+bMCA9T7QRzpF3sJRskz2Y0PvxcfDMizUg8Puhs7nG5L9plwz8\nRPA2xvZriaiWiPeUJJQmQRYhXirxI6GqIsajDmPtciy5jok9CziMVzgKGDsMNNWgnNTA4bwv3023\naQDzdphvcgk4ptjPODp/OuSkv0qXEU1m0NYDGfeuDli1W3zefxN7zlEmlpIY0UDCLquSJCagXH7u\nWIfL0Dxc51huzUdehdKlRL7ARC44HBgN2SOLUHBFwgKI1lLH2EMW4ToR9C0Gj6lPqgB6TPBljIhi\n2w6fRyEJh/OIKpEGBpM0qUIbX7ymXE2/zUIlVASrLhy/rpc7btAETKEs+l1ynzKRNiP6SFViUYo4\nxq1E9Nr7VDYmlr2QNTJrhVMyHATA4bCszoB75DHc408Q/clzyHALeXJY3xwsFIF5PQ0X2obEgdmA\nTAPzuegg4mbtrh7Nc6qyQ5NoKKoyrA9tp97QmWwxaS3ha/abq0ddnQ6885Yjve9fYbdfBQTxEwQH\n1iAre7Cyhx920EsryBtQXhJ8DuojIu/qpRSlOJ9gPzbrnBPbC09iH5JBYAQ1FvfoOdz99xJv/Gl4\nvvTv41Zhsi2I25jxBs4VB0GYOsvi/Zd30MrjxTBqH2O79wBnei/A7sF9qVounP80p069RLe3gTXQ\nDARkfx+/2AEKvK7hqnOQWPyxNczW5o3jI/GYB68i4300qelbZYkeO1b/Nvze7rxO68V/RvbkL91g\nKt7pwL/8lxm//MspL79spp81MZmEvnvunOef/JP8pkSw6L13qD77OewrL8/ZS8y1UxnY7H51LuN6\np0P01hu3zZJ5s4h235iy+d7/Rh2i3Tc+8nK6s2c93/2uvSORy3gMn/zkXUDsbtw+7oJid+Nu3GmU\nJen/8VvY114No9Dm7lzkxN/9DvFz38Y9+gny//wf3hrs+KsoGjH/dPfLDB4ecW7jjzm+8zqVK3E2\n5uKJH+ed+z5PkfSYvANf/aryla+Ud1bEXg89vopsXTtQN1UYjgyurIgXstqXBfBKVvbYWhfSxLO8\nPDdedQ77+muYvV3Kp57G35cj0QhIMOvrmPUr6Ooa/vQyZnSJkKXJo0lCku7TwAfGeJyLaqP98FkD\ndPR6A0ajHnFc0umPWIxH2KGCy6DMIO7Ur0OGL0wU1sYlCuBYLw76l60tsFAWJqQ1747xuz3SSYZp\nlegjFdlLLVzcpdMQXZaXWDbK8nI9C6+GmN1dvJ3j4DvH+5ohu4NUblcJRveJ7Coew0hX2dEbATHB\nc0ZegMRzaSMh+8QjtN77Hr4s6Xb2aLf3cK7LzuYxFtweLR2HDHZ4dmWZN+yjtOIJv7H+X/H9+PP8\nNL/PTx37PZwWWBNBbAPwY2opqisRV88wIwfW1xn1PJolITOWzs3mVefoVQFr8ZMcdRbdV+zCBLUG\n9YIaxeUR5TghSqualeDppPt0EqHcjdHKgglG1SsLm6z111mw+xS+hW9ZorgismVgMcwDXnPg0kG2\nl87AmPq32Lm58qFTJ1MmUwBzmplzbEoWW/s4b/FewoS3hE4yDP53kISTAAAgAElEQVQvpvY0M36G\nc2lot8bUu2muG8Ccw5P1eeDiEFijjoMss3mA7SgAQAkTxFLR2qtICWyOJqGFmMPObvX+G28jPfT/\nKFbUUds3zT9XlmmTGkI/E2beXhJYJV4MiS2m9wBt5uniMG03A5caVtDhtrwJgKJlTaiYl1E22x8G\nGlNmBvtNWzSsrDuNI1hNUxDrcGT13xPAD+rPIoJcMiKAYp25cjT/m33G9fYwY4RtEjJrmvp/4zfm\nbyzDtKgW2AIfh6QGnc9P6KxNQruoQCbIokdSF+TjQwnnsR38p2QJ7IpiKdASqu0AIFv8FH/zXhjR\nJe7m2L7jwu79rLBNnrdox0FuVrqYSMsbGWPzYKPO/ZkZMHbDuToKnGqA5FoyayqPdoSVJ7Zp/aDA\nG8E+6aC+5bvS0mFM+0SG6Tqi1FNqTFkmFAWkqccVPvihHSHNPhB1/6du0obIJOrRsVJiidsawOwK\nKKHxYfRigxca1AsDil/wxFLAacU/EZO/2cHkHiFklBQDaixEinEOyf0sGymKJLNFhgPXb+NSUPcb\nJfhhCTUrtBXa2liP/rwwfmMBc0Gww5KODnk3PstmfILFT+6SPlbQSsZY6zDWob5AvSGSIbInsG9v\nBMScg87SdLxSffZZ4vXfQyZ70OrP/BSbFbvGx82G+65G4SYkWsG1vAYrJWRA7VdIWaKqpMVoBoYF\nhAw3iWnne3SybXb695L6ARf90wAYSp5x/xS7c3UmabPRrPwu3GiMbMLidcZv9ChzS+JLIGR6bTHB\nYSknEZPrCd21Ca4wIamoCpVp4+OESavH0uMP4R55FLv7FjiH7528PdOooSCKYPbPB5aR90Qvv4Rs\nbQBCP0nYy4TIOFb2z7Oydx7jCpJ8QJH2D+5OLVeu/BjeFzzyyHuobkKZISQ4exZf3M/8gqt77Bzy\nwvPBq3ZuvCmnNqDtMaMCt0QAxDpdqsceP1j+2/hpdTrwK7+Sc+0a/Pqvx7z4oqUsw6E++1nHl79c\nctM8UE2UFViLe+JJXFFgLpzH7GyHfmct/sTJoIQ4LEUpj7qBv8/wH3DbD7rdhyiefdbx3HN3pnBR\nDdvdjbtxu7gLit2Nu3EnUZa0v/pryNbm0WywGiCzb75O66v/jOwrv/RXC4wNh8T/77+dstVeeynh\nZPUo1dnP89rZL/IaXzxys3YbtraEr3894ktfurMHZfXk0yR/9s2w/BLHqML6FVhcyunG+6iY2itJ\n0chgerDGecbjPi+8AE8/DUYd0QvPhxXCBz8G3W7whGgGRMnMVJUshRNzlAw/QawB51EFaz1VFXyD\nrPVTUMO5iDTNaKUTSo2JKGm3BBlvgSvBmFDWqI2YGTogquCC55jv3YO/ZwWbZZS7I1QNYoSoW1Dt\nVViXB8bKKcFjsQsDCuNJOwkiuwd9x6IYxpPpYAmY/b/d81oV+9br2PQVTGfEJPe0Vses8Balsyy6\n1znNc0x8j31/irhlyWSFBbmMasS7+iwFlvP7H+NE6yIu3qXlxpSZcqy1wfETVxld7uN9mDaN6ZLR\n4ln3LfbdIv/Txf+e3PYwnwBdgq1iibXjFkYjQJCiCFIS6wPrYIriUE/cPJKUwZdNtPa60gMTU1c1\nWbOUaj/CdiuMqVBVXI1EFMMUMUqVRZBZol5BVJaoGJJWiQ4rnEb0/T6lRsT9gigtiX2JqywkgvMG\nK77O3lYDJ8zmvFMfsYYNUAMeOvfdTQGdpkoC6iW4v9QTRCsuZHlTyCcJxoeJvY0PMdbmgZ8YbOyD\nrAtAA/tM6ksB5soyP8lvkIP5sWMjZTqKXTbPhKrrPGXPNcy4umxGfM1Q8DdO2g+XpWGNzL8/DC7c\nBhwLE/05bM2HiTU1sDlluJlg9m3ri2nm6VYfdp7hBTMG3Xw7zoOKzWc1+BMYNXPtdbie830jmfu+\n8SO7E4bY7cIQAKZ87rOKIHcsCMyuZO51BPSZAhAH4vA5qRMhTPff2PYNmbVZzE3rIiVBSrkItqew\nXm/TqgEnNADGe6Bdgh/h8Vo+PM80rDN3eiBaK9GxwQ0tERUVEY1nUpyUFCbhePca6aggdhUtnyGV\nx1jFe0vlhcSUt5RSijIbEd/uPB3u5xbkZGgzcYqsKrzjaD9RBZZnKXhrMKuOdqfCrjrUGkSU2BRY\nW+GcIY7KenHmFseev0fU71XBVxYxDp8J1rlwnTT2eT4koWjazGgVXqUE9qeC8wZTBMqofaxA7nH4\nDUvxwwQ0MDBjX4TMh5FBR4L0ddoXpWnDQ203n0mUBZD9uhQ9ptlWdQKiiul72veNqO6Pqa7F7L60\nzCju8uh/+kMWF3cw7RIb1z5nSAAPpQbu1kBXBLYjyKLgCeYUTYTivqdnBarGyJKDsQWbIRMPVRXg\nQtUASjVpiYXAdk58YAMer0Lmzm0PgwTtBxlcq9gH7wMYpopxJUYcuiVYX05ByNPX/j3fWPplsPAE\nv0m5u8Hz3+/jPFgDj2wLq+0ONhuDMcggMN/yWODjGdVLbdpnBsixIPuXymG2lMXzu7zyF49z8tNX\nSHslxpnwTAHUec6Xp3jPP8ljHqLRVbS1iFs5d5tOzuyCNDEm28Z7f9AMH1hYVPb2Qnu52pessjHH\nd97g+vLHbwDGAJxLWF5+iGr/FH51DYlypDpiHGot1dPPHGRhtS2mNwZnUa2gCAyx6rHHbwRE4Wg/\nrXJIfOlbRLtvgq94wET8D//g45T/3c19yG4acRSSNAAkCf7swzeo0W+63QcNE4HLb/+7o7b7K4rh\nEL7zHcvbb8+8ms+e/cv3au71QkK0N980tNu3//1kEn5/p/bJd+NvZ9z1FPvR466n2N+iSH/7NzGX\nLtzezD6Okf09zLVruCeevPMDDYfE/+4PSP+/f0v83eeIX3ge2dnBnzwVBiNlSffrvwW//du4d99D\ngHLiePNlx+nxWzx04Y9ZGF7h2rFzYXX36CKyvi4884y7Iz81yXehPcFMCmQwZGvD0epexaaeqCyb\nxF14E5OnfbQGNLzG2GjA5cv3cOr6S5j9vSCjfOJJsBZjLxwwPAdCGuu8QDSDtgFfYfIdvIlR13gD\nCc5FeG8xxmGM4p0lihzGBD+qy+P7aMUFy+k2QhmYTSYNEroqGP4HGoiAq9OxuwoZl8iwZMcvULog\nK5l6KPkKV8RwymBWK2yvgMKgxpC3eqRLk5nv2LDWLRU5phzBfTlmdRO5p0DS65jdK8i1EbI3DO73\nSRIGeKqY9XVkMsKeuYC0M6Q9gXSMcxbjczrRPkmUk9icTjSkLTts7nY4Zt4k0QHX8zOMzQmiyJAX\nEVG5S9vvId7RznZIfB48bWJha3SM63KSoVnASYTEnmwjZfnyLi+YZ/jHn/pfeGDhbVQhZhJMfX0e\nsnNZN5PXhdNy8LVX0GgOFGs+B18KOLBF8L2xpUM6OodXKIph791l2ks53gtxtyQyFbae6olVfC6U\ngY5CuzOm1c4wVnClxSZVMF5XW/thhfPYgCZTRlD9v/FXm37WvL8NiNMAN0bqCR8N6yz8WesxkcNG\nDkGDX9bccaf7mTvWFJgrOcjGOCoaoKe57OdAtmmbH/aymjvu9Jh1+cUTfKJMyEyHBL8gI3Obzu/n\nMKg0/9nNvNBuEc25adrI12y5RiI5PU8a/I3E+5l8cg40Q5l6Q93wdzOZ3HzZ5dD2h+t9+HUT5ojt\n/jKi8S1rGF27BACqOaYAA+AcsMQM7GpinuXWxHwZm4SQKwRgrKz3J8zYZM1va+YjI2bS0eX6/RC4\nF2SBAML0CL5qdbJZjWtgrAHa5sDZKThsCP5xqcdm4Zr3GDpM6LRHtCVnNdqib/eJbIVNHCbSabZW\nUwMeDbCLzpFgDgHHdwxeNr+3IGl9rcYQP14iXUUHBnvCE605oparWWWCqq2zz9YZaiUAzo0sW25W\njnnwtf5ThSqL8d5w/tX70C0D++ATwaSEBQCYPrsEgvyxRouds7jS4vcF4z1qFBWwZxz2sQpiSHoZ\nduJCv2kHtFoKnWVFbcp1uNwNkOrqe5wN53/a7iJUo8AEBNAtg1QO37dUp1Me/PG3SZYKWq0cI0oU\nObxEqBiMcYGp2vQTQNoamGi9CqIajX2jj710mej117D5S5h4AFkSyt5thXvHZFKXv95R20HLQ1Q/\nr7IAlknLI30PJpj4iwkZKbwKkS+IfIUYj44tOrR1mwsxE7guTDYW+NbWIzzivk5lerRaoV2ch/T6\nZfaLNnExIs32Ee8oq+CtltyfYU+UmL6bPVsii64a4vsLer0hl374AGmvpLVYBEa082zHx/mL1Z8h\nsRO2NypOrGX4Y08e6bN1Q1erMiTfoUHYzIXdqWfttPsbKIpgZC4CxhbsDc6wXT1IK99HjcW6Ypps\nqKrgZH/IieUC98gnyH/hy8Q/+H6wYTjy+jLoiRP4e86Et711pJWBROjiIuXf+bv4U/dwNOJdR+On\ntfAA6cv/O+3v/yrJ5W9h99/F7l9E8j3M+Brx+ndv6UN2ZBvt7GDfefvoREk3i/GY6pNP4R/6YP5e\nku1g996+M0+xakx1/Cn80l+up1hZwte+FvG7vxtx6VK4+J0L/eGddwzPPWdZXxcefdTfqYXxTePR\nRz2vvGL+f/beNdaS67rz+629q+o876Nv39vv5kMkm6T4kChRlh2N4/HMILLhD/EgEix5DAHGACMg\nRoAMgiDwzBd/yXxNgmCcKMHACBI4o7ESj2HAAyMj2xJF+iHSsimyxUeTVD/Y3ff9Os+q2nvlw646\np+655z6abFGm2AvovveeU1V71967du313//1X+zsyKGcg34/6MF95Sv5XSv7nv147IPSFLsHir1/\nuweK/bisFCf94R8R33yeePm7yGAT3z57Zy+L41qnQ+0P/t/jZ2yMY8ytm2Sf+ezxX5hZRu3rv0vt\nD34fe+N6WOQ5h2QZ9u23iJ9/DnPjOslz3yK5fRPabbLCq/nhD8NLgjjB2YTZzi3Orr/C9TPPHgiM\n7RF7Pab59lnim9/Bn73IcOk8u8vXaES7KFHIemQsw6RNHhWppwiL8t2dc8Rxik+3OXlrLWTZWlwM\nCxrA2JtBr2PSrA3O1ck6km1j0l3EGHIi8EHTRF2ARnyR2t4YjzEeT0ROzG5+ghPNXeqmF3bLTMJI\n1dyXKRdNyBzpUsaojcG7mGxniJXwWR7VcKaOmAwz6zGJA2ew5OS9BsNkBueEJBFETRCjn+mhO03k\nYx3sqZUgytzrID5H8Jh6Bzk3hFaG2C5mfhkzs4GpLSNmgF5KkFYWslBFOdnA0GGWuJaNRNpzIrwa\nIslJbMpq7xQbgyVq0iUerPLu1hk+1v0+zZVVGtEmdb+L5pBLhKohquX0dxp0dBZEiKKMXrfFS3/z\nU8y4HU7mt/nMJ/+cizPXaMZdrOTEfgimoiNzEDBQgF+k41BBVYOkgh8EQMz4kMGQBKROyJYmBKcn\nVtQFZoVEnijxRPWcSPNKAYIXQ57HIEptboiJQJ3gncEmvtDK0RFLbFJHbZSxrXI/JU67J4SyenuT\na/ESTIKx9I9WD9TCeQhDccR0mrzGJNhSML0kJ7TTZHtPssBKsKMEZHTi+Iny9n1X0TQayReZ0J7V\nttNSj6wKhlH5exKUmyz3TswUlymAxFGoW6UYishtmQR7poFy09qQid+nteU0m+yvSWCs2gfv18rr\nlBv/fQIoViYsLIDMEQh1GvYISJZA3UGssfL7kh3nCQLjdYLgfp0AmPWKMlP2hlJa0DpIF1iqAGJJ\n+E5sAMGkXgBiVSCuZGyVgveMp2qx4RxrHEmaEpNDnSKc2mMjxXYctqa4AoAP4Jjuydo6Armrz8Tk\nWDiOTfZxAUhLAtIEEyn2Yx4zpwHkr4W5zGmETfwI+AqA2JgBKpPXPqRc78GpxYnh5vJZ6BjECX2a\ntOe7pGmMWkFsmeVTQhKPMswbGHYT8k5CTIptO0wbbJEARGPBisfOeMxsOEFjAijWDNlfy/svQ5lH\nuoVlfwKaGRSD1LWYDwOS5jODpuE7HLADimHg6sQXBjRP9YhNSJyRmxq5rRFJhrVhsg6sMZ1oM4Gh\nhHGa1NBbJ7DXbyBbm8iZrcAIq9XQYQ1ma0jUC9kkE4XYQStspoFALkgvglwCA96AqEEiD9aiDkxi\nkDSlZPlpLujQYBbCGkFPWLy3dF8+Seut11g5P8vSzDJLp6I981TiBrTTDQZaZ2b7JiYbYtyQ6GyK\n1MM6I+8mGByKIZMa3ll6voVtO6Ilx83vnae7skDftFmevY8/n/k8cbvJVuNTfGv7K8xFtzl7+gAA\nasI0aWN3r4YHTwW5sTN1LdtsQbcXwBBjHbdufoLcNFBj+daz/zXOJtSzHbJcSGbrfPwrnyD91X+C\n+/SzYePzOMCStejCScypTZidQRsN/IMPoftSQk6xgunWeOV/J775HCYfIMYWQLnDDDaw3VuIT0Fz\n7MYPyE99+ljAmD9zlvj55+4MFEtThl/+tTs7p1pm+yzxu8/dmZ/jUoaP/9r78o0mQbEsg699LebG\nDUOrtf92kiT8W14WLl82PPPM3QHGrIVnnvEsLwu3bglZtrfsXi8AtY8+6vnKV/IPWsXmnv0I7B4o\n9uGxe6DYB20uo/b671K78vvY3QI4Uof4DLv91pFZZ96rxX/6TeyN63fwIksx5i3i1W8TDV8/GrQr\nQjPNjesc9oaJv/2n2DfeIHrkYTCGLAtA0ltv7fVwvI2pD7aZ6d7m1ulPTL+nGDodubOsLDbBdG9i\nere5diMiyq4zqJ9kUJuj21gkdkNkJF4EgiPNWqTDGVQtc9wgv2qoLcyRf+KZikfUx5gN9gv9AF7x\nzbMYs4HJ+4jmqBq8MfR8C6eBxZXnCdudeSSGAQ0wkGuNpB4Rx5440hAquSd3eh52lnNXePiF96wm\npLbvDTFDN9ILyaMag2SGdrQBalA15LaGMwlZt1EwvAAJDHlRA3GGObcSHMJUkW4/LDQbjeAUDQbQ\nHiBnhsjJIdITJE/DIu1MhLnQgcYAEchTYagxcTsjiop2yAWXR3T8LEPXCKCVcVjjmE02OVlb5ZL9\nLtLp402brY0ZZtwqNs6IyIjUY8RRZ0AtG9C3TVZWTvHSS5+CzHFBr/Jz/Amf+txf0066RJIj3mMk\n35/Bb/L3UR8CqUA/RqxHPPhMwWjI4hYXYFLR/BIH8EfiMET8usGZGFCSuQHW5JQYasDcDN5Y3DAi\nauTEzQz1BpdakqjQFYLgQBVso7Kek5Bw9Z6k+jdT/OZDnNeShRZYVQEhck4CGaEElqacv0eXRyrX\nKhlSh+kdHeTgH9fhn+zPyrVKYGJkBQtkH6ulOp1M1mcaaHRUfaogxmRZZfvo+E9J2Z8L5CCW10FA\n3eTnRzGIjvPdewXEpu1ZlOysDgEUaxAYYTXCJoISQIGF4vNq5EypdXZQPUsr26xRqUNanF8vPrfs\nSSwwGivhUQ39Msv+9iv12apjuTzmAAB1NP4TAtiXBKDJlP3oQXoho2UUO4xTKDKVlWG4JesQZZxd\nsrRyfN2pVeuaEUJFayANxswvQt0lAVvzBVt1DDDvmWOmjUud8rNgvGX9CJfH3Lh6njOzK+SZZcZ3\nqM2kgY2dJWTDOGwIGEWSAEyJB7rguxE1BthZH5irZbZawKhiYg+9ok9jkDkwjRB+Ofns7wmhLOrq\nfIQ6sLjRfI5V1EoAWxJFI2Ww02Szt4AjIrEpyYUUEzvEGnbsSTJTI/MJiR0EVt1k85fApCj4CJUG\n2m9ghhuwDEQRnBsi3oVsgQtZYO4NcyTLw8kJxbMikBnoVwbISg1ihdgj1hblxMjAoUkeNoiK5YWp\neTQyuCTGD2NcLyFa6lJr7PJg4wesLTxKeyKyMLN1Hr72LU50bmC8w2NDaHG9YFDbHN0RtqJFKJIV\noCFbZ64xg0aDjblFXl9/mhfNL/C/yn/Pixt/n/jBz7JpLmHihHR7kyfOvomNj7GOFYvJOiE6oGOR\nrk7NsCgCMzNKnmdsrC+xuXkea0OyHmdi/vrcL/Hm0s8Q//x/ws//y2cxjz0Cko42tY29RvzuX4BN\nUZ1h6hqw7OIyosD5UZTBkaae2tv/Hsm6EDX3+wXGgrFI1sEMt9G4gemv4BaPEeWRJJhbNzHLt48n\nldLv4R59HPepZ48+9iCrrMGD1u4RlvdwJx/HnX4fZbIfFPt3/y6ww44KY4xj2NkRlpeFp566O4L3\n1sJTT3k+85mwCOx0BBGhXlc+8QnPl76U8+lP3z122j27e9bpwJ/8ieWP/zjihRciXnrJsLkpnDmj\nB7rX97JP3rN7Ns1cRuPlf43016bH/hcZWQ7LOvNeLbryxjFZYg4bvYoxq2GBtd4l+9hFcEPim98h\nvvkcbuFxhpe+tKdutW98PWiVHRaamaaY7e3gZX//+/DJT45LnUKyyuImZ9Yvk6Qd0mR6YP97SW88\nvPQlGi//a8zmS1hbkcQSYWvmAjPdZZK8BzhyX6ezG3bzrE+p5126F+ZofuozezQgvL8fy7XpBcYx\nZnMTf+EUWpvDbr1FrN2g0ZQY8rjGem+JbTfDxRPv0Ey6o9CQjs6CSWi2MmAOzbuIzwpHuyg/CBWB\nBJBLvII6RD2YCCkUhSOfEvUHRG5A3ooY6vy4/kbACtaF8Mw8E6gX4SrNAdRSpF9D20MkyYPzmq0h\nXR8000QhK3bb51K4XSA3tSDqJPUMvJAPGmRJg7rdRtUUu/1CTKFHg1C3fRrNPpvDDOcjGtolilPy\n1g7oLgt5F3s7x2HRBSU6kWETpdHIsVmOv6LcuHKWX3a/zww7tOhRe2pA5IKosMVj1O3X2DrIkSvC\nZ7AO6fjAJIkVTcGU4TcUDI5Cs0hhrO+Vg23nxDtDUl9DvQQgrQWSEwTkcyUixxtD1MpQZyCFhHTk\nqOwhbFVCOEcsg6r+VVH/ku01lU00ef9M+a4K1ihFuOfEOWU99tHQjmhfJj6/W0yk8n4r9RGtMGzK\n46rhZtPqUAAVd6Ve1froxO8V0I4SNJkU0j8I/DpOWbAfLLlbbX1cK+vh2MvMKldxpa/RJAjrv80o\nVI2csQ7YQWy3av+V/yLGk/scgRlWJncr27ZMIrBOYJPtEEI2LUHHrHpsaYeBttPYehOsPykBsMb4\nb3GE8GI0ADr18vKKFNO9t2AcAcyphnDfrX6UcO09rLxyXqnIYo72ZLzuuUeZvP9Jq/YNQTdbXWBh\nDVdj7h/ewPicudlt2BbyvsXO5eRqieo5PpLRkkOLLKnShRpDtJoZdQqRSEowdKE4v3IfYV4VUA2/\n54Q5vqSYZoF1RhSASI3ApKCp4hGcLQT855S67dNdaWHmPTbOcTYM8PJazkPuLZg46DSihEEqgTUu\n4K0h8wnx7mxgA7V20GQ+1MsLxAIn+4gYlBaSepRGEHS3LhxnJIROthx07fjlsR5BlOBPWohSdHEG\nLg+RzSb56T7SdmAsjhg3jMk7ddSH93Q2hGjR8bHF19hY+ByO+qh9jct48sofYn1gnKkJKnCmBerD\nC1IlJCmYzTZYZwkjDjGB7S0oNssZnqnz2yv/DbVTp8kyOHnS73E0r+Q/x813/4z7jxlFl598kni4\nBR0dab1OMyHj5GKLRuNxjFE2NgRnW1zovE7tZ35hrC3lMmo/+LejjJvErTA/LM1jN97GJtfwvshA\nORUcs5ANKpkmj7Zo9W+RwRY69+DhB5oYybrY7XcAs1eH7BAbfvFLY63hw9bv/R7+5BLDL/zKsep9\naJnFGlz6awHoO8jyHr6xxPCR919m1Tod+MEPzLH1whoNeO01Q6czXY75vVq7DZ//vOPzn78npP93\n3bIMfu/3Il57zezNUZcKL7xgef55y2OPeb74xR8fu+8eKHbPDrZOh/g73x6JuBNH5A9fIvvcz97d\nWe0OrPbm149+CUDIOrN9k+b/8y/R1fN3p/7HyhbjiOLvIvQYURV8ZWfkINCu08H+4PKR9TLXroZF\naxTDykrgCBdm7XRgDOBj177Faw9PF92P4/2r8OVl+Df/JubK3/R5cvXPeGD4BhdOD/nUZw2NZ0Ib\n9p/+DfLv/DMUsKTjzIcidFsL9HUO7QrDnRbGgBfDdvssuUnQRsx+UdQE75dCtiWZMiP64MWY7ipk\nDnKPxWBzxavjfOsq97UzBI/HolhyTZixm1izggxjtHYC4jaqPmSd9EEPRHyGYkFrSDYIO77IPkdY\nC6pMze7g0hjjc7yJCGy4OfrtE8z0VoizXuFoGTAeSVKwGSyuQ2rRuUVkYyNktFpyeAs6FKRbhPk0\nwy669waRPs7HxKo4MeiMoabDsUh8GZ4C1LUfQokkJByIyHDeYsnwIsRRStR31Bt99DxIZkJGvtzg\nUwni8DvKufO3OHP+j+iuNjAvg4kc8akMs+6RpoKVoo0KO8ixnGRQCFBT6BlohwW/5ARGXhkSWJwn\nAZcMN5aCWkN0MkO8BnH6AizTwtE3kQ/aZLseM++wJjAZqOhPldcUUymrCvwUbepdQNDEThGTr5w2\nTVj6wIOLiBw/ytg2pY2O+vsg4Omg796vTQJ7MgbGptZ12rkQAIEyHO+91O0g8HFaWGLZt9Xp+iCQ\nsfr3QWBECZ6U4F5ljFZDTI8ERt8LA6l6nbK8o8ZLGTo5U/ws9cTK+5xWj0nAdxIgLlldCdPbKWIc\nVnmi+DkorhEz7pOyLUZ6UpWfOvH7JPhcWgnMmv3nyeQuc61SRIWFimFvcpOjwKjjmmEvQ7Fa73K1\nXSlHJu+LI/4u+9AzYnrpOvjM0l2ZYYYONvPUdod0NtsQKbXzw5BtVwiTZRQKFgkhlLQDMKZxpRkG\ne4sWD9ogzOEVLTut3JcvxPjVCZoV7C8P2rUh/J2if5Qw32fhpiS8qcnSBO8McTNj5vwu4IlskWVY\nhIbbIScmMiFeO/MJOUpkclQEQwGQiaBZhE8tva7QtsXNnB3A9SZsRfB4Z6wV5seovc7EIQuwKuq1\n2L5QfOJwtxNM6rH1GBoNNEsgU9TU0LQNS0rUqNHbsqONxvR0B/QAACAASURBVCohPS8SVog3xDXH\nQ+m3eSP6z0bfP/b2HxNnPQbJHFE+DGDeiaIupd6aBy2SiZxgnWU9zbt6AYwQReDFctU+zFNnX+TV\n7JdotZSPf3zvQLKNNm9sPcn9+atHr6MB3IDBpS+TXPv/kJFoYWWQawaieA1AVpJYHnpowCOPXA3J\nk6yQnXLkq5fI4p+mcfn/mLqpnT/xJPFLPej1MNEaEr9Inj3LJDDm+y3MXLo/0+RB5lPM1ptofEzZ\nExNjequ4mfuIbzxH9uAvHn1OHNP/6m9Q+8bXQ1Z62LuB3uuBashK/4VfuTvJt2xM/+nfoPbm10Mi\nARj5FwDkRZknHw+A2F0iB5T2/PP28Oy4U0wknHcPwProWRlqu74uh+Wo4803DV/7WsxXv5r9WICx\ne6DYPdtvWUbt9/5tmNz3wLlD4he+Q/z8c2Fy/+KXfrSZFffVq4Ndv3z0zk01ZbRkZPoPgOT917+a\nZeYAs9GrARCrgjoHZMSppoqOn3+O47xhzOYGVGnv77wDFx4AYGFBuX59v/BkGrc4tfn61EyUvR58\n8pN+z9+/+Zs1Xn/F8Uub/xdfdq+iGAa2xfUrcO1NuPhHf8HP/r3nkCcfZ5Un2UgvcKH/IrN6Ewl7\nm+zIWa42fpq02Q7MhYq1+mtEMp1C7fInkPi7iE60IYB47M23MNlWQQ2oo9omtgNqsoXiUbUhoyQC\nElOLQUQCRcClSH8VbSyF1WqxIJS8h5Y7zWle6IZMeokTVYk82o+JfY9hbRYEBv15EMNu6wyijla2\nRdv2gjB+PQ8UfVW0Nh92pCMbhHJtP7AekgCEuW6MIQ/O5YZBxGFNRu5Ce9Ski8cWGmLjOilCI+qO\nMrMhENsM8oKQoELd9lCNUA/2RI5xgu+MF53W5yFkIwuhLu3FLvrTBrcWxrDbsERnXBAwnnRsD7KS\n/VVmw4uBnkd2gMXK+TnB6SoZLSVLpQDTTL0o0+cYq+WQCCBWT0ZsheR8hokKh69ax9LjK6NkpwnS\nK7iOxWGxNTc+ZnIoVB3Uw+5/0rE3FUziKBDxsOsdh91yN1lj0xz5yWtPq1vFiR8509POPcymsecm\nQZTSfOU7IYw3y9ErnWmPeTlutwjAUrWMyfs46H6O0wd32k9CeEYiRpkxmWXvfScEZlefAAyXfTIF\nCJ4KulYtYgzqCXvBxup5M4yBMFc5F8ag6GHA4CQYdhzgFfaCfTFwkunhuyUoFxF00Kphf3cDEJtW\n14PadtrcUb1/rQwLMzFESgwnI2wWdA3RSsa8bhHhwq14iDsppgnaB9t04CWEmOcG50JHxGQhycpC\nMZci+NRgdJxQpqxuCYpKJ2xw0fSB0ZuZsKFBkYjDgliFYfFOM4rbtUUdprWZhndutyjLQ32uT9QI\ngvXl67xGnxo9rHhUiw0cPOICEGYKSq8ioIIYyJxCuxfY1gu7MJ+HDZmGQzJBbQTD4bjxreI0BgJL\nvHjLILHS222Q5XWkJ0Rxk9bFJezqbUw3RU84JHbgLK0WeFWGQ8gzCeCl9dQXBkSNLICzNc9i/BY/\npENKmyTtMNNbHovRR3UiN8Q0c1QFaWjQOxsK0YUM7Qt2K2fFnRox6PIMpK6cad3gnzz2Na7zMoun\nIlb1Ud72P0fKeN38Uvar/KPG/3B8ltFjv4r5sy2ku4kxBdhVUHG9O4P391PSc230coiSgPCZTZCs\nS3zzO9S//7+BCO7Up/aXZQzZp3+K6PIryNoaItvY6FVcXoQwFsBS9ug/wF68Mn1dPcXszjVMPsDN\n3Hes44EwnnqrRFtvkFEBxSayVmIi8vlLZBdC1srhl38tkAmefy5ElpSb8Z945kdDJrAxw8d+rajX\nc0Rbb4zrtfTMqF4/CrtyxdxxRsdmM5x3DxT76Nk3vhGxvi5Hhto2GrC+LnzjGxFf/vJxiCh31+6B\nYvdsrxW6VrK+Nn0CL2ZB++br1L/22wy++l9+YMBYfOM5jlwhT6aMVsG4q3hfcMXfR/3zhy8Rv/Cd\nQ0Io02IxUAGtsgx/+uz0wyupoo8VmpmmmJUVpNcNu5uRDfd56hwkCRcveq5dmx5Ab910YVVV+Nzn\nwguq14N/+k/r7Kzn/Pr2/8S8W6Nvx6IXZTO9u9XmD76p/Oe8xid3/5RtbWAiw645N741n3Kf/wtW\nWeK15GkcFiPQaChz0SIPuGvYK28GkK/IR+4XTuIu3kfOZ0L4qVQWVmmKGd4KmRwjQTWh32uTZ0Kj\nMcBGBhGLkTElR002pg4UugviMxhuoMkJJKxakeEgHC++SG0XFtWjWLYi9GEkLC6K5hHiQ4p764cM\n3Byq47bP1cLCAn7hBNa+FgA+DwxTJO0i/UFYHCYaQLwilZ9EDlPLkVTRJoy9iOAJhXookQyJ3BCv\nBofFSQHkWEemMd5brAkqx7FmKIIRsOoY+pikFcTxTaz4wh8wxkEv3KQh7OxHuZI1hfipPGQ0Q8hW\nY5LTQ4iKZjpkyKIVAKp05ktcuU4ALXaAZuGWFCLy2gUNSSSROmOgDAKgWjrphayLaYS+0kFgRJTO\nZOj0vVUSy9hpL+tVAcZs5HC5HYtmT4Ru7WGqHHHvI6uyZKbU6dhWdZynsXvuBgg2adNYO4eVU/2u\nvG/LGEy5Gxofk21QBYSq7CZ/wPFa+XkQ+FJea8g4FNOwt5zjtPdBjKfJzw663rQ6VkMgYwJAYirf\nVUEXM3F8tdzj9GP5s8qUmwQ3yusnjLXOqmVXAajjjp3jjuVqX1jCvFIALAwZA4gwbqMpYPhdf34O\nGk+lTRu/k6cXDFotji9fb6oELbkusCxEOCz9osk9mkP8ZI9s25JeqWEveCQOzCg768L7rlpBC2pA\nh4LrhndJGY4/qlqNEKoOIUyxa9BEIYfcR0TtPIQlFvUL9RCkDlE9gG/AOES+LD4D14uweFSV2mwK\nxmPjAEqJhEyjIorFYcWN+srnJlymBPGU8HOYY+tDmuc8XvPQ1ULQA3ugD4lHEwEnSJahUYR0u6gZ\nO2FeLLnESHEffi6is3OCHTuPDg0n3005ff4M8Y1r0BxANl73GYFGHWh4aHeRJCMtmNiokvs6sR3w\nuP0PrOolGrc39vS9M3HQZZ3JsbWSLh36RyODzgt23vNI901+sPoEqnD21E1cO6KZDLD1OlofAkMe\n4jke4tvc9h/ne/mv4omxcXTHLKNy/etbj+wJfqjUen+URHX9a2JMugMiyPJ3yU9/Zv9L1BjyJ5+G\nNMVev4bZuEluBOLmHmCp9oP/E7v5+rGYbtK9jU/a+3XEDrNCmN/5Yjy4jNobEyGfMF0Spd0m+/wv\nkn3+GAyzu2Vxm+zBX9wL4P2I7b3Irryf8+7Zh9f+roTaHsfugWL3bI8dS9cKoNHErK9S+8bXw+7I\nB2DR1ht7X9zTjrn8SkB3SgRHYozd2P8StxHRi3/FzN/8dXgJHyO0MvvczwZG1wFmzNX9Hyq4++7f\n81GawrVrQVjQOsv1v3mBxy87Hjh7gIa/c9jLr2LWVpGd7cB8ggCMbWwQP/8curgETzzJ4qJhYyNQ\n6fdcYgp1ut8PqY1LLO5f/IsaGxvCP979XeazNYZ2+hiIIhj2PNf+8FUufaJPPtNlJ77AsA95HnZG\n06wGCjWzzmPxX/Jq+7M4sexue1x/jej2CibzFY0Khx3+LdGtv0DbTfzps7i8zEq5g1l9F846VJbY\n2hqiGnZ3rXHYKA07xwqeIGJuxBcaSA61CYhFfEi1KXkPhoXXJhY0LHpRH3bZyti6yAVQ1Y8bMwBi\nFpeHHfG+raM9WF49AeKZ002a0kNUmYsEpAaLQyTNGOmtpCHcQBOPaeQVXSYJYRIagB0jis5qsUsM\n4jziNWTOij04E3bKCZ/F0TAIyANOTRD4r3hbEXlwWESDWHJxT6bu8X2DQfFbQlRxhkCwuSIzHrcl\neBeRLUdIrCTJEKlNHSIjB089aIdQXhmCVTp/MQEUE2ANWAE9zxg0axSAmFT+lTbxu0SEsMgknL8n\nq1xZn0nnmf3HaAZEECdpwEYLjTepCpPfyZquek7V8X0/68JJYOwwoOVuWHktx977mOb0H3Z+Kcg+\nrT8POn8SUFLGjLNJgKs8pvp5VVw/YwyUycR3k2VX+2l2on5Vp/44wNhxwZaj2m+yz8vyy5C98vdq\nG7UJYGSNvdc/bijntGeuOq1Mho8aAjutvH7M3r660zIP+n4as6vaNmX4Zvl5OXarIaFCaJvqs31c\noHNa2YedO1nnaXPBlPtSIbCIi4QWZUKDUXV9YPSOqyFhQ8MCsxCvezzK8EZCfCrDthzqBGO1YIMx\nbhcPw92EMptxSoLBB3H8MlTel2UHUE2HBh8ZolmHMcU7d3SEgArqIjT3WJON590UNBN0IKOdFVWo\ntwaogag2XrCpCiqFSH/J3ZJiuMd+v/6ZA4kdUeZwRXZtxCO5wnCASo50BJ03EA2LbJ05mjm0pqPG\nFVUMObmP2OyexDVi1ndOYot+2t2BtUc+wyflWtAIHUxQIESRud2gUaZmD5Cc2wY4iOIBLdZYar5B\nutMqO5E+NebO7CBxwYojhKT6kpLnQvu3ml3On7+OCNSiIe/4j5GIpVkfP+Ap4bqn5TV+Nv6f+ePt\n/4pPftLcMcvoqPXv1CiJyvrXbLyF2dpChhmqOfb6LfKFJ3EX75uaVMo99DDu/nPkZz+5L4TxTvS0\niBpo62whlXEn5sBEP1Yd47/LFsdKmt75ZDlNruWe/WTbhynU9h4ods/Gdkxdq5E1miHE8oOCc496\nqaUpsrY6BVmqPFRlaOXaKqWqrTzwYMjqeFRoZbuNe+xx7JuvTwUNA6V8L0usKgbqPbz6qmFtXcKm\nZQLQYsa9wTvXE959O2NxUXniCT9mhjtH9NJ3Q7hdkqDtGWRne5xxJ4ogSZCNNaIXv8sTz3yGF78X\n0+8zAsaSrMv1M5/eU9d+H06eVL7whdCmy8vwyiuGxXqHh3qv7GGITbNL6ffJB32yrSXi+lV2+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BDJltEnUH5D5GAD+MsD6lEfXxqSHPY2zkMFpCYgHg3EgWQQwi0E52udU5y6trT7O4KDhgZwe2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858h7mV1zA2w5qYs/OP8Mrs52nsrPH1r2d0bqIodSPxFKMExkAgEAjcngRRLHDrya5yI3er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\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yS6jcG2dwmWFFhIAKbWo0CUkJSN34nQITVfhI4zv8wpE/Ivyf/xmlr/8bSjK+5DBev+1j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KqlUrOHv2DEWKFKVDh6d58MGGcf0OHz4EQOvWDwOwcuXlfe7mzp3FrFnfsGvX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\nERERERERiUBubi633vp3HnjgbrKydnHhhRfzj3/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334OMHXs3778/jW+//YYzzhhMu3bt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\nTaEJozWEq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hnZifPCp0m+8Gm0k8Rtu9H07mu/jbBuxSwehDiTSIBPLAw6PPHjsSjFX/sN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3RM0tW8yAyIhrazOjLk6aDMKOUbRlv/ByVd/JPUki995PzoXnJi888UR48slBN7VfXNeM7Kcb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\nAWGfD958s4U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+ "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "height": 277, + "width": 892 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "theta0 = fit_cp90['theta'][:, 0]\n", + "logtau0 = fit_cp90['tau_log_']\n", + "divergent0 = fit_cp90['diverging']\n", + "\n", + "theta1 = fit_cp99['theta'][:, 0]\n", + "logtau1 = fit_cp99['tau_log_']\n", + "divergent1 = fit_cp99['diverging']\n", + "\n", + "thetan = fit_ncp80['theta'][:, 0]\n", + "logtaun = fit_ncp80['tau_log_']\n", + "divergentn = fit_ncp80['diverging']\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(thetan[divergentn == 0], logtaun[divergentn == 0],\n", + " color='b', alpha=.5, label='Non-Centered, delta=0.80')\n", + "plt.scatter(theta1[divergent1 == 0], logtau1[divergent1 == 0],\n", + " color='r', alpha=.5, label='Centered, delta=0.99')\n", + "plt.scatter(theta0[divergent0 == 0], logtau0[divergent0 == 0],\n", + " color=[1, 0.5, 0], alpha=.5, label='Centered, delta=0.90')\n", + "plt.axis([-20, 50, -6, 4])\n", + "plt.ylabel('log(tau)')\n", + "plt.xlabel('theta[0]')\n", + "plt.title('scatter plot between log(tau) and theta[1]')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(15, 4))\n", + "plt.axhline(0.7657852, lw=2.5, color='gray')\n", + "mlogtaun = [np.mean(logtaun[:i]) for i in np.arange(1, len(logtaun))]\n", + "plt.plot(mlogtaun, color='b', lw=2.5, label='Non-Centered, delta=0.80')\n", + "\n", + "mlogtau1 = [np.mean(logtau1[:i]) for i in np.arange(1, len(logtau1))]\n", + "plt.plot(mlogtau1, color='r', lw=2.5, label='Centered, delta=0.99')\n", + "\n", + "mlogtau0 = [np.mean(logtau0[:i]) for i in np.arange(1, len(logtau0))]\n", + "plt.plot(mlogtau0, color=[1, 0.5, 0], lw=2.5, label='Centered, delta=0.90')\n", + "plt.ylim(0, 2)\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('MCMC mean of log(tau)')\n", + "plt.title('MCMC estimation of log(tau)')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/notebooks/sampler-stats.ipynb b/docs/source/notebooks/sampler-stats.ipynb index 4c20957733..23d0f8524c 100644 --- a/docs/source/notebooks/sampler-stats.ipynb +++ b/docs/source/notebooks/sampler-stats.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, @@ -72,7 +72,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2000/2000 [00:02<00:00, 784.84it/s]\n" + "100%|██████████| 2000/2000 [00:01<00:00, 1361.25it/s]\n" ] } ], @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, @@ -116,7 +116,7 @@ " 'tune'}" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -168,7 +168,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 5, @@ -177,9 +177,9 @@ }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, @@ -212,18 +212,18 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 19, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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MGNYkzb6eovq8y2pJz/5+wKL7VwgTVF1IF7oUuOSWdLFKWEkqZdRGjWWFQfAm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\n1ScXsC5mbbvQfpo9HUysMxP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gR1YJOZ++MSfk3NthEbvdroBc5bwcF3JXppuTMlfShW/9XEnAlCSRSYl25aIx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\nWGt9i9Z6QmvdQ+Kk9ZoZ8Tq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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -239,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, @@ -249,18 +249,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -274,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, @@ -284,10 +284,10 @@ { "data": { "text/plain": [ - "0.81124010706223437" + "0.80329863296935067" ] }, - "execution_count": 21, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -308,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, @@ -321,7 +321,7 @@ "(array([], dtype=int64),)" ] }, - "execution_count": 22, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -344,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, @@ -354,18 +354,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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a6rOlxq7u7JTI2kAtAL0T/XmLSawNiyZ3rfU+4JBSaj/ZWvqnlFIPKaXeMXXI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\nrkJggg==\n", 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EccrNhS36fOHzQVGRQ29fEtt2MM1U3b2uuIZzo230TPZSVbQ6N1WFWIqMUBXr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\n9d4mzOIxHnnpWLbDEXlAkru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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, @@ -420,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, @@ -431,7 +431,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10000/10000 [00:03<00:00, 2630.30it/s]\n" + "100%|██████████| 10000/10000 [00:02<00:00, 3695.88it/s]\n" ] } ], @@ -444,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, @@ -457,7 +457,7 @@ "{'accept', 'p_jump', 'tune'}" ] }, - "execution_count": 29, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -478,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, @@ -488,16 +488,16 @@ { "data": { "text/plain": [ - "array([[ 1.00000000e+00, 1.77752562e-03],\n", - " [ 2.50000000e-01, 7.91482991e-02],\n", - " [ 2.50000000e-01, 5.37823368e-03],\n", + "array([[ 1.00000000e+00, 2.99250475e-05],\n", + " [ 2.50000000e-01, 2.58944314e-04],\n", + " [ 1.00000000e+00, 5.89596665e-03],\n", " ..., \n", - " [ 1.00000000e+00, 5.90932306e-03],\n", - " [ 1.00000000e+00, 1.02647227e+00],\n", - " [ 2.50000000e-01, 9.01210431e-04]])" + " [ 2.50000000e-01, 7.71917301e-02],\n", + " [ 1.00000000e+00, 2.43450517e-01],\n", + " [ 1.00000000e+00, 2.40661294e-02]])" ] }, - "execution_count": 30, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -523,7 +523,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.5.2" } }, "nbformat": 4, From 8af47d845421ae22c7b4dd52fdebe1c401762ff2 Mon Sep 17 00:00:00 2001 From: Thomas Wiecki Date: Tue, 7 Mar 2017 22:05:39 +0100 Subject: [PATCH 05/53] DOC Change heading names. --- docs/source/examples.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 6c0a8a125e..c517a2f259 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -39,8 +39,8 @@ GLM notebooks/hierarchical_partial_pooling.ipynb notebooks/GLM-negative-binomial-regression.ipynb -GP -== +Gaussian Processes +================== .. toctree:: notebooks/GP-introduction.ipynb @@ -58,8 +58,8 @@ Mixture Models notebooks/dp_mix.ipynb notebooks/dependent_density_regression.ipynb -ADVI -==== +Variational Inference +===================== .. toctree:: notebooks/GLM-hierarchical-ADVI.ipynb From 07b3d15c1273e3a73aafde3bc53f66743ef10992 Mon Sep 17 00:00:00 2001 From: Colin Date: Tue, 7 Mar 2017 16:22:11 -0500 Subject: [PATCH 06/53] Make install scripts idempotent (#1879) --- scripts/create_testenv.sh | 15 ++++++++++-- scripts/install_miniconda.sh | 45 +++++++++++++++++++++--------------- 2 files changed, 39 insertions(+), 21 deletions(-) diff --git a/scripts/create_testenv.sh b/scripts/create_testenv.sh index 2b14398561..35336759d1 100755 --- a/scripts/create_testenv.sh +++ b/scripts/create_testenv.sh @@ -15,12 +15,23 @@ do shift done +command -v conda >/dev/null 2>&1 || { + echo "Requires conda but it is not installed. Run install_miniconda.sh." >&2; + exit 1; +} + +ENVNAME="testenv" PYTHON_VERSION=${PYTHON_VERSION:-3.6} # if no python specified, use 3.6 if [ -z ${GLOBAL} ] then - conda create -n testenv --yes pip python=${PYTHON_VERSION} - source activate testenv + if conda env list | grep -q ${ENVNAME} + then + echo "Environment ${ENVNAME} already exists, keeping up to date" + else + conda create -n ${ENVNAME} --yes pip python=${PYTHON_VERSION} + source activate ${ENVNAME} + fi fi pip install jupyter diff --git a/scripts/install_miniconda.sh b/scripts/install_miniconda.sh index db603857a9..bf35345c7f 100755 --- a/scripts/install_miniconda.sh +++ b/scripts/install_miniconda.sh @@ -9,32 +9,39 @@ if conda --version > /dev/null 2>&1; then PYTHON_VERSION=${PYTHON_VERSION:-3.6} # if no python specified, use 3.6 -if [ "$(uname)" == "Darwin" ]; then - URL_OS="MacOSX" -elif [ "$(expr substr "$(uname -s)" 1 5)" == "Linux" ]; then - URL_OS="Linux" -elif [ "$(expr substr "$(uname -s)" 1 10)" == "MINGW32_NT" ]; then - URL_OS="Windows" -fi - -echo "Downloading miniconda for $URL_OS" -DOWNLOAD_PATH="miniconda.sh" - if [ ${PYTHON_VERSION} == "2.7" ]; then - wget http://repo.continuum.io/miniconda/Miniconda-latest-$URL_OS-x86_64.sh -O ${DOWNLOAD_PATH}; INSTALL_FOLDER="$HOME/miniconda2" else - wget http://repo.continuum.io/miniconda/Miniconda3-latest-$URL_OS-x86_64.sh -O ${DOWNLOAD_PATH}; INSTALL_FOLDER="$HOME/miniconda3" fi -echo "Installing miniconda for python-$PYTHON_VERSION to $INSTALL_FOLDER" -# install miniconda to home folder -bash ${DOWNLOAD_PATH} -b -p $INSTALL_FOLDER - -# tidy up -rm ${DOWNLOAD_PATH} +if [ ! -d $INSTALL_FOLDER ]; then + if [ "$(uname)" == "Darwin" ]; then + URL_OS="MacOSX" + elif [ "$(expr substr "$(uname -s)" 1 5)" == "Linux" ]; then + URL_OS="Linux" + elif [ "$(expr substr "$(uname -s)" 1 10)" == "MINGW32_NT" ]; then + URL_OS="Windows" + fi + + echo "Downloading miniconda for $URL_OS" + DOWNLOAD_PATH="miniconda.sh" + if [ ${PYTHON_VERSION} == "2.7" ]; then + wget http://repo.continuum.io/miniconda/Miniconda-latest-$URL_OS-x86_64.sh -O ${DOWNLOAD_PATH}; + else + wget http://repo.continuum.io/miniconda/Miniconda3-latest-$URL_OS-x86_64.sh -O ${DOWNLOAD_PATH}; + fi + + echo "Installing miniconda for python-$PYTHON_VERSION to $INSTALL_FOLDER" + # install miniconda to home folder + bash ${DOWNLOAD_PATH} -b -p $INSTALL_FOLDER + + # tidy up + rm ${DOWNLOAD_PATH} +else + echo "Miniconda already installed at ${INSTALL_FOLDER}. Updating, adding to path and exiting" +fi export PATH="$INSTALL_FOLDER/bin:$PATH" echo "Adding $INSTALL_FOLDER to PATH. Consider adding it in your .rc file as well." From bc62f3e2c580be233067ab2e2a5bf2062ea3c770 Mon Sep 17 00:00:00 2001 From: domenzain Date: Tue, 7 Mar 2017 22:23:28 +0100 Subject: [PATCH 07/53] Add examples of censored data models (#1870) --- pymc3/examples/censored_data.py | 148 ++++++++++++++++++++++++++++++++ 1 file changed, 148 insertions(+) create mode 100644 pymc3/examples/censored_data.py diff --git a/pymc3/examples/censored_data.py b/pymc3/examples/censored_data.py new file mode 100644 index 0000000000..084be25c93 --- /dev/null +++ b/pymc3/examples/censored_data.py @@ -0,0 +1,148 @@ +import numpy as np +import matplotlib.pyplot as plt +import pymc3 as pm +import theano.tensor as tt +''' +Data can be left, right, or interval censored. + +In this example we take interval censoring as it is the most general. In order +to deal with data censored only on one side one can simply remove one of the +sides. + +Many modeling problems are of this nature, two common examples are: +- Survival analysis: at the end of a clinical trial to study the impact of a + new drug on lifespan, it is almost never possible to follow through with the + study until all subjects have died. At the end of the study, the only + information known for many subjects is that a subject was still alive. +- Sensor saturation: a sensor might have a limited dynamic range and the upper + and lower limits would simply be the lowest and highest values a sensor can + report. An 8-bit pixel value can only hold values from 0 to 255. + +This example presents two different ways of dealing with censored data in +PyMC3: + +- An imputed censored model, which represents censored data as parameters and + makes up plausible values for all censored values. This produces as a + byproduct a plausible set of made up values that would have been censored. + Each censored element introduces a random variable. +- An unimputed censored model, where the censored data are integrated out and + accounted for only through the log-likelihood. This method deals more + adequately with large amounts of censored data and converges more quickly. + +To establish a baseline they compare to an uncensored model of uncensored data. +''' + + +# Helper functions +def normal_lcdf(mu, sigma, x): + z = (x - mu) / sigma + return tt.switch( + tt.lt(z, -1.0), + tt.log(tt.erfcx(-z / tt.sqrt(2.)) / 2.) - tt.sqr(z) / 2, + tt.log1p(-tt.erfc(z / tt.sqrt(2.)) / 2.) + ) + + +def normal_lccdf(mu, sigma, x): + z = (x - mu) / sigma + return tt.switch( + tt.gt(z, 1.0), + tt.log(tt.erfcx(z / tt.sqrt(2.)) / 2) - tt.sqr(z) / 2., + tt.log1p(-tt.erfc(-z / tt.sqrt(2.)) / 2.) + ) + + +# Produce normally distributed samples +np.random.seed(123) +size = 500 +sigma = 5. +mu = 13. +samples = np.random.normal(mu, sigma, size) + +# Set censoring limits. +high = 16. +low = -1. + +# Truncate samples +truncated = samples[(samples > low) & (samples < high)] + +# Omniscient model +with pm.Model() as omniscient_model: + mu = pm.Normal('mu', mu=0., sd=(high - low) / 2.) + sigma = pm.HalfNormal('sigma', sd=(high - low) / 2.) + observed = pm.Normal('observed', mu=mu, sd=sigma, observed=samples) + +# Imputed censored model +# Keep tabs on left/right censoring +n_right_censored = len(samples[samples >= high]) +n_left_censored = len(samples[samples <= low]) +n_observed = len(samples) - n_right_censored - n_left_censored +with pm.Model() as imputed_censored_model: + mu = pm.Normal('mu', mu=0., sd=(high - low) / 2.) + sigma = pm.HalfNormal('sigma', sd=(high - low) / 2.) + right_censored = pm.Bound(pm.Normal, lower=high)( + 'right_censored', mu=mu, sd=sigma, shape=n_right_censored + ) + left_censored = pm.Bound(pm.Normal, upper=low)( + 'left_censored', mu=mu, sd=sigma, shape=n_left_censored + ) + observed = pm.Normal( + 'observed', + mu=mu, + sd=sigma, + observed=truncated, + shape=n_observed + ) + + +# Unimputed censored model +def censored_right_likelihood(mu, sigma, n_right_censored, upper_bound): + return n_right_censored * normal_lccdf(mu, sigma, upper_bound) + + +def censored_left_likelihood(mu, sigma, n_left_censored, lower_bound): + return n_left_censored * normal_lcdf(mu, sigma, lower_bound) + +with pm.Model() as unimputed_censored_model: + mu = pm.Normal('mu', mu=0., sd=(high - low) / 2.) + sigma = pm.HalfNormal('sigma', sd=(high - low) / 2.) + observed = pm.Normal( + 'observed', + mu=mu, + sd=sigma, + observed=truncated, + ) + right_censored = pm.Potential( + 'right_censored', + censored_right_likelihood(mu, sigma, n_right_censored, high) + ) + left_censored = pm.Potential( + 'left_censored', + censored_left_likelihood(mu, sigma, n_left_censored, low) + ) + + +def run(n=1500): + if n == 'short': + n = 50 + + print('Model with no censored data (omniscient)') + with omniscient_model: + trace = pm.sample(n) + pm.plot_posterior(trace[-1000:], varnames=['mu', 'sigma']) + plt.show() + + print('Imputed censored model') + with imputed_censored_model: + trace = pm.sample(n) + pm.plot_posterior(trace[-1000:], varnames=['mu', 'sigma']) + plt.show() + + print('Unimputed censored model') + with unimputed_censored_model: + trace = pm.sample(n) + pm.plot_posterior(trace[-1000:], varnames=['mu', 'sigma']) + plt.show() + +if __name__ == '__main__': + run() From 6261937c8e337e777d36bcc473c6dda74dc232c4 Mon Sep 17 00:00:00 2001 From: "Peter St. John" Date: Tue, 7 Mar 2017 14:27:30 -0700 Subject: [PATCH 08/53] Don't include `-np.inf` in calculating average ELBO (#1880) * Adds an infmean for advi reporting * fixing typo --- pymc3/variational/advi.py | 11 ++++++++--- pymc3/variational/advi_minibatch.py | 4 ++-- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/pymc3/variational/advi.py b/pymc3/variational/advi.py index 8690a025e7..a7dd905b73 100644 --- a/pymc3/variational/advi.py +++ b/pymc3/variational/advi.py @@ -160,7 +160,7 @@ def advi(vars=None, start=None, model=None, n=5000, accurate_elbo=False, if n < 10: progress.set_description('ELBO = {:,.5g}'.format(elbos[i])) elif i % (n // 10) == 0 and i > 0: - avg_elbo = elbos[i - n // 10:i].mean() + avg_elbo = infmean(elbos[i - n // 10:i]) progress.set_description('Average ELBO = {:,.5g}'.format(avg_elbo)) if i % eval_elbo == 0: @@ -193,14 +193,14 @@ def advi(vars=None, start=None, model=None, n=5000, accurate_elbo=False, pm._log.info('Interrupted at {:,d} [{:.0f}%]: ELBO = {:,.5g}'.format( i, 100 * i // n, elbos[i])) else: - avg_elbo = elbos[i - n // 10:i].mean() + avg_elbo = infmean(elbos[i - n // 10:i]) pm._log.info('Interrupted at {:,d} [{:.0f}%]: Average ELBO = {:,.5g}'.format( i, 100 * i // n, avg_elbo)) else: if n < 10: pm._log.info('Finished [100%]: ELBO = {:,.5g}'.format(elbos[-1])) else: - avg_elbo = elbos[-n // 10:].mean() + avg_elbo = infmean(elbos[-n // 10:]) pm._log.info('Finished [100%]: Average ELBO = {:,.5g}'.format(avg_elbo)) finally: progress.close() @@ -410,3 +410,8 @@ def rvs(x): trace.record(point) return MultiTrace([trace]) + + +def infmean(input_array): + """Return the mean of the finite values of the array""" + return np.mean(np.asarray(input_array)[np.isfinite(input_array)]) diff --git a/pymc3/variational/advi_minibatch.py b/pymc3/variational/advi_minibatch.py index f0e8d9add8..fad8cf5561 100644 --- a/pymc3/variational/advi_minibatch.py +++ b/pymc3/variational/advi_minibatch.py @@ -9,7 +9,7 @@ import pymc3 as pm from pymc3.theanof import reshape_t, inputvars, floatX -from .advi import ADVIFit, adagrad_optimizer, gen_random_state +from .advi import ADVIFit, adagrad_optimizer, gen_random_state, infmean __all__ = ['advi_minibatch'] @@ -529,7 +529,7 @@ def is_shared(t): if n < 10: progress.set_description('ELBO = {:,.2f}'.format(elbos[i])) elif i % (n // 10) == 0 and i > 0: - avg_elbo = elbos[i - n // 10:i].mean() + avg_elbo = infmean(elbos[i - n // 10:i]) progress.set_description('Average ELBO = {:,.2f}'.format(avg_elbo)) pm._log.info('Finished minibatch ADVI: ELBO = {:,.2f}'.format(elbos[-1])) From 5417f0c4d421b034e77284b5980cfb1bfb607b81 Mon Sep 17 00:00:00 2001 From: Chris Fonnesbeck Date: Wed, 8 Mar 2017 04:42:26 -0500 Subject: [PATCH 09/53] Raise TypeError on non-data values of observed (#1872) * Raise TypeError on non-data values of observed * Added check for observed TypeError --- pymc3/distributions/distribution.py | 6 ++++-- pymc3/tests/test_model.py | 6 ++++++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/pymc3/distributions/distribution.py b/pymc3/distributions/distribution.py index 423746b972..064dda54d6 100644 --- a/pymc3/distributions/distribution.py +++ b/pymc3/distributions/distribution.py @@ -3,7 +3,7 @@ from theano import function import theano from ..memoize import memoize -from ..model import Model, get_named_nodes +from ..model import Model, get_named_nodes, FreeRV, ObservedRV from ..vartypes import string_types from .dist_math import bound @@ -30,11 +30,13 @@ def __new__(cls, name, *args, **kwargs): if isinstance(name, string_types): data = kwargs.pop('observed', None) + if isinstance(data, ObservedRV) or isinstance(data, FreeRV): + raise TypeError("observed needs to be data but got: {}".format(type(data))) total_size = kwargs.pop('total_size', None) dist = cls.dist(*args, **kwargs) return model.Var(name, dist, data, total_size) else: - raise TypeError("Name needs to be a string but got: %s" % name) + raise TypeError("Name needs to be a string but got: {}".format(name)) def __getnewargs__(self): return _Unpickling, diff --git a/pymc3/tests/test_model.py b/pymc3/tests/test_model.py index 24d783d8e3..6b3e33d374 100644 --- a/pymc3/tests/test_model.py +++ b/pymc3/tests/test_model.py @@ -136,6 +136,12 @@ def test_model_root(self): with pm.Model() as sub: self.assertTrue(model is sub.root) +class TestObserved(unittest.TestCase): + def test_observed_rv_fail(self): + with self.assertRaises(TypeError): + with pm.Model() as model: + x = Normal('x') + Normal('n', observed=x) class TestScaling(unittest.TestCase): def test_density_scaling(self): From e6317ac948ad6114307158f52442b922c4c0950d Mon Sep 17 00:00:00 2001 From: AustinRochford Date: Wed, 8 Mar 2017 07:57:43 -0500 Subject: [PATCH 10/53] Make exponential mode have the correct shape --- pymc3/distributions/continuous.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pymc3/distributions/continuous.py b/pymc3/distributions/continuous.py index 36db33927c..bb011a41ad 100644 --- a/pymc3/distributions/continuous.py +++ b/pymc3/distributions/continuous.py @@ -517,7 +517,7 @@ def __init__(self, lam, *args, **kwargs): self.lam = lam = tt.as_tensor_variable(lam) self.mean = 1. / self.lam self.median = self.mean * tt.log(2) - self.mode = 0 + self.mode = tt.zeros_like(self.lam) self.variance = self.lam**-2 From 04d3fba0b059da1cd23284dfcc68c8ef27e80efc Mon Sep 17 00:00:00 2001 From: David Brochart Date: Tue, 7 Mar 2017 13:34:57 +0100 Subject: [PATCH 11/53] Added tutorial notebook on updating priors --- docs/source/notebooks/updating_priors.ipynb | 285 ++++++++++++++++++++ 1 file changed, 285 insertions(+) create mode 100644 docs/source/notebooks/updating_priors.ipynb diff --git a/docs/source/notebooks/updating_priors.ipynb b/docs/source/notebooks/updating_priors.ipynb new file mode 100644 index 0000000000..3c70f02a64 --- /dev/null +++ b/docs/source/notebooks/updating_priors.ipynb @@ -0,0 +1,285 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Updating priors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, I will show how it is possible to update the priors as new data becomes available. The example is a slightly modified version of the linear regression in the [Getting started with PyMC3](https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/getting_started.ipynb) notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "from pymc3 import Model, Normal, Slice\n", + "from pymc3 import sample\n", + "from pymc3 import traceplot\n", + "from pymc3.distributions import Continuous\n", + "from theano import as_op\n", + "import theano.tensor as tt\n", + "import numpy as np\n", + "from scipy import stats\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize random number generator\n", + "np.random.seed(123)\n", + "\n", + "# True parameter values\n", + "alpha_true, beta0_true, beta1_true = 5, 7, 13\n", + "\n", + "# Size of dataset\n", + "size = 100\n", + "\n", + "# Predictor variable\n", + "X1 = np.random.randn(size)\n", + "X2 = np.random.randn(size) * 0.2\n", + "\n", + "# Simulate outcome variable\n", + "Y = alpha_true + beta0_true * X1 + beta1_true * X2 + np.random.randn(size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model specification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our initial beliefs about the parameters are quite informative (sd=1) and a bit off the true values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "basic_model = Model()\n", + "\n", + "with basic_model:\n", + " \n", + " # Priors for unknown model parameters\n", + " alpha = Normal('alpha', mu=0, sd=1)\n", + " beta0 = Normal('beta0', mu=12, sd=1)\n", + " beta1 = Normal('beta1', mu=18, sd=1)\n", + " \n", + " # Expected value of outcome\n", + " mu = alpha + beta0 * X1 + beta1 * X2\n", + " \n", + " # Likelihood (sampling distribution) of observations\n", + " Y_obs = Normal('Y_obs', mu=mu, sd=1, observed=Y)\n", + " \n", + " # draw 1000 posterior samples\n", + " trace = sample(1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "traceplot(trace)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to update our beliefs about the parameters, we use the posterior distributions, which will be used as the prior distributions for the next inference. The data used for each inference iteration has to be independent from the previous iterations, otherwise the same (possibly wrong) belief is injected over and over in the system, misleading the inference. By ensuring the data is independent, the system should converge to the true parameter values.\n", + "\n", + "Because we draw samples from the posterior distribution (shown on the right in the figure above), we need to estimate their probability density (shown on the left in the figure above). Kernel density estimation (KDE) is a way to achieve this, and we will use this technique here. In any case, it is an empirical distribution that cannot be expressed analytically. Fortunately PyMC3 provides a way to built custom distributions. We just need to inherit the *Continuous* class and provide our own *logp* method. The code below does just that." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def from_posterior(param, samples):\n", + "\n", + " class From_posterior(Continuous):\n", + " def __init__(self, *args, **kwargs):\n", + " self.from_posterior_logp = _from_posterior_logp()\n", + " super(From_posterior, self).__init__(*args, **kwargs)\n", + " def logp(self, value):\n", + " return self.from_posterior_logp(value)\n", + "\n", + " class From_posterior_logp:\n", + " def __init__(self, samples):\n", + " smin, smax = np.min(samples), np.max(samples)\n", + " self.x = np.linspace(smin, smax, 100)\n", + " self.y = stats.gaussian_kde(samples)(self.x)\n", + " #self.y /= np.sum(self.y)\n", + " def from_posterior_logp(self, value):\n", + " return np.array(np.log(np.interp(value, self.x, self.y, left=0, right=0)))\n", + " \n", + " from_posterior_logp = From_posterior_logp(samples)\n", + "\n", + " def _from_posterior_logp():\n", + " @as_op(itypes=[tt.dscalar], otypes=[tt.dscalar])\n", + " def logp(value):\n", + " return from_posterior_logp.from_posterior_logp(value)\n", + " return logp\n", + "\n", + " return From_posterior(param, testval=np.median(samples))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we just need to generate more data and build our Bayesian model so that the prior distributions for the current iteration are the posterior distributions from the previous iteration. We save the posterior samples for each iteration so that we can plot their distribution and see it changing from one iteration to the next (first iterations are plotted in yellow, last iterations are plotted in red)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "update_i = 0\n", + "traces = [trace]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for _ in range(10):\n", + "\n", + " # generate more data\n", + " X1 = np.random.randn(size)\n", + " X2 = np.random.randn(size) * 0.2\n", + " Y = alpha_true + beta0_true * X1 + beta1_true * X2 + np.random.randn(size)\n", + "\n", + " model = Model()\n", + " with model:\n", + " burnin = int(len(trace) / 5)\n", + " # Priors for unknown model parameters\n", + " alpha = from_posterior('alpha', trace['alpha'][burnin:])\n", + " beta0 = from_posterior('beta0', trace['beta0'][burnin:])\n", + " beta1 = from_posterior('beta1', trace['beta1'][burnin:])\n", + "\n", + " # Expected value of outcome\n", + " mu = alpha + beta0 * X1 + beta1 * X2\n", + "\n", + " # Likelihood (sampling distribution) of observations\n", + " Y_obs = Normal('Y_obs', mu=mu, sd=1, observed=Y)\n", + " \n", + " step = Slice([alpha, beta0, beta1])\n", + " \n", + " # draw 1000 posterior samples\n", + " trace = sample(1000, step=step)\n", + " traces.append(trace)\n", + " \n", + " update_i += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cmap = mpl.cm.autumn\n", + "for param in ['alpha', 'beta0', 'beta1']:\n", + " plt.figure(figsize=(8, 2))\n", + " for update_i, trace in enumerate(traces):\n", + " samples = trace[param][burnin:]\n", + " smin, smax = np.min(samples), np.max(samples)\n", + " x = np.linspace(smin, smax, 100)\n", + " y = stats.gaussian_kde(samples)(x)\n", + " plt.plot(x, y, color=cmap(1 - update_i / len(traces)))\n", + " plt.axvline({'alpha': alpha_true, 'beta0': beta0_true, 'beta1': beta1_true}[param], c='k')\n", + " plt.ylabel('Frequency')\n", + " plt.title(param)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can re-execute the last two cells to generate more updates.\n", + "\n", + "What is interesting to note is that the posterior distributions for our parameters tend to get centered on their true value (vertical lines), and the distribution gets thiner and thiner. This means that we get more confident each time, and the (false) belief we had at the beginning gets flushed away by the new data we incorporate." