/
log_ndtr.py
68 lines (52 loc) · 1.85 KB
/
log_ndtr.py
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import numpy
from chainer.backends import cuda
from chainer import function_node
from chainer.functions.math import erfcx
from chainer import utils
from chainer.utils import type_check
_log_ndtr_cpu = None
class LogNdtr(function_node.FunctionNode):
@property
def label(self):
return 'log_ndtr'
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):
global _log_ndtr_cpu
if _log_ndtr_cpu is None:
try:
from scipy import special
_log_ndtr_cpu = special.log_ndtr
except ImportError:
raise ImportError('SciPy is not available. Forward computation'
' of log_ndtr can not be done.')
self.retain_inputs((0,))
return utils.force_array(_log_ndtr_cpu(x[0]), dtype=x[0].dtype),
def forward_gpu(self, x):
self.retain_inputs((0,))
return cuda.elementwise(
'T x', 'T y',
'''
if (x > 0) {
y = log1p(-normcdf(-x));
} else {
y = log(0.5 * erfcx(-sqrt(0.5) * x)) - 0.5 * x * x;
}
''',
'elementwise_log_ndtr',
)(x[0]),
def backward(self, indexes, gy):
x = self.get_retained_inputs()[0]
return (2 / numpy.pi) ** 0.5 / erfcx.erfcx(- x / 2 ** 0.5) * gy[0],
def log_ndtr(x):
"""Logarithm of cumulative distribution function of normal distribution.
.. 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 LogNdtr().apply((x,))[0]