Draw samples from a Binomial distribution.

The values are drawn from a Binomial distribution with specified trial count and probability of success.

Usage

random_binomial(shape, counts, probabilities, dtype = NULL, seed = NULL)

Arguments

shape

The shape of the random values to generate.

counts

A number or array of numbers representing the number of trials. It must be broadcastable with probabilities.

probabilities

A float or array of floats representing the probability of success of an individual event. It must be broadcastable with counts.

dtype

Optional dtype of the tensor. Only floating point types are supported. If not specified, config_floatx() is used, which defaults to "float32" unless you configured it otherwise (via config_set_floatx(float_dtype)).

seed

Optional R integer or instance of random_seed_generator(). By default, the seed argument is NULL, and an internal global random_seed_generator() is used. The seed argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the seed value will produce the same random values for each call. To generate different random values for repeated calls, an instance of random_seed_generator() must be provided as the seed value.

Remark concerning the JAX backend: When tracing functions with the JAX backend the global random_seed_generator() is not supported. Therefore, during tracing the default value seed=NULL will produce an error, and a seed argument must be provided.

Value

A tensor of random values.

See also

• https://www.tensorflow.org/api_docs/python/tf/keras/random/binomial

Other random:
random_beta()
random_categorical()
random_dropout()
random_gamma()
random_integer()
random_normal()
random_seed_generator()
random_shuffle()
random_truncated_normal()
random_uniform()