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ebb48c99fad63650facd29ce0b8411203a5c2455 Mon Sep 17 00:00:00 2001 From: David Brochart Date: Wed, 8 Mar 2017 10:08:29 +0100 Subject: [PATCH 12/53] Made small changes and executed the notebook --- docs/source/notebooks/updating_priors.ipynb | 204 ++++++++++++++++---- 1 file changed, 168 insertions(+), 36 deletions(-) diff --git a/docs/source/notebooks/updating_priors.ipynb b/docs/source/notebooks/updating_priors.ipynb index 3c70f02a64..7d67ede9a3 100644 --- a/docs/source/notebooks/updating_priors.ipynb +++ b/docs/source/notebooks/updating_priors.ipynb @@ -2,21 +2,27 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "# Updating priors" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "In this notebook, I will show how it is possible to update the priors as new data becomes available. The example is a slightly modified version of the linear regression in the [Getting started with PyMC3](https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/getting_started.ipynb) notebook." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, @@ -40,16 +46,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Generating data" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -57,7 +68,9 @@ "np.random.seed(123)\n", "\n", "# True parameter values\n", - "alpha_true, beta0_true, beta1_true = 5, 7, 13\n", + "alpha_true = 5\n", + "beta0_true = 7\n", + "beta1_true = 13\n", "\n", "# Size of dataset\n", "size = 100\n", @@ -72,27 +85,45 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Model specification" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Our initial beliefs about the parameters are quite informative (sd=1) and a bit off the true values." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using advi...\n", + "Average ELBO = -178.23: 100%|██████████| 200000/200000 [00:11<00:00, 17384.94it/s]\n", + "Finished [100%]: Average ELBO = -178.25\n", + "100%|██████████| 1000/1000 [00:01<00:00, 901.60it/s]\n" + ] + } + ], "source": [ "basic_model = Model()\n", "\n", @@ -115,29 +146,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ],\n", + " [,\n", + " ],\n", + " [,\n", + " ]], dtype=object)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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xmFdesU7MtWvtb9orr/RcbGBTj51InYd6Vmi6Xf3FGLNYRO7DClS7A+/EFtBc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\nPji1zz5w0EH+fwVfpZ2aB5xnZvvSul7nnsClZnZ2MKNz7rJCFx6TaX5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HMcoRmMqXMdW3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Bd0PPA8a4+2clKF+1KWR/vgZ0N7MjAcysC3Aq8K8Sl7VaFLJP2wCrU+atBvY2s5YlKWWVMrNW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RrXMyr1PvXWCTga/wHzq/hDCeYoJ0nzMQv4U402yktrc7IUmF817zfAQuUkWU46Oofqdg7w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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "traceplot(trace)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "In order to update our beliefs about the parameters, we use the posterior distributions, which will be used as the prior distributions for the next inference. The data used for each inference iteration has to be independent from the previous iterations, otherwise the same (possibly wrong) belief is injected over and over in the system, misleading the inference. By ensuring the data is independent, the system should converge to the true parameter values.\n", + "In order to update our beliefs about the parameters, we use the posterior distributions, which will be used as the prior distributions for the next inference. The data used for each inference iteration has to be independent from the previous iterations, otherwise the same (possibly wrong) belief is injected over and over in the system, amplifying the errors and misleading the inference. By ensuring the data is independent, the system should converge to the true parameter values.\n", "\n", "Because we draw samples from the posterior distribution (shown on the right in the figure above), we need to estimate their probability density (shown on the left in the figure above). Kernel density estimation (KDE) is a way to achieve this, and we will use this technique here. In any case, it is an empirical distribution that cannot be expressed analytically. Fortunately PyMC3 provides a way to built custom distributions. We just need to inherit the *Continuous* class and provide our own *logp* method. The code below does just that." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -155,9 +219,10 @@ " smin, smax = np.min(samples), np.max(samples)\n", " self.x = np.linspace(smin, smax, 100)\n", " self.y = stats.gaussian_kde(samples)(self.x)\n", - " #self.y /= np.sum(self.y)\n", " def from_posterior_logp(self, value):\n", - " return np.array(np.log(np.interp(value, self.x, self.y, left=0, right=0)))\n", + " x, y = self.x, self.y\n", + " y0 = np.min(y) / 10 # what was never sampled should have a small probability but not 0\n", + " return np.array(np.log(np.interp(value, x, y, left=y0, right=y0)))\n", " \n", " from_posterior_logp = From_posterior_logp(samples)\n", "\n", @@ -172,30 +237,54 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "Now we just need to generate more data and build our Bayesian model so that the prior distributions for the current iteration are the posterior distributions from the previous iteration. We save the posterior samples for each iteration so that we can plot their distribution and see it changing from one iteration to the next (first iterations are plotted in yellow, last iterations are plotted in red)." + "Now we just need to generate more data and build our Bayesian model so that the prior distributions for the current iteration are the posterior distributions from the previous iteration. The NUTS sampling method cannot be used anymore since it requires to provide the gradient of the distribution, so we will use Slice sampling. We save the posterior samples for each iteration so that we can plot their distribution and see it changing from one iteration to the next (first iterations are plotted in yellow, last iterations are plotted in red)." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ - "update_i = 0\n", "traces = [trace]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true, + "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1000/1000 [00:03<00:00, 331.87it/s]\n", + "100%|██████████| 1000/1000 [00:04<00:00, 236.99it/s]\n", + "100%|██████████| 1000/1000 [00:03<00:00, 307.75it/s]\n", + "100%|██████████| 1000/1000 [00:03<00:00, 315.12it/s]\n", + "100%|██████████| 1000/1000 [00:03<00:00, 320.33it/s]\n", + "100%|██████████| 1000/1000 [00:03<00:00, 266.63it/s]\n", + "100%|██████████| 1000/1000 [00:03<00:00, 328.09it/s]\n", + "100%|██████████| 1000/1000 [00:02<00:00, 334.17it/s]\n", + "100%|██████████| 1000/1000 [00:03<00:00, 319.34it/s]\n", + "100%|██████████| 1000/1000 [00:03<00:00, 326.96it/s]\n" + ] + } + ], "source": [ "for _ in range(10):\n", "\n", @@ -207,7 +296,8 @@ " model = Model()\n", " with model:\n", " burnin = int(len(trace) / 5)\n", - " # Priors for unknown model parameters\n", + "\n", + " # Priors are posteriors from previous iteration\n", " alpha = from_posterior('alpha', trace['alpha'][burnin:])\n", " beta0 = from_posterior('beta0', trace['beta0'][burnin:])\n", " beta1 = from_posterior('beta1', trace['beta1'][burnin:])\n", @@ -222,19 +312,58 @@ " \n", " # draw 1000 posterior samples\n", " trace = sample(1000, step=step)\n", - " traces.append(trace)\n", - " \n", - " update_i += 1" + " traces.append(trace)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Posterior distributions after 51 iterations.\n" + ] + }, + { + "data": { + "image/png": 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ocNsE7PEmkCVrYUEzsHv7nDf5zVHmg10JKABmPQ4zJkGoUmtuM/MgEEZrEoC6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\nx76KS75Fe3eYdJUQjo21+NYWISzf87l+GOJAXCI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CSul1YiTCO0RH1X/g6pTSrkVRPE29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\n+hkUMkG/7Cvf5zaG/to40z/6e43YZPe59CS9+JlMJpPJZDKZzIBja/H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o0okZYmJ1lEJHH2wohLQiKMuCU36nJcDGXKH3WPaMtidtDMTU6fsiILRSr52Q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Z4k8/1n0jJ0C0wLKlrXY7jTHGtJxwg9wX0WoGW0WkWETyG2/NOD5jTHtRsBY+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "print('Posterior distributions after ' + str(len(traces)) + ' iterations.')\n", "cmap = mpl.cm.autumn\n", "for param in ['alpha', 'beta0', 'beta1']:\n", " plt.figure(figsize=(8, 2))\n", @@ -252,7 +381,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "You can re-execute the last two cells to generate more updates.\n", "\n", From a42e25cab45eababd1ab2fe5f018cd4138bff30a Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Tue, 7 Mar 2017 16:54:59 -0600 Subject: [PATCH 13/53] Fixed y-axis bug in forestplot; added transform argument to summary --- pymc3/plots/forestplot.py | 2 +- pymc3/stats.py | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/pymc3/plots/forestplot.py b/pymc3/plots/forestplot.py index 0ab79e5553..ca2ba2ebdd 100644 --- a/pymc3/plots/forestplot.py +++ b/pymc3/plots/forestplot.py @@ -257,7 +257,7 @@ def forestplot(trace_obj, varnames=None, transform=identity_transform, alpha=0.0 gs.update(left=left_margin, right=0.95, top=0.9, bottom=0.05) # Define range of y-axis - interval_plot.set_ylim(-var + 0.5, -0.5) + interval_plot.set_ylim(-var-0.5, -0.5) datarange = plotrange[1] - plotrange[0] interval_plot.set_xlim(plotrange[0] - 0.05 * datarange, plotrange[1] + 0.05 * datarange) diff --git a/pymc3/stats.py b/pymc3/stats.py index 18451b095e..89a54704c7 100644 --- a/pymc3/stats.py +++ b/pymc3/stats.py @@ -632,8 +632,8 @@ def _hpd_df(x, alpha): return pd.DataFrame(hpd(x, alpha), columns=cnames) -def summary(trace, varnames=None, alpha=0.05, start=0, batches=None, roundto=3, - include_transformed=False, to_file=None): +def summary(trace, varnames=None, transform=lambda x: x, alpha=0.05, start=0, + batches=None, roundto=3, include_transformed=False, to_file=None): R""" Generate a pretty-printed summary of the node. @@ -644,6 +644,8 @@ def summary(trace, varnames=None, alpha=0.05, start=0, batches=None, roundto=3, varnames : list of strings List of variables to summarize. Defaults to None, which results in all variables summarized. + transform : callable + Function to transform data (defaults to identity) alpha : float The alpha level for generating posterior intervals. Defaults to 0.05. @@ -682,7 +684,7 @@ def summary(trace, varnames=None, alpha=0.05, start=0, batches=None, roundto=3, for var in varnames: # Extract sampled values - sample = trace.get_values(var, burn=start, combine=True) + sample = transform(trace.get_values(var, burn=start, combine=True)) fh.write('\n%s:\n\n' % var) From e125a7122befe7dc34785fa23d7f2dd26fd68914 Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Wed, 8 Mar 2017 09:39:28 -0600 Subject: [PATCH 14/53] Style cleanup --- pymc3/plots/forestplot.py | 2 +- pymc3/stats.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pymc3/plots/forestplot.py b/pymc3/plots/forestplot.py index ca2ba2ebdd..3a6ddc5af8 100644 --- a/pymc3/plots/forestplot.py +++ b/pymc3/plots/forestplot.py @@ -257,7 +257,7 @@ def forestplot(trace_obj, varnames=None, transform=identity_transform, alpha=0.0 gs.update(left=left_margin, right=0.95, top=0.9, bottom=0.05) # Define range of y-axis - interval_plot.set_ylim(-var-0.5, -0.5) + interval_plot.set_ylim(-var - 0.5, -0.5) datarange = plotrange[1] - plotrange[0] interval_plot.set_xlim(plotrange[0] - 0.05 * datarange, plotrange[1] + 0.05 * datarange) diff --git a/pymc3/stats.py b/pymc3/stats.py index 89a54704c7..0d21688a4b 100644 --- a/pymc3/stats.py +++ b/pymc3/stats.py @@ -633,7 +633,7 @@ def _hpd_df(x, alpha): def summary(trace, varnames=None, transform=lambda x: x, alpha=0.05, start=0, - batches=None, roundto=3, include_transformed=False, to_file=None): + batches=None, roundto=3, include_transformed=False, to_file=None): R""" Generate a pretty-printed summary of the node. From 28022c3dca08fc581071e8a5560f5387631aa6db Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Wed, 8 Mar 2017 14:35:51 -0600 Subject: [PATCH 15/53] Added probit and invprobit functions --- pymc3/math.py | 10 ++++++++-- pymc3/tests/test_math.py | 6 +++++- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/pymc3/math.py b/pymc3/math.py index af0c932804..f83fc49e40 100644 --- a/pymc3/math.py +++ b/pymc3/math.py @@ -6,8 +6,8 @@ from theano.tensor import ( constant, flatten, zeros_like, ones_like, stack, concatenate, sum, prod, lt, gt, le, ge, eq, neq, switch, clip, where, and_, or_, abs_, exp, log, - cos, sin, tan, cosh, sinh, tanh, sqr, sqrt, erf, erfinv, dot, maximum, - minimum, sgn, ceil, floor) + cos, sin, tan, cosh, sinh, tanh, sqr, sqrt, erf, erfc, erfinv, erfcinv, dot, + maximum, minimum, sgn, ceil, floor) from theano.tensor.nlinalg import det, matrix_inverse, extract_diag, matrix_dot, trace from theano.tensor.nnet import sigmoid from theano.gof import Op, Apply @@ -70,3 +70,9 @@ def __str__(self): return "LogDet" logdet = LogDet() + +def probit(p): + return -sqrt(2) * erfcinv(2 * p) + +def invprobit(x): + return 0.5 * erfc(-x / sqrt(2)) \ No newline at end of file diff --git a/pymc3/tests/test_math.py b/pymc3/tests/test_math.py index 49dd235d66..5fe14465b9 100644 --- a/pymc3/tests/test_math.py +++ b/pymc3/tests/test_math.py @@ -1,9 +1,13 @@ import numpy as np import theano from theano.tests import unittest_tools as utt -from pymc3.math import LogDet, logdet +from pymc3.math import LogDet, logdet, probit, invprobit from .helpers import SeededTest +def test_probit(): + p = np.array([0.01, 0.25, 0.5, 0.75, 0.99]) + assert np.all(invprobit(probit(p)).eval()==p) + class TestLogDet(SeededTest): def setUp(self): From c5fb96e2b8715479ba61d15de80f46bf7df5d0e2 Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Wed, 8 Mar 2017 14:37:09 -0600 Subject: [PATCH 16/53] Added carriage return to end of file --- pymc3/math.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pymc3/math.py b/pymc3/math.py index f83fc49e40..fe9acd61c0 100644 --- a/pymc3/math.py +++ b/pymc3/math.py @@ -75,4 +75,5 @@ def probit(p): return -sqrt(2) * erfcinv(2 * p) def invprobit(x): - return 0.5 * erfc(-x / sqrt(2)) \ No newline at end of file + return 0.5 * erfc(-x / sqrt(2)) + \ No newline at end of file From 695b49b9d76df6e7c0a56a5ba71fa62b974f450f Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Wed, 8 Mar 2017 16:27:55 -0600 Subject: [PATCH 17/53] Fixed indentation --- pymc3/math.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pymc3/math.py b/pymc3/math.py index fe9acd61c0..bf58294814 100644 --- a/pymc3/math.py +++ b/pymc3/math.py @@ -76,4 +76,3 @@ def probit(p): def invprobit(x): return 0.5 * erfc(-x / sqrt(2)) - \ No newline at end of file From caa531844b69ae94fb69f4604b15caeee6e86806 Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Wed, 8 Mar 2017 16:30:48 -0600 Subject: [PATCH 18/53] Changed probit test to use assert_allclose --- pymc3/tests/test_math.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pymc3/tests/test_math.py b/pymc3/tests/test_math.py index 5fe14465b9..d160df5f8b 100644 --- a/pymc3/tests/test_math.py +++ b/pymc3/tests/test_math.py @@ -6,7 +6,7 @@ def test_probit(): p = np.array([0.01, 0.25, 0.5, 0.75, 0.99]) - assert np.all(invprobit(probit(p)).eval()==p) + np.testing.assert_allclose(invprobit(probit(p)).eval(), p, atol=1e-5) class TestLogDet(SeededTest): From c12288582262469ed3303e7bc26b7b36ea2e7afd Mon Sep 17 00:00:00 2001 From: Adrian Seyboldt Date: Sun, 5 Mar 2017 12:22:19 +0100 Subject: [PATCH 19/53] Fix support of LKJCorr --- pymc3/distributions/multivariate.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/pymc3/distributions/multivariate.py b/pymc3/distributions/multivariate.py index a15c6f3fb9..0d880d5821 100755 --- a/pymc3/distributions/multivariate.py +++ b/pymc3/distributions/multivariate.py @@ -20,6 +20,7 @@ from .continuous import ChiSquared, Normal from .special import gammaln, multigammaln from .dist_math import bound, logpow, factln +from . import transforms __all__ = ['MvNormal', 'MvStudentT', 'Dirichlet', 'Multinomial', 'Wishart', 'WishartBartlett', 'LKJCorr'] @@ -583,12 +584,17 @@ class LKJCorr(Continuous): 100(9), pp.1989-2001. """ - def __init__(self, n, p, *args, **kwargs): + def __init__(self, n, p, transform='interval', *args, **kwargs): self.n = n self.p = p n_elem = int(p * (p - 1) / 2) self.mean = np.zeros(n_elem, dtype=theano.config.floatX) - super(LKJCorr, self).__init__(shape=n_elem, *args, **kwargs) + + if transform == 'interval': + transform = transforms.interval(-1, 1) + + super(LKJCorr, self).__init__(shape=n_elem, transform=transform, + *args, **kwargs) self.tri_index = np.zeros([p, p], dtype='int32') self.tri_index[np.triu_indices(p, k=1)] = np.arange(n_elem) From e3cf77ba2a76a3281def622732087a9ead8488bc Mon Sep 17 00:00:00 2001 From: Adrian Seyboldt Date: Thu, 9 Mar 2017 11:57:47 +0100 Subject: [PATCH 20/53] Fix tests for LKJCorr --- pymc3/distributions/multivariate.py | 1 - pymc3/tests/test_distributions.py | 5 +++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pymc3/distributions/multivariate.py b/pymc3/distributions/multivariate.py index 0d880d5821..03c1350c10 100755 --- a/pymc3/distributions/multivariate.py +++ b/pymc3/distributions/multivariate.py @@ -20,7 +20,6 @@ from .continuous import ChiSquared, Normal from .special import gammaln, multigammaln from .dist_math import bound, logpow, factln -from . import transforms __all__ = ['MvNormal', 'MvStudentT', 'Dirichlet', 'Multinomial', 'Wishart', 'WishartBartlett', 'LKJCorr'] diff --git a/pymc3/tests/test_distributions.py b/pymc3/tests/test_distributions.py index eec59ff080..8705e4e0d1 100644 --- a/pymc3/tests/test_distributions.py +++ b/pymc3/tests/test_distributions.py @@ -530,7 +530,7 @@ def test_zeroinflatednegativebinomial(self): def test_mvnormal(self, n): self.pymc3_matches_scipy(MvNormal, Vector(R, n), {'mu': Vector(R, n), 'tau': PdMatrix(n)}, normal_logpdf) - + def test_mvnormal_init_fail(self): with Model(): with self.assertRaises(ValueError): @@ -556,7 +556,8 @@ def test_wishart(self, n): @parameterized.expand(gen_lkj_cases) def test_lkj(self, x, n, p, lp): with Model() as model: - LKJCorr('lkj', n=n, p=p) + LKJCorr('lkj', n=n, p=p, transform=None) + pt = {'lkj': x} assert_almost_equal(model.fastlogp(pt), lp, decimal=6, err_msg=str(pt)) From a3825d772ab4fd49e1802223bc79ccf38ba0bbc2 Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Fri, 10 Mar 2017 07:45:17 -0600 Subject: [PATCH 21/53] Added warning for ignoring init arguments in sample --- pymc3/sampling.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/pymc3/sampling.py b/pymc3/sampling.py index ac56d9385b..6633df2bdd 100644 --- a/pymc3/sampling.py +++ b/pymc3/sampling.py @@ -13,6 +13,8 @@ Slice, CompoundStep) from tqdm import tqdm +import warnings + import sys sys.setrecursionlimit(10000) @@ -81,7 +83,7 @@ def assign_step_methods(model, step=None, methods=(NUTS, HamiltonianMC, Metropol return steps -def sample(draws, step=None, init='advi', n_init=200000, start=None, +def sample(draws, step=None, init='ADVI', n_init=200000, start=None, trace=None, chain=0, njobs=1, tune=None, progressbar=True, model=None, random_seed=-1): """ @@ -157,6 +159,8 @@ def sample(draws, step=None, init='advi', n_init=200000, start=None, if start is None: start = start_ else: + if step is not None and init is not None: + warnings.warn('Instantiated step methods cannot be automatically initialized. init argument ignored.') step = assign_step_methods(model, step) if njobs is None: @@ -452,7 +456,7 @@ def init_nuts(init='ADVI', njobs=1, n_init=500000, model=None, """ model = pm.modelcontext(model) - + pm._log.info('Initializing NUTS using {}...'.format(init)) random_seed = int(np.atleast_1d(random_seed)[0]) From 6fcee1516c9ef67547f53e06275baf4e24435011 Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Fri, 10 Mar 2017 09:42:51 -0600 Subject: [PATCH 22/53] Kill stray tab --- pymc3/sampling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pymc3/sampling.py b/pymc3/sampling.py index 6633df2bdd..cc85ab8797 100644 --- a/pymc3/sampling.py +++ b/pymc3/sampling.py @@ -456,7 +456,7 @@ def init_nuts(init='ADVI', njobs=1, n_init=500000, model=None, """ model = pm.modelcontext(model) - + pm._log.info('Initializing NUTS using {}...'.format(init)) random_seed = int(np.atleast_1d(random_seed)[0]) From 030205ebdd6abba24d3d1262772ee56b747e7c75 Mon Sep 17 00:00:00 2001 From: Adrian Seyboldt Date: Sat, 11 Mar 2017 16:46:57 +0100 Subject: [PATCH 23/53] Improve performance of transformations --- pymc3/distributions/transforms.py | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/pymc3/distributions/transforms.py b/pymc3/distributions/transforms.py index 1f05984a47..6ed4ae8e11 100644 --- a/pymc3/distributions/transforms.py +++ b/pymc3/distributions/transforms.py @@ -86,6 +86,9 @@ def backward(self, x): def forward(self, x): return tt.log(x) + def jacobian_det(self, x): + return x + log = Log() @@ -109,20 +112,22 @@ class Interval(ElemwiseTransform): name = "interval" - def __init__(self, a, b, eps=1e-6): + def __init__(self, a, b): self.a = a self.b = b - self.eps = eps def backward(self, x): a, b = self.a, self.b - r = (b - a) / (1 + tt.exp(-x)) + a + r = (b - a) * tt.nnet.sigmoid(x) + a return r def forward(self, x): - a, b, e = self.a, self.b, self.eps - r = tt.log(tt.maximum((x - a) / tt.maximum(b - x, e), e)) - return r + a, b = self.a, self.b + return tt.log(x - a) - tt.log(b - x) + + def jacobian_det(self, x): + s = tt.nnet.softplus(-x) + return tt.log(self.b - self.a) - 2 * s - x interval = Interval @@ -145,6 +150,9 @@ def forward(self, x): r = tt.log(x - a) return r + def jacobian_det(self, x): + return x + lowerbound = LowerBound @@ -166,6 +174,9 @@ def forward(self, x): r = tt.log(b - x) return r + def jacobian_det(self, x): + return x + upperbound = UpperBound From de6c5a0d5837dfb08393cb6bda1dbb9a343971ab Mon Sep 17 00:00:00 2001 From: Thomas Wiecki Date: Mon, 13 Mar 2017 22:54:52 +0100 Subject: [PATCH 24/53] DOC Add new features --- RELEASE-NOTES.md | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md index 06544112c7..7f76255c68 100644 --- a/RELEASE-NOTES.md +++ b/RELEASE-NOTES.md @@ -4,9 +4,23 @@ ### New features -* Experimental GPU support. +* Theano's floatX setting is no respected, enabling GPU support. -* Gaussian Process support. +* [Add Gaussian Process submodule](http://pymc-devs.github.io/pymc3/notebooks/GP-introduction.html) + +* Many optimizations and speed-ups. + +* NUTS implementation now matches current Stan implementation. + +* Add higher-order integrators for HMC. + +* [Add sampler statistics.](http://pymc-devs.github.io/pymc3/notebooks/sampler-stats.html) + +* ADVI stopping criterion implemented. + +* [Add Elliptical Slice Sampler.](http://pymc-devs.github.io/pymc3/notebooks/GP-slice-sampling.html) + +* Add Stein-Variational Gradient Descent (experimental). * `Model` can now be inherited from and act as a base class for user specified models (see pymc3.models.linear). From a47f27c986fdb704db2d44aa49db3e7a4674a580 Mon Sep 17 00:00:00 2001 From: Thomas Wiecki Date: Mon, 13 Mar 2017 23:33:25 +0100 Subject: [PATCH 25/53] Bump version. --- pymc3/__init__.py | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pymc3/__init__.py b/pymc3/__init__.py index 5f8cc7fe19..df5a3504e1 100644 --- a/pymc3/__init__.py +++ b/pymc3/__init__.py @@ -1,5 +1,5 @@ # pylint: disable=wildcard-import -__version__ = "3.0" +__version__ = "3.1.rc1" from .blocking import * from .distributions import * diff --git a/setup.py b/setup.py index 907c23ae86..1fbc1bcbbe 100755 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ AUTHOR_EMAIL = 'chris.fonnesbeck@vanderbilt.edu' URL = "http://github.com/pymc-devs/pymc3" LICENSE = "Apache License, Version 2.0" -VERSION = "3.0" +VERSION = "3.1.rc1" classifiers = ['Development Status :: 5 - Production/Stable', 'Programming Language :: Python', From 416e6f2610a07c30bc9f3bf9a99e1eaea7db13b7 Mon Sep 17 00:00:00 2001 From: Maxim Kochurov Date: Wed, 15 Mar 2017 12:04:13 +0300 Subject: [PATCH 26/53] WIP: Implement opvi (#1694) * migrate useful functions from previous PR (cherry picked from commit 9f61ab4) * opvi draft (cherry picked from commit d0997ff) * made some test work (cherry picked from commit b1a87d5) * refactored approximation to support aevb (without test) * refactor opvi delete unnecessary methods from operator, change method order * change log_q_local computation * add full rank approximation * add more_params argument to ObjectiveFunction.updates (aevb case) * refactor density computation in full rank approximation * typo: cast dict values to list * typo: cast dict values to list * typo: undefined T in dist_math * refactor gradient scaling as suggested in approximateinference.org/accepted/RoederEtAl2016.pdf * implement Langevin-Stein (LS) operator * fix docstring * add blank line in docs * refactor ObjectiveFunction * add not working LS Op test * experiments with not working LS Op * change activations * refactor networks * add step_function * remove Langevin Stein, done refactoring * remove Langevin Stein, done refactoring * change optimizers * refactor init params * implement tests * implement Inference * code style * test fix * add minibatch test (fails now) * add more tests for minibatch training * add logdet to FullRank approximation * add conversion of arrays to floatX * tiny changes * change number of iterations * fix test and pylint check * memoize functions in Objective function * Optimize code a lot * a bit more efficient pickling * add docs * Add MeanField -> FullRank parameter transfer * refactor MeanField and FullRank a bit * fix FullRank bug with shapes in random * refactor Model.flatten (CC @taku-y) * add `approximate` to inference * rename approximate->fit * change abbreviations * Fix bug with scaling input variable in aevb * fix theano bottleneck in graph * more efficient scaling for local vars * fix typo in local Q * add aevb test * refactor memoize to work with my objects * add tests for numpy view usage * pickle-hash fix * pickle-hash fix again * add node sampling + make up some code * add notebook with example * sample_proba explained --- .../bayesian_neural_network_opvi-advi.ipynb | 865 ++++++++++++++++++ pymc3/distributions/dist_math.py | 117 +++ pymc3/math.py | 6 +- pymc3/memoize.py | 17 +- pymc3/model.py | 40 +- pymc3/tests/helpers.py | 9 +- pymc3/tests/test_examples.py | 1 + pymc3/tests/test_math.py | 4 +- pymc3/tests/test_variational_inference.py | 244 +++++ pymc3/theanof.py | 87 +- pymc3/variational/__init__.py | 31 +- pymc3/variational/inference.py | 544 +++++++++++ pymc3/variational/opvi.py | 808 ++++++++++++++++ 13 files changed, 2745 insertions(+), 28 deletions(-) create mode 100644 docs/source/notebooks/bayesian_neural_network_opvi-advi.ipynb create mode 100644 pymc3/tests/test_variational_inference.py create mode 100644 pymc3/variational/inference.py create mode 100644 pymc3/variational/opvi.py diff --git a/docs/source/notebooks/bayesian_neural_network_opvi-advi.ipynb b/docs/source/notebooks/bayesian_neural_network_opvi-advi.ipynb new file mode 100644 index 0000000000..66c75cf91d --- /dev/null +++ b/docs/source/notebooks/bayesian_neural_network_opvi-advi.ipynb @@ -0,0 +1,865 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# Variational Inference: Bayesian Neural Networks\n", + "\n", + "(c) 2016 by Thomas Wiecki & Maxim Kochurov (opvi)\n", + "\n", + "Original blog post: http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Current trends in Machine Learning\n", + "\n", + "There are currently three big trends in machine learning: **Probabilistic Programming**, **Deep Learning** and \"**Big Data**\". Inside of PP, a lot of innovation is in making things scale using **Variational Inference**. In this blog post, I will show how to use **Variational Inference** in [PyMC3](http://pymc-devs.github.io/pymc3/) to fit a simple Bayesian Neural Network. I will also discuss how bridging Probabilistic Programming and Deep Learning can open up very interesting avenues to explore in future research.\n", + "\n", + "### Probabilistic Programming at scale\n", + "**Probabilistic Programming** allows very flexible creation of custom probabilistic models and is mainly concerned with **insight** and learning from your data. The approach is inherently **Bayesian** so we can specify **priors** to inform and constrain our models and get uncertainty estimation in form of a **posterior** distribution. Using [MCMC sampling algorithms](http://twiecki.github.io/blog/2015/11/10/mcmc-sampling/) we can draw samples from this posterior to very flexibly estimate these models. [PyMC3](http://pymc-devs.github.io/pymc3/) and [Stan](http://mc-stan.org/) are the current state-of-the-art tools to consruct and estimate these models. One major drawback of sampling, however, is that it's often very slow, especially for high-dimensional models. That's why more recently, **variational inference** algorithms have been developed that are almost as flexible as MCMC but much faster. Instead of drawing samples from the posterior, these algorithms instead fit a distribution (e.g. normal) to the posterior turning a sampling problem into and optimization problem. [ADVI](http://arxiv.org/abs/1506.03431) -- Automatic Differentation Variational Inference -- is implemented in [PyMC3](http://pymc-devs.github.io/pymc3/) and [Stan](http://mc-stan.org/), as well as a new package called [Edward](https://github.com/blei-lab/edward/) which is mainly concerned with Variational Inference. \n", + "\n", + "Unfortunately, when it comes to traditional ML problems like classification or (non-linear) regression, Probabilistic Programming often plays second fiddle (in terms of accuracy and scalability) to more algorithmic approaches like [ensemble learning](https://en.wikipedia.org/wiki/Ensemble_learning) (e.g. [random forests](https://en.wikipedia.org/wiki/Random_forest) or [gradient boosted regression trees](https://en.wikipedia.org/wiki/Boosting_(machine_learning)).\n", + "\n", + "### Deep Learning\n", + "\n", + "Now in its third renaissance, deep learning has been making headlines repeatadly by dominating almost any object recognition benchmark, [kicking ass at Atari games](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), and [beating the world-champion Lee Sedol at Go](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html). From a statistical point, Neural Networks are extremely good non-linear function approximators and representation learners. While mostly known for classification, they have been extended to unsupervised learning with [AutoEncoders](https://arxiv.org/abs/1312.6114) and in all sorts of other interesting ways (e.g. [Recurrent Networks](https://en.wikipedia.org/wiki/Recurrent_neural_network), or [MDNs](http://cbonnett.github.io/MDN_EDWARD_KERAS_TF.html) to estimate multimodal distributions). Why do they work so well? No one really knows as the statistical properties are still not fully understood.\n", + "\n", + "A large part of the innoviation in deep learning is the ability to train these extremely complex models. This rests on several pillars:\n", + "* Speed: facilitating the GPU allowed for much faster processing.\n", + "* Software: frameworks like [Theano](http://deeplearning.net/software/theano/) and [TensorFlow](https://www.tensorflow.org/) allow flexible creation of abstract models that can then be optimized and compiled to CPU or GPU.\n", + "* Learning algorithms: training on sub-sets of the data -- stochastic gradient descent -- allows us to train these models on massive amounts of data. Techniques like drop-out avoid overfitting.\n", + "* Architectural: A lot of innovation comes from changing the input layers, like for convolutional neural nets, or the output layers, like for [MDNs](http://cbonnett.github.io/MDN_EDWARD_KERAS_TF.html).\n", + "\n", + "### Bridging Deep Learning and Probabilistic Programming\n", + "On one hand we Probabilistic Programming which allows us to build rather small and focused models in a very principled and well-understood way to gain insight into our data; on the other hand we have deep learning which uses many heuristics to train huge and highly complex models that are amazing at prediction. Recent innovations in variational inference allow probabilistic programming to scale model complexity as well as data size. We are thus at the cusp of being able to combine these two approaches to hopefully unlock new innovations in Machine Learning. For more motivation, see also [Dustin Tran's](https://twitter.com/dustinvtran) recent [blog post](http://dustintran.com/blog/a-quick-update-edward-and-some-motivations/).\n", + "\n", + "While this would allow Probabilistic Programming to be applied to a much wider set of interesting problems, I believe this bridging also holds great promise for innovations in Deep Learning. Some ideas are:\n", + "* **Uncertainty in predictions**: As we will see below, the Bayesian Neural Network informs us about the uncertainty in its predictions. I think uncertainty is an underappreciated concept in Machine Learning as it's clearly important for real-world applications. But it could also be useful in training. For example, we could train the model specifically on samples it is most uncertain about.\n", + "* **Uncertainty in representations**: We also get uncertainty estimates of our weights which could inform us about the stability of the learned representations of the network.\n", + "* **Regularization with priors**: Weights are often L2-regularized to avoid overfitting, this very naturally becomes a Gaussian prior for the weight coefficients. We could, however, imagine all kinds of other priors, like spike-and-slab to enforce sparsity (this would be more like using the L1-norm).\n", + "* **Transfer learning with informed priors**: If we wanted to train a network on a new object recognition data set, we could bootstrap the learning by placing informed priors centered around weights retrieved from other pre-trained networks, like [GoogLeNet](https://arxiv.org/abs/1409.4842). \n", + "* **Hierarchical Neural Networks**: A very powerful approach in Probabilistic Programming is hierarchical modeling that allows pooling of things that were learned on sub-groups to the overall population (see my tutorial on [Hierarchical Linear Regression in PyMC3](http://twiecki.github.io/blog/2014/03/17/bayesian-glms-3/)). Applied to Neural Networks, in hierarchical data sets, we could train individual neural nets to specialize on sub-groups while still being informed about representations of the overall population. For example, imagine a network trained to classify car models from pictures of cars. We could train a hierarchical neural network where a sub-neural network is trained to tell apart models from only a single manufacturer. The intuition being that all cars from a certain manufactures share certain similarities so it would make sense to train individual networks that specialize on brands. However, due to the individual networks being connected at a higher layer, they would still share information with the other specialized sub-networks about features that are useful to all brands. Interestingly, different layers of the network could be informed by various levels of the hierarchy -- e.g. early layers that extract visual lines could be identical in all sub-networks while the higher-order representations would be different. The hierarchical model would learn all that from the data.\n", + "* **Other hybrid architectures**: We can more freely build all kinds of neural networks. For example, Bayesian non-parametrics could be used to flexibly adjust the size and shape of the hidden layers to optimally scale the network architecture to the problem at hand during training. Currently, this requires costly hyper-parameter optimization and a lot of tribal knowledge." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Bayesian Neural Networks in PyMC3" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Generating data\n", + "\n", + "First, lets generate some toy data -- a simple binary classification problem that's not linearly separable." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import theano\n", + "floatX = theano.config.floatX\n", + "import pymc3 as pm\n", + "import theano.tensor as T\n", + "import sklearn\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from warnings import filterwarnings\n", + "filterwarnings('ignore')\n", + "sns.set_style('white')\n", + "from sklearn import datasets\n", + "from sklearn.preprocessing import scale\n", + "from sklearn.cross_validation import train_test_split\n", + "from sklearn.datasets import make_moons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "X, Y = make_moons(noise=0.2, random_state=0, n_samples=1000)\n", + "X = scale(X)\n", + "X = X.astype(floatX)\n", + "Y = Y.astype(floatX)\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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3ID7fcwZuhYA1MWJhLbgVaCTHWTBkQDJMEXp8IWmVSoJ2zZQ0U+l2g0EPvU7n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\nrmwefMXhw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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.scatter(X[Y==0, 0], X[Y==0, 1], label='Class 0')\n", + "ax.scatter(X[Y==1, 0], X[Y==1, 1], color='r', label='Class 1')\n", + "sns.despine(); ax.legend()\n", + "ax.set(xlabel='X', ylabel='Y', title='Toy binary classification data set');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Model specification\n", + "\n", + "A neural network is quite simple. The basic unit is a [perceptron](https://en.wikipedia.org/wiki/Perceptron) which is nothing more than [logistic regression](http://pymc-devs.github.io/pymc3/notebooks/posterior_predictive.html#Prediction). We use many of these in parallel and then stack them up to get hidden layers. Here we will use 2 hidden layers with 5 neurons each which is sufficient for such a simple problem." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def construct_nn(ann_input, ann_output):\n", + " n_hidden = 5\n", + " \n", + " # Initialize random weights between each layer\n", + " init_1 = np.random.randn(X.shape[1], n_hidden).astype(floatX)\n", + " init_2 = np.random.randn(n_hidden, n_hidden).astype(floatX)\n", + " init_out = np.random.randn(n_hidden).astype(floatX)\n", + " \n", + " with pm.Model() as neural_network:\n", + " # Weights from input to hidden layer\n", + " weights_in_1 = pm.Normal('w_in_1', 0, sd=1, \n", + " shape=(X.shape[1], n_hidden), \n", + " testval=init_1)\n", + " \n", + " # Weights from 1st to 2nd layer\n", + " weights_1_2 = pm.Normal('w_1_2', 0, sd=1, \n", + " shape=(n_hidden, n_hidden), \n", + " testval=init_2)\n", + " \n", + " # Weights from hidden layer to output\n", + " weights_2_out = pm.Normal('w_2_out', 0, sd=1, \n", + " shape=(n_hidden,), \n", + " testval=init_out)\n", + " \n", + " # Build neural-network using tanh activation function\n", + " act_1 = pm.math.tanh(pm.math.dot(ann_input, \n", + " weights_in_1))\n", + " act_2 = pm.math.tanh(pm.math.dot(act_1, \n", + " weights_1_2))\n", + " act_out = pm.math.sigmoid(pm.math.dot(act_2, \n", + " weights_2_out))\n", + " \n", + " # Binary classification -> Bernoulli likelihood\n", + " out = pm.Bernoulli('out', \n", + " act_out,\n", + " observed=ann_output,\n", + " total_size=Y_train.shape[0] # IMPORTANT for minibatches\n", + " )\n", + " return neural_network\n", + "\n", + "# Trick: Turn inputs and outputs into shared variables. \n", + "# It's still the same thing, but we can later change the values of the shared variable \n", + "# (to switch in the test-data later) and pymc3 will just use the new data. \n", + "# Kind-of like a pointer we can redirect.\n", + "# For more info, see: http://deeplearning.net/software/theano/library/compile/shared.html\n", + "ann_input = theano.shared(X_train)\n", + "ann_output = theano.shared(Y_train)\n", + "neural_network = construct_nn(ann_input, ann_output)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "That's not so bad. The `Normal` priors help regularize the weights. Usually we would add a constant `b` to the inputs but I omitted it here to keep the code cleaner." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Variational Inference: Scaling model complexity\n", + "\n", + "We could now just run a MCMC sampler like [`NUTS`](http://pymc-devs.github.io/pymc3/api.html#nuts) which works pretty well in this case but as I already mentioned, this will become very slow as we scale our model up to deeper architectures with more layers.\n", + "\n", + "Instead, we will use the brand-new [ADVI](http://pymc-devs.github.io/pymc3/api.html#advi) variational inference algorithm which was recently added to `PyMC3`. This is much faster and will scale better. Note, that this is a mean-field approximation so we ignore correlations in the posterior." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (theano.tensor.blas): We did not found a dynamic library into the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n", + "Average Loss = 131.3: 100%|██████████| 20000/20000 [00:22<00:00, 880.39it/s] \n", + "Finished [100%]: Average Loss = 131.32\n", + "Average Loss = 128.89: 100%|██████████| 10000/10000 [00:10<00:00, 997.84it/s]\n", + "Finished [100%]: Average Loss = 128.92\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 33 s, sys: 1min 39s, total: 2min 12s\n", + "Wall time: 36.2 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "with neural_network:\n", + " # Run ADVI to estimate posterior means, standard deviations, and the evidence lower bound (ELBO)\n", + " # here is a good chance to demonstrate `cost_part_grad_scale` parameter usage\n", + " # the reason is described here: approximateinference.org/accepted/RoederEtAl2016.pdf\n", + " # to be short it is used to reduce variance of gradient on final iterations\n", + " s = theano.shared(pm.floatX(1))\n", + " inference = pm.ADVI(cost_part_grad_scale=s)\n", + " # ADVI has nearly converged\n", + " inference.fit(n=20000)\n", + " # It is time to set `s` to zero\n", + " s.set_value(0)\n", + " approx = inference.fit(n=10000)\n", + " # or you can just use `pm.fit` if you don't need ELBO history, ADVI will be used as default\n", + " #approx = pm.fit(n=30000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "~ 30 sec on my laptop. That's pretty good considering that NUTS is having a really hard time. Further below we make this even faster. To make it really fly, we probably want to run the Neural Network on the GPU.\n", + "\n", + "As samples are more convenient to work with, we can very quickly draw samples from the variational posterior using `approx.sample_vp()` (this is just sampling from Normal distributions, so not at all the same like MCMC):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "trace = approx.sample_vp(draws=5000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Plotting the objective function (ELBO) we can see that the optimization slowly improves the fit over time." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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QoKCgAOPGjQMAREVFoaCgAAqFAmvWrMFbb73l5pK6zkffHbF9YQuXVw3J+uJS\nLfafuAa9ntdtrjJteWKT7P5YaueSi747ir/O/Bl5hazjQtLh8tv4tQ0bNgzDhg0DAPTo0cPQ3K62\nli1bYtmyZa4umoEk7iI0YBcWfnMYKWnX8eYz/4dH/9zdMeXgeYMkSe3Pui/5KgDgclYhOrVv5ebS\nEDlGo7myb6zknVq7uwi2MRPoer0wPLOv60KmBkmnVQCAhOOZ2LLnvGFeTT2BazY0D9ykPIcfd/9h\nZ4GJqLn65WA6lm9KdncxmhWGvRVN9cq+skqP//2UgulfJCIm/oJhuk5n3Ivd+19XP/Ne9N1RrI09\nXW89tjShXvfzGXz3y1mT865dd1xfAvaqrNLh5IXrfBRRS0WljseD3O6LzScQd8jG+kjkEAx7a5rI\n76KszqX9mGnbEbv/Ek5fyjOafvxcjiuLhVcWKg3/FyYOpjqvFPuOX3XKtr+MScWML/YjztZKjjYo\nLa/EzBX7ceK8a4+jI+j1AlHv7MCbyxIcul6p9qkj0d2iZophb4WpgGrKqnT29U/v7P1/9YPd+Oi7\nI7jshGaBR85Wd8z0x+U8K0va7rcjGUhJu47//u+Aw9bpKjp99d8+LcO1PSdqiiuweP1Rt97lIWru\nGPZWNJWrFlufNlTY0JGP0frM7H+51jGD6FTpqjdQWKp1yPqcrfYd8FsbSMjxH6xVMSex50iG+S3e\nwiYtdXVs7YTw21/OYO/RTCz61o7WJU5w4lwOSsrYpK6p2pd8FT/9dmvDgDdnDHsr7uzm4+4iOFS9\nkefMnCXU/Lab+xlf+dNJu7fdVE6cbNWYdqdKp8f2fRfxyYZjDl/33K8O4a8zf27w+8tvjOxYUu6a\nURZNOXE+B/9deQDvrT7ktjLQrfno2yNYs8P6MOBkmlub3jUF3i2kdT5U7wqtzuvM7CKU2vCjfOpS\nriOL1SQ1pYF9gIafnBw+rbY4v259EYcXwAFqBp06k27HI52m9eclskhaSdaM2Vqz1VpF7Ikf/1av\nUp/DSPjH88gZNXI1lkfJi94rzVuQUqvXQiRFDHsJSMsowA+7bGvnnnrxuvHFfJ3b+FU6AY9ak8xe\nvfL33eBaTjHeW30I//pQaXG5r7ffvAVp602BxvyM2e5WqW5sxdo0G9ASOQ7DXgKK7Kjctveo8fC4\ner3AM7N+MV6ozi+jTqfHwZPX7KqQVlSqxXOzf7V5eWeePCgPZ+DURcc8dqh9FVsT2AXFFQCAsgrb\nKj/a6tSXdnUNAAAgAElEQVTFXPztvz/j+19N92FgL7c/duAJIpHbMOwl4Ltf7etP/cgZ42ewhSXG\nJwu1n8EKAezYfwkL1h7GG0vj8cWWEzY131uwNskQgoZ1ufHX/oedju/hz9TezFyxH5o6+91Qh2/0\nbvjTb+dNzk/LLIDy8BWHbOuWWPmzNop+qRpQCD6ecD63n4A2Iwx7CTh3xbHtpms3qxNCYPXWVABA\nZnYxfjmQjoMpWSbfVzvkUi9YvpKurNIj9cLNoXsrKh17VVyPmd/6zOwi5BeWQ1NcgesFlp+512Pi\nhyol7bpTmgelZRTgq22p0NWqdPHGJ/FY+sNxFJdqcdKWYZAd/Ltqa3ya+j0vLqvEhrizDjsxIiLL\nWBuf6qk98lpNO/jaKipN385/dvav2L44AnuPmm/rXWPNjlPYvu+i4fXqral44B55A0prG3PB9K8P\n9xi9jvkoHJtrjRGg0+nh6XnznLioxPozdL0NVyvZ+aVWl6ntjaXxAIDB/brUO06VOj1mrTpo1/oc\nqSHnEGt3nELcocu4rC7CO88PdXiZ6moMNxeI3IlX9mSRKrek3rRPf0xGlonpQPVdgcXrTbf1FqK6\nPbgQAkfrPEq4mlOMxBNXsWT9Uau39n4+cAkLv0mycQ+qHT+Xg3wbhiyNP34V39V6Rj5/jfF2TFWE\nrNv0zJY7kzsSLyEt8+YdGZ2FZhK155jqFMnWpm91t6DT6TH3q0PYn3LNpvfX37Dp7er0wuiK3dRi\nOTfuouTYedIjdTqdHou+PYKjZy03dySyF8OeLPr9lMqu5S0NslJZpcfot7djwdokk4H44boj+O1o\nptkTiRortqTggJlHCZY8/16c0eus6/W3U1BkfEJQt35DbQLVjzn+uJJvNP3Uxev4308pVgecOX/j\nfd/Enkbk1G1ml6vdEZKp58iWHkfr9QLqvFKTlSvPXs7H4dNqfPDNYYvlNGzbSh8NNeZ+dQjPzv7V\nalNEU7LzShETf6HZDtaTejEXCclXMedLdv5DjsXb+FY01VHvGqP8ouqrvUOpKsg73WZ2ueLSSnz7\nyxlczSnGtOcecOjfIDuvFB3atURZRRVeXri73vw1O+qP/GeOENUnQ19tSzWanpapQVqmBg8P6opB\nd3Ux//4b/9Z+bFDbFhPP/u2tz7Q29rShff/wwd0M09f9fBpdO7cxvNZW6uDdwtPiuoSwrZ7bsbPZ\nAKrv1nT2sTxEdN39mbZ8H65rynF7h1YIGHyH9Y05mKU7LK7g6JOcC5kF6OnXDi28LP9tSfoY9uRQ\nl7OKzM5LqTVSnDrP/O3bNz+9OSqbOrQUfp3bQK8XUOWWGE4Yaisu1SI9qxCHT6sxPuxeyGQylJRV\nmryaffH9XQCAf48bbNP+WGOpIqK20r5Bh2xiIgssnQztrDXi3/4TN2/Xb1Ian2BkZhejzx12dg0t\nkzW4D2RzJb6uqb6zojHxd74Vtpyk/HzgElZsSXHodt3p+B/ZmLXqIIYP6oZ3/mFbvYg9RzKw92gG\nZr/0kFFdFWr6GPbkUG8v32d2XkKy/UPZfvjtEVzNLjK0Ya99NVrj6Xdv9hPw6P91x53dfPC3/1ru\ny335phM2l+HbX87gb4r+9a6OhBC31DxL6IXdrRBs3Z5eL+DhIbO5Ux5bmkDd6jWnXcfKiXfUdidd\nQWWVDk8+fKfR9K+2Gt+hsXZILl3ToH0bb6t3L9ylpk6IPXUyasZWSM8qRN/uHZxSLnIPnrpRo5aW\nUWDUWY215/mTFu9FtoW7Bg2xcfc5jJm2A19uNR78x9otVyGExdvC/4s+iah3dthVFlMBVDcXw9/c\nisi3t9lV41+I6p4ALYa+mXm30lZaoDo0XdkE79Mfj+MLE1fw2rqDRFkxafFejJ+701HFAuC8c5xc\nTRnKGjhKY73BsyRsf8o1HP8j293FcAqGPUlOza16R9uWcNHo9d5jmRYvdwWAMdO2O7QMNZUCdbU6\nNjKVD0IA0z4zf5elruj4NLzygRI/779kdpmzl/OxfFOyYdu3kks1jx7KyisxafFePDv7V6MhcB0f\neg1f4e+pWdiacMGBZXG98XN3YvzcOOsL1rFiywmMmbYdeTa0ZJGCD7457NZmrM7EsCdqoKs5xZav\neoTjK1xBCEz5NAGRb9c6iTCTjDXPv22ReON5ft3WF7Wv2t/5PBFxhy4j6bR9LTRM5WzNemsPe1v7\nMU9jqhY7f02SoWMpV0rLKMCeI47rIdGW0Sxr1PzVfz6QXl2WTMd23GXYTvNsdOEWDHuiBoo7dBm/\nHEw3O3/e1787fJvaKj3SMox/eB0RjOZOSkxVWKupeGjt6jtDVWT1ZMfcKvIKK+x+PHA+Ix9TlyUg\nJ79+k7+m0Kim7qOMN5bG45MNx1Fa7v7BkJrA4SMrGPZETcgXm22vWNgQx8/lGL02dTJTE8HWsvh/\n0Sexac85i8uYW8UPu/7Atn0Xzcw1bcGaJJy9nI/1cY4ZOMiVNu85j2dn/4pDqfXvmpjqxdLVmkIT\n5BPncvDKwt0Or7Njq6RTKrzzeaJdA4a5EsPeisb/EafmxFSFP1dHgbkr7gMp1xD+5lajaabCC7At\nPPYcsaHb5e2n8NnG5OpyGdZt9W02eX9NkmEwolshhEC5tgon066bPXY1J1UHTzawN0MHa4q/ewu+\nScK16yVuq18x7+vfcepiLg6m2t/hlyu4POyzsrLw3HPPYdSoUQgNDcU333wDACgoKMCECRMQHByM\nCRMmQKPRAKj+osyfPx8KhQLh4eE4deqUpdU7nPvPqYkse9pKM8OGSknLMTl92Y/J0OtFvVBdaGNP\nfEYsVv63/O3TFFfgp71pRn0JOFKVTo+5X9n3KObY2WyjPiTeX/M7ot7ZgSXrj2HGiv04eNJ0ELgq\nXIUQWLA2yeLjJ6D+n8WjCVzZ15xAuvs3W4jG2YLB5WHv6emJd955Bz///DN+/PFHrF+/HmlpaVi1\nahX8/f2xc+dO+Pv7Y9WqVQCAhIQEpKenY+fOnZg3bx7mzJnj0vJyCEZqjlLScjBzxQGT86p0epzL\nuNlFsBBAgZlOcKxFRGGp1uy8mq/e1ZxiQ38B13KKDUH17OxfDcsWl2otPlZwRVTlF5Vj9pcH8VKt\n1iCHUlXQVukNIX/xmsZ0+WwooM6GoaWtKSzR4uDJLLsfBwkI5BeVY93Pp1Fs4W9mjiu6P645hMKF\nvSDGxF/AiTqPvr6MOYkx07bbP4qmk7k87H19fXHfffcBANq2bYs+ffpArVZDqVQiMjISABAZGYnd\nu6u7Mq2ZLpPJMGTIEBQWFiI723XtIJn11NwUlWrrVQKs6/TFmz0Hbt5zHs/N+dXC0uZZCgEhBMor\nqvDqB0r8c0H178GrHyrxxeYTOFdnPILaHStlZhfb1O1trqbMrgDNzi+1eMVWZqG2uyHMrRTLXD8K\nX21LReTb282ON1BZpWvQycCGuLM4ZMNt5zlfHsKS749hk/I8Fqw9bFfgf7HlBCKmbrP6LFuvFzh8\nWlWvQmJhidZij5s1ao6xtb98Uam2QScsdWkrdfhqWyr+u9L4pLj4xonpmfS8W96GI7n1mX1mZibO\nnDmDwYMHIzc3F76+vgCALl2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(inference.hist)\n", + "plt.ylabel('ELBO')\n", + "plt.xlabel('iteration');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now that we trained our model, lets predict on the hold-out set using a posterior predictive check (PPC). \n", + "\n", + "1. We can use [`sample_ppc()`](http://pymc-devs.github.io/pymc3/api.html#pymc3.sampling.sample_ppc) to generate new data (in this case class predictions) from the posterior (sampled from the variational estimation).