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Diffstat (limited to 'tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md')
| -rw-r--r-- | tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md | 41 |
1 files changed, 20 insertions, 21 deletions
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md index e9fb06bcaf..76a28f8d17 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md @@ -34,37 +34,36 @@ Construct Chi2 distributions with parameter `df`. * <b>`df`</b>: Floating point tensor, the degrees of freedom of the - distribution(s). `df` must contain only positive values. -* <b>`validate_args`</b>: Python `Boolean`, default `False`. When `True` distribution + distribution(s). `df` must contain only positive values. +* <b>`validate_args`</b>: 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. -* <b>`allow_nan_stats`</b>: Python `Boolean`, default `True`. When `True`, statistics +* <b>`allow_nan_stats`</b>: 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 + result is undefined. When `False`, an exception is raised if one or more of the statistic's batch members are undefined. -* <b>`name`</b>: `String` name prefixed to Ops created by this class. +* <b>`name`</b>: Python `str` name prefixed to Ops created by this class. - - - #### `tf.contrib.distributions.Chi2.allow_nan_stats` {#Chi2.allow_nan_stats} -Python boolean describing behavior when a stat is undefined. +Python `bool` describing behavior when a stat is undefined. -Stats return +/- infinity when it makes sense. E.g., the variance -of a Cauchy distribution is infinity. However, sometimes the -statistic is undefined, e.g., if a distribution's pdf does not achieve a -maximum within the support of the distribution, the mode is undefined. -If the mean is undefined, then by definition the variance is undefined. -E.g. the mean for Student's T for df = 1 is undefined (no clear way to say -it is either + or - infinity), so the variance = E[(X - mean)^2] is also -undefined. +Stats return +/- infinity when it makes sense. E.g., the variance of a +Cauchy distribution is infinity. However, sometimes the statistic is +undefined, e.g., if a distribution's pdf does not achieve a maximum within +the support of the distribution, the mode is undefined. If the mean is +undefined, then by definition the variance is undefined. E.g. the mean for +Student's T for df = 1 is undefined (no clear way to say it is either + or - +infinity), so the variance = E[(X - mean)**2] is also undefined. ##### Returns: -* <b>`allow_nan_stats`</b>: Python boolean. +* <b>`allow_nan_stats`</b>: Python `bool`. - - - @@ -276,7 +275,7 @@ Indicates that `batch_shape == []`. ##### Returns: -* <b>`is_scalar_batch`</b>: `Boolean` `scalar` `Tensor`. +* <b>`is_scalar_batch`</b>: `bool` scalar `Tensor`. - - - @@ -293,7 +292,7 @@ Indicates that `event_shape == []`. ##### Returns: -* <b>`is_scalar_event`</b>: `Boolean` `scalar` `Tensor`. +* <b>`is_scalar_event`</b>: `bool` scalar `Tensor`. - - - @@ -389,7 +388,7 @@ Mode. Additional documentation from `Gamma`: The mode of a gamma distribution is `(shape - 1) / rate` when -`shape > 1`, and `NaN` otherwise. If `self.allow_nan_stats` is `False`, +`shape > 1`, and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception will be raised rather than returning `NaN`. @@ -432,8 +431,8 @@ param_shapes with static (i.e. `TensorShape`) shapes. This is a class method that describes what key/value arguments are required to instantiate the given `Distribution` so that a particular shape is -returned for that instance's call to `sample()`. Assumes that -the sample's shape is known statically. +returned for that instance's call to `sample()`. Assumes that the sample's +shape is known statically. Subclasses should override class method `_param_shapes` to return constant-valued tensors when constant values are fed. @@ -581,7 +580,7 @@ survival_function(x) = P[X > x] #### `tf.contrib.distributions.Chi2.validate_args` {#Chi2.validate_args} -Python boolean indicated possibly expensive checks are enabled. +Python `bool` indicating possibly expensive checks are enabled. - - - |