mxfusion.components.distributions.dirichlet¶
Members¶
-
class
mxfusion.components.distributions.dirichlet.DirichletLogPDFDecorator¶ Bases:
mxfusion.components.distributions.distribution.LogPDFDecorator
-
class
mxfusion.components.distributions.dirichlet.DirichletDrawSamplesDecorator¶ Bases:
mxfusion.components.distributions.distribution.DrawSamplesDecorator
-
class
mxfusion.components.distributions.dirichlet.Dirichlet(a, normalization=True, rand_gen=None, dtype=None, ctx=None)¶ Bases:
mxfusion.components.distributions.distribution.DistributionThe Dirichlet distribution.
Parameters: - a (Variable) – alpha, the concentration parameters of the distribution.
- normalization (boolean) – If true, L1 normalization is applied.
- rand_gen (RandomGenerator) – the random generator (default: MXNetRandomGenerator).
- dtype (numpy.float32 or numpy.float64) – the data type for float point numbers.
- ctx (None or mxnet.cpu or mxnet.gpu) – the mxnet context (default: None/current context).
-
log_pdf(F, variables, targets=None)¶ Computes the logarithm of the probability density/mass function (PDF/PMF) of the distribution. The inputs and outputs variables are fetched from the variables argument according to their UUIDs.
Parameters: - F (mxnet.symbol or mxnet.ndarray) – the MXNet computation mode
- variables – the set of MXNet arrays that holds the values of
variables at runtime. :type variables: {str(UUID): MXNet NDArray or MXNet Symbol} :returns: log pdf of the distribution :rtypes: MXNet NDArray or MXNet Symbol
-
draw_samples(F, variables, num_samples=1, always_return_tuple=False, targets=None)¶ Draw a set of samples from the distribution. The inputs variables are fetched from the variables argument according to their UUIDs.
Parameters: - F (mxnet.symbol or mxnet.ndarray) – the MXNet computation mode
- variables – the set of MXNet arrays that holds the values of
variables at runtime. :type variables: {str(UUID): MXNet NDArray or MXNet Symbol} :param num_samples: the number of drawn samples (default: one) :int num_samples: int :param always_return_tuple: Whether return a tuple even if there is only one variables in outputs. :type always_return_tuple: boolean :returns: a set samples of the distribution :rtypes: MXNet NDArray or MXNet Symbol or [MXNet NDArray or MXNet Symbol]
-
static
define_variable(a, shape=None, normalization=True, rand_gen=None, dtype=None, ctx=None)¶ Creates and returns a random variable drawn from a Dirichlet distribution.
Parameters: - a (Variable) – alpha, the concentration parameters of the distribution.
- normalization (boolean) – If true, L1 normalization is applied.
- rand_gen (RandomGenerator) – the random generator (default: MXNetRandomGenerator).
- dtype (numpy.float32 or numpy.float64) – the data type for float point numbers.
- ctx (None or mxnet.cpu or mxnet.gpu) – the mxnet context (default: None/current context).
Returns: the random variables drawn from the Dirichlet distribution.
Rtypes: Variable
-
replicate_self(attribute_map=None)¶ This functions as a copy constructor for the object. In order to do a copy constructor we first call
__new__on the class which creates a blank object. We then initialize that object using the methods standard init procedures, and do any extra copying of attributes.Replicates this Factor, using new inputs, outputs, and a new uuid. Used during model replication to functionally replicate a factor into a new graph.
Parameters: