Registering documentation for contrib.distributions.

Change: 121899093

tensorflow/contrib/distributions/__init__.py

@@ -12,17 +12,38 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Ops for representing statistical distributions.
+"""Classes representing statistical distributions.  Ops for working with them.
 
-## This package provides classes for statistical distributions.
+## Classes for statistical distributions.
 
+Classes that represent batches of statistical distributions.  Each class is
+initialized with parameters that define the distributions.
+
+### Univariate (scalar) distributions
+
+@@Gaussian
+
+### Multivariate distributions
+
+@@MultivariateNormal
+@@DirichletMultinomial
+
+## Posterior inference with conjugate priors.
+
+Functions that transform conjugate prior/likelihood pairs to distributions
+representing the posterior or posterior predictive.
+
+### Gaussian likelihood with conjugate prior.
+
+@@gaussian_conjugates_known_sigma_posterior
+@@gaussian_congugates_known_sigma_predictive
 """
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 # pylint: disable=unused-import,wildcard-import,line-too-long
-from tensorflow.contrib.distributions.python.ops import gaussian_conjugate_posteriors
 from tensorflow.contrib.distributions.python.ops.dirichlet_multinomial import *
 from tensorflow.contrib.distributions.python.ops.gaussian import *
+from tensorflow.contrib.distributions.python.ops.gaussian_conjugate_posteriors import *
 from tensorflow.contrib.distributions.python.ops.mvn import *

tensorflow/contrib/distributions/python/kernel_tests/gaussian_conjugate_posteriors_test.py

@@ -22,7 +22,7 @@ import math
 
 import tensorflow as tf
 
-gaussian_conjugate_posteriors = tf.contrib.distributions.gaussian_conjugate_posteriors  # pylint: disable=line-too-long
+distributions = tf.contrib.distributions
 
 
 class GaussianTest(tf.test.TestCase):
@@ -35,12 +35,12 @@ class GaussianTest(tf.test.TestCase):
       x = tf.constant([-2.5, 2.5, 4.0, 0.0, -1.0, 2.0])
       s = tf.reduce_sum(x)
       n = tf.size(x)
-      prior = tf.contrib.distributions.Gaussian(mu=mu0, sigma=sigma0)
-      posterior = gaussian_conjugate_posteriors.known_sigma_posterior(
+      prior = distributions.Gaussian(mu=mu0, sigma=sigma0)
+      posterior = distributions.gaussian_conjugates_known_sigma_posterior(
           prior=prior, sigma=sigma, s=s, n=n)
 
       # Smoke test
-      self.assertTrue(isinstance(posterior, tf.contrib.distributions.Gaussian))
+      self.assertTrue(isinstance(posterior, distributions.Gaussian))
       posterior_log_pdf = posterior.log_pdf(x).eval()
       self.assertEqual(posterior_log_pdf.shape, (6,))
 
@@ -54,12 +54,12 @@ class GaussianTest(tf.test.TestCase):
           tf.constant([[-2.5, 2.5, 4.0, 0.0, -1.0, 2.0]], dtype=tf.float32))
       s = tf.reduce_sum(x)
       n = tf.size(x)
-      prior = tf.contrib.distributions.Gaussian(mu=mu0, sigma=sigma0)
-      posterior = gaussian_conjugate_posteriors.known_sigma_posterior(
+      prior = distributions.Gaussian(mu=mu0, sigma=sigma0)
+      posterior = distributions.gaussian_conjugates_known_sigma_posterior(
           prior=prior, sigma=sigma, s=s, n=n)
 
       # Smoke test
-      self.assertTrue(isinstance(posterior, tf.contrib.distributions.Gaussian))
+      self.assertTrue(isinstance(posterior, distributions.Gaussian))
       posterior_log_pdf = posterior.log_pdf(x).eval()
       self.assertEqual(posterior_log_pdf.shape, (6, 2))
 
@@ -75,12 +75,12 @@ class GaussianTest(tf.test.TestCase):
       s = tf.reduce_sum(x, reduction_indices=[1])
       x = tf.transpose(x)  # Reshape to shape (6, 2)
       n = tf.constant([6] * 2)
-      prior = tf.contrib.distributions.Gaussian(mu=mu0, sigma=sigma0)
-      posterior = gaussian_conjugate_posteriors.known_sigma_posterior(
+      prior = distributions.Gaussian(mu=mu0, sigma=sigma0)
+      posterior = distributions.gaussian_conjugates_known_sigma_posterior(
           prior=prior, sigma=sigma, s=s, n=n)
 
