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import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
_digamma_cpu = None
class DiGamma(function_node.FunctionNode):
def label(self):
return 'digamma'
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 _digamma_cpu
if _digamma_cpu is None:
from scipy import special
_digamma_cpu = special.digamma
except ImportError:
raise ImportError('SciPy is not available. Forward computation'
' of digamma can not be done.')
return utils.force_array(_digamma_cpu(x[0]), dtype=x[0].dtype),
def forward_gpu(self, x):
return utils.force_array(
cuda.cupyx.scipy.special.digamma(x[0]), dtype=x[0].dtype),
def backward(self, indexes, gy):
z = self.get_retained_inputs()[0]
xp = backend.get_array_module(*gy)
return chainer.functions.polygamma(xp.array(1), z) * gy[0],
def digamma(x):
"""Digamma function.
.. note::
Forward computation in CPU can not be done if
`SciPy <>`_ is not available.
x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
~chainer.Variable: Output variable.
return DiGamma().apply((x,))[0]
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