\n", + "2. It is better to get the node directly and build theano graph using our approximation (`approx.sample_node`) , we get a lot of speed up" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sigmoid.0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can get predicted probability from model\n", + "neural_network.out.distribution.p" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# create symbolic input\n", + "x = T.matrix('X')\n", + "# symbolic number of samples is supported, we build vectorized posterior on the fly\n", + "n = T.iscalar('n')\n", + "# Do not forget test_values or set theano.config.compute_test_value = 'off'\n", + "x.tag.test_value = np.empty_like(X_train[:10])\n", + "n.tag.test_value = 100\n", + "_sample_proba = approx.sample_node(neural_network.out.distribution.p, size=n,\n", + " more_replacements={ann_input:x})\n", + "# It is time to compile the function\n", + "# No updates are needed for Approximation random generator \n", + "# Efficient vectorized form of sampling is used\n", + "sample_proba = theano.function([x, n], _sample_proba)\n", + "\n", + "# Create bechmark functions\n", + "def production_step1():\n", + " ann_input.set_value(X_test)\n", + " ann_output.set_value(Y_test)\n", + " ppc = pm.sample_ppc(trace, model=neural_network, samples=500, progressbar=False)\n", + "\n", + " # Use probability of > 0.5 to assume prediction of class 1\n", + " pred = ppc['out'].mean(axis=0) > 0.5\n", + " \n", + "def production_step2():\n", + " sample_proba(X_test, 500).mean(0) > 0.5" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "See the difference" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loop, best of 3: 6.88 s per loop\n" + ] + } + ], + "source": [ + "%timeit production_step1()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loop, best of 3: 153 ms per loop\n" + ] + } + ], + "source": [ + "%timeit production_step2()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "pred = sample_proba(X_test, 500).mean(0) > 0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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Y3A8LvnUR8nIzDfOJZa8n8hm99LKcrDCe/9m32+t/u7UJkhEGZsfEu0ZrrE03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\nlm8+IxuoaMXSYBSlb0aop2B1NDchAU4QVhAVeF75wEX/iJS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CABLgBEEQbpOerkabP/CA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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.scatter(X_test[pred==0, 0], X_test[pred==0, 1])\n", + "ax.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r')\n", + "sns.despine()\n", + "ax.set(title='Predicted labels in testing set', xlabel='X', ylabel='Y');" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy = 96.39999999999999%\n" + ] + } + ], + "source": [ + "print('Accuracy = {}%'.format((Y_test == pred).mean() * 100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Hey, our neural network did all right!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Lets look at what the classifier has learned\n", + "\n", + "For this, we evaluate the class probability predictions on a grid over the whole input space." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "grid = np.mgrid[-3:3:100j,-3:3:100j].astype(floatX)\n", + "grid_2d = grid.reshape(2, -1).T\n", + "dummy_out = np.ones(grid.shape[1], dtype=np.int8)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# Creater posterior predictive samples\n", + "ppc = sample_proba(grid_2d ,500)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Probability surface" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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nIMsybr75ZkvHMrARhYzZWqW6hpZc5zsb68Tk7CTEfFWrWKCNGQQR8cY2zZs8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\nl9DVat7ww4zeAHXDljfdPbD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4447D6tWrcfXVV+PFF180PZZr2Igc0ltXow7Q4k2dkDQ6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\n6dSpU9c8v2zZMk2dOlVlZWUqK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmap = sns.diverging_palette(250, 12, s=85, l=25, as_cmap=True)\n", + "fig, ax = plt.subplots(figsize=(16, 9))\n", + "contour = ax.contourf(grid[0], grid[1], ppc.mean(axis=0).reshape(100, 100), cmap=cmap)\n", + "ax.scatter(X_test[pred==0, 0], X_test[pred==0, 1])\n", + "ax.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r')\n", + "cbar = plt.colorbar(contour, ax=ax)\n", + "_ = ax.set(xlim=(-3, 3), ylim=(-3, 3), xlabel='X', ylabel='Y');\n", + "cbar.ax.set_ylabel('Posterior predictive mean probability of class label = 0');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Uncertainty in predicted value\n", + "\n", + "So far, everything I showed we could have done with a non-Bayesian Neural Network. The mean of the posterior predictive for each class-label should be identical to maximum likelihood predicted values. However, we can also look at the standard deviation of the posterior predictive to get a sense for the uncertainty in our predictions. Here is what that looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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3KHpuBjYiA9hljyqt55m6fdPH7WjzhzGk1o0Z4xsN+/qkLrD1ahmUaktMEbtF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\nWmYVxNtvbLC8qcqUKki+WiHjYPAlK//f+UvWhelCUHd7ZP24WgQap6J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2btyITZs24aqr\nrsItt9ySUxlPjra2NtTX1yd/rqurw8GDB029B4VihMwZNV4Q8estn6OjKyr7/lQhRmJckm8zEiNi\nTU91rZDILTSlXX82GoG9O4hYpQeCw5l2nuJuP8fBd90N8K9YBc7vQ3nTOcmde6GyAoD2olFt1gYA\n+NU3pe2A5yv4tuKMcXkXbXruVwjhprbBosf9UQ49z10yaF46V+DwQSjxu2FhVZ+h+5PACDwad21G\n/bFDcHX7Ea6sQeu4KTg8ewlEVv13U75aIdOw2fTlGPZHZbinTdN1/yRmmX2otT5394C7cx34/+9J\n2eNKEQNiRYX89fQ6WVIoQ5xc9MgXX3yBqqoqrF69Gi0tLbjwwgtx2223gePSf39KGur3v/89nnvu\nOZSVJX5vXXXVVfjHf/xHonup/k20fPlyXHDBBVi2bBl++tOfgmVZiKIIhmHw/vvvE92AQikVlGbU\nREDWiEAi1UFSzbikaWINnnv7S80ZuEJXQsxohbQapYWmf/k1AM9j1JZNqD68H86AD5FqLwKNU9Gy\naDnAcUSLR9HpRPnsqfIHNRaNarM2sm2OebTu1lNtK1SrYj6Fm9oGC6n7Y660Hgvgk75K2WNH+uyY\nX9mHUy3WPGeNuzZj/IH3kj+Xd/uSP380Z6nieQURa6m4ytQNRjKiMlTbBVNbDQFrnsG6OogjR4Bp\nbpE9zOzclfgcmfdUqcyhp0f+WgZmXymUQlPfUIHhleabFzm6BdOvmUo8HsfevXuxefNmNDQ0YM2a\nNXj55Zfxne98R/b9fr8fDsdAF4Pdboff7ye6l6pgO3jwINauXYu/+7u/w/XXXy/bl2mUuro6tLYO\nOKi1tbVZ0npJoZCiZBbicqh/7zNdKJWMS0RAcwau0K2QpSDWAOWFZmu/cK7f9VbyWJm/M/lzy2Ur\nzfsQSotGlVkb1TbHPFp358NZMtfr6hVuRirXahssubg/6plb6+ZZBAX53zFBgcXRlhCs8GDkYlHU\nHzske6z+2CF8fOHiommP1EtmVIZsu2BGqyHcbogAmN5eiKNGmTsP5nZDvGg2kDLDlgpz8qS8yFKp\nzCkh1p0GWLDwpVAGK7nokfr6epx99tnJdsr58+fjwIEDiu+fOXMmvve97yUrbn/4wx+IM9kUfxP9\n7Gc/w7Zt27B+/Xp8/etfJ7qYHqZMmYIvvvgCzc3NqKurw5YtW/Dzn//c9PtQKCSozbKEo8o7NPPO\nHZ7lQilnXAIAt/z7h7LXkGbgYi1fGvz08hSTM52ZqC00qz/aD6UldvXhD9F30w1EXpVEu/wqcE88\nBr7KY3mboxnIVd0KbQSSipZwyzVGY+lEEbEutyH3Rzn05q1VcgI8rICgkC0yPaxgWdC8MxSEq1t+\nZ9fV44czFETIMzzrWMGra1qozJCmtgtmthqipyf5u0NW4OXoxsj/9AEwf3wNrExlTNFgSM2UqKIC\njMy1mBMnYbv4UmpAQqEQkosemTJlCrq6uuDz+eD1evHBBx9g8uTJiu//l3/5F2zatAlvvPEGAGDe\nvHn4+7//e6J7KT7JPp8PmzdvRoVCn3Su2Gw23HPPPbjhhhvA8zyWLl2KiRMnWnIvCkULtVkWJYZ7\nnLhx0XjFRWGqcUmrL6xpRmLNk0ZOIaprn38VwPiR+hZ1agtNR8CnKNicQR84vw/xevVFYa6LRklY\n5KvN0SyssO83EyVBaTRGQ7oexzJYOd2DxVMq0RKIYVS1HVVlxrpJjGyS2BngrLJYcmYtlZGxXsvi\nMCJuD8KVNSjvzt6oClfUIOL2ZH+eYhdrUJ8hTbYL1tUptxqmvn/rtoQd/4MbcndjrKqC+A8r9BkM\nqZkSrVgOnmUTm0LNfwPT/zVhQA1IKBQ9KOmRxx9/HJMnT8b8+fNx8OBBrF69Gl1dXXj33Xfx5JNP\nYsuWLeA4DnfeeSeuvfZaAEBjY6NiOySQaIFctWoVVq1apf9zKh148MEHdV9ML3PnzsXcuXMtvw+F\nooXaLIvLwcpW2WZMIgvk1rp+rccJe+cJwKTwXaA0WiE//4r8nqnmImoLTcHhAO8uhzOQfSxeWwu+\nxqt6H7PEWpI8tjkWC/mYwZTuEYmL2H1EXrxnxmgofS5eELFpXxf2NffB1yvAW86iaXSiwqYn6F7P\nM5f5vC2oTBiLHElxiZxUFkNj0DrzH97uQOu4KWmtxRKt46ZktUPm1cI/B4hmSAlbDZmvvgJ35zpw\nv8vBjj8FIwZDqufYbMBtt8J20cWJtsrMz08NSCgUIuT0yC233JL8/+eeey527JCv3M+aNQuvvvoq\n0X2OHz+OtWvXoq2tDe+88w4OHz6Md955B//0T/+keS6tlVOKnny4KqrNssz72mlgGSanQG61659X\nT5bnRMpgsvBXMhdpPWMyxh/M/uVpi0YQqa2TFWyhaTMgOp1Zr5tFMbURDhUCYV6xcn0qEMHfDh9F\ng8eueo1N+7rw5icDuXqdvULy55XTs6tMuSK3OcIyCTfI+ZV9yRw2q4xGUjk8ewmAxMyaq8ePcMWA\nS2Q+Mau6BiBhAHTZYvVKlkqrYdr7R4wAs2On7DFDYkgyGLrtVuD//g845xygdhjZOUrV+u4uMG0a\nFcUhtmlEoRQrP/nJT/CDH/wg2XJ59tln44477qCCjVLa5DqbohclsxDpfrkEcqtdf+lEa1qeSCmE\nhT9pdU3JXOTY5FmI2p1wxLIX61yoF20XXgLPJwfhCPgQrfYicuEFyYw1JXJZNFKxliDfDqfVLg7e\nchadvfJzpm990otrZipXeyJxEfua5W3z97f0Ydn5VaaGY2thZwCvTcjbxojIcvhozlJ8fOFi1Ry2\nUqmuSWhWslRaDVMRL5oFdtMLsscMiaFcwq6VqvVqc25Ks3EUCqUgdHd3Y86cOXjkkUcAJHLd7Hb1\nTUUJKtgoRYvR2RSjyJmFpAqzXAO55a6fMBoZWq2QpKi62H3xEWwyYg0AHEE/Ts1ZiK8WXQV7dxCV\nU8ZqVtaoWMudQsRROG0MvjayDO98GpI9fuBEBJG4KCu6InERn3dE4VMQe6Q5bLm0QhYLvN0hazAC\nWC/WTK2ugXyGNFPUwe2CKAJMKJRwifz2QvBr7wSz88+miSGlTLXk5zWC2pybmisthULJOxzHIRaL\ngWESfye1tbURO/BTwUYpStRcGzNnU8wmV2FGev1CW/jrwWyxNn5ktWaVTc1cpKy3C33lHrh7g1nH\notXeZFh2xbQzFS0bmEgEnN+nOdemBhVrhc8NvPQst6JgkxNdqTNrnb0CWED2O0KSw2aFWCtkXEa+\nMSTW+sOw5UKziZ/HfsdH/p516aIOSBd48ThQ7QHkBJteMaSSqZbrrJmR2TgKhZJ/VqxYgdWrV8Pv\n9+PJJ5/E5s2bsWbNGqJzqWCj5B2SmTQ110bJVdFKUTUUyMcCUhJlep0gAW0Xu+7J58H9/jtZxwKN\n50NwqFTUeB7eZzfCvWc3bB3tEOrqEJvehNAdtwIWueIOVgot1gBgWLkNwxTaIuVEV+bMmlJoh1YO\n21AQa0XlCpkRhi3U1yE6bw7Ca27ObidUaj38138B96/3qbckprQdcnffC/bQR1kfRZgyWb8YUjE6\nyXnWTGvOjUKhFAVLlizBqFGj8O677yIcDmPDhg2YNm0a0blUsFHyhp6ZNC1XRSnbrFQpdHUt361Z\nmdU0EgGn5mLX/bUmtCxaDnAcqg9/mJxVCzSen3gdygtI77Mb4dn6WvJnrrUV3Ktb4HjnXUQuXyy/\nAJRhKFfXSL+/kbiIQJhHtctcY51UnDYGTaPL0kSYRKboUptZYxlAFIFhBDlsg6ENUotim1vLDMPm\nTpxM/hy+/da051Gp9ZD58/+mCTDVlkSVihiCXUA0qs/WPx+zZkPQlZZCKRV4nse9996L+++/n1ik\npUIFGyVv6JlJc9o5NE2owbZ9rchk+pnkdvrFSCmJNTOqa2rHtISbkoudv1+stVy2EicWLoO9O5hs\ngwSUF5BMJAL3nt2yx9jeUNoCUA1dYi3HwN2czzcZku+vWVb5pEjiSi38WmtmTRSBOy71Ynytw7TK\nmh6KrbpmJbqrayph2I7tO8D97N8GXlBrPfy/j+Vfl2tJNLsiRmfNKJQhDcdxOHLkiOHzqWCj5AU9\nM2lSJW7f0cT8EssAgggM9zgwY9IwXXb6VpCPmAFSinVuTU/GmhpyLnb142rT3iM4nIgMO43oepzf\nB1tHu+p7HNt3ILz6h+nzMSlzMxXnnEP24XNxhDPj/AKSb6t8Kfx62flVWRU90pm1YRWsqlgz8qzR\nVshsjMytqYVhs21t4FPFk1rGGs/LviwrwCyoiNFZMwplaHPBBRdg/fr1WLJkCdwpmzQTJkzQPLe4\n/9anDBr0zKRlVuKE/tVV00QvvrtwHCIxHu2B/AsmM2IGCj3zk4/2LFKxpmeuTc3FLhO1BSRf40W8\ndjjs7acU38O2tSXE2ehRWXMzekRTro5wljjK5QG1tsO9x0NYUBWCQ+WxzcU10GljslwdzZhZo2JN\nnnxZ+KuHYY9KF09qGWscJyvaZAWYWkXs0m8Yq3rTWTMKZUizZcsWAMD27duTrzEMg7ffflvzXCrY\nKHmBdCZNrRK3/zM//nPL59h31J+XXLZMco0ZGAqtkGZV1pQgWUymuj9m2vmLTidC02ekzbBlItTV\nJRzokD03QyyacnWEs9BRzmoCYV6x7dAfAYIxYLiKYJO+12bYvec6s2a0gj0U5taMYPjP1FWG6Lw5\nac+iRFY7oZrQOudsMDImIkotiVkVsREjgGoPmDffgn3jfxmvetNZMwplSPLOO9lGaaRQwUbJC047\nhxmTvGmCRyJ1Jk2tEncqGEmbabM6ly2VXGMGCi3W9JCPHX8jrpGai0mex8SdryTdH+POSn/SAAAg\nAElEQVS1wxGaPiMRmM0N/NnwP/kxwtXlcP7hVbC92UYV0XlzEu2QKnMzmqIp1/kXKx3lcoDke6wW\nZl3jBDxkGaGmCDc18ag1s5YPsVZs1TU95DsgO7zmZgCJlmW2rQ1CXR1w2WLZdkLF1kPJJZK0JTGj\nIsb98j/APfV08nCpVL0pFErx8Omnn2L37sQs/QUXXEDUDgkAZGltFIoJXLvgDCya0YDTqp1gGeC0\naicWzWhIm0mTKnFyKBXR9nzqQyQmP5tgFiQtncWM1e1Zn38VsLy6psXEna/As/U12NtPgRFF2NtP\nwbP1NXif3Zj+RpsN4dtvReD1V9G3eBH4hnqIHAt+RAPCK65KLgzV5maSokmJ/rYsOYjmX1TPH2GO\no5xFSK6NckypgWo7pBzBoyeT/+hFEo9yqM2sDUWxVvQB2f3PbfClTQi+8iL4vX9JiCS5yla/0Ir/\nZSdie99H/C87E+8tK5N/Xas65nYDdXVg3nhL9jCzdVvCHIhCoVBUeO6553D99dfjyJEjOHLkCL77\n3e/i+eefJzqXVtgoeYNjGXx34TisnD9W0bRDrRInKCQg5yOXLZeYgUJX16xuz9Ir1KyorrFRZfdH\n997d8K9YBdHpTF80VlYgtP4exSBe9bkZDdGVqyOc260Y2AuPZ+D8InOQlJDaC/ceD8EfSVTWptQA\nS07P7bqp330SAaDH8j/XqvVQEWtGMKO9NYmrLDFfSvJ9V2o9NNKSWKRVbwqFUjo8++yz2Lx5M4YN\nGwYA8Pl8uPrqq7FixQrNc6lgo+Qdp51TFVdSxW3PpwPmHlMn1GDvpz50dGVXsvKRy0ba0plJKYm1\nYltE6mG4R1B0f7R1dIDz+1A+e6r8ydICUOZ14rkZGXJyhAuFAL/Cn10gCHR1gXtwQ94dJCvOGEf0\nnZZcGxdUhRCMJdog9VbWtCBtmVSz/DertbhUZ9aMkO9WSDkKkoGYjxw1CoUyqCkvL0+KNQDwer0o\nLy8nOpcKNkrRoVSJ41hGt2AyEzkhOf1Mb8FjBsygWE1GALLdfzX3x3htLfgar6F7c088Br7KY0x0\n5eII19YG5sQJ2UPMiRPg7loH7vlNA68V4SxN8OhJODh1gxGz7pNJqohLtfxv/qQVHrsABxdCzzFz\nWthIxFpMBLp5FpWcgFMtxbUxUvStkBkULLDe7Yb4zUuBlBk2CZqjRqFQSJg1axbWrVuHZcuWAQBe\neeUVXHTRRTh69CgAdXt/KtgoRUtmJa7QgomkpTOVUqmu5VOsGWmH1GLYGdUQAUX3x9C0GahqPN3Y\nxc2w4TbSfqW2m9/QAGbnLtnTit1BMl8oPSvD5UfrDKP1jAki8GZ3GT7psyMosKgQ4xjLspgpBIpi\ngLzgrZAK7chFh5SJuO3NRIYfxwECD3H0aIjf/hbNUaNQKERItv7vv/9+2uuvvvqqpr0/FWyUkkGv\nYLIKrZZOoHTEmlHyZTCiZ0Hpu+Y6AImZNVtHB+K1tQhNS7hEGolqTtvJz7cNt9oM3EWzwf7+BdnT\n6CxN/iB5xt7sLsMHoQEh0sPYcZhL2GReKJRWG6WprZAZ+YZCfR2i8+YkDH9UWnoLVV3LzESUstyE\nSy/Nf0W7SOdWKRSKNtTWnzKkIBFMg4lis/AvtBtkKmmLSI6D77ob4F+xKi2HzTOyBmxzi65d/IK1\nXaWgOAO39k4wu/5MZ2kKCGkb5Cd98hkGX7IuTBeCsEHBSSkPFLIVMjPfkDtxMvlz+PZbZc8p2DOp\nlon41p8SAiofwkmq8uV5bpVCoRQH9CmnUEym5/gxROIiAmEe1S5O1jZcL1EexOYNVrZC5iLW9LZD\nai0olRaRotOJeH0DwPPwbnwKFX/dp2sXv+Ck7KArtWPm5EBpBia2sqV+X4vB0EINPZXrbp5FUJBv\nfOyBDSFwqELcrI+mi4K2QqrkGzq270B49Q+Lqz2ySNwhM6t8xTi3SqFQrKOIVy0USukR/PxzbNrX\nhX3NffD1CvCWs2ganXCk45SC5FTwfXYSm78ADvmRZY/OyVzOylbIYqqskeB9dmPaTBvJLj5QwJ18\ntR30jAVhTg6UOX5G18OPQG8rmxKZ39fMn0kEnJ7NjFzQ+2xVcgIqxDh6mOwqWwXicMPa7EizIBXR\nTCSSrGyroZZvyLa1JTYCMlxbC1rxLgZ3SLUqH51bpVCGBFSwUSgm0XP8GDbt60rLfOrsFZI/r5yu\nb5IqeDQh1t5rHXjNFxn4eWnGpq6VFv75FmtGq2sSTCSCir/ukz2mtotfyIWhrh10M8xQDH5Gh85W\nNiVIvq9q1TdehK7NDKMY3QSxM8BYIZycWUtlrBDWbIfkYlE4Q0FE3B7wdvNiS0xvheR5eJ/dCPee\n3bB1tENoqFcV8Wr5hkJdXaJqW0zkmqloBkVS5aNQKMbheR6//OUvcfPNNxs6vxiMqiiUQUEkLmJf\nc5/ssf0tfYjE9c2rRPnEYlSOQ/7EcSNoibU4GHTBhjjMW/XqaYc0o12L8/s0d/GLCq0d9JCCBb1k\nhpKPRaPKZ3Rs3wGE5b/7ZtF5PJD8B0ByM8MXAUQMbGZs/sLc+xnlq+YuzBQCaOS7UCHGAFFEhRhD\nI594XQlG4DF5x//g4ucexPxn78fFzz2IyTv+B4yQe0XOilZIqZJtbz8FRhSTIt716BPyJ/TnG8oR\nnTcnayOlWOZJ+ZtuhDBmDESOgzBmDPibbrS+oh0KAcePA5VViWqeDHRulUIpDTiOw44d8u3gJNAK\nG4ViAj3HjyEQ5uHrFWSP+3oEBMI86irJHrng0ZMIxhKVAzn8kUQbmJRxZcbcmgDgA7YaX7Iu9MCG\nCsQxVgijtvmLotrZIdn1L286R/cufkEXhqWwg67yGZVa2ayi9VgAf22vBJDdA3nIDywek1t7ZK6t\nxdJzxiLhBjldCCIEDm7wmpW1xl2bMf7Ae8mfy7t9yZ8/mrM0p8+lB5LnjIlE4N6zW/aYWiU7vObm\n5HvYtjYIdSmttSkUg1gDMFDRvu1W4P/+DzjnHKB2mPZ5RpFpj0a1B5Bry6QZcBRKyTBv3jz85je/\nwZIlS+BOeW5dLm0jPSrYKJQckSz8q10cvOUsOmVEm7eCRbWLbAUpuUJ67Ik2L5+MaKtxJo4D5s2t\nfcBW4zA3sAPfg4QF+aiaBkz0W+dUmYppFYD+XfxUJzoJuV38gqMyJwMA3C/+A/yGBwprlqLyGfPd\nyqZm6JG5mSFH5tybmbOfcpsiNohEBiNcLIr6Y4dkj9UfO4SPL1xsuD3SCldIzu+DraNd9piqiLfZ\nEF79Q0SuuBwQAWHUyOJ7JlPJs0OjbHt0czOEKZOBYFd+51YpFIpp/OIXvwAAPPzww8nXGIbBxx9/\nrHkuFWwUikk4bQyaRpelzbBJTB1VJusWmekmmWrh7+ASMzmpM2wSU2r0VxDUqmtxMPiSld/haXdX\nYVygFZxozILczLBskoWk5FBHuosPFMFOvsqcDMPz4H7zNGC3FdYNTuUz6hXBuQqkSk6AhxUQFLIf\nAg/Lgz/RjU5Gfe7NFxHhYQWcVRbDgkrAgCdQGkYD6FNxhoJwdcv3Qbt6/HCGggh5huu+riTW2GgE\n9u4gYpUeCA5nTp8VAPgaL+K1w2FvP5V1TFHEE2awFfyZTCGvDo0qrccIdiH+zptAdxfNYaNQSpBP\nPvnE8LlUsFEoOZAZkL28KbEw2t/SB1+PAG8Fi6mjypKvS/CCKOsm+a3qdMOEJacn/lfOWAEwz8I/\nBA49Cr8OIjYHopwdrniU6F5GMX2+xmZD+PZbEV79Q9Ms6K2Ev/8nQDwOduMzYPjseaVicINLd6ds\nURXBVmJngLPKYvgglC3YJpXFYO9/hjKfj21dqUHWDIICl7zGwirjM3hmiDUAiLg9CFfWoLzbl3Us\nXFGDiNtIBDwAnseoLZtQfXg/nAEfItVeBBqnomXRcoBL/2+oJ1pBdDoRmj4jzY1VQknEG8lg04We\nYGmS9+bboVGrPbq7q/Dt0RQKxTB+vx8HDhwAAJx33nmorib7nUsFG6VkicR4+LujqKl0wGm30M9b\ngUyxBgAcy2DldA+WnV+lmsOm5CYZqU93f+SYxM+Lx2Rbl5uZt+YGjwrE0YNsRztnPAoHHyO6VyaF\nqq6l4SpTna0qmp18mw38j24C+5uNsoeLYpYtxZ0ytHdvQUXwgsqEwDrSZ0dQYOFhBUwqiyVfz0Qt\nyPpInx3zK/uSQk8PZoi1VEfI1nFT0mbYJFrHTTHUDjlydBVG/fE51O96K/lamb8z+XPLZSuTrxvJ\nweN/8mOEq8tBUskmzWAz9EzqaVvU8958z5cWQ4wAhUKxhJ07d+L222/H2WefDQBYu3YtHn74Ycya\nNUvzXCrYKCUHL4h45s3j2H3Eh45gBLUeJ2ZM8uLaBWcYyjozgpxYS8VpYxQNRtTcJJUMExxc+kyO\n2XlrNoiKFuTDQ12G2iHz7Qw5KKirgzhqVPEv1tzuvBmMKMEyiarY/Mo+dPMsKjlBVXCpzb0FBRbd\nPAuvLTF/GhNBdM1cxRoj8GjctRn1xw7B1e1HuLIGrWdMxufnzkH98Y/g6vEjXFGD1nFTcHj2Et3X\nHzm6Cmw0gurD+2WPVx/+ECcWLsutPVJHJZskg809R95BUgs9bYu6WhzzLaCKIUaAQqFYwqOPPorn\nnnsO48ePBwB8/vnnuP3224kEWzGZv1EoRDzz5nFs2X0S7cEIRADtwQi27D6JZ948XuiPhkhcRFt3\nXNXCX81NUjJMMAs9C8qZQgCjgu1wxiKAKMIZi2BUsB3j82Q4oobh6poGRVNdk5AWazJYtliTrMOV\nogMU0PpvZ+TPwwh2BvDa1IUVMDD3JoeHFVDJCRDERNvkL9sr8WRHJX7ZXoltXWUQMh7nr5q7TKms\nSY6Q5d0+sBATjpAHdwAMg3dXrsXbq+7GuyvX4qM5SyGy+roIpE0Qe3cQzkB2iyUAOAI+2LuDAIxV\n19L+jKVKtkrFVcpgkz2Wi3GNnlgMvREaRp5Jg8+URMFiBCgUiqXE4/GkWAOA8ePHIx7XNqQCaIWN\nUmJEYjx2H5FffOz51IeV88da3h4pV11Tmklb3lSVVfVTc5NMdX9UwsxWyFSOfxXARAQwLtCKKGeH\ng48ZNhrRQ64h2UYpOrHWT/qcmIVucHl2vis0JHNv6TNukJ1xM2tejcQR0ojBSCaxSg8i1V6U+Tuz\njkWrvYhVGpuLMyTINdxbK845x9Bn0dW2aKDFkfiZNOuZSmk9Jp7Ho1AoRY/X68XLL7+MK6+8EgDw\nyiuvwOv1Ep07+P5Wpgxq/N1RdATlw8k6ghH4u6Oo92rnWRhFqRVSaSYNAFZOT18QqblJark/mt0K\nKfH5VwPX5UQxZ4MRM2fXSMhXNScv5GmxllfnuyJBbe5Na8bt7GC7ZoaaHqxyhATSN0EEhxOBxqlp\nM2wSgcbzITiclm2KyKHm3lph9KJ62haNtDgSPpOmP1NuNzUYoVAGEevXr8dtt92Ge++9FwzD4Oyz\nz06z+FeDCjZKSVFT6UCtx4l2GdFW63GiptJYRpEakrmJvfOEojW/0kza/pY+LDu/Kuu85U1ViARC\niu6PcugRa2ZVAazGjOraoGiFlCOXxZqW+12+ne9yQPoumzHnqDb3Fogrz7gFeBYhcERZaqRY5Qgp\n99+pZdFyAImZNUfAh2i1F4HG89GyaHnurZB6UZh503wm1b7Teua+cpkRU3smS+iZolAohWHMmDF4\n4YUX0NvbCwAoLy8nPpcKNkpJ4bRzmDHJiy27s+eqpp/p1dUOqeUymWluotTmqDaT5usREAjzWQYk\nHMsouj/mil6xllpdM4N8V9f0UhJizSikLVkmOd9VnDFO04BHjmFnVBNtQKR+l5W+10aEnDT3lopa\ntlsF4nAjO2ohF3i7w3RHSEU4Di2XrcSJhcvMyWEL9+UelZHi3qr6TBJ+p/W0ElvSdpxvN0kKhVIy\nNDc3Y/To0Th69Kjs8QkTJmhegwo2Sslx7YLEX3p7Ph1wiZx+pjf5uhZyLpNNE2rw7ZkjUOsZEG+S\nuYmEUpuj2kyat4JFtSt7ASgFZGe6Pyph1dya2WJND4Wqrg1miFuy8uR855nQkBYGbwWZ33mjlTi1\nGbexQtjUdkgJyfmx/tihnB0hAe1/d8HhRGTYacmfdVfXeB71f3wBWqHXZkL8ndbTSmxF2zG146dQ\nKArcf//9+NWvfoUbb7wx6xjDMHj77bc1r0EFG6Xk4FgG3104DivnjzWUw5YpxNqDEWzb14pt+1ox\nvD8iYPnFYxTNTTLbHNVm0qaOKstqh9S7gM3H3JpZFHt1bVCjpyXLROtwrSobE4mA8/vA13ghOnOo\n6hCipxKX+d5GdKGHrcaXrAs9sKECcYwVwpgpWPMMiiyHj+YsxccXLk7msBmtrOkVqkZaIev/+ILp\nodeq1TUjbYZ6WonNnBGjdvwUCkWBX/3qVwCAzZs3o6rK2KYiFWyUksVp53QbjKi5TAIDEQG9fXFF\ncxO5NsflTYkHcH9LH3w9ArwVLKaOKku+LmFltaFU5tYAOrtmCTpbslTbwrRm4EiIx+F69AlUvvUu\nbB3tiNcOR2j6DPiuuQ7gtDdYzP4+k1yPBXChEMB0IYgQOLjBW1JZy4S3O0xxhLQSJhIBSei1qZRY\nmyF//0+AeBzM1tfBtJ2yzuFVwoznlEKh5AVRFLF8+XJs3brV0PlUsFEGFVpzaWouk6kc/rILw6oc\n6OjKdkuUa3PkWAYrp3uw7PwqBMI8ql2crEGJXgZjK+SgpZCLp1AICPdBHDkCTHNL1mHZliy5tjCH\nwzSrf9ejT6RVY+ztp+DZ+hoAwHfdDfr/HfOIDaKpBiNWk4/qWnWVTTP0Wm+YuuYGSim1GUqzdm+8\nBeZkK8T6eoiXfsOamIwhFslBoQwGGIZBQ0MDgsEgPB79plL0yaYMCuTm0mZMSsy1pRqEqLlMptLZ\nFcHXzyjDLhkdJNfmKOG0MVkGIxJWtUIWi1gjbYccdNW1Qi6eMu+tIBRVW7JS2sK4u9YZsiXPaosM\n90GpGuPeuxv+FauIjUco6uRDrAEDodfciezfYzmFXqtRQm2GWbN2J08Cv3kasNtMj8kYipEcFMpg\noKKiAldccQXmzJkDd8rvrzvuuEPzXHkPYwqlxJDm0tqDEYgYaG185s3jae+TXCa18JazWDndgwVn\nuVFbwYIFUFvBYsFZ7qw2RxIG89zaUEdaPLHNzWAEAWxzM7j/+DW4u+8190ahEHD8eOJ/le7d0wMA\nECsrIHIchDFjwN90I1lLlta8UCh7RlMJtqNDsRpj6+gA51duS6YUH54JDcnQazmi8+bobock3UDh\n7/8J+JtuhDBmjP7vdL4w8dkpqntRKBRTmThxIq688krU1tbC7XYn/yGBVtgoJY/aXNqeT31YOX9s\nWntkqsvkqYB8pW3qqDK4HawpbY7FMrdWDGJt0FXX8pG9pFTBW3un4r1FTzXib2wFTh9Lfn8T54XU\nqjHx2lrwNdqbJhRt8lFdS33e1EKvNUmJAqg45xzyD5CnIPmcyOesXYnN9VEolAFWr15t+Fwq2Cgl\nj9pcWkcwAn93NM2cJNVlsiMYwdbdJ7H/qD+ZtZZpFqLW5igRiYumza5Z0QpptVgbsu6QeVg8KbY/\nBbuU733yZKLioWdhm+O8UFpbZH81JnWGTSI0bQaRW+TI0VUlZaSTb/LVCpmGQui1Kv3mM6lRALhs\nsf6WYTMdHc0mn7N2pTTXR6FQ0ujs7MRDDz2EkydP4rnnnsMnn3yCDz/8EFdffbXmubQlklLySHNp\nctR6nKiplLfJdto5jKx143vfHo/HfnA+nvzRVDy4+DSsnO5Jm3tTgxdEPLcniB//8RTu3NyOH//x\nFJ7bEwQvJJzlimVurRgoyeqaTBtiGv2LJzmyFk9a11K4v2IFb+cuiCNHkN2bBGleSO56BuaFwmtu\nRnjFVeBHNEDkWMROOw3Bb/9dwiWyH1NExBDEaNacXhSfNyn0mqANUjKf4U6cBCMI4E6ctKZluJCY\n/OwUzb0oFIqp3H333WhqakJXV2INN27cODz//PNE51LBRil51ObSpp/pJcpoc9o5VARP6q6ObdrX\nhTc/CaGzV4CIgXDtTfu6hszcWjFX1wyLtXgc3F3rYJs5G/amC2CbORvcXeuAeIZzIMniifRacqhV\n8E6ehHjRbPV768TUeaH+akzwpU0IvvIiuje/mHCHJLD0zwUuFoU72A4ulu3wKhEHgy7YEEfuTq6l\nQK6tkIZRMZ8ZbPNW+Zy1K4m5PgqFkkVbWxuuvvpqcP1/DzocDrAsmRSjLZGUQUHqXJrkEjn9TG/y\ndS3Ugn+ViMRF7Gvukz2293gIC6oAhwVr02JqhSSlUNU1o+hxYVPNMyO9llIkgEb7E//TB4CqKsV7\n60ZpXigUApqbjc0PSdUYi2EEHo27NqP+2CG4uv0IV9agddwUHJ69BCKbeBAFAB8oBGOXyu5lQVoh\nDaJmPjPo5q3yOWtXCnN9FAolC1tGG3hXVxdEkSzrkwo2yqAgdS5NLYdNDiNiDQACYR6+XkH2mD8C\nBGPAcELBVopza8Agra7pNRJRWzyFQmC2yIdkMlu3AWvvBPfgBuVIAC1b86oqaxZu0rxQf3WQNLIg\ny96fALPs/Rt3bcb4A+8lfy7v9iV//mjOUgAJsXaYGxA8PbDjMGcHkAjMLnYK3gqpEzXzmUE7b5XP\nWbtinuujUChZXHrppbjnnnvQ29uLl19+Gc8//zyWLl1KdG6pbCpSKEQ47RzqvS5isZYL1S4O3nL5\nR6jGCXjsZNcp1bm1Ys9dMwyJkYgc0uJJEkzxOLj/d6dskLV0LSn3TC0SgKj9KfPeJpG3yAIFSAUK\nF4ui/tgh2WP1xw6Bi0URB4MvWZfse75kXYOyPbLgM4IqUQBDet7KyDwrhUIpeb73ve9h2rRpaGxs\nxHvvvYdVq1bh2muvJTqXCjbKkMZodU1yhTxvpLzZyZQasnbIUp1bK3ZyMhrRYySiAnf3veB+t0lR\nBogNDWB27pI9ljbf01/Bi/9lJ2J730f8LzsTVTWrQ7ktyHtSEt65CgtnKAhXt1/2mKvHD2coiBA4\n9Cg0lfTAhhCs3+TJhXy1Qpq9ORJeczOdt5LIZZ6VQqGUPO+//z4uu+wyPPbYY3j88cdx+eWX4/33\n3yc6lwo2ypDFiFjLdIX861cRjKmxYVg5AwaA1wnMrQeWnG7uZy22VkhSSq661j9LJi64VPYwcVVA\nRewkr3XRbDBfnZA9JlvJs6iKpojRSmMBiLg9CFfWyB4LV9Qg4vbADR4VkF8YVyAON3grP2JOlFor\nZBqF2nAoQgpdsaZQKIXl3/7t34hek2Po/cakUGC8sia5Qkp09gro7BVwyZluzCoPwWMnNxop1bk1\nYBDOrsmEUwtTJgOBIJgTJ/SbeaiIHQAQzjwT/IYHwOz6c+HylJSMTiQM5j0ZmWNTgySPjbc70Dpu\nStoMm0TruCng7Q7YIGKsEE7OrKUyVgjDBrLB73xjRKwVvBUyheTzONTnrfTOxlIolEHDl19+iS++\n+AI9PT14772Bv6e6u7sRDoeJrkEFG2XIkUsbpJIr5F+/DOHbXzNfrOmhmCprQGlV12SdHJubwd/w\nXcR/dJN+M4+6ukTLo5JoC4cBm03dUMSqxZuMOJU1EtEyPDH4+TwTGmQjL3I1Hzk8ewmAxMyaq8eP\ncMWAS6TEzH5jETmXyKFOXivZQxGSivVQFrQUyiBm//79ePnll9HR0YGnnnoq+XpFRQXuuusuomtQ\nwUahEGKWK6SeRWmxGY0Ag7C6prbz/eafgPX36hcnbjfEORcBv9skf90TJ4C2Ns1IACswM7IgX5BU\n2USWw0dzluLjCxfDGQoi4vaAtzvS3sMi4QY5XUjMtLnBF21lDRhE1TWK4Yo1hUIpfa644gpcccUV\nePnll3HllVcaugadYaMMKXJp1TLLFZKUYmyFJKWUqmtWzWrxGx6AWFEheyy5QMv3fI9eIxGbDfw9\n6xDf9FvEdr5L/PkKuVDn7Q6EPMOzxFoqNoioQpyKtX6seNaoWMtAqljLMKQdMymUIcTo0aPR29sL\nAHjxxRdxzz33oFlmE0cOKtgoQ4Zc52qcNgZNo8tkj5ntClmsYq2Yq2uGMckVMo1QCOjshHDV38tf\nN3OBli9DET3iNNXRbvY82K5aCW79A6Y42hlxi8yX8YYVcLEo3MF2cLEo0fvz+e9qqlgL94FtbgHC\n8q3jRUee7fWJIjooFEre2bFjB775zW/i0ksvxa9/nT0GsGfPHlxxxRU455xzsG3btuTrH3/8Ma66\n6iosWrQIixcvxtat8rmrEuvXr4fb7cZnn32GjRs3YsSIEVi3bh3RZ6QtkZQhgVkmCMubEgup/S19\n6OwRUONMiDUSV8h8z63xDIMoZ4eDj4ET81dJKFR1zfCOvpmzWmaYl2iZgeSCjrYsPa2TFHkYgUfj\nrs2JubpuP8KVA3N1ImtujEBBWyHjcbgefQKO7TvAtrZpBqwXHNI5TrPpr6ibHnRPoVAMw/M81q9f\nj40bN6Kurg7Lli3DJZdcggkTJiTf09DQgIceeghPP/102rllZWXYsGEDTj/9dLS1tWHp0qWYPXs2\nqqrk10E2mw0Mw2DHjh24+uqrsWrVqjQBqEYR/ialUMzFTMc6jmWwcroHC6pCCMZA7AqZz7k1AcDn\nNQ1od1chYnPAGY9ieKgL4/0naUldAcVZrbV3JnbgCRdWOZmX5GMRSSpOTXC003KLNGI+QjLLVkw0\n7tqc5lxZ3u1L/vzRnKWy55RiK6Tr0Sfgev73yZ+LXdxbthlButky1B0zKZQi4uDBgxg7dixGjx4N\nAFi0aBHefvvtNME2atQoAADLpq+izkh5juvq6uD1euHz+RQFWzwex4EDB/DWW2/hvvvuA5AQjCTQ\n9RuFopPg0ZNwcMDwMnJXSFLMaIX8vKYBLZ7hiNidAMMgYneixTMcn9fktjgjaXR7zawAACAASURB\nVIcsueqaROYs2a53Ey/PmkcecKtlXqKxiNOd0WSwnYuoLasEMtj0thnmGy4WRf2xQ7LH6o8dkv3c\npSjWEO6DY/sO2UNGA9YtxYJAeBqITaGULm1tbaivr0/+XFdXhzYDf8cdPHgQsVgMY8aMUXzPLbfc\ngnvuuQdf+9rXMHHiRBw/fhxjx44luj6tsFEsJxLj4e+OoqbSAafdZIWjgZnVNQCyFQEt8jm3xjMM\n2t3yi752dxXGBVrz2h5ZcvTvfHN3rdO/A5+LbbeeilaulTiStiyTHO2MZrKpVtlGlKNm07N5aTPM\nBWcoCFe3X/aYq8cPZyiIkGd48rVSndFjOzrAtsovbrK+91a2+yqReU8L7PVp+zCFkhvVo6swrMb8\ntu6YX97Z22xOnTqF22+/HRs2bMiqwqXyjW98A9/4xjeSP59xxhn4xS9+QXQPWmGjWAYviHh62zHc\n8u8fYvUv9uOWf/8QT287Bl7Ij2AwW6wZId9za1HOjohN3h0vYnMgKhMaTMKgrq5lYnQHPhfzEh0V\nLcVK3M1r9FUH1IxO8uRoZ+TPe9SWTRh/4D2Ud/vAQky2GTbu2mzKZzKLiNuDcGWN7LFwRQ0ibk/y\nZ6NireDVNQBCbS2EevnvdvJ7X4gKlNI9hw0z12TIioodhULJG3V1dWhtbU3+3NbWhjodvwd6enrw\n/e9/H2vWrMF5552n+t5wOIyf//znWLp0KZYuXYpHHnmEODibCjaKZTzz5nFs2X0S7cEIRADtwQi2\n7D6JZ948bvm9rRBrRqprpJBW17QcIR18DM64fIuYMx6Fg4/p/mxDDqPtgLmIHFKxp7I4ZJ/fBNv0\nr5u2EC5GRzs2GkH14f2yx5TaDAsFb3egddwU2WOt46aoxg6QUDR5a64yROfNkT0kfe91t/uagOI9\nH9xg7mZECbQPUygUZaZMmYIvvvgCzc3NiEaj2LJlCy655BKic6PRKH70ox/h8ssvx8KFCzXff999\n9+HUqVNYu3Yt1q5di/b2dqxfv57oXrQlkmIJkRiP3Ud8ssf2fOrDyvljLWuPLBaxZkUrpBacKGJ4\nqAstKa1WEsNDXYbaIYdUdQ3IqR3QcNA0qRmI2uIQ/QtEs1qxTHK0M9N8xN4dhDMg/3tFrs2w0Bye\nvQRAQky6evwIVwy0b0rkuxXSisy18Jqb4ajyyH/vTTCw0Y3GPaUZVVMC4WkgNoVS0thsNtxzzz24\n4YYbwPM8li5diokTJ+Lxxx/H5MmTMX/+fBw8eBCrV69GV1cX3n33XTz55JPYsmULXn/9dezduxeB\nQACvvPIKAOCnP/0pzj77bNl7HTp0CK+++mry56lTp+Kyyy4j+5y5/6tSKNn4u6PoCEZkj3UEI/B3\nR1HvdeX5UxmjWMQaad7aeH/i88q5RA4mLAvmzcXmPweRIyv2Lv0G+OuvS7RVud2qi8NUTF0IF5Gj\nXazSg0i1F2X+zqxjmW2GxYDIcvhozlJ8fOFiOEPB5OdzdfsQcXtQP67W0HWLoRUylYqJZyp/75ub\nTZ8Z00Sr6tXZaZ69vpmxIBQKpSDMnTsXc+fOTXvtlltuSf7/c889Fzt2ZJsrXX755bj88st13SsU\nCsHd/3uBtB0SoIKNYhE1lQ7UepxolxFttR4naipzawdSYijOrWXCApjoP4lxgdacc9iKubpmJYYr\nZbmQKva++grcr54C88ZbsG/8rzRjEaXFYSqWLYQNYpb5iOBwItA4FfW73sp6rxlthlbB2x0IV3rT\nMtmiNV4EGqeiZdFygCPvNiiaVkg55MR9ISpQpPc0aTOiIL8vKBRKybF48eJk0DYAbN26lVjwUcFG\nsQSnncOMSV5s2Z1d1Zl+pteSdshiaYUkxay5NSU4UYRLYZ6NooHRSpkZWWpuN7jf/Be4pwYCOtNc\n59beCea3z4Pt6VG8RKm1Yim1RcrRsmg5AKD68IdwBHwIV1RntRkWI5mZbGX+zqTwbLlspeX3t6y6\nplXpLkQFyqp7Krlc0kBsCoVCwI033ohJkybhL3/5CwDgtttuw5w58jPAmVDBRrGMaxckdi73fOpD\nRzCCWo8T08/0Jl83k2IRa4WYW7OKYq6uWdYOmQnJDnzKIo5b/0Du9t5aMz/X/AMYDee5wdSKlTXL\nxnFouWwlTixcBnt3ELFKD5rb5NuviwW1TLbqwx/ixMJlEBxOzevktRUy3Ae2owNCbS3gKlN+jYBC\nVKBMvSfpRkwRtQ9TKJTiZO7cuZg2bRoAoLy8nPg8KtgolsGxDL67cBxWzh9raQ7bYBZrRqtrlDyQ\ntYgbAfjl/7x0zZRpzd8Ayu1eHAfhumtLshVLT5UNSLRHRoadBgAYOdpZ1JsgaplsjoAP9u5g8t9F\niby1QsbjcD36BBzbd4BtbYNQX4fonIsAiHDs2DXw2rw54J54jOyahahAmXhPmrNGoVDM4PPPP8cd\nd9yBzz77DABw5plnYsOGDRg/frzmudTWn2I5TjuHeq+LijWdFFKs0eqaNtm24S2KbYq67L21LP5P\nH6toSy7847Xgf7aBvP0yj2j+uYX7YGs9CSaSXS0j+S4Vc/B0xO1BtMYreyxa7UWsUt0sJRexpvcZ\ncz36BFzP/x7ciZNgBAHciZNwbXoBrk0vpr/2/O/12/KrZf9ZRa73pDlrFArFJH784x9j1apVOHDg\nAA4cOIBVq1bhxz/+MdG5VLBRKHnE6rk1Sp5QWcTJoWumjCDPTTEjbUMJ7vbH43A9/Ag8y5Zj1M0/\nxMg1/wTvxqcAnk97W1GbbWhQP64WgcapsscCjecTtUMaQfeGSLgPju3ZTmhKDAnBUuictVAIOH58\n8P93plCGAKFQCEuWLAHDMGAYBpdffjmxU2TxbcNSKISUWnVtMIm1oV5dU1vEyaF3pkxz/qZYTQ6U\nTBlUkCo6Evb2U/BsfQ0A4LvuBl23Hzm6quhaI6VnJdMsJVrtRaDx/OTrSuRTqLIdHWBbyQUIsRup\nge+Fbqy6x7BhEN1uMDLVc0vNfcwwMKJQKEVFY2Mj9u7dm5xh27dvHyZPnkx0Ln3qKSXJYBVrxQBJ\nO+SQR802vLICoqcazMmTxo0OSAVZsZgcEC4us+z9VSo67r274V+xCqJzoPokF6adSTGJtrSNDRmz\nFK3KGolYYyIRcH4f+Bpv2n8rIxsiQm0thPo6cCfIfhdqChYrREemMLNY2HAPblBsdbbS3IfOzVEo\ng49PPvkEq1atwpgxYwAAzc3NOPPMM7Fs2TIAwEsvvaR4LhVslJKj1MSaHmh1rURQsQ0XVq4Ab1bl\nq1gEmQZGF5dqFR1bRwc4vw/xev3fFTnRxsWiyfBqPXltRs9Tek5SzVLU0HyOeB7eZzfCvWc3bB3t\niNcOR2j6DPiuuU5XrlsarjJE581Jq3iqoSVYdH0vtCpkCsIMggDu10+R3SMTrXuqtD6LlRXg196p\nfn2jaM3NkRoYUSiUomLdunWGz6WCjVJSFEMwtl5KqRWymKtrRdMO2Y9q26LNVhJCSzdyC9wcFpdq\nFZ14bS14GaOOzCobG43IVqwk0cYIfFpgdbiyJpnbJrLKwoYReEze8T+oP3YIZb1BhCu9ROdJ91ZD\n6TOn/jtq4X12Y7J1FEhvJeUf+BfN85UIr7kZAODYvgNsWxuEugyXyLZTZJVj0u8FYYVMSfyJFRXa\n98iEtCqn1vocCgOdnUCVBWY3JHNzg/H3C4UyyJkxY4bhc6lgo5QMVok12gpJjhnVtUFDsc6RWYHa\nAlfn4jKtLVKlohOaNiOtxS8LnseoLZtQfXg/nAEfItVeBBqnJmbC+itMI0dXoea5jWmB1eXdvuTP\nH81ZKv+5BR5zfv8zVHd8pes8TadKgs9MAhOJwL1nt+yxir/uQzDcpysnLQ2bDeHbb0V49Q+zMte4\nR34GnvS7Tvi9IKrCqZn8aDmzyggb4sqfWuuzlfNrhbovhUKxhPvvvx833ngjTjtNvrviT3/6EyKR\nCBYtWqR4jYK4RL7++utYtGgRzjrrLBw6JB8mSqHkg2IRa0OpulbSZiNyFMKqPM9kRxg0g/uPXyds\n3bViCDQWl+E1NyO84irwIxogciz4EQ0Ir7gq0dqnwLAzqjFqyybU73oLZf5OMKKIMn8n6ne9hVFb\nNiXfx0YjGPm3j2SvUX/sELhYVPbY5B3/kybWtM4bObqKKFaA5DOTbHpwfh9sHe2yx9i2NrAdHZrX\n0MRVBmH0qIRYC/ehAkziddLvOsn3gtQyX6fJT9o9MtFj00/g2GoJhbovhUKxhK9//eu4/vrrce21\n1+KRRx7BM888g/+/vXsPj6I+9wD+nd1NQsKSDTc3EC6CgjcooKgUETSSRkmxKlBbrEfwUPRYUKTF\nC1gooB4raIRSUQ4i5bEWtQoWoiKFItjiradHbNFaLNEgZCEQLjGbLHs5f2x22c3Ozs7szuxc9vt5\nnj5H9jqB3Zz5zvv+3t+qVavw4IMPoqKiArt27cLIkSMlX0OXwDZw4ED86le/wqWXXqrH25MJcd2a\n/lhdy1GpTnAB5SeX3hbY6g4A3pZoRefE79fjxIZXcOL36+GdMxuu83olPSShtRVd//mx6H0l//gb\nbL7wXm55p06g4Pgx0ccVNjWioPlEwu320z6U/jv5hcTCU8finid3/zebrxUl//hfyWOW+x0KdO4C\nf7fuovcF3e5wVUwNMVsu5F0yAo7LR8H+wDzA70/9XDmhQ+7IfKnwl6QlMulnT+GY/qRbaGi8Ob1e\n70tE6isvL8emTZswc+ZMdOjQAV988QUaGhpwySWXYP369Vi4cCE6d+4s+Rq6tETK2dGbKMKMYc1M\n69YAVtdIgowT3JTbEES0tVaWtLVWBkvd8F01OrxuKlLRkUGqwpR//BjyTp1Aa9ezcLqTC60lXdCh\n8WjC47zOzmgtStywuqD5BDp8k/z729LRhdYil+KNuqXCY/7xY+juCkJGDAIAhAoK0HzpZXFr2CJ8\nV41Ovx2ynfZbLiidVJjycyG39U9iyE9o8g8QsNlSf/YilLYb6tX6nEst10Q5Yvjw4dGR/kpxDRsZ\nGoeMGAOrayai9n5Uck5wZZ5ctl87ZD94KBoIvHNmJzzedW4P0YsrkQpT3pHDCff5SrrgdKdwEAvm\nF+D4RRej9N2tCY87NeQSlPbvFvd9FYIBnPO3PwGCAIRCYn8bOPmt8POUkgqPge7iA1akRFpGiz76\nAI6jDeHhIJHwqwaJLRdkTypM9bmQCmLtKmQph/zIDTYK3rP983QZ9GGSSbFEpC3NAtuUKVPQINJH\nP2vWLIwdO1artyUL4ZCR7GB1zSLEBoNUViBwxzSgrCz98KbkBFfq5FKitTJ/x054Z9wluzIkVWE6\nftGwuMmLqTasjr0Y0esPv0XpJ+8mfd9vevbBgetvSXq/1PRHqfCYcsCKGLsdx6ZOQ+PkW1FS7Igb\nDqIGqS0XFE8qlPhcyK7Oygh/co9H9nsSERmEZoFt7dq1Wr005QAzhjUlWF0jtYlOvlu9BrbVaxDq\n3TujzYRVOcGVaK2MDMoQa4lMVmWLqzA1NKDVFR/EomRuWC21xiwoCGi4ZBS+mjBFfJqjzOmP7cNj\noHs3NA+/THLASirFF52NYNrPTi7YrVt2JhUqbf1To+LEdkMiMhm2RJLhGCmsKWHGVkhW11Smdjui\ngvdNOhgEKmwmrMYJrkRrZVqDMmIqTPbGYzhywiYaxKLvkWLDaqk1ZkIoBNfnf0evmvWiI/gj0x8j\nItMfAcRX5GLCY3dXEIHOXZRX1mJougF9YYf0WgelSH0/9Gj9Y7shEWVRTU0NKisr4UjjwqkuUyK3\nbt2K0aNH429/+xvuuOMO/Od//qceh0GUktqtkEYKa3KwuiaT3w/7A/PguHxU6ml6zc3A/v3x48Mz\nJXPsubD5jTPv2/445PwMSrcwiH0PiamBqQZlSAWTUEEB/KU90Pm81BUfm68VBUcPR6dIxoqsMRMj\nACg42Zgwgj/ymqmmP7bX+Tw3/KU9MgprWnP266/epEIl3w8iIovavHkzysvLsWzZMng84i3nyehS\nYauoqEBFRYUeb00GZ6TqmhatkEbC6pp6ZG3EK7X5dBpX2+JIVK9iCQcOwD57DlBcDOHNLXHHgWAQ\n9lWrpX8GMZGqSadi4NTJcPUkP1/8Z/3Fz8OvHdNa2Trq26oNyujar0T8eyujZVFqjVmskn/8DQev\nnRit5qWa/hiZWBl7jGrQtLoWoVLroOyNqo1Ir6o5EVnOypUrceDAAbz00kuYMGECLr74YkyePBkj\nRoxI+Vy2RJJhmDWsZbu69vnhIxh4lvgeTGpidU2mVPuUtU3T0/SkVWIwSNzxALCvfzn+trbjCCbZ\nzyrpRMDYAFpXFw4+gQBCvXoBnUtg++TMhtUJP2tMALAXFQEyvvvJ1rK1Jxba5LYsRtaYdd7zEfJP\nNka2io7TPoRJTX+MnVgZOTY1JA1r3pbwWsAMB5AkXDDJpHVQ5vfDcLS8wEJEOatXr1746U9/iquv\nvhqzZ8/Grl270KtXLyxYsEBy5L8uLZFE7TGsyfP5YfG9p4wqK1UAvdV+CeHAAdG7otP0Up20qtAe\nGfjFzxEcPAghux3iA+mlCU1N4reLbCYMnKma2OrqwuvkAgEIAGwHDsSFtbjXivysSlsrFYoNRopa\nFtvWmO2dtQi+YvFNTNuHsEhlTkzsxEpNw1rMBteuGybBNfEHKFzyZFoth7Kq20raehVuVG0UcZ/v\nYBC2ujrYn1kF+0ML9D40IjIpn8+HjRs34uabb8ajjz6KWbNm4f3338eCBQtw3333ST6XgY10Z6Sw\npoSeYS3T4JaqHVLP6ppp2iEj63Junpx0z67oNL0snLTaf7EYtk/+Hg1OahGdCCgRQKUk+1nl/pun\ncwFATstiewFnJzR+S/xKZ/ttA4BwZa5+VAVaOndDULChpXM31I+qiFbs5HxfhNZWOOoPQWhNXPOW\nSmSDa/vBQxCCwej+dkWLHgW8LYpfT1RzM/Cvf8H+s/uVrUVra9cVk9a0SS3WgIq8h9YXWIgo95SX\nl2PXrl144IEH8Pvf/x433HAD8vPzMXz4cHz729+WfC7r+qQro4U1o65bM1tlDbB+da19i6OY6DQ9\nOZtPZyLNABWnkxM4lVhlE50IKHPIScJrqTkOPoVISGr0tcpuWYyVav+2OBJbB6QMa4EAuqx7HkUf\nfgBHwxH4u3VH86WXofHmybCfPBE3SVL0OyWxwXXBphrkffhX+MrHhNcJpmjlEw3O7VpfYy8GyGrr\nTXejaqnjEGtRVHOtmZwLLJwuSUQKvfbaazjrrPhpxU1NTXA6nXjkEemlEQxspButwlq6jNgKqUVQ\nY3VNBRIBKQQg1KsXQt8dd2aanlonrcmkGaBiBX/4A8Bmk7fXmswhJ+2p8bPKXcsW0fk8d9JhImLV\nsiiZ+7fFit06QO73pMu65+M2AM87chiuNzbDuX0bbK0t0QAXWPig6PMlN7gGYK+vR+GLLwEAvHNm\nyzqmWHIuTKRaixaYez9w4iSEXe9COHQorX38kq4BDQbDn1s115ppfYGFiHLSHXfcgQ0bNsTdduut\ntybcJoaBjXShZVizwro1M1bUYlm9uiYZkOw2+F9+EbjwgribVdl8OhmFASp40YXAl1+dWbfWNnAk\n8PBCeRMBZQw5CQ4eFD5Jl/mzOvv1R9PevbKGZigNbd4Zd+JEcQEKdr+XulrW/udIsX+bGLlhTWht\nRdGHH4jeZ2/xAjgT4LwlHUUDV7BbNwRL3bAflP77yN+xE94ZdyX9exW9WCKzcpu06pRQFeuJ4M2T\nwtW4YukLQ3KPQ3hxPWwx6y9VGeaj9QUWIsopfr8fp0+fRjAYREtLC0JtyyhOnToFr9cr6zUY2MhS\nrLBuTcuwxuqaSiSvwPcCzu6b+ByVRqSLkjklMurLr+JOctHUFB7pb7OFj1FGu9eZANo2JdLWNiWy\nd+8zFQ6fT97P2nZi7/rDJtjqPQiWuuG7arSsNj5Z2jbZFibfilOffCmrWmbztcqurEUo/X7YG4/B\n0SDv+540cBV2gO+q0dEqWjI2jycchnv3Srgv6XdPZuU2WdUpsSp2AHhxPVBcrCxMSbUoJhuWk+EE\nysDDC4HT/vDn2+MJV83VusBCRDnlmWeewYoVKyAIAoYOHRq93el0YurUqbJeg4GNso7r1pIze2UN\nyIHqGpDZFfhMRqRLCDy8EMKf/5J0QmMsVU5y2wfQ2H3YIs93OGT9rO1P7CNDM4DkbXxKq2xAeJNt\n5/CB0t95GXu2tZfuhYxA5y7wd+uOvCOHUz5WKnBF9rHL3/4ObPX1okNngm53uHKphMzKrehnXs1x\n/mm04Ga01ixSGXx7azislboR+s5YjvQnorTMmDEDM2bMwKJFizB//vy0XoNTIimruG4tOa3DWqYb\nZbO6Fq/9GP2Q3Y7g4EHRzaGzzucDjidOPFQirYmVbUNVEsKaXBIn9vk7dqo35TBG134l0f+1F9mz\nrUPjUQihUHTPtl4165O+TrpCBQVovvQyWY+VDFwOB7xzZuPEay+hdXyV6EN8V40WbYeU/O5FLkyI\nCAlAsE8fBO6cLl51UnMyqsRxIMn+gZmsNUsY6X/wEOyr13CkPxFlJBLWfD4fvF5v9H9y8FIRZQ3X\nrSVnhMpaqnZIOXKiutYmMkY/KhCA8MnfYf/F4sw3wk6HksEjTicgUmVTfJKrxubCEsctVVUC0quy\ntRcbuBr/6ZHcs63lzmnRiY1qOfYf4XaYoo8+gKOhAcGCAthF/h+4aOBqv1F2YQc0z5+LUCcn8nfs\nhM3jQdAd017aTkJYazgK7N0LXHgh0K0rgCRrLyvGInDnj4GynskDusqDO5KtAUUwGG7nbf8e6a41\nM+tG30RkeFu3bsXixYtx+PBhCIKAUCgEQRDw6aefpnwuAxtlhdHCmhJGDGsDz+qu6PGsrqnMiCd1\nUifIba18ap/kJp3cBwUDHySOW04bnxqhLaK7K5h0z7aCE8dgbzwGf6nKFyXa1tc1Tr4V9sZjCBS7\n4N7yOiQDl9+Pwurl4ceIrPnzzpkN74y7ZA1wAQC0tMBRcR2EvZ8CgQBgtyN04QXwb30T6NAhvbWX\nag/uSLYG1O+XP91UDo70JyKNPP7443jqqacwdOhQ2GzKmhwZ2EhzRgxrRlm3ZoTKGsDqmmJGPKmT\nOEEOTrkNgRl3qnuSq1ZolTjuZG187aUb2oTW1nBIatvrTGpNmb9bNwQ6d1H8HnKFCgrgL+0B17k9\n4P2WdOCKbJQdIbrmr7BD0sokEH+hxFFxnWi12FFxHfy7/hS+LY21l5pMRm1/HGoN84ns49apmCP9\niUgTLpcLF198cVrPZWAjTZk9rGlZXUs3rGW7uqYlU1bXAMPu0yR5ghzboqjGSa6KoTXhuN1noXXU\nFaJtfKpIsln1sf+YiuZLL4vbFy2iefhlSdsh2wc/KVKPjbvokSxwSWyUnb/9HbTe8D0Ee5XJCroA\ngIaj4cqa2LHu/TTcJtnWHqmYlpNR20t3mI9IWy9KXIDYd5sj/YkoAxUVFXjxxRcxbtw4FMT8/i8s\nLEz5XAY20ozRBowA5g9rWlBjlH9OVdcA4+7TpPQEOZOJlWqGVocDgYcXwn7aD7zxJoRD9ch/98+A\nwy57tL+SKluyzaqBxDVl/m7d0Dz8sujtcSSCn+D3xwcziccmmz4pRmqjbFt9PVw334Jgj1LJbRHi\nLpTs3RtugxQTCITvH32l7OMTpdFkVDWItvXW1SneR5CIKJXq6moAwKJFi7iGjXKDmdetZTOsGbm6\nZnaaboSdqWycIKscWu0PLYD9uTVn/ixjtH97ckKb1GbVRR99gMbJt8avKZOomiULfh32/h22b5rj\nghlCIbjerEl4LAAcmzpN9kUPqY2yBQAIhST/7pz9+p9p/3O7wwNG7Hbx0GazGTZoqUJqY/ATJ+Hf\n/nb600+JiNr57LPP0n4ux/qTJszeCmlUStshU+FG2Rloq2b539uF0x/thv+9XeHqlpX2aWpuBvbv\nD/9fEYGHFyJw53QE+/QJb2sgNeY9xfuoNdo/VfCR2qza0dAAe2N46EhkTZlUG2Sy4FdQW4u8I4ch\nhELRYObcsV30sc7/+ytcZZ0ljzlO20bZciT83fn9sD8wD47LRyHvkhFwXD4K9qVPInTBeeIvEAzC\nMe562B+YF173aDWp2npPnQwHVoY1ItKZhc4syCjMHtasUl3Llpxrh2zPwO1eaZM7rl+tNUoZjPZX\nSq3BIlLBT4wtyV476fx80Y2yd+yErb4eCIZEN8tu/9qu534jOtUzeNGFou8jIM3Jn2Zh0LWoRGQt\nt912G37zm99gxIgREIQzv60jLZG7d+9O+RoMbKQqhrXkMg1rag8bYXWNklE8rj/T0JrhaP/2pFoj\nI5tVSw0WkTNERCr4KZHOzxc3uv/rr9Fp5mzY6+slX9vpLk0+1fOzf6Z8S9mTP2PbLY1emTLqWlQi\nspQlS5YAAF599dW0X4OBjVRjxCEjShg5rBlVzlfXAHOdoMqhxx5zzV6Ehn5LdDKf3NH+7UmFtqSD\nRW75D3R5frWswSBSwU9MsLBQ/obYchV2QPDcc+ArHxM35l/0taU2Vk82dCRGdPKn2y3+eVdjE3Ud\nGHotKhFZwllnnQUAKCsrS/s1jPtblExF67Bm5nVreoQ1VteyQM0TVCOFvmzuMdduw+aQIAAOOxAI\nItSrF0LjroX94YVA3VdpvXzkgkLC74/2m1W3VdK6PL86+fTIqdMSXj8a/D58H44jR+Dv0gVBZycU\nfPVlwmObxlwN2Gxw/t9fk2+Inaa4FkmR144MGklWxUw6dCRGqKwn7CuegfD2VtHPuyqbqOshm1sP\nEFFOO3ToEJYsWYLPPvsMra2t0du3bduW8rkMbJQxK4Q1rapraoU1tYeNqCHXq2uqnKAqCX3ZCnVZ\nXNeTsGFzKASc9iM4cCD8O7aq9nMmq7ZFBosA8qZHirZHBoOwnToFAHAc/4uBaAAAIABJREFUO4Zg\nczNa+54NW3MzHEfjtwVwndcLJ7wtSTfETltsi2S7145eIJFq/7vwAgix/w5iXK64KZ5xn/f587Jf\nlVWbFdeiEpGhzJ07F+PGjcOnn36KpUuX4ne/+x369Okj67mcEkkZYVhLTq82SCNX1ywjVdtgkqmK\n7UVCn62uDkIwCFtdHezPrIL9oQVnHiQ22U/LqX2RE3sRqq7rkdqw+YsvgOYz7YNqVGXVmh4Zq8u6\n5+F66w3YW1rCAzoA2FtaUPBlLZovvgQHlv0aXz/5KwQe+Tlc57UNFYlsiJ1uWPO2wFZ3QHxyZorX\nTjbV07/1zTO322wIOp0IdXKeecy024FG8d9/whtvAbVfpq7Kqi3FBFMiIqNpbGzEpEmT4HA4MGzY\nMDz22GN45513ZD2XFTYyLK33WgOsF9ayJd3qmmXaIdVoG5S5VkyPVrOsrOvJxobN7Uita1M6PVJo\nbUXRB+8nfa+i//0IjbdOQfFFZ2d0zGcOwo/C6uUIT4b0IFjqltwcGxD5vkm0/yXcDsStWctbs1b0\nPSLfg6xNWzTpWjkiory8PABAUVERDh48iG7duuHYscSLgWJYYaO0GXXIiNrr1tIZMqImJe2QrK5l\nSVvboBjZJ6hyQp9KlTzFsrHHXGTDZjF2e/j+GGmH/XYVqWQXGyJDRMREpkfGHWKKsf6Oo0dRUqzC\n31fb8RcuqUbhiy/BfvAQhGAwujl2YfVy0adJ/n1F2v/aV0tjb4/971Sf97P7ZqcqC5lVaSIiAxo+\nfDiOHz+OH/7wh7jpppswduxYlJeXy3ouL0dRWnKpFVIpVtfEWaa6BqgzDlzOWrFsDgARI2ddT7pr\n67p1Rei88yDs3ZtwV+jCC4BuXRUebDsSFanYz3Ds75qk0yPbbo8VrcglCW1B91nKx/XHOtWEoiVP\nIu/D8JASxOzdEyt/x054Z9yl3no4MTI+71mpyuoxwZSISCX3338/AOCGG27AZZddhqamJgwcOFDW\ncxnYSLFcCmtmaoVkdS27Mj5BlRP6jLyxbyataW3PxcmTCMXebrcjdOEF8G8VPyl39usv+/dPYfXy\nuFH3kYoUAHjnzI7eHjdJMsn0SDGhggI0X3Z50rH+p4dfIus4E7QFzYLXN8H2TUwFNRQSfbjYxtta\nXBxJ+XnPxrRFvS9gEBFl4J577sGyZcsAAD179ky4TQoDGyli1LCmhJnCmtGmQ7K6FkOFE9SUJ8EG\n3tg3k7V17Z8bEbj1FgSeekL5wbSfvOhtQf6OnaIPTVaRig1usdMjk3Gd2wOBhQ/CW1yIgk01ENrC\nVcjhAPLzUVDzJvL++r8p15m11z5optJ+423NvmtyP+9aTls08gUMIqIUvvoqcYuaf/9b3nk1AxvJ\nZuSwpvd+a3q3QbK6pqNMTlBlnAQbcmPfTFrTpJ67fUe4xVIiiMZV2ZK0PbZOmgBbvfhkQrGKVCxF\nFyUcDnjv/xm8d8+A7euv0eH5dejwxlvRCZ7JqnpJSQTNZDLaeDsd6X7e1diWwsAXMIiIknn55Zfx\n0ksvoba2FhMnTozefurUKfST+fuUgY1kMeqAEUD/Vki9w1q2sLqmIamTYCNu7JtJa5qKbW1J2x79\nAQRL3bAfTLwI1L4ipYrCDgiWlSHv/z4WvVvuOjNbQ0PSoBkRsoVnhcVNiWxjyO+aylMdDXkBg4hI\nwhVXXIG+ffti8eLFuO+++6K3O51OnHfeebJeg4GNUspGWDPzujWtyG2HZHUtRxhpY99MWtNUaGtz\n9uuPpr17kbTt8d0/wzdqJApffjXhPq0qUlJhy3bwEGweD4Jn95V8jWC3bkmDZkTLxBvR+qPJCRtv\nGzKsQaUN5mMZ8QIGEZGEsrIylJaWYujQobjsMvFpxKlwrD9JYliTxuqaNKOeRFKGMtlcW6WNuZ2F\nhZJtj60//D68k29GoGcPhOw2BHr2gHfyzXEVqQRSm1KnEAlbYgQABb97OfWLFHYIB0qx1+9YFD7+\nObMTNsc27PdMy20pkm1NQERkQHa7Hf/85z/Tfj4rbJSUkcOaFqwY1lJV18ii1FgvlEImrWmqtLW5\n3dJtj+5SeOfMhnfGXQkDSWyH6uMrVGlsSp2gsEPSqh4Qrvp5vTNTVvcigTJ/x85wVa77WTh96SVo\nvm824HQmPD6rYU3p54pTHYmIokaMGIFFixbhhhtuQFHM79Bzzz035XMZ2EiU0cOa3vutaR3W1GqH\nTIXtkBrJQmASpfJ6IUmZtKap0dZWVARcP150AEVc22Nhh3BFyu9H4ZInIRbK5G4BkErrD7+PDi+/\nCrEd01INO4lyOMSDpp7S/VxxqiMRmcDOnTvxyCOPIBgMYtKkSZg+fXrc/T6fD/fddx/+8Y9/oKSk\nBNXV1ejVqxdOnz6Nhx56CHv37oXf78cNN9yAO+64I+n71NTUAAB27NgRvU0QBGzbti3lMTKwkekY\noRVSSxzlb2LZDEwiVF8vJEcma+syXJcXeHghfCdPIFqNcicO4oiQGlCS/+6fRV9f6abUQXcpgj17\nqDPsJBI0JWTrO5b254pTHYnI4AKBABYtWoTnn38ebrcbEydORHl5eVzV65VXXkFxcTG2bt2Kmpoa\nLF26FE899RTeeust+Hw+bNq0CV6vF1VVVaiqqkKvXuK/u7dv3572cXINGyUwcnXNCGHNKK2QHDZi\nPJETW1tdHYRgELa6OtifWRXeJFprWq4XMiqHA/anV+DE79fjxIZXcOL368MVsfbhOMW+bLZD9aL3\nRapiskmsQVN72EnWLohk+LkKPLwQgTunI9inD0J2O4J9+iBw53ROdSQiQ9izZw/69u2L3r17Iz8/\nH1VVVQkVr+3bt+PGG28EAFRWVmL37t0IhUIQBAFerxd+vx8tLS3Iy8uDU6R1Pdbu3bvxwgsvAACO\nHj2K/fv3yzpOBjaKY+SwpgWzhrVsYXVNAb0Dk5z1QhblvPDChEEcsSQnODY0INhdvPKVzhYA3nvv\nVj7sRKGsfr8y/Vy1tb/639uF0x/thv+9XeGqXBYqzkREqXg8HpSWlkb/7Ha74Wn3e83j8aBHj/D5\nkMPhQKdOndDY2IjKykoUFhZi1KhRuPrqq3H77bejpCT5xfBVq1ZhxYoVWLduHQDg9OnTmDt3rqzj\n5G9MijJ6WLP6ujWAo/xNTe8BC1wvlJTUuPxgqRu+EZeh8LXXE+5Lqyqm8Rq0rF8MUetzZaRtKYjI\nUIr7ngWXBstBmg9rG3P27NkDm82GXbt24eTJk5g8eTJGjhyJ3r17iz5+8+bNePXVVzFp0iQAQGlp\nKZqammS9FytsBCA3wxo3x5bG6ppCbSe2YrISmFQal29Wkp87qXH5zk7I3/0+QghvSh0CEOhRmnlV\nLLIGTe+BIZnK8c8VEVmb2+1Gff2ZtniPxwN3u/9/7Xa7cehQ+BzW7/fj1KlT6Ny5MzZv3owrr7wS\neXl56Nq1Ky6++GJ88sknSd+rQ4cOyMvLi7tNEMTGVCViYKOshLVM5EpYY3XN5AxwYpvr64WkQptY\nq+LpgQOR9/nnsB+qhwBACAYhAPBdOSp+LVwG+7OpSa+LIbn+uSIi6xo8eDBqa2tRV1cHn8+Hmpoa\nlJeXxz2mvLwcGzZsAABs2bIFI0aMgCAI6NGjB95//30AQHNzMz7++GP075/893RpaSk++ugjCIKA\nYDCIp59+GgMGDJB1nGyJzHHZCmtGWrdGqaVbXct1quwvlgk1xuVbVftWRacTrh9NEX1odM+0PEfm\n+7OpRNfKNT9XRGRRDocD8+fPx7Rp0xAIBDBhwgQMGDAAy5Ytw6BBg3DNNddg4sSJmDNnDioqKuBy\nuVBdXQ0AuOWWW/Dggw+iqqoKoVAIN910E84///yk7/Xzn/8c999/P/71r39hyJAhGD58OJYsWSLv\nOFX5acmUzBDWWF2LZ/TqWs62Q0YY5cQ2h9cLOfv1l/7d1taqaKs7kHwQSdt0yIL1L6uyP1umDPO9\nkvu50msfQiKiNIwZMwZjxoyJu+2ee+6J/ndBQQGWL1+e8LyOHTuK3p5M9+7dsWbNGni9XgSDQXTs\n2FH2c9kSmaMY1lLLxXVrAKtrqoic2JrxZLW5Gdi/39TbAMgJOJFBJKL3ud0IOp2Q2gogW+2Rhglr\ncvj9sD8wD47LRyHvkhFwXD4K9gfmAX6/3kdGRKS7jRs34sSJEygsLETHjh1x/Phx/OEPf5D1XAa2\nHMSwlpoRwxqra6Qpi51sp/w8ptgzzdbUlLICpzWzfad024fQAhcZiMj61qxZA5fLFf1zSUkJ1qxZ\nI+u5DGyUE5Tut5ZNctshs4HVtdyl66bfGkkVeKKDSHqUImSzxU2HTFmBU7g/m1JmC2u67ENosYsM\nRJR7AoGArMcxsOWYXK2uKcHqmnKmO7mkeHpv+q0hWZ/NEIBQKPx/I1JU4LQc12/K75MOG7db8SID\nEVlX9+7d8fbbb0f/vGXLFnTt2lXWczl0JIfkalgzeytktrC6lsP03vRbY8kGkRRWL48fKlJfHzdU\nJLIPW/6OnbB5PAi6Y6ZEanisppTtjdtTXWSYP8+ca0iJyLLmzp2Lu+66KzoZ0m634+mnn5b1XAa2\nHMGwlpoeYU1OOySra6S5bJ9s6yAhtHlbIDVUxDvjLqCwQ/xWAN26namseVsSb8vw+Ewtsg/hM6sS\n7gpdMVL997P4RQYisp5zzjkHb7zxBvbv3w8A6NevH+x2u6znsiUyB1gtrGkhlytrRLpv+p2loRGx\nocjW0CB/qEjbVgAo7AD4/Shc8iRcE38A1w2T4Jr4AxQueTKjdVOmD2tt4jbYttkQdDoRcjphe+ll\n9deXtV1kEKP6RQYONSEilfh8vmhI279/P/bt2yfreaywWZwZwppSalfX9ApragwbUau6xnZI0mXT\nb78f9ocWQKh588x7Vl0Xfk+NNqaOVNoiQ0XsBxN/d0kNFUloo8xwbzarhDUAcfsQ2n92P+wvro/e\nJdTVRatvgcceyfy9pCp6al1k0OHzSUTW9dvf/hZLly5FSUkJBEEAAAiCgG3btqV8Ln/jWFi2wlqm\n9G6FNLJU7ZB6s9TJZq7TYdPvyNCIiIxO6hVs1hwJbb6rRseFr4ikQ0VktlHKYfXvjrDrz+K3q7i+\nTOuLDKp+Poko561ZswabN29GWZLuAClsibSobIY1rltTjtU1Mqxsbfqt1mRKqdHuEq1szn79z4z1\n79kDIbsNgZ49omP9xShqo5Rg9bCWtYmRbRcZ/O/twumPdsP/3q5wkFKj+mXhyalEpI/u3bunFdYA\nVtgsyYphTS4zhDW5WF0jS1NpaESyKojw578Ax09ItrI5BwwEnl6BE3v3yhogkm4bZfT9cuU7k+0h\nNpGLDGriUBMiUtnIkSPx+OOPo6qqCgUFBdHbzz333JTPZWCzGLOENaW02G9NL6yuEUGdk3qJKojt\nk79H/ztVK5vzwgsByPj92bY3m6I2SuRQUIvIxvoyreXA5FQiyq6NGzcCAN56663obVzDRprKNKzl\naiukXKyukeWpcVIvUQURk2r9VLL92mLJ3ZvN9N8RBWsCxegyxEZNVgidRGQo27dvT/u5DGwWwiEj\n8ugZ1lhdI8PL8ERdiYxP6iWqIGLktLKJBa24360OR9K92Uwf0gD1JiPqMMRGbaYPnURkCAcPHoz7\nsyAI6NKlS1xbZCoMbBZhllZIrltLzejVNbIoPUaYZ3pSL1EFEZNuK5toEGtuBgoLTRlEpKg+GVGL\n9WXZYoHQSUT6u+mmmyAIAkKhUPS2pqYmDB06FI8//jh69uyZ8jUY2CzAqmFN7XVreoe1bFTX5Mqk\numaJKgIl0HWEeQYn9WJVELiK49awRajSymblvblSTUZUaRy/6Zg5dBKR7t57772E2wKBANavX4/F\nixdj5cqVKV+DY/1NzixhTalc228tItPqmtx2SKI4Zh5hLjLa3f+nrQjcOR3BPn0QstsR7NMHgTun\nq9LKFgm2tro6CMEgbHV1sD+zCvaHFqjww+gsW+P4iYhynN1uxy233IL6+npZj2dgMzEzhbVcXrdm\nNKyuUQIrnKjH7h+n1f5cZg62crStCRSj62REif30iIjMLBAIyHocA5tJmWXACMB1a4C8dshU1TW1\nho1QjpFzsit5ot7TvCPM1d4E3ArBVkpkTaAIXSYjSm2KTkRkEl6vN+F/hw4dQnV1NQYMGCDrNUze\ncJ+bsh3WuG6NAFbXTEfJWquiIqDEBYhNW3S5jLFuKYvTK5PKgb25jDQZUdd1lUREKhk2bFjc0JHI\nlMiRI0di3rx5sl6Dgc1kzBTWlLLqujUjVdc4yj93KDrZbW4GGpN8r46fCN+vV0gy0pAPs+3NlU7I\nNcpkRMn20zdzdwAKEZnOZ599lvFrsCXSRMwW1rhuzTpYXTMZpWutPB4I7faJiT7+4EFdW/2MNuQj\n8PBCzQaaqEaNVkK120mV8niS7q8n1NWZv/2UiEgBBjYSlc2wJpdVwxqra6Q6pWutDDxswnBDPrQa\naKIio4XctHQqBux28fts9vD9REQ5goHNJKw6ERKw9ro1NfZeI1JMaQAz2rCJCCMP+dC7ApWMEUNu\nOk6dBJJNTwsEwvcTEeUIBjYTMNNESKXUboU0UlhTgxGqa2yHNKE0ApghW/2MWvkzmthJoEYOuUq4\n3Qj16iV6V6h3b/7bE1FOMU4PB4niujXzDRmJUGPYCFG6FE/7M8qwiVhmG/KRbWIDWb5TYY1JlkVF\nCH13nPi/fdV1/LcnopzCwGZgVg5rcll13ZocrK5RRtINYJFWP4Mw0ph5oxGdBPrcGgQHDxLdosFs\nIZf/9kREYQxsBmX1sGbldWsAq2tkIAYLYIoZsfJnBBJr1XD8BALTbofw9h/NHXT4b09EBICBzZAY\n1sJydd0aoF51LROsrpGhmD14qk1qrdrBg/D/5E5g0QJrBB3+2xNRjuPQEYOx8oARwPrr1gBjVdc4\nyp9MKXaIBomTM5DFqJMsiYhIEQY2A9EjrHHdmvEYobpGpAs1NnzOFUbdioGIiFTHlkiDyIWwZvV1\na4B1qmtshyQ9iA7RaPtz4LFH9Dosw+JQDiKi3KBLYPvlL3+JP/3pT8jLy0OfPn3w3//93ygulq4q\nWBnD2hm5vG4NSF1dI7KsVBs+z5/HqlF7HMpBRJQTdGmJvOKKK7B582Zs2rQJZ599Np599lk9DsMQ\nzBjWlMqFdWtAdqprHOVPlmWVDZ/1wLVqRESWpktgGzVqFByOcHFv6NChqK+v1+MwcpIaYY3r1rTB\n6hrlNDlDNIiIiHKQ7kNHXn31VYwePVrvw9CFGSdCct1a+lhdI5LAIRpERESiNFvDNmXKFDQ0NCTc\nPmvWLIwdOxYAsHLlStjtdlx//fVaHYZhmbEVkuvWkpPTDklE0jhEg4iIKJFmgW3t2rWS97/22mvY\nsWMH1q5dC0EQtDoMQ2JYO8Ps69bkSlVdU2uUP6trZGocokFERJRAlymRO3fuxOrVq/HCCy+gsLBQ\nj0PQjRnDmlassm6N1TUilUWGaBAREZE+gW3x4sXw+XyYOnUqAGDIkCFYtGiRHoeSVWYNa1y3lhkz\nVNeIiIiIyJh0CWxbt27V4211ZcYBI4D+rZBGD2tWqa6xHZKIiIjImHSfEpkL9AprXLd2xmcNX+Gz\nhq+y/r6srhERERFRJhjYNGbWsKaUFmFNrepabFBTM7SxukZEREREWmNg05CZw5oWm2MroUVYy7Zs\nVdeIiIiIyLoY2DSSS2HNqOvWtAxrRqqucZQ/ERERkXXpMnSEtMGwFqZnVS2C1TUiIiIiUgMrbBrg\nRMh42Rwyko2wZpXqGhEREREZHwObyszcCqm3TKtrRqisyWGU6hrbIYmIiIiMj4FNRWYOa3pX18wS\n1uRU11K1Q6qF1TUiIiIi62NgUwnDWiKrhTU1sLpGREREREowsKmAYS2RHptjGwGra0RERESkJga2\nDJk5rBmBmaprmQ4bSVVdyxZW14iIiIjMg4EtA2adBhmhd3XNTGFNjkyra3LbIVldIyIiIlLHzp07\nUVlZiYqKCqxatSrhfp/Ph1mzZqGiogKTJk3CgQMH4u4/ePAghg0bhueee06zY2RgS5OeYc0KrZB6\nhbXzu/VJ63msrhERERFZSyAQwKJFi7B69WrU1NRg8+bN2LdvX9xjXnnlFRQXF2Pr1q2YMmUKli5d\nGnf/Y489hiuvvFLT42RgSwPDmrhsrVszWmUNYHWNiIiIyGz27NmDvn37onfv3sjPz0dVVRW2bdsW\n95jt27fjxhtvBABUVlZi9+7dCIVCAIA//vGPKCsrw4ABAzQ9TgY2hXItrGkhk+qaHmHNKtU1IiIi\nIjrD4/GgtLQ0+me32w2Px5PwmB49whfMHQ4HOnXqhMbGRnzzzTf4n//5H8yYMUPz43Ro/g4WYvaw\nlg4jtUIasbIGZK+6lim2QxIREZHRFPXuA2dP9TuIigoKVX/NWCtWrMBtt92Gjh07avo+AANbTtG7\nFVLvsJbO+jUjVdfYDklEqmluBjwewO0Gior0PhoiIl243W7U19dH/+zxeOB2uxMec+jQIZSWlsLv\n9+PUqVPo3LkzPv74Y2zZsgVLly7FyZMnYbPZUFBQgB/96EeqHycDm0xmr67pHdYyYdTKGsDqGhGZ\njN8P+0MLINS8CeHrrxEqK0Oo6joEHl4IOHhKQES5ZfDgwaitrUVdXR3cbjdqamrwxBNPxD2mvLwc\nGzZswLBhw7BlyxaMGDECgiDgxRdfjD7mV7/6FYqKijQJawADmyy5Fta0kG51Ta2wpkd1TU2srhGR\nGuwPLYD9mTNjq4W6OqDtz4HHHtHrsIiIdOFwODB//nxMmzYNgUAAEyZMwIABA7Bs2TIMGjQI11xz\nDSZOnIg5c+agoqICLpcL1dXV2T/OrL+jyeRiWDNSK6SRpaqupWqHZHWNiLKquRlCzZuidwlvvAXM\nn8f2SCLKOWPGjMGYMWPibrvnnnui/11QUIDly5dLvsbMmTM1ObYITomUYPawlg4jhTVW18JYXSMi\nVXg8EL7+WvQu4euvw2vaiIjIcBjYkrBCWOO6Ne2YpbpGRBTldiNUViZ6V6isLDyAhIiIDIeBTYSe\nYU0tWoU1JfRet5YuK1XX2A5JRFFFRQhVXSd6V2jctWyHJCIyKK5ha0fvsGb0ISNat0KqHdbSaYdM\nhdU1IjKrwMMLAYTXrEWnRI67Nno7EREZDwNbDCuEtXQYZd2a3pU1o2F1jYhU53CEp0HOn8d92IiI\nTIKBrY1VwpqZ160ZgdYbZbO6RkSGUFQE9Oun91EQEZEMXMMGhjU1Gam6pkc7pFpYXSMiIiIigIFN\nd3qFNSVypRVS6+oaEREREZFSOR/Y9K6uqcHMm2MbJazJkWl1TW47JKtrRERERBSR04FN77BmlSEj\n6dIyrClth2R1jYiIiIiMKGcDm1XCmlnXrZmpsgZkr7pGRERERBQrJwNbroY1JbRshTRaWDNSdY3t\nkEREREQUK+cCWy6HNaOsW9Oa2tMhWV0jIiIiIr3kVGBjWEvNzOvW0sHqGhEREREZWc4ENr3Dmp5y\nad0aq2tEREREZCU5EdiMENZyfd2aEbG6RkRERERGZ/nAluthzSjr1lhdIyIiIiJSztKBjWGN69aS\nsVJ1jYiIiIisy7KBzUphLR25tG4NyO3qGtshiYiIiKzLkoHNamHNrOvWjFhZMxpW14iIiIhIiuUC\nmxHCmpqM0ApptbCWqh0yVXUtVTskq2tEREREpBbLBTYj4Lq17FK7HTJbWF0jIiIiolQsFdiMUF0z\nQ1hTgtW1eKyuEREREVE2WSawWSmsac1KrZCsrhERERGRlVkisFktrBmhFdIMYU0pVteIiIiIyGxM\nH9gY1nJz3Rpg3uoaEREREZFcpg5sDGtctyaX1tU1JTJth2R1jYiIiCh3mDawGSGsqUnrsJbLrZDZ\nkM12SCIiIiLKHaYMbEYJaxwyol9YU9IOmaq6loqaa9c4bISIiIiIlDBdYGuuM0Y1xyytkFquW7NK\nZS1VO6SRsB2SiIiIKLeYLrAZgVnCmhLpVNfMQOvqmhKsrhERERGRUgxsCpkprFmxFRJQdzpkptU1\njvInIiIiIi0xsCmgd1hTwqphTQlW14iIiIjI7BjYTITr1nK3ukZEREREuYmBTSa9q2tct6aM1apr\nbIckIiIiyk0MbDKYKaxZuRWS1TUiIiIiyjUMbCkwrIXpHdayidU1IiIiIjIKBjYJZtkYG7D2ujVA\n3Y2yWV0jIiIiIrNgYEtC7bBm1nVrRghr2cTqGhEREREZCQNbFpi5FdIIWF0jIiIiolzFwCaC69bC\nWF3TF6trRERERMTA1o7eYU0Jq69bA8xbXeNG2URERESkBga2GEYYMsJ1awSwukZEREREYQxsbcw2\nZMTq69aUyrS6lqodktU1IiIiItIDAxsY1mIZqbqm5kbZRERERERmxMCmMq5byz6rVdfYDklERERE\nETkf2IwwZITr1hKxukZERERElOOBzWxDRrRqhTRaWFNC6+qaEqyuEREREZHacjawcd2acRmpusaN\nsomIiIhITzkZ2IwQ1pTgujVxrK4RERERkdXlXGAzSliTW11TEtas0ArJ6hoRERER0Rk5FdjMFtaU\nsEJYU8Jq1TUiIiIiIjG6BLannnoK48ePx/e+9z3cfvvt8Hg8ehyGLrhuLblcrq6xHZKIiIgo+3bu\n3InKykpUVFRg1apVCff7fD7MmjULFRUVmDRpEg4cOBC979lnn0VFRQUqKyuxa9cuzY5Rl8A2bdo0\nbNq0Ca+//jquuuoq/PrXv9b8PY1QXdMirKXD6tW1VFhdIyIiIqJAIIBFixZh9erVqKmpwebNm7Fv\n3764x7zyyisoLi7G1q1bMWXKFCxduhQAsG/fPtTU1KCmpgarV6/GwoULEQgENDlOXQKb0+mM/rfX\n64UgCJq+nxHCmhK5tm5NbanaIVNhdY2IiIjI+vbs2YO+ffuid++mgRWbAAAIZ0lEQVTeyM/PR1VV\nFbZt2xb3mO3bt+PGG28EAFRWVmL37t0IhULYtm0bqqqqkJ+fj969e6Nv377Ys2ePJsfp0ORVZaiu\nrsbGjRvRqVMnrFu3LuXjI4n18NFjit7n5JeH0zq+ZI6nuf6s/lCT7Mee8sl77L8V/l0AgD/kU/yc\nbDinSxm8AW/Kx/Xv2iXl388xb/LrEKU9nDhySjoQn24MpjyOiObDmX+FigoKM34NIiIiyl31nvD5\nrlYVHi15Dqt7rq7kdT0eD0pLS6N/drvdCaHL4/GgR49wR5XD4UCnTp3Q2NgIj8eDIUOGxD1Xq2Ve\nmgW2KVOmoKGhIeH2WbNmYezYsbj33ntx77334tlnn8ULL7yAu+++W/L1jhwJV5Lu/MXDmhwv6evI\n0S9kPe69ozIe9Hlmx0JERERkRkeOHEHfvn31PgxZnE4nXC4Xbpt+l2bv4XK54jr7zEqzwLZ27VpZ\njxs/fjymT5+eMrANGjQIv/3tb9G9e3fY7XYVjpCIiIiIyPwCgQCOHDmCQYMG6X0ospWUlODtt99G\nU5P8LjSlnE4nSkqSL3Vxu92or6+P/tnj8cDtdic85tChQygtLYXf78epU6fQuXNnWc9Viy4tkbW1\ntTj77LMBANu2bUP//qnX8HTo0AHDhw/X+MiIiIiIiMzHLJW1WCUlJZKBSmuDBw9GbW0t6urq4Ha7\nUVNTgyeeeCLuMeXl5diwYQOGDRuGLVu2YMSIERAEAeXl5fjpT3+KqVOnwuPxoLa2Ft/61rc0OU5d\nAtsTTzyB/fv3QxAElJWVYeHChXocBhERERER5SiHw4H58+dj2rRpCAQCmDBhAgYMGIBly5Zh0KBB\nuOaaazBx4kTMmTMHFRUVcLlcqK6uBgAMGDAA1113HcaNGwe73Y758+dr1gUohEKhkCavTERERERE\nRBnRZaw/ERERERERpcbARkREREREZFCmC2xPPfUUxo8fj+9973u4/fbbNdvvgLLvl7/8Ja699lqM\nHz8eP/nJT3DyZHp73pHxvPnmm6iqqsL555+PTz75RO/DIRXs3LkTlZWVqKiowKpVq/Q+HFLJgw8+\niG9/+9v47ne/q/ehkMoOHTqEW2+9FePGjUNVVRV+85vf6H1IpJLW1lZMnDgR119/PaqqqrB8+XK9\nD4lUZro1bE1NTdH9FNatW4d9+/Zh0aJFOh8VqeHdd9/FiBEj4HA4sGTJEgDAnDlzdD4qUsMXX3wB\nQRCwYMEC3HfffRg8eLDeh0QZCAQCqKysxPPPPw+3242JEyfiy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmap = sns.cubehelix_palette(light=1, as_cmap=True)\n", + "fig, ax = plt.subplots(figsize=(16, 9))\n", + "contour = ax.contourf(grid[0], grid[1], ppc.std(axis=0).reshape(100, 100), cmap=cmap)\n", + "ax.scatter(X_test[pred==0, 0], X_test[pred==0, 1])\n", + "ax.