/
ndtri.py
61 lines (48 loc) · 1.64 KB
/
ndtri.py
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try:
from scipy import special
available_cpu = True
except ImportError as e:
available_cpu = False
_import_error = e
import math
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Ndtri(function_node.FunctionNode):
@property
def label(self):
return 'ndtri'
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 1)
type_check.expect(in_types[0].dtype.kind == 'f')
def forward_cpu(self, x):
if not available_cpu:
raise ImportError('SciPy is not available. Forward computation'
' of ndtri in CPU can not be done.' +
str(_import_error))
self.retain_outputs((0,))
return utils.force_array(special.ndtri(x[0]), dtype=x[0].dtype),
def forward_gpu(self, x):
self.retain_outputs((0,))
return cuda.elementwise(
'T x', 'T y',
'y = normcdfinv(x)',
'elementwise_ndtri',
)(x[0]),
def backward(self, indexes, gy):
y, = self.get_retained_outputs()
sqrt_2pi = (2 * math.pi) ** 0.5
return sqrt_2pi * chainer.functions.exp(0.5 * y ** 2) * gy[0],
def ndtri(x):
"""Elementwise inverse function of ndtr.
.. 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 Ndtri().apply((x,))[0]