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lgamma.py
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lgamma.py
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import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
_lgamma_cpu = None
class LGamma(function_node.FunctionNode):
@property
def label(self):
return 'lgamma'
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x',))
type_check.expect(in_types[0].dtype.kind == 'f')
def forward_cpu(self, x):
global _lgamma_cpu
if _lgamma_cpu is None:
try:
from scipy import special
_lgamma_cpu = special.gammaln
except ImportError:
raise ImportError('SciPy is not available. Forward computation'
' of lgamma can not be done.')
self.retain_inputs((0,))
return utils.force_array(_lgamma_cpu(x[0]), dtype=x[0].dtype),
def forward_gpu(self, x):
self.retain_inputs((0,))
return utils.force_array(
cuda.cupyx.scipy.special.gammaln(x[0]), dtype=x[0].dtype),
def backward(self, indexes, gy):
z = self.get_retained_inputs()[0]
return chainer.functions.digamma(z) * gy[0],
def lgamma(x):
"""logarithm of gamma function.
.. note::
Forward computation in CPU can not be done if
`SciPy <https://www.scipy.org/>`_ is not available.
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
Returns:
~chainer.Variable: Output variable.
"""
return LGamma().apply((x,))[0]