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r')\n", + "cbar = plt.colorbar(contour, ax=ax)\n", + "_ = ax.set(xlim=(-3, 3), ylim=(-3, 3), xlabel='X', ylabel='Y');\n", + "cbar.ax.set_ylabel('Uncertainty (posterior predictive standard deviation)');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can see that very close to the decision boundary, our uncertainty as to which label to predict is highest. You can imagine that associating predictions with uncertainty is a critical property for many applications like health care. To further maximize accuracy, we might want to train the model primarily on samples from that high-uncertainty region." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Mini-batch ADVI: Scaling data size\n", + "\n", + "So far, we have trained our model on all data at once. Obviously this won't scale to something like ImageNet. Moreover, training on mini-batches of data (stochastic gradient descent) avoids local minima and can lead to faster convergence.\n", + "\n", + "Fortunately, ADVI can be run on mini-batches as well. It just requires some setting up:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# Generator that returns mini-batches in each iteration\n", + "def create_minibatch(data):\n", + " rng = np.random.RandomState(0)\n", + " \n", + " while True:\n", + " # Return random data samples of set size 100 each iteration\n", + " ixs = rng.randint(len(data), size=50)\n", + " yield data[ixs]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# Minibatch ADVI" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "All you need to train with minibatches is to wrap python generators with `pm.generator` function\n", + "The rest code should work without changes, let's see it" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Loss = 126.51: 100%|██████████| 40000/40000 [00:23<00:00, 1711.01it/s]\n", + "Finished [100%]: Average Loss = 126.74\n" + ] + } + ], + "source": [ + "minibatch_x = pm.generator(create_minibatch(X_train))\n", + "minibatch_y = pm.generator(create_minibatch(Y_train))\n", + "neural_network_minibatch = construct_nn(minibatch_x, minibatch_y)\n", + "with neural_network_minibatch:\n", + " inference = pm.ADVI()\n", + " approx = inference.fit(40000)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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4Mu/y/3P+QSz93yV89t8QbNwfjeLiRwn80JnbCL+hxrqga1JZ2Rn13vpyLwBA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\noQPmzp0LAOjWrRtef/11DB0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(inference.hist)\n", + "plt.ylabel('ELBO')\n", + "plt.xlabel('iteration');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It works!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Summary\n", + "\n", + "Hopefully this blog post demonstrated a very powerful new inference algorithm available in [PyMC3](http://pymc-devs.github.io/pymc3/): [ADVI](http://pymc-devs.github.io/pymc3/api.html#advi). I also think bridging the gap between Probabilistic Programming and Deep Learning can open up many new avenues for innovation in this space, as discussed above. Specifically, a hierarchical neural network sounds pretty bad-ass. These are really exciting times.\n", + "\n", + "## Next steps\n", + "\n", + "[`Theano`](http://deeplearning.net/software/theano/), which is used by `PyMC3` as its computational backend, was mainly developed for estimating neural networks and there are great libraries like [`Lasagne`](https://github.com/Lasagne/Lasagne) that build on top of `Theano` to make construction of the most common neural network architectures easy. Ideally, we wouldn't have to build the models by hand as I did above, but use the convenient syntax of `Lasagne` to construct the architecture, define our priors, and run ADVI. \n", + "\n", + "You can also run this example on the GPU by setting `device = gpu` and `floatX = float32` in your `.theanorc`.\n", + "\n", + "You might also argue that the above network isn't really deep, but note that we could easily extend it to have more layers, including convolutional ones to train on more challenging data sets.\n", + "\n", + "\n", + "## Acknowledgements\n", + "\n", + "[Taku Yoshioka](https://github.com/taku-y) did a lot of work on ADVI in PyMC3, including the mini-batch implementation as well as the sampling from the variational posterior. I'd also like to the thank the Stan guys (specifically Alp Kucukelbir and Daniel Lee) for deriving ADVI and teaching us about it. Thanks also to Chris Fonnesbeck, Andrew Campbell, Taku Yoshioka, and Peadar Coyle for useful comments on an earlier draft. After that [Maxim Kochurov](https://github.com/ferrine) implemented OPVI framework, as a unified interface for variational (including minibatch training and AEVB) methods" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.3" + }, + "latex_envs": { + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 0 + }, + "nav_menu": {}, + "toc": { + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 6, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/pymc3/distributions/dist_math.py b/pymc3/distributions/dist_math.py index 22aa4c6085..e609ed011b 100644 --- a/pymc3/distributions/dist_math.py +++ b/pymc3/distributions/dist_math.py @@ -9,6 +9,9 @@ import theano.tensor as tt from .special import gammaln +from ..math import logdet as _logdet + +c = - 0.5 * np.log(2 * np.pi) def bound(logp, *conditions, **kwargs): @@ -96,3 +99,117 @@ def i1(x): x**9 / 1474560 + x**11 / 176947200 + x**13 / 29727129600, np.e**x / (2 * np.pi * x)**0.5 * (1 - 3 / (8 * x) + 15 / (128 * x**2) + 315 / (3072 * x**3) + 14175 / (98304 * x**4))) + + +def sd2rho(sd): + """ + `sd -> rho` theano converter + :math:`mu + sd*e = mu + log(1+exp(rho))*e`""" + return tt.log(tt.exp(sd) - 1) + + +def rho2sd(rho): + """ + `rho -> sd` theano converter + :math:`mu + sd*e = mu + log(1+exp(rho))*e`""" + return tt.log1p(tt.exp(rho)) + + +def log_normal(x, mean, **kwargs): + """ + Calculate logarithm of normal distribution at point `x` + with given `mean` and `std` + Parameters + ---------- + x : Tensor + point of evaluation + mean : Tensor + mean of normal distribution + kwargs : one of parameters `{sd, tau, w, rho}` + Notes + ----- + There are four variants for density parametrization. + They are: + 1) standard deviation - `std` + 2) `w`, logarithm of `std` :math:`w = log(std)` + 3) `rho` that follows this equation :math:`rho = log(exp(std) - 1)` + 4) `tau` that follows this equation :math:`tau = std^{-1}` + ---- + """ + sd = kwargs.get('sd') + w = kwargs.get('w') + rho = kwargs.get('rho') + tau = kwargs.get('tau') + eps = kwargs.get('eps', 0.0) + check = sum(map(lambda a: a is not None, [sd, w, rho, tau])) + if check > 1: + raise ValueError('more than one required kwarg is passed') + if check == 0: + raise ValueError('none of required kwarg is passed') + if sd is not None: + std = sd + elif w is not None: + std = tt.exp(w) + elif rho is not None: + std = rho2sd(rho) + else: + std = tau**(-1) + std += eps + return c - tt.log(tt.abs_(std)) - (x - mean) ** 2 / (2 * std ** 2) + + +def log_normal_mv(x, mean, gpu_compat=False, **kwargs): + """ + Calculate logarithm of normal distribution at point `x` + with given `mean` and `sigma` matrix + Parameters + ---------- + x : Tensor + point of evaluation + mean : Tensor + mean of normal distribution + kwargs : one of parameters `{cov, tau, chol}` + + Flags + ---------- + gpu_compat : False, because LogDet is not GPU compatible yet. + If this is set as true, the GPU compatible (but numerically unstable) log(det) is used. + + Notes + ----- + There are three variants for density parametrization. + They are: + 1) covariance matrix - `cov` + 2) precision matrix - `tau`, + 3) cholesky decomposition matrix - `chol` + ---- + """ + if gpu_compat: + def logdet(m): + return tt.log(tt.abs_(tt.nlinalg.det(m))) + else: + logdet = _logdet + + T = kwargs.get('tau') + S = kwargs.get('cov') + L = kwargs.get('chol') + check = sum(map(lambda a: a is not None, [T, S, L])) + if check > 1: + raise ValueError('more than one required kwarg is passed') + if check == 0: + raise ValueError('none of required kwarg is passed') + # avoid unnecessary computations + if L is not None: + S = L.dot(L.T) + T = tt.nlinalg.matrix_inverse(S) + log_det = -logdet(S) + elif T is not None: + log_det = logdet(T) + else: + T = tt.nlinalg.matrix_inverse(S) + log_det = -logdet(S) + delta = x - mean + k = S.shape[0] + result = k * tt.log(2 * np.pi) - log_det + result += delta.dot(T).dot(delta) + return -1 / 2. * result diff --git a/pymc3/math.py b/pymc3/math.py index bf58294814..c4a307ac1b 100644 --- a/pymc3/math.py +++ b/pymc3/math.py @@ -37,13 +37,17 @@ def logit(p): return tt.log(p / (1 - p)) +def flatten_list(tensors): + return tt.concatenate([var.ravel() for var in tensors]) + + class LogDet(Op): """Computes the logarithm of absolute determinant of a square matrix M, log(abs(det(M))), on CPU. Avoids det(M) overflow/ underflow. Note: Once PR #3959 (https://github.com/Theano/Theano/pull/3959/) by harpone is merged, - this must be removed. + this must be removed. """ def make_node(self, x): x = theano.tensor.as_tensor_variable(x) diff --git a/pymc3/memoize.py b/pymc3/memoize.py index 5d25e03b87..e936190099 100644 --- a/pymc3/memoize.py +++ b/pymc3/memoize.py @@ -1,4 +1,5 @@ import functools +import pickle def memoize(obj): @@ -23,8 +24,16 @@ def hashable(a): Turn some unhashable objects into hashable ones. """ if isinstance(a, dict): - return hashable(a.items()) + return hashable(tuple((hashable(a1), hashable(a2)) for a1, a2 in a.items())) try: - return tuple(map(hashable, a)) - except: - return a + return hash(a) + except TypeError: + pass + # Not hashable >>> + try: + return hash(pickle.dumps(a)) + except Exception: + if hasattr(a, '__dict__'): + return hashable(a.__dict__) + else: + return id(a) diff --git a/pymc3/model.py b/pymc3/model.py index 090d89885a..c7b46e89d5 100644 --- a/pymc3/model.py +++ b/pymc3/model.py @@ -1,14 +1,15 @@ +import collections import threading import six import numpy as np import scipy.sparse as sps -import theano -import theano.tensor as tt import theano.sparse as sparse +from theano import theano, tensor as tt from theano.tensor.var import TensorVariable import pymc3 as pm +from pymc3.math import flatten_list from .memoize import memoize from .theanof import gradient, hessian, inputvars, generator from .vartypes import typefilter, discrete_types, continuous_types, isgenerator @@ -19,6 +20,8 @@ 'Point', 'Deterministic', 'Potential' ] +FlatView = collections.namedtuple('FlatView', 'input, replacements, view') + class InstanceMethod(object): """Class for hiding references to instance methods so they can be pickled. @@ -172,8 +175,10 @@ def fastd2logp(self, vars=None): @property def logpt(self): """Theano scalar of log-probability of the model""" - - return tt.sum(self.logp_elemwiset) * self.scaling + if getattr(self, 'total_size', None) is not None: + return tt.sum(self.logp_elemwiset) * self.scaling + else: + return tt.sum(self.logp_elemwiset) @property def scaling(self): @@ -659,6 +664,33 @@ def profile(self, outs, n=1000, point=None, profile=True, *args, **kwargs): return f.profile + def flatten(self, vars=None): + """Flattens model's input and returns: + FlatView with + * input vector variable + * replacements `input_var -> vars` + * view {variable: VarMap} + + Parameters + ---------- + vars : list of variables or None + if None, then all model.free_RVs are used for flattening input + + Returns + ------- + flat_view + """ + if vars is None: + vars = self.free_RVs + order = ArrayOrdering(vars) + inputvar = tt.vector('flat_view', dtype=theano.config.floatX) + inputvar.tag.test_value = flatten_list(vars).tag.test_value + replacements = {self.named_vars[name]: inputvar[slc].reshape(shape).astype(dtype) + for name, slc, shape, dtype in order.vmap} + view = {vm.var: vm for vm in order.vmap} + flat_view = FlatView(inputvar, replacements, view) + return flat_view + def fn(outs, mode=None, model=None, *args, **kwargs): """Compiles a Theano function which returns the values of `outs` and diff --git a/pymc3/tests/helpers.py b/pymc3/tests/helpers.py index f23c7481e1..efbd1468e7 100644 --- a/pymc3/tests/helpers.py +++ b/pymc3/tests/helpers.py @@ -1,6 +1,8 @@ import unittest -import numpy.random as nr from logging.handlers import BufferingHandler +import numpy.random as nr +from theano.sandbox.rng_mrg import MRG_RandomStreams +from ..theanof import set_tt_rng, tt_rng class SeededTest(unittest.TestCase): @@ -12,6 +14,11 @@ def setUpClass(cls): def setUp(self): nr.seed(self.random_seed) + self.old_tt_rng = tt_rng() + set_tt_rng(MRG_RandomStreams(self.random_seed)) + + def tearDown(self): + set_tt_rng(self.old_tt_rng) class TestHandler(BufferingHandler): def __init__(self, matcher): diff --git a/pymc3/tests/test_examples.py b/pymc3/tests/test_examples.py index 926fd0929a..91b5533d0f 100644 --- a/pymc3/tests/test_examples.py +++ b/pymc3/tests/test_examples.py @@ -234,6 +234,7 @@ class TestLatentOccupancy(SeededTest): Copyright (c) 2008 University of Otago. All rights reserved. """ def setUp(self): + super(TestLatentOccupancy, self).setUp() # Sample size n = 100 # True mean count, given occupancy diff --git a/pymc3/tests/test_math.py b/pymc3/tests/test_math.py index d160df5f8b..7073817238 100644 --- a/pymc3/tests/test_math.py +++ b/pymc3/tests/test_math.py @@ -4,13 +4,15 @@ from pymc3.math import LogDet, logdet, probit, invprobit from .helpers import SeededTest + def test_probit(): p = np.array([0.01, 0.25, 0.5, 0.75, 0.99]) np.testing.assert_allclose(invprobit(probit(p)).eval(), p, atol=1e-5) -class TestLogDet(SeededTest): +class TestLogDet(SeededTest): def setUp(self): + super(TestLogDet, self).setUp() utt.seed_rng() self.op_class = LogDet self.op = logdet diff --git a/pymc3/tests/test_variational_inference.py b/pymc3/tests/test_variational_inference.py new file mode 100644 index 0000000000..4dac00663e --- /dev/null +++ b/pymc3/tests/test_variational_inference.py @@ -0,0 +1,244 @@ +from six.moves import cPickle as pickle +import unittest +import numpy as np +from theano import theano, tensor as tt +import pymc3 as pm +from pymc3 import Model, Normal +from pymc3.variational.inference import ( + KL, MeanField, ADVI, FullRankADVI, + fit +) + +from pymc3.tests import models +from pymc3.tests.helpers import SeededTest + + +class TestELBO(SeededTest): + def test_elbo(self): + mu0 = 1.5 + sigma = 1.0 + y_obs = np.array([1.6, 1.4]) + + post_mu = np.array([1.88], dtype=theano.config.floatX) + post_sd = np.array([1], dtype=theano.config.floatX) + # Create a model for test + with Model() as model: + mu = Normal('mu', mu=mu0, sd=sigma) + Normal('y', mu=mu, sd=1, observed=y_obs) + + # Create variational gradient tensor + mean_field = MeanField(model=model) + elbo = -KL(mean_field)()(mean_field.random()) + + mean_field.shared_params['mu'].set_value(post_mu) + mean_field.shared_params['rho'].set_value(np.log(np.exp(post_sd) - 1)) + + f = theano.function([], elbo) + elbo_mc = sum(f() for _ in range(10000)) / 10000 + + # Exact value + elbo_true = (-0.5 * ( + 3 + 3 * post_mu ** 2 - 2 * (y_obs[0] + y_obs[1] + mu0) * post_mu + + y_obs[0] ** 2 + y_obs[1] ** 2 + mu0 ** 2 + 3 * np.log(2 * np.pi)) + + 0.5 * (np.log(2 * np.pi) + 1)) + np.testing.assert_allclose(elbo_mc, elbo_true, rtol=0, atol=1e-1) + + +class TestApproximates: + class Base(SeededTest): + inference = None + NITER = 30000 + + def test_vars_view(self): + _, model, _ = models.multidimensional_model() + with model: + app = self.inference().approx + posterior = app.random(10) + x_sampled = app.view(posterior, 'x').eval() + self.assertEqual(x_sampled.shape, (10,) + model['x'].dshape) + + def test_vars_view_dynamic_size(self): + _, model, _ = models.multidimensional_model() + with model: + app = self.inference().approx + i = tt.iscalar('i') + i.tag.test_value = 1 + posterior = app.random(i) + x_sampled = app.view(posterior, 'x').eval({i: 10}) + self.assertEqual(x_sampled.shape, (10,) + model['x'].dshape) + x_sampled = app.view(posterior, 'x').eval({i: 1}) + self.assertEqual(x_sampled.shape, (1,) + model['x'].dshape) + + def test_vars_view_dynamic_size_numpy(self): + _, model, _ = models.multidimensional_model() + with model: + app = self.inference().approx + i = tt.iscalar('i') + i.tag.test_value = 1 + x_sampled = app.view(app.random_fn(10), 'x') + self.assertEqual(x_sampled.shape, (10,) + model['x'].dshape) + x_sampled = app.view(app.random_fn(1), 'x') + self.assertEqual(x_sampled.shape, (1,) + model['x'].dshape) + x_sampled = app.view(app.random_fn(), 'x') + self.assertEqual(x_sampled.shape, () + model['x'].dshape) + + def test_sample_vp(self): + n_samples = 100 + xs = np.random.binomial(n=1, p=0.2, size=n_samples) + with pm.Model(): + p = pm.Beta('p', alpha=1, beta=1) + pm.Binomial('xs', n=1, p=p, observed=xs) + app = self.inference().approx + trace = app.sample_vp(draws=1, hide_transformed=True) + self.assertListEqual(trace.varnames, ['p']) + self.assertEqual(len(trace), 1) + trace = app.sample_vp(draws=10, hide_transformed=False) + self.assertListEqual(sorted(trace.varnames), ['p', 'p_logodds_']) + self.assertEqual(len(trace), 10) + + def test_sample_node(self): + n_samples = 100 + xs = np.random.binomial(n=1, p=0.2, size=n_samples) + with pm.Model(): + p = pm.Beta('p', alpha=1, beta=1) + pm.Binomial('xs', n=1, p=p, observed=xs) + app = self.inference().approx + app.sample_node(p).eval() # should be evaluated without errors + + def test_optimizer_with_full_data(self): + n = 1000 + sd0 = 2. + mu0 = 4. + sd = 3. + mu = -5. + + data = sd * np.random.randn(n) + mu + + d = n / sd ** 2 + 1 / sd0 ** 2 + mu_post = (n * np.mean(data) / sd ** 2 + mu0 / sd0 ** 2) / d + + with Model(): + mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0) + Normal('x', mu=mu_, sd=sd, observed=data) + pm.Deterministic('mu_sq', mu_**2) + inf = self.inference() + self.assertEqual(len(inf.hist), 0) + inf.fit(10) + self.assertEqual(len(inf.hist), 10) + self.assertFalse(np.isnan(inf.hist).any()) + approx = inf.fit(self.NITER) + self.assertEqual(len(inf.hist), self.NITER + 10) + self.assertFalse(np.isnan(inf.hist).any()) + trace = approx.sample_vp(10000) + np.testing.assert_allclose(np.mean(trace['mu']), mu_post, rtol=0.1) + np.testing.assert_allclose(np.std(trace['mu']), np.sqrt(1. / d), rtol=0.2) + + def test_optimizer_minibatch_with_generator(self): + n = 1000 + sd0 = 2. + mu0 = 4. + sd = 3. + mu = -5. + + data = sd * np.random.randn(n) + mu + + d = n / sd**2 + 1 / sd0**2 + mu_post = (n * np.mean(data) / sd**2 + mu0 / sd0**2) / d + + def create_minibatch(data): + while True: + data = np.roll(data, 100, axis=0) + yield data[:100] + + minibatches = create_minibatch(data) + with Model(): + mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0) + Normal('x', mu=mu_, sd=sd, observed=minibatches, total_size=n) + inf = self.inference() + approx = inf.fit(self.NITER) + trace = approx.sample_vp(10000) + np.testing.assert_allclose(np.mean(trace['mu']), mu_post, rtol=0.4) + np.testing.assert_allclose(np.std(trace['mu']), np.sqrt(1. / d), rtol=0.4) + + def test_optimizer_minibatch_with_callback(self): + n = 1000 + sd0 = 2. + mu0 = 4. + sd = 3. + mu = -5. + + data = sd * np.random.randn(n) + mu + + d = n / sd ** 2 + 1 / sd0 ** 2 + mu_post = (n * np.mean(data) / sd ** 2 + mu0 / sd0 ** 2) / d + + def create_minibatch(data): + while True: + data = np.roll(data, 100, axis=0) + yield data[:100] + + minibatches = create_minibatch(data) + with Model(): + data_t = theano.shared(next(minibatches)) + + def cb(*_): + data_t.set_value(next(minibatches)) + mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0) + Normal('x', mu=mu_, sd=sd, observed=data_t, total_size=n) + inf = self.inference() + approx = inf.fit(self.NITER, callbacks=[cb]) + trace = approx.sample_vp(10000) + np.testing.assert_allclose(np.mean(trace['mu']), mu_post, rtol=0.4) + np.testing.assert_allclose(np.std(trace['mu']), np.sqrt(1. / d), rtol=0.4) + + def test_pickling(self): + with models.multidimensional_model()[1]: + inference = self.inference() + + inference = pickle.loads(pickle.dumps(inference)) + inference.fit(20) + + def test_aevb(self): + _, model, _ = models.exponential_beta() + x = model.x + y = model.y + mu = theano.shared(x.init_value) * 2 + sd = theano.shared(x.init_value) * 3 + with model: + inference = self.inference(local_rv={y: (mu, sd)}) + inference.fit(3) + + +class TestMeanField(TestApproximates.Base): + inference = ADVI + + def test_approximate(self): + with models.multidimensional_model()[1]: + fit(10, method='advi') + + +class TestFullRank(TestApproximates.Base): + inference = FullRankADVI + + def test_from_mean_field(self): + with models.multidimensional_model()[1]: + advi = ADVI() + full_rank = FullRankADVI.from_mean_field(advi.approx) + full_rank.fit(20) + + def test_from_advi(self): + with models.multidimensional_model()[1]: + advi = ADVI() + full_rank = FullRankADVI.from_advi(advi) + full_rank.fit(20) + + def test_combined(self): + with models.multidimensional_model()[1]: + fit(10, method='advi->fullrank_advi', frac=.5) + + def test_approximate(self): + with models.multidimensional_model()[1]: + fit(10, method='fullrank_advi') + +if __name__ == '__main__': + unittest.main() diff --git a/pymc3/theanof.py b/pymc3/theanof.py index 1f1b35fe6d..b1a99a5d17 100644 --- a/pymc3/theanof.py +++ b/pymc3/theanof.py @@ -1,19 +1,29 @@ import numpy as np import theano -from .vartypes import typefilter, continuous_types from theano import theano, scalar, tensor as tt +from theano.sandbox.rng_mrg import MRG_RandomStreams from theano.gof.graph import inputs from theano.gof import Op from theano.configparser import change_flags + +from .vartypes import typefilter, continuous_types from .memoize import memoize from .blocking import ArrayOrdering from .data import DataGenerator -__all__ = ['gradient', 'hessian', 'hessian_diag', 'inputvars', - 'cont_inputs', 'floatX', 'jacobian', - 'CallableTensor', 'join_nonshared_inputs', - 'make_shared_replacements', 'generator'] +__all__ = ['gradient', + 'hessian', + 'hessian_diag', + 'inputvars', + 'cont_inputs', + 'floatX', + 'jacobian', + 'CallableTensor', + 'join_nonshared_inputs', + 'make_shared_replacements', + 'generator', + 'GradScale'] def inputvars(a): @@ -60,6 +70,7 @@ def floatX(X): Theano derivative functions """ + def gradient1(f, v): """flat gradient of f wrt v""" return tt.flatten(tt.grad(f, v, disconnected_inputs='warn')) @@ -100,6 +111,17 @@ def jacobian(f, vars=None): return empty_gradient +def jacobian_diag(f, x): + idx = tt.arange(f.shape[0], dtype='int32') + + def grad_ii(i): + return theano.grad(f[i], x)[i] + + return theano.scan(grad_ii, sequences=[idx], + n_steps=f.shape[0], + name='jacobian_diag')[0] + + @memoize def hessian(f, vars=None): return -jacobian(gradient(f, vars), vars) @@ -325,3 +347,58 @@ def generator(gen, default=None): - var.set_default(value) : sets new default value (None erases default value) """ return GeneratorOp(gen, default)() + + +@change_flags(compute_test_value='off') +def launch_rng(rng): + """ + Helper function for safe launch of theano random generator. + If not launched, there will be problems with test_value + + Parameters + ---------- + rng : `theano.sandbox.rng_mrg.MRG_RandomStreams` instance + """ + state = rng.rstate + rng.inc_rstate() + rng.set_rstate(state) + +_tt_rng = MRG_RandomStreams() +launch_rng(_tt_rng) + + +def tt_rng(): + """ + Get the package-level random number generator. + + Returns + ------- + `theano.sandbox.rng_mrg.MRG_RandomStreams` instance + `theano.sandbox.rng_mrg.MRG_RandomStreams` + instance passed to the most recent call of `set_tt_rng` + """ + return _tt_rng + + +def set_tt_rng(new_rng): + """ + Set the package-level random number generator. + + Parameters + ---------- + new_rng : `theano.sandbox.rng_mrg.MRG_RandomStreams` instance + The random number generator to use. + """ + # pylint: disable=global-statement + global _tt_rng + # pylint: enable=global-statement + _tt_rng = new_rng + launch_rng(_tt_rng) + + +class GradScale(theano.compile.ViewOp): + def __init__(self, multiplier): + self.multiplier = multiplier + + def grad(self, args, g_outs): + return [self.multiplier * g_out for g_out in g_outs] diff --git a/pymc3/variational/__init__.py b/pymc3/variational/__init__.py index 8211c23b62..63d35470fc 100644 --- a/pymc3/variational/__init__.py +++ b/pymc3/variational/__init__.py @@ -1,18 +1,25 @@ from .advi import advi, sample_vp from .advi_minibatch import advi_minibatch -from .updates import (sgd, - apply_momentum, - momentum, - apply_nesterov_momentum, - nesterov_momentum, - adagrad, - rmsprop, - adadelta, - adam, - adamax, - norm_constraint, - total_norm_constraint +from .updates import ( + sgd, + apply_momentum, + momentum, + apply_nesterov_momentum, + nesterov_momentum, + adagrad, + rmsprop, + adadelta, + adam, + adamax, + norm_constraint, + total_norm_constraint ) from .svgd import svgd + +from .inference import ( + ADVI, + FullRankADVI, + fit, +) diff --git a/pymc3/variational/inference.py b/pymc3/variational/inference.py new file mode 100644 index 0000000000..cfaed1c7f9 --- /dev/null +++ b/pymc3/variational/inference.py @@ -0,0 +1,544 @@ +from __future__ import division + +import logging + +import numpy as np +import theano +from theano import tensor as tt +import tqdm + +import pymc3 as pm +from pymc3.distributions.dist_math import log_normal, rho2sd, log_normal_mv +from pymc3.variational.opvi import Operator, Approximation, TestFunction + +logger = logging.getLogger(__name__) + +__all__ = [ + 'TestFunction', + 'KL', + 'MeanField', + 'FullRank', + 'ADVI', + 'FullRankADVI', + 'Inference' +] +# OPERATORS + + +class KL(Operator): + """ + Operator based on Kullback Leibler Divergence + .. math:: + + KL[q(v)||p(v)] = \int q(v)\log\\frac{q(v)}{p(v)}dv + """ + def apply(self, f): + z = self.input + return self.logq(z) - self.logp(z) + +# APPROXIMATIONS + + +class MeanField(Approximation): + """ + Mean Field approximation to the posterior where spherical Gaussian family + is fitted to minimize KL divergence from True posterior. It is assumed + that latent space variables are uncorrelated that is the main drawback + of the method + + Parameters + ---------- + local_rv : dict + mapping {model_variable -> local_variable} + Local Vars are used for Autoencoding Variational Bayes + See (AEVB; Kingma and Welling, 2014) for details + + model : PyMC3 model for inference + + cost_part_grad_scale : float or scalar tensor + Scaling score part of gradient can be useful near optimum for + archiving better convergence properties. Common schedule is + 1 at the start and 0 in the end. So slow decay will be ok. + See (Sticking the Landing; Geoffrey Roeder, + Yuhuai Wu, David Duvenaud, 2016) for details + + References + ---------- + Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 + Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI + approximateinference.org/accepted/RoederEtAl2016.pdf + """ + @property + def mu(self): + return self.shared_params['mu'] + + @property + def rho(self): + return self.shared_params['rho'] + + @property + def cov(self): + return tt.diag(rho2sd(self.rho)) + + def create_shared_params(self): + return {'mu': theano.shared( + self.input.tag.test_value[self.global_slc]), + 'rho': theano.shared( + np.zeros((self.global_size,), dtype=theano.config.floatX)) + } + + def log_q_W_global(self, z): + """ + log_q_W samples over q for global vars + Gradient wrt mu, rho in density parametrization + is set to zero to lower variance of ELBO + """ + mu = self.scale_grad(self.mu) + rho = self.scale_grad(self.rho) + z = z[self.global_slc] + logq = tt.sum(log_normal(z, mu, rho=rho)) + return logq + + def random_global(self, size=None, no_rand=False): + initial = self.initial(size, no_rand, l=self.global_size) + sd = rho2sd(self.rho) + mu = self.mu + return sd * initial + mu + + +class FullRank(Approximation): + """ + Full Rank approximation to the posterior where Multivariate Gaussian family + is fitted to minimize KL divergence from True posterior. In contrast to + MeanField approach correlations between variables are taken in account. The + main drawback of the method is computational cost. + + Parameters + ---------- + local_rv : dict + mapping {model_variable -> local_variable} + Local Vars are used for Autoencoding Variational Bayes + See (AEVB; Kingma and Welling, 2014) for details + + model : PyMC3 model for inference + + cost_part_grad_scale : float or scalar tensor + Scaling score part of gradient can be useful near optimum for + archiving better convergence properties. Common schedule is + 1 at the start and 0 in the end. So slow decay will be ok. + See (Sticking the Landing; Geoffrey Roeder, + Yuhuai Wu, David Duvenaud, 2016) for details + + References + ---------- + Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 + Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI + approximateinference.org/accepted/RoederEtAl2016.pdf + """ + def __init__(self, local_rv=None, model=None, cost_part_grad_scale=1, gpu_compat=False): + super(FullRank, self).__init__( + local_rv=local_rv, model=model, + cost_part_grad_scale=cost_part_grad_scale + ) + self.gpu_compat = gpu_compat + + @property + def L(self): + return self.shared_params['L_tril'][self.tril_index_matrix] + + @property + def mu(self): + return self.shared_params['mu'] + + @property + def cov(self): + L = self.L + return L.dot(L.T) + + @property + def num_tril_entries(self): + n = self.global_size + return int(n * (n + 1) / 2) + + @property + def tril_index_matrix(self): + n = self.global_size + num_tril_entries = self.num_tril_entries + tril_index_matrix = np.zeros([n, n], dtype=int) + tril_index_matrix[np.tril_indices(n)] = np.arange(num_tril_entries) + tril_index_matrix[np.tril_indices(n)[::-1]] = np.arange(num_tril_entries) + return tril_index_matrix + + def create_shared_params(self): + n = self.global_size + L_tril = ( + np.eye(n) + [np.tril_indices(n)] + .astype(theano.config.floatX) + ) + return {'mu': theano.shared( + self.input.tag.test_value[self.global_slc]), + 'L_tril': theano.shared(L_tril) + } + + def log_q_W_global(self, z): + """ + log_q_W samples over q for global vars + Gradient wrt mu, rho in density parametrization + is set to zero to lower variance of ELBO + """ + mu = self.scale_grad(self.mu) + L = self.scale_grad(self.L) + z = z[self.global_slc] + return log_normal_mv(z, mu, chol=L, gpu_compat=self.gpu_compat) + + def random_global(self, size=None, no_rand=False): + # (samples, dim) or (dim, ) + initial = self.initial(size, no_rand, l=self.global_size).T + # (dim, dim) + L = self.L + # (dim, ) + mu = self.mu + # x = Az + m, but it assumes z is vector + # When we get z with shape (samples, dim) + # we need to transpose Az + return L.dot(initial).T + mu + + @classmethod + def from_mean_field(cls, mean_field, gpu_compat=False): + """ + Construct FullRank from MeanField approximation + + Parameters + ---------- + mean_field : MeanField + approximation to start with + + Flags + ----- + gpu_compat : bool + use GPU compatible version or not + + Returns + ------- + FullRank + """ + full_rank = object.__new__(cls) # type: FullRank + full_rank.gpu_compat = gpu_compat + full_rank.__dict__.update(mean_field.__dict__) + full_rank.shared_params = full_rank.create_shared_params() + full_rank.shared_params['mu'].set_value( + mean_field.shared_params['mu'].get_value() + ) + rho = mean_field.shared_params['rho'].get_value() + n = full_rank.global_size + L_tril = ( + np.diag(np.log1p(np.exp(rho))) # rho2sd + [np.tril_indices(n)] + .astype(theano.config.floatX) + ) + full_rank.shared_params['L_tril'].set_value(L_tril) + return full_rank + + +class Inference(object): + """ + Base class for Variational Inference + + Communicates Operator, Approximation and Test Function to build Objective Function + + Parameters + ---------- + op : Operator class + approx : Approximation class or instance + tf : TestFunction instance + local_rv : list + model : PyMC3 Model + kwargs : kwargs for Approximation + """ + def __init__(self, op, approx, tf, local_rv=None, model=None, **kwargs): + self.hist = np.asarray(()) + if isinstance(approx, type) and issubclass(approx, Approximation): + approx = approx( + local_rv=local_rv, + model=model, **kwargs) + elif isinstance(approx, Approximation): + pass + else: + raise TypeError('approx should be Approximation instance or Approximation subclass') + self.objective = op(approx)(tf) + + approx = property(lambda self: self.objective.approx) + + def run_profiling(self, n=1000, score=True, **kwargs): + fn_kwargs = kwargs.pop('fn_kwargs', dict()) + fn_kwargs.update(profile=True) + step_func = self.objective.step_function( + score=score, fn_kwargs=fn_kwargs, + **kwargs + ) + progress = tqdm.trange(n) + try: + for _ in progress: + step_func() + except KeyboardInterrupt: + pass + finally: + progress.close() + return step_func.profile + + def fit(self, n=10000, score=True, callbacks=None, callback_every=1, + **kwargs): + """ + Performs Operator Variational Inference + + Parameters + ---------- + n : int + number of iterations + score : bool + evaluate loss on each iteration or not + callbacks : list[function : (Approximation, losses, i) -> any] + callback_every : int + call callback functions on `callback_every` step + kwargs : kwargs for ObjectiveFunction.step_function + + Returns + ------- + Approximation + """ + if callbacks is None: + callbacks = [] + step_func = self.objective.step_function(score=score, **kwargs) + i = 0 + scores = np.empty(n) + scores[:] = np.nan + progress = tqdm.trange(n) + if score: + try: + for i in progress: + e = step_func() + if np.isnan(e): + scores = scores[:i] + self.hist = np.concatenate([self.hist, scores]) + raise FloatingPointError('NaN occurred in optimization.') + scores[i] = e + if i % 10 == 0: + avg_elbo = scores[max(0, i - 1000):i+1].mean() + progress.set_description('Average Loss = {:,.5g}'.format(avg_elbo)) + if i % callback_every == 0: + for callback in callbacks: + callback(self.approx, scores[:i+1], i) + except KeyboardInterrupt: + scores = scores[:i] + if n < 10: + logger.info('Interrupted at {:,d} [{:.0f}%]: Loss = {:,.5g}'.format( + i, 100 * i // n, scores[i])) + else: + avg_elbo = scores[min(0, i - 1000):i].mean() + logger.info('Interrupted at {:,d} [{:.0f}%]: Average Loss = {:,.5g}'.format( + i, 100 * i // n, avg_elbo)) + else: + if n < 10: + logger.info('Finished [100%]: Loss = {:,.5g}'.format(scores[-1])) + else: + avg_elbo = scores[max(0, i - 1000):i].mean() + logger.info('Finished [100%]: Average Loss = {:,.5g}'.format(avg_elbo)) + finally: + progress.close() + else: + try: + for _ in progress: + step_func() + except KeyboardInterrupt: + pass + finally: + progress.close() + self.hist = np.concatenate([self.hist, scores]) + return self.approx + + +class ADVI(Inference): + """ + Automatic Differentiation Variational Inference (ADVI) + + Parameters + ---------- + local_rv : dict + mapping {model_variable -> local_variable} + Local Vars are used for Autoencoding Variational Bayes + See (AEVB; Kingma and Welling, 2014) for details + + model : PyMC3 model for inference + + cost_part_grad_scale : float or scalar tensor + Scaling score part of gradient can be useful near optimum for + archiving better convergence properties. Common schedule is + 1 at the start and 0 in the end. So slow decay will be ok. + See (Sticking the Landing; Geoffrey Roeder, + Yuhuai Wu, David Duvenaud, 2016) for details + + References + ---------- + - Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., + and Blei, D. M. (2016). Automatic Differentiation Variational + Inference. arXiv preprint arXiv:1603.00788. + + - Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 + Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI + approximateinference.org/accepted/RoederEtAl2016.pdf + + - Kingma, D. P., & Welling, M. (2014). + Auto-Encoding Variational Bayes. stat, 1050, 1. + """ + def __init__(self, local_rv=None, model=None, cost_part_grad_scale=1): + super(ADVI, self).__init__( + KL, MeanField, None, + local_rv=local_rv, model=model, cost_part_grad_scale=cost_part_grad_scale) + + +class FullRankADVI(Inference): + """ + Full Rank Automatic Differentiation Variational Inference (ADVI) + + Parameters + ---------- + local_rv : dict + mapping {model_variable -> local_variable} + Local Vars are used for Autoencoding Variational Bayes + See (AEVB; Kingma and Welling, 2014) for details + + model : PyMC3 model for inference + + cost_part_grad_scale : float or scalar tensor + Scaling score part of gradient can be useful near optimum for + archiving better convergence properties. Common schedule is + 1 at the start and 0 in the end. So slow decay will be ok. + See (Sticking the Landing; Geoffrey Roeder, + Yuhuai Wu, David Duvenaud, 2016) for details + + References + ---------- + - Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., + and Blei, D. M. (2016). Automatic Differentiation Variational + Inference. arXiv preprint arXiv:1603.00788. + + - Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 + Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI + approximateinference.org/accepted/RoederEtAl2016.pdf + + - Kingma, D. P., & Welling, M. (2014). + Auto-Encoding Variational Bayes. stat, 1050, 1. + """ + def __init__(self, local_rv=None, model=None, cost_part_grad_scale=1, gpu_compat=False): + super(FullRankADVI, self).__init__( + KL, FullRank, None, + local_rv=local_rv, model=model, cost_part_grad_scale=cost_part_grad_scale, gpu_compat=gpu_compat) + + @classmethod + def from_mean_field(cls, mean_field, gpu_compat=False): + """ + Construct FullRankADVI from MeanField approximation + + Parameters + ---------- + mean_field : MeanField + approximation to start with + + Flags + ----- + gpu_compat : bool + use GPU compatible version or not + + Returns + ------- + FullRankADVI + """ + full_rank = FullRank.from_mean_field(mean_field, gpu_compat) + inference = object.__new__(cls) + objective = KL(full_rank)(None) + inference.objective = objective + inference.hist = np.asarray(()) + return inference + + @classmethod + def from_advi(cls, advi, gpu_compat=False): + """ + Construct FullRankADVI from ADVI + + Parameters + ---------- + advi : ADVI + + Flags + ----- + gpu_compat : bool + use GPU compatible version or not + + Returns + ------- + FullRankADVI + """ + inference = cls.from_mean_field(advi.approx, gpu_compat) + inference.hist = advi.hist + return inference + + +def fit(n=10000, local_rv=None, method='advi', model=None, **kwargs): + """ + Handy shortcut for using inference methods in functional way + + Parameters + ---------- + n : int + number of iterations + local_rv : dict + mapping {model_variable -> local_variable} + Local Vars are used for Autoencoding Variational Bayes + See (AEVB; Kingma and Welling, 2014) for details + method : str or Inference + string name is case insensitive in {'advi', 'fullrank_advi', 'advi->fullrank_advi'} + model : None or Model + frac : float + if method is 'advi->fullrank_advi' represents advi fraction when training + kwargs : kwargs for Inference.fit + + Returns + ------- + Approximation + """ + if model is None: + model = pm.modelcontext(model) + _select = dict( + advi=ADVI, + fullrank_advi=FullRankADVI, + ) + if isinstance(method, str) and method.lower() == 'advi->fullrank_advi': + frac = kwargs.pop('frac', .5) + if not 0. < frac < 1.: + raise ValueError('frac should be in (0, 1)') + n1 = int(n * frac) + n2 = n-n1 + inference = ADVI(local_rv=local_rv, model=model) + logger.info('fitting advi ...') + inference.fit(n1, **kwargs) + inference = FullRankADVI.from_advi(inference) + logger.info('fitting fullrank advi ...') + return inference.fit(n2, **kwargs) + + elif isinstance(method, str): + try: + inference = _select[method.lower()]( + local_rv=local_rv, model=model + ) + except KeyError: + raise KeyError('method should be one of %s ' + 'or Inference instance' % + set(_select.keys())) + elif isinstance(method, Inference): + inference = method + else: + raise TypeError('method should be one of %s ' + 'or Inference instance' % + set(_select.keys())) + return inference.fit(n, **kwargs) diff --git a/pymc3/variational/opvi.py b/pymc3/variational/opvi.py new file mode 100644 index 0000000000..8d694b2698 --- /dev/null +++ b/pymc3/variational/opvi.py @@ -0,0 +1,808 @@ +import numpy as np +import theano +import theano.tensor as tt + +import pymc3 as pm +from .updates import adam +from ..distributions.dist_math import rho2sd, log_normal +from ..model import modelcontext, ArrayOrdering +from ..theanof import tt_rng, memoize, change_flags, GradScale + + +class ObjectiveUpdates(theano.OrderedUpdates): + """ + OrderedUpdates extension for storing loss + """ + loss = None + + +class Operator(object): + """ + Base class for Operator + + Parameters + ---------- + approx : Approximation + + Subclassing + ----------- + For implementing Custom operator it is needed to define `.apply(f)` method + """ + + NEED_F = False + + def __init__(self, approx): + self.model = approx.model + self.approx = approx + + flat_view = property(lambda self: self.approx.flat_view) + input = property(lambda self: self.approx.flat_view.input) + + def logp(self, z): + p = self.approx.to_flat_input(self.model.logpt) + p = theano.clone(p, {self.input: z}) + return p + + def logq(self, z): + return self.approx.logq(z) + + def apply(self, f): + """ + Operator itself + .. math:: + + (O^{p,q}f_{\theta})(z) + + Parameters + ---------- + f : function or None + function that takes `z = self.input` and returns + same dimension output + + Returns + ------- + symbolically applied operator + """ + raise NotImplementedError + + def __call__(self, f=None): + if f is None: + if self.NEED_F: + raise ValueError('Operator %s requires TestFunction' % self) + else: + f = TestFunction() + elif not isinstance(f, TestFunction): + f = TestFunction.from_function(f) + f.setup(self.approx.total_size) + return ObjectiveFunction(self, f) + + def __getstate__(self): + # pickle only important parts + return self.approx + + def __setstate__(self, approx): + self.__init__(approx) + + def __str__(self): + return '%(op)s[%(ap)s]' % dict(op=self.__class__.__name__, + ap=self.approx.__class__.__name__) + + +class ObjectiveFunction(object): + """ + Helper class for construction loss and updates for variational inference + + Parameters + ---------- + op : Operator + tf : TestFunction + """ + def __init__(self, op, tf): + self.op = op + self.tf = tf + + obj_params = property(lambda self: self.op.approx.params) + test_params = property(lambda self: self.tf.params) + approx = property(lambda self: self.op.approx) + + def random(self, size=None): + """ + Posterior distribution from initial latent space + + Parameters + ---------- + size : int + number of samples from distribution + + Returns + ------- + posterior space (theano) + """ + return self.op.approx.random(size) + + def __call__(self, z): + if z.ndim > 1: + a = theano.scan( + lambda z_: theano.clone(self.op.apply(self.tf), {self.op.input: z_}), + sequences=z, n_steps=z.shape[0])[0].mean() + else: + a = theano.clone(self.op.apply(self.tf), {self.op.input: z}) + return tt.abs_(a) + + def updates(self, obj_n_mc=None, tf_n_mc=None, obj_optimizer=adam, test_optimizer=adam, + more_obj_params=None, more_tf_params=None, more_updates=None): + """ + Calculates gradients for objective function, test function and then + constructs updates for optimization step + + Parameters + ---------- + obj_n_mc : int + Number of monte carlo samples used for approximation of objective gradients + tf_n_mc : int + Number of monte carlo samples used for approximation of test function gradients + obj_optimizer : function (loss, params) -> updates + Optimizer that is used for objective params + test_optimizer : function (loss, params) -> updates + Optimizer that is used for test function params + more_obj_params : list + Add custom params for objective optimizer + more_tf_params : list + Add custom params for test function optimizer + more_updates : dict + Add custom updates to resulting updates + + Returns + ------- + ObjectiveUpdates + """ + if more_obj_params is None: + more_obj_params = [] + if more_tf_params is None: + more_tf_params = [] + if more_updates is None: + more_updates = dict() + resulting_updates = ObjectiveUpdates() + + if self.test_params: + tf_z = self.random(tf_n_mc) + tf_target = -self(tf_z) + resulting_updates.update(test_optimizer(tf_target, self.test_params + more_tf_params)) + else: + pass + obj_z = self.random(obj_n_mc) + obj_target = self(obj_z) + resulting_updates.update(obj_optimizer(obj_target, self.obj_params + more_obj_params)) + resulting_updates.update(more_updates) + resulting_updates.loss = obj_target + return resulting_updates + + @memoize + def step_function(self, obj_n_mc=None, tf_n_mc=None, + obj_optimizer=adam, test_optimizer=adam, + more_obj_params=None, more_tf_params=None, + more_updates=None, score=False, + fn_kwargs=None): + """ + Step function that should be called on each optimization step. + + Generally it solves the following problem: + .. math:: + + \textbf{\lambda^{*}} = \inf_{\lambda} \sup_{\theta} t(\mathbb{E}_{\lambda}[(O^{p,q}f_{\theta})(z)]) + + Parameters + ---------- + obj_n_mc : int + Number of monte carlo samples used for approximation of objective gradients + tf_n_mc : int + Number of monte carlo samples used for approximation of test function gradients + obj_optimizer : function (loss, params) -> updates + Optimizer that is used for objective params + test_optimizer : function (loss, params) -> updates + Optimizer that is used for test function params + more_obj_params : list + Add custom params for objective optimizer + more_tf_params : list + Add custom params for test function optimizer + more_updates : dict + Add custom updates to resulting updates + score : bool + calculate loss on each step? Defaults to False for speed + fn_kwargs : dict + Add kwargs to theano.function (e.g. `{'profile': True}`) + Returns + ------- + theano.function + """ + if fn_kwargs is None: + fn_kwargs = {} + updates = self.updates(obj_n_mc=obj_n_mc, tf_n_mc=tf_n_mc, + obj_optimizer=obj_optimizer, + test_optimizer=test_optimizer, + more_obj_params=more_obj_params, + more_tf_params=more_tf_params, + more_updates=more_updates) + if score: + step_fn = theano.function([], updates.loss, updates=updates, **fn_kwargs) + else: + step_fn = theano.function([], None, updates=updates, **fn_kwargs) + return step_fn + + @memoize + def score_function(self, sc_n_mc=None, fn_kwargs=None): + if fn_kwargs is None: + fn_kwargs = {} + return theano.function([], self(self.random(sc_n_mc)), **fn_kwargs) + + def __getstate__(self): + return self.op, self.tf + + def __setstate__(self, state): + self.