       # Smoke test
-      self.assertTrue(isinstance(posterior, tf.contrib.distributions.Gaussian))
+      self.assertTrue(isinstance(posterior, distributions.Gaussian))
 
       # Calculate log_pdf under the 2 models
       posterior_log_pdf = posterior.log_pdf(x)
@@ -96,14 +96,15 @@ class GaussianTest(tf.test.TestCase):
       x = tf.constant([-2.5, 2.5, 4.0, 0.0, -1.0, 2.0])
       s = tf.reduce_sum(x)
       n = tf.size(x)
-      prior = tf.contrib.distributions.Gaussian(mu=mu0, sigma=sigma0)
-      predictive = gaussian_conjugate_posteriors.known_sigma_predictive(
+      prior = distributions.Gaussian(mu=mu0, sigma=sigma0)
+      predictive = distributions.gaussian_congugates_known_sigma_predictive(
           prior=prior, sigma=sigma, s=s, n=n)
 
       # Smoke test
-      self.assertTrue(isinstance(predictive, tf.contrib.distributions.Gaussian))
+      self.assertTrue(isinstance(predictive, distributions.Gaussian))
       predictive_log_pdf = predictive.log_pdf(x).eval()
       self.assertEqual(predictive_log_pdf.shape, (6,))
 
+
 if __name__ == '__main__':
   tf.test.main()

tensorflow/contrib/distributions/python/ops/dirichlet_multinomial.py

@@ -12,10 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""The Dirichlet Multinomial distribution class.
-
-@@DirichletMultinomial
-"""
+"""The Dirichlet Multinomial distribution class."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -61,6 +58,11 @@ def _log_combinations(counts, name='log_combinations'):
 class DirichletMultinomial(object):
   """DirichletMultinomial mixture distribution.
 
+  This distribution is parameterized by a vector `alpha` of concentration
+  parameters for `k` classes.
+
+  #### Mathematical details
+
   The Dirichlet Multinomial is a distribution over k-class count data, meaning
   for each k-tuple of non-negative integer `counts = [c_1,...,c_k]`, we have a
   probability of these draws being made from the distribution.  The distribution
@@ -85,7 +87,7 @@ class DirichletMultinomial(object):
   same shape (if possible).  In all cases, the last dimension of alpha/counts
   represents single Dirichlet Multinomial distributions.
 
-  Examples:
+  #### Examples
 
   ```python
   alpha = [1, 2, 3]

tensorflow/contrib/distributions/python/ops/gaussian.py

@@ -12,10 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""The Normal (Gaussian) distribution class.
-
-@@Gaussian
-"""
+"""The Normal (Gaussian) distribution class."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -44,10 +41,47 @@ def _assert_all_positive(x):
 class Gaussian(object):
   """The scalar Gaussian distribution with mean and stddev parameters mu, sigma.
 
+  #### Mathematical details
+
   The PDF of this distribution is:
 
   ```f(x) = sqrt(1/(2*pi*sigma^2)) exp(-(x-mu)^2/(2*sigma^2))```
 
+  #### Examples
+
+  Examples of initialization of one or a batch of distributions.
+
+  ```python
+  # Define a single scalar Gaussian distribution.
+  dist = tf.contrib.Gaussian(mu=0, sigma=3)
+
+  # Evaluate the cdf at 1, returning a scalar.
+  dist.cdf(1)
+
+  # Define a batch of two scalar valued Gaussians.
+  # The first has mean 1 and standard deviation 11, the second 2 and 22.
+  dist = tf.contrib.distributions.Gaussian(mu=[1, 2.], sigma=[11, 22.])
+
+  # Evaluate the pdf of the first distribution on 0, and the second on 1.5,
+  # returning a length two tensor.
+  dist.pdf([0, 1.5])
+
+  # Get 3 samples, returning a 3 x 2 tensor.
+  dist.sample(3)
+  ```
+
+  Arguments are broadcast when possible.
+
+  ```python
+  # Define a batch of two scalar valued Gaussians.
+  # Both have mean 1, but different standard deviations.
+  dist = tf.contrib.distributions.Gaussian(mu=1, sigma=[11, 22.])
+
+  # Evaluate the pdf of both distributions on the same point, 3.0,
+  # returning a length 2 tensor.
+  dist.pdf(3.0)
+  ```
+
   """
 
