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Bernoulli Distribution

Generates Bernoulli distributed random numbers.

Details

Bernoulli random number generator fills the \(n \times p\) numeric table with Bernoulli distributed values with the \(p\) probability of success on a single trial, where \(p \in R\), \(0 \leq p \leq 1\).

A variate is called Bernoulli distributed if after a trial it is equal to \(1\) with the probability of success \(p\) and to \(0\) with the probability \(1 - p\). The probability distribution is given by:

\[p\{x = 1\} = p\]

\[p\{x = 0\} = 1 - p\]

The cumulative distribution function is as follows:

\[\begin{split}F_p(x) = \begin{cases} 0, & x < 0 \\ 1 - p, & 0 \leq x < 1, x \in \mathbb{R} \\ 1, & x \geq 1 \end{cases}\end{split}\]

Batch Processing

Algorithm Parameters

Bernoulli distribution algorithm has the following parameters in addition to the common parameters specified in Distributions:

Algorithm Parameters for Bernoulli Distribution (Batch Processing)

Parameter	Default Value	Description
algorithmFPType	float	The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
method	defaultDense	Performance-oriented computation method, the only method supported by the algorithm.
p	Not applicable	Success probability of a trial, required parameter.

Examples

Python*

Batch Processing:

• https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/distributions_bernoulli.py

Performance Considerations

To get the best overall performance when using the Bernoulli distribution random number generator, provide the 32-bit signed integer homogeneous numeric table constructed with enabled equal features.

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex​. Notice revision #20201201

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