__init__(*state) + + +def cast_to_list(params): + """ + Helper function for getting a list from + usable representation of parameters + + Parameters + ---------- + params : {list|tuple|dict|theano.shared|None} + + Returns + ------- + list + """ + if isinstance(params, list): + return params + elif isinstance(params, tuple): + return list(params) + elif isinstance(params, dict): + return list(params.values()) + elif isinstance(params, theano.compile.SharedVariable): + return [params] + elif params is None: + return [] + else: + raise TypeError('Unknown type %s for %r, need list, dict or shared variable') + + +class TestFunction(object): + def __init__(self): + self._inited = False + self.shared_params = None + + def create_shared_params(self, dim): + """ + Returns + ------- + {dict|list|theano.shared} + """ + pass + + @property + def params(self): + return cast_to_list(self.shared_params) + + def __call__(self, z): + raise NotImplementedError + + def setup(self, dim): + if not self._inited: + self._setup(dim) + self.shared_params = self.create_shared_params(dim) + self._inited = True + + def _setup(self, dim): + """ + Does some preparation stuff before calling `.create_shared_params()` + + Parameters + ---------- + dim : int dimension of posterior distribution + """ + pass + + @classmethod + def from_function(cls, f): + if not callable(f): + raise ValueError('Need callable, got %r' % f) + obj = TestFunction() + obj.__call__ = f + return obj + + +class Approximation(object): + """ + Base class for approximations. + + Parameters + ---------- + local_rv : dict + mapping {model_variable -> local_variable} + Local Vars are used for Autoencoding Variational Bayes + See (AEVB; Kingma and Welling, 2014) for details + + model : PyMC3 model for inference + + cost_part_grad_scale : float or scalar tensor + Scaling score part of gradient can be useful near optimum for + archiving better convergence properties. Common schedule is + 1 at the start and 0 in the end. So slow decay will be ok. + See (Sticking the Landing; Geoffrey Roeder, + Yuhuai Wu, David Duvenaud, 2016) for details + + Subclassing + ----------- + Defining an approximation needs + custom implementation of the following methods: + - `.create_shared_params()` + Returns {dict|list|theano.shared} + + - `.random_global(size=None, no_rand=False)` + Generate samples from posterior. If `no_rand==False`: + sample from MAP of initial distribution. + Returns TensorVariable + + - `.log_q_W_global(z)` + It is needed only if used with operator + that requires :math:`logq` of an approximation + Returns Scalar + + Notes + ----- + There are some defaults for approximation classes that can be + optionally overriden. + - `initial_dist_name` + string that represents name of the initial distribution. + In most cases if will be `uniform` or `normal` + - `initial_dist_map` + float where initial distribution has maximum density + + References + ---------- + - Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 + Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI + approximateinference.org/accepted/RoederEtAl2016.pdf + + - Kingma, D. P., & Welling, M. (2014). + Auto-Encoding Variational Bayes. stat, 1050, 1. + """ + initial_dist_name = 'normal' + initial_dist_map = 0. + + def __init__(self, local_rv=None, model=None, cost_part_grad_scale=1): + model = modelcontext(model) + self.model = model + self.check_model(model) + if local_rv is None: + local_rv = {} + + def get_transformed(v): + if hasattr(v, 'transformed'): + return v.transformed + return v + + known = {get_transformed(k): v for k, v in local_rv.items()} + self.known = known + self.local_vars = [v for v in model.free_RVs if v in known] + self.global_vars = [v for v in model.free_RVs if v not in known] + self.order = ArrayOrdering(self.local_vars + self.global_vars) + self.flat_view = model.flatten( + vars=self.local_vars + self.global_vars + ) + self.grad_scale_op = GradScale(cost_part_grad_scale) + self.shared_params = self.create_shared_params() + + def __getstate__(self): + state = self.__dict__.copy() + # can be inferred from the rest parts + state.pop('flat_view') + state.pop('order') + return state + + def __setstate__(self, state): + self.__dict__.update(state) + self.order = ArrayOrdering(self.local_vars + self.global_vars) + self.flat_view = self.model.flatten( + vars=self.local_vars + self.global_vars + ) + + _view = property(lambda self: self.flat_view.view) + input = property(lambda self: self.flat_view.input) + + @staticmethod + def check_model(model): + """ + Checks that model is valid for variational inference + """ + vars_ = [var for var in model.vars if not isinstance(var, pm.model.ObservedRV)] + if any([var.dtype in pm.discrete_types for var in vars_]): + raise ValueError('Model should not include discrete RVs') + + def create_shared_params(self): + """ + Returns + ------- + {dict|list|theano.shared} + """ + pass + + def _local_mu_rho(self): + mu = [] + rho = [] + for var in self.local_vars: + mu.append(self.known[var][0].ravel()) + rho.append(self.known[var][1].ravel()) + mu = tt.concatenate(mu) + rho = tt.concatenate(rho) + return mu, rho + + def construct_replacements(self, include=None, exclude=None, + more_replacements=None): + """ + Construct replacements with given conditions + + Parameters + ---------- + include : list + latent variables to be replaced + exclude : list + latent variables to be excluded for replacements + more_replacements : dict + add custom replacements to graph, e.g. change input source + + Returns + ------- + dict + Replacements + """ + if include is not None and exclude is not None: + raise ValueError('Only one parameter is supported {include|exclude}, got two') + if include is not None: + replacements = {k: v for k, v + in self.flat_view.replacements.items() if k in include} + elif exclude is not None: + replacements = {k: v for k, v + in self.flat_view.replacements.items() if k not in exclude} + else: + replacements = self.flat_view.replacements + if more_replacements is not None: + replacements.update(more_replacements) + return replacements + + def apply_replacements(self, node, deterministic=False, + include=None, exclude=None, + more_replacements=None): + """ + Replace variables in graph with variational approximation. By default, replaces all variables + + Parameters + ---------- + node : Variable + node for replacements + deterministic : bool + whether to use zeros as initial distribution + if True - zero initial point will produce constant latent variables + include : list + latent variables to be replaced + exclude : list + latent variables to be excluded for replacements + more_replacements : dict + add custom replacements to graph, e.g. change input source + + Returns + ------- + node with replacements + """ + replacements = self.construct_replacements( + include, exclude, more_replacements + ) + node = theano.clone(node, replacements, strict=False) + posterior = self.random(no_rand=deterministic) + return theano.clone(node, {self.input: posterior}) + + def sample_node(self, node, size=100, + more_replacements=None): + if more_replacements is not None: + node = theano.clone(node, more_replacements) + posterior = self.random(size) + node = self.to_flat_input(node) + + def sample(z): return theano.clone(node, {self.input: z}) + nodes, _ = theano.scan(sample, posterior, n_steps=size) + return nodes + + def scale_grad(self, inp): + """ + Rescale gradient of input + + References + ---------- + - Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 + Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI + approximateinference.org/accepted/RoederEtAl2016.pdf + """ + return self.grad_scale_op(inp) + + def to_flat_input(self, node): + """ + Replaces vars with flattened view stored in self.input + """ + return theano.clone(node, self.flat_view.replacements, strict=False) + + @property + def params(self): + return cast_to_list(self.shared_params) + + def initial(self, size, no_rand=False, l=None): + """ + Initial distribution for constructing posterior + + Parameters + ---------- + size : int - number of samples + no_rand : bool - return zeros if True + l : length of sample, defaults to latent space dim + + Returns + ------- + Tensor + sampled latent space shape == size + latent_dim + """ + + theano_condition_is_here = isinstance(no_rand, tt.Variable) + if l is None: + l = self.total_size + if size is None: + shape = (l, ) + else: + shape = (size, l) + if theano_condition_is_here: + no_rand = tt.as_tensor(no_rand) + sample = getattr(tt_rng(), self.initial_dist_name)(shape) + space = tt.switch( + no_rand, + tt.ones_like(sample) * self.initial_dist_map, + sample + ) + else: + if no_rand: + return tt.ones(shape) * self.initial_dist_map + else: + return getattr(tt_rng(), self.initial_dist_name)(shape) + return space + + def random_local(self, size=None, no_rand=False): + """ + Implements posterior distribution from initial latent space + + Parameters + ---------- + size : number of samples from distribution + no_rand : whether use deterministic distribution + + Returns + ------- + local posterior space + """ + + mu, rho = self._local_mu_rho() + e = self.initial(size, no_rand, self.local_size) + return e * rho2sd(rho) + mu + + def random_global(self, size=None, no_rand=False): + """ + Implements posterior distribution from initial latent space + + Parameters + ---------- + size : number of samples from distribution + no_rand : whether use deterministic distribution + + Returns + ------- + global posterior space + """ + raise NotImplementedError + + def random(self, size=None, no_rand=False): + """ + Implements posterior distribution from initial latent space + + Parameters + ---------- + size : number of samples from distribution + no_rand : whether use deterministic distribution + + Returns + ------- + posterior space (theano) + """ + if size is None: + ax = 0 + else: + ax = 1 + if self.local_vars and self.global_vars: + return tt.concatenate([ + self.random_local(size, no_rand), + self.random_global(size, no_rand) + ], axis=ax) + elif self.local_vars: + return self.random_local(size, no_rand) + elif self.global_vars: + return self.random_global(size, no_rand) + else: + raise ValueError('No FreeVARs in model') + + @property + @memoize + @change_flags(compute_test_value='off') + def random_fn(self): + """ + Implements posterior distribution from initial latent space + + Parameters + ---------- + size : number of samples from distribution + no_rand : whether use deterministic distribution + + Returns + ------- + posterior space (numpy) + """ + In = theano.In + size = tt.iscalar('size') + no_rand = tt.bscalar('no_rand') + posterior = self.random(size, no_rand) + fn = theano.function([In(size, 'size', 1, allow_downcast=True), + In(no_rand, 'no_rand', 0, allow_downcast=True)], + posterior) + + def inner(size=None, no_rand=False): + if size is None: + return fn(1, int(no_rand))[0] + else: + return fn(size, int(no_rand)) + + return inner + + def sample_vp(self, draws=1, hide_transformed=False): + """ + Draw samples from variational posterior. + + Parameters + ---------- + draws : int + Number of random samples. + hide_transformed : bool + If False, transformed variables are also sampled. Default is True. + + Returns + ------- + trace : pymc3.backends.base.MultiTrace + Samples drawn from the variational posterior. + """ + if hide_transformed: + vars_sampled = [v_ for v_ in self.model.unobserved_RVs + if not str(v_).endswith('_')] + else: + vars_sampled = [v_ for v_ in self.model.unobserved_RVs] + posterior = self.random_fn(draws) + names = [var.name for var in self.local_vars + self.global_vars] + samples = {name: self.view(posterior, name) + for name in names} + + def points(): + for i in range(draws): + yield {name: samples[name][i] + for name in names} + + trace = pm.sampling.NDArray(model=self.model, vars=vars_sampled) + try: + trace.setup(draws=draws, chain=0) + for point in points(): + trace.record(point) + finally: + trace.close() + return pm.sampling.MultiTrace([trace]) + + def log_q_W_local(self, z): + """ + log_q_W samples over q for local vars + Gradient wrt mu, rho in density parametrization + is set to zero to lower variance of ELBO + """ + if not self.local_vars: + return tt.constant(0) + mu, rho = self._local_mu_rho() + mu = self.scale_grad(mu) + rho = self.scale_grad(rho) + logp = log_normal(z[self.local_slc], mu, rho=rho) + scaling = [] + for var in self.local_vars: + scaling.append(tt.ones(var.dsize)*var.scaling) + scaling = tt.concatenate(scaling) + + if z.ndim > 1: + logp *= scaling[:, None] + else: + logp *= scaling + return self.to_flat_input(tt.sum(logp)) + + def log_q_W_global(self, z): + """ + log_q_W samples over q for global vars + """ + raise NotImplementedError + + def logq(self, z): + """ + Total logq for approximation + """ + return self.log_q_W_global(z) + self.log_q_W_local(z) + + def view(self, space, name, reshape=True): + """ + Construct view on a variable from flattened `space` + + Parameters + ---------- + space : space to take view of variable from + name : str + name of variable + reshape : bool + whether to reshape variable from vectorized view + + Returns + ------- + variable view + """ + theano_is_here = isinstance(space, tt.TensorVariable) + slc = self._view[name].slc + _, _, _shape, dtype = self._view[name] + if space.ndim == 2: + view = space[:, slc] + elif space.ndim < 2: + view = space[slc] + else: + raise ValueError('Space should have no more than 2 dims, got %d' % space.ndim) + if reshape: + if len(_shape) > 0: + if theano_is_here: + shape = tt.concatenate([space.shape[:-1], + tt.as_tensor(_shape)]) + else: + shape = np.concatenate([space.shape[:-1], + _shape]).astype(int) + + else: + shape = space.shape[:-1] + if theano_is_here: + view = view.reshape(shape, ndim=space.ndim + len(_shape) - 1) + else: + view = view.reshape(shape) + return view.astype(dtype) + + @property + def total_size(self): + return self.order.dimensions + + @property + def local_size(self): + size = sum([0] + [v.dsize for v in self.local_vars]) + return size + + @property + def global_size(self): + return self.total_size - self.local_size + + @property + def local_slc(self): + return slice(0, self.local_size) + + @property + def global_slc(self): + return slice(self.local_size, self.total_size) From 966b1ed1420b03acddaf655af61843531c18f037 Mon Sep 17 00:00:00 2001 From: Maxim Kochurov Date: Thu, 16 Mar 2017 11:55:53 +0300 Subject: [PATCH 27/53] delete unnecessary text and add some benchmarks (#1901) --- .../bayesian_neural_network_opvi-advi.ipynb | 290 +++++++++++------- 1 file changed, 176 insertions(+), 114 deletions(-) diff --git a/docs/source/notebooks/bayesian_neural_network_opvi-advi.ipynb b/docs/source/notebooks/bayesian_neural_network_opvi-advi.ipynb index 66c75cf91d..8e4e8d3ca8 100644 --- a/docs/source/notebooks/bayesian_neural_network_opvi-advi.ipynb +++ b/docs/source/notebooks/bayesian_neural_network_opvi-advi.ipynb @@ -7,49 +7,13 @@ "editable": true }, "source": [ - "# Variational Inference: Bayesian Neural Networks\n", + "# Changes in API: Variational Inference: Bayesian Neural Networks\n", "\n", - "(c) 2016 by Thomas Wiecki & Maxim Kochurov (opvi)\n", + "(c) 2017 by Thomas Wiecki & Maxim Kochurov (opvi)\n", "\n", - "Original blog post: http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "## Current trends in Machine Learning\n", - "\n", - "There are currently three big trends in machine learning: **Probabilistic Programming**, **Deep Learning** and \"**Big Data**\". Inside of PP, a lot of innovation is in making things scale using **Variational Inference**. In this blog post, I will show how to use **Variational Inference** in [PyMC3](http://pymc-devs.github.io/pymc3/) to fit a simple Bayesian Neural Network. I will also discuss how bridging Probabilistic Programming and Deep Learning can open up very interesting avenues to explore in future research.\n", - "\n", - "### Probabilistic Programming at scale\n", - "**Probabilistic Programming** allows very flexible creation of custom probabilistic models and is mainly concerned with **insight** and learning from your data. The approach is inherently **Bayesian** so we can specify **priors** to inform and constrain our models and get uncertainty estimation in form of a **posterior** distribution. Using [MCMC sampling algorithms](http://twiecki.github.io/blog/2015/11/10/mcmc-sampling/) we can draw samples from this posterior to very flexibly estimate these models. [PyMC3](http://pymc-devs.github.io/pymc3/) and [Stan](http://mc-stan.org/) are the current state-of-the-art tools to consruct and estimate these models. One major drawback of sampling, however, is that it's often very slow, especially for high-dimensional models. That's why more recently, **variational inference** algorithms have been developed that are almost as flexible as MCMC but much faster. Instead of drawing samples from the posterior, these algorithms instead fit a distribution (e.g. normal) to the posterior turning a sampling problem into and optimization problem. [ADVI](http://arxiv.org/abs/1506.03431) -- Automatic Differentation Variational Inference -- is implemented in [PyMC3](http://pymc-devs.github.io/pymc3/) and [Stan](http://mc-stan.org/), as well as a new package called [Edward](https://github.com/blei-lab/edward/) which is mainly concerned with Variational Inference. \n", + "See original blog post for old interface and more explanations of bayesian approach in Deep Learning:\n", "\n", - "Unfortunately, when it comes to traditional ML problems like classification or (non-linear) regression, Probabilistic Programming often plays second fiddle (in terms of accuracy and scalability) to more algorithmic approaches like [ensemble learning](https://en.wikipedia.org/wiki/Ensemble_learning) (e.g. [random forests](https://en.wikipedia.org/wiki/Random_forest) or [gradient boosted regression trees](https://en.wikipedia.org/wiki/Boosting_(machine_learning)).\n", - "\n", - "### Deep Learning\n", - "\n", - "Now in its third renaissance, deep learning has been making headlines repeatadly by dominating almost any object recognition benchmark, [kicking ass at Atari games](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), and [beating the world-champion Lee Sedol at Go](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html). From a statistical point, Neural Networks are extremely good non-linear function approximators and representation learners. While mostly known for classification, they have been extended to unsupervised learning with [AutoEncoders](https://arxiv.org/abs/1312.6114) and in all sorts of other interesting ways (e.g. [Recurrent Networks](https://en.wikipedia.org/wiki/Recurrent_neural_network), or [MDNs](http://cbonnett.github.io/MDN_EDWARD_KERAS_TF.html) to estimate multimodal distributions). Why do they work so well? No one really knows as the statistical properties are still not fully understood.\n", - "\n", - "A large part of the innoviation in deep learning is the ability to train these extremely complex models. This rests on several pillars:\n", - "* Speed: facilitating the GPU allowed for much faster processing.\n", - "* Software: frameworks like [Theano](http://deeplearning.net/software/theano/) and [TensorFlow](https://www.tensorflow.org/) allow flexible creation of abstract models that can then be optimized and compiled to CPU or GPU.\n", - "* Learning algorithms: training on sub-sets of the data -- stochastic gradient descent -- allows us to train these models on massive amounts of data. Techniques like drop-out avoid overfitting.\n", - "* Architectural: A lot of innovation comes from changing the input layers, like for convolutional neural nets, or the output layers, like for [MDNs](http://cbonnett.github.io/MDN_EDWARD_KERAS_TF.html).\n", - "\n", - "### Bridging Deep Learning and Probabilistic Programming\n", - "On one hand we Probabilistic Programming which allows us to build rather small and focused models in a very principled and well-understood way to gain insight into our data; on the other hand we have deep learning which uses many heuristics to train huge and highly complex models that are amazing at prediction. Recent innovations in variational inference allow probabilistic programming to scale model complexity as well as data size. We are thus at the cusp of being able to combine these two approaches to hopefully unlock new innovations in Machine Learning. For more motivation, see also [Dustin Tran's](https://twitter.com/dustinvtran) recent [blog post](http://dustintran.com/blog/a-quick-update-edward-and-some-motivations/).\n", - "\n", - "While this would allow Probabilistic Programming to be applied to a much wider set of interesting problems, I believe this bridging also holds great promise for innovations in Deep Learning. Some ideas are:\n", - "* **Uncertainty in predictions**: As we will see below, the Bayesian Neural Network informs us about the uncertainty in its predictions. I think uncertainty is an underappreciated concept in Machine Learning as it's clearly important for real-world applications. But it could also be useful in training. For example, we could train the model specifically on samples it is most uncertain about.\n", - "* **Uncertainty in representations**: We also get uncertainty estimates of our weights which could inform us about the stability of the learned representations of the network.\n", - "* **Regularization with priors**: Weights are often L2-regularized to avoid overfitting, this very naturally becomes a Gaussian prior for the weight coefficients. We could, however, imagine all kinds of other priors, like spike-and-slab to enforce sparsity (this would be more like using the L1-norm).\n", - "* **Transfer learning with informed priors**: If we wanted to train a network on a new object recognition data set, we could bootstrap the learning by placing informed priors centered around weights retrieved from other pre-trained networks, like [GoogLeNet](https://arxiv.org/abs/1409.4842). \n", - "* **Hierarchical Neural Networks**: A very powerful approach in Probabilistic Programming is hierarchical modeling that allows pooling of things that were learned on sub-groups to the overall population (see my tutorial on [Hierarchical Linear Regression in PyMC3](http://twiecki.github.io/blog/2014/03/17/bayesian-glms-3/)). Applied to Neural Networks, in hierarchical data sets, we could train individual neural nets to specialize on sub-groups while still being informed about representations of the overall population. For example, imagine a network trained to classify car models from pictures of cars. We could train a hierarchical neural network where a sub-neural network is trained to tell apart models from only a single manufacturer. The intuition being that all cars from a certain manufactures share certain similarities so it would make sense to train individual networks that specialize on brands. However, due to the individual networks being connected at a higher layer, they would still share information with the other specialized sub-networks about features that are useful to all brands. Interestingly, different layers of the network could be informed by various levels of the hierarchy -- e.g. early layers that extract visual lines could be identical in all sub-networks while the higher-order representations would be different. The hierarchical model would learn all that from the data.\n", - "* **Other hybrid architectures**: We can more freely build all kinds of neural networks. For example, Bayesian non-parametrics could be used to flexibly adjust the size and shape of the hidden layers to optimally scale the network architecture to the problem at hand during training. Currently, this requires costly hyper-parameter optimization and a lot of tribal knowledge." + "http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/" ] }, { @@ -132,7 +96,7 @@ "data": { "image/png": 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ZAPhoxefOncPSpUths9kAAC+88AKGDRuGyspKPPHEE2hqaoLb7cZvf/tbDB06FM899xwK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\nE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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -226,26 +190,24 @@ "editable": true }, "source": [ - "That's not so bad. The `Normal` priors help regularize the weights. Usually we would add a constant `b` to the inputs but I omitted it here to keep the code cleaner." + "That's not so bad. The `Normal` priors help regularize the weights. Usually we would add a constant `b` to the inputs but I omitted it here to keep the code cleaner. Let's train the model using new ADVI implemented via [OPVI](https://arxiv.org/abs/1610.09033) framework" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 5, "metadata": { - "deletable": true, - "editable": true + "collapsed": true }, + "outputs": [], "source": [ - "### Variational Inference: Scaling model complexity\n", - "\n", - "We could now just run a MCMC sampler like [`NUTS`](http://pymc-devs.github.io/pymc3/api.html#nuts) which works pretty well in this case but as I already mentioned, this will become very slow as we scale our model up to deeper architectures with more layers.\n", - "\n", - "Instead, we will use the brand-new [ADVI](http://pymc-devs.github.io/pymc3/api.html#advi) variational inference algorithm which was recently added to `PyMC3`. This is much faster and will scale better. Note, that this is a mean-field approximation so we ignore correlations in the posterior." + "from pymc3.theanof import set_tt_rng, MRG_RandomStreams\n", + "set_tt_rng(MRG_RandomStreams(42))" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, @@ -257,18 +219,18 @@ "output_type": "stream", "text": [ "WARNING (theano.tensor.blas): We did not found a dynamic library into the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n", - "Average Loss = 131.3: 100%|██████████| 20000/20000 [00:22<00:00, 880.39it/s] \n", - "Finished [100%]: Average Loss = 131.32\n", - "Average Loss = 128.89: 100%|██████████| 10000/10000 [00:10<00:00, 997.84it/s]\n", - "Finished [100%]: Average Loss = 128.92\n" + "Average Loss = 112.04: 100%|██████████| 20000/20000 [00:24<00:00, 821.54it/s] \n", + "Finished [100%]: Average Loss = 112.05\n", + "Average Loss = 109.76: 100%|██████████| 10000/10000 [00:08<00:00, 1225.38it/s]\n", + "Finished [100%]: Average Loss = 109.79\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 33 s, sys: 1min 39s, total: 2min 12s\n", - "Wall time: 36.2 s\n" + "CPU times: user 32.4 s, sys: 1min 38s, total: 2min 10s\n", + "Wall time: 35.3 s\n" ] } ], @@ -291,6 +253,94 @@ " #approx = pm.fit(n=30000)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's compare performance with no grad scaling" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "set_tt_rng(MRG_RandomStreams(42))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Loss = 109.83: 100%|██████████| 30000/30000 [00:32<00:00, 917.54it/s] \n", + "Finished [100%]: Average Loss = 109.86\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 34.4 s, sys: 1min 37s, total: 2min 12s\n", + "Wall time: 36.7 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "with neural_network:\n", + " inference_no_s = pm.ADVI()\n", + " approx_no_s = inference_no_s.fit(n=30000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And using old interface. Performance is nearly the same" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average ELBO = -151.21: 100%|██████████| 30000/30000 [00:24<00:00, 1236.55it/s]\n", + "Finished [100%]: Average ELBO = -135.19\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 26.5 s, sys: 1min 11s, total: 1min 38s\n", + "Wall time: 26.1 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "with neural_network:\n", + " advifit = pm.advi(n=30000)" + ] + }, { "cell_type": "markdown", "metadata": { @@ -305,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, @@ -328,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, @@ -337,9 +387,9 @@ "outputs": [ { "data": { - "image/png": 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QoKCgAOPGjQMAREVFoaCgAAqFAmvWrMFbb73l5pK6zkffHbF9YQuXVw3J+uJS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\nJHEMeyIiIolj2BMREUkcw56IiEji/h/WKT1R2F6BxQAAAABJRU5ErkJggg==\n", 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PzwMoQTJ8aaSVw2ow4B2dPcUt+5tFodPXElmGEuQy3/zytVQFSYJagZt+uazU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\nGfJ3NwAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -347,11 +397,43 @@ } ], "source": [ - "plt.plot(inference.hist)\n", + "plt.plot(-inference.hist, label='new ADVI', alpha=.3)\n", + "plt.plot(-inference_no_s.hist, label='new ADVI no scaling', alpha=.3)\n", + "plt.plot(advifit.elbo_vals, label='old ADVI', alpha=.3)\n", + "plt.legend()\n", "plt.ylabel('ELBO')\n", "plt.xlabel('iteration');" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Grad scaling seemd to give no effect, but let's see difference in variance of ELBO, that's really what is expected" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(63.055858777972695, 64.433532941142403)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference.hist[25000:].var(), inference_no_s.hist[25000:].var()" + ] + }, { "cell_type": "markdown", "metadata": { @@ -367,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, @@ -380,7 +462,7 @@ "sigmoid.0" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -392,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, @@ -439,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, @@ -450,7 +532,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1 loop, best of 3: 6.88 s per loop\n" + "1 loop, best of 3: 6.75 s per loop\n" ] } ], @@ -460,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, @@ -471,7 +553,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1 loop, best of 3: 153 ms per loop\n" + "1 loop, best of 3: 146 ms per loop\n" ] } ], @@ -481,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "collapsed": true, "deletable": true, @@ -494,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, @@ -503,9 +585,9 @@ "outputs": [ { "data": { - "image/png": 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Y3A8LvnUR8nIzDfOJZa8n8hm99LKcrDCe/9m32+t/u7UJkhEGZsfEu0ZrrE03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\n", 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FugdV8TbxVIr9nrCmevGEqhon7i8MqXKUDUAQYpAADzi3zRqJ2VOL0b9PLqIRoH+fXMye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\n2TzMHHxEBat2I9c2HFE01aIift67iBVD+ay6Ov3DhZHg1Lv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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -522,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, @@ -533,7 +615,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy = 96.39999999999999%\n" + "Accuracy = 95.8%\n" ] } ], @@ -565,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, @@ -580,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, @@ -604,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, @@ -613,9 +695,9 @@ "outputs": [ { "data": { - "image/png": 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nIMsybr75ZkvHMrARhYzZWqW6hpZc5zsb68Tk7CTEfFWrWKCNGQQR8cY2zZs8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\nl9DVat7ww4zeAHXDljfdPbD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4447D6tWrcfXVV+PFF180PZZr2Igc0ltXow7Q4k2dkDQ6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\n6dSpU9c8v2zZMk2dOlVlZWUqK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ERFoY2IjIFUYBoOoCgg1awXUodcCf14QhhypAEJqNEBFZcfToUWzYsAEffvghAGDq1Kk4\n55xz0NDQYGp7Nh0hIvfkAoBW8DC6vQak+vei58B29HRvQ8+B7Uj1763514TICW618ufcNSKyYt26\ndbjwwgvx7LPP4v3338f777+PZ599FsuWLcPatWtN7YMVNiIqz9iizyUVIa3fUylWtqgGBaXZCBGR\nm77//e9j9erVOO6448b9vrOzE1/60pdw7rnnGu6DgY2IbCtc9DkrD2NobM6V1u9NYdAjojJV0kLZ\nrK4RVbdMJlMS1gBg5syZkGXZ1D4Y2IjIltyizzlSKIpYfArCdXHURWIlvwdgGNrKCnpEVDGC0mzE\nKjYbISKr5s2bh3vuuQef+cxnMG3aNADABx98gGeeeQYnnniiqX0wsBGRdTqLPofr6lV/H6lvQSq5\nX7NqphUAAeOgR0TVrxqHQ7K6RlT97r//fvzkJz/BHXfcgQ8++ACCIGDatGm46KKLcOedd5raBwMb\nEVmmv+izoL6NVDc61FFtrpZOADQKehXH6yGfHGJKNaaSmo0QUfWLRqO45ZZbcMstt9jeBwMbEVmm\nv+izArXQJssjo6FBhV4A1A16FcbRIZ8mgljVDzFlGK1IlToc0mmsrhGRWQxsRGSdzqLP6ZGj4+aw\n5Qwf7dU8qdYLgHpBr5I4OeTTTBCr9iGmVR9GaRyzwyFZXSOiasR12IjIltGFr7uQyQxBUbLIZIaQ\nSnah99AO1d/rnkyPBUA1ekGvYhgM+YRg/k9xLohJoSgEQcgHsVhTuyuPF0SmXgOiAGN1jYisYIWN\niKwpGIY2Gtr2lwxL0/q9nlygi9S3QJLqIMsjGK6SqoljQz5NzvWr6iGmtTTfsQq5ufYaq2tEVK0q\n+zIrERkTREhSxJGqSqypHa2T52FCYgFaJ88brWjkFn0uPknW+r2OVP9e9BzYjp7ubeg5sN3fsObg\n65Yb8ql6m4Uhn2aCmJOPF0RmXwOioGJ1jah2/fznPx/3v2YxsBFVMdWAVca+PBmGZiPoOc3J1y1n\nZDip+nsrQz5NB7EqHmJazWGUgofVNSJy0nPPPTfuf83ikEiiKuVo04kaGobmdLOO8c0x0lAgQBRF\ne0M+dZq9FAexqh1iauE1oGCpxOGQTmN1jYgAQFEUS/dnYCOqRg4HrKqeE1XI4detOPwJ0uhwvcGB\ngxjo32MrXFgNYsLY/1dfHQ8V2Rq/asMoBQqra0QUFAxsRFXI6YDlWNv9gIcDR183nfBXF40D/XaP\n0lxTFzOVwkpujW+nsQ35h9U1VteIyD4GNqIq5Pi6Zg4MQwtcOFAJj06+bq5XJXNz/dSYqBTG4m2V\nv06b3mtAVAZW14goSBjYiKqRC/N8yhmGFrRFnDXDo4Ovm5+LgRuHxbqamZNIlclsdc0sv6trRESA\n9blrOQxsRFXKjXk+toahBaxhiVZ4FEUJyb49zr1uPjbHMAqLEFAbcxIpEOwMhzTL7HBIv3E4JBEB\nwB133DHuf81iYCOqFipD/FyZ52NxGFqgGpbohMdow0TUReL5apsTr5tvzTEMwqIsj/hW/SPyGhfK\nJqKgOPvss8f9r1kMbERVQHd+mM/zfPwcGlhMLzwWri0HjIYtJ143v5pjGIVFtsYnLwSh2YjfWF0j\nonIxsBFVuKDNDysRoHWz9MJjIceHavoUmvXCoqXqX8C7exI5hdU1IgoiBjaiShaw+WGBpxMeC1XV\nPC6dsGh2eYBAdfckAlv5E1FtEf0+ACKyz8z8MN8ZhEoI3v4ZGg0pXchkhjS7NdXUPK5coNNZy00K\nRccNGY01tftwoBVIECFJEc8/40Hh5nBIN7C6RkRBxQobkZtcHkrm+/wwE88vUE1HxuQqS41NM9DQ\nOKnkds7jgv3qLYdPAmBlMghYXSOiIDjrrLMgCELJ7xVFgSAI2Lx5s+E+GNiIXOLJCZuV+WEOn0ib\nfX6+h0otShYDfZ1QFNn7Lo4VwE7QZkgZFfh5pR5gsxEiolG/+c1vyt4HAxuRC7RP2ASk+vc4+lhm\nmkc4fSJt6YTU6aYjDgdP1XlcTj1GBVebrAZthpQxnFcaCH638md1jYhy2tra8v89MDCA999/H/Pm\nzbO0DwY2IqfpnLCNDr9THD+B1Wse4fiJtI0TUqfWI3OtglPQmMOpx/C02uRGMLRYvWVIGRXEIcDV\nhNU1IqpU69evxz333ANJkvDaa6/h7bffxn/+53/i0UcfNdyWgY3IYfprfYnuVR3UugG6cCJt94S0\n3PXIvKjgOPUYXlab3AyGZoM2Q8oxgR0C7KFabzbC6hoRqfn3f/93/Pd//zdWrlwJAFiwYAH27DE3\n6qo2W1cRuSh3wqbHq+6IbnSR1Ht+hiekOh0JdXnRadLgMaRQ1NzjeNgV04sujqn+veg5sB093dvQ\nc2C77jxFNbUSUvLGKpNq2MzGG343GyEi0jJp0vhGZ3V1daa2Y2AjcprOCVuOVy33XTmR9uGE1Ivl\nC/QfI4IJk+ejdfI8wzDk2VIL5QRDq+3mjYI2Q8o445eOyCKTGUIq2VVbc/ks8LPZCKtrROSVWCyG\nQ4cO5TtGbtmyBfF43NS2HBJJ5IJU/16E6+II1zWotnL1rOrgdMOPMU7NSTPLi2Fmeo+Rew/NDG30\nakic3WGIbg2h9PozEXTlDgGuVHaGQzqJ1TUiCqqvf/3rWLlyJfbt24fPfe5z6OzsxCOPPGJqWwY2\nIhfEmtpRF4lp3u5l1cHNE2lh7P+XRlKHuRQ8zT5GMd35f14cK+wFQ7fn1tVqSNGkNq+UxmF1jYhq\nxcknn4xf/OIXeOONNwAAp512Gpqamkxty8BG5DSdoWqKksXgwAHPqw5On0j70cLdiwpO8WMAgmqF\n1KiRhifVJp1gKAoSYvG28Y/nVSdHv0JKBS+hQM5gdY2Igi6dTiObHf03KpPJmN6OgY3IYXpD1QAB\nQ6kDnh5PnlMn0j62cPeignPsMerQMnGO7aGNXh0rAEQbJkKSjv05F6VwSYCu5k6OXLA7GNwaDlkJ\n1TUiIiMvv/wy/uVf/gXz58+Hoii466678K1vfQsXXHCB4bYMbEQO0x+qNlzxHfN8P/H3ooKjZCHL\nQ+UPbfTgWFPJ/WMBuvTPeWGArtZ281ywu3L52crfaRwOSURGfvjDH+KXv/wljj/+eABAZ2cnvvSl\nL5kKbOwSSeS0Ku+Y53oLd6sdDF1ku9ufnedg83mb7UoZi7dBECTV+1Xs59LDJRRIH5uNEBHpi0Qi\n+bAGADNnzkQ0WnoRVQ0rbEQuqOqOeTpzp7KybHzirzXXSBARb56Bukjc/tC2cucxqWxvdWhj8fC8\nkeEkkn17dLezPKSv4DjNVM6Kq1DHbs9gaPDQsccK6jwwjePyvdpLtrHZCBHViqNHjwIAzj//fDzy\nyCO46qqroCgK/ud//gfnn3++qX0wsBG5xJeOeR6dcOeWLSjuhFkXiSHW1K4ZNrSCSaypHfUNrRCl\nY2uUWR3aVu48Jt3tTQ5tVBueVx+KIhJtwdHBw6rHY3VIn9px6g7dBHSa4GRGh0waPX+3mPi86h1X\ntQ7zrDSsrhERaTvttNMgCAIURQEAPPTQQ/nbBEHAl7/8ZcN9MLARucnDjnmennALIiRJfXidVuMR\nrWCiFvzM7M/MvgHzYa/seVA6w/PUmoAYbaP2vLWOM5XsQirZpVrRlaSIYRUqGpvs+TwwM59Xw/fF\noyUUyB+srhFRNdi5c2fZ++AAf6IqkDuxlUJRCIKQP7GNNbW78nhm503l6QSTcF29/mOp7c/kvk3N\nYypn+4J5Z/rdQdX3Z+l1NBHueg5sR0/3NvQc2F5ShVIjyyOQlYzn88BMfV5Nvi+25xmSb5xsNsLq\nGhHVAlbYiILMzBBHH9rsWx2KZrTUge5jGQxtK3cek63tBRGNTTMQiRbOt+vTfE209mfldTR7nCXH\nalCFkoSQt/PATH5erbwvXLDbP7Xcyp/VNSKyYufOnVi1ahV27tyJkZGR/O/feecdw20Z2IgCyuwQ\nR8uBw4l5bhaHoukFE0CBXmgzGtpW7jwmq9uPzrebCLFg3bPRClEUI8MpSDp/VUv2Z+F1LOd56jbB\nEURP54GZDp5Wn69fC3aTb1hdI6JK8q//+q/4p3/6J9x///144oknsHr1asRi2lNCCjGwEQWQlTlV\nVk5snZznZqkTpk4wSY8cVZ3DVtLBUEu585gsbK/VbTFHlCSkkl0lC1nrHY/p19HoOAFIUkQziGtW\noTyeB2b688r5aVXJr7XXWF0jIi0bNmzAfffdh2w2i6uvvho33XTTuNu/853vYMuWLQCAoaEhHD58\nGH/6058AACeeeCLmzp0LAJg6dSoeffRRzccZGRnB2WefDUVRMHnyZHz1q1/FlVdeWfJ4ahjYiIJG\nZ8hYtGFi6RBHkye2biwwbGUo2vhgEkGusiZJEkaGUxAlKR9YRoaSGOjXb4WvvW/ryyiY2l7nfcmR\npDoMpQ4gldyPxqYZqIvGTR2P2ddR6zgBoHXyPOMgrlGF8nQZCgtBrKqXx6gClTQckohIjSzLuPfe\ne/HTn/4UiUQCV111Fc477zzMnj07f5+77ror/99PPfUUduzYkf85Go3i+eefN/VYuWZtzc3N2Llz\nJxKJBI4cOWJqWwY2ooDRHzIWQmPTDBwd+LBkrTBA58TWzXluFoai5Y5n9GR9dBikFIpCCgGpZDeG\nUt22h2qWO4/JaHszTUXyFSIli4G+TqDfwvBTk69j8XHG4m2OBHEv54FZCWKcn1Y9/Go2wuoaEWl5\n6623cNxxx6G9fbTp1aWXXopXX311XGArtGbNGnzlK1+x9ViXXHIJjhw5gptuugnXXnststksbr31\nVlPbMrARBYz+fC+gPtaK+tjEkkqK3oltYBYY1g2OzUgl95V3Ql7uPCad7Y3eF0BlqJ5b86py+3U6\niHs4D8xSEOP8tMBhdY2IqkF3dzemTDl20TORSOCtt95Sve/+/fuxb98+nHXWWfnfDQ8PY/ny5QiF\nQrjppptwwQUXaD7W5z//eQDAkiVLsHXrVgwPD6OxsdHUcTKwEQWNksXIcBL1GsFAGGtnrlpJ0Tix\nDcoCw4EJjnboDOWT5TSGNBbGdpNnr2dxoxqnFmhnECMb/Gw2wuoakfNmnX0WEhMmOL7f7p4eYO1r\nju1vzZo1uOiii8atQ7t27VokEgns3bsXK1aswNy5czFjxvgLT++9957ufrWqeYUY2IgCKNm3B5Fo\nC0RJZ/2xMaYqKUFo4CCIAIRABEe71IbypYeTSPaZn2/nJC+CeHGjGlmWIUmSNwu0U9WolmYjRFRd\nEokEurq68j93d3cjkUio3vell17CPffcU7I9ALS3t2PhwoXYsWNHSWDTayoiCAJeffVVw+NkYCMK\nIiWLo4OHdTsS5pitpPjZwKHwpF/JZlTvUymd/wI1p8rlIK7WqKaw+aUTjWuo8nA4JBFViwULFqCz\nsxN79+5FIpHAmjVr8MADD5Tcb/fu3ejv78dpp52W/11fXx/q6+tRV1eHnp4evPHGG7jxxhtLtn3t\ntfKrfAxsVLucGtblkuKABRwbDlnISiXFj7BRfNIvjFUNZTkDURSdD45evK8BGsrnWhA30RUzx60F\n2qk6sNkIEQVVKBTCPffcgxtvvBGyLOPKK6/EnDlz8NBDD2H+/Pk4//zzAYxW1y655BIIwrF1Y3fv\n3o1Vq1ZBEAQoioKVK1eaGt5o6zhd2StRwDm5HpmbCgNWNJZALF5aprdcSTETNpwKPTon/YqSQc+B\nXZDlEcdO9H17X30O/24EcTNdMfP3Dfr8Qwo8VteIyC8dHR3o6OgY97vbbrtt3M9qnSE/9rGP4YUX\nXnD12HIY2KjmuLEemavGAlaqfw8AxfUhjU6GHqOmGFCU8eFCLfiYDEN+va+ehUSj10EtiBduA1gK\ndNHYZOTWyjNSCfMPHRPwyrzbrA6HZHWNiKh8DGxUW9xcj8wjwtj/Nz6Nts7p0GOlKYZa8AFgLgz5\n9L56FRLthMLx8wZlAAoEMWRq++LnZaRS5h+Wq1Iq85WG1TUiIn2lE2KIqpiZNuhBlTuJlkJRCIKQ\nDwexpnZnHsAg9EBl/pyhsaYYagpP8jWfm8nn68v76sbrpcLO+168jSiFIEphc9vrDmPNYmQ4hUxm\nCIqSRSYUg5ncAAAgAElEQVQzhFSyqyZCi+vfvwrgVrMRM9jKn4gq3Xe/+10kk0lkMhlcd911OPXU\nU/H888+b2paBjWpKruKjeluQh3V5EA7cCj2j86u6Sk/yk/shSRFAlEw3twDUn68f76snIdHO+26y\nWYjW9kZz15JHdqPnwHb0dG9Dz4HtNRHWvArn1Yat/ImIjnn99dcRj8exadMmJBIJ/P73v8eTTz5p\nalsOiaTaEoT1yGxwbIFknfk3bq7pVdwUIxZvQ+vkeWNDy0YgjnXBNEP1+frwvnqxBpqd991ssxCt\n7U09rwB1yfRCRS/4HnBmhkOyukZE1eSPf/wjLrzwQiQSiXFdJ/XwsiDVHM2KT4ArBU5UkGJN7Wid\nPA8TEgvQOnle6VAuk8MXbRs7yY/F24qGlkVM/8ECtJ+v5++r268X7L3vetuY2d6L51VpKrYy7yA/\nm41YweoaEQVVa2srVq1ahd/97ndYtGgRMpkMZFk2tS0rbFSTArX4sRllVpDMNsdwfXFtC2t7adF7\nvl6/r66/Xnbed51tTG0PfxdZD6QKrcwHXdCbjbC6RkROeuCBB/Db3/4Wn/70p9Hc3Ix9+/bh85//\nvKltGdiodlXYsC7bJ9EWOyi6GXr0hpYpigJZHoEkhfPPLXeMlp6vx++r2yHRzvtevE02mwWgQBQl\n069jxV3UcFkth9hKaTbC6hoRBdmECRPwD//wDwCAw4cP49ChQ1i+fLmpbRnYiCqInZNoW/NvXAo9\n+vOjhtFzcAckITTuuVVEaHA5JNp530u2gbV12AB4E34raF0zhlhz/BoO6SRW14jIaddddx0ee+wx\nKIqCK664Ak1NTViyZAnuuOMOw20Z2IgqjcWTaC+aY5gydmKuO7QsK0NG0XhuF0OD1crB+q3bXDkO\nU+y8DkXbBK2iXJHrmlVYZT6onG42wuoaEQXd4OAg4vE4nn/+eVx++eX4+te/jk996lMMbESEsRNM\nGZLKtz0ry55UCYpPzEeGUxAlyfOhZeUO7cpt72tw84rLlS+teZWCIGGgfw+rVwFRKc1GnMTqGhG5\nYWRkBACwZcsWXHrppRBFEZIkmdqWgY2oUhWeUENnuJug/QdBlKTRNaRcPDlWOzGXQsDR1CH0JT+E\nnB3x5OTcTljLZoFMVkBIVCAW9NSt9uDmeOWrOPzpzKusj01EJBqvjGob2cLqGhHVooULF+KSSy6B\nLMv4P//n/6C/vx+iaK5hPwMbUQUqPKFWshkAAgRRUj259nUNKZ0T82hDKyLRZhwdPOzqiXkuXGmF\nLzWKAnT3hZA8KuW3idfLSDRnULgCQWEIrJbwZrajqJX9FYe/odQBzc/k6FIP5T0mOcPPZiNERNVm\n1apV2LlzJ9rb2xEOh5FMJvHtb3/b1LZch40oqAQRkhQZrYAVyJ1Q59YxE6UwRCk07kS3cI01P9eQ\n0guLuWMvPl4ndSycD0UBunpD2N0dyf9fV28IiqK9XXdfCEdSYWSyIgABmayII6kwuvu0r3FVxcmt\nQUfR4s+ikeLPau7zGY0lTK0VZ+cxyT9mhkM63crf6eoah0MSkVsEQcCJJ56I4eFhfPDBBxgaGkJL\ni7mljnytsN15551Yt24dWltb8eKLL/p5KESBojkkzeQ6ZuPa9fu4hpRew5NCassLlKMwPOXCV04m\nK+BIajQETGnJlGybzQLJo+pDSJNHJUxuymhW6BwbKulH50RBRDgcc64aqxv+mk2tFed6BZgCycpw\nSCKiSrF582Z885vfxOHDhyGKItLpNFpaWrB582bDbX29dLl8+XI88cQTfh4CUeBoVSViTe26FatC\nuRPdnNF25F3IZIagKFlkMkNIJbvcH242FhaNFB9vOQrDmlH4yqpkoUxWQCYrlN5gcJvWMVgVa2pH\n6+R5mJBYgNbJ81yrPqo9ZsvEvwOgXnq0Wo01Goo7lDpQ8Jl05jEdp1HlrgVsNkJE5Kwf/OAH+NnP\nfobZs2fjzTffxL333otrrrnG1La+/iv08Y9/HM3NzX4eAlGwGAxJk5WMqaFkaie6qf696DmwHT3d\n29BzYLtnc4NGw2I3FJ1KkSCIiMYSZT9W8UmmnfAVEhWERPUAoXeb0bGYoRfW3VL8mIJGONGtxqoE\nGzNDcXOfyaHBQ9Yf02V+BOdqx2YjRFTrjj/+eGQyGQiCgKuvvhobN240tR2bjhAFiGGDECFkaiiZ\n5omuT2tIpfr3AFB0jztS34xU0n7HSrWAlAtYVoKZKALxejk/bLJQvF42bFiidkymhkgahHUnh4ya\neczRgC1Alod1l13QHL5rNBQXgCRFIGfTSPZ2IpuVEalv8XypBzVON16xpYIWFK8GrK4RkdtCodHY\nlUgk8Nprr6GtrQ19fX3mtnXzwIjIGjOLXA+lDqChMQFBKA0hiqJgaPBQfr5bkE74Uv17IQgS6mMT\nVY+9nPlKWtUsu+Er0Tw6t02tS6QdHQvnG4Y2P7p5Gg2x7T30LtLplObnxyjY5MJNcRADgNbJ80pC\nXiq53//PrB/BuYjfC4q7MRyykqproWwWjek0BsJhZKxcoSEi0nHDDTegr68Pt912G772ta8hmUzi\nzjvvNLUtAxvVpoCFmTwTDUL0Q90wkn17fD/h0zLQvweRaFw3kFpldHJpJ3wJwmhDkslNGdNLAZg5\nTr3QZiasjx6czc+uynZGj6kX1swGm+IgFou36YY8vxuM+LoMBgJS3asxueqaoChYun8f5vT2Ip5O\nIxkOY1dLC9a1TYeicpGJiMiKyy67DABw8skn45VXXrG0LQMb1ZyghpkcrapE/hgNQp3RCbHjrASI\ncjpW2gwq5YQvUQTqTM5ZM0M3tJl4bex+dm0PW9R5nS0Fm9xQ3ABUr4yYDs5uCMDr41ezkSB0hly6\nfx/OOHgw/3NzOp3/ee10zmEkInvWr1+ve3tHR4fhPnwNbLfffju2bt2KI0eOYMmSJfjKV76Cq6++\n2s9DoipXKVevjYaHaYa65H60Tp6nuk/VE74yK412AoRhILXwOFZOLrXCl5UFtZ2gF9r0Xhu7n127\nwxaN3kc7wcbv6pUpPi6DURGvjw1BXnstV10LZbOY06ve0XZ2bx82Tmvj8EgiskWvI74gCMEPbA8+\n+KCfD0+1JgBXry0xaBCiFuokKWL6hK/cSmM54dfKfCWtx5k+pRWAvTllAKAoo2u0qQ2VdHv0k1Fo\nK3lt7H52bQ5bNPU9sBFsyq5eeTSU2W6ILZev1b0a15hOI55Wf33j6RE0ptPojRgvqUJEVOypp54q\nex+8XEQ1w8zV64qTC3VFc5LUFJ7wld0+3iAImFq3qujYrT6O1jpqZuUW1M5kRQACMlkRR1JhdPd5\ncx2rY+F87Qph0Wtj97NrabuCCwRm1x6zvL6fzrp8RtUrr9vs+7IMRhmvjxNqrdlIYWfIgXAYybD6\n9ygZrsOAxm1ERGY999xz47pC9vb24re//a2pbRnYqGaYDTMVzcwJnwNhy6vwq/c4ZhexVmNnQe1s\nFhjJCGWFRDVmTpLtfnatbmcnFFkNNnYWcfdjfToA5i4qqCljwW3fFrmvcRlRxK4W9b+L77U0czgk\nEZXtySefHLf+dEtLC5588klT27LpCNUOH+emeMloOJcT82S8Grql9zhWFrEuZmZB7dx8Ny+GThq2\n/bf72bWwXVnzOy2u72dpCGaFDWV2oqmRH0scVFt1TUqnEUmlMByLQVapjqmtu7aubTqA0Tlr8fQI\nkuE6vNfSnP89EZHTZFk2dT8GNqopfs1N8ZreCZ8jYcur8KvzOFYXsS5kZUHt3NDJnExWyK/rNqXF\n/hy6Ykahze5n19R2foQikyGvkhpxONrUyKdF7iudkM3ixA3rMXX3bkT7+zHU1IQPZ83CO0s6oBj8\nwVAEAWunt2PjtDauw0ZEjps0aRJefvllLFu2DADw+9//Hq2traa2ZWCjyuBgs4HALNDrNq0TPofC\nlqPhV+f9ze2vuXmyI4tYA/oLajdGR4Ngx5kLkM5k8cwre6DW3CSj1OMTp8/A63/ebvs4rLL72S3Z\nDqPz1PLNagIciiqmEUeFVQILWa2u+cFsde3EDesx6y9/yf/c0N+f/3nH0nNN7SMjimwwQkSOu+uu\nu3DzzTfjBz/4AQBAkiT86Ec/MrUtAxsFnivrptX41WunwpYT4dfM+5vq34uPzW12tP1+8YLaAgQo\nANLZKELRRmSzCgaHZAwcVQ+GycEMunuG8InT5yEcOnZA67e8bfuYDIdGAvY/u2Pbqb7eyf3BDUUV\nMpQ5yKHXaX4MhzRDSqcxdfdu1dum7N6NdxcthhwOqw6HJCJy26xZs/DSSy/hb3/7GwDg+OOPhySp\nz6cvxsBGgVYp66ZVIs2wZbWaWUb4Nfv+5ioATi5inVtQW1GA3sEwcntODcl4e/doF6eF81rRWB9S\nDW0CgBc3fYDG+hCOnxbD2QsmQhSF/Mms3eBmKrTZpPd6ux6KyqiSW7rAUPw4Hi0FUDGVwApktroW\nSaUQ7e9Xva0+mUQklcKgRmORUDbLYZBE5DpJkjB79mzL2zGwUXBV8BCjilEUtlypZmox+f66OVzr\nE6fP0xzy2PlhCgvnteL4abF8gCuUC3gDRzP52xedMil/eznBzZXQZvB69xzYnv9vp+d3an6uLIQp\nM9Xc4seRZRmSJHnzeTZbCfQoQJpVCc1GzBqOxTDU1IQGldB2NB7HcCxWUl0TFAVL9+/DnN5exNNp\nJMNh7Gppwbq26VDcXpCRiMgkBjYKrFoaYhSEkzivq5l+vr+5k86+gbTmkMeBwQwGh2ScvWAigNEA\nlxzMQMCxsFYoF/AKh0cWPpbV4OZ0aDPzersxv1PrcxWui1sPUzrVXLXHkQr+hfOiOm9UCfT0gkgN\nksNhfDhr1rg5bDlds2apdotcun8fzjh4MP9zczqd/3ntdJeXjSAiMol1fwqsmlg3Dd4vCKzKiYWw\nLTLz/rpRXSusEDREJTTWq1+3amwIoSEqQRQFLDplEq65YAYuWzxNNawBxwKemcc1fawOPn/T3ye7\na4+p0flc1UVizq2rpvM4xdz6POdorUvn21pyDrLzGVbj5kLZ7yzpwO7TTkOqqQlZQUCqqQm7TzsN\n7yzpKKmuhbJZzOlVX7dydm8fQk4vukhENen2228HAPz85z+3vQ8GNgouM4tAV7ignMR5tRD2OD68\nv8UnnOGQiOOnxVTvO3NqbFy1LBwSkZgQNQx4Vh7fDMdCm1uvt84i0XqfKzV2w5SVx3Ht81yoOPT6\ncEHEDDcuiJgZDukmRRSxY+m5WH/DCqz9h89j/Q0rsGPpuaot/RvTacTT6hf+4ukRNGrcRkRkxa5d\nuwAAzz33nO19cEgkBVpVr5vm9Rw9nWGXfjVM0Ht/nT6Z1ApLhUMeBwYzaGwIYebUWP73hXIBT21O\nW3HAMzoOK0MknRoe6fT3yWiIn97nSo3dobBWHseP6nxNDe92kNXqWiE5HB7XYEStM+RAOIxkOIxm\nlWCWDNdhQGUIJRGRVfPnz8fpp5+O4eFhnH322fnfK4oCQRCwefNmw30wsFHgVcS6aTbmoHl5Emc4\nd8bH1ulq769XYQ0ARFHAwnmtOGFmEwCgKRbWDV5WAp7RMfkV2pz4PmnPeRSQ6t8z+kudz5Ua22HK\nwuP4UZ2vhg6SQW42YldGFLGrpWXcHLac91qa2S2SiBxx//3342tf+xpWrFiBxx9/3NY+GNioMgR4\n3TS7jQS8Ookz20zE12qmi++v3olmNqtg89uH8LcPUhg4milp0a8mN6dt4bxWDA7JaIhKpipresdW\nztpttpT7egsi6htaVW9qaJwEQMl/btQ+V1lZRl2kdChqOWFK63FEKQRJCvtbnQ/gWnJBXyy7nOpa\nyb50QuK6tukARuesxdMjSIbr8F5Lc/73REROmDhxIn71q18hFlOfhmGEgY2oDGV1VnTrJK6w2gdY\nGnYZhGqmkyeSRlWBzW8fGje8UatFv5pwSERzozNX4M1W29xco80KSayDIKr/8yEIYsl3IJXcj6HU\nQUAYvRgBJYtYU7vjFwcKP7/R2OSxCyl1yMojGD7a5+tQ6koe3l2N1bUcRRCwdno7Nk5r4zpsROSq\n4eFh/PM//zNef/11CIKARYsW4e6778aECRMMt+VfJSK7HGgkMHqC2YVMZgiKkkUmM4RUsqusOUWF\nHSfjzTOsNxNxskugj4xOMtOZLP72QUr1ts4PU0hnvH3+ZhuSBKIyYmJ5qtx34Nhncj5aWucgFm8D\noN1NUfsxtZubjKNkEY1NLmrmE0Esnhht5mN2Py6w/JxdEojPkEf0qmuFMqKI3kiEYY2IXLNq1SrM\nnDkTv/3tb/Hcc8/huOOOwz333GNqW1bYiGxyag6am3OK6kNRZOU0BKk0mAVx7oxTJ5Jmws/gkGy4\nBluugma1892Gt/ZYun9OpVTaZHkESlaGIGn/EyJJdYg3z0B97FilsqQCbXJopqVhxzoXUqINE/1f\nBy3Aw7vd5GYrfyOhbJbVMyLy3Z49e/Af//Ef+Z9vvfVWfOpTnzK1LQMbkU2OzkFzYE6R1kmqolEO\nqZalEYqZrVTl1mBTC22NDSEsO2MmInX6bfq1FAe8XIBLZ7KG896sNiPxhZLF0cFDuk0+ZHkEdZG4\n6m1WuqBaHXasfyElhNw/e14spB1EVi+KODUc0g9CNotz9+3FnN5exNNpJMNh7Gppwbq26VAEE2Vi\nIiIHZbNZHD58GK2to3PADx8+jKzJ9R4Z2IjssjMHzUY3STP0TlJFUcTgwEHURePuzZ1x4Hl5PUxL\nr0X/ogVTbIc1NYvmTcfql9/Dpre6TDU3MRPaHK+yWXwPc5+faMPEsSA03shQEvUx9c6ZkhRBvHkG\nkr2dhsdkdekLq0sJuLKEBtnmZHVtyv+3elwHyOZ0Ov/z2umVs2A5EVWHf/zHf8QVV1yBpUuXAgDW\nr1+Pr33ta6a2ZWAjKoOVRgJ2u0maYVTtG+jfA/TDlbDoxPMqN6xls0AmK2DpmSdZ2q6wRX/qaAat\nzVGc/ncTcf2y2WUdT7HVL7+H/7tlX/5nM81NvAxtdt/D3HDexqYZpRcEkvsRicZVP5OCIKA+NgnZ\nrKz7OLaGHVtcSqCW1kHzq7rmR7MRKZ3GnF71heJn9/Zh47Q2Do8kIk9dccUVmDdvHrZs2QIAuOGG\nGzBnzhxT2zKwEZXJzBy0srpJmmGy2uf0Sanrz8uAogDdfSEkj0rIZEV0v7LHsC1/oVyL/ts/cyp6\nB4bR0hhxtLIGAMMjMv60s3SdJ2A0KC6c11rW8MhyQ1vZ76GSxUBfJ9BfWqEzCk5G1S27w46Nqn+a\n+7FaKVa7v0tV9FrgZHVt/2uvIa6yIDYAxNMjaEyn0RtRvxhAROSWOXPmmA5phRjYiJygNwfNxrAu\nU4pODD1vG+7W87Kguy+EI6ljDVWstOXPyVUIEhManD9AAL0Dwzjcp/7ZSB0d39xEjauhzcn3UOU7\nkOrfC0GQUB+bCEFlzpBhdcvu0heCiKHUwbHnpv/PXG4/VquMavcH4H9TEw8FtboGAAPhMJLhMJpV\nQlsyXIeBsEqHXCKigOJ4ACKXmRnWZVVx+/5Y0+h8DC/bhjv1vOwOh8xmgeRR9WqY2bb8XjRLaGmM\noLVZ/XVqbY5i2RkzDfdhZmianddR/z0cnWdWroH+PchqBDIzzXmsLn1RuIyA1nNTFAVyZji/n1yV\n8dgyAKNVxtz3Su