   def __init__(self, mu, sigma, name=None):

tensorflow/contrib/distributions/python/ops/gaussian_conjugate_posteriors.py

@@ -12,11 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""The Gaussian distribution: conjugate posterior closed form calculations.
-
-@@known_sigma_posterior
-@@known_sigma_predictive
-"""
+"""The Gaussian distribution: conjugate posterior closed form calculations."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -27,7 +23,7 @@ from tensorflow.contrib.distributions.python.ops.gaussian import Gaussian  # pyl
 from tensorflow.python.ops import math_ops
 
 
-def known_sigma_posterior(prior, sigma, s, n):
+def gaussian_conjugates_known_sigma_posterior(prior, sigma, s, n):
   """Posterior Gaussian distribution with conjugate prior on the mean.
 
   This model assumes that `n` observations (with sum `s`) come from a
@@ -86,7 +82,7 @@ def known_sigma_posterior(prior, sigma, s, n):
       sigma=math_ops.sqrt(sigmap_2))
 
 
-def known_sigma_predictive(prior, sigma, s, n):
+def gaussian_congugates_known_sigma_predictive(prior, sigma, s, n):
   """Posterior predictive Gaussian distribution w. conjugate prior on the mean.
 
   This model assumes that `n` observations (with sum `s`) come from a

tensorflow/contrib/distributions/python/ops/mvn.py

@@ -12,10 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""The Multivariate Normal distribution class.
-
-@@MultivariateNormal
-"""
+"""The Multivariate Normal distribution class."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -101,6 +98,8 @@ class MultivariateNormal(object):
   or alternatively mean `mu` and factored covariance (cholesky decomposed
   `sigma`) called `sigma_chol`.
 
+  #### Mathematical details
+
   The PDF of this distribution is:
 
   ```
@@ -123,6 +122,34 @@ class MultivariateNormal(object):
   ```
 
   where `tri_solve()` solves a triangular system of equations.
+
+  #### Examples
+
+  A single multi-variate Gaussian distribution is defined by a vector of means
+  of length `k`, and a covariance matrix of shape `k x k`.
+
+  Extra leading dimensions, if provided, allow for batches.
+
+  ```python
+  # Initialize a single 3-variate Gaussian with diagonal covariance.
+  mu = [1, 2, 3]
+  sigma = [[1, 0, 0], [0, 3, 0], [0, 0, 2]]
+  dist = tf.contrib.distributions.MultivariateNormal(mu=mu, sigma=sigma)
+
+  # Evaluate this on an observation in R^3, returning a scalar.
+  dist.pdf([-1, 0, 1])
+
+  # Initialize a batch of two 3-variate Gaussians.
+  mu = [[1, 2, 3], [11, 22, 33]]
+  sigma = ...  # shape 2 x 3 x 3
+  dist = tf.contrib.distributions.MultivariateNormal(mu=mu, sigma=sigma)
+
+  # Evaluate this on a two observations, each in R^3, returning a length two
+  # tensor.
+  x = [[-1, 0, 1], [-11, 0, 11]]  # Shape 2 x 3.
+  dist.pdf(x)
+  ```
+
   """
 
   def __init__(self, mu, sigma=None, sigma_chol=None, name=None):

tensorflow/python/framework/gen_docs_combined.py

@@ -50,6 +50,7 @@ def get_module_to_name():
       tf.train: "tf.train",
       tf.python_io: "tf.python_io",
       tf.test: "tf.test",
+      tf.contrib.distributions: "tf.contrib.distributions",
       tf.contrib.layers: "tf.contrib.layers",
       tf.contrib.learn: "tf.contrib.learn",
       tf.contrib.util: "tf.contrib.util",
@@ -125,6 +126,8 @@ def all_libraries(module_to_name, members, documented):
                                "RankingExample", "SequenceExample"]),
       library("script_ops", "Wraps python functions", prefix=PREFIX_TEXT),
       library("test", "Testing", tf.test),
+      library("contrib.distributions", "Statistical distributions (contrib)",
+              tf.contrib.distributions),
       library("contrib.layers", "Layers (contrib)", tf.contrib.layers),
       library("contrib.learn", "Learn (contrib)", tf.contrib.learn),
       library("contrib.util", "Utilities (contrib)", tf.contrib.util),