# Random Sampling (cupy.random)¶

CuPy’s random number generation routines are based on cuRAND. They cover a small fraction of numpy.random.

The big difference of cupy.random from numpy.random is that cupy.random supports dtype option for most functions. This option enables us to generate float32 values directly without any space overhead.

## Sample random data¶

cupy.random.rand(*size, **kwarg)[source]

Returns an array of uniform random values over the interval [0, 1).

Each element of the array is uniformly distributed on the half-open interval [0, 1). All elements are identically and independently distributed (i.i.d.).

Parameters: size (tuple of ints) – The shape of the array. dtype – Data type specifier. Only numpy.float32 and numpy.float64 types are allowed. The default is numpy.float64. A random array. cupy.ndarray
cupy.random.randn(*size, **kwarg)[source]

Returns an array of standard normal random values.

Each element of the array is normally distributed with zero mean and unit variance. All elements are identically and independently distributed (i.i.d.).

Parameters: size (tuple of ints) – The shape of the array. dtype – Data type specifier. Only numpy.float32 and numpy.float64 types are allowed. The default is numpy.float64. An array of standard normal random values. cupy.ndarray
cupy.random.randint(low, high=None, size=None)[source]

Returns a scalar or an array of integer values over [low, high).

Each element of returned values are independently sampled from uniform distribution over left-close and right-open interval [low, high).

Parameters: low (int) – If high is not None, it is the lower bound of the interval. Otherwise, it is the upper bound of the interval and lower bound of the interval is set to 0. high (int) – Upper bound of the interval. size (None or int or tuple of ints) – The shape of returned value. If size is None, it is single integer sampled. If size is integer, it is the 1D-array of length size element. Otherwise, it is the array whose shape specified by size. int or cupy.ndarray of ints
cupy.random.random_integers(low, high=None, size=None)[source]

Return a scalar or an array of integer values over [low, high]

Each element of returned values are independently sampled from uniform distribution over closed interval [low, high].

Parameters: low (int) – If high is not None, it is the lower bound of the interval. Otherwise, it is the upper bound of the interval and the lower bound is set to 1. high (int) – Upper bound of the interval. size (None or int or tuple of ints) – The shape of returned value. If size is None, it is single integer sampled. If size is integer, it is the 1D-array of length size element. Otherwise, it is the array whose shape specified by size. int or cupy.ndarray of ints
cupy.random.random_sample(size=None, dtype=<type 'float'>)[source]

Returns an array of random values over the interval [0, 1).

This is a variant of cupy.random.rand().

Parameters: size (int or tuple of ints) – The shape of the array. dtype – Data type specifier. Only numpy.float32 and numpy.float64 types are allowed. An array of uniformly distributed random values. cupy.ndarray
cupy.random.random(size=None, dtype=<type 'float'>)

Returns an array of random values over the interval [0, 1).

This is a variant of cupy.random.rand().

Parameters: size (int or tuple of ints) – The shape of the array. dtype – Data type specifier. Only numpy.float32 and numpy.float64 types are allowed. An array of uniformly distributed random values. cupy.ndarray
cupy.random.ranf(size=None, dtype=<type 'float'>)

Returns an array of random values over the interval [0, 1).

This is a variant of cupy.random.rand().

Parameters: size (int or tuple of ints) – The shape of the array. dtype – Data type specifier. Only numpy.float32 and numpy.float64 types are allowed. An array of uniformly distributed random values. cupy.ndarray
cupy.random.sample(size=None, dtype=<type 'float'>)

Returns an array of random values over the interval [0, 1).

This is a variant of cupy.random.rand().

Parameters: size (int or tuple of ints) – The shape of the array. dtype – Data type specifier. Only numpy.float32 and numpy.float64 types are allowed. An array of uniformly distributed random values. cupy.ndarray

## Distributions¶

cupy.random.gumbel(loc=0.0, scale=1.0, size=None, dtype=<type 'float'>)[source]

Returns an array of samples drawn from a Gumbel distribution.

The samples are drawn from a Gumbel distribution with location loc and scale scale. Its probability density function is defined as

$f(x) = \frac{1}{\eta} \exp\left\{ - \frac{x - \mu}{\eta} \right\} \exp\left[-\exp\left\{-\frac{x - \mu}{\eta} \right\}\right],$

where $$\mu$$ is loc and $$\eta$$ is scale.

Parameters: loc (float) – The location of the mode $$\mu$$. scale (float) – The scale parameter $$\eta$$. size (int or tuple of ints) – The shape of the array. If None, a zero-dimensional array is generated. dtype – Data type specifier. Only numpy.float32 and numpy.float64 types are allowed. Samples drawn from the Gumbel destribution. cupy.ndarray
cupy.random.lognormal(mean=0.0, sigma=1.0, size=None, dtype=<type 'float'>)[source]

Returns an array of samples drawn from a log normal distribution.