0xVO9vYR9ec2M4pJOc7gyZEUXsalG/GPFeSzOHQxJRReFfLCKX5YZ1qd5mo7W+\n4cmlR+uoOf28rMhmgaMjIjJZ9WGPubb8erzqbBepk3DGCerVvtP/biIidRKWnDzD8HjcOIHWew9z\n88zKDhxjVTI1ZjuVmr0QUfzdUKvqAaOfz8MHx/ZjdT1Fnfub3oceH9eJc5pf1bXc2mvr2qbjT5Mm\noTdcBxlAb7gOf5o0CevapvtyXEREdlX+vwhEQadkIcvq4SEry9aClQOLdTvGgRNxq1f9FQXo6g1h\nd3cEew7Xad6vsSGEhqj2XDSv25Bfv2w2Lj5zOia1RCEKwKSWKC4+c3pJc5NyQ5vq66kXAHTewxwn\nPleOLBBvdCHCQpAaPnoEyI5+J61WivXub3YfWrQq5+UKeit/p6trOYogYO30dvz0pJPw5Enz8NOT\nTsLa6e1s6U9Evrj44ovx9NNPY2BgwPK2nMNG5DZBhCSphwdRkkZPhk2GNqcW63aK1/PmiuesaZk5\nNabZyMOPNaMkScQNn5yLz5w/y7C5yZKTZ+guvG00p61wPpuZeVnG88wio+9tZsjMU9Xk1ALxWvS+\nG4qiABhtulP8+bTa2MTqsgFmq81+N/CpFjtVqnoZUWSDESLy3YMPPojVq1fjRz/6ES688EJcf/31\nmDt3rqltGdiIXOZkyHJ0sW6H2D0Rt3rVX2/OmgBAARBvCGHm1Fi+XX8xJ8OaleFeU8bWXInUSaaa\nm5Qb2gBrAWCgf49mC34AaGmd40wDjXIXiNeh/90YRt/hXZDlkdLPp9XGJhaXDTBVbQ5AAx8rgtxs\nhNwTymbRmE5jIBzmHEAiG0466STcd9996O/vx29+8xusXLkS06dPx4oVK7Bs2TLdbRnYiFzmaMiy\n2zXPbS6eiOdksoLmnDUFwGWLpyExIepaZa2cE9DCbXPhzUjueLWCm15oO+eM+dixV/21Ug0AOp+r\nwnmSQICrPQbfDb0KoW6lWKVNv9b9NfdhwM3KeS01G1GrrlH5BEXB0v37MKe3F/F0GslwGLtaWrCu\nbTqHlxLZ8Oabb2LLli2IRqM455xz8Mtf/hIvvfQS/u3f/k1zGwY2onKYWXOpjNbkavv2vH2/C+x0\nNAyJCkKiohra4g0h18Ka05WC3P6sBDe9apuaTFaAFLIWAMZ/riKqwyM9rfbYWM+snO+GWqVYb0ip\nVmXZTrU5iJXzcrC6Vl2W7t+HMw4eW0uyOZ3O/7x2ejC6oBJVgp/85Cd45pln0N7ejs997nPo6OiA\nIAj44he/iAsvvFB3WwY2IpusrNtk9UTSaN9uzwdyk902/qIIxOtlHEmVhjK9OWvlcPPE00pw0wpt\nWlU2vXBrtOD0UOogJiTU3yOv5klaXROtMNyV9d0oqBSbGlKqVlm2U20OSOW8kpuNsLrmjlA2izm9\n6o2JZvf2YeO0Ng6PJDJp//79eOSRRzBr1qyS2374wx/qbstvGZENVtdtAuy3Jtfct5KFnE2PdqCr\nghbgZiSaM1gwqxnxhhAEjFbWFsxq1pyzBtg/wfSqStC1bt2xxxochPC//wsMDpbcT+t5qJ1k58Kt\nGqMAIGdHfFuuAbD+3VLtrFju0hY+dGN1pJNmEbsXR8rh1PdGSqfR0NsLSWXha/JOYzqNuMZ7EE+P\noJHvD5FpU6dOLQlrP/7xjwEA8+fr/71mhY3IqnIaBKhdfS8c+gWY3rflKkQAlHsCufSs0XCycF4r\nBodkNEQl3cqanbDmy3AuWUbm+uvR9OabEPftQ3b6dGQuvRQj990HhI79mbZSaUs0ZwCMNmpJyzA/\nPNDPao/F75ZbnRX96sbqZ+Xc6+qaFiGbxYkb1mPq7t2I9vdjqKkJH86ahXeWdEDRqOSwuuaegXAY\nyXAYzSrBLBmuw0DY3JIVRAS89NJLWLlypeHv1DCwEVlkfDJXB0AxdcJVHLqGh5KmThRrvQV4OCSi\nuVG/yuFWWBOGhxHq6UFmwgQoDrUKn/jkk/jICy/kf5b27IH0yCMAgJHvfW/cfc2GNkEAprRkMLkp\ng0xWwOa/bPdkLlg5LAUlpzsrFlw48XVOmUMNfIJeXdMaDnnihvWY9Ze/5H9u6O/P/7xj6bnlHB7Z\nkBFF7GppGTeHLee9lmYOhyQy4Q9/+AM2bdqEAwcO4Pvf/37+9wMDA/llZ4wwsBEBlhoc6J3MZbMy\nWibOMVX1UgtdDY1RZOUMBKn0q5k/UaywFuA55Z5Aut29zvBkU5Yx8ckn0bhlC0IHDyIzaRIGzjwT\nh77wBUBjnT0zhOFhNG7Zonpb6KWXMLJqFdAwfikAK5U2UQTqRAUdHz8pvz6bGX5Ue6wEJSerYGrV\nakerjDYaqASNF9U1KZ3G1N27VW+bsns33l20GHJRRYfVNfeta5sOYHTOWjw9gmS4Du+1NOd/T0T6\nwuEwYrEYBEFAQ8G/55MnT8ZNN91kah8MbFTzLA8tVLKQZRkqmQqSFAYwekKhWfUSREhSnWboUqB+\ntSV3oihJkUAtnh1EVk8uzVQGiqtg4QMH8j8fMjGcQUuopwchlavXACDu3QuhqwvKRz9acptWaPvE\n6fOwbssOhEQFxRe/CxfVNsWD5RqKH89sUHKqCqZVrU4lu5BKdpVdZfRj6HKltvKPpFKI9ver3laf\nTCKSSmGwRf3vJrlHEQSsnd6OjdPauA4bkQ0LFy7EwoULsWzZMtMLZRdjYKOaZmtooSBCslBRKax6\nFZ68aRFFCUdTBxGOxFVPFKutBbgZVk4oTYe1wUEIXV3o2rULMBjaqFcFa9yyBYdvuMH28MjMhAnI\nTJqE8IEDpbdNnIiuXbuQUAlswPjQls0q2Pz2IfztgxQGjkYQEhXE62UkmjMI5FJJ5S5b4cRcO4Nq\ndc+B7cZVRp3qWS0NXXZi7udwLIahpiY0qIS2o/E4hmOxsh+D7MuIInodGgZOVEt+97vf4ZOf/CT+\n+Mc/4o9//GPJ7ddff73hPhjYqHbZHFqoNxRLTa7qFY1NVj25LCbLI0j27ck/VsmJYEBagFvhx3wa\nTZkM6u6+G6E1ayDu3YvjTAxt1KuChQ4dQqinB+mpU20djhKJYODMM8dV73IGzjwTSiSCrnXrNNv/\n50Lb5rcP4e3dfWO/HV1kPLcEwpSWTP7+lqtsLnBq2Ypy59qZHVapVWXUfR4VMnQ5SK385XAYH86a\nNW4OW07XrFkcDklEFWnXrl345Cc/iW3b7P/by8BGzqmweRp258DoVbjUyPIIZCWjefJWrDB05dr2\nV4ThlegAACAASURBVOPi2WY5XV2ru/tuRMYaegDmhjYaVcF2vL0N8s53TR/nCecuHffzoS98AcBo\ntS506BAyEyceC5Fj9ELbmSe04enfdareljwqYXJTpmR4pF9MV51MDscsZ65dOdVqo+fhV6fJoDcb\nMfLOkg4Ao3PW6pNJHI3H0TXWJdIroWzW1aF/bu+fiILl1ltvBQDcf//9tvfBwEaOqMQW87ZP1nQq\nXGqGj/ZCEkKaJ2+jHYKUktBVLYtnl3MC6fhQyMFBhNasUb1Jb2ijXhVsT1tbyZV/I8VVhhPOXYpD\nK1fi8A036Hag1AptvQPDGDiaKfk9AGSyo9W2OvHY3EjfqmxuVZ3szrWzW6028TyqZeiy1wtlK6KI\nHUvPxbuLFiOSSmE4FlP9frlRXRMUBUv378Oc3l7E02kkw2HsamnBurbpUBwYV+z2/onIng0bNuC+\n++5DNpvF1VdfrdoI5KWXXsLDDz8MQRBwwgkn4IEHHgAAPPvss3hk7CLwl770JXz6058u2Xb9+vW6\nj9/RYXxBioGNylax8zTKGFqoVeFS+12qfy8giDonb8PoO7QLcnZk3BprTlYhAFRcBTQnncmaWnPN\nDKGrC+K+faq3GQ1tLKyCSQcPOnrlP3cie8K5Sw2HVqqFtpbGCCY2R3Cor/SzEG8IIyQOlfzej9Dm\nV9VJj51qtdnn4fXQ5aA3G7FCDoc9bzCydP++ce3rm9Pp/M9rp48u3F5OdczM/onIW7Is495778VP\nf/pTJBIJXHXVVTjvvPMwe/bs/H06Ozvx+OOP47/+67/Q3NyMw4cPAwB6e3vx8MMP4ze/+Q0EQcDy\n5ctx3nnnobm5edxjPPHEE5qPLwgCAxt5oELmaWhJJfdDFCXNBh+622pUuFSrXgbhUJYLTqhdeE1N\nV0AdDnXlVNcUBfjDmwfHmmhk0FgfwvHTYjh7wUSI4vir0WarAMqUKchMnKg5tDEzYYL2xpKETbPn\nQDpupu6V/3LsXLuuZLikmtwQtFxwi9RJOOOESfi/W0rD6MypMWSGkqr78Sy05T5XSiaQVSer1Wqz\n1bNKH7ps5ntldjikmeqaWW5U10LZLOb09qreNru3D5umTsPiDz+wXR0z2v/GaW0cHknkg7feegvH\nHXcc2ttHL5pceumlePXVV8cFtl/96le4/vrr80GstbUVALBp0yYsWrQILWMXlxYtWoSNGzfisssu\nG/cYTz31VNnHycBGZQniFXOzikPM0dRhDPTvsRZU1CpcGlUvsydvTr+mZqt1QRvWGq6fUtBEAxg4\nmsn/vOiUSbb22bV1KyYaNPhQU3iy6faV/8Jqm5HCatv1y0b/cfnzu4dwqHcIjQ0hzJyaC7iTStZn\ny3E7tBV+rpRsBhDUG7v43jDHSrXa4AJMYfCzPHTZ5kWTaqqu+aExnUY8rX7BIJ4ewfn79mJBT0/+\nd1arY0b7b0yn2YGRyAfd3d2YMuXY3/JEIoG33npr3H06OzsBAH//93+PbDaLL3/5y1iyZInqtt3d\n3SWPsXfvXrS3t+O9995TPYbCcKiFgY3KUqnzNLQWrVYU2dWQYubkzdHX1GS1zo1hreVU1z5x+jw8\n80rpGmMA0PlhCgvnteaHR5qtruUqAWYafBRysjJghdnglgttkiTihk/OxWfOn4XegWFs6zwwbgip\n2qLahsqsuBZ/rgSptCIpy2kMDR6umKpTjtoFmKwsI1rfgobGxPiLHibDYNAumhhxstmI3wbCYSTD\nYTSrhKqBcBgzNNaHM1sd09t/MlyHAYer9VawCQr5bdJZZ2HKtGmO7zf7wQeO7EeWZbz//vt46qmn\n0NXVhc9+9rN4QeXCr5Zvf/vbeOyxx1TnxgmCgFdffdVwHwxsVJ4KbDHv+zBOo5M3B19TU9W6bDpw\nw1oHh2TNJhoDgxkMDslobhTtNUSQJFMNPgD/wlrxMZiptuVE6iQkJjQgMWFmycLaWqFNrcpWdnjQ\n+Z4VUhR59DNWgQovwERjCcTiifxtVi96eDkXOEit/M2S0mlEUim89cYbcKPlaUYUsaulZdwcs5w9\n8TjmFVTXCpmtjunt/72WZl+CEpugEI1Wxbq6uvI/d3d3I5FIlNznlFNOQTgcRnt7O2bOnInOzk4k\nEgls3bp13LYLFy4seYzHHnsMAPDaa6/ZPk5eSqGyjZ60dCGTGYKiZJHJDCGV7ArslWEzIcZvTr2m\nuWqd6m1j1To3Xo9yO0M2RCU01qtfT2psCKEhan7hckC9EqBEIkhPnRrosJazc+063ePpWrdO9Tmq\nnXRrnawXvme58CCFohAEIR8eYk3mGyOYXa8wKN8525Ts2EWPZtWbI/UtgGDwT63BRSSj7Su9lb8e\nIZvFSevWYukvfo5zf/okvrBjO87dtxeCohhvbNG6tun406RJ6A3XQQbQG67DnyZNwqvT25HUqIBZ\nqY5p7X9d23TnnoQFuSYozek0RBwb5rl0v3pjJqJqtGDBAnR2dmLv3r0YGRnBmjVrcN555427zwUX\nXJAPZj09Pejs7ER7ezsWL16MTZs2oa+vD319fdi0aRMWL16s+3h//etf8fTTT+Ppp5/WHCKphhU2\nckSltJgHKmcYpyOvqYlqXRBfj3BIxPHTYuPmsOXMnBpDOGS+umbnxDJIYa2QUbVNrYNkbmFt0xyq\nQJtdrzBI3zm7yp136tdcYK0OrEGqrp24Yf24hbTd7KyoCALWTm/HxmltJUMEnaiO6e3fa2yCQjQq\nFArhnnvuwY033ghZlnHllVdizpw5eOihhzB//nycf/75OOecc/CHP/wBl1xyCSRJwje+8Q185CMf\nAQDcfPPNuOqqqwAAt9xyS74BiZrVq1fj0UcfxdKxf6cff/xxfPGLX8R1111nfJzlP1WiMXbXQvJa\nJQ3jdOA1Ha3KCWPD2+ogy8Pjm504/Ho4te7a2QsmAhidszYwmBnXRMNNQQ1rOXZCWzG9oZGb/rzL\nmfBgcr3CwH3nbCj3okc529tpNpLNKtj89iFTHVjVeFVdk9JpTN29W/U2N0NFRhRLhjjmqmCze/sQ\nT48gGa7Dey3Ntqpjavv3GpugEB3T0dFR0lr/tttuy/+3IAi48847ceedd5Zse9VVV+UDm5Ff/OIX\neO655/JdJnt6enDttdcysBFpqfR221aMzkVqhijVIavxPIP4eoiigEWnTMLCea0lVQC3qmtBD2s5\nRg1JikObWpVNLbRls8AZC+bivQ+dqbgWf66yWRmAAFEUA/EZc0y5Fz08voi0+e1Dmh1Y7/zc6Y4+\nVjkiqRSiGs0+vA4VQaqOOSHITVCIqlUsFsuHNQCYMGECYrGYqW0Z2KhmVdIwTrtKGxlENBsZ+P16\naM2tCodENDdaPzGq1rBWSK/aZiW0KQrQ3RdC8qiETFaAIMiq+7QTHko+V0BVfufKvejhxUWTjjMX\nIJ3J4m8fpFRv7/wwheERGZE6a3NEtZT7nRqOxTDU1IQGldDmV6gIQnXMCUFsgkJUrXJz1RYtWoS7\n7747X5F79tlncc4555jaBwMb1bZKGcZph525SGW+Hl40P3Byfk01sNJFUiu0PfP7d3AkdezkV5RG\n/2mQ5TREUSo/PBR9rqr1O1fuRQ+r29v5vhl1YO0dGEZiQoPm9l628pfDYXw4a9a4OWw5DBXlc3KY\nJxFpK27nv3nz5vx/C4KAr371q4b7YGAjqlKVtKi504v42qmu5dqGD8dikCtsOJBWaDPThCSdySJ5\nVL2ioigyerp3Qs6OeFMNK3Pdt0Ao9yKQSxeRct+xXAdWtdA2sSWKlkZnqkdOVazfWdKBI/v2MVS4\noNqGeRIFVTnt/HMY2Ki6VMMJn0O87v5YqdW1d199DSdtWI+pu3cj2t+PoaYmfDhrFt5Z0gGlgk5e\n7Ia2wSEZmaz68wyFIgAUT75LlbZotN/sft/0OrCe/ncTdYdD+rFQ9jsbNuAdhgpXVcswT6JKcfjw\nYQwPH7swN83EouH8q0dVI9bUjtbJ8zAhsQCtk+dZWjOqKo01MlBTDZ35tFg5qdy5dl2+bXhDfz9E\nAA39/Zj1l7/gxA3rXTtGt2hVNfTWaNNb8y4kKvjEaXOcOjxNTqz75hpBhCRFjNdSC7DiCvbZCyZi\nwaxmxBtCEADEG0K4+MzpuH7ZbEcez435oLlQwbBGRJVq8+bN+Y6UF198Mc4//3xceeWVprblXz6q\nCoE+4fOKyomlV4uaO9XKX4+Z6prVCoBe2/Apu3dD0mh7HWRWQhtwrOKiJl4vQxRdrp6WuWi0mzy7\nCGQxFJb7fuQ6sF5zwQz8/bLjcM0FM3DDJ+dCkrQf34/qGhFRNfnBD36An/3sZ5g9ezbefPNN3Hvv\nvbjmmmtMbcshkVT5HFrot5LpDSfzu/tjUO1cuw4NOm3D65NJRFIpDKosgmllvttOCye6Jxisn2aW\n2eGRuaGRhWveJQfTCIkK4vUyEs3qzSmcFNS5lqUdVqOaHVYN6QzVjjXNyK+R6PVQ0FwH1iAtlD1u\nXwyJRFRljj/+eGQyGQiCgKuvvhrLly9n0xGqDa6f8AV8XpypE8uAdsP0q7qWO6nUaxt+NB7HcNH6\nKEI2ixNNznezc7JZvE05Ac5qaCtc8+6Nt3eieORZx8L5WL91m+3j0eL1XEtTHLwIpHcxpWXiSaiL\nHPuMmQmFdhbKJiIi/4VCo7ErkUjgtddeQ1tbG/r6SucTq+GQSKp4uRM+1dvKPOFzfUhUufNjAjCc\nzItmIyUGByH87/8Cg4OWNy2sAOTahqvpmjWrpHpmZr7bznXrxgWvUDaLluFhhLLWw37xvixvb3J4\nZC4Qj1Zcwjj3bIsn+eV8jgM419LMRSAz9IZqx5rax4W1Qr4NBdX4Xpm9GMLqGhGRthtuuAF9fX24\n7bbbcP/992PFihW47bbbTG3LChtVvrETvsIqU045J3yODonS2H+5XfGCOpzMDFvVtUwGdXffjdCa\nNRD37UN2+nRkLr0Ue5YtAyR7i/2+s6QDwOictfpkEkfjcXSNVc0K6c13m/T2NnRmZAyFjv1JFRQF\nS/fvw5zeXsTTaSTDYexqacG6tulQBMHSMeZOXu1U3Kys01Yot6j2uN+pVNmc+ByXtWi00xVwQQQE\nofyqn8HFFAHanwFJiqh+d92ori05eYbm92rkvvuAEE8TiIiccNlllwEATj75ZLzyyiuWtuVfYqps\nYydrqeR+ADZP+Ar2kz/pc3lenFNh0O/hZF5X1+ruvhuRRx7J/yzt2QPpkUcwcd8+HFq50nB7tQqA\nIop4d9Fi7Jm/AAKAweZm1XlpEZ35bvFMGive2YG/fuQj+UC2dP8+nHHwYP4+zel0/ue10+1Vau0G\nN7XQpjU0spBRaHPyooaduZZOLwVQuD8lK6vex+xFIP2LKfot1LMeDwXV+l4BwJ5PftKz4yhXKJtl\n638iCqxMJoNnnnkGW7ZsAQCcddZZuOaaa/JDJfUwsFHFUjtZ6zmw3fLVdrX9DKUOuFe5cjIMulRd\ndJut6trgIEJr1qjer3HLFhy+4QYoFtcSsjInTW++mwCgKZPJB7KN09owp1d9mN/s3j5snNZW1gml\nneBmN7Sp6Vg4H+v/uMP5ixoW5lo6XQEv3p8gjf7zKMtpiKJk+SKQ/sWUYQhjx6xmSOW761p1Ted7\nFXrpJQjnnWfqe+XncEgnq9lERG659957sX//flxxxRUAgOeffx47d+7Evffea7gtL0FRRdKcGxJv\nGz3hsxDW1PYTjSVcmxfn1PyYHK9a9xfzuromdHVB3LdP9bbQoUMI9fTobq92QmllDTa9+W6FZvf2\noXl4GHGNJQHi6RE0OrRcgNUTW7XXQGs+W47Wib/Tn2NLnJ67qbM/RZHR070dPQe2W/tOGczN07pt\nZDiFVL9xaHaK3vdK3LvX8HsVBLlqdnM6DRHHqtlL96s/L7PKmX8a5MciIn9s3boVP/7xj3H55Zfj\n8ssvx6OPPpqvthlhYKPK49TJmu5+ml1rhOBGk5RU/170HNiOnu5t1k8sPWa3M6QyZQqy06er3jcz\ncSIyEyZo7kstqNhZg+2dJR3Yfdpp6A+FoWg8Vjw9AgBIarT7T4brMGCwFIAVfoW2T5w2x7WLGkac\nDotG+wMUW995vYsppbcNI5XsQu+hHZYfx47ce1zO9yrHz+paKJvVrWbbCUCCouDcfXvxhR3b8Y87\ntuMLO7bj3H17ISha33r7vHwsIvJXS0sLRkZG8j9nMhlMMPE3FmBgo6DS6Trn1Mma0X6GUgeQSnZB\nltNQFAWKoiArO7AulVtd8XLDyTwYBulLZ8iGBmQuvVT1poEzz7Q8HFJvTlpuDbZiiijifyDg5yee\niKTGmPNkuA59kQh2qazfBgDvtTQ7Pr/GajdJO6GtmCgCE5vVG724PRzX6Ysebnaa1buYMv62bf60\n8nf4e+W1xnTa8Wq2WxU7vx+LiPyxevVqrF69GnPmzMFnPvMZPPbYY3jsscdw7bXXYs6cOab2wcBG\ngWPUSt+pkyuz+5GkMARBgCAIEKVQvi13Ofwaxui3ctddG7nvPgx/6UuQjzsOiigiPXkyjlx+OQ59\n4Qua+9K6+p+bk6ZGbQ024NjV/2FJwqBGYMsFsnVt0/GnSZPQG66DDKA3XIc/TZqEdW3q1QwnlBva\n9Ki9d4nmDD4SS3v/OXb6oofbSwvoXUzx8EJLTvH3a9z3SpIgH3ec4ffKDXZa+Q+Ew0hrXABJi6Ll\narYbFbsgPBYR+Wfbtm3Ytm0bMpkMTjrpJHR2dqKzsxMnnHAC0iYvKrHpCAWK2UWgHWm0YbQfwNVO\nkXa64gWBL9W1nFAII9/7Hvaedx5CPT3ITJhguwKQm5M26y9/KblNbQ22wpPJpfv3YcrQUOl20Wg+\nkCmCgLXT27FxWpunnet2rltne8FtoyYkxV0jBQGY0pLB5Cbg9b9s8/RzXNZSAB7szymeLJQ99r0a\nWbUKQlcXlClTcGjrVlObOjkc0i4nBw+aqdj1OlR19PKxiMg/999/f9n7YGCj4LDQPdGpkyu9/UhS\nxP01zix0xat05VbXCimRCNJTpxrez+hk0uwabMULYWtdFY9mZEiKgkxBZ7qMKHp+0mW2i6SdzpFq\nrf5FEb58jp2+6OHI/pxeF85LDQ1QPvpR0wtlO8nuQtmN6TTqNCpR4bE2/1a+fwPhMJLhMJpVglQ5\n80/Vlhxw67GIKJgURcEzzzyD119/HQCwePFiXH311RBMdLNlYKPAsLoItFMna1r78XuNM1Ln5Mmk\nIorYsfRcvLtoMSKpFIZj/z975x4dN3nm/69GmosvYzuJHdtxnBDsFHKjDWRJWVJyg6YQyjVtCPkV\n2kDaZssBenaBLrRsF9pSSmkPt5PSLklblgL7O1xaNuXWkCvll5ZLSUgcSkxD7CR24ji2x2N7PBrp\n94etycxYl1caSSPZz+ecHPBoXknWSOP3+z7P831KdCNrgP6qeLmYxIUth/DqlKm+sRO3S7SpNdV2\nBbsXPWQJKSmp/b2iI8js7gvnmJV/Ln196cgaiouZj+eF6Jqu6BGCpkWPGAjgo4qKrB6KClbqT/Va\nDth9LIIgvM1PfvITNDU14aqrrgIAvPjiizh48CBuv/12w7H0bUB4Bku1aRmTNS2TEibU6kicrmvx\nIU6nQ7JE11gxM5lMBYPoq6hQbZidizJBVIMDMKezU9UwoBC23axRCxYTEhYKmi5rE3o1tEbbVFuN\n5Fnv6iiiiNAdd6D43HNRcvbZKD73XIirVwMp9abhTmE1ugacElhqRKQUPnfksKrjot7zaGf9qZGp\nSCFqXQmCKAw7d+7Ef/3Xf+Gyyy7DZZddhscffxw7duxgGksRNsI7WKxNs3tVOxOv1rX4DUt1NXmQ\n78o/n0wiHI9j97vvDuX7ZaC3Kq6Q2Rybk2UsaW3B9K4ulIgiU1NftfQpq7DWtKlF2jJhibIBBYy0\n2YBeDS0A7fra2GHb613diK6F7roL4fXr0z/zhw5h3KGhz7hj7VrdfXkhuqagiJvZJ04gkiHAIpKU\nfk63TB4SzixNtu2qPzUyFVG+IwpR60oQRGHITH9kSYVUIMFGeAqzAonJpMSGc/KjOYjdeCG65nRt\nDSdJmLF9G2qbmxHp6cE8DXG1tW4yIqKIWSdPQu3rVjEM6A6F8H/2N2UZlCgr7MCpSWT6+AyTSStY\nrWmzkhrpWwxqaLWufrioAgPx487Xu9pNXx+ETZtUN5Xu2oUT113neUt/BZnjsGNSHaZ3dWUJNoVM\ncaREvBT0nsd860/NmIoUotaVIAh3WbBgAdauXYsrr7wSwFBK5IIFC5jG0jIO4TmYm0Db1UCbhQJY\nb48W/BRdm7F9Gxreew/FPT2aPZEUUVXf26u5H8UwYHFri6qbJKBu2+10TyaW1LPc62elqbYfUyON\namh1G2tzsLWPmxvRNa6tDYFW9ftK6OiA0Nmpua9CNsrWgkUcuW2jr5c+TaYiBDH2uO2223DRRRfh\n9ddfx+uvv44LL7wQt912G9NYEmyEN2EQSHY10HYMnebfRDZeiK7xySRqm5tVt2VO5jJFlVbU5UBF\nOQBoTg6BoUlkbTye3q9bk0knRJsafhNtRjW0uoIsNWhbvatb102uqYE0Wb1OSqyshDh+vCvnYRcs\n4siJJtt66NXX5ZqKFKLGlSAI90ilUnj00UexatUqPPzww3j44YexatUqBBhToGkmSfgWuxpoO4FR\n82+/YXUS6YXoGp9MorirC7zBZCwcjyPS06O6jWWFXgbQJQTThgGlySRKRVH3mF8+8BHW7NuLxa0t\niA4OujaZZI1qcIkEgkePgkskdAWz1ufsN9GWGIipv97fZSjIhlKn29xvIs6AqsAuLoa4fLnq+3vn\nz1dNh+QSCRx64UXDZ4kVu6JrAJs4KkTEa4SpiBDEnvHjsbN2EoChiP3i1has2bcXN+zbm/4+UDNK\nIQjCv/A8j+3bt1seX9Aatu3bt+OHP/whJEnCl770JXz9618v5OkQfkPHpCTACSiJ1hVksuRGXd1o\nwqnoWm492kBZGY4O91iTVVa0EiUlhj2R9FboZQDPNzbiRFERAH27cQDgh/+rpD1ysuxqTyY9MxJO\nkhD4j//AlMOHIRw/DrGqCr3z5wMLFgDC0J+N0WRCkm1cJEKGjECAV62h1auvzbfe1YrAzWdRZPCH\nPwQACH/8IwItLRArK9E7fz461qzJfmMqhcoNG1C6axdOP3bM8FkqFIr5SGNXN6LJQcSCIRyoKE+/\nXggbfcXAZGftJCxtbcGUWAyzOjsxJRYbEpiyjHkdHen369XUEQThbxYtWoQnnngCV1xxBYozWqgU\nDc8b9NAUbM888wyuueYae85QhVQqhXvuuQcbN25EdXU1VqxYgSVLlqCxsdGxYxKjD2WyFCmeAJ4/\nNaEN8EJhRJKJ5t9+wa/RNaUeTaG4pyf9875Fi0eM3/vmm5hoMJnTE2E9wRC6M6ISLG6SmTR096C5\nvBxnZ0zeco9vN1qiLffaBY8dw7iXXsLJ66+H8NRT6ddHg2jLXWDh+KE/i/3x44h1H8p6XpkEWR59\n4SQJECUOQkDONSd1BkHA4P33o2XJEgidnRDHj1eNrFVu2IBxL72U/tnoWSoULO6ORqLOKRYcPYI5\nGXWBijBLaHzQmUYpBEGMDh599FEAwAMPPJB+jeM4NDU1GY7V/CZ49dVXccMNN6C9vd2GUxzJ7t27\nMXXqVNTX1yMUCmH58uXYvHmzI8ciRjdDIki9b5Ct5iMMNWmer6vzIVaia3r1aLUffYRgX5/qNqOe\nSGZqUtT2F+N5aCU6RZODeKdqous9mXLT0vSuXemuXWh/9dWs1+zsnec6OgsswXBUfYxDBkQlZfVo\nbg+n/7V1CTDKirPcKDsHORxGsrZWMw2ydNcu1XE1zc2W0yPtTIfMRXFcVBM7iqjbOHMmNsychY0z\nZ2LL5HpHG93rpVKHNGrWnKipIwiisOzfv3/EPxaxBuhE2DZu3IhnnnkGK1euxC233JK2oLSL9vZ2\n1NScWtWsrq7G7t27bT0GMTZgEUmmVry5wIgVdNZeb0pdHS9ERm5T6upU9u9VnI6u5TvZ5xIJCJ2d\n2LfnAyAjZVCvHq2otxcLn/pvHJk+PZ3SpUwe7Vihz+2hlrm/AZ7HdfubtNMeQ6GC92TSu3aKe2Cu\n3X8mnoqyGTxrtn93WESJ8onDpyhKHE7Ghz73mgr9Osh8MVoQETo7IWhEiYtiMYTjcfRpLGJ4GTdt\n9PVSqbVkIrlIEsTopLOzE++//z4A4DOf+QzGjRvHNE63hu2aa67BZz/7WaxYsQI//vGPEQgEIMsy\nOI7DW2+9lf9ZE4QNMIkkRtSEGaDTMDdXtBk0/y6J1jnW5Hs0ojmZzKipEY4fx8RoNF1TAwCnv/MO\nwHFQC1FwGBJteildepM5LVGnmAeo9VDL3B9LDY3bPZkyUyMTJSUYKCtDsYpoy3QPzBRtXkyNZFlk\nsfO7wzI6Ub5YP4+JZaJqeqRd0TUjxPHjIVZVIXjs2Iht/dEoEiUlpvfpZHTNLHY2qdfCqJ5VDafS\noAmCKByvvfYavve972HWrFkAgDvvvBP33nsvLrzwQsOxuoJt9+7duPPOO3HppZfihhtuYLaeZKG6\nuhptbW3pn9vb21FdXW3b/okxhIFIYo1iaZmFSCn1FW6tmjSt5t+ACeHnAbzQKFsNLpFA1fr1KH/j\njfRrmTU1ADBt9/tM+6ppbsYfJBlWCoZyRRVrQ95C1dAYoYi2VDCIow0NWddTQcs9EPCWaGM2/rHp\nuyMf+EAQvKB+TUWJgyhxCAWyFx7sqg9lSTeWw2H0zp+fVcOWHt/QgJRPo0BONalXQwwEcCgazaph\ny0QC0CsEUSImPfN9QBCE/fz85z/HM888g2nTpgEADh48iHXr1uUn2H7605/ilVdewT333IN//ud/\ntu9sh5kzZw4OHjyIlpYWVFdXY9OmTXjwwQdtPw4xNtASScxCSGeVmwvwqq/rpUyNMCcAMGHi5hOp\neAAAIABJREFULNX9+NWMRAvHJpNKVO3//T/NFK2aAwc0U4zUKIrFUJpM5h3NMuqhlmkewJJ2WSgU\n0aZEKmuam1EUi6E/GkVbQwOaTm/AGRnvz02N9IRoM2n8k/d3R57889zpaG6XIUoj71whIEMIWLN3\nt7O2sGPNGnS2to68H4bvEzN4JbrGusCikG8kbvPkenyqqwthlZq1nmAIT555JiKplKe+DwiCsJdw\nOJwWawBw2mmnIRIZmeGhhqZg6+zsxIsvvojS0tL8z1DtwIKAu+++GzfeeCNSqRSuvvpqTJ8+3ZFj\nET7FZK1XPpbaerUsWqimTOWcsyLmeD7siVoZVrwYXct1qlOjKKbeR0uLHiFoS50IS0PeXFHodtoj\nK4po27doMT48fwHC8TgSJSXpSMr+LVtx5uJF6fd7TbRZqUvL144/HwIBIFqUStesZRItSo0I/roZ\nXVPYv30HoHE/+BEzCyx2ReKSPI89EyZopkMPCAIGhIJ2WiIIwmGWLl2K9evXY8WKFZBlGc8//zyW\nLl2KgYEByLKsa++v+e3wox/9yJGTzWThwoVYuND8Ch0x+mE1+RiBRUttvVoWSRKzWgYo5KZM6Z2z\nJ2plXMCpyWSgpwelb75pOK4/GgUHoJhRuNlVJ6JXo+JH84DM9EgWQwk9ExI9nBBtlp+1POz4raIs\njFSXD6Vdx/r5tK1/tCiVft0sTjl3st4PWnglumZmgcVsJE4Pr6ZDEwThDo899hgA4KGHHsp6/dFH\nHzW096flHMJzFKTxtE4ty0DfCQD6KVOG5+yBWhmvYGoyqaRB/vnPEDTqPzJpG+7jqFZ/1VVVhWAi\nkU7pagqHTU+UtNKiCtGQt5DkRtkAayYkgAOizSfPWmYUm+OG3CAnlom6fdjc7m0IjOxvOBpgXWAx\nE4ljwcvp0ARBOM/+/fstjyXBRniLAjaeNqpl0UyZYjznQtfKsFLoRtmZ0TWWNEgZQF80irbGxqya\nGrV6m0AqhXA8jt3vvmtqosSSFjXaVs+1mmqnt6uItkzMiDa78cuzlksggBEGI2ZhWRCx0tswX7wS\nXQPYF1ispDqzHt+L6dAEQXgXEmyEpyh0XyTdWhaNlCkz51zIWhkvYCa6ptewN5OWmTPxwZKlWTU1\nWvVXqUAAfRUVple1WdKiRuPquVnR5qV6Ni8/a1YWRSi6Zi8sCyyjLdWZIAj/4u/ZBDHqUOpPVLe5\nVeulCDPGCZ7pcza5fzdx2mzEDHoNe2UAfaWlaJ47F7sv+ryqAYJSb5O7zexKv1FalJDj+qasnjsp\n1gRJQkUiMeLYTmB0vXIn9bnRm1yRriU8HLn3PPisOfmMeTW6Zid23fvKAsvGmTOxYeYsbJw5E1sm\n12cZiYiBAD4qL1cdf6C8zPcLMgRB+AeKsBHeIt/6E5POkrbgk5oZJ3EiHVK3YW9pKbav/j9IFhfn\nfUwju26n0qKs4GbvqEyMIm25GJmQ6EXaALjWXNsv+D26Zkc6pFP3vnF6ota+nXveCIIgciHBRngO\nq/Unlp0lbcCvNTNuYta5Tq9h79Hp0y2JtcyJI+sE0EtpUXY61plFT7SZrWcDXDYi8QgUXbNOIe59\nQZIwvVsjut7djR115kxHCIIY2yQSCfzhD39AS0sLRPGUC/Dtt99uOJa+aQhPEu9pQefxfejq+Ds6\nj+9jEmsl0RrwQgQcx6VdGkvKnJ3EZhLvaUHnsb3obP8Ancf2+k6seclsRKFjzRqc/OIXkZw4EXIg\ngOTEiWieO9dSw95clAlgeTKJAE5NABcdbs16n2JQoIabDpBmUzOdQC9SYjY10ggvpecWEoquFe7e\nZ4muEwRBsHLLLbfglVdeAc/zKC4uTv9jgSJshCcxFS1z21lSL+2yAL2c/IDlvlA8j461a3Hiuusg\ndHZi354PLDfszZw4mrXr9oIDpFdSM81E2qyakKS3j6JIG0XX2MlNUy7Uve+l6DpBEP7nk08+wcsv\nv2xpLAk2wnOY7cPmprNkIdMuncSL0bVM5HAYydpapPZ/aMvxzE4ArThAGtXGmcWvk0cSbYV/vsxQ\nSGdIrTTlnbWTCnLvO91f0e7vCIIgvE19fT16e3tRWlpqeiwJNsJbWIiWKS6NvBAZMcZOZ0lXG3oX\nwjzFISxH13JgnUjyySTC8TiSoRCCg4NIlJRg75tvZr3Hqvhh6Z/kpDmCV5pzm61ns2pCkt7uY9Hm\ndGqnl6NrZtMh9erUCnXvOxFdL5R5EEEQhSUajeLqq6/G5z73OYRCofTrLDVsJNgIT2EpWuaGS6OL\naZduR/EKvfpv12SSkyTM2L4NtQcOIBKLARwHTpbRHy3DxEg4azLkpPhx0hzB6uTR7ZV8u01IAH+K\ntnzEWiGia4XEKE351zNmpP/fzbRkJ/orFtI8iCCIwjFt2jRMmzbN0lgSbISnsBotc9ql0a20S1ej\neD6CJbo2Y/s2NLz33qkXZBkAUBzrwbzY0EuZkyEnVs7N1saZxezk0cmVfLubagOjS7S5Idbsjq4V\n0mzEKE25RBQL2pheDATQGwzmfWynvyMIgvAuN910k+WxJNgIb5FHtCze04J47LAjqYSupF26bZ4C\nb6Rr2QGfTKK2uVn3PbmTISdWzt0yR2BJzQScX8nPtz/baBdtBDusacqs976d2Lnw4RXzIIIgCsPO\nnTvR1NSEROLUIj+LkKNlHMJzDAmvNojiAGRZgigOIB5rY4swKS6Ndtd9DQtJNexKu2SJ4nkFr6VD\nhuNxRHp6dN+jZcOtTADtNAZRw21jELes0M1Y/QNsdv9G95fXLf8pumZ+X15pn6EGawsQFrz0HUEQ\nhLv89Kc/xa9+9Sv8+te/xrFjx/D000/j4MGDTGNJsBGexIs9zfISkgwoUTzVbTaapyj4JbrGMpFM\nlJRgoKxM9z1uTIZYJp2CJKEikXC8b5reSn5ZchDlCfvaT5gVbXaw8NzZnhRuauckScCgyMGFVnm+\nZmvdZLxdVYWuYAgpAF3BEN6uqnK1fUYudi98eFmYEgThLNu2bcMTTzyBCRMm4J577sHzzz+P7u5u\nprGUEkl4Fw/2NHMy7dIV8xQb8Fp0DQBSwSCONjRk17Dl4NZkSKs2btukOixubXHNGU4vxYwDcPWB\nj/DRuHG2Hd/O/myAcWpk+n0eSpHMFWuyDLR3C4j18xAlDkJARrQohepyEWqXfKxF13LNcJxIU84X\nJ1IYvdDXkSAI9wmFQhAEARzHIZlMorq6Gm1tbUxjSbARhFkcFJJOm6cojKbomkLTBQsBADUHDqAo\nFoOMIWHSEwziwLAwcgOtSefi1hZXneH0nDA5AOWiaPvxCynaABRUuKk9U+3dAk7GT0V1RYnDyfiQ\nAKmpELPHjyFXSKOasELUqWnRGwxiMBBARCWSlhw2IjFLPsKUercRhH8pKSlBf38/5s6di+985zuo\nqqpCJDLSG0ENEmwE4TEcjeLliRejawpyIIB9ixbjw/MX4PAbb2CA5xFJpdL/5WUZoos9jjInnYVy\nhlNE6vSuLpQlk1D77QvpTGenaAMKE23TWvyQJCDWz6tui/XzmFgmwsolHw3RNb/Z2jv1rWFGmFLv\nNoLwPz/72c/A8zzuuOMObNy4EbFYDA899BDTWFqeIQgv4pR5igsUIrqWSSoYRFc4jATPY+7xY7hu\nfxNu2LcXa/btxeLWFnDDdv9uwpJW5QTKSv5zDY3Q+q2tHF+vDq8QJiRZ73Wxtk3vOKLEQZTUJ9K5\n2+xMhfQ6bpnh2EVpMomgxjkFh6NdbmCn8QlBEIWhsrISoVAI/f39+Jd/+RfccccdmDRpEtNYEmwE\nMcbwolGDnSiCwUsTnEI7w3WHw7Ycn5NlLG5twZp9ey2LYBYhbka0aRl6OH2fG+1fCMgQAurXJXOb\n3amQhYqusVKoxQurFPrZBfwncgmCUOf999/H4sWLceWVVwIA9uzZg+9973tMY0mwEQTBhJvpkPlO\nJL02wSm0M5xdx2cVwWZt3dXuCS3RptyHsgy0dQlobg+n/7V1CcjUjkq0zU7xxrq/QACIFqVUt0WL\nUobpkElRQndvEklx6F71enSN9TP3ggAyQ6GfXcB/IpcgCHXuu+8+/OpXv8K4ceMAAHPmzMG7777L\nNJYEG0GMIVgmmvlYkHtlUunFCU6hLcvzPb5ZEZxvaiSgfT8tnD8nbeghSgEAHEQpgJPxINq71Uuz\n8xFuVoVfdbmIcSVJCAEJgAwhIGFcSRLV5WL698hFkmS8+f5xPPv6ITz92id49vVDePP940iljB9I\nr0fXAG8IILMU6tlVUo8HeN5XIpcgCHWSySQaGxuzXgsyPr9kOkIQBAB9C/JFn82eWCZFCX0DKRRH\neAQF9gmWE2YjmSgiQc/SvlATHLPOcHa7weVrmW63vXmuayQw0oREi6QoISkVARBHbDMy9NASXblm\nJXZE5ThuyA1yYpmYfqaU89KKWL+1pwN7mk/15entF7GnuRtPvXYA1138qbzPyQnMRlS9ZGvP8pwp\nz85bNbWo6u/H8aIiDAjOTZ/UDEb6eV71+8yrIpcgiJGEQiHE43Fww0ZBBw4cQJjx7yYJNoIgAOhb\nkCtIkoy39nTgH0fi6O0XUVokYNqkEpw3pxKLPjPVlvOwY+Vfz9K+0BMcI2c4p93grFqmWxHBejb/\nAJtoU3OO7BtIobd/pFgDThl6hDTqx7RwsuYtEEDW+WiJtaQo4R9H4qrb3vmwAyuXNiAcUnee9EN0\nTcEL/dbMPGduOzSquWiWJ5Noi0QQSUkFF7kEQVjjm9/8Jm644QYcO3YM3/nOd7Bjxw488MADTGNJ\nsBHEGEFvQqpnQS7KRUiKEoJCQHP1H4ChYHMruqbgpVV8M3jV8tyqCLYi2nLJFW3FER6lRYKqaNMz\n+/ACerWgekL0RPcAunoTqB5fbPnYXCIBobMTfDKJlAfS6ArZb83Mc+bmM6mXehxJSXjyzDMRSaWo\nDxtB+JCFCxfi9NNPx44dOyDLMtatW4epU9kWu0mwEQSha0He2ycOpz9Cc/X/4NE4EoMpzdV/Vuxc\n+ffCKr5ZCtWvjRWrIthItOWilhqZKdqCQgDTJpVkLR4osBh6eBU9ITqhPIKKUnVxY7gYkkqhcsMG\nlO7aBeH4cUyMRnG0oQFNFyyEnOfFMpsO6TQsKY5mnjO3n0mj1ONIKuWZpuIEQZinvr4e1157relx\nPv2zRhCEGfKxIC8tFlAc4XVX/+P9Irp6E3mfpxMoq/heF2uAN81SMlFE8MaZM7Fh5ixsnDkTWybX\nM6WF2W1Cct6cSsxpKEe0WAAHIFosZBl6eBEjp1VFiKpxzhmVlhdEKjdswLiXXkLw2DFwsozinh40\nvPceZmzfZml/XsRMy4no4CDzc+b2M+k3F02CGA1s374dy5Ytw0UXXYRf/vKXmu979dVXccYZZ2DP\nnj0AgNbWVpx11lm4/PLLcfnll+Puu+/WPc7bb7+Na6+9FgsWLMB5552Hz372szjvvPOYzpEibARB\npC3Ic2vWAOC02hIEhQCKI7C0+g84b+XvtVV+LYxW/71olqKGE6lsrCYkSqQtEOBw/qercO6sCVkG\nONt27bH1vOyCtS3GeXMqAQBtJxI40T2ACeURnHNGJVZ/vlH1/UbPFpdIoHTXLtVtNc3N+PD8BZbT\nI7303LGmLQqShPltRzVXq3OfM7efSS/X3xLEaCSVSuGee+7Bxo0bUV1djRUrVmDJkiUj3Bx7e3vx\n29/+Fp/+9KezXp8yZQp+//vfMx3rrrvuwq233orZs2cjYPJZJsFGEHbCBcAHgkhJSUD2RjNTVjMF\nJTKR6RI5Y9q49ARSLw0tn9X/sQCraUEhJmt2u1HqYYcJCTAyPbK89NR5K8LIS8LNTA/DQIDDv3/l\nHCQGU+jqTaCiNJzXsyV0dkJQuZ8AoCgWQzgeR5+Gzb5fYElbTHFc+hks04mKfVxelvUcFOKZ9Gv9\nLUH4kd27d2Pq1Kmorx9a2Fm+fDk2b948QrA99NBDWLt2LZ544gnLxyorK8PFF19saSwJNoKwiZKy\nekSKKhDgw5BSCQz0dyHe01Lo02Im14J80fyZIyz7FfF28GgcvX0iSosFnD+nRnP1H3DfbMSLaK3+\nc7KMdydWZ4kltyZrbjvf5YORaFNj4fw5nhBtVhvOh0O8ocEIy7Mljh8PsaoKwWPHRmzrj0aRKFFP\nwTTCS88dS9ri3OPHVEVXJhKAd6omjnjdbQHlx/pbgvAr7e3tqKmpSf9cXV2N3bt3Z71n7969aGtr\nw6JFi0YIttbWVlxxxRUoLS3Frbfeinnz5mke69JLL8XTTz+Niy++OMvOv6ioyPA8SbARhA2UlNWj\nJHrqgeeFSPrnQoo2K1bligW5Wn+13DS0z887zZbIWqFtxp1Eb/X/Mx0dmNvRMUIsuTFZK5QbpVXX\nSKuiDShctM2KWGNtPs+6ECKHw+idPx/jXnpp5D4aGtLpkHwyiXA8jkRJiSccJM1glLY4wPOaz2Am\nPcEQekOhEa8XSkAV0kWTINzkL/uPYHyH/fXHnR0jF6rMIkkSfvzjH+O+++4bsW3ixInYsmULxo0b\nhw8++ADf+ta3sGnTJpSWlqrua8KECfje976He+65BwAgyzI4jkNTU5PheZBgIwgjjNIcuQAiReop\nReGiCsRjhz2THskKizlCeWnAUKxRdE1/9V+5empiycnJWqHdKO2w+lcwEm2A+9E2q1E1VrFmlo41\na9DZ2oqa5mYUxWLoj0bRNuwSyUkSZmzfhtrmZkR6ejBQVmboIFmI504vddcobTGSSmk+g7nv1bvv\nSUARxOijuroabW1t6Z/b29tRXV2d/jkej+Pvf/87rrvuOgDA8ePHsW7dOqxfvx5z5sxBaHiRZ/bs\n2ZgyZQr+8Y9/YM4c9b8BP/vZz/Db3/4Ws2bNoho2grATljRHPhBEgFf/I87zoSGxl3LfQdHJRsCA\nfZPL0RxdA/RX/3Nxy7qfJYXM6YmpXfVsALtoA5yPtjku1vr6cOL3vwc3fjxkxs9o//YdwKLF+PD8\nBSOiaDO3bkHDe++l36s4SALAvkWLVffnZt0ja+quXtoiL8uaz6AMoFsI4sC4CqoRI4gxyJw5c3Dw\n4EG0tLSguroamzZtwoMPPpjeHo1GsSvDuOkrX/kKbr/9dsyZMwednZ0oLy8Hz/NoaWnBwYMH07Vw\nakycOFFTzBlBgo0gNGBNc0xJSUipBHghMmIfqdTgUGTOR1idcLqNH6JrwNCqfD/PMwk2t8SSX9wo\n1chHtAHOCjdHnx1RROiuuyBs2oTSlhaIVVXonT8fHWvWALx2pDtzQSQVDGYZjPDJJGqbm1XHqTlI\ncpKEmt89hQtcrHtkTd3VS1sUOU4zAvfB+PH4U/0UqhEjiDGKIAi4++67ceONNyKVSuHqq6/G9OnT\n8dBDD2H27NlYunSp5ti//vWvePjhhyEIAgKBAP7zP/8TFTomTp/97GfxwAMP4JJLLsmqYcs1OFE9\nT3O/FkGMEcykOcoSBvq7ssSdQqK/y3fpkHbhtJW/F1GLPAiShCKRLTffLbHkpvOdXjTGzno2gF20\nAfYJNztEGkt0LXTXXQivX5/+OXjsWLourWPtWkvHDcfjiPT0qG5Tc5CcsX0bGlyse7SSuquVtmgU\ngatIJMjcgyDGKAsXLsTChQuzXrvllltU3/vkk0+m/3/ZsmVYtmwZ83H+8Ic/AABefvnl9Gscx2Hz\n5s2GY0mwEYQKZtMclYhbuKgCPB9CKjWIRAFdIv2SDmkVr0XX9NK2SpNJRBkFmyKW3Eg505rA7qyd\nhAn9/QCA7jwajrOmsjkh2gCYFm6AOfFmVzSN6Vnq64OwaZPqptJdu3DiuutU0yONFkQSJSUYKCtD\nsYpoy3WQ5JNJVH7wgep+nErltTN1Vy0Cl2n1b1fE0M10UTePRRBE/rzxxhuWx5JgI0YXNvVBs5Lm\nGO9pQTx22HN92CQJ6b5qRn/T7ZqEOm024jX00rZ2TKrTTD9MDf9XEUvbJtVhcWuLK1b7uRPYuCBg\nwZEj+OYHexCWhu7dwUAAe8aPx9bJ9aaPb8aF0m7RBpiLtim4nQ7MuvDBtbUh0Nqquk3o6IDQ2Ylk\nba3p46eCQRxtaMiqYVPIdJAEhqJxbtc9OpG6mxmBW9zaYptTqpttMvzUkoMgCHugJRli1FBSVo8J\nE2dhfPUcTJg4CyVleaToDKc5qqGb5ihLQ5G3Aoo1Jbomy0Bbl4Dm9nD6X1uXAFnOb/9kNpKNUdoW\nAHykkdP+t8pKbJg5CxtnzsSWyfVYeOQw5h0/jvJkEgGcmkAuOqw+WbcDZQK74OgRzOs4jogkgQPA\nAQhLEuZ1dJg+vtE1ESTzz4fW/aK3OFDoSLAeZs5NrqmBWFmpuk2srIQ4fvyI11mfr6YLFqJ57lzE\ny8ogcRziZWVonjsXTRdkpwclSkoQ0xBITqXyKqm7auSbumv3PaosULjx7Lp5LIIgvAEJNsK/cAHw\nfBjgAmmDEF6IgOO4tEFIPqJtKGLWBlEcgCxLEMUBxGNtvmmG3d4t4GQ8CFEKAOAgSgGcjAfR3q0e\nWPdLdM1r6ZBGaVvliQT+VlmFdyor0RUMIQWgKxjC21VV2DK5Hl3DaYdOiBxW9I4NAI0nu0wdnyWV\nLZd8Ple/iTaz59T2l7+gd/581W298+czu0WqIQcC2LdoMbZddz22fPVr2Hbd9di3aPEIS/+9b77p\nmHjSY2vdZLxdVTXi2cnX0dHKPaqFm89uIb8nCIIoHJQSSfiSXLt9jlN3Scu3D5pX0xyNkCQg1q9+\nTWL9PCaWiYbpkWpQdG0kemlbyUAAVx/4CFFRRCwYRHN5Od6pmojeUGjEBLeQVvt6xwaAqJg0dXyr\nqWz59GczSo8E2OvanMTqM9SxZg2AoZo1oaMDYmXlKZfIHKw8X7kOkmroGXc4hVNNq+1Mt3Tz2fVC\nSw6CINyHImyE71CLpgV49T+uikFIXrCkOWZE+wqJkg4pShxESb2WQW8bYR69tK2IJKFcFNNpS2d3\ndGBux3HVCacygVTDaffIuCAgqTMJjglBU8d3MpVNT4wYRXcLHW2zcvz078Tz6Fi7Fp889hg+Wb8e\nnzz22JA7pI6lv10o0U9FPG2cOTMrldeNuiklddeuSJ6d96ibz24hvycIgsiPgwcPYtWqVViyZAkA\nYO/evXjkkUeYxpJgI/yFjt2+Gm70QbO1ds4mhIAMIaBerKa2jdIh1REkCRWJhGGaUW7aVncwiITG\nhE8rbclJkWPEgqNH0kYjqscfV2H6+FZT2Vg+Yz+KtrzEWgZyOIxkba1mGqQb0Wu7xVOhsCvd0s1n\nt5DfEwRB5Mf3v/99rFu3DtFoFAAwY8YMvPLKK0xjKSWS8BV6dvtqON0HjbW5thtkWvkHAkC0KIWT\n8ZF/vKNFKUqHNMCsC1tu2hYvSfjq/ibVfeulLRUi5UyvJkYC8N6ECZaOn08qm1FqJKCdHsklEjjx\n9NOYcPnlQHGx6tgLGivBtbVhR0cSUqSI6ZzyodCRvXzwWs2ondiZbunms1uI7wmCIPInFovhggsu\nwM9+9jMAQCAQQJAxKk6CjfAV+nb7ImRZdK8Pmpnm2gWgunyo91esn0/b+keLUunXFSi6NhIzlvSZ\nKJEHQZIs1cc4Va+jh15NjAzg3eoa8LKM0sFBS+ej1cjYDrJEWyqFyg0bhmq8jh+H+L3vQbr6agz+\n8IeAMPynThQRuusuCJs2IdDais9Pngxx+XJsXvkNyIL9fw7zEWpWnic/LIZ4ETvuUTef3UJ8TxAE\nkT88zyOZTIIbXvhtb29HgPHZJcFG+Ithu/3MqJbCQF+HqwYhZptrO4lao2yOA2oqREwsE5n7sGkx\nlqJrRi5sLA2CUxyHfp5XFWwsaUtOipxc9M0XgjjnWDsaurtd7/fEEmXLpHLDBox76aX0z8Fjx4D1\n6wEAg/ffDwAI3XUXwsOvAQB/6BD49euxdPg9dpmSmH5e+vrAtbVBrqkBios908dwNEfXnMLNZ9fN\nYxEEkT/XXnstbrrpJpw8eRKPPPIIXnzxRXz7299mGktLMoTv0LXbd7EPmhLtU93mQu0cK4EAEBLU\nxZrbjYL9gB1234sOt6JmYGDE622RiOfSlvRqYgZ4Hmd3dBSs3xNrPRuXSKB01y7V7YHnngP6+oC+\nPgibNqm+R/jjH4G+Plxw1pS8FydMjRdFhO64A8XnnouSs89G8bnnQly9GkiljMfm4IfFED/DWs9K\nEAShxRVXXIG1a9di+fLl6O/vx/33349LL72UaSxF2Ahf4gm7fZ1on9O1c5moRdfcZjSlQ+Zr9y1I\nEqafPKm6LSKmwMsyRBdc9cygVhPTXF6GxjwjjXbAEmn75I8voyEjhTUToaMDJ37/e1TOn49Aq7rQ\nDLS2DkW4Tj8dwEjRpRV5y1fcqUX8xh0aOlbH2rXM+3FCrFF0bQiz9awEQRBavP3225g3bx7mzZtn\neiwJNsK/KNG0AqLUyIWLKtyrnbMJ1ujaWEuHLE0m0VxejrM7OkZsZ0lnLE0mERVF1W1lJvuZuYVa\nTUxpMom5KtcA8F6/p0RJCfqjURT39IzYJlZWQhw/Hm0ffYSplZVDqZI5SJMnD6UjauCIaYhOxK90\n1y6cuO66vBpiE/ZgtZ6VIAgil/vuuw+xWAxXXHEFrrrqKtTo/N3JhVIiCSJP4j0t6Dy2F53tH6Dz\n2F5XxRpF1+yBk2Usbm3Bmn17ccO+vWjo7kZbJIIuIWja7nuA56HeUGHIxGPAhb5ZVsm0a/dCvycl\nDe3AG2/ovi8VDOJoQ4Pqtt758yGHw5DDYfTOn6/6HvGSSzQdJZ2Ca2vTjPgJHR0QOjuZ9kPRNecw\nqmel9EiCIMzw3HPP4ZFHHkEsFsOXvvQlrFmzBv/7v//LNJYibARhBx6I9pmBomvZqK2ilyeTeKey\nEu9OrDblwhZJpTRXwrjh7QMOOBLajVLbNk8l1dDpfk+qaWhHDqPt2tWQNY7bdMFCAEDKZz9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cnlAB/AdfubcMO+vVizby8Wt7aAk+URx2stjWKA4yADkDFUH/S2TgNwVmGqdz20hA2fTKL6Y/Xo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\nysjI0MlfK2z7GhkZGS0m+9zKt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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -647,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, @@ -656,9 +738,9 @@ "outputs": [ { "data": { - "image/png": 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3KHpuBjYiA9hljyqt55m6fdPH7WjzhzGk1o0Z4xsN+/qkLrD1ahmUaktMEbtF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\nWmYVxNtvbLC8qcqUKki+WiHjYPAlK//f+UvWhelCUHd7ZP24WgQap6J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2btyITZs24aqr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\nQRCwYMEC3HfffRg8eLDeh0QZCAQCqKysxPPPPw+3242JEyfiy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1q47x16O6xrBGZB2mVdgcDgduv/12jB8/Hp2dnViwYAHOPvtsjB492qxTIqIspGSy5aDK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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -745,15 +827,17 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Average Loss = 126.51: 100%|██████████| 40000/40000 [00:23<00:00, 1711.01it/s]\n", - "Finished [100%]: Average Loss = 126.74\n" + "Average Loss = 117.12: 100%|██████████| 40000/40000 [00:26<00:00, 1526.66it/s]\n", + "Finished [100%]: Average Loss = 117.17\n" ] } ], @@ -770,14 +854,17 @@ "cell_type": "code", "execution_count": 26, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true, + "scrolled": false }, "outputs": [ { "data": { - "image/png": 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ix2CwGj82NhZBQUEoKirCwIEDsWnTJigUCri4uMDb29tcMRIREdFjMPhkb2NjAwBo2LAh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\nTaKiogDU7e9XKY1GAz8/P3z44YcA6uB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jX7p0CQqFAhUVFRg6dCjWrVsHmUwGPz8/rcltCLiaVYguHVujrLwSn607AQDYtiTcxlER\nERE1ULJ3cnICALRo0QJdu3bV6nffokULy0ZmZzKyiwAA6irdh/dlqkpUVJo2nwAREVFTM1qyLyoq\nQlxcHACgpKRE8xqofp5PtYw10BsXqUBLJzk2fsTaECIisj6jyb5r166aKWo9PDy0pqutOzkN1Sq5\nVaF3ebmKJXsiIrINo8n+p59+MriuqKioyYORgg9XJ9g6BCIiIi0NzmdvSFhYWFPGIRmXrxXYOgQi\nIiItjU72gqPIaOFMvURE1Fw1OtlzHnptvPchIqLmyugz+5SUFIPrKisrmzwYIiIianpGk/1LL71k\ncF3Lli2bPBgiIiJqeg22xu/evbvedcnJyRYJiIiIiJqW0Wf2r776quZ1/fnk33vvPctERERERE3K\naLKv2+K+/jN6tsbXxj8HERE1V0aTfd0W9/Vb37M1vrZyFRssEhFR82T0mX15eTkuXboEIYTW65p1\nVCsq/hLGh9xj6zCIiIh0GE32ZWVlePHFFzXv675myV5bSRlL9kRE1DwZTfZ79uyxVhxERERkIY0e\nQY906ZvLnoiIyNaY7JvQ3mPptg6BiIhIB5N9E7p+o9jWIRAREelgsiciIpI4JnsiIiKJY7JvQuyO\nSEREzRGTPRERkcQx2RMREUmc0UF1LOHy5cuYPn265n16ejqmTZuGoqIirF+/Hp06dQIAzJgxA/7+\n/gCAFStWYOPGjXBwcMC7776LYcOGWTtsIiIiu2X1ZO/j44OoqCgAgFqthp+fH4KCgrBp0yZMnDgR\nkydP1to+JSUFCoUCCoUCSqUSkyZNwo4dOyCXy60deoPSsgq13ucW3IKrS2sbRUNERFTNptX4hw4d\ngqenJ7p3725wm5iYGISGhsLJyQmenp7w9vZGUlKSFaM03eHkLK33ExfstFEkREREtWya7BUKBUaO\nHKl5v3btWoSFhSEyMhIFBQUAAKVSCQ8PD8027u7uUCqVVo+ViIjIXtks2atUKuzZswdPPfUUAGD8\n+PHYtWsXoqKi4ObmhkWLFtkqNCIiIkmxWbKPj4/H/fffj86dOwMAOnfuDLlcDgcHB4wbNw6nTp0C\nUF2Sz8qqrR5XKpVwd3e3ScxERET2yGbJXqFQIDQ0VPM+Oztb83r37t3w9fUFAAQEBEChUEClUiE9\nPR2pqano37+/1eMlIiKyV1ZvjQ8ApaWlOHjwIBYsWKBZ9sknn+DcuXMAgO7du2vW+fr6Yvjw4Rgx\nYgTkcjnmzJnTLFviExERNVc2SfZt2rTBkSNHtJZ98sknBrefMmUKpkyZYumwiIiIJIkj6BEREUkc\nkz0REZHEMdkTERFJHJO9DairBIQQtg6DiIjuEEz2NvDse3/g9c/ibB0GERHdIZjsbaCkrBKXrxXY\nOgwiIrpDMNlb2JJf/rJ1CEREdIdjsrew2L8ybB0CERHd4ZjsiYiIJI7JnoiISOKY7ImIiCSOyZ6I\niEjimOyJiIgkjsnezqmrBD74/gj2n7xm61CIiKiZYrK3c5cy8nHkdBY+/vGYrUMhIqJmisneisor\n1E2+T46xT0REDWGyt6IXPtyls+zLDYmY/OEuJm0iIrIYR1sHcCfJLyrXWbbj8FUbREJERHcSluyt\nrKqqtgTPyXCIiMgamOytLPzNrZrXX2xI1LxmLT4REVkKkz0REZHEMdk3E40t2LNCgIiIGsJkT0RE\nJHFM9s0FH9oTEZGFMNk3Iz//eRYnL+TYOgwiIpIYJvtmIif/Fn7bdQHvrjho61CsLuFMFk6cz7Z1\nGEREksVkb0OyOq8rKqu01mXllmj1ya+vUl1lcJ29ef+7I5iz8pCtwyAikiwm+2bocHImXly4Gz9E\nn4YQAsqbpVrD6a6OPo2n39qG7JulNozSulQVatwqr7R1GEREdonJ3oYupudrXtct2dc8t485moad\nR67ihQ93YUvcJc363/emAACSL+daKVLbG//uH3hmtsLWYRAR2SWbjY0fEBCAtm3bwsHBAXK5HJs2\nbUJ+fj6mT5+Oa9euoXv37vj888/h4uICIQQ+/PBDxMXFoVWrVli0aBHuv/9+W4VuEa8tjdW7/OCp\nTADA/pPX8PTjd+tuUK+mv6JSjdyCMni4tm3iCG1LVSmdxxZERNZm05L9mjVrEBUVhU2bNgEAVq5c\nicGDB2Pnzp0YPHgwVq5cCQCIj49Hamoqdu7ciffffx/z5s2zYdS2df1GsdH1s5cfwIsLd0N5B1Xx\nExGRcc2qGj8mJgYREREAgIiICOzevVtruUwmw8CBA1FYWIjs7Duz9fbLH8VoXstkuuvPXc0DAGTd\nKLFWSERE1MzZNNlPnjwZo0ePxm+//QYAyM3NhZubGwCgS5cuyM2tfiatVCrh4eGh+ZyHhweUSqX1\nA7YhjrlDRESNZbNn9uvWrYO7uztyc3MxadIk+Pj4aK2XyWSQ6Su63kGE0O6eR0RE1Bg2K9m7u7sD\nAFxdXREUFISkpCS4urpqquezs7PRqVMnzbZZWVmaz2ZlZWk+L0WmFuLTlUXYuu+yyfvdHJuCU5du\nNC4oIiKyWzZJ9qWlpSguLta8PnDgAHx9fREQEIAtW7YAALZs2YLAwEAA0CwXQiAxMRHt2rXTVPdL\nkeLAFZ1l+m4ANsRcxL7Eaybts6hUhe+3ncbs5QeQkV2k1W+fiIikzSbV+Lm5uXjllVcAAGq1GiNH\njoSfnx/69euH119/HRs3bkS3bt3w+eefAwD8/f0RFxeHoKAgtG7dGgsXLrRF2DbRVI8yKut0XZvy\n8R78d0x/DH+sZ5Psm4iImjebJHtPT09s3bpVZ3nHjh2xZs0aneUymQxz5861Rmh3jIQzSiZ7A/Yc\nS0cH55Z44B7p1h4R0Z3FZg307EVzqu4+9PcAO03hds5LCAEhAAcHaTYf/GzdcQDAtiXhNo6EiKhp\nNKt+9s2RLXN93UMXFJdj4eoEm8VS1+yvD2DC3O22DgMAcOV6Ab7dckpSEwMRETU1JvsG2LL3X8mt\nCs3kL2VmTgJzIT3PEiEBAJIv5aKotMJi+zfHtCWx2LrvMg4mXbd1KEREzRaTfQNs3df/dCMnu/nx\nj7NNHIl1XUjLw1tf7ENuwS2Tti9TqS0cERGR/WKytxOmlKSj4i9pva+qElqz6TXkcHImtpnRb9+S\n3v/uCM6m3sRvuy8Y3KY5tacgImrO2EBPQlZFJWu9n7pkL9KyijDz2Qfh4dqmwc9/+EN1m4CwYT4N\nbGl5oqbFgoF8Pu/bQzhz5abmvQzAsbNKeLi2wV1u7SwfIBGRHWGyl7C0rCIAwKdr/zK4zbLfTqBn\nN5dmkeDrkv09ULChsvtf57QnQlJVqDF/1WEAbEVPRFQfk/0dqvhWBS6k5WFXQhqA6mRZ41Z5JeQO\nMqRmFsLXs4PF2i3cyL+F+BMGRgD8+5D1q+p/230e//dkH53NK9Ss0iciMoTJ/g51/moe3vhfvOb9\nasUZzetnZis0r9/45wN4/EFPi8Tw3oqDyMgu1rvO0O3Fz9vP6U32d/icSVRHpboKcgdOpFWjUl2F\nmKPpeLSvB1ycW9o6HLIRNtAjo86m1j4XLy2rgGK//gZ86iqBb6NOYcfhqybv21CiB2qTN9vgkTlU\nFWo8/dY2fLTmqK1DaTZ2HErFlxsSsfinY7YOhWyIJXsyqm7paLXiDLYfTNXZJir+klbjwJBHvZvi\nyADY4r65mfm/eNzt2QH/Gd3f1qHolV9UDqBpR5u0d1k3SwEAFxsYe+NA0nUsWnMUy954HD27uVgj\nNLIiluzJZBlK/SXx+r0AmoK5NbCssDWPEALqRow6eD4tT++sjGT/vvk9CQD03tCT/WOyJ5PZ4hFo\n3e51zYFaXYXrOYYfP9iLGZ/HYfTb0bYOg4ishMmejKrJ78WlKiSl3DDpM+t2nsemvReNbpNwJsuk\n417LKcaV6wUmHbepbT+UinU7zmkt+2rjSby8KAaJF7L1f8hOpGQUoKpKgo9IWMVDpBeTPZnkgx9M\nn4Tnlx3n8EP0GaPbvP/dEeM7qVONoPz7maPx7WtfqirUuJFv2jC7xizfeBK/7DwPoPqc9v6Vrumq\neLaZ1TjcCbbGX8KBk5wDwdIkeAtIYLInEzV2jH6g+vnwyYs5KC5VmfyZuo8MTGmjF5OQrnk9bcle\nTHp/J0rLzJus5/j5bJy8kKN33bqd57H0l+Nm7Y+qFRSX48yVxn9/anwblYxFP7KVvcU001qR8go1\ntu27jMIS3d+PlPR8fPjDEZTcah4TczVnTPbUoLoD7jTGqJlb8e43BzH76wMmf8bY707yJd3HCZfr\nVPVfyykBAL0/DvUlpeRgxaYkCCEwd+UhvLvioMkx3sne++Ygiky8eZv66V7M+nI/snJLLBxV7ciL\ndPsqKtX4bmsy0pVFNo1j056LWLnlFD7/VfdmO3L5fhxOzkK0gS7BVIvJnoyKPnAFYww05Np/8hrm\nmJEcr1wvRHmFGn+dUza8sVZrQO2ifeRy028aGvLO1wcRfeAKLqbnm/W5pq7q3HXkKi5f026bkJFd\nhIrKKpSpKjFpwQ5siUtp4qM2XuLFHESbOGlS3t/d4Wq6xZF92J2Qhi1xlxC5fL9N46jpOng1S/em\no2a2y4pG9Cy507CfPTXaxz+aP0jHik1Jmufexli7fFZp5MfC0g3Z8grLsGx9IoDacf1T0vMx/fM4\nDLrPHWOe8MWNgjJ8t/U0Ivzvtmgs5pBi+z6qHduipKwSAFBQbPrjN4vS8zxPJuPAW6ZiyZ4sZkvc\nJZ1liRf1PxMHgNjjGdi27zK+2ZTUJMc3Z7hUYz8YyZdN64XQWOV6HpPUPJY4esaEWhA9muNgRNYI\niSPkNp5d/+ma39e92WGyJ4vR1/0uJ89wK/kla//Cyi2noDhwRetHu7DEto1vKivt65ckr6gMo2Zu\nxbq/exIQSYKeO7maJfb1P9Q2mOzJYvKKyhtdwqzb0OrLDYlNFZIWk0f+01Pkqag07Rlh3e2EEPhu\na7LZPRvMLa0mp1Tv/5d6YwQ0NXPjElb4SbZmyV4IgR+2ndbbYLQ5aoaVPTrW7TiH1dGn9a/UdwJ2\nUpVTUam+7YbOt4vJnizq+20G/uM2IDvPhL71tykqXvcxg6k27qmttSgtq0BeUZnONsmXbmD0rG3Y\nfrB6eNnzaXnYEncJb3/VcIMnQz9hpt5kkOWlZhZiU2xKkzYYbU5skUZ/2Xkev+81vyFqc3xsVdcz\nsxUGGzpbC5M9WZS+5/amUDdB6y9zfqzmrDzU6P2Mf287np+3A0B1n/KaMef3/pUBAPh11wUAjU/U\ndWs5lv7yV6P2YW05ebcw+cNdpvW8sFO88bK9mv8ZG2Iu3tZYIJZWqbb9zQiTPd2Rth9K1XpvrIrN\n2I0AUNtaPyu3BBPm/ok3lsXr3a7uTUNhiQpb4i6hTFXZYKx1ayr3mzmCXEWlGsmXbjRq0pvboThw\nGdk3S7GwzsiLzbzwZbbbrUE25dpbk6VqxC9fK8Ctcsuca92YTakxux3LN55EzNGGexLVZ8p4H9bA\nZE/SVe/Hq7hUpRnmdvnGk01+uPmrDgMALmVUt6Q3VrX4xfoT+G5rMt74n4Ebg9v54a3z2Wdm/4HI\n5QcQ8dY2g3MM7DpyFXNXHjK7NkVfiPXP2dL53ZTq21OXbmDBd4cbTK5lqkokX7phcpXw7Qzgs+Pw\nVYyLVDQ4R4S9S1cW4bWlsU2SiG15r6iqUGP7oVR8/usJsz97Ia12auH4ExlNGJV5mOzpjjFz2T68\n9eU+hL0RZZH9Z2Trnw2vui+wwIGk2lJ5wunqH/m0rCLkFug+79fegZmB1PlVrDt+wLxv9ddQLFuf\niOPns5F5oxh5RWV4bu6fZg9vK4TAfxbtxqiZW3E1q9DMgBsnev9ljJq51WgPDwCYvfwAjp5RIu64\n8R/aJWv/QuTyAzicbPkEXNNeZM/R9Aa2tG81oybWHzCq6VinZUFT3Wh88rPtHsMx2ZNkLVn7l9bE\nKddsODXt0TNKRO+vnQe+biH6k5+ND05k7s/ZYgP7K/97tLHcAsPJcfbyA8gvLsesL80riV1Mz9cM\nU7zzyFXNcktW3a/YfAoAkHA68+9jCc2Iavo0VHFRk+RNnWUxtoGbh/rUVQKnL+fqDOBUUWlaK+2T\nF3L0Xrsjyl8rAAAgAElEQVSSWxX4fttpg9e1pqbJ1DYGNdesuTV01xeOtWJsZn+KRmGyJ8k6c+Um\nFv141GJjsgshDP5QHzp1XTNSoAwwGkPdkn3tzYllfl4upOVh4oKdetd9tfGkwdoJver80moNDCTM\nG9CoqXy05ij+syjmtvdTZeIdirm9ObbEpuDtr/ZrjX9wIOk6Rs+KbnBmx5y8W3h3xUG8tHC3zrp1\nO89jc2yKwVLjqb+7Bjb8mMay1yyvsIEarAbYshq/ud34NIbVk31mZiaee+45jBgxAqGhoVizZg0A\n4IsvvsCwYcMQHh6O8PBwxMXFaT6zYsUKBAUFISQkBPv27bN2yGTnXly42yJToxaVVmD0LP3daRau\nbtzsbLWzutX+tDWUOE+l3NBpcKiPAHSq51MyaucESL7UNK2Zt+67jE2xNd2nzPuJzsm7hZijaY3q\nSnXoVKbW+7yiMmTXSaJ/Hkw1+nnNn9lCWSXp76SbeCFbZ90LH+7C7oSrOstrFJZUzyug0lM6Lyiu\nXle3ZL9GcQYTF+y4rYaZTV0r8/z8HU0+O531crD9Z3urj40vl8vx9ttv4/7770dxcTHGjBmDIUOG\nAAAmTpyIyZMna22fkpIChUIBhUIBpVKJSZMmYceOHZDL5dYOneyYJaZGNWc2sNv53WzomX7NbILu\nHdvggXvcDMcgdH/Ap38Wp39jzWcEvtmUhIG93TC4X1eD29X/KazpoWBOwjicnIkP/269f/pyLjp3\naI1/htyjd1tTamtqukPWuHy9ACkZ+bj7rg6aZVM/3QvlzVL89uEIyGQyCGF46J/Ssgq0bukIdZVA\nooGpkG/H/35LxBMPeeHwqUz8+McZfDLNDxnZRfD2aG9STUndBoM140DkGihNqyrUqKoSaNXSuikg\nv7gcKRn52BBzAbMnPozWLR1RfKsCt8orIXdovglVCiV7qyd7Nzc3uLlV/yA5OzvDx8cHSqXhvrgx\nMTEIDQ2Fk5MTPD094e3tjaSkJPzjH/+wVshEepnawvhGQVkjSqq1vy4LVydorfnX/B0YdJ87Xh03\nUGv5XAMN8G5HTv4t/HEwFX8cTNVM0qOJUFb9fLura1uDnzenhf+KOnMi1DwCMZTstZ6rm/FLXL9k\nmZpZ3Zgw6eKN2qFX9Vyr6znFeHlRDIY/1gOdXVrjp+1ntdaHvRGFL2c+Ae+u7U2ORZ8/D6Vq5oZY\nHX0auxLS4NaxNd799yMGP2P0q6Vn3f6T1zSTWNW/ptX7q/5Q/T+rukpg1pf7EPiQJ4Y/1tPoeRjz\n7jfVM2XuPZaOK5mF2HHYcI2GvjhuV1T8JexOSMNn0/3hKNet3M4rLEPrlo63dSO0L/Ea8ovKETbM\n53ZCbVI2fWafkZGBs2fPYsCAAQCAtWvXIiwsDJGRkSgoqP7PrFQq4eHhofmMu7u70ZsDouaobuM8\n0xj+Bb9ZWKb5gTQnmd4qr8SR0+a1NDc24196VhGmLYnFuNkKk0aRq5tEUzMLdbrCmXomNdXWpsZp\nykFyC8s0pWd9ybPm8cf2g6laXanq+vNQaqOOXVfdfWf+XXuR3UBvAw09SbH+Ia9cL9CarVIIgWW/\nncChU5kNJtXYv9Jx/moelv9ee1MWvf8yXlq4W3PT1JC634HKKmEw0X/y8zHNzbTRm5lG3AmsikpG\namYhruqJWQiB5+fvwMQFO+otN+8Yi386hpVbqhuQmtuzxVJsluxLSkowbdo0zJ49G87Ozhg/fjx2\n7dqFqKgouLm5YdGiRbYKjajJNdQAq77//dbwfABL1v6FiDe3mrVfc0YZSziThRfrNAir/4Men3jN\nrGPXNHxLVxZh6qd78e7X1SW8qiqBw8mZDXdB/NuEuX+iqt6jaH3DFesTZ6Cfs0xWmzfK9AwAs/bP\npptnoDH9841V49c8eMi8UYL/e0ehNdR0/VqKaUtitd5fyynGroQ0ndojIQR+iD6jea+8WarVx3z5\n7yexJS4FKzafQmZuCaZ+utek86gbjrG5KeJPXMPpy7l62xxUVQmDU1LPX3UYH3x/BKujT+PkxRzE\nn8gweHO2PuaCweOXlFXi0KnrZjfuLa9QY+9ftd0pL18rwIYY3QnBbMEm89lXVFRg2rRpCAsLQ3Bw\nMACgc+fOmvXjxo3Df/7zHwDVJfmsrNrSiFKphLu7u3UDJrIiUyf+Mbfrl7ne/+6I1vvf9cxiaI4v\n15/EishApP3d1uH83z/CKzYn4Q8Djed2HbmKoEe8dZbX/RH+ZlOSydMi70pIQ0snOR6+zwP9fbto\nlm/cc1HTNS36wBVEH7iCqc8MhOLAFTw3/F7cMPFGxJAb+bdw/Hx1wzxTJwSq22DS1NuD0rJK7DlW\nm2waKpHWHc667rb1H3d8tEb7ZmB7A40dm0rEW9t0lk3/LA6Xrxdg25JwnYL9sbPVtb5HTkNrjP2o\nT0bBoV6bgMQLOQh/cyuqqoTmEUzdv0FNI9ttS8Kxbd9lzXJVhRqZN0r0PrL5eftZrb/pa0tjdbap\nqhKI3n8Zj/btCrdObQyffBOzesleCIF33nkHPj4+mDRpkmZ5dnZtC9Xdu3fD19cXABAQEACFQgGV\nSoX09HSkpqaif//+1g6byGoaeoZpK0kXb69RWmZuCUbN3IpFa2obS6qrhMFED1QP+FNcqtIpoepr\nlW6q6P1XMGflIby2pLY0mpal29jyi/WJuHytQDMyYg1Dj0KuZBbqfaySfOkGJr2vv7ujyerkqYvp\neUYf39SthTBUk1HD0Hetol7J+aYJNztFpQ0PC9sUE9ZcrtNew9SboEPJmTrLSssqNY9/akrfhqL7\noc5MfC8u3I1XP92Li+m6NQamjNGw51g6vo1KxqwvrduzzOol+7/++gtRUVHo3bs3wsOrG4fMmDED\n0dHROHeu+kvavXt3LFiwAADg6+uL4cOHY8SIEZDL5ZgzZw5b4hPZgCUGyDHlMcR7Kw+hTb3GUk0x\nfe9VPQn+dpy+nIuIN7di+VsByMm/hQf6VDdE1teewVBPjnw97REA7aQ24/N4eLq3wz3eHRH0sLfR\ntgDmjFxXU7NxMKl2jIgaeUX646rrn+9tx1vPPYQ+3h3h1lF/ifV2v0If/nCk4Y30+HbLKQzp383g\nenOmX775dw+Hq5mF8PXsiNXRp3E29SY+fnWYSb0m/vdb9eOQ260tMpfVk/1DDz2E8+fP6yz39/c3\n+JkpU6ZgypQplgyLiBpgq4lsUtLzG96oGfnv4j0AgO/eDTKY9Aw5fk63Dz6g2xAzXVmEdGURdiWk\noW8vV4P7qz/2gDE1JfOSssZPWrP4J8Ot/G+XENAZytjYiIl11bQHWdfATeKOw6kmx1PTcLLmcYEQ\nwuxn/Cnp+bjbs0PDGzYBjqBHRCYxVOok/QqKy/XOE9CYBno1/eb1aarBkJrS2EgDc7ffzg1jvbvN\n0jLzB+j5ZaduQROobhC4fONJfP27btsPQ11a1+08rzXfwnPz/kRWrnkNcdc2QQ2VqWzSQI+ISOpm\nfK5/RsPGTG0bf8K8ng+2Vq5SIyu3BEfPaneT1tc2wlT1q73NnSN+/0njf0NDo1Aaqm0BgE/X1g5R\nXFBs/lS2lpr6Vx8mexN06di6wZm1iIhM0dRtBZqrF/WM429okqbGeHbOdrO2rzu+QHNhTlfY28Vq\nfBPMfPZBW4dARETUaEz2JmjZgq3/iYjIfjHZm6BnNxdbh0BERNRoTPYmqD/yEhERkT1hsiciIpI4\nJnsiIiKJY7InIiKSOCZ7E61dMNzgulnPP2TFSIiIiMzDZG+i9m2dcG+PTnrXmTL5QV3zXxrcFCER\nERGZhMneDI5y/rmIiMj+MHuZ4T4f/SX7uha9MrThHdlo9jAiIrozMdmb4f8F9dGpgn/oXnet96aM\ntmfO3MlERES3i8neDI5yBzzQxw1OLeTo1rktPvrvEMx67iH09uwIAAh+xBvtnZ20PvPBy4/ZIlSj\n3p30sK1DICIiK+Ksd42w/sMRkMlkmpH1WrV0xIaPQtGyhRwymQx+A7sjPrF6OsUBvbvofF7YuGCv\nr0Ghl0c7zfST818cbHAOZ0PatWmBolLz55cmIiLLY8m+EeRyB50hdFs5OWqS6Ovj/2H08+6d2jR4\njAnD7zE5npWRT2LGPx8weXt97vGubY/wjz7aNygjh/TUer9tSbjO5797N/i2jk9ERJbDZG8BLRyN\nP7f3dG+Hpa/76TzvB4D3/v0Itn46Cv/3ZB/NslHDfIzur2vntnjiQU/8NO8preUrIgM1rx9/8C69\nn5U7yLTGEOjUvpVWyX/uC4/iuRH3Gj2+g4MMrVs66r0JICIi22M1vo34enbEtGcG4qftZ7ErIQ0A\nsOWTUZDrmXSnby9XbN13ucF9dmjXUut9t87O+PWDEbiaVYj7erri+LlsFJao8I8+XTDAtzNCh/TE\n4H7djO5T3w0JADg5OkBVWVV9XOeWercBgNYt5Wjp5IjRj9+NzBsl2H4otcHzICKipsVkbyE/z38K\n8gb65Xds3wrT/u8f6OPdEUJAb6I3pF2bFvDp7oLhg3vqXf/mhAcBAG1bt8B9PV0BAD+8FwxVhRot\nHOX44D9DtLbv0bU9gOobC1P88sEIFJeq8MuO84jw76VZvuiVoYg7ngFvj3YoLFHhmaA+Wuf17FP3\nYN6qw0hJzzf5XImI6PYw2VuIS53S7pczn8D1GyVYuDoB7do46Wwb8mgPk/c7aeR9+CH6DPp4d8Lc\nFx41uN2wgd11ljm1kMPJQNfAEY/1QOcOrTHw7waFn73uj/zicp3tOndoDaC6i2FLl9aY+sxArfX3\n+7jifh/DNwwuzi3RohGDEzm1kENVoTb7c0REzdVzw40/Im1KTPZW4N21Pby7tsf8lwaj598laHM/\nDwAP3OOGsGG90La1Ex7r31XvtuveH44yldrsIXzlcgcM7le7z7s9O+jdrqm77X3zdiAupudjSP9u\nGD1rm2b5t7OfREZ2MR68xw3Xb5SgW+e2GDVzKwBg8qj7Ee7XC5VqgXnfHkKn9q3QoV1LbIm7pLXv\ndm2c8N6/H8FbX+7TWj7/pcGYu9K83gamaOUkR5mq6W5IBvh2xsmLN/Suc2ohR7ifDzbEXGyy4xGR\ndQ1/rIfVjsVkb0UP9HFr1Oe6dXbG9+8Go1P7lpDLHRDyqLfBbZ3bOMG54cb+jdbrLv03Aeb4f8F9\nMHflITzWvyu6d3FG9y7OAIA2rRxRWlaJUX4+8HBtCw/XtgCgWf/bhyMgRPWjCQBo4SjDh1OqH0eo\nKtTo1rkthg3sjiW/HMexs0rcfZcL7u3ZCQv/OwRrFGfw9vODoKpUo1tnZ8x78VHM+/YwgOreBaVl\nFZi5LB7pymJsXhyG0rJKbD90BRfT8nHkdBYA4JWxAxD0sBci3tqG+j6b7o+77+oAVYUafx5OxdAB\n1TUrm/am4NCp68jOu4XeXh3wxj8fRG5hGfr16owyVSXW/nkOOfm3cODkdQDAg/e4YcRjPeHr2QEd\n27cCAPzv1xPYfbS6XceQAd3w8H0eCHjIEwAgd3DAr7vOA6ju5fHt7CdxIOk6Pv7xmE6MjnIZxgT4\n4mpmIU5fvgn/B7pjSP9uiFx+AEB1I87YvzI027dr0wL97u6Mg0mZ+ODlx/DuioOYPKovwv188OYX\n+3D+ap7e6xvwkCf2HEvHsjcex+VrBfj81xMAgP883Q/DH+uJg6dq4/vl/eEouVWBFxfuxv0+rujZ\ntT08OrdF185t0dW1Lf67eI/mO3Atp1jv8fr16oxTl27AuXULFN+q7v7Zo2t7pGYWYtjA7ujYviW6\ndGiN77aexqN9PXA4OUvz2X+F3ocDJ68hJaNA776fGtwDx88p8ck0P7RykmP8u3+gSgBD+neDqlKN\nfr064/ttp7U+88nUYejSsTVeWxqLgmKVZvkD97jh+LlsvDpuADw6tcXOI1c1XXT1aekkx8evDMXr\nn8XprJPJarvwOsodMC7QFxH+vXD8fDbuvqsDylVqKA5cQUpGPi6a+cjs1XED8MfBVIQN7QkHBxk+\nW3fC5M/6enYw+3h1PTnICx3bt8SGmItYOGUIim+psHD1Ua1txgf3wbqd53U+O338A1i5OQklZZWa\n70T/uzujZzcXRMVf0tm+LrdObZB9s1RrWf+7OyMpRf/N9v9mPI7XlsZq3vfs1h5XrheaeJa12rS0\nXgqWCWHrXt9NLyMjA4GBgYiJicFdd+lvhW4Pwt6IAqC/q5u1NXUsQgid2ofvt53G5tgUzH3hUYMN\nA02RfbMUq7YmY/Kovka7Oc799hAuXyvQ9GKoVFehTKWG8983EzXGvB0NVYVac+5nr9zEW1/uw6p3\ngpCRXYSDSZl4ddwAs2tT6lr75zlczSrE7Im6NSdl5ZX463w2HrnfQ+/8DFVVQqcr6MY9F6E4cAU3\n8m8BAKY9MxBPPuxlMMaafairBJIv3cB9PTtpepVUqqvgKHdARWUVWjhWH//6jWL8+MdZ/Hvk/Wjp\nJEcLRwdUqgVKyyo0N2lNQV0lcColB/f1dMUP0afR26sjHn/gLny18SQAYPjgHjo3oHXj1EcIgYvp\n+ejRtb3msdZHaxJwMCkTC14ajKtZhejSsQ26dW6LHl3bN3hdhRBIvJCDvr1ctXriqNVVUFcJnL1y\nE2nKIoQZ6FVzPaf6b3lLVYnj57IxffwDeOLBuyBEdU+XPcfS8Nm6E/D17IAlr/lp4jHl/6QQAhti\nLsKnuwsG+HbR1J4NG9gd+xKv4Z1JD+OHbacxPrgPHn/QU+8+ar7vdW36OAy3yivx4x9nsOPwVc3y\nmliqb/KOY+RQH/h6dsDOI1fh/8BdaOXkiMQL2Rg1rBfKVJVIyyqCUws58ovKkaYsQoR/LwghUHyr\nQvPIs6KyCrO+3If+d3fGv0Lvg0wm08Tk4uyEn+drz0haWlaBNq20/w8XFJfDwUGGX3edx/jge1Bc\nqsKV6wVYuPoo+vZyxcIpQyCTyVBeoUbM0TQ8/kB13li/+wKGDeyO3QlpiD5wBa4urfDGsw+iX6/O\nOJycCbmDDIPu8wAA/Lz9LLYfSkXxrQq8/fwgJF7Ixh8HUwFUF1R2H02DENW9q2Qymd7/t7ejobzH\nZN+MHT2ThZuF5UZL8tZijRsPIQRy8m/BraMFqyb0HLOhH/OKSjWqhGlDITcnJbcqsCshDcGPeOn8\n+JG2iko1ruWUaBqq2kJVlUBBSTk6tmuls07f9zTxQjZyC8oQOMirUcfTlxQNKSuvhDKvFK9+shcj\nHuuBKWMGaNZdzSrED9tOY+aEh3RulC0p7ngG7vdx1bQjaoys3BK4dWxjUtI15beivsvXCtC1c1u0\ntkIJnsnejpN9czJzWTzOX81rFrUMRGQb5RVqu7vpvVM0lPf4zJ5M8snUYaiqktx9IRGZgYnefjHZ\nk0lkMhnk8qZ7vkRERNbD4XKJiIgkzm6SfXx8PEJCQhAUFISVK1faOhwiIiK7YRfJXq1WY8GCBVi1\nahUUCgWio6ORkpJi67CIiIjsgl0k+6SkJHh7e8PT0xNOTk4IDQ1FTEyMrcMiIiKyC3aR7JVKJTw8\nPDTv3d3doVQqbRgRERGR/bCLZE9ERESNZxfJ3t3dHVlZteNZK5VKuLs3fjhVIiKiO4ldJPt+/foh\nNTUV6enpUKlUUCgUCAgIsHVYREREdsEuBtVxdHTEnDlz8MILL0CtVmPMmDHw9fW1dVhERER2wS6S\nPQD4+/vD39/f1mEQERHZHbtJ9uZQq9UAoPWcn4iISKpq8l1N/qtPksk+JycHAPDss8/aOBIiIiLr\nycnJgbe37rTokpzitqysDMnJyejSpQvkcs7SRERE0qZWq5GTk4O+ffuiVatWOuslmeyJiIioll10\nvSMiIqLGY7InIiKSOCZ7IiIiiWOyJyIikjgm+wbEx8cjJCQEQUFBWLlypa3DMSggIABhYWEIDw/H\n6NGjAQD5+fmYNGkSgoODMWnSJBQUFAAAhBD44IMPEBQUhLCwMJw+fVqzn82bNyM4OBjBwcHYvHmz\nVWKPjIzE4MGDMXLkSM2ypow9OTkZYWFhCAoKwgcffABLtUnVdx5ffPEFhg0bhvDwcISHhyMuLk6z\nbsWKFQgKCkJISAj27dunWW7oO5eeno5x48YhKCgIr7/+OlQqlUXOAwAyMzPx3HPPYcSIEQgNDcWa\nNWsA2Od1MXQu9nZtysvLMXbsWIwaNQqhoaFYtmyZ0WOrVCq8/vrrCAoKwrhx45CRkdHo87PWubz9\n9tsICAjQXJOzZ88CaN7frxpqtRoRERF4+eWXATTD6yLIoMrKShEYGCjS0tJEeXm5CAsLExcvXrR1\nWHo98cQTIjc3V2vZxx9/LFasWCGEEGLFihVi8eLFQgghYmNjxeTJk0VVVZU4ceKEGDt2rBBCiLy8\nPBEQECDy8vJEfn6+CAgIEPn5+RaPPSEhQSQnJ4vQ0FCLxD5mzBhx4sQJUVVVJSZPnixiY2Otdh7L\nli0Tq1at0tn24sWLIiwsTJSXl4u0tDQRGBgoKisrjX7npk2bJqKjo4UQQrz33nti7dq1FjkPIYRQ\nKpUiOTlZCCFEUVGRCA4OFhcvXrTL62LoXOzt2lRVVYni4mIhhBAqlUqMHTtWnDhxwuCxf/75Z/He\ne+8JIYSIjo4Wr732WqPPz1rnMmvWLLF9+3ad7Zvz96vG999/L2bMmCFeeuklIYTh74StrgtL9kYk\nJSXB29sbnp6ecHJyQmhoKGJiYmwdlsliYmIQEREBAIiIiMDu3bu1lstkMgwcOBCFhYXIzs7G/v37\nMWTIEHTo0AEuLi4YMmSI1t2lpQwaNAguLi4WiT07OxvFxcUYOHAgZDIZIiIiLHYN9Z2HITExMQgN\nDYWTkxM8PT3h7e2NpKQkg985IQQOHz6MkJAQAMDTTz9t0e+im5sb7r//fgCAs7MzfHx8oFQq7fK6\nGDoXQ5rrtZHJZGjbti0AoLKyEpWVlZDJZAaPvWfPHjz99NMAgJCQEBw6dAhCCLPPzxIMnYshzfn7\nBVSPXhcbG4uxY8cCgNHvhK2uC5O9EUqlEh4eHpr37u7uRn8kbG3y5MkYPXo0fvvtNwBAbm4u3Nzc\nAABdunRBbm4uAN3z8vDwgFKpbFbn21SxG9remtauXYuwsDBERkZqqr1NjbdmeV5eHtq3bw9HR0er\nn0dGRgbOnj2LAQMG2P11qXsugP1dG7VajfDwcDz22GN47LHH4OnpafDYSqUSXbt2BVA9mVi7du2Q\nl5dn9vlZSv1zqbkmn332GcLCwrBw4UJN1Xdz/34tXLgQb775JhwcqlOqse+Era4Lk71ErFu3Dps3\nb8a3336LtWvX4ujRo1rrZTKZ0Tvn5syeYx8/fjx27dqFqKgouLm5YdGiRbYOySwlJSWYNm0aZs+e\nDWdnZ6119nZd6p+LPV4buVyOqKgoxMXFISkpCZcvX7Z1SI1W/1wuXLiAGTNm4M8//8Tvv/+OgoKC\nZt1OqsbevXvRqVMn9O3b19ahGMVkb4S7u7vWZDpKpRLu7u42jMiwmrhcXV0RFBSEpKQkuLq6Ijs7\nGwCQnZ2NTp06abate15ZWVlwd3dvVufbVLEb2t5aOnfuDLlcDgcHB4wbNw6nTp3Sex6G4q1Z3rFj\nRxQWFqKystJq51FRUYFp06YhLCwMwcHBAOz3uug7F3u+Nu3bt8cjjzyCxMREg8d2d3dHZmYmgOqq\n8qKiInTs2NHs87O0mnPZt28f3NzcIJPJ4OTkhNGjRxu8Js3p+3X8+HHs2bMHAQEBmDFjBg4fPowP\nP/yw2V0XJnsj+vXrh9TUVKSnp0OlUkGhUCAgIMDWYekoLS1FcXGx5vWBAwfg6+uLgIAAbNmyBQCw\nZcsWBAYGAoBmuRACiYmJaNeuHdzc3DB06FDs378fBQUFKCgowP79+zF06FCbnFNTxe7m5gZnZ2ck\nJiZCCKG1L2uoSYwAsHv3bvj6+mrOQ6FQQKVSIT09Hampqejfv7/B75xMJsMjjzyCHTt2AKhugWzJ\n76IQAu+88w58fHwwadIkzXJ7vC6GzsXers3NmzdRWFgIoHr+j4MHD6JXr14Gjx0QEKBpnb5jxw48\n+uijkMlkZp+fJeg7Fx8fH801EULoXJPm+v164403EB8fjz179mDp0qV49NFHsWTJkuZ3XRrV7PAO\nEhsbK4KDg0VgYKBYvny5rcPRKy0tTYSFhYmwsDAxYsQITZw3b94Uzz//vAgKChL/+te/RF5enhCi\nuiXsvHnzRGBgoBg5cqRISkrS7GvDhg3iySefFE8++aTYuHGjVeKfPn26GDJkiLjvvvvEsGHDxPr1\n65s09qSkJBEaGioCAwPF/PnzRVVVldXOY+bMmWLkyJFi5MiR4uWXXxZKpVKz/fLly0VgYKAIDg7W\nails6DuXlpYmxowZI5588kkxdepUUV5ebpHzEEKIo0ePit69e4uRI0eKUaNGiVGjRonY2Fi7vC6G\nzsXers3Zs2dFeHi4GDlypAgNDRVffPGF0WOXlZWJqVOniieffFKMGTNGpKWlNfr8rHUuzz33nGbZ\nG2+8oWmx35y/X3UdPnxY0xq/uV0XToRDREQkcazGJyIikjgmeyIiIoljsiciIpI4JnsiIiKJY7In\nIiKSOCZ7ojtQeHg4ysrKAACrV6/WDHvblDIyMjRDN9d48cUXkZaW1uTHIiLjmOyJ7kBRUVFo1aoV\nAODHH39sVLKvGR3MkGvXrukk+2+//RZeXl5mH4uIbg/72RPdgfr06YPjx4/jxx9/xFdffYW77roL\nLVu2xJIlS+Dl5YXPPvsMR48ehUqlQp8+fTBv3jy0bdsWb7/9NuRyOa5cuYKSkhJERUXhjTfewJUr\nV1BRUQEvLy8sXLgQLi4uCA0NRUZGBnr06AFvb28sW7YMAQEB+Oabb9C7d29cvXoVc+bMwc2bN+Ho\n6Ijp06fDz89PE9/06dOxa9cu5Ofn46233tLMIEZEjdDIgYKIyI717t1bMzrZE088Ic6fP69Z99VX\nX17co3EAAAIMSURBVImvvvpK837x4sVi6dKlQgghZs2aJZ5++mlRUlKiWZ+bm6t5vXTpUvHJJ58I\nIapHE3v66ae1jlv3WGPHjhXr168XQlTP5f3www9r9tW7d2/x008/CSGEOHbsmBg6dGjTnDjRHcrR\n1jcbRNS87NmzB8XFxZpxvVUqFe655x7N+qeeegpt2rTRvI+KisK2bdtQUVGB0tJS9OjRo8FjFBcX\n4+zZsxgzZgwA4O6778a9996LxMREzbjfI0aMAAAMHDgQ2dnZKC8vR8uWLZvqNInuKEz2RKRFCIG5\nc+di8ODBetfXTfTHjh3DunXr8Ouvv6JTp07Ytm0b1q9f3yRx1CR2uVwOoLqNAJM9UeOwgR7RHa5t\n27YoKirSvA8ICMDq1as1rfWLi4tx6dIlvZ8tLCyEs7MzOnToAJVKhd9//12zztnZWTMbY33Ozs64\n9957NbN/Xbp0CefOncPAgQOb6rSIqA4me6I73PPPP4/Zs2cjPDwcKSkpeOmll3DPPfdg7NixCAsL\nwz//+U+DyX7YsGHw8vJCSEgIJkyYgPvuu0+zrk+fPujZsydGjhyJadOm6Xz2008/xdatWxEWFoaZ\nM2di8eLF6NSpk8XOk+hOxtb4REREEseSPRERkcQx2RMREUkckz0REZHEMdkTERFJHJM9ERGRxDHZ\nExERSRyTPRERkcQx2RMREUnc/wcmirEPXQhBMgAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -796,31 +883,6 @@ "source": [ "It works!" ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "## Summary\n", - "\n", - "Hopefully this blog post demonstrated a very powerful new inference algorithm available in [PyMC3](http://pymc-devs.github.io/pymc3/): [ADVI](http://pymc-devs.github.io/pymc3/api.html#advi). I also think bridging the gap between Probabilistic Programming and Deep Learning can open up many new avenues for innovation in this space, as discussed above. Specifically, a hierarchical neural network sounds pretty bad-ass. These are really exciting times.