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.rk_seed += numpy.prod(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 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
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.rk_seed += cupy.core.internal.prod(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
