Bounded now works with array bounds

pymc3/distributions/continuous.py

@@ -1114,31 +1114,55 @@ class Bounded(Continuous):
 
     def __init__(self, distribution, lower, upper, transform='infer', *args, **kwargs):
         self.dist = distribution.dist(*args, **kwargs)
-
         self.__dict__.update(self.dist.__dict__)
         self.__dict__.update(locals())
 
         if hasattr(self.dist, 'mode'):
             self.mode = self.dist.mode
 
         if transform == 'infer':
+            self.transform, self.testval = self._infer(lower, upper)
 
-            default = self.dist.default()
-
-            if not np.isinf(lower) and not np.isinf(upper):
-                self.transform = transforms.interval(lower, upper)
-                if default <= lower or default >= upper:
-                    self.testval = 0.5 * (upper + lower)
+    def _infer(self, lower, upper):
+        """Infer proper transforms for the variable, and adjust test_value.
 
-            if not np.isinf(lower) and np.isinf(upper):
-                self.transform = transforms.lowerbound(lower)
-                if default <= lower:
-                    self.testval = lower + 1
+        In particular, this deals with the case where lower or upper may be +/-inf, or an
+        `ndarray` or a `theano.tensor.TensorVariable`
+        """
+        if isinstance(upper, tt.TensorVariable):
+            _upper = upper.tag.test_value
+        else:
+            _upper = upper
+        if isinstance(lower, tt.TensorVariable):
+            _lower = lower.tag.test_value
+        else:
+            _lower = lower
+
+        testval = self.dist.default()
+        if not self._isinf(_lower) and not self._isinf(_upper):
+            transform = transforms.interval(lower, upper)
+            if (testval <= _lower).any() or (testval >= _upper).any():
+                testval = 0.5 * (_upper + _lower)
+        elif not self._isinf(_lower) and self._isinf(_upper):
+            transform = transforms.lowerbound(lower)
+            if (testval <= _lower).any():
+                testval = lower + 1
+        elif self._isinf(_lower) and not self._isinf(_upper):
+            transform = transforms.upperbound(upper)
+            if (testval >= _upper).any():
+                testval = _upper - 1
+        else:
+            transform = None
+        return transform, testval
 
-            if np.isinf(lower) and not np.isinf(upper):
-                self.transform = transforms.upperbound(upper)
-                if default >= upper:
-                    self.testval = upper - 1
+    def _isinf(self, value):
+        """Checks whether the value is +/-inf, or else returns 0 (even if an ndarray with
+        entries that are +/-inf)
+        """
+        try:
+            return bool(np.isinf(value))
+        except ValueError:
+            return False
 
     def _random(self, lower, upper, point=None, size=None):
         samples = np.zeros(size).flatten()
@@ -1165,38 +1189,17 @@ def logp(self, value):
                      value >= self.lower, value <= self.upper)
 
 
-class Bound(object):
-    R"""
-    Creates a new upper, lower or upper+lower bounded distribution
-
-    Parameters
-    ----------
-    distribution : pymc3 distribution
-        Distribution to be transformed into a bounded distribution
-    lower : float (optional)
-        Lower bound of the distribution
-    upper : float (optional)
-
-    Example
-    -------
-    boundedNormal = pymc3.Bound(pymc3.Normal, lower=0.0)
-    par = boundedNormal(mu=0.0, sd=1.0, testval=1.0)
-    """
-
-    def __init__(self, distribution, lower=-np.inf, upper=np.inf):
-        self.distribution = distribution
-        self.lower = lower
-        self.upper = upper
-
-    def __call__(self, *args, **kwargs):
-        first, args = args[0], args[1:]
+def Bound(distribution, lower=-np.inf, upper=np.inf):
+    class _BoundedDist(Bounded):
+        def __init__(self, *args, **kwargs):
+            first, args = args[0], args[1:]
+            super(self, _BoundedDist).__init__(first, distribution, lower, upper, *args, **kwargs)
 
-        return Bounded(first, self.distribution, self.lower, self.upper,
-                       *args, **kwargs)
+        @classmethod
+        def dist(cls, *args, **kwargs):
+            return Bounded.dist(distribution, lower, upper, *args, **kwargs)
 
-    def dist(self, *args, **kwargs):
-        return Bounded.dist(self.distribution, self.lower, self.upper,
-                            *args, **kwargs)
+    return _BoundedDist
 
 
 def StudentTpos(*args, **kwargs):

pymc3/examples/garch_example.py

@@ -1,4 +1,4 @@
-from pymc3 import Normal, sample, Model, Bound
+from pymc3 import Normal, sample, Model, Bound, summary
 import theano.tensor as tt
 import numpy as np
 
@@ -29,29 +29,35 @@
   r ~ normal(mu,sigma);
 }
 """
-J = 8
-r = np.array([28, 8, -3, 7, -1, 1, 18, 12])
-sigma1 = np.array([15, 10, 16, 11, 9, 11, 10, 18])
-alpha0 = np.array([10, 10, 16, 8, 9, 11, 12, 18])
 
-with Model() as garch:
-    alpha1 = Normal('alpha1', 0, 1, shape=J)
-    BoundedNormal = Bound(Normal, upper=(1 - alpha1))
-    beta1 = BoundedNormal('beta1', 0, sd=1e6)
-    mu = Normal('mu', 0, sd=1e6)
 
-    theta = tt.sqrt(alpha0 + alpha1 * tt.pow(r - mu, 2) +
-                    beta1 * tt.pow(sigma1, 2))
+def get_garch_model():
+    r = np.array([28, 8, -3, 7, -1, 1, 18, 12])
+    sigma1 = np.array([15, 10, 16, 11, 9, 11, 10, 18])
+    alpha0 = np.array([10, 10, 16, 8, 9, 11, 12, 18])
+    shape = r.shape
 
-    obs = Normal('obs', mu, sd=theta, observed=r)
+    with Model() as garch:
+        alpha1 = Normal('alpha1', mu=np.zeros(shape=shape), sd=np.ones(shape=shape), shape=shape)
+        BoundedNormal = Bound(Normal, upper=(1 - alpha1))
+        beta1 = BoundedNormal('beta1',
+                              mu=np.zeros(shape=shape),
+                              sd=1e6 * np.ones(shape=shape),
+                              shape=shape)
+        mu = Normal('mu', mu=np.zeros(shape=shape), sd=1e6 * np.ones(shape=shape), shape=shape)
+        theta = tt.sqrt(alpha0 + alpha1 * tt.pow(r - mu, 2) +
+                        beta1 * tt.pow(sigma1, 2))
+        Normal('obs', mu, sd=theta, observed=r)
+    return garch
 
 
 def run(n=1000):
     if n == "short":
         n = 50
-    with garch:
-        tr = sample(n)
+    with get_garch_model():
+        tr = sample(n, n_init=10000)
+    return tr
 
 
 if __name__ == '__main__':
-    run()
+    print(summary(run()))