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from __future__ import absolute_import, print_function, division
# Definitions of theano.scalar ops that have their python implementation taken
# from SciPy. As SciPy is not always available, we treat them separately.
import numpy as np
import theano
from theano.gradient import grad_not_implemented
from theano.scalar.basic import (UnaryScalarOp, BinaryScalarOp,
exp, upgrade_to_float,
upgrade_to_float64,
float_types)
from theano.scalar.basic import (upgrade_to_float_no_complex,
complex_types, discrete_types,
upcast)
imported_scipy_special = False
try:
import scipy.special
import scipy.stats
imported_scipy_special = True
# Importing scipy.special may raise ValueError.
# See http://projects.scipy.org/scipy/ticket/1739
except (ImportError, ValueError):
pass
class Erf(UnaryScalarOp):
nfunc_spec = ('scipy.special.erf', 1, 1)
def impl(self, x):
if imported_scipy_special:
return scipy.special.erf(x)
else:
super(Erf, self).impl(x)
def L_op(self, inputs, outputs, grads):
x, = inputs
gz, = grads
if x.type in complex_types:
raise NotImplementedError()
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
return [x.zeros_like()]
cst = np.asarray(2. / np.sqrt(np.pi),
dtype=upcast(x.type.dtype, gz.type.dtype))
return gz * cst * exp(-x * x),
def c_code(self, node, name, inp, out, sub):
x, = inp
z, = out
if node.inputs[0].type in complex_types:
raise NotImplementedError('type not supported', type)
cast = node.outputs[0].type.dtype_specs()[1]
return "%(z)s = erf((%(cast)s)%(x)s);" % locals()
erf = Erf(upgrade_to_float, name='erf')
class Erfc(UnaryScalarOp):
nfunc_spec = ('scipy.special.erfc', 1, 1)
def impl(self, x):
if imported_scipy_special:
return scipy.special.erfc(x)
else:
super(Erfc, self).impl(x)
def L_op(self, inputs, outputs, grads):
x, = inputs
gz, = grads
if x.type in complex_types:
raise NotImplementedError()
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
return [x.zeros_like()]
cst = np.asarray(2. / np.sqrt(np.pi),
dtype=upcast(x.type.dtype, gz.type.dtype))
return - gz * cst * exp(-x * x),
def c_code(self, node, name, inp, out, sub):
x, = inp
z, = out
if node.inputs[0].type in complex_types:
raise NotImplementedError('type not supported', type)
cast = node.outputs[0].type.dtype_specs()[1]
return "%(z)s = erfc((%(cast)s)%(x)s);" % locals()
# scipy.special.erfc don't support complex. Why?
erfc = Erfc(upgrade_to_float_no_complex, name='erfc')
class Erfcx(UnaryScalarOp):
"""
Implements the scaled complementary error function exp(x**2)*erfc(x) in a
numerically stable way for large x. This is useful for calculating things
like log(erfc(x)) = log(erfcx(x)) - x ** 2 without causing underflow.
Should only be used if x is known to be large and positive, as using
erfcx(x) for large negative x may instead introduce overflow problems.
Notes
-----
This op can still be executed on GPU, despite not having c_code. When
running on GPU an optimization will replace it with a gpu version.
"""
nfunc_spec = ('scipy.special.erfcx', 1, 1)
def impl(self, x):
if imported_scipy_special:
return scipy.special.erfcx(x)
else:
super(Erfcx, self).impl(x)
def L_op(self, inputs, outputs, grads):
x, = inputs
gz, = grads
if x.type in complex_types:
raise NotImplementedError()
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
return [x.zeros_like()]
cst = np.asarray(2. / np.sqrt(np.pi),
dtype=upcast(x.type.dtype, gz.type.dtype))
return gz * (-cst + (2. * x) * erfcx(x)),
erfcx = Erfcx(upgrade_to_float_no_complex, name='erfcx')
class Erfinv(UnaryScalarOp):
"""
Implements the inverse error function.
