/
_generator.py
1243 lines (1034 loc) · 43.3 KB
/
_generator.py
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import atexit
import binascii
import functools
import hashlib
import operator
import os
import time
import numpy
import warnings
from numpy.linalg import LinAlgError
import cupy
from cupy import _core
from cupy import cuda
from cupy.cuda import curand
from cupy.cuda import device
from cupy.random import _kernels
from cupy import _util
import cupyx
_UINT32_MAX = 0xffffffff
_UINT64_MAX = 0xffffffffffffffff
class RandomState(object):
"""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 :mod:`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
:func:`cupy.random.get_random_state` function.
Args:
seed (None or int): Seed of the random number generator. See the
:meth:`~cupy.random.RandomState.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_PSEUDO_XORWOW
cupy.cuda.curand.CURAND_RNG_PSEUDO_MRG32K3A
cupy.cuda.curand.CURAND_RNG_PSEUDO_MTGP32
cupy.cuda.curand.CURAND_RNG_PSEUDO_MT19937
cupy.cuda.curand.CURAND_RNG_PSEUDO_PHILOX4_32_10
"""
def __init__(self, seed=None, method=curand.CURAND_RNG_PSEUDO_DEFAULT):
self._generator = curand.createGenerator(method)
self.method = method
self.seed(seed)
def __del__(self, is_shutting_down=_util.is_shutting_down):
# When createGenerator raises an error, _generator is not initialized
if is_shutting_down():
return
if hasattr(self, '_generator'):
curand.destroyGenerator(self._generator)
def _update_seed(self, size):
self._rk_seed = (self._rk_seed + size) % _UINT64_MAX
def _generate_normal(self, func, size, dtype, *args):
# curand functions below don't support odd size.
# * curand.generateNormal
# * curand.generateNormalDouble
# * curand.generateLogNormal
# * curand.generateLogNormalDouble
size = _core.get_size(size)
element_size = _core.internal.prod(size)
if element_size % 2 == 0:
out = cupy.empty(size, dtype=dtype)
func(self._generator, out.data.ptr, out.size, *args)
return out
else:
out = cupy.empty((element_size + 1,), dtype=dtype)
func(self._generator, out.data.ptr, out.size, *args)
return out[:element_size].reshape(size)
# NumPy compatible functions
def beta(self, a, b, size=None, dtype=float):
"""Returns an array of samples drawn from the beta distribution.
.. seealso::
- :func:`cupy.random.beta` for full documentation
- :meth:`numpy.random.RandomState.beta`
"""
a, b = cupy.asarray(a), cupy.asarray(b)
if size is None:
size = cupy.broadcast(a, b).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.beta_kernel(a, b, self._rk_seed, y)
self._update_seed(y.size)
return y
def binomial(self, n, p, size=None, dtype=int):
"""Returns an array of samples drawn from the binomial distribution.
.. seealso::
- :func:`cupy.random.binomial` for full documentation
- :meth:`numpy.random.RandomState.binomial`
"""
n, p = cupy.asarray(n), cupy.asarray(p)
if size is None:
size = cupy.broadcast(n, p).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.binomial_kernel(n, p, self._rk_seed, y)
self._update_seed(y.size)
return y
def chisquare(self, df, size=None, dtype=float):
"""Returns an array of samples drawn from the chi-square distribution.
.. seealso::
- :func:`cupy.random.chisquare` for full documentation
- :meth:`numpy.random.RandomState.chisquare`
"""
df = cupy.asarray(df)
if size is None:
size = df.shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.chisquare_kernel(df, self._rk_seed, y)
self._update_seed(y.size)
return y
def dirichlet(self, alpha, size=None, dtype=float):
"""Returns an array of samples drawn from the dirichlet distribution.
.. seealso::
- :func:`cupy.random.dirichlet` for full documentation
- :meth:`numpy.random.RandomState.dirichlet`
"""
alpha = cupy.asarray(alpha)
if size is None:
size = alpha.shape
elif isinstance(size, (int, cupy.integer)):
size = (size,) + alpha.shape
else:
size += alpha.shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.standard_gamma_kernel(alpha, self._rk_seed, y)
y /= y.sum(axis=-1, keepdims=True)
self._update_seed(y.size)
return y
def exponential(self, scale=1.0, size=None, dtype=float):
"""Returns an array of samples drawn from a exponential distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.exponential` for full documentation
- :meth:`numpy.random.RandomState.exponential`
"""
scale = cupy.asarray(scale, dtype)
if (scale < 0).any(): # synchronize!
raise ValueError('scale < 0')
if size is None:
size = scale.shape
x = self.standard_exponential(size, dtype)
x *= scale
return x
def f(self, dfnum, dfden, size=None, dtype=float):
"""Returns an array of samples drawn from the f distribution.