\n", - "\n", - "## Next steps\n", - "\n", - "[`Theano`](http://deeplearning.net/software/theano/), which is used by `PyMC3` as its computational backend, was mainly developed for estimating neural networks and there are great libraries like [`Lasagne`](https://github.com/Lasagne/Lasagne) that build on top of `Theano` to make construction of the most common neural network architectures easy. Ideally, we wouldn't have to build the models by hand as I did above, but use the convenient syntax of `Lasagne` to construct the architecture, define our priors, and run ADVI. \n", - "\n", - "You can also run this example on the GPU by setting `device = gpu` and `floatX = float32` in your `.theanorc`.\n", - "\n", - "You might also argue that the above network isn't really deep, but note that we could easily extend it to have more layers, including convolutional ones to train on more challenging data sets.\n", - "\n", - "\n", - "## Acknowledgements\n", - "\n", - "[Taku Yoshioka](https://github.com/taku-y) did a lot of work on ADVI in PyMC3, including the mini-batch implementation as well as the sampling from the variational posterior. I'd also like to the thank the Stan guys (specifically Alp Kucukelbir and Daniel Lee) for deriving ADVI and teaching us about it. Thanks also to Chris Fonnesbeck, Andrew Campbell, Taku Yoshioka, and Peadar Coyle for useful comments on an earlier draft. After that [Maxim Kochurov](https://github.com/ferrine) implemented OPVI framework, as a unified interface for variational (including minibatch training and AEVB) methods" - ] } ], "metadata": { From 8e0c48971ba7c819ef4560bdb4c0df6c0bd61e92 Mon Sep 17 00:00:00 2001 From: alexandercbooth Date: Thu, 16 Mar 2017 04:57:27 -0400 Subject: [PATCH 28/53] doc(DiagInferDiv): formatting fix in blog post quote. Closes #1895. (#1909) --- ...ng_biased_Inference_with_Divergences.ipynb | 196 ++++++++++++++---- 1 file changed, 151 insertions(+), 45 deletions(-) diff --git a/docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb b/docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb index c4159cba15..4cc3e62173 100644 --- a/docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb +++ b/docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb @@ -2,7 +2,10 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "# Diagnosing Biased Inference with Divergences\n", "\n", @@ -13,11 +16,16 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "#### More formally, as explained in [the original post](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html) (in markdown block, same below): \n", "> \n", - "Markov chain Monte Carlo (MCMC) approximates expectations with respect to a given target distribution, $$ \\mathbb{E}{\\pi} [ f ] = \\int \\mathrm{d}q \\, \\pi (q) \\, f(q), $$ using the states of a Markov chain, ${q{0}, \\ldots, q_{N} }$, $$ \\mathbb{E}{\\pi} [ f ] \\approx \\hat{f}{N} = \\frac{1}{N + 1} \\sum_{n = 0}^{N} f(q_{n}). $$ These estimators, however, are guaranteed to be accurate only asymptotically as the chain grows to be infinitely long, $$ \\lim_{N \\rightarrow \\infty} \\hat{f}{N} = \\mathbb{E}{\\pi} [ f ]. $$\n", + "Markov chain Monte Carlo (MCMC) approximates expectations with respect to a given target distribution, $$ \\mathbb{E}{\\pi} [ f ] = \\int \\mathrm{d}q \\, \\pi (q) \\, f(q), $$ using the states of a Markov chain, ${q{0}, \\ldots, q_{N} }$, $$ \\mathbb{E}{\\pi} [ f ] \\approx \\hat{f}{N} = \\frac{1}{N + 1} \\sum_{n = 0}^{N} f(q_{n}). $$ \n", + "\n", + ">These estimators, however, are guaranteed to be accurate only asymptotically as the chain grows to be infinitely long, $$ \\lim_{N \\rightarrow \\infty} \\hat{f}{N} = \\mathbb{E}{\\pi} [ f ]. $$\n", "> \n", "To be useful in applied analyses, we need MCMC estimators to converge to the true expectation values sufficiently quickly that they are reasonably accurate before we exhaust our finite computational resources. This fast convergence requires strong ergodicity conditions to hold, in particular geometric ergodicity between a Markov transition and a target distribution. Geometric ergodicity is usually the necessary condition for MCMC estimators to follow a central limit theorem, which ensures not only that they are unbiased even after only a finite number of iterations but also that we can empirically quantify their precision using the MCMC standard error.\n", "> \n", @@ -50,7 +58,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## The Eight Schools Model\n", "> \n", @@ -69,7 +80,9 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -82,7 +95,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## A Centered Eight Schools Implementation \n", "\n", @@ -115,7 +131,9 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -128,14 +146,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "> Unfortunately, this direct implementation of the model exhibits a pathological geometry that frustrates geometric ergodicity. Even more worrisome, the resulting bias is subtle and may not be obvious upon inspection of the Markov chain alone. To understand this bias, let's consider first a short Markov chain, commonly used when computational expediency is a motivating factor, and only afterwards a longer Markov chain." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "#### A Dangerously-Short Markov Chain" ] @@ -144,7 +168,9 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -165,7 +191,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "In the [the original post](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html) a single chain of 1200 sample is applied. However, since split $\\hat{R}$ is not implemented in `PyMC3` we fit 2 chains with 600 sample each instead. \n", "The Gelman-Rubin diagnostic $\\hat{R}$ doesn’t indicate any problems (value close to 1) and the effective sample size per iteration is reasonable" @@ -175,7 +204,9 @@ "cell_type": "code", "execution_count": 20, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -197,7 +228,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "> Moreover, the trace plots all look fine. Let's consider, for example, the hierarchical standard deviation $\\tau$, or more specifically, its logarithm, $log(\\tau)$. Because $\\tau$ is constrained to be positive, its logarithm will allow us to better resolve behavior for small values. Indeed the chains seems to be exploring both small and large values reasonably well," ] @@ -206,7 +240,9 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -233,7 +269,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "> Unfortunately, the resulting estimate for the mean of $log(\\tau)$ is strongly biased away from the true value, here shown in grey." ] @@ -242,7 +281,9 @@ "cell_type": "code", "execution_count": 27, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -277,7 +318,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "> Hamiltonian Monte Carlo, however, is not so oblivious to these issues as 2% of the iterations in our lone Markov chain ended with a divergence." ] @@ -286,7 +330,9 @@ "cell_type": "code", "execution_count": 28, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -308,7 +354,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "> Even with a single short chain these divergences are able to identity the bias and advise skepticism of any resulting MCMC estimators.\n", "\n", @@ -319,7 +368,9 @@ "cell_type": "code", "execution_count": 38, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -355,7 +406,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "In the current example, the pathological samples from the trace is not necessary concentrated at the funnel (unlike in `Stan`), the follow figure is from the [the original post](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html) as comparison.\n", "\n", @@ -369,7 +423,9 @@ "cell_type": "code", "execution_count": 41, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -412,7 +468,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "#### A Safer, Longer Markov Chain \n", "> \n", @@ -423,7 +482,9 @@ "cell_type": "code", "execution_count": 46, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -501,7 +562,9 @@ "cell_type": "code", "execution_count": 47, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -523,7 +586,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "> \n", "Similar to the result in `Stan`, $\\hat{R}$ does not indicate any serious issues. However, the effective sample size per iteration has drastically fallen, indicating that we are exploring less efficiently the longer we run. This odd behavior is a clear sign that something problematic is afoot. As shown in the trace plot, the chain occasionally \"sticking\" as it approaches small values of $\\tau$, exactly where we saw the divergences concentrating. This is a clear indication of the underlying pathologies. These sticky intervals induce severe oscillations in the MCMC estimators early on, until they seem to finally settle into biased values. \n", @@ -535,7 +601,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Mitigating Divergences by Adjusting PyMC3's Adaptation Routine\n", "> \n", @@ -550,7 +619,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "#### Adjusting Adaptation Routine" ] @@ -559,7 +631,9 @@ "cell_type": "code", "execution_count": 48, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -592,7 +666,9 @@ "cell_type": "code", "execution_count": 50, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -626,7 +702,9 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "source": [ "Interestingly, unlike in `Stan`, the number of divergent transitions decrease since we increased the adapt_delta and decreased the step size. \n", @@ -642,7 +720,9 @@ "cell_type": "code", "execution_count": 54, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -717,7 +797,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## A Non-Centered Eight Schools Implementation \n", "> \n", @@ -764,7 +847,9 @@ "cell_type": "code", "execution_count": 57, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -780,7 +865,9 @@ "cell_type": "code", "execution_count": 58, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -801,7 +888,9 @@ "cell_type": "code", "execution_count": 59, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -826,7 +915,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "> \n", "As shown above, the effective sample size per iteration has drastically improved, and the trace plots no longer show any \"stickyness\". However, we do still see the rare divergence. These infrequent divergences do not seem concentrate anywhere in parameter space, which is indicative of the divergences being false positives." @@ -836,7 +928,9 @@ "cell_type": "code", "execution_count": 60, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -899,7 +993,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "> As expected of false positives, we can remove the divergences entirely by decreasing the step size," ] @@ -908,7 +1005,9 @@ "cell_type": "code", "execution_count": 61, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -938,7 +1037,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "> The more agreeable geometry of the non-centered implementation allows the Markov chain to explore deep into the neck of the funnel, capturing even the smallest values of $\\tau$ that are consistent with the measurements. Consequently, MCMC estimators from the non-centered chain rapidly converge towards their true expectation values." ] @@ -947,7 +1049,9 @@ "cell_type": "code", "execution_count": 65, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -1030,7 +1134,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [] @@ -1038,7 +1144,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -1052,7 +1158,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.1" } }, "nbformat": 4, From c05282be9952665f8d03b34b5dc0dbcdace135ae Mon Sep 17 00:00:00 2001 From: Austin Rochford Date: Wed, 15 Mar 2017 14:17:45 -0400 Subject: [PATCH 29/53] Revert "small fix for multivariate mixture models" --- pymc3/distributions/mixture.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/pymc3/distributions/mixture.py b/pymc3/distributions/mixture.py index 95d1378862..1e0b06149f 100644 --- a/pymc3/distributions/mixture.py +++ b/pymc3/distributions/mixture.py @@ -61,9 +61,7 @@ def __init__(self, w, comp_dists, *args, **kwargs): try: comp_modes = self._comp_modes() - # for logp the different modes are like different observations, i.e. rows, hence use comp_modes.T - # logPs is a vector, hence self.logp(..).T == self.logp(..) - comp_mode_logps = self.logp(comp_modes.T) + comp_mode_logps = self.logp(comp_modes) self.mode = comp_modes[tt.argmax(w * comp_mode_logps, axis=-1)] if 'mode' not in defaults: From 6c7c60055dfa319d1aef95c7ccc164e6e158dc5e Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Wed, 15 Mar 2017 16:48:09 -0500 Subject: [PATCH 30/53] Added message about init only working with auto-assigned step methods --- pymc3/sampling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pymc3/sampling.py b/pymc3/sampling.py index cc85ab8797..60caa4eacf 100644 --- a/pymc3/sampling.py +++ b/pymc3/sampling.py @@ -101,7 +101,7 @@ def sample(draws, step=None, init='ADVI', n_init=200000, start=None, specified, or are partially specified, they will be assigned automatically (defaults to None). init : str {'ADVI', 'ADVI_MAP', 'MAP', 'NUTS', None} - Initialization method to use. + Initialization method to use. Only works for auto-assigned step methods. * ADVI : Run ADVI to estimate starting points and diagonal covariance matrix. If njobs > 1 it will sample starting points from the estimated posterior, otherwise it will use the estimated posterior mean. From de77fca296b4b5408df00a3e68bdaa97dc5e2b3a Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Tue, 14 Mar 2017 17:33:29 -0500 Subject: [PATCH 31/53] Added docs and scripts to MANIFEST --- MANIFEST.in | 3 ++- setup.py | 5 ++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/MANIFEST.in b/MANIFEST.in index 0b9e226d86..1632cc8d7e 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,5 +1,6 @@ recursive-include pymc3/examples/data * recursive-include source * +recursive-include docs * include requirements.txt include *.md *.rst - +include scripts/*.sh \ No newline at end of file diff --git a/setup.py b/setup.py index 1fbc1bcbbe..38db85ce4b 100755 --- a/setup.py +++ b/setup.py @@ -60,10 +60,9 @@ 'pymc3.variational', 'pymc3.external', 'pymc3.gp', - 'pymc3.plots', - 'docs', - '.', + 'pymc3.plots' ], + package_data={'docs':['*'],}, classifiers=classifiers, install_requires=install_reqs, tests_require=test_reqs, From ce3a5e630838afbdb122394ffaf9ac70c2a97428 Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Tue, 14 Mar 2017 17:40:51 -0500 Subject: [PATCH 32/53] Added newline to MANIFEST --- MANIFEST.in | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/MANIFEST.in b/MANIFEST.in index 1632cc8d7e..77b370e791 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -3,4 +3,4 @@ recursive-include source * recursive-include docs * include requirements.txt include *.md *.rst -include scripts/*.sh \ No newline at end of file +include scripts/*.sh From 9025d7f02470bca8f795fde991e6b1c322f864ea Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Tue, 14 Mar 2017 18:02:58 -0500 Subject: [PATCH 33/53] Replaced package list with find_packages in setup.py; removed examples/data/__init__.py --- pymc3/examples/data/__init__.py | 0 setup.py | 19 +++---------------- 2 files changed, 3 insertions(+), 16 deletions(-) delete mode 100644 pymc3/examples/data/__init__.py diff --git a/pymc3/examples/data/__init__.py b/pymc3/examples/data/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/setup.py b/setup.py index 38db85ce4b..29babfb033 100755 --- a/setup.py +++ b/setup.py @@ -1,5 +1,5 @@ #!/usr/bin/env python -from setuptools import setup +from setuptools import setup, find_packages import sys @@ -48,21 +48,8 @@ license=LICENSE, url=URL, long_description=LONG_DESCRIPTION, - packages=['pymc3', - 'pymc3.backends', - 'pymc3.distributions', - 'pymc3.examples', - 'pymc3.glm', - 'pymc3.step_methods', - 'pymc3.step_methods.hmc', - 'pymc3.tuning', - 'pymc3.tests', - 'pymc3.variational', - 'pymc3.external', - 'pymc3.gp', - 'pymc3.plots' - ], - package_data={'docs':['*'],}, + packages=find_packages(), + package_data={'docs':['*'], 'pymc3.examples':['data/*']}, classifiers=classifiers, install_requires=install_reqs, tests_require=test_reqs, From 75da69cb96e1f249494beaf91da87e071d749f87 Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Thu, 16 Mar 2017 12:59:16 -0500 Subject: [PATCH 34/53] Updated version to rc2 --- pymc3/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pymc3/__init__.py b/pymc3/__init__.py index df5a3504e1..1d49a4d7c9 100644 --- a/pymc3/__init__.py +++ b/pymc3/__init__.py @@ -1,5 +1,5 @@ # pylint: disable=wildcard-import -__version__ = "3.1.rc1" +__version__ = "3.1.rc2" from .blocking import * from .distributions import * From d6bd88615875c9fc0da28acb168097bf4ed45d48 Mon Sep 17 00:00:00 2001 From: Christopher Fonnesbeck Date: Thu, 16 Mar 2017 13:03:51 -0500 Subject: [PATCH 35/53] Fixed stray version string --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 29babfb033..13d08c1964 100755 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ AUTHOR_EMAIL = 'chris.fonnesbeck@vanderbilt.edu' URL = "http://github.com/pymc-devs/pymc3" LICENSE = "Apache License, Version 2.0" -VERSION = "3.1.rc1" +VERSION = "3.1.rc2" classifiers = ['Development Status :: 5 - Production/Stable', 'Programming Language :: Python', From 47f6e1b8186dff982f7162b98b2d8faaf8c1a04f Mon Sep 17 00:00:00 2001 From: Maxim Kochurov Date: Fri, 17 Mar 2017 16:52:52 +0300 Subject: [PATCH 36/53] refactor variational module, add histogram approximation (#1904) * refactor module, add histogram * add more tests * refactor some code concerning AEVB histogram * fix test for histogram * use mean as deterministic point in Histogram * remove unused import * change names of shortcuts * add names to shared params * add new line at the end of `approximations.py` --- pymc3/tests/test_variational_inference.py | 76 ++++- pymc3/variational/__init__.py | 4 + pymc3/variational/approximations.py | 331 ++++++++++++++++++++++ pymc3/variational/inference.py | 237 +--------------- pymc3/variational/operators.py | 17 ++ pymc3/variational/opvi.py | 48 ++-- 6 files changed, 456 insertions(+), 257 deletions(-) create mode 100644 pymc3/variational/approximations.py create mode 100644 pymc3/variational/operators.py diff --git a/pymc3/tests/test_variational_inference.py b/pymc3/tests/test_variational_inference.py index 4dac00663e..fe11d1b041 100644 --- a/pymc3/tests/test_variational_inference.py +++ b/pymc3/tests/test_variational_inference.py @@ -1,13 +1,16 @@ -from six.moves import cPickle as pickle +import pickle import unittest import numpy as np from theano import theano, tensor as tt import pymc3 as pm from pymc3 import Model, Normal -from pymc3.variational.inference import ( - KL, MeanField, ADVI, FullRankADVI, +from pymc3.variational import ( + ADVI, FullRankADVI, + Histogram, fit ) +from pymc3.variational.operators import KL +from pymc3.variational.approximations import MeanField from pymc3.tests import models from pymc3.tests.helpers import SeededTest @@ -186,7 +189,7 @@ def cb(*_): mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0) Normal('x', mu=mu_, sd=sd, observed=data_t, total_size=n) inf = self.inference() - approx = inf.fit(self.NITER, callbacks=[cb]) + approx = inf.fit(self.NITER, callbacks=[cb], obj_n_mc=10) trace = approx.sample_vp(10000) np.testing.assert_allclose(np.mean(trace['mu']), mu_post, rtol=0.4) np.testing.assert_allclose(np.std(trace['mu']), np.sqrt(1. / d), rtol=0.4) @@ -199,14 +202,23 @@ def test_pickling(self): inference.fit(20) def test_aevb(self): - _, model, _ = models.exponential_beta() + _, model, _ = models.exponential_beta(n=2) x = model.x y = model.y mu = theano.shared(x.init_value) * 2 - sd = theano.shared(x.init_value) * 3 + rho = theano.shared(np.zeros_like(x.init_value)) with model: - inference = self.inference(local_rv={y: (mu, sd)}) - inference.fit(3) + inference = self.inference(local_rv={y: (mu, rho)}) + approx = inference.fit(3, obj_n_mc=2) + approx.sample_vp(10) + approx.apply_replacements( + y, + more_replacements={x: np.asarray([1, 1], dtype=x.dtype)} + ).eval() + + def test_profile(self): + with models.multidimensional_model()[1]: + self.inference().run_profiling(10) class TestMeanField(TestApproximates.Base): @@ -214,7 +226,10 @@ class TestMeanField(TestApproximates.Base): def test_approximate(self): with models.multidimensional_model()[1]: - fit(10, method='advi') + meth = ADVI() + fit(10, method=meth) + self.assertRaises(KeyError, fit, 10, method='undefined') + self.assertRaises(TypeError, fit, 10, method=1) class TestFullRank(TestApproximates.Base): @@ -234,11 +249,54 @@ def test_from_advi(self): def test_combined(self): with models.multidimensional_model()[1]: + self.assertRaises(ValueError, fit, 10, method='advi->fullrank_advi', frac=1) fit(10, method='advi->fullrank_advi', frac=.5) def test_approximate(self): with models.multidimensional_model()[1]: fit(10, method='fullrank_advi') + +class TestHistogram(SeededTest): + def test_sampling(self): + with models.multidimensional_model()[1]: + full_rank = FullRankADVI() + approx = full_rank.fit(20) + trace0 = approx.sample_vp(10000) + histogram = Histogram(trace0) + trace1 = histogram.sample_vp(100000) + np.testing.assert_allclose(trace0['x'].mean(0), trace1['x'].mean(0), atol=0.01) + np.testing.assert_allclose(trace0['x'].var(0), trace1['x'].var(0), atol=0.01) + + def test_aevb_histogram(self): + _, model, _ = models.exponential_beta(n=2) + x = model.x + mu = theano.shared(x.init_value) + rho = theano.shared(np.zeros_like(x.init_value)) + with model: + inference = ADVI(local_rv={x: (mu, rho)}) + approx = inference.approx + trace0 = approx.sample_vp(1000) + histogram = Histogram(trace0, local_rv={x: (mu, rho)}) + trace1 = histogram.sample_vp(10000) + histogram.random(no_rand=True) + histogram.random_fn(no_rand=True) + np.testing.assert_allclose(trace0['y'].mean(0), trace1['y'].mean(0), atol=0.02) + np.testing.assert_allclose(trace0['y'].var(0), trace1['y'].var(0), atol=0.02) + np.testing.assert_allclose(trace0['x'].mean(0), trace1['x'].mean(0), atol=0.02) + np.testing.assert_allclose(trace0['x'].var(0), trace1['x'].var(0), atol=0.02) + + def test_random(self): + with models.multidimensional_model()[1]: + full_rank = FullRankADVI() + approx = full_rank.approx + trace0 = approx.sample_vp(10000) + histogram = Histogram(trace0) + histogram.randidx(None).eval() + histogram.randidx(1).eval() + histogram.random_fn(no_rand=True) + histogram.random_fn(no_rand=False) + histogram.histogram_logp.eval() + if __name__ == '__main__': unittest.main() diff --git a/pymc3/variational/__init__.py b/pymc3/variational/__init__.py index 63d35470fc..1945dacb05 100644 --- a/pymc3/variational/__init__.py +++ b/pymc3/variational/__init__.py @@ -23,3 +23,7 @@ FullRankADVI, fit, ) +from .approximations import Histogram + +from . import approximations +from . import operators diff --git a/pymc3/variational/approximations.py b/pymc3/variational/approximations.py new file mode 100644 index 0000000000..8fcdc5070d --- /dev/null +++ b/pymc3/variational/approximations.py @@ -0,0 +1,331 @@ +import numpy as np +import theano +from theano import tensor as tt + +from pymc3 import ArrayOrdering, DictToArrayBijection +from pymc3.distributions.dist_math import rho2sd, log_normal, log_normal_mv +from pymc3.variational.opvi import Approximation +from pymc3.theanof import tt_rng, memoize, change_flags + + +__all__ = [ + 'MeanField', + 'FullRank', + 'Histogram' +] + + +class MeanField(Approximation): + """ + Mean Field approximation to the posterior where spherical Gaussian family + is fitted to minimize KL divergence from True posterior. It is assumed + that latent space variables are uncorrelated that is the main drawback + of the method + + Parameters + ---------- + local_rv : dict + mapping {model_variable -> local_variable} + Local Vars are used for Autoencoding Variational Bayes + See (AEVB; Kingma and Welling, 2014) for details + + model : PyMC3 model for inference + + cost_part_grad_scale : float or scalar tensor + Scaling score part of gradient can be useful near optimum for + archiving better convergence properties. Common schedule is + 1 at the start and 0 in the end. So slow decay will be ok. + See (Sticking the Landing; Geoffrey Roeder, + Yuhuai Wu, David Duvenaud, 2016) for details + + References + ---------- + Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 + Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI + approximateinference.org/accepted/RoederEtAl2016.pdf + """ + @property + def mean(self): + return self.shared_params['mu'] + + @property + def rho(self): + return self.shared_params['rho'] + + @property + def cov(self): + return tt.diag(rho2sd(self.rho)) + + def create_shared_params(self): + return {'mu': theano.shared( + self.input.tag.test_value[self.global_slc], + 'mu'), + 'rho': theano.shared( + np.zeros((self.global_size,), dtype=theano.config.floatX), + 'rho') + } + + def log_q_W_global(self, z): + """ + log_q_W samples over q for global vars + Gradient wrt mu, rho in density parametrization + is set to zero to lower variance of ELBO + """ + mu = self.scale_grad(self.mean) + rho = self.scale_grad(self.rho) + z = z[self.global_slc] + logq = tt.sum(log_normal(z, mu, rho=rho)) + return logq + + def random_global(self, size=None, no_rand=False): + initial = self.initial(size, no_rand, l=self.global_size) + sd = rho2sd(self.rho) + mu = self.mean + return sd * initial + mu + + +class FullRank(Approximation): + """ + Full Rank approximation to the posterior where Multivariate Gaussian family + is fitted to minimize KL divergence from True posterior. In contrast to + MeanField approach correlations between variables are taken in account. The + main drawback of the method is computational cost. + + Parameters + ---------- + local_rv : dict + mapping {model_variable -> local_variable} + Local Vars are used for Autoencoding Variational Bayes + See (AEVB; Kingma and Welling, 2014) for details + + model : PyMC3 model for inference + + cost_part_grad_scale : float or scalar tensor + Scaling score part of gradient can be useful near optimum for + archiving better convergence properties. Common schedule is + 1 at the start and 0 in the end. So slow decay will be ok. + See (Sticking the Landing; Geoffrey Roeder, + Yuhuai Wu, David Duvenaud, 2016) for details + + References + ---------- + Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 + Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI + approximateinference.org/accepted/RoederEtAl2016.pdf + """ + def __init__(self, local_rv=None, model=None, cost_part_grad_scale=1, gpu_compat=False): + super(FullRank, self).__init__( + local_rv=local_rv, model=model, + cost_part_grad_scale=cost_part_grad_scale + ) + self.gpu_compat = gpu_compat + + @property + def L(self): + return self.shared_params['L_tril'][self.tril_index_matrix] + + @property + def mean(self): + return self.shared_params['mu'] + + @property + def cov(self): + L = self.L + return L.dot(L.T) + + @property + def num_tril_entries(self): + n = self.global_size + return int(n * (n + 1) / 2) + + @property + def tril_index_matrix(self): + n = self.global_size + num_tril_entries = self.num_tril_entries + tril_index_matrix = np.zeros([n, n], dtype=int) + tril_index_matrix[np.tril_indices(n)] = np.arange(num_tril_entries) + tril_index_matrix[np.tril_indices(n)[::-1]] = np.arange(num_tril_entries) + return tril_index_matrix + + def create_shared_params(self): + n = self.global_size + L_tril = ( + np.eye(n) + [np.tril_indices(n)] + .astype(theano.config.floatX) + ) + return {'mu': theano.shared( + self.input.tag.test_value[self.global_slc], + 'mu'), + 'L_tril': theano.shared(L_tril, 'L_tril') + } + + def log_q_W_global(self, z): + """ + log_q_W samples over q for global vars + Gradient wrt mu, rho in density parametrization + is set to zero to lower variance of ELBO + """ + mu = self.scale_grad(self.mean) + L = self.scale_grad(self.L) + z = z[self.global_slc] + return log_normal_mv(z, mu, chol=L, gpu_compat=self.gpu_compat) + + def random_global(self, size=None, no_rand=False): + # (samples, dim) or (dim, ) + initial = self.initial(size, no_rand, l=self.global_size).T + # (dim, dim) + L = self.L + # (dim, ) + mu = self.mean + # x = Az + m, but it assumes z is vector + # When we get z with shape (samples, dim) + # we need to transpose Az + return L.dot(initial).T + mu + + @classmethod + def from_mean_field(cls, mean_field, gpu_compat=False): + """ + Construct FullRank from MeanField approximation + + Parameters + ---------- + mean_field : MeanField + approximation to start with + + Flags + ----- + gpu_compat : bool + use GPU compatible version or not + + Returns + ------- + FullRank + """ + full_rank = object.__new__(cls) # type: FullRank + full_rank.gpu_compat = gpu_compat + full_rank.__dict__.update(mean_field.__dict__) + full_rank.shared_params = full_rank.create_shared_params() + full_rank.shared_params['mu'].set_value( + mean_field.shared_params['mu'].get_value() + ) + rho = mean_field.shared_params['rho'].get_value() + n = full_rank.global_size + L_tril = ( + np.diag(np.log1p(np.exp(rho))) # rho2sd + [np.tril_indices(n)] + .astype(theano.config.floatX) + ) + full_rank.shared_params['L_tril'].set_value(L_tril) + return full_rank + + +class Histogram(Approximation): + def __init__(self, trace, local_rv=None, model=None): + self.trace = trace + self._histogram_logp = None + super(Histogram, self).__init__(local_rv=local_rv, model=model) + + @staticmethod + def check_model(model): + return True + + def get_global_vars(self): + return [var for var in self.model.unobserved_RVs + if var.name in self.trace.varnames] + + def _setup(self): + self._histogram_order = ArrayOrdering(self.global_vars) + self._bij = DictToArrayBijection(self._histogram_order, dict()) + + def create_shared_params(self): + trace = self.trace + histogram = np.empty((len(trace), self.global_size)) + for i in range(len(trace)): + histogram[i] = self._bij.map(trace[i]) + return theano.shared(histogram, 'histogram') + + def randidx(self, size=None): + if size is None: + size = () + elif isinstance(size, tt.TensorVariable): + if size.ndim < 1: + size = size[None] + elif size.ndim > 1: + raise ValueError('size ndim should be no more than 1d') + else: + pass + else: + size = tuple(np.atleast_1d(size)) + return (tt_rng() + .uniform(size=size, low=0.0, high=self.histogram.shape[0] - 1e-16) + .astype('int64')) + + def random_global(self, size=None, no_rand=False): + theano_condition_is_here = isinstance(no_rand, tt.Variable) + if theano_condition_is_here: + return tt.switch(no_rand, + self.mean, + self.histogram[self.randidx(size)]) + else: + if no_rand: + return self.mean + else: + return self.histogram[self.randidx(size)] + + @property + def histogram(self): + return self.shared_params + + @property + def histogram_logp(self): + if self._histogram_logp is None: + node = self.to_flat_input(self.model.logpt) + + def mapping(z): + return theano.clone(node, {self.input: z}) + x = self.histogram + self._histogram_logp, _ = theano.scan( + mapping, x, n_steps=x.shape[0] + ) + return self._histogram_logp + + @property + def mean(self): + return self.histogram.mean(0) + + @property + def params(self): + return [] + + @property + @memoize + @change_flags(compute_test_value='off') + def random_fn(self): + """ + Implements posterior distribution from initial latent space + + Parameters + ---------- + size : number of samples from distribution + no_rand : whether use deterministic distribution + + Returns + ------- + posterior space (numpy) + """ + In = theano.In + size = tt.iscalar('size') + no_rand = tt.bscalar('no_rand') + posterior = self.random(size, no_rand=no_rand) + fn = theano.function([In(size, 'size', 1, allow_downcast=True), + In(no_rand, 'no_rand', 0, allow_downcast=True)], + posterior) + + def inner(size=None, no_rand=False): + if size is None: + return fn(1, int(no_rand))[0] + else: + return fn(size, int(no_rand)) + + return inner diff --git a/pymc3/variational/inference.py b/pymc3/variational/inference.py index cfaed1c7f9..331750f964 100644 --- a/pymc3/variational/inference.py +++ b/pymc3/variational/inference.py @@ -3,242 +3,21 @@ import logging import numpy as np -import theano -from theano import tensor as tt import tqdm import pymc3 as pm -from pymc3.distributions.dist_math import log_normal, rho2sd, log_normal_mv -from pymc3.variational.opvi import Operator, Approximation, TestFunction +from pymc3.variational.approximations import MeanField, FullRank +from pymc3.variational.operators import KL +from pymc3.variational.opvi import Approximation, TestFunction logger = logging.getLogger(__name__) __all__ = [ 'TestFunction', - 'KL', - 'MeanField', - 'FullRank', 'ADVI', 'FullRankADVI', 'Inference' ] -# OPERATORS - - -class KL(Operator): - """ - Operator based on Kullback Leibler Divergence - .. math:: - - KL[q(v)||p(v)] = \int q(v)\log\\frac{q(v)}{p(v)}dv - """ - def apply(self, f): - z = self.input - return self.logq(z) - self.logp(z) - -# APPROXIMATIONS - - -class MeanField(Approximation): - """ - Mean Field approximation to the posterior where spherical Gaussian family - is fitted to minimize KL divergence from True posterior. It is assumed - that latent space variables are uncorrelated that is the main drawback - of the method - - Parameters - ---------- - local_rv : dict - mapping {model_variable -> local_variable} - Local Vars are used for Autoencoding Variational Bayes - See (AEVB; Kingma and Welling, 2014) for details - - model : PyMC3 model for inference - - cost_part_grad_scale : float or scalar tensor - Scaling score part of gradient can be useful near optimum for - archiving better convergence properties. Common schedule is - 1 at the start and 0 in the end. So slow decay will be ok. - See (Sticking the Landing; Geoffrey Roeder, - Yuhuai Wu, David Duvenaud, 2016) for details - - References - ---------- - Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 - Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI - approximateinference.org/accepted/RoederEtAl2016.pdf - """ - @property - def mu(self): - return self.shared_params['mu'] - - @property - def rho(self): - return self.shared_params['rho'] - - @property - def cov(self): - return tt.diag(rho2sd(self.rho)) - - def create_shared_params(self): - return {'mu': theano.shared( - self.input.tag.test_value[self.global_slc]), - 'rho': theano.shared( - np.zeros((self.global_size,), dtype=theano.config.floatX)) - } - - def log_q_W_global(self, z): - """ - log_q_W samples over q for global vars - Gradient wrt mu, rho in density parametrization - is set to zero to lower variance of ELBO - """ - mu = self.scale_grad(self.mu) - rho = self.scale_grad(self.rho) - z = z[self.global_slc] - logq = tt.sum(log_normal(z, mu, rho=rho)) - return logq - - def random_global(self, size=None, no_rand=False): - initial = self.initial(size, no_rand, l=self.global_size) - sd = rho2sd(self.rho) - mu = self.mu - return sd * initial + mu - - -class FullRank(Approximation): - """ - Full Rank approximation to the posterior where Multivariate Gaussian family - is fitted to minimize KL divergence from True posterior. In contrast to - MeanField approach correlations between variables are taken in account. The - main drawback of the method is computational cost. - - Parameters - ---------- - local_rv : dict - mapping {model_variable -> local_variable} - Local Vars are used for Autoencoding Variational Bayes - See (AEVB; Kingma and Welling, 2014) for details - - model : PyMC3 model for inference - - cost_part_grad_scale : float or scalar tensor - Scaling score part of gradient can be useful near optimum for - archiving better convergence properties. Common schedule is - 1 at the start and 0 in the end. So slow decay will be ok. - See (Sticking the Landing; Geoffrey Roeder, - Yuhuai Wu, David Duvenaud, 2016) for details - - References - ---------- - Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 - Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI - approximateinference.org/accepted/RoederEtAl2016.pdf - """ - def __init__(self, local_rv=None, model=None, cost_part_grad_scale=1, gpu_compat=False): - super(FullRank, self).__init__( - local_rv=local_rv, model=model, - cost_part_grad_scale=cost_part_grad_scale - ) - self.gpu_compat = gpu_compat - - @property - def L(self): - return self.shared_params['L_tril'][self.tril_index_matrix] - - @property - def mu(self): - return self.shared_params['mu'] - - @property - def cov(self): - L = self.L - return L.dot(L.T) - - @property - def num_tril_entries(self): - n = self.global_size - return int(n * (n + 1) / 2) - - @property - def tril_index_matrix(self): - n = self.global_size - num_tril_entries = self.num_tril_entries - tril_index_matrix = np.zeros([n, n], dtype=int) - tril_index_matrix[np.tril_indices(n)] = np.arange(num_tril_entries) - tril_index_matrix[np.tril_indices(n)[::-1]] = np.arange(num_tril_entries) - return tril_index_matrix - - def create_shared_params(self): - n = self.global_size - L_tril = ( - np.eye(n) - [np.tril_indices(n)] - .astype(theano.config.floatX) - ) - return {'mu': theano.shared( - self.input.tag.test_value[self.global_slc]), - 'L_tril': theano.shared(L_tril) - } - - def log_q_W_global(self, z): - """ - log_q_W samples over q for global vars - Gradient wrt mu, rho in density parametrization - is set to zero to lower variance of ELBO - """ - mu = self.scale_grad(self.mu) - L = self.scale_grad(self.L) - z = z[self.global_slc] - return log_normal_mv(z, mu, chol=L, gpu_compat=self.gpu_compat) - - def random_global(self, size=None, no_rand=False): - # (samples, dim) or (dim, ) - initial = self.initial(size, no_rand, l=self.global_size).T - # (dim, dim) - L = self.L - # (dim, ) - mu = self.mu - # x = Az + m, but it assumes z is vector - # When we get z with shape (samples, dim) - # we need to transpose Az - return L.dot(initial).T + mu - - @classmethod - def from_mean_field(cls, mean_field, gpu_compat=False): - """ - Construct FullRank from MeanField approximation - - Parameters - ---------- - mean_field : MeanField - approximation to start with - - Flags - ----- - gpu_compat : bool - use GPU compatible version or not - - Returns - ------- - FullRank - """ - full_rank = object.__new__(cls) # type: FullRank - full_rank.gpu_compat = gpu_compat - full_rank.__dict__.update(mean_field.__dict__) - full_rank.shared_params = full_rank.create_shared_params() - full_rank.shared_params['mu'].set_value( - mean_field.shared_params['mu'].get_value() - ) - rho = mean_field.shared_params['rho'].get_value() - n = full_rank.global_size - L_tril = ( - np.diag(np.log1p(np.exp(rho))) # rho2sd - [np.tril_indices(n)] - .astype(theano.config.floatX) - ) - full_rank.shared_params['L_tril'].set_value(L_tril) - return full_rank class Inference(object): @@ -262,9 +41,9 @@ def __init__(self, op, approx, tf, local_rv=None, model=None, **kwargs): approx = approx( local_rv=local_rv, model=model, **kwargs) - elif isinstance(approx, Approximation): + elif isinstance(approx, Approximation): # pragma: no cover pass - else: + else: # pragma: no cover raise TypeError('approx should be Approximation instance or Approximation subclass') self.objective = op(approx)(tf) @@ -318,7 +97,7 @@ def fit(self, n=10000, score=True, callbacks=None, callback_every=1, try: for i in progress: e = step_func() - if np.isnan(e): + if np.isnan(e): # pragma: no cover scores = scores[:i] self.hist = np.concatenate([self.hist, scores]) raise FloatingPointError('NaN occurred in optimization.') @@ -329,7 +108,7 @@ def fit(self, n=10000, score=True, callbacks=None, callback_every=1, if i % callback_every == 0: for callback in callbacks: callback(self.approx, scores[:i+1], i) - except KeyboardInterrupt: + except KeyboardInterrupt: # pragma: no cover scores = scores[:i] if n < 10: logger.info('Interrupted at {:,d} [{:.0f}%]: Loss = {:,.5g}'.format( @@ -346,7 +125,7 @@ def fit(self, n=10000, score=True, callbacks=None, callback_every=1, logger.info('Finished [100%]: Average Loss = {:,.5g}'.format(avg_elbo)) finally: progress.close() - else: + else: # pragma: no cover try: for _ in progress: step_func() diff --git a/pymc3/variational/operators.py b/pymc3/variational/operators.py new file mode 100644 index 0000000000..fba69ff375 --- /dev/null +++ b/pymc3/variational/operators.py @@ -0,0 +1,17 @@ +from pymc3.variational.opvi import Operator + +__all__ = [ + 'KL' +] + + +class KL(Operator): + """ + Operator based on Kullback Leibler Divergence + .. math:: + + KL[q(v)||p(v)] = \int q(v)\log\\frac{q(v)}{p(v)}dv + """ + def apply(self, f): + z = self.input + return self.logq(z) - self.logp(z) diff --git a/pymc3/variational/opvi.py b/pymc3/variational/opvi.py index 8d694b2698..20b09cd8c8 100644 --- a/pymc3/variational/opvi.py +++ b/pymc3/variational/opvi.py @@ -46,7 +46,7 @@ def logp(self, z): def logq(self, z): return self.approx.logq(z) - def apply(self, f): + def apply(self, f): # pragma: no cover """ Operator itself .. math:: @@ -83,7 +83,7 @@ def __getstate__(self): def __setstate__(self, approx): self.__init__(approx) - def __str__(self): + def __str__(self): # pragma: no cover return '%(op)s[%(ap)s]' % dict(op=self.__class__.__name__, ap=self.approx.__class__.__name__) @@ -230,7 +230,7 @@ def step_function(self, obj_n_mc=None, tf_n_mc=None, return step_fn @memoize - def score_function(self, sc_n_mc=None, fn_kwargs=None): + def score_function(self, sc_n_mc=None, fn_kwargs=None): # pragma: no cover if fn_kwargs is None: fn_kwargs = {} return theano.function([], self(self.random(sc_n_mc)), **fn_kwargs) @@ -387,15 +387,25 @@ def get_transformed(v): known = {get_transformed(k): v for k, v in local_rv.items()} self.known = known - self.local_vars = [v for v in model.free_RVs if v in known] - self.global_vars = [v for v in model.free_RVs if v not in known] + self.local_vars = self.get_local_vars() + self.global_vars = self.get_global_vars() self.order = ArrayOrdering(self.local_vars + self.global_vars) self.flat_view = model.flatten( vars=self.local_vars + self.global_vars ) self.grad_scale_op = GradScale(cost_part_grad_scale) + self._setup() self.shared_params = self.create_shared_params() + def _setup(self): + pass + + def get_global_vars(self): + return [v for v in self.model.free_RVs if v not in self.known] + + def get_local_vars(self): + return [v for v in self.model.free_RVs if v in self.known] + def __getstate__(self): state = self.