diff options
-rw-r--r-- | tensorflow/g3doc/api_docs/python/contrib.distributions.md | 47 | ||||
-rw-r--r-- | tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md | 47 |
2 files changed, 44 insertions, 50 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md index 6386bdba8c..d48816ba7d 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md +++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md @@ -5989,25 +5989,22 @@ dist.pmf(counts) # Shape [2] ``` - - - -#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, allow_arbitrary_counts=False, validate_args=True, allow_nan_stats=False, name='DirichletMultinomial')` {#DirichletMultinomial.__init__} +#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, validate_args=True, allow_nan_stats=False, name='DirichletMultinomial')` {#DirichletMultinomial.__init__} Initialize a batch of DirichletMultinomial distributions. ##### Args: -* <b>`n`</b>: Non-negative `float` or `double` tensor with shape - broadcastable to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch - of `N1 x ... x Nm` different Dirichlet multinomial distributions. Its - components should be equal to integral values. -* <b>`alpha`</b>: Positive `float` or `double` tensor with shape broadcastable to - `[N1,..., Nm, k]` `m >= 0`. Defines this as a batch of `N1 x ... x Nm` - different `k` class Dirichlet multinomial distributions. -* <b>`allow_arbitrary_counts`</b>: Boolean. This represents whether the pmf/cdf - allows for the `counts` tensor to be non-integral values. - The pmf/cdf are functions that can be evaluated at non-integral values, - but are only a distribution over non-negative integers. If - `validate_args` is `False`, this assertion is turned off. +* <b>`n`</b>: Non-negative `float` or `double` tensor, whose dtype is the same as + `alpha`. The shape is broadcastable to `[N1,..., Nm]` with `m >= 0`. + Defines this as a batch of `N1 x ... x Nm` different Dirichlet + multinomial distributions. Its components should be equal to integral + values. +* <b>`alpha`</b>: Positive `float` or `double` tensor, whose dtype is the same as + `n` with shape broadcastable to `[N1,..., Nm, k]` `m >= 0`. Defines + this as a batch of `N1 x ... x Nm` different `k` class Dirichlet + multinomial distributions. * <b>`validate_args`</b>: Whether to assert valid values for parameters `alpha` and `n`, and `x` in `prob` and `log_prob`. If False, correct behavior is not guaranteed. @@ -6176,12 +6173,12 @@ probability includes a combinatorial coefficient. ##### Args: -* <b>`counts`</b>: Non-negative `float` or `double` tensor whose shape can - be broadcast with `self.alpha`. For fixed leading dimensions, the last - dimension represents counts for the corresponding Dirichlet Multinomial - distribution in `self.alpha`. `counts` is only legal if it sums up to - `n` and its components are equal to integral values. The second - condition is relaxed if `allow_arbitrary_counts` is set. +* <b>`counts`</b>: Non-negative `float` or `double` tensor whose dtype is the same + `self` and whose shape can be broadcast with `self.alpha`. For fixed + leading dimensions, the last dimension represents counts for the + corresponding Dirichlet Multinomial distribution in `self.alpha`. + `counts` is only legal if it sums up to `n` and its components are + equal to integral values. * <b>`name`</b>: Name to give this Op, defaults to "log_prob". ##### Returns: @@ -6246,12 +6243,12 @@ probability includes a combinatorial coefficient. ##### Args: -* <b>`counts`</b>: Non-negative `float`, `double` tensor whose shape can - be broadcast with `self.alpha`. For fixed leading dimensions, the last - dimension represents counts for the corresponding Dirichlet Multinomial - distribution in `self.alpha`. `counts` is only legal if it sums up to - `n` and its components are equal to integral values. The second - condition is relaxed if `allow_arbitrary_counts` is set. +* <b>`counts`</b>: Non-negative `float` or `double` tensor whose dtype is the same + `self` and whose shape can be broadcast with `self.alpha`. For fixed + leading dimensions, the last dimension represents counts for the + corresponding Dirichlet Multinomial distribution in `self.alpha`. + `counts` is only legal if it sums up to `n` and its components are + equal to integral values. * <b>`name`</b>: Name to give this Op, defaults to "prob". ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md index f5a88c11dd..f3434ce299 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md @@ -67,25 +67,22 @@ dist.pmf(counts) # Shape [2] ``` - - - -#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, allow_arbitrary_counts=False, validate_args=True, allow_nan_stats=False, name='DirichletMultinomial')` {#DirichletMultinomial.__init__} +#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, validate_args=True, allow_nan_stats=False, name='DirichletMultinomial')` {#DirichletMultinomial.__init__} Initialize a batch of DirichletMultinomial distributions. ##### Args: -* <b>`n`</b>: Non-negative `float` or `double` tensor with shape - broadcastable to `[N1,..., Nm]` with `m >= 0`. Defines this as a batch - of `N1 x ... x Nm` different Dirichlet multinomial distributions. Its - components should be equal to integral values. -* <b>`alpha`</b>: Positive `float` or `double` tensor with shape broadcastable to - `[N1,..., Nm, k]` `m >= 0`. Defines this as a batch of `N1 x ... x Nm` - different `k` class Dirichlet multinomial distributions. -* <b>`allow_arbitrary_counts`</b>: Boolean. This represents whether the pmf/cdf - allows for the `counts` tensor to be non-integral values. - The pmf/cdf are functions that can be evaluated at non-integral values, - but are only a distribution over non-negative integers. If - `validate_args` is `False`, this assertion is turned off. +* <b>`n`</b>: Non-negative `float` or `double` tensor, whose dtype is the same as + `alpha`. The shape is broadcastable to `[N1,..., Nm]` with `m >= 0`. + Defines this as a batch of `N1 x ... x Nm` different Dirichlet + multinomial distributions. Its components should be equal to integral + values. +* <b>`alpha`</b>: Positive `float` or `double` tensor, whose dtype is the same as + `n` with shape broadcastable to `[N1,..., Nm, k]` `m >= 0`. Defines + this as a batch of `N1 x ... x Nm` different `k` class Dirichlet + multinomial distributions. * <b>`validate_args`</b>: Whether to assert valid values for parameters `alpha` and `n`, and `x` in `prob` and `log_prob`. If False, correct behavior is not guaranteed. @@ -254,12 +251,12 @@ probability includes a combinatorial coefficient. ##### Args: -* <b>`counts`</b>: Non-negative `float` or `double` tensor whose shape can - be broadcast with `self.alpha`. For fixed leading dimensions, the last - dimension represents counts for the corresponding Dirichlet Multinomial - distribution in `self.alpha`. `counts` is only legal if it sums up to - `n` and its components are equal to integral values. The second - condition is relaxed if `allow_arbitrary_counts` is set. +* <b>`counts`</b>: Non-negative `float` or `double` tensor whose dtype is the same + `self` and whose shape can be broadcast with `self.alpha`. For fixed + leading dimensions, the last dimension represents counts for the + corresponding Dirichlet Multinomial distribution in `self.alpha`. + `counts` is only legal if it sums up to `n` and its components are + equal to integral values. * <b>`name`</b>: Name to give this Op, defaults to "log_prob". ##### Returns: @@ -324,12 +321,12 @@ probability includes a combinatorial coefficient. ##### Args: -* <b>`counts`</b>: Non-negative `float`, `double` tensor whose shape can - be broadcast with `self.alpha`. For fixed leading dimensions, the last - dimension represents counts for the corresponding Dirichlet Multinomial - distribution in `self.alpha`. `counts` is only legal if it sums up to - `n` and its components are equal to integral values. The second - condition is relaxed if `allow_arbitrary_counts` is set. +* <b>`counts`</b>: Non-negative `float` or `double` tensor whose dtype is the same + `self` and whose shape can be broadcast with `self.alpha`. For fixed + leading dimensions, the last dimension represents counts for the + corresponding Dirichlet Multinomial distribution in `self.alpha`. + `counts` is only legal if it sums up to `n` and its components are + equal to integral values. * <b>`name`</b>: Name to give this Op, defaults to "prob". ##### Returns: |