.. seealso::
- :func:`cupy.random.f` for full documentation
- :meth:`numpy.random.RandomState.f`
"""
dfnum, dfden = cupy.asarray(dfnum), cupy.asarray(dfden)
if size is None:
size = cupy.broadcast(dfnum, dfden).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.f_kernel(dfnum, dfden, self._rk_seed, y)
self._update_seed(y.size)
return y
def gamma(self, shape, scale=1.0, size=None, dtype=float):
"""Returns an array of samples drawn from a gamma distribution.
.. seealso::
- :func:`cupy.random.gamma` for full documentation
- :meth:`numpy.random.RandomState.gamma`
"""
shape, scale = cupy.asarray(shape), cupy.asarray(scale)
if size is None:
size = cupy.broadcast(shape, scale).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.standard_gamma_kernel(shape, self._rk_seed, y)
y *= scale
self._update_seed(y.size)
return y
def geometric(self, p, size=None, dtype=int):
"""Returns an array of samples drawn from the geometric distribution.
.. seealso::
- :func:`cupy.random.geometric` for full documentation
- :meth:`numpy.random.RandomState.geometric`
"""
p = cupy.asarray(p)
if size is None:
size = p.shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.geometric_kernel(p, self._rk_seed, y)
self._update_seed(y.size)
return y
def hypergeometric(self, ngood, nbad, nsample, size=None, dtype=int):
"""Returns an array of samples drawn from the hypergeometric distribution.
.. seealso::
- :func:`cupy.random.hypergeometric` for full documentation
- :meth:`numpy.random.RandomState.hypergeometric`
"""
ngood, nbad, nsample = \
cupy.asarray(ngood), cupy.asarray(nbad), cupy.asarray(nsample)
if size is None:
size = cupy.broadcast(ngood, nbad, nsample).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.hypergeometric_kernel(ngood, nbad, nsample, self._rk_seed, y)
self._update_seed(y.size)
return y
_laplace_kernel = _core.ElementwiseKernel(
'T x, T loc, T scale', 'T y',
'y = loc + scale * ((x <= 0.5) ? log(x + x): -log(x + x - 1.0))',
'cupy_laplace_kernel')
def laplace(self, loc=0.0, scale=1.0, size=None, dtype=float):
"""Returns an array of samples drawn from the laplace distribution.
.. seealso::
- :func:`cupy.random.laplace` for full documentation
- :meth:`numpy.random.RandomState.laplace`
"""
loc = cupy.asarray(loc, dtype)
scale = cupy.asarray(scale, dtype)
if size is None:
size = cupy.broadcast(loc, scale).shape
x = self._random_sample_raw(size, dtype)
RandomState._laplace_kernel(x, loc, scale, x)
return x
def logistic(self, loc=0.0, scale=1.0, size=None, dtype=float):
"""Returns an array of samples drawn from the logistic distribution.
.. seealso::
- :func:`cupy.random.logistic` for full documentation
- :meth:`numpy.random.RandomState.logistic`
"""
loc, scale = cupy.asarray(loc), cupy.asarray(scale)
if size is None:
size = cupy.broadcast(loc, scale).shape
x = cupy.empty(shape=size, dtype=dtype)
_kernels.open_uniform_kernel(self._rk_seed, x)
self._update_seed(x.size)
x = (1.0 - x) / x
cupy.log(x, out=x)
cupy.multiply(x, scale, out=x)
cupy.add(x, loc, out=x)
return x
def lognormal(self, mean=0.0, sigma=1.0, size=None, dtype=float):
"""Returns an array of samples drawn from a log normal distribution.
