# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""The Binomial distribution class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.contrib.distributions.python.ops import distribution
from tensorflow.contrib.distributions.python.ops import distribution_util
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops


_binomial_sample_note = """
For each batch member of counts `value`, `P[value]` is the probability that
after sampling `self.total_count` draws from this Binomial distribution, the
number of successes is `value`. Since different sequences of draws can result in
the same counts, the probability includes a combinatorial coefficient.

Note: `value` must be a non-negative tensor with dtype `dtype` and whose shape
can be broadcast with `self.probs` and `self.total_count`. `value` is only legal
if it is less than or equal to `self.total_count` and its components are equal
to integer values.
"""


class Binomial(distribution.Distribution):
  """Binomial distribution.

  This distribution is parameterized by `probs`, a (batch of) probabilities for
  drawing a `1` and `total_count`, the number of trials per draw from the
  Binomial.

  #### Mathematical Details

  The Binomial is a distribution over the number of `1`'s in `total_count`
  independent trials, with each trial having the same probability of `1`, i.e.,
  `probs`.

  The probability mass function (pmf) is,

  ```none
  pmf(k; n, p) = p**k (1 - p)**(n - k) / Z
  Z = k! (n - k)! / n!
  ```

  where:
  * `total_count = n`,
  * `probs = p`,
  * `Z` is the normalizing constant, and,
  * `n!` is the factorial of `n`.

  #### Examples

  Create a single distribution, corresponding to 5 coin flips.

  ```python
  dist = Binomial(total_count=5., probs=.5)
  ```

  Create a single distribution (using logits), corresponding to 5 coin flips.

  ```python
  dist = Binomial(total_count=5., logits=0.)
  ```

  Creates 3 distributions with the third distribution most likely to have
  successes.

  ```python
  p = [.2, .3, .8]
  # n will be broadcast to [4., 4., 4.], to match p.
  dist = Binomial(total_count=4., probs=p)
  ```

  The distribution functions can be evaluated on counts.

  ```python
  # counts same shape as p.
  counts = [1., 2, 3]
  dist.prob(counts)  # Shape [3]

  # p will be broadcast to [[.2, .3, .8], [.2, .3, .8]] to match counts.
  counts = [[1., 2, 1], [2, 2, 4]]
  dist.prob(counts)  # Shape [2, 3]

  # p will be broadcast to shape [5, 7, 3] to match counts.
  counts = [[...]]  # Shape [5, 7, 3]
  dist.prob(counts)  # Shape [5, 7, 3]
  ```
  """

  def __init__(self,
               total_count,
               logits=None,
               probs=None,
               validate_args=False,
               allow_nan_stats=True,
               name="Binomial"):
    """Initialize a batch of Binomial distributions.

    Args:
      total_count: Non-negative floating point tensor with shape broadcastable
        to `[N1,..., Nm]` with `m >= 0` and the same dtype as `probs` or
        `logits`. Defines this as a batch of `N1 x ...  x Nm` different Binomial
        distributions. Its components should be equal to integer values.
      logits: Floating point tensor representing the log-odds of a
        positive event with shape broadcastable to `[N1,..., Nm]` `m >= 0`, and
        the same dtype as `total_count`. Each entry represents logits for the
        probability of success for independent Binomial distributions. Only one
        of `logits` or `probs` should be passed in.
      probs: Positive floating point tensor with shape broadcastable to
        `[N1,..., Nm]` `m >= 0`, `probs in [0, 1]`. Each entry represents the
        probability of success for independent Binomial distributions. Only one
        of `logits` or `probs` should be passed in.
      validate_args: Python `bool`, default `False`. When `True` distribution
        parameters are checked for validity despite possibly degrading runtime
        performance. When `False` invalid inputs may silently render incorrect
        outputs.
      allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
        (e.g., mean, mode, variance) use the value "`NaN`" to indicate the
        result is undefined. When `False`, an exception is raised if one or
        more of the statistic's batch members are undefined.
      name: Python `str` name prefixed to Ops created by this class.
    """
    parameters = locals()
    with ops.name_scope(name, values=[total_count, logits, probs]):
      self._total_count = self._maybe_assert_valid_total_count(
          ops.convert_to_tensor(total_count, name="total_count"),
          validate_args)
      self._logits, self._probs = distribution_util.get_logits_and_probs(
          logits=logits,
          probs=probs,
          validate_args=validate_args,
          name=name)
    super(Binomial, self).__init__(
        dtype=self._probs.dtype,
        reparameterization_type=distribution.NOT_REPARAMETERIZED,
        validate_args=validate_args,
        allow_nan_stats=allow_nan_stats,
        parameters=parameters,
        graph_parents=[self._total_count,
                       self._logits,
                       self._probs],
        name=name)

  @property
  def total_count(self):
    """Number of trials."""
    return self._total_count

  @property
  def logits(self):
    """Log-odds of drawing a `1`."""
    return self._logits

  @property
  def probs(self):
    """Probability of of drawing a `1`."""
    return self._probs

  def _batch_shape_tensor(self):
    return array_ops.broadcast_dynamic_shape(
        array_ops.shape(self.total_count),
        array_ops.shape(self.probs))

  def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.total_count.get_shape(),
        self.probs.get_shape())

  def _event_shape_tensor(self):
    return constant_op.constant([], dtype=dtypes.int32)

  def _event_shape(self):
    return tensor_shape.scalar()

  @distribution_util.AppendDocstring(_binomial_sample_note)
  def _log_prob(self, counts):
    return self._log_unnormalized_prob(counts) - self._log_normalization(counts)

  @distribution_util.AppendDocstring(_binomial_sample_note)
  def _prob(self, counts):
    return math_ops.exp(self._log_prob(counts))

  def _log_unnormalized_prob(self, counts):
    counts = self._maybe_assert_valid_sample(counts)
    return (counts * math_ops.log(self.probs) +
            (self.total_count - counts) * math_ops.log1p(-self.probs))

  def _log_normalization(self, counts):
    counts = self._maybe_assert_valid_sample(counts)
    return (math_ops.lgamma(1. + self.total_count - counts)
            + math_ops.lgamma(1. + counts)
            - math_ops.lgamma(1. + self.total_count))

  def _mean(self):
    return self.total_count * self.probs

  def _variance(self):
    return self._mean() * (1. - self.probs)

  @distribution_util.AppendDocstring(
      """Note that when `(1 + total_count) * probs` is an integer, there are
      actually two modes. Namely, `(1 + total_count) * probs` and
      `(1 + total_count) * probs - 1` are both modes. Here we return only the
      larger of the two modes.""")
  def _mode(self):
    return math_ops.floor((1. + self.total_count) * self.probs)

  def _maybe_assert_valid_total_count(self, total_count, validate_args):
    if not validate_args:
      return total_count
    return control_flow_ops.with_dependencies([
        check_ops.assert_non_negative(
            total_count,
            message="total_count must be non-negative."),
        distribution_util.assert_integer_form(
            total_count,
            message="total_count cannot contain fractional componentes."),
    ], total_count)

  def _maybe_assert_valid_sample(self, counts, check_integer=True):
    """Check counts for proper shape, values, then return tensor version."""
    if not self.validate_args:
      return counts

    counts = distribution_util.embed_check_nonnegative_discrete(
        counts, check_integer=check_integer)
    return control_flow_ops.with_dependencies([
        check_ops.assert_less_equal(
            counts, self.total_count,
            message="counts are not less than or equal to n."),
    ], counts)
