# RandomPoisson

public final class RandomPoisson

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

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

### Nested Classes

 class RandomPoisson.Options Optional attributes for ``` RandomPoisson ```

### Constants

 String OP_NAME The name of this op, as known by TensorFlow core engine

### Public Methods

 Output () Returns the symbolic handle of the tensor. static RandomPoisson ( Scope scope, Operand shape, Operand rate, Class dtype, Options... options) Factory method to create a class wrapping a new RandomPoisson operation. static RandomPoisson < TInt64 > ( Scope scope, Operand shape, Operand rate, Options... options) Factory method to create a class wrapping a new RandomPoisson operation using default output types. Output () A tensor with shape `shape + shape(rate)`. static RandomPoisson.Options (Long seed) static RandomPoisson.Options (Long seed2)

## Constants

#### public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "RandomPoissonV2"

## Public Methods

#### public Output <V> asOutput ()

Returns the symbolic handle of the tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

#### public static RandomPoisson <V> create ( Scope scope, Operand <? extends TNumber > shape, Operand <? extends TNumber > rate, Class<V> dtype, Options... options)

Factory method to create a class wrapping a new RandomPoisson operation.

##### Parameters
 scope current scope 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in rate. A tensor in which each scalar is a "rate" parameter describing the associated poisson distribution. carries optional attributes values
##### Returns
• a new instance of RandomPoisson

#### public static RandomPoisson < TInt64 > create ( Scope scope, Operand <? extends TNumber > shape, Operand <? extends TNumber > rate, Options... options)

Factory method to create a class wrapping a new RandomPoisson operation using default output types.

##### Parameters
 scope current scope 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in rate. A tensor in which each scalar is a "rate" parameter describing the associated poisson distribution. carries optional attributes values
##### Returns
• a new instance of RandomPoisson

#### public Output <V> output ()

A tensor with shape `shape + shape(rate)`. Each slice `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for `rate[i0, i1, ...iN]`.

#### public static RandomPoisson.Options seed (Long seed)

##### Parameters
 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.

#### public static RandomPoisson.Options seed2 (Long seed2)

##### Parameters
 seed2 A second seed to avoid seed collision.
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