.. seealso::
- :func:`cupy.random.lognormal` for full documentation
- :meth:`numpy.random.RandomState.lognormal`
"""
if any(isinstance(arg, cupy.ndarray) for arg in (mean, sigma)):
x = self.normal(mean, sigma, size, dtype)
cupy.exp(x, out=x)
return x
if size is None:
size = ()
dtype = _check_and_get_dtype(dtype)
if dtype.char == 'f':
func = curand.generateLogNormal
else:
func = curand.generateLogNormalDouble
return self._generate_normal(func, size, dtype, mean, sigma)
def logseries(self, p, size=None, dtype=int):
"""Returns an array of samples drawn from a log series distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.logseries` for full documentation
- :meth:`numpy.random.RandomState.logseries`
"""
p = cupy.asarray(p)
if cupy.any(p <= 0): # synchronize!
raise ValueError('p <= 0.0')
if cupy.any(p >= 1): # synchronize!
raise ValueError('p >= 1.0')
if size is None:
size = p.shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.logseries_kernel(p, self._rk_seed, y)
self._update_seed(y.size)
return y
def multivariate_normal(self, mean, cov, size=None, check_valid='ignore',
tol=1e-08, method='cholesky', dtype=float):
"""Returns an array of samples drawn from the multivariate normal
distribution.
.. warning::
This function calls one or more cuSOLVER routine(s) which may yield
invalid results if input conditions are not met.
To detect these invalid results, you can set the `linalg`
configuration to a value that is not `ignore` in
:func:`cupyx.errstate` or :func:`cupyx.seterr`.
.. seealso::
- :func:`cupy.random.multivariate_normal` for full documentation
- :meth:`numpy.random.RandomState.multivariate_normal`
"""
_util.experimental('cupy.random.RandomState.multivariate_normal')
mean = cupy.asarray(mean, dtype=dtype)
cov = cupy.asarray(cov, dtype=dtype)
if size is None:
shape = []
elif isinstance(size, (int, cupy.integer)):
shape = [size]
else:
shape = size
if len(mean.shape) != 1:
raise ValueError('mean must be 1 dimensional')
if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]):
raise ValueError('cov must be 2 dimensional and square')
if mean.shape[0] != cov.shape[0]:
raise ValueError('mean and cov must have same length')
final_shape = list(shape[:])
final_shape.append(mean.shape[0])
if method not in {'eigh', 'svd', 'cholesky'}:
raise ValueError(
"method must be one of {'eigh', 'svd', 'cholesky'}")
if check_valid != 'ignore':
if check_valid != 'warn' and check_valid != 'raise':
raise ValueError(
"check_valid must equal 'warn', 'raise', or 'ignore'")
if check_valid == 'warn':
with cupyx.errstate(linalg='raise'):
try:
decomp = cupy.linalg.cholesky(cov)
except LinAlgError:
with cupyx.errstate(linalg='ignore'):
if method != 'cholesky':
if method == 'eigh':
(s, u) = cupy.linalg.eigh(cov)
psd = not cupy.any(s < -tol)
if method == 'svd':
(u, s, vh) = cupy.linalg.svd(cov)
psd = cupy.allclose(cupy.dot(vh.T * s, vh),
cov, rtol=tol, atol=tol)
decomp = u * cupy.sqrt(cupy.abs(s))
if not psd:
warnings.warn("covariance is not positive-" +
"semidefinite, output may be " +
"invalid.", RuntimeWarning)
else:
warnings.warn("covariance is not positive-" +
"semidefinite, output *is* " +
"invalid.", RuntimeWarning)
decomp = cupy.linalg.cholesky(cov)
else:
with cupyx.errstate(linalg=check_valid):
try:
if method == 'cholesky':
decomp = cupy.linalg.cholesky(cov)
elif method == 'eigh':
(s, u) = cupy.linalg.eigh(cov)
decomp = u * cupy.sqrt(cupy.abs(s))
elif method == 'svd':
(u, s, vh) = cupy.linalg.svd(cov)
decomp = u * cupy.sqrt(cupy.abs(s))
except LinAlgError:
raise LinAlgError("Matrix is not positive definite; if " +
"matrix is positive-semidefinite, set" +
"'check_valid' to 'warn'")
x = self.standard_normal(final_shape,
dtype=dtype).reshape(-1, mean.shape[0])
x = cupy.dot(decomp, x.T)
x = x.T
x += mean
x.shape = tuple(final_shape)
return x
def negative_binomial(self, n, p, size=None, dtype=int):
"""Returns an array of samples drawn from the negative binomial distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.negative_binomial` for full documentation
- :meth:`numpy.random.RandomState.negative_binomial`
"""
n = cupy.asarray(n)
p = cupy.asarray(p)
if cupy.any(n <= 0): # synchronize!
raise ValueError('n <= 0')
if cupy.any(p < 0): # synchronize!
raise ValueError('p < 0')
if cupy.any(p > 1): # synchronize!
raise ValueError('p > 1')
y = self.gamma(n, (1-p)/p, size)
return self.poisson(y, dtype=dtype)
def normal(self, loc=0.0, scale=1.0, size=None, dtype=float):
"""Returns an array of normally distributed samples.