The samples are natural log of samples drawn from a normal distribution with mean mean and deviation sigma.

Parameters: mean (float) – Mean of the normal distribution. sigma (float) – Standard deviation of the normal distribution. size (int or tuple of ints) – The shape of the array. If None, a zero-dimensional array is generated. dtype – Data type specifier. Only numpy.float32 and numpy.float64 types are allowed. Samples drawn from the log normal distribution. cupy.ndarray
cupy.random.normal(loc=0.0, scale=1.0, size=None, dtype=<type 'float'>)[source]

Returns an array of normally distributed samples.

Parameters: loc (float or array_like of floats) – Mean of the normal distribution. scale (float or array_like of floats) – Standard deviation of the normal distribution. size (int or tuple of ints) – The shape of the array. If None, a zero-dimensional array is generated. dtype – Data type specifier. Only numpy.float32 and numpy.float64 types are allowed. Normally distributed samples. cupy.ndarray
cupy.random.standard_normal(size=None, dtype=<type 'float'>)[source]

Returns an array of samples drawn from the standard normal distribution.

This is a variant of cupy.random.randn().

Parameters: size (int or tuple of ints) – The shape of the array. If None, a zero-dimensional array is generated. dtype – Data type specifier. Samples drawn from the standard normal distribution. cupy.ndarray
cupy.random.uniform(low=0.0, high=1.0, size=None, dtype=<type 'float'>)[source]

Returns an array of uniformly-distributed samples over an interval.

Samples are drawn from a uniform distribution over the half-open interval [low, high).

Parameters: low (float) – Lower end of the interval. high (float) – Upper end of the interval. size (int or tuple of ints) – The shape of the array. If None, a zero-dimensional array is generated. dtype – Data type specifier. Samples drawn from the uniform distribution. cupy.ndarray

## Random number generator¶

cupy.random.seed(seed=None)[source]

Resets the state of the random number generator with a seed.

This function resets the state of the global random number generator for the current device. Be careful that generators for other devices are not affected.

Parameters: seed (None or int) – Seed for the random number generator. If None, it uses os.urandom() if available or time.clock() otherwise. Note that this function does not support seeding by an integer array.
cupy.random.get_random_state()[source]

Gets the state of the random number generator for the current device.

If the state for the current device is not created yet, this function creates a new one, initializes it, and stores it as the state for the current device.

Returns: The state of the random number generator for the device. RandomState
class cupy.random.RandomState(seed=None, method=100)[source]

Portable container of a pseudo-random number generator.

An instance of this class holds the state of a random number generator. The state is available only on the device which has been current at the initialization of the instance.

Functions of cupy.random use global instances of this class. Different instances are used for different devices. The global state for the current device can be obtained by the cupy.random.get_random_state() function.

Parameters: seed (None or int) – Seed of the random number generator. See the seed() method for detail. method (int) – Method of the random number generator. Following values are available: cupy.cuda.curand.CURAND_RNG_PSEUDO_DEFAULT cupy.cuda.curand.CURAND_RNG_XORWOW cupy.cuda.curand.CURAND_RNG_MRG32K3A cupy.cuda.curand.CURAND_RNG_MTGP32 cupy.cuda.curand.CURAND_RNG_MT19937 cupy.cuda.curand.CURAND_RNG_PHILOX4_32_10 
interval(mx, size)[source]

Generate multiple integers independently sampled uniformly from [0, mx].

Parameters: mx (int) – Upper bound of the interval size (None or int or tuple) – Shape of the array or the scalar returned. If None, an cupy.ndarray with shape () is returned. If int, 1-D array of length size is returned. If tuple, multi-dimensional array with shape size is returned. Currently, each element of the array is numpy.int32. int or cupy.ndarray
lognormal(mean=0.0, sigma=1.0, size=None, dtype=<type 'float'>)[source]

Returns an array of samples drawn from a log normal distribution.

normal(loc=0.0, scale=1.0, size=None, dtype=<type 'float'>)[source]

Returns an array of normally distributed samples.

rand(*size, **kwarg)[source]

Returns uniform random values over the interval [0, 1).

randn(*size, **kwarg)[source]

Returns an array of standard normal random values.

random_sample(size=None, dtype=<type 'float'>)[source]

Returns an array of random values over the interval [0, 1).

seed(seed=None)[source]

Resets the state of the random number generator with a seed.

standard_normal(size=None, dtype=<type 'float'>)[source]

Returns samples drawn from the standard normal distribution.

uniform(low=0.0, high=1.0, size=None, dtype=<type 'float'>)[source]

Returns an array of uniformly-distributed samples over an interval.