Method __init__

tensorflow/contrib/distributions/python/ops/mixture.py:80–215

Initialize a Mixture distribution. A `Mixture` is defined by a `Categorical` (`cat`, representing the mixture probabilities) and a list of `Distribution` objects all having matching dtype, batch shape, event shape, and continuity properties (the components). The `num_classes` o

(self,
               cat,
               components,
               validate_args=False,
               allow_nan_stats=True,
               use_static_graph=False,
               name="Mixture")

      "instead of `tf.contrib.distributions`.",
      warn_once=True)
  def __init__(self,
               cat,
               components,
               validate_args=False,
               allow_nan_stats=True,
               use_static_graph=False,
               name="Mixture"):
    """Initialize a Mixture distribution.

    A `Mixture` is defined by a `Categorical` (`cat`, representing the
    mixture probabilities) and a list of `Distribution` objects
    all having matching dtype, batch shape, event shape, and continuity
    properties (the components).

    The `num_classes` of `cat` must be possible to infer at graph construction
    time and match `len(components)`.

    Args:
      cat: A `Categorical` distribution instance, representing the probabilities
          of `distributions`.
      components: A list or tuple of `Distribution` instances.
        Each instance must have the same type, be defined on the same domain,
        and have matching `event_shape` and `batch_shape`.
      validate_args: Python `bool`, default `False`. If `True`, raise a runtime
        error if batch or event ranks are inconsistent between cat and any of
        the distributions. This is only checked if the ranks cannot be
        determined statically at graph construction time.
      allow_nan_stats: Boolean, default `True`. If `False`, raise an
       exception if a statistic (e.g. mean/mode/etc...) is undefined for any
        batch member. If `True`, batch members with valid parameters leading to
        undefined statistics will return NaN for this statistic.
      use_static_graph: Calls to `sample` will not rely on dynamic tensor
        indexing, allowing for some static graph compilation optimizations, but
        at the expense of sampling all underlying distributions in the mixture.
        (Possibly useful when running on TPUs).
        Default value: `False` (i.e., use dynamic indexing).
      name: A name for this distribution (optional).

    Raises:
      TypeError: If cat is not a `Categorical`, or `components` is not
        a list or tuple, or the elements of `components` are not
        instances of `Distribution`, or do not have matching `dtype`.
      ValueError: If `components` is an empty list or tuple, or its
        elements do not have a statically known event rank.
        If `cat.num_classes` cannot be inferred at graph creation time,
        or the constant value of `cat.num_classes` is not equal to
        `len(components)`, or all `components` and `cat` do not have
        matching static batch shapes, or all components do not
        have matching static event shapes.
    """
    parameters = dict(locals())
    if not isinstance(cat, categorical.Categorical):
      raise TypeError("cat must be a Categorical distribution, but saw: %s" %
                      cat)
    if not components:
      raise ValueError("components must be a non-empty list or tuple")
    if not isinstance(components, (list, tuple)):
      raise TypeError("components must be a list or tuple, but saw: %s" %