.. seealso::
- :func:`cupy.random.normal` for full documentation
- :meth:`numpy.random.RandomState.normal`
"""
dtype = _check_and_get_dtype(dtype)
if size is None:
size = cupy.broadcast(loc, scale).shape
if dtype.char == 'f':
func = curand.generateNormal
else:
func = curand.generateNormalDouble
if isinstance(scale, cupy.ndarray):
x = self._generate_normal(func, size, dtype, 0.0, 1.0)
cupy.multiply(x, scale, out=x)
cupy.add(x, loc, out=x)
elif isinstance(loc, cupy.ndarray):
x = self._generate_normal(func, size, dtype, 0.0, scale)
cupy.add(x, loc, out=x)
else:
x = self._generate_normal(func, size, dtype, loc, scale)
return x
def pareto(self, a, size=None, dtype=float):
"""Returns an array of samples drawn from the pareto II distribution.
.. seealso::
- :func:`cupy.random.pareto` for full documentation
- :meth:`numpy.random.RandomState.pareto`
"""
a = cupy.asarray(a)
if size is None:
size = a.shape
x = self._random_sample_raw(size, dtype)
cupy.log(x, out=x)
cupy.exp(-x/a, out=x)
return x - 1
def noncentral_chisquare(self, df, nonc, size=None, dtype=float):
"""Returns an array of samples drawn from the noncentral chi-square
distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.noncentral_chisquare` for full documentation
- :meth:`numpy.random.RandomState.noncentral_chisquare`
"""
df, nonc = cupy.asarray(df), cupy.asarray(nonc)
if cupy.any(df <= 0): # synchronize!
raise ValueError('df <= 0')
if cupy.any(nonc < 0): # synchronize!
raise ValueError('nonc < 0')
if size is None:
size = cupy.broadcast(df, nonc).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.noncentral_chisquare_kernel(df, nonc, self._rk_seed, y)
self._update_seed(y.size)
return y
def noncentral_f(self, dfnum, dfden, nonc, size=None, dtype=float):
"""Returns an array of samples drawn from the noncentral F distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.noncentral_f` for full documentation
- :meth:`numpy.random.RandomState.noncentral_f`
"""
dfnum, dfden, nonc = \
cupy.asarray(dfnum), cupy.asarray(dfden), cupy.asarray(nonc)
if cupy.any(dfnum <= 0): # synchronize!
raise ValueError('dfnum <= 0')
if cupy.any(dfden <= 0): # synchronize!
raise ValueError('dfden <= 0')
if cupy.any(nonc < 0): # synchronize!
raise ValueError('nonc < 0')
if size is None:
size = cupy.broadcast(dfnum, dfden, nonc).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.noncentral_f_kernel(dfnum, dfden, nonc, self._rk_seed, y)
self._update_seed(y.size)
return y
def poisson(self, lam=1.0, size=None, dtype=int):
"""Returns an array of samples drawn from the poisson distribution.
.. seealso::
- :func:`cupy.random.poisson` for full documentation
- :meth:`numpy.random.RandomState.poisson`
"""
lam = cupy.asarray(lam)
if size is None:
size = lam.shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.poisson_kernel(lam, self._rk_seed, y)
self._update_seed(y.size)
return y
def power(self, a, size=None, dtype=float):
"""Returns an array of samples drawn from the power distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.power` for full documentation
- :meth:`numpy.random.RandomState.power`
"""
a = cupy.asarray(a)
if cupy.any(a < 0): # synchronize!
raise ValueError('a < 0')
if size is None:
size = a.shape
x = self.standard_exponential(size=size, dtype=dtype)
cupy.exp(-x, out=x)
cupy.add(1, -x, out=x)
cupy.power(x, 1./a, out=x)
return x
def rand(self, *size, **kwarg):
"""Returns uniform random values over the interval ``[0, 1)``.