Notes
-----
This op can still be executed on GPU, despite not having c_code. When
running on GPU, an optimization will replace it with a GPU version.
(TODO) Find a C implementation of erfinv for CPU.
"""
nfunc_spec = ('scipy.special.erfinv', 1, 1)
def impl(self, x):
if imported_scipy_special:
return scipy.special.erfinv(x)
else:
super(Erfinv, self).impl(x)
def L_op(self, inputs, outputs, grads):
x, = inputs
gz, = grads
if x.type in complex_types:
raise NotImplementedError()
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
return [x.zeros_like()]
cst = np.asarray(np.sqrt(np.pi) / 2.,
dtype=upcast(x.type.dtype, gz.type.dtype))
return gz * cst * exp(erfinv(x) ** 2),
# TODO: erfinv() is not provided by the C standard library
# def c_code(self, node, name, inp, out, sub):
# x, = inp
# z, = out
# if node.inputs[0].type in complex_types:
# raise NotImplementedError('type not supported', type)
# return "%(z)s = erfinv(%(x)s);" % locals()
erfinv = Erfinv(upgrade_to_float_no_complex, name='erfinv')
class Erfcinv(UnaryScalarOp):
nfunc_spec = ('scipy.special.erfcinv', 1, 1)
def impl(self, x):
if imported_scipy_special:
return scipy.special.erfcinv(x)
else:
super(Erfcinv, self).impl(x)
def L_op(self, inputs, outputs, grads):
x, = inputs
gz, = grads
if x.type in complex_types:
raise NotImplementedError()
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
return [x.zeros_like()]
cst = np.asarray(np.sqrt(np.pi) / 2.,
dtype=upcast(x.type.dtype, gz.type.dtype))
return - gz * cst * exp(erfcinv(x) ** 2),
# TODO: erfcinv() is not provided by the C standard library
# def c_code(self, node, name, inp, out, sub):
# x, = inp
# z, = out
# if node.inputs[0].type in complex_types:
# raise NotImplementedError('type not supported', type)
# return "%(z)s = erfcinv(%(x)s);" % locals()
erfcinv = Erfcinv(upgrade_to_float_no_complex, name='erfcinv')
class Gamma(UnaryScalarOp):
nfunc_spec = ('scipy.special.gamma', 1, 1)
@staticmethod
def st_impl(x):
return scipy.special.gamma(x)
def impl(self, x):
if imported_scipy_special:
return Gamma.st_impl(x)
else:
super(Gamma, self).impl(x)
def L_op(self, inputs, outputs, gout):
(x,) = inputs
(gz,) = gout
if x.type in complex_types:
raise NotImplementedError()
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
return [x.zeros_like()]
return gz * gamma(x) * psi(x),
def c_code(self, node, name, inputs, outputs, sub):
(x,) = inputs
(z,) = outputs
if node.inputs[0].type in float_types:
return """%(z)s = tgamma(%(x)s);""" % locals()
raise NotImplementedError('only floating point is implemented')
gamma = Gamma(upgrade_to_float, name='gamma')
class GammaLn(UnaryScalarOp):
"""
Log gamma function.
"""
nfunc_spec = ('scipy.special.gammaln', 1, 1)
@staticmethod
def st_impl(x):
return scipy.special.gammaln(x)
def impl(self, x):
if imported_scipy_special:
return GammaLn.st_impl(x)
else:
super(GammaLn, self).impl(x)
def L_op(self, inputs, outputs, grads):
x, = inputs
gz, = grads
if x.type in complex_types:
raise NotImplementedError()
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
return [x.zeros_like()]
return [gz * psi(x)]
def c_code(self, node, name, inp, out, sub):
x, = inp
z, = out
# no c code for complex
# [u]int* will be casted to float64 before computation
if node.inputs[0].type in complex_types:
raise NotImