RandomPoisson

tensorflow C++ API

tensorflow::ops::RandomPoisson

Outputs random values from the Poisson distribution(s) described by rate.

Summary

This op uses two algorithms, depending on rate. If rate >= 10, then the algorithm by Hormann is used to acquire samples via transformation-rejection. See http://www.sciencedirect.com/science/article/pii/0167668793909974.

Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform random variables. See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Addison Wesley

Arguments:

• scope: A Scope object
• shape: 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in rate.
• rate: A tensor in which each scalar is a "rate" parameter describing the associated poisson distribution.

Optional attributes (see Attrs):

• seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
• seed2: A second seed to avoid seed collision.

Returns:

• Output : A tensor with shape shape + shape(rate). Each slice [:, ..., :, i0, i1, ...iN] contains the samples drawn for rate[i0, i1, ...iN] . The dtype of the output matches the dtype of rate.

RandomPoisson block

Source link : https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_random.cpp

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input shape: connect Input node.
• Input rate: connect Input node.
• RandomPoisson ::Attrs attrs : Input attrs in value. ex) seed_ = 0;seed2_ = 0;

Return:

• Output output : Output object of RandomPoisson class object.

Result:

• std::vector(Tensor) product_result : Returned object of executed result by calling session.

Using Method