.. seealso::
- :func:`cupy.random.rand` for full documentation
- :meth:`numpy.random.RandomState.rand`
"""
dtype = kwarg.pop('dtype', float)
if kwarg:
raise TypeError('rand() got unexpected keyword arguments %s'
% ', '.join(kwarg.keys()))
return self.random_sample(size=size, dtype=dtype)
def randn(self, *size, **kwarg):
"""Returns an array of standard normal random values.
.. seealso::
- :func:`cupy.random.randn` for full documentation
- :meth:`numpy.random.RandomState.randn`
"""
dtype = kwarg.pop('dtype', float)
if kwarg:
raise TypeError('randn() got unexpected keyword arguments %s'
% ', '.join(kwarg.keys()))
return self.normal(size=size, dtype=dtype)
_mod1_kernel = _core.ElementwiseKernel(
'', 'T x', 'x = (x == (T)1) ? 0 : x', 'cupy_random_x_mod_1')
def _random_sample_raw(self, size, dtype):
dtype = _check_and_get_dtype(dtype)
out = cupy.empty(size, dtype=dtype)
if dtype.char == 'f':
func = curand.generateUniform
else:
func = curand.generateUniformDouble
func(self._generator, out.data.ptr, out.size)
return out
def random_sample(self, size=None, dtype=float):
"""Returns an array of random values over the interval ``[0, 1)``.
.. seealso::
- :func:`cupy.random.random_sample` for full documentation
- :meth:`numpy.random.RandomState.random_sample`
"""
if size is None:
size = ()
out = self._random_sample_raw(size, dtype)
RandomState._mod1_kernel(out)
return out
def rayleigh(self, scale=1.0, size=None, dtype=float):
"""Returns an array of samples drawn from a rayleigh distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.rayleigh` for full documentation
- :meth:`numpy.random.RandomState.rayleigh`
"""
scale = cupy.asarray(scale)
if size is None:
size = scale.shape
if cupy.any(scale < 0): # synchronize!
raise ValueError('scale < 0')
x = self._random_sample_raw(size, dtype)
x = cupy.log(x, out=x)
x = cupy.multiply(x, -2., out=x)
x = cupy.sqrt(x, out=x)
x = cupy.multiply(x, scale, out=x)
return x
def _interval(self, mx, size):
"""Generate multiple integers independently sampled uniformly from ``[0, mx]``.
Args:
mx (int): Upper bound of the interval
size (None or int or tuple): Shape of the array or the scalar
returned.
Returns:
int or cupy.ndarray: If ``None``, an :class:`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, only 32 bit or 64 bit integers can be sampled.
""" # NOQA
if size is None:
size = ()
elif isinstance(size, int):
size = size,
if mx == 0:
return cupy.zeros(size, dtype=numpy.uint32)
if mx < 0:
raise ValueError(
'mx must be non-negative (actual: {})'.format(mx))
elif mx <= _UINT32_MAX:
dtype = numpy.uint32
upper_limit = _UINT32_MAX - (1 << 32) % (mx + 1)
elif mx <= _UINT64_MAX:
dtype = numpy.uint64
upper_limit = _UINT64_MAX - (1 << 64) % (mx + 1)
else:
raise ValueError(
'mx must be within uint64 range (actual: {})'.format(mx))
n_sample = functools.reduce(operator.mul, size, 1)
if n_sample == 0:
return cupy.empty(size, dtype=dtype)
sample = self._curand_generate(n_sample, dtype)
mx1 = mx + 1
if mx1 != (1 << (mx1.bit_length() - 1)):
# Get index of samples that exceed the upper limit
ng_indices = self._get_indices(sample, upper_limit, False)
n_ng = ng_indices.size
while n_ng > 0:
n_supplement = max(n_ng * 2, 1024)
supplement = self._curand_generate(n_supplement, dtype)
# Get index of supplements that are within the upper limit
ok_indices = self._get_indices(supplement, upper_limit, True)
n_ok = ok_indices.size
# Replace the values that exceed the upper limit
if n_ok >= n_ng:
sample[ng_indices] = supplement[ok_indices[:n_ng]]
n_ng = 0
else:
sample[ng_indices[:n_ok]] = supplement[ok_indices]
ng_indices = ng_indices[n_ok:]
n_ng -= n_ok
sample %= mx1
else:
mask = (1 << mx.bit_length()) - 1
sample &= mask
return sample.reshape(size)
def _curand_generate(self, num, dtype):
sample = cupy.empty((num,), dtype=dtype)
# Call 32-bit RNG to fill 32-bit or 64-bit `sample`
size32 = sample.view(dtype=numpy.uint32).size
curand.generate(self._generator, sample.data.ptr, size32)
return sample
def _get_indices(self, sample, upper_limit, cond):
dtype = numpy.uint32 if sample.size < 2**32 else numpy.uint64
flags = (sample <= upper_limit) if cond else (sample > upper_limit)
csum = cupy.cumsum(flags, dtype=dtype)
del flags
indices = cupy.empty((int(csum[-1]),), dtype=dtype)
self._kernel_get_indices(csum, indices, size=csum.size)
return indices
_kernel_get_indices = _core.ElementwiseKernel(
'raw U csum', 'raw U indices',
'''
int j = 0;
if (i > 0) { j = csum[i-1]; }
if (csum[i] > j) { indices[j] = i; }
''',
'cupy_get_indices')
def seed(self, seed=None):
"""Resets the state of the random number generator with a seed.
.. seealso::
- :func:`cupy.random.seed` for full documentation
- :meth:`numpy.random.RandomState.seed`
"""
if seed is None:
try:
seed_str = binascii.hexlify(os.urandom(8))
seed = int(seed_str, 16)
except NotImplementedError:
seed = (time.time() * 1000000) % _UINT64_MAX
else:
if isinstance(seed, numpy.ndarray):
seed = int(hashlib.md5(seed).hexdigest()[:16], 16)
else:
seed = int(
numpy.asarray(seed).astype(numpy.uint64, casting='safe'))
curand.setPseudoRandomGeneratorSeed(self._generator, seed)
if (self.method not in (curand.CURAND_RNG_PSEUDO_MT19937,
curand.CURAND_RNG_PSEUDO_MTGP32)):
curand.setGeneratorOffset(self._generator, 0)
self._rk_seed = seed
def standard_cauchy(self, size=None, dtype=float):
"""Returns an array of samples drawn from the standard cauchy distribution.
.. seealso::
- :func:`cupy.random.standard_cauchy` for full documentation
- :meth:`numpy.random.RandomState.standard_cauchy`
"""
x = self.uniform(size=size, dtype=dtype)
return cupy.tan(cupy.pi * (x - 0.5))
def standard_exponential(self, size=None, dtype=float):
"""Returns an array of samples drawn from the standard exp distribution.
.. seealso::
- :func:`cupy.random.standard_exponential` for full documentation
- :meth:`numpy.random.RandomState.standard_exponential`
"""
if size is None:
size = ()
x = self._random_sample_raw(size, dtype)
return -cupy.log(x, out=x)
def standard_gamma(self, shape, size=None, dtype=float):
"""Returns an array of samples drawn from a standard gamma distribution.
.. seealso::
- :func:`cupy.random.standard_gamma` for full documentation
- :meth:`numpy.random.RandomState.standard_gamma`
"""
shape = cupy.asarray(shape)
if size is None:
size = shape.shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.standard_gamma_kernel(shape, self._rk_seed, y)
self._update_seed(y.size)
return y
def standard_normal(self, size=None, dtype=float):
"""Returns samples drawn from the standard normal distribution.
.. seealso::
- :func:`cupy.random.standard_normal` for full documentation
- :meth:`numpy.random.RandomState.standard_normal`
"""
return self.normal(size=size, dtype=dtype)
def standard_t(self, df, size=None, dtype=float):
"""Returns an array of samples drawn from the standard t distribution.
.. seealso::
- :func:`cupy.random.standard_t` for full documentation
- :meth:`numpy.random.RandomState.standard_t`
"""
df = cupy.asarray(df)
if size is None:
size = df.shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.standard_t_kernel(df, self._rk_seed, y)
self._update_seed(y.size)
return y
def tomaxint(self, size=None):
"""Draws integers between 0 and max integer inclusive.
Return a sample of uniformly distributed random integers in the
interval [0, ``np.iinfo(np.int_).max``]. The `np.int_` type translates
to the C long integer type and its precision is platform dependent.
Args:
size (int or tuple of ints): Output shape.
Returns:
cupy.ndarray: Drawn samples.
.. seealso::
:meth:`numpy.random.RandomState.tomaxint`
"""
if size is None:
size = ()
sample = cupy.empty(size, dtype=cupy.int_)
# cupy.random only uses int32 random generator
size_in_int = sample.dtype.itemsize // 4
curand.generate(
self._generator, sample.data.ptr, sample.size * size_in_int)
# Disable sign bit
sample &= cupy.iinfo(cupy.int_).max
return sample
_triangular_kernel = _core.ElementwiseKernel(
'L left, M mode, R right', 'T x',
"""
T base, leftbase, ratio, leftprod, rightprod;
base = right - left;
leftbase = mode - left;
ratio = leftbase / base;
leftprod = leftbase*base;
rightprod = (right - mode)*base;
if (x <= ratio)
{
x = left + sqrt(x*leftprod);
} else
{
x = right - sqrt((1.0 - x) * rightprod);
}
""",
'cupy_triangular_kernel'
)
def triangular(self, left, mode, right, size=None, dtype=float):
"""Returns an array of samples drawn from the triangular distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.triangular` for full documentation
- :meth:`numpy.random.RandomState.triangular`
"""
left, mode, right = \
cupy.asarray(left), cupy.asarray(mode), cupy.asarray(right)
if cupy.any(left > mode): # synchronize!
raise ValueError('left > mode')
if cupy.any(mode > right): # synchronize!
raise ValueError('mode > right')
if cupy.any(left == right): # synchronize!
raise ValueError('left == right')
if size is None:
size = cupy.broadcast(left, mode, right).shape
x = self.random_sample(size=size, dtype=dtype)
return RandomState._triangular_kernel(left, mode, right, x)
_scale_kernel = _core.ElementwiseKernel(
'T low, T high', 'T x',
'x = T(low) + x * T(high - low)',
'cupy_scale')
def uniform(self, low=0.0, high=1.0, size=None, dtype=float):
"""Returns an array of uniformly-distributed samples over an interval.
.. seealso::
- :func:`cupy.random.uniform` for full documentation
- :meth:`numpy.random.RandomState.uniform`
"""
dtype = numpy.dtype(dtype)
rand = self.random_sample(size=size, dtype=dtype)
if not numpy.isscalar(low):
low = cupy.asarray(low, dtype)
if not numpy.isscalar(high):
high = cupy.asarray(high, dtype)
return RandomState._scale_kernel(low, high, rand)
def vonmises(self, mu, kappa, size=None, dtype=float):
"""Returns an array of samples drawn from the von Mises distribution.
.. seealso::
- :func:`cupy.random.vonmises` for full documentation
- :meth:`numpy.random.RandomState.vonmises`
"""
mu, kappa = cupy.asarray(mu), cupy.asarray(kappa)
if size is None:
size = cupy.broadcast(mu, kappa).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.vonmises_kernel(mu, kappa, self._rk_seed, y)
self._update_seed(y.size)
return y
_wald_kernel = _core.ElementwiseKernel(
'T mean, T scale, T U', 'T X',
"""
T mu_2l;
T Y;
mu_2l = mean / (2*scale);
Y = mean*X*X;
X = mean + mu_2l*(Y - sqrt(4*scale*Y + Y*Y));
if (U > mean/(mean+X))
{
X = mean*mean/X;
}
""",
'cupy_wald_scale')
def wald(self, mean, scale, size=None, dtype=float):
"""Returns an array of samples drawn from the Wald distribution.
.. seealso::
- :func:`cupy.random.wald` for full documentation
- :meth:`numpy.random.RandomState.wald`
"""
mean, scale = \
cupy.asarray(mean, dtype=dtype), cupy.asarray(scale, dtype=dtype)
if size is None:
size = cupy.broadcast(mean, scale).shape
x = self.normal(size=size, dtype=dtype)
u = self.random_sample(size=size, dtype=dtype)
return RandomState._wald_kernel(mean, scale, u, x)
def weibull(self, a, size=None, dtype=float):
"""Returns an array of samples drawn from the weibull distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.weibull` for full documentation
- :meth:`numpy.random.RandomState.weibull`
"""
a = cupy.asarray(a)
if cupy.any(a < 0): # synchronize!
raise ValueError('a < 0')
if size is None:
size = a.shape
x = self.standard_exponential(size, dtype)
cupy.power(x, 1./a, out=x)
return x
def zipf(self, a, size=None, dtype=int):
"""Returns an array of samples drawn from the Zipf distribution.
.. warning::
This function may synchronize the device.
.. seealso::