__dict__.copy() # can be inferred from the rest parts @@ -419,7 +429,7 @@ def check_model(model): Checks that model is valid for variational inference """ vars_ = [var for var in model.vars if not isinstance(var, pm.model.ObservedRV)] - if any([var.dtype in pm.discrete_types for var in vars_]): + if any([var.dtype in pm.discrete_types for var in vars_]): # pragma: no cover raise ValueError('Model should not include discrete RVs') def create_shared_params(self): @@ -461,15 +471,15 @@ def construct_replacements(self, include=None, exclude=None, """ if include is not None and exclude is not None: raise ValueError('Only one parameter is supported {include|exclude}, got two') - if include is not None: + if include is not None: # pragma: no cover replacements = {k: v for k, v in self.flat_view.replacements.items() if k in include} - elif exclude is not None: + elif exclude is not None: # pragma: no cover replacements = {k: v for k, v in self.flat_view.replacements.items() if k not in exclude} else: replacements = self.flat_view.replacements - if more_replacements is not None: + if more_replacements is not None: # pragma: no cover replacements.update(more_replacements) return replacements @@ -506,7 +516,7 @@ def apply_replacements(self, node, deterministic=False, def sample_node(self, node, size=100, more_replacements=None): - if more_replacements is not None: + if more_replacements is not None: # pragma: no cover node = theano.clone(node, more_replacements) posterior = self.random(size) node = self.to_flat_input(node) @@ -554,7 +564,7 @@ def initial(self, size, no_rand=False, l=None): """ theano_condition_is_here = isinstance(no_rand, tt.Variable) - if l is None: + if l is None: # pragma: no cover l = self.total_size if size is None: shape = (l, ) @@ -593,7 +603,7 @@ def random_local(self, size=None, no_rand=False): e = self.initial(size, no_rand, self.local_size) return e * rho2sd(rho) + mu - def random_global(self, size=None, no_rand=False): + def random_global(self, size=None, no_rand=False): # pragma: no cover """ Implements posterior distribution from initial latent space @@ -630,11 +640,11 @@ def random(self, size=None, no_rand=False): self.random_local(size, no_rand), self.random_global(size, no_rand) ], axis=ax) - elif self.local_vars: + elif self.local_vars: # pragma: no cover return self.random_local(size, no_rand) elif self.global_vars: return self.random_global(size, no_rand) - else: + else: # pragma: no cover raise ValueError('No FreeVARs in model') @property @@ -725,14 +735,14 @@ def log_q_W_local(self, z): for var in self.local_vars: scaling.append(tt.ones(var.dsize)*var.scaling) scaling = tt.concatenate(scaling) - - if z.ndim > 1: - logp *= scaling[:, None] + if z.ndim > 1: # pragma: no cover + # rare case when logq(z) is called directly + logp *= scaling[None] else: logp *= scaling return self.to_flat_input(tt.sum(logp)) - def log_q_W_global(self, z): + def log_q_W_global(self, z): # pragma: no cover """ log_q_W samples over q for global vars """ @@ -767,7 +777,7 @@ def view(self, space, name, reshape=True): view = space[:, slc] elif space.ndim < 2: view = space[slc] - else: + else: # pragma: no cover raise ValueError('Space should have no more than 2 dims, got %d' % space.ndim) if reshape: if len(_shape) > 0: From 0dbd346ee0d8be45e0071a01bc2096f08330b98f Mon Sep 17 00:00:00 2001 From: Adrian Seyboldt Date: Thu, 16 Mar 2017 21:13:53 +0100 Subject: [PATCH 37/53] Fix indexing traces with steps greater one --- pymc3/backends/hdf5.py | 15 ++++----------- pymc3/backends/ndarray.py | 28 ++++++++++++++++------------ pymc3/backends/sqlite.py | 5 +++++ pymc3/tests/backend_fixtures.py | 19 +++++++++++++++++++ pymc3/tests/test_hdf5_backend.py | 7 ++++++- 5 files changed, 50 insertions(+), 24 deletions(-) diff --git a/pymc3/backends/hdf5.py b/pymc3/backends/hdf5.py index 69a8c6b75f..f38c2c0dbf 100644 --- a/pymc3/backends/hdf5.py +++ b/pymc3/backends/hdf5.py @@ -185,19 +185,12 @@ def get_values(self, varname, burn=0, thin=1): def _slice(self, idx): with self.activate_file: - if idx.start is None: - burn = 0 - else: - burn = idx.start - if idx.step is None: - thin = 1 - else: - thin = idx.step - + start, stop, step = idx.indices(len(self)) sliced = ndarray.NDArray(model=self.model, vars=self.vars) sliced.chain = self.chain - sliced.samples = {v: self.get_values(v, burn=burn, thin=thin) + sliced.samples = {v: self.samples[v][start:stop:step] for v in self.varnames} + sliced.draw_idx = (stop - start) // step return sliced def point(self, idx): @@ -233,4 +226,4 @@ def load(name, model=None): trace = HDF5(name, model=model) trace.chain = chain straces.append(trace) - return base.MultiTrace(straces) \ No newline at end of file + return base.MultiTrace(straces) diff --git a/pymc3/backends/ndarray.py b/pymc3/backends/ndarray.py index 454172281d..0fbc4c8263 100644 --- a/pymc3/backends/ndarray.py +++ b/pymc3/backends/ndarray.py @@ -154,6 +154,8 @@ def _slice(self, idx): sliced.samples = {varname: values[idx] for varname, values in self.samples.items()} sliced.sampler_vars = self.sampler_vars + sliced.draw_idx = (idx.stop - idx.start) // idx.step + if self._stats is None: return sliced sliced._stats = [] @@ -163,7 +165,6 @@ def _slice(self, idx): for key, vals in vars.items(): var_sliced[key] = vals[idx] - sliced.draw_idx = idx.stop - idx.start return sliced def point(self, idx): @@ -176,17 +177,20 @@ def point(self, idx): def _slice_as_ndarray(strace, idx): - if idx.start is None: - burn = 0 - else: - burn = idx.start - if idx.step is None: - thin = 1 - else: - thin = idx.step - sliced = NDArray(model=strace.model, vars=strace.vars) sliced.chain = strace.chain - sliced.samples = {v: strace.get_values(v, burn=burn, thin=thin) - for v in strace.varnames} + + # Happy path where we do not need to load everything from the trace + if ((idx.step is None or idx.step >= 1) and + (idx.stop is None or idx.stop == len(strace))): + start, stop, step = idx.indices(len(strace)) + sliced.samples = {v: strace.get_values(v, burn=idx.start, thin=idx.step) + for v in strace.varnames} + sliced.draw_idx = (stop - start) // step + else: + start, stop, step = idx.indices(len(strace)) + sliced.samples = {v: strace.get_values(v)[start:stop:step] + for v in strace.varnames} + sliced.draw_idx = (stop - start) // step + return sliced diff --git a/pymc3/backends/sqlite.py b/pymc3/backends/sqlite.py index 9409fdba85..67de56205b 100644 --- a/pymc3/backends/sqlite.py +++ b/pymc3/backends/sqlite.py @@ -207,6 +207,11 @@ def get_values(self, varname, burn=0, thin=1): ------- A NumPy array """ + if burn is None: + burn = 0 + if thin is None: + thin = 1 + if burn < 0: burn = max(0, len(self) + burn) if thin < 1: diff --git a/pymc3/tests/backend_fixtures.py b/pymc3/tests/backend_fixtures.py index 2c760b3c7b..16fee02266 100644 --- a/pymc3/tests/backend_fixtures.py +++ b/pymc3/tests/backend_fixtures.py @@ -319,6 +319,25 @@ def test_get_slice(self): npt.assert_equal(result.get_values(varname, chains=[chain]), expected[chain][varname]) + def test_get_slice_step(self): + result = self.mtrace[:] + self.assertEqual(len(result), self.draws) + + result = self.mtrace[::2] + self.assertEqual(len(result), self.draws // 2) + + + def test_get_slice_neg_step(self): + if hasattr(self, 'skip_test_get_slice_neg_step'): + return + + result = self.mtrace[::-1] + self.assertEqual(len(result), self.draws) + + result = self.mtrace[::-2] + self.assertEqual(len(result), self.draws // 2) + + def test_get_neg_slice(self): expected = [] for chain in [0, 1]: diff --git a/pymc3/tests/test_hdf5_backend.py b/pymc3/tests/test_hdf5_backend.py index c934d2cbf9..ddbf58b08a 100644 --- a/pymc3/tests/test_hdf5_backend.py +++ b/pymc3/tests/test_hdf5_backend.py @@ -60,6 +60,7 @@ class TestHDF50dSelection(bf.SelectionTestCase): backend = hdf5.HDF5 name = DBNAME shape = () + skip_test_get_slice_neg_step = True class TestHDF50dSelectionStats1(bf.SelectionTestCase): @@ -67,6 +68,7 @@ class TestHDF50dSelectionStats1(bf.SelectionTestCase): name = DBNAME shape = () sampler_vars = STATS1 + skip_test_get_slice_neg_step = True class TestHDF50dSelectionStats2(bf.SelectionTestCase): @@ -74,18 +76,21 @@ class TestHDF50dSelectionStats2(bf.SelectionTestCase): name = DBNAME shape = () sampler_vars = STATS2 + skip_test_get_slice_neg_step = True class TestHDF51dSelection(bf.SelectionTestCase): backend = hdf5.HDF5 name = DBNAME shape = 2 + skip_test_get_slice_neg_step = True class TestHDF52dSelection(bf.SelectionTestCase): backend = hdf5.HDF5 name = DBNAME shape = (2, 3) + skip_test_get_slice_neg_step = True class TestHDF5DumpLoad(bf.DumpLoadTestCase): @@ -100,4 +105,4 @@ class TestNDArrayHDF5Equality(bf.BackendEqualityTestCase): name0 = None backend1 = hdf5.HDF5 name1 = DBNAME - shape = (2, 3) \ No newline at end of file + shape = (2, 3) From 3a0d654153d01c1cccba659568008ca169bc9777 Mon Sep 17 00:00:00 2001 From: Maxim Kochurov Date: Sat, 18 Mar 2017 16:08:30 +0300 Subject: [PATCH 38/53] SVGD problems (#1916) * fix some svgd problems * switch -> ifelse * except in record --- pymc3/variational/svgd.py | 40 +++++++++++++++++++++++++-------------- 1 file changed, 26 insertions(+), 14 deletions(-) diff --git a/pymc3/variational/svgd.py b/pymc3/variational/svgd.py index 8d4663bd29..bca03e4864 100644 --- a/pymc3/variational/svgd.py +++ b/pymc3/variational/svgd.py @@ -5,6 +5,7 @@ import numpy as np import theano +from theano.ifelse import ifelse import theano.tensor as tt from tqdm import tqdm from .updates import adagrad @@ -12,6 +13,7 @@ import pymc3 as pm from pymc3.model import modelcontext + def rbf_kernel(X): # TODO. rbf may not be a good choice for high dimension data XY = tt.dot(X, X.transpose()) @@ -22,11 +24,11 @@ def rbf_kernel(X): V = H.flatten() # median distance - h = tt.switch(tt.eq((V.shape[0] % 2), 0), - # if even vector - tt.mean(tt.sort(V)[ ((V.shape[0] // 2) - 1) : ((V.shape[0] // 2) + 1) ]), - # if odd vector - tt.sort(V)[V.shape[0] // 2]) + h = ifelse(tt.eq((V.shape[0] % 2), 0), + # if even vector + tt.mean(tt.sort(V)[ ((V.shape[0] // 2) - 1) : ((V.shape[0] // 2) + 1) ]), + # if odd vector + tt.sort(V)[V.shape[0] // 2]) h = tt.sqrt(0.5 * h / tt.log(X.shape[0].astype('float32') + 1.0)) @@ -35,7 +37,7 @@ def rbf_kernel(X): sumkxy = tt.sum(Kxy, axis=1).dimshuffle(0, 'x') dxkxy = tt.add(dxkxy, tt.mul(X, sumkxy)) / (h ** 2) - return (Kxy, dxkxy) + return Kxy, dxkxy def _make_vectorized_logp_grad(vars, model, X): @@ -70,7 +72,7 @@ def svgd(vars=None, n=5000, n_particles=100, jitter=.01, random_seed=None, model=None): if random_seed is not None: - seed(random_seed) + np.random.seed(random_seed) model = modelcontext(model) if vars is None: @@ -102,18 +104,28 @@ def svgd(vars=None, n=5000, n_particles=100, jitter=.01, else: progress = np.arange(n) - for ii in progress: - svgd_step(ii) + try: + for ii in progress: + svgd_step(ii) + except KeyboardInterrupt: + pass + finally: + if hasattr(progress, 'close'): + progress.close() theta_val = theta.get_value() # Build trace - strace = pm.backends.NDArray() - strace.setup(theta_val.shape[0], 1) - for p in theta_val: - strace.record(model.bijection.rmap(p)) - strace.close() + strace = pm.backends.NDArray() + try: + strace.setup(theta_val.shape[0], 1) + for p in theta_val: + strace.record(model.bijection.rmap(p)) + except KeyboardInterrupt: + pass + finally: + strace.close() trace = pm.backends.base.MultiTrace([strace]) From 31d514d80b6f97f5477803127467fa07a10f3db6 Mon Sep 17 00:00:00 2001 From: Maxim Kochurov Date: Sat, 18 Mar 2017 17:20:33 +0300 Subject: [PATCH 39/53] Histogram docs (#1914) * add docs * delete redundant code * add usage example * remove unused import --- pymc3/variational/approximations.py | 62 ++++++++++++++--------------- 1 file changed, 29 insertions(+), 33 deletions(-) diff --git a/pymc3/variational/approximations.py b/pymc3/variational/approximations.py index 8fcdc5070d..4f11c6f68c 100644 --- a/pymc3/variational/approximations.py +++ b/pymc3/variational/approximations.py @@ -5,7 +5,7 @@ from pymc3 import ArrayOrdering, DictToArrayBijection from pymc3.distributions.dist_math import rho2sd, log_normal, log_normal_mv from pymc3.variational.opvi import Approximation -from pymc3.theanof import tt_rng, memoize, change_flags +from pymc3.theanof import tt_rng __all__ = [ @@ -221,6 +221,28 @@ def from_mean_field(cls, mean_field, gpu_compat=False): class Histogram(Approximation): + """ + Builds Approximation instance from a given trace, + it has the same interface as variational approximation + + Prameters + ---------- + trace : MultiTrace + local_rv : dict + Experimental for Histogram + mapping {model_variable -> local_variable} + Local Vars are used for Autoencoding Variational Bayes + See (AEVB; Kingma and Welling, 2014) for details + + model : PyMC3 model + + Usage + ----- + >>> with model: + ... step = NUTS() + ... trace = sample(1000, step=step) + ... histogram = Histogram(trace[100:]) + """ def __init__(self, trace, local_rv=None, model=None): self.trace = trace self._histogram_logp = None @@ -275,10 +297,16 @@ def random_global(self, size=None, no_rand=False): @property def histogram(self): + """ + Shortcut to flattened Trace + """ return self.shared_params @property def histogram_logp(self): + """ + Symbolic logp for every point in trace + """ if self._histogram_logp is None: node = self.to_flat_input(self.model.logpt) @@ -297,35 +325,3 @@ def mean(self): @property def params(self): return [] - - @property - @memoize - @change_flags(compute_test_value='off') - def random_fn(self): - """ - Implements posterior distribution from initial latent space - - Parameters - ---------- - size : number of samples from distribution - no_rand : whether use deterministic distribution - - Returns - ------- - posterior space (numpy) - """ - In = theano.In - size = tt.iscalar('size') - no_rand = tt.bscalar('no_rand') - posterior = self.random(size, no_rand=no_rand) - fn = theano.function([In(size, 'size', 1, allow_downcast=True), - In(no_rand, 'no_rand', 0, allow_downcast=True)], - posterior) - - def inner(size=None, no_rand=False): - if size is None: - return fn(1, int(no_rand))[0] - else: - return fn(size, int(no_rand)) - - return inner From 4240ea57466f11b21bcbd8964eaff3ff7d7b9b78 Mon Sep 17 00:00:00 2001 From: aloctavodia Date: Sat, 18 Mar 2017 18:14:42 -0300 Subject: [PATCH 40/53] improve aesthetics --- pymc3/plots/compareplot.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/pymc3/plots/compareplot.py b/pymc3/plots/compareplot.py index 352884e855..763f5f2c6d 100644 --- a/pymc3/plots/compareplot.py +++ b/pymc3/plots/compareplot.py @@ -24,14 +24,20 @@ def compare_plot(comp_df, ax=None): if ax is None: _, ax = plt.subplots() - yticks_pos = np.linspace(0, -1, (comp_df.shape[0] * 2) - 1) - yticks_labels = np.repeat(comp_df.index, 2)[1:] + yticks_pos, step = np.linspace(0, -1, (comp_df.shape[0] * 2) - 1, retstep=True) + yticks_pos[1::2] = yticks_pos[1::2] + step / 2 + + yticks_labels = [''] * len(yticks_pos) + yticks_labels[0] = comp_df.index[0] + yticks_labels[1::2] = comp_df.index[1:] data = comp_df.values min_ic = data[0, 0] - ax.errorbar(x=data[:, 0], y=yticks_pos[::2], xerr=data[:, 4], fmt='ko', mfc='None', mew=1) - ax.errorbar(x=data[1:, 0], y=yticks_pos[1::2], xerr=data[1:, 5], fmt='^', color='grey') + ax.errorbar(x=data[:, 0], y=yticks_pos[::2], xerr=data[:, 4], + fmt='ko', mfc='None', mew=1) + ax.errorbar(x=data[1:, 0], y=yticks_pos[1::2], + xerr=data[1:, 5], fmt='^', color='grey') ax.plot(data[:, 0] - (2 * data[:, 1]), yticks_pos[::2], 'ko') ax.axvline(min_ic, ls='--', color='grey') From f6190fc13ee99eb9872fe4bf25a1ea7e692f766b Mon Sep 17 00:00:00 2001 From: Colin Date: Mon, 20 Mar 2017 04:02:41 -0400 Subject: [PATCH 41/53] Bump theano to 0.9.0rc4 (#1921) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index b9bc902eb3..94cc0223f1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ numpy>=1.11.0 scipy>=0.12.0 matplotlib>=1.5.0 -theano>=0.8.2 +theano==0.9.0rc4 pandas>=0.18.0 patsy>=0.4.0 joblib>=0.9 From 96ca0ac848e37215370c86c15e79d5b004ca7d79 Mon Sep 17 00:00:00 2001 From: Maxim Kochurov Date: Tue, 21 Mar 2017 17:21:12 +0300 Subject: [PATCH 42/53] Histogram: use only free RVs from trace (#1926) * use only free RVs from trace * use memoize in Histogram.histogram_logp * Change tests for histogram --- pymc3/tests/test_variational_inference.py | 17 +++++++----- pymc3/variational/approximations.py | 34 ++++++++++------------- pymc3/variational/opvi.py | 3 +- 3 files changed, 26 insertions(+), 28 deletions(-) diff --git a/pymc3/tests/test_variational_inference.py b/pymc3/tests/test_variational_inference.py index fe11d1b041..b7e2d8e74a 100644 --- a/pymc3/tests/test_variational_inference.py +++ b/pymc3/tests/test_variational_inference.py @@ -276,7 +276,7 @@ def test_aevb_histogram(self): with model: inference = ADVI(local_rv={x: (mu, rho)}) approx = inference.approx - trace0 = approx.sample_vp(1000) + trace0 = approx.sample_vp(10000) histogram = Histogram(trace0, local_rv={x: (mu, rho)}) trace1 = histogram.sample_vp(10000) histogram.random(no_rand=True) @@ -286,17 +286,20 @@ def test_aevb_histogram(self): np.testing.assert_allclose(trace0['x'].mean(0), trace1['x'].mean(0), atol=0.02) np.testing.assert_allclose(trace0['x'].var(0), trace1['x'].var(0), atol=0.02) - def test_random(self): - with models.multidimensional_model()[1]: - full_rank = FullRankADVI() - approx = full_rank.approx - trace0 = approx.sample_vp(10000) - histogram = Histogram(trace0) + def test_random_with_transformed(self): + p = .2 + trials = (np.random.uniform(size=10) < p).astype('int8') + with pm.Model(): + p = pm.Uniform('p') + pm.Bernoulli('trials', p, observed=trials) + trace = pm.sample(1000, step=pm.Metropolis()) + histogram = Histogram(trace) histogram.randidx(None).eval() histogram.randidx(1).eval() histogram.random_fn(no_rand=True) histogram.random_fn(no_rand=False) histogram.histogram_logp.eval() + if __name__ == '__main__': unittest.main() diff --git a/pymc3/variational/approximations.py b/pymc3/variational/approximations.py index 4f11c6f68c..61e219091f 100644 --- a/pymc3/variational/approximations.py +++ b/pymc3/variational/approximations.py @@ -5,7 +5,7 @@ from pymc3 import ArrayOrdering, DictToArrayBijection from pymc3.distributions.dist_math import rho2sd, log_normal, log_normal_mv from pymc3.variational.opvi import Approximation -from pymc3.theanof import tt_rng +from pymc3.theanof import tt_rng, memoize __all__ = [ @@ -245,16 +245,12 @@ class Histogram(Approximation): """ def __init__(self, trace, local_rv=None, model=None): self.trace = trace - self._histogram_logp = None super(Histogram, self).__init__(local_rv=local_rv, model=model) - @staticmethod - def check_model(model): - return True - - def get_global_vars(self): - return [var for var in self.model.unobserved_RVs - if var.name in self.trace.varnames] + def check_model(self, model): + if not all([var.name in self.trace.varnames + for var in model.free_RVs]): + raise ValueError('trace has not all FreeRV') def _setup(self): self._histogram_order = ArrayOrdering(self.global_vars) @@ -303,20 +299,20 @@ def histogram(self): return self.shared_params @property + @memoize def histogram_logp(self): """ Symbolic logp for every point in trace """ - if self._histogram_logp is None: - node = self.to_flat_input(self.model.logpt) - - def mapping(z): - return theano.clone(node, {self.input: z}) - x = self.histogram - self._histogram_logp, _ = theano.scan( - mapping, x, n_steps=x.shape[0] - ) - return self._histogram_logp + node = self.to_flat_input(self.model.logpt) + + def mapping(z): + return theano.clone(node, {self.input: z}) + x = self.histogram + _histogram_logp, _ = theano.scan( + mapping, x, n_steps=x.shape[0] + ) + return _histogram_logp @property def mean(self): diff --git a/pymc3/variational/opvi.py b/pymc3/variational/opvi.py index 20b09cd8c8..ca4bec26f4 100644 --- a/pymc3/variational/opvi.py +++ b/pymc3/variational/opvi.py @@ -423,8 +423,7 @@ def __setstate__(self, state): _view = property(lambda self: self.flat_view.view) input = property(lambda self: self.flat_view.input) - @staticmethod - def check_model(model): + def check_model(self, model): """ Checks that model is valid for variational inference """ From 33406e88305a45b9920610ca8290df6a84211c3b Mon Sep 17 00:00:00 2001 From: aloctavodia Date: Tue, 21 Mar 2017 20:09:14 -0300 Subject: [PATCH 43/53] small fix to prevent a TypeError with the ufunc true_divide --- pymc3/plots/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pymc3/plots/utils.py b/pymc3/plots/utils.py index b4ff6f5d4c..156948175d 100644 --- a/pymc3/plots/utils.py +++ b/pymc3/plots/utils.py @@ -99,6 +99,6 @@ def fast_kde(x): norm_factor = n * dx * (2 * np.pi * std_x ** 2 * scotts_factor ** 2) ** 0.5 - grid /= norm_factor + grid = grid / norm_factor return grid, xmin, xmax From 6f984b3807a4b810b3ebe53d8136e2850d6afd24 Mon Sep 17 00:00:00 2001 From: colin Date: Tue, 21 Mar 2017 17:29:41 -0400 Subject: [PATCH 44/53] Bump theano to be at least 0.9.0 --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 94cc0223f1..04e58121f5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ numpy>=1.11.0 scipy>=0.12.0 matplotlib>=1.5.0 -theano==0.9.0rc4 +theano>=0.9.0 pandas>=0.18.0 patsy>=0.4.0 joblib>=0.9 From 4d531bed6767f0a463b9dd61ef425691982e51ca Mon Sep 17 00:00:00 2001 From: Adrian Seyboldt Date: Tue, 14 Mar 2017 17:36:44 +0100 Subject: [PATCH 45/53] Add LKJCholeskyCov --- pymc3/distributions/multivariate.py | 41 ++++++++++++++++++++++++++++- pymc3/distributions/transforms.py | 16 +++++++++++ 2 files changed, 56 insertions(+), 1 deletion(-) diff --git a/pymc3/distributions/multivariate.py b/pymc3/distributions/multivariate.py index 03c1350c10..4fb12853f4 100755 --- a/pymc3/distributions/multivariate.py +++ b/pymc3/distributions/multivariate.py @@ -22,7 +22,8 @@ from .dist_math import bound, logpow, factln __all__ = ['MvNormal', 'MvStudentT', 'Dirichlet', - 'Multinomial', 'Wishart', 'WishartBartlett', 'LKJCorr'] + 'Multinomial', 'Wishart', 'WishartBartlett', + 'LKJCorr', 'LKJCholeskyCov'] def get_tau_cov(mu, tau=None, cov=None): @@ -543,6 +544,44 @@ def WishartBartlett(name, S, nu, is_cholesky=False, return_cholesky=False, testv return Deterministic(name, tt.dot(tt.dot(tt.dot(L, A), A.T), L.T)) +class LKJCholeskyCov(Continuous): + def __init__(self, eta, sd_dist, *args, **kwargs): + self.n = sd_dist.shape[0] + + if 'transform' in kwargs: + raise ValueError('Invalid parameter transform') + + shape = self.n * (self.n + 1) // 2 + transform = transforms.CholeskyCovPacked(self.n) + super(LKJCholeskyCov, self).__init__( + *args, **kwargs, transform=transform, shape=(shape,)) + self.eta = eta + assert sd_dist.shape.ndim == 1 + self.sd_dist = sd_dist + self.diag_idxs = transform.diag_idxs + + self.mode = np.zeros(shape) + self.mode[self.diag_idxs] = 1 + + def logp(self, x): + diag_idxs = self.diag_idxs + cumsum = tt.cumsum(x ** 2) + rowlengths = tt.zeros(self.n) + rowlengths = tt.set_subtensor(rowlengths[0], x[0] ** 2) + rowlengths = tt.set_subtensor( + rowlengths[1:], + cumsum[diag_idxs[1:]] - cumsum[diag_idxs[:-1]]) + sd_vals = tt.sqrt(rowlengths) + logp_sd = self.sd_dist.logp(sd_vals) + + corr_diag = x[diag_idxs] / rowlengths + corr_logdet = np.log(corr_diag).sum() + + det_invjac = np.log(0.5 * rowlengths).sum() + + return (self.n - 1) * corr_logdet + logp_sd + det_invjac + + class LKJCorr(Continuous): R""" The LKJ (Lewandowski, Kurowicka and Joe) log-likelihood. diff --git a/pymc3/distributions/transforms.py b/pymc3/distributions/transforms.py index 6ed4ae8e11..bb4198d8ed 100644 --- a/pymc3/distributions/transforms.py +++ b/pymc3/distributions/transforms.py @@ -266,3 +266,19 @@ def jacobian_det(self, x): return 0 circular = Circular() + + +class CholeskyCovPacked(Transform): + name = "cholesky_cov_packed" + + def __init__(self, n): + self.diag_idxs = np.arange(1, n + 1).cumsum() - 1 + + def backward(self, x): + return tt.advanced_set_subtensor1(x, tt.exp(x[self.diag_idxs]), self.diag_idxs) + + def forward(self, y): + return tt.advanced_set_subtensor1(y, tt.log(y[self.diag_idxs]), self.diag_idxs) + + def jacobian_det(self, y): + return tt.sum(y[self.diag_idxs]) From 6a718fa216afb69789d301978333362bf02d31fa Mon Sep 17 00:00:00 2001 From: Adrian Seyboldt Date: Wed, 15 Mar 2017 19:38:02 +0100 Subject: [PATCH 46/53] Fix log jacobian in LKJCholeskyCov --- pymc3/distributions/multivariate.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/pymc3/distributions/multivariate.py b/pymc3/distributions/multivariate.py index 4fb12853f4..0daabaf136 100755 --- a/pymc3/distributions/multivariate.py +++ b/pymc3/distributions/multivariate.py @@ -550,11 +550,15 @@ def __init__(self, eta, sd_dist, *args, **kwargs): if 'transform' in kwargs: raise ValueError('Invalid parameter transform') + if 'shape' in kwargs: + raise ValueError('Invalid shape parameter. Shape is set by sd_dist.') shape = self.n * (self.n + 1) // 2 transform = transforms.CholeskyCovPacked(self.n) - super(LKJCholeskyCov, self).__init__( - *args, **kwargs, transform=transform, shape=(shape,)) + + kwargs['shape'] = shape + kwargs['transform'] = transform + super(LKJCholeskyCov, self).__init__(*args, **kwargs) self.eta = eta assert sd_dist.shape.ndim == 1 self.sd_dist = sd_dist @@ -572,12 +576,14 @@ def logp(self, x): rowlengths[1:], cumsum[diag_idxs[1:]] - cumsum[diag_idxs[:-1]]) sd_vals = tt.sqrt(rowlengths) - logp_sd = self.sd_dist.logp(sd_vals) + logp_sd = self.sd_dist.logp(sd_vals).sum() - corr_diag = x[diag_idxs] / rowlengths + corr_diag = x[diag_idxs] / sd_vals corr_logdet = np.log(corr_diag).sum() - det_invjac = np.log(0.5 * rowlengths).sum() + count = np.arange(self.n - 1) + det_invjac = - (count * tt.log(sd_vals[1:])).sum() + det_invjac += - tt.log(x[diag_idxs]).sum() + tt.log(x[0]) return (self.n - 1) * corr_logdet + logp_sd + det_invjac From 34b716f7e535861ab919326bafcd6544f64d3abe Mon Sep 17 00:00:00 2001 From: Adrian Seyboldt Date: Fri, 17 Mar 2017 19:33:35 +0100 Subject: [PATCH 47/53] Add documentation for LKJCholeskyCov --- pymc3/distributions/multivariate.py | 124 +++++++++++++++++++++++++++- 1 file changed, 120 insertions(+), 4 deletions(-) diff --git a/pymc3/distributions/multivariate.py b/pymc3/distributions/multivariate.py index 0daabaf136..ffed69ff21 100755 --- a/pymc3/distributions/multivariate.py +++ b/pymc3/distributions/multivariate.py @@ -544,14 +544,128 @@ def WishartBartlett(name, S, nu, is_cholesky=False, return_cholesky=False, testv return Deterministic(name, tt.dot(tt.dot(tt.dot(L, A), A.T), L.T)) +def expand_packed_triangular(packed, lower=False, diagonal_only=False): + # TODO + pass + + class LKJCholeskyCov(Continuous): - def __init__(self, eta, sd_dist, *args, **kwargs): - self.n = sd_dist.shape[0] + """Covariance matrix with LKJ distributed correlations. + + This defines a distribution over cholesky decomposed covariance + matrices, such that the underlying correlation matrices follow an + LKJ distribution [1] and the standard deviations follow an arbitray + distribution specified by the user. + + Parameters + ---------- + n : int + The number of rows of the covariance matrix. + eta : float + The shape parameter of the LKJ distribution. A value of one + implies a uniform distribution of the correlation matrices; + larger values put more weight on matrices with few correlations. + sd_dist : pm.Distribution + A distribution for the standard deviations. + + Notes + ----- + Since the cholesky factor is a lower triangular matrix, we use + packed storge for the matrix: We store and return the values of + the lower triangular matrix in a one-dimensional array, numbered + by row. + + [[0 - - -] + [1 2 - -] + [3 4 5 -] + [6 7 8 9]] + + You can use `pm.expand_packed_triangular(packed_cov, lower=True)` + to convert this to a regular two-dimensional array. + + Examples + -------- + + with pm.Model() as model: + # Note that we access the distribution for the standard + # deviations, and do not create a new random variable. + sd_dist = pm.HalfCauchy.dist(beta=2.5) + packed_chol = pm.LKJCholeskyCov('chol_cov', 10, 2, sd_dist) + + # Define a new MvNormal with the given covariance + vals = pm.MvNormal('vals', mu=np.zeros(10), packed_chol=packed_col) + + # Or transform a uncorrelated normal: + vals_raw = pm.Normal('vals_raw', mu=np.zeros(10), sd=1) + chol = pm.expand_packed_triangular(packed_chol, lower=True) + vals = tt.dot(chol, vals_raw) + + # Or compute the covariance matrix + chol = pm.expand_packed_triangular(packed_chol, lower=True) + cov = pm.dot(chol, chol.T) + + # Extract the standard deviations + stds = pm.extracet_packed_triangular( + packed_chol, lower=True, diagonal_only=True) + + Implementation + -------------- + In the unconstrained space all values of the cholesky factor + are stored untransformed, except for the diagonal entries, where + we use a log-transform to restrict them to positive values. + + To correctly compute log-likelihoods for the standard deviations + and the correlation matrix seperatly, we need to consider a + second transformation: Given a cholesky factorization + :math:`LL^T = \Sigma` of a covariance matrix we can recover the + standard deviations :math:`\sigma` as the euclidean lengths of + the rows of :math:`L`, and the cholesky factor of the + correlation matrix as :math:`U = \text{diag}(\sigma)^{-1}L`. + Since each row of :math:`U` has length 1, we do not need to + store the diagonal. We define a transformation :math:`\phi` + such that `\phi(L)` is the lower triangular matrix containing + the standard deviations :math:`\sigma` on the diagonal and the + correlation matrix :math:`U` below. In this form we can easily + compute the different likelihoods seperatly, as the likelihood + of the correlation matrix only depends on the values below the + diagonal, and the likelihood of the standard deviation depends + only on the diagonal values. + + We still need the determinant of the jacobian of :math:`\phi^{-1}`. + If we think of :math:`\phi` as an automorphism on + :math:`\mathbb{R}^{\tfrac{n(n+1)}{2}}`, where we order + the dimensions as described in the notes above, the jacobian + is a block-diagonal matrix, where each block corresponds to + one row of :math:`U`. Each block has arrowhead shape, and we + can compute the determinant of that as described in [2]. Since + the determinant of a block-diagonal matrix is the product + of the determinants of the blocks, we get + + .. math:: + + \text{det}(J_{\phi^{-1}}(U)) = + \left[ + \prod^{i=2}^N u_{ii}^(i - 1) L_{ii} + \right]^{-1} + + References + ---------- + [1] Lewandowski, D., Kurowicka, D. and Joe, H. (2009). + "Generating random correlation matrices based on vines and + extended onion method." Journal of multivariate analysis, + 100(9), pp.1989-2001. + [2] J. M. isn't a mathematician (http://math.stackexchange.com/users/498/ + j-m-isnt-a-mathematician), Different approaches to evaluate this + determinant, URL (version: 2012-04-14): + http://math.stackexchange.com/q/130026 + """ + def __init__(self, n, eta, sd_dist, *args, **kwargs): + self.n = n if 'transform' in kwargs: - raise ValueError('Invalid parameter transform') + raise ValueError('Invalid parameter: transform.') if 'shape' in kwargs: - raise ValueError('Invalid shape parameter. Shape is set by sd_dist.') + raise ValueError('Invalid shape parameter: shape.') shape = self.n * (self.n + 1) // 2 transform = transforms.CholeskyCovPacked(self.n) @@ -581,6 +695,8 @@ def logp(self, x): corr_diag = x[diag_idxs] / sd_vals corr_logdet = np.log(corr_diag).sum() + # Compute the log det jacobian of the second transformation + # described in the docstring. count = np.arange(self.n - 1) det_invjac = - (count * tt.log(sd_vals[1:])).sum() det_invjac += - tt.log(x[diag_idxs]).sum() + tt.log(x[0]) From d8566f926a8d0175783b5d117c658f0ca1988c5d Mon Sep 17 00:00:00 2001 From: Adrian Seyboldt Date: Sat, 18 Mar 2017 17:32:47 +0100 Subject: [PATCH 48/53] Add expand_packed_triangular --- pymc3/distributions/multivariate.py | 52 ++++++++++++++--------------- pymc3/math.py | 50 +++++++++++++++++++++++++-- 2 files changed, 72 insertions(+), 30 deletions(-) diff --git a/pymc3/distributions/multivariate.py b/pymc3/distributions/multivariate.py index ffed69ff21..eb40012c02 100755 --- a/pymc3/distributions/multivariate.py +++ b/pymc3/distributions/multivariate.py @@ -544,9 +544,23 @@ def WishartBartlett(name, S, nu, is_cholesky=False, return_cholesky=False, testv return Deterministic(name, tt.dot(tt.dot(tt.dot(L, A), A.T), L.T)) -def expand_packed_triangular(packed, lower=False, diagonal_only=False): - # TODO - pass +def _lkj_normalizing_constant(n, p): + if n == 1: + result = gammaln(2. * tt.arange(1, int((p - 1) / 2) + 1)).sum() + if p % 2 == 1: + result += (0.25 * (p ** 2 - 1) * tt.log(np.pi) + - 0.25 * (p - 1) ** 2 * tt.log(2.) + - (p - 1) * gammaln(int((p + 1) / 2))) + else: + result += (0.25 * p * (p - 2) * tt.log(np.pi) + + 0.25 * (3 * p ** 2 - 4 * p) * tt.log(2.) + + p * gammaln(p / 2) - (p - 1) * gammaln(p)) + else: + result = -(p - 1) * gammaln(n + 0.5 * (p - 1)) + k = tt.arange(1, p) + result += (0.5 * k * tt.log(np.pi) + + gammaln(n + 0.5 * (p - 1 - k))).sum() + return result class LKJCholeskyCov(Continuous): @@ -595,18 +609,18 @@ class LKJCholeskyCov(Continuous): # Define a new MvNormal with the given covariance vals = pm.MvNormal('vals', mu=np.zeros(10), packed_chol=packed_col) - # Or transform a uncorrelated normal: + # Or transform an uncorrelated normal: vals_raw = pm.Normal('vals_raw', mu=np.zeros(10), sd=1) - chol = pm.expand_packed_triangular(packed_chol, lower=True) + chol = pm.expand_packed_triangular(10, packed_chol, lower=True) vals = tt.dot(chol, vals_raw) # Or compute the covariance matrix - chol = pm.expand_packed_triangular(packed_chol, lower=True) + chol = pm.expand_packed_triangular(10, packed_chol, lower=True) cov = pm.dot(chol, chol.T) # Extract the standard deviations stds = pm.extracet_packed_triangular( - packed_chol, lower=True, diagonal_only=True) + 10, packed_chol, lower=True, diagonal_only=True) Implementation -------------- @@ -623,7 +637,7 @@ class LKJCholeskyCov(Continuous): correlation matrix as :math:`U = \text{diag}(\sigma)^{-1}L`. Since each row of :math:`U` has length 1, we do not need to store the diagonal. We define a transformation :math:`\phi` - such that `\phi(L)` is the lower triangular matrix containing + such that :math:`\phi(L)` is the lower triangular matrix containing the standard deviations :math:`\sigma` on the diagonal and the correlation matrix :math:`U` below. In this form we can easily compute the different likelihoods seperatly, as the likelihood @@ -701,7 +715,9 @@ def logp(self, x): det_invjac = - (count * tt.log(sd_vals[1:])).sum() det_invjac += - tt.log(x[diag_idxs]).sum() + tt.log(x[0]) - return (self.n - 1) * corr_logdet + logp_sd + det_invjac + norm = _lkj_normalizing_constant(eta, self.n) + + return norm + (self.n - 1) * corr_logdet + logp_sd + det_invjac class LKJCorr(Continuous): @@ -760,24 +776,6 @@ def __init__(self, n, p, transform='interval', *args, **kwargs): self.tri_index[np.triu_indices(p, k=1)] = np.arange(n_elem) self.tri_index[np.triu_indices(p, k=1)[::-1]] = np.arange(n_elem) - def _normalizing_constant(self, n, p): - if n == 1: - result = gammaln(2. * tt.arange(1, int((p - 1) / 2) + 1)).sum() - if p % 2 == 1: - result += (0.25 * (p ** 2 - 1) * tt.log(np.pi) - - 0.25 * (p - 1) ** 2 * tt.log(2.) - - (p - 1) * gammaln(int((p + 1) / 2))) - else: - result += (0.25 * p * (p - 2) * tt.log(np.pi) - + 0.25 * (3 * p ** 2 - 4 * p) * tt.log(2.) - + p * gammaln(p / 2) - (p - 1) * gammaln(p)) - else: - result = -(p - 1) * gammaln(n + 0.5 * (p - 1)) - k = tt.arange(1, p) - result += (0.5 * k * tt.log(np.pi) - + gammaln(n + 0.5 * (p - 1 - k))).sum() - return result - def logp(self, x): n = self.n p = self.p diff --git a/pymc3/math.py b/pymc3/math.py index c4a307ac1b..3e2aea992c 100644 --- a/pymc3/math.py +++ b/pymc3/math.py @@ -6,7 +6,7 @@ from theano.tensor import ( constant, flatten, zeros_like, ones_like, stack, concatenate, sum, prod, lt, gt, le, ge, eq, neq, switch, clip, where, and_, or_, abs_, exp, log, - cos, sin, tan, cosh, sinh, tanh, sqr, sqrt, erf, erfc, erfinv, erfcinv, dot, + cos, sin, tan, cosh, sinh, tanh, sqr, sqrt, erf, erfc, erfinv, erfcinv, dot, maximum, minimum, sgn, ceil, floor) from theano.tensor.nlinalg import det, matrix_inverse, extract_diag, matrix_dot, trace from theano.tensor.nnet import sigmoid @@ -14,7 +14,7 @@ import numpy as np # pylint: enable=unused-import -def tround(*args, **kwargs): +def tround(*args, **kwargs): """ Temporary function to silence round warning in Theano. Please remove when the warning disappears. @@ -77,6 +77,50 @@ def __str__(self): def probit(p): return -sqrt(2) * erfcinv(2 * p) - + def invprobit(x): return 0.5 * erfc(-x / sqrt(2)) + + +def expand_packed_triangular(n, packed, lower=False, diagonal_only=False): + """Convert a packed triangular matrix into a two dimensional array. + + Triangular matrices can be stored with better space efficiancy by + storing the non-zero values in a one-dimensional array. We number + the elements by row like this (for lower or upper triangular matrices): + + [[0 - - -] [[0 1 2 3] + [1 2 - -] [- 4 5 6] + [3 4 5 -] [- - 7 8] + [6 7 8 9]] [- - - 9] + + Parameters + ---------- + n : int + The number of rows of the triangular matrix. + packed : ndarray or theano.vector + The matrix in packed format. + lower : bool + If true, assume that the matrix is lower triangular. + diagonal_only : bool + If true, return only the diagonal of the matrix. + """ + if packed.ndim != 1: + raise ValueError('Packed triagular is not one dimensional.') + + if diagonal_only and lower: + diag_idxs = np.arange(1, n + 1).cumsum() - 1 + return packed[diag_idxs] + elif diagonal_only and not lower: + diag_idxs = np.arange(n)[::-1].cumsum() - n + return packed[diag_idxs] + elif lower: + out = tt.zeros((n, n), dtype=theano.config.floatX) + idxs = np.tril_indices(n) + return tt.advanced_set_subtensor(out, packed, idxs) + elif not lower: + out = tt.zeros((n, n), dtype=theano.config.floatX) + idxs = np.triu_indices(n) + return tt.advanced_set_subtensor(out, packed, idxs) + else: + assert False From 62835aebb40de636dd50b0952393459db05ecd16 Mon Sep 17 00:00:00 2001 From: Adrian Seyboldt Date: Mon, 20 Mar 2017 16:51:43 +0100 Subject: [PATCH 49/53] Add tests for LKJCholeskyCov --- pymc3/__init__.py | 2 +- pymc3/distributions/__init__.py | 2 ++ pymc3/distributions/multivariate.py | 39 +++++++++++++++++------------ pymc3/math.py | 6 ++--- pymc3/tests/sampler_fixtures.py | 37 ++++++++++++++++++++++++++- pymc3/tests/test_posteriors.py | 8 ++++++ 6 files changed, 72 insertions(+), 22 deletions(-) diff --git a/pymc3/__init__.py b/pymc3/__init__.py index 1d49a4d7c9..49b71570d7 100644 --- a/pymc3/__init__.py +++ b/pymc3/__init__.py @@ -6,7 +6,7 @@ from .external import * from .glm import * from . import gp -from .math import logsumexp, logit, invlogit +from .math import logsumexp, logit, invlogit, expand_packed_triangular from .model import * from .stats import * from .sampling import * diff --git a/pymc3/distributions/__init__.py b/pymc3/distributions/__init__.py index 5a6236e6cb..0cb62e80b4 100644 --- a/pymc3/distributions/__init__.py +++ b/pymc3/distributions/__init__.py @@ -58,6 +58,7 @@ from .multivariate import Multinomial from .multivariate import Wishart from .multivariate import WishartBartlett +from .multivariate import LKJCholeskyCov from .multivariate import LKJCorr from .timeseries import AR1 @@ -118,6 +119,7 @@ 'Multinomial', 'Wishart', 'WishartBartlett', + 'LKJCholeskyCov', 'LKJCorr', 'AR1', 'GaussianRandomWalk', diff --git a/pymc3/distributions/multivariate.py b/pymc3/distributions/multivariate.py index eb40012c02..590354629b 100755 --- a/pymc3/distributions/multivariate.py +++ b/pymc3/distributions/multivariate.py @@ -619,8 +619,7 @@ class LKJCholeskyCov(Continuous): cov = pm.dot(chol, chol.T) # Extract the standard deviations - stds = pm.extracet_packed_triangular( - 10, packed_chol, lower=True, diagonal_only=True) + stds = tt.sqrt(tt.diag(cov)) Implementation -------------- @@ -679,16 +678,19 @@ def __init__(self, n, eta, sd_dist, *args, **kwargs): if 'transform' in kwargs: raise ValueError('Invalid parameter: transform.') if 'shape' in kwargs: - raise ValueError('Invalid shape parameter: shape.') + raise ValueError('Invalid parameter: shape.') shape = self.n * (self.n + 1) // 2 + + if sd_dist.shape.ndim not in [0, 1]: + raise ValueError('Invalid shape for sd_dist.') + transform = transforms.CholeskyCovPacked(self.n) kwargs['shape'] = shape kwargs['transform'] = transform super(LKJCholeskyCov, self).__init__(*args, **kwargs) self.eta = eta - assert sd_dist.shape.ndim == 1 self.sd_dist = sd_dist self.diag_idxs = transform.diag_idxs @@ -696,28 +698,33 @@ def __init__(self, n, eta, sd_dist, *args, **kwargs): self.mode[self.diag_idxs] = 1 def logp(self, x): + n = self.n + eta = self.eta + diag_idxs = self.diag_idxs cumsum = tt.cumsum(x ** 2) - rowlengths = tt.zeros(self.n) - rowlengths = tt.set_subtensor(rowlengths[0], x[0] ** 2) - rowlengths = tt.set_subtensor( - rowlengths[1:], + variance = tt.zeros(n) + variance = tt.inc_subtensor(variance[0], x[0] ** 2) + variance = tt.inc_subtensor( + variance[1:], cumsum[diag_idxs[1:]] - cumsum[diag_idxs[:-1]]) - sd_vals = tt.sqrt(rowlengths) - logp_sd = self.sd_dist.logp(sd_vals).sum() + sd_vals = tt.sqrt(variance) + logp_sd = self.sd_dist.logp(sd_vals).sum() corr_diag = x[diag_idxs] / sd_vals - corr_logdet = np.log(corr_diag).sum() + + logp_lkj = (2 * eta - 3 + n - tt.arange(n)) * np.log(corr_diag) + logp_lkj = tt.sum(logp_lkj) # Compute the log det jacobian of the second transformation # described in the docstring. - count = np.arange(self.n - 1) - det_invjac = - (count * tt.log(sd_vals[1:])).sum() - det_invjac += - tt.log(x[diag_idxs]).sum() + tt.log(x[0]) + idx = tt.arange(n) + det_invjac = tt.log(corr_diag) - idx * tt.log(sd_vals) + det_invjac = det_invjac.sum() norm = _lkj_normalizing_constant(eta, self.n) - return norm + (self.n - 1) * corr_logdet + logp_sd + det_invjac + return norm + logp_lkj + logp_sd + det_invjac class LKJCorr(Continuous): @@ -783,7 +790,7 @@ def logp(self, x): X = x[self.tri_index] X = tt.fill_diagonal(X, 1) - result = self._normalizing_constant(n, p) + result = _lkj_normalizing_constant(n, p) result += (n - 1.) * tt.log(det(X)) return bound(result, tt.all(X <= 1), tt.all(X >= -1), diff --git a/pymc3/math.py b/pymc3/math.py index 3e2aea992c..7cdea058ae 100644 --- a/pymc3/math.py +++ b/pymc3/math.py @@ -117,10 +117,8 @@ def expand_packed_triangular(n, packed, lower=False, diagonal_only=False): elif lower: out = tt.zeros((n, n), dtype=theano.config.floatX) idxs = np.tril_indices(n) - return tt.advanced_set_subtensor(out, packed, idxs) + return tt.set_subtensor(out[idxs], packed) elif not lower: out = tt.zeros((n, n), dtype=theano.config.floatX) idxs = np.triu_indices(n) - return tt.advanced_set_subtensor(out, packed, idxs) - else: - assert False + return tt.set_subtensor(out[idxs], packed) diff --git a/pymc3/tests/sampler_fixtures.py b/pymc3/tests/sampler_fixtures.py index 693c607df4..2efe65b9ed 100644 --- a/pymc3/tests/sampler_fixtures.py +++ b/pymc3/tests/sampler_fixtures.py @@ -4,7 +4,7 @@ import numpy as np import numpy.testing as npt from scipy import stats - +import theano.tensor as tt from .helpers import SeededTest @@ -29,6 +29,7 @@ class KnownCDF(unittest.TestCase): def test_kstest(self): for varname, cdf in self.cdfs.items(): + print('checking', varname) samples = self.samples[varname] if samples.ndim == 1: t, p = stats.kstest(samples[::self.ks_thin], cdf=cdf) @@ -94,6 +95,35 @@ def make_model(cls): return model +class LKJCholeskyCovFixture(KnownCDF): + cdfs = { + 'log_stds': [stats.norm(loc=x, scale=x / 10).cdf + for x in [1, 2, 3, 4, 5]], + # The entries of the correlation matrix should follow + # beta(eta - 1 + d/2, eta - 1 + d/2) on (-1, 1). + # See https://arxiv.org/abs/1309.7268 + 'corr_entries_unit': [ + stats.beta(3 - 1 + 2.5, 3 - 1 + 2.5).cdf + for _ in range(10) + ], + } + + @classmethod + def make_model(cls): + with pm.Model() as model: + sd_mu = np.array([1, 2, 3, 4, 5]) + sd_dist = pm.Lognormal.dist(mu=sd_mu, sd=sd_mu / 10, shape=5) + chol_packed = pm.LKJCholeskyCov('chol_packed', 5, 3, sd_dist) + chol = pm.expand_packed_triangular(5, chol_packed, lower=True) + cov = tt.dot(chol, chol.T) + stds = tt.sqrt(tt.diag(cov)) + pm.Deterministic('log_stds', tt.log(stds)) + corr = cov / stds[None, :] / stds[:, None] + corr_entries_unit = (corr[np.tril_indices(5, -1)] + 1) / 2 + pm.Deterministic('corr_entries_unit', corr_entries_unit) + return model + + class BaseSampler(SeededTest): @classmethod def setUpClass(cls): @@ -125,6 +155,11 @@ def make_step(cls): args = {} if hasattr(cls, 'step_args'): args.update(cls.step_args) + if 'scaling' not in args: + mu, stds, elbo = pm.advi(n=50000) + scaling = cls.model.dict_to_array(stds) ** 2 + args['scaling'] = scaling + args['is_cov'] = True return pm.NUTS(**args) def test_target_accept(self): diff --git a/pymc3/tests/test_posteriors.py b/pymc3/tests/test_posteriors.py index 402c1e470a..e3ff3d9402 100644 --- a/pymc3/tests/test_posteriors.py +++ b/pymc3/tests/test_posteriors.py @@ -90,3 +90,11 @@ class NUTSNormalLong(sf.NutsFixture, sf.NormalFixture): min_n_eff = 300000 rtol = 0.01 atol = 0.001 + + +class NUTSLKJCholeskyCov(sf.NutsFixture, sf.LKJCholeskyCovFixture): + n_samples = 2000 + tune = 1000 + burn = 1000 + chains = 2 + min_n_eff = 200 From 69aab7486cd37df2547f95a5df54bafc41cebd0a Mon Sep 17 00:00:00 2001 From: Adrian Seyboldt Date: Wed, 22 Mar 2017 10:56:05 +0100 Subject: [PATCH 50/53] Fix tests for py2 --- pymc3/distributions/multivariate.py | 2 +- pymc3/tests/sampler_fixtures.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pymc3/distributions/multivariate.py b/pymc3/distributions/multivariate.py index 590354629b..11984794e7 100755 --- a/pymc3/distributions/multivariate.py +++ b/pymc3/distributions/multivariate.py @@ -713,7 +713,7 @@ def logp(self, x): logp_sd = self.sd_dist.logp(sd_vals).sum() corr_diag = x[diag_idxs] / sd_vals - logp_lkj = (2 * eta - 3 + n - tt.arange(n)) * np.log(corr_diag) + logp_lkj = (2 * eta - 3 + n - tt.arange(n)) * tt.log(corr_diag) logp_lkj = tt.sum(logp_lkj) # Compute the log det jacobian of the second transformation diff --git a/pymc3/tests/sampler_fixtures.py b/pymc3/tests/sampler_fixtures.py index 2efe65b9ed..3fef766b44 100644 --- a/pymc3/tests/sampler_fixtures.py +++ b/pymc3/tests/sampler_fixtures.py @@ -97,7 +97,7 @@ def make_model(cls): class LKJCholeskyCovFixture(KnownCDF): cdfs = { - 'log_stds': [stats.norm(loc=x, scale=x / 10).cdf + 'log_stds': [stats.norm(loc=x, scale=x / 10.).cdf for x in [1, 2, 3, 4, 5]], # The entries of the correlation matrix should follow # beta(eta - 1 + d/2, eta - 1 + d/2) on (-1, 1). @@ -112,7 +112,7 @@ class LKJCholeskyCovFixture(KnownCDF): def make_model(cls): with pm.Model() as model: sd_mu = np.array([1, 2, 3, 4, 5]) - sd_dist = pm.Lognormal.dist(mu=sd_mu, sd=sd_mu / 10, shape=5) + sd_dist = pm.Lognormal.dist(mu=sd_mu, sd=sd_mu / 10., shape=5) chol_packed = pm.LKJCholeskyCov('chol_packed', 5, 3, sd_dist) chol = pm.expand_packed_triangular(5, chol_packed, lower=True) cov = tt.dot(chol, chol.T) From 83267415e3c926bf9d2cc1d20a84cb02edeb02e8 Mon Sep 17 00:00:00 2001 From: Kyle Beauchamp Date: Wed, 22 Mar 2017 21:51:53 -0700 Subject: [PATCH 51/53] Add floatX wrappers in test_advi --- pymc3/tests/test_advi.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pymc3/tests/test_advi.py b/pymc3/tests/test_advi.py index 29d13670d6..9d8c173584 100644 --- a/pymc3/tests/test_advi.py +++ b/pymc3/tests/test_advi.py @@ -5,6 +5,7 @@ from pymc3.variational import advi, advi_minibatch, sample_vp from pymc3.variational.advi import _calc_elbo, adagrad_optimizer from pymc3.theanof import CallableTensor +from pymc3.theanof import floatX from theano import function, shared import theano.tensor as tt @@ -82,7 +83,7 @@ def test_check_discrete(self): def test_check_discrete_minibatch(self): disaster_data_t = tt.vector() - disaster_data_t.tag.test_value = np.zeros(len(self.disaster_data)) + disaster_data_t.tag.test_value = floatX(np.zeros(len(self.disaster_data))) def create_minibatches(): while True: @@ -172,13 +173,13 @@ def test_advi_minibatch(self): sd = 3. mu = -5. - data = sd * np.random.randn(n) + mu + data = floatX(sd * np.random.randn(n) + mu) d = n / sd**2 + 1 / sd0**2 mu_post = (n * np.mean(data) / sd**2 + mu0 / sd0**2) / d data_t = tt.vector() - data_t.tag.test_value = np.zeros(1,) + data_t.tag.test_value = floatX(np.zeros(1,)) def create_minibatch(data): while True: From 45d0887a20741ac468c031d2eed8c04e9d9c6b3e Mon Sep 17 00:00:00 2001 From: David Brochart Date: Thu, 23 Mar 2017 20:53:50 +0100 Subject: [PATCH 52/53] Changed the API to pm.sample(..., live_plot=True) --- docs/source/notebooks/live_sample_plots.ipynb | 806 +----------------- pymc3/plots/__init__.py | 2 +- pymc3/plots/traceplot.py | 171 +--- pymc3/sampling.py | 30 +- 4 files changed, 68 insertions(+), 941 deletions(-) diff --git a/docs/source/notebooks/live_sample_plots.ipynb b/docs/source/notebooks/live_sample_plots.ipynb index d923527ac7..e851acea72 100644 --- a/docs/source/notebooks/live_sample_plots.ipynb +++ b/docs/source/notebooks/live_sample_plots.ipynb @@ -17,12 +17,12 @@ "editable": true }, "source": [ - "This notebook illustrates how we can have live sample plots using the `live_traceplot` function with an `iter_sample` generator. It is based on the \"Coal mining disasters\" case study in the [Getting started notebook](https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/getting_started.ipynb)." + "This notebook illustrates how we can have live sample plots when calling the `sample` function with `live_plot=True`. It is based on the \"Coal mining disasters\" case study in the [Getting started notebook](https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/getting_started.ipynb)." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, @@ -31,8 +31,7 @@ "outputs": [], "source": [ "import numpy as np\n", - "import pymc3 as pm\n", - "from pymc3 import Model, Exponential, DiscreteUniform, Poisson\n", + "from pymc3 import Model, Exponential, DiscreteUniform, Poisson, sample\n", "from pymc3.math import switch\n", "\n", "%matplotlib notebook" @@ -40,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, @@ -60,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, @@ -84,806 +83,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Assigned Metropolis to switchpoint\n", - "Assigned NUTS to early_rate_log_\n", - "Assigned NUTS to late_rate_log_\n", - "Assigned Metropolis to disasters_missing\n" - ] - }, - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. 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');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width);\n", - " canvas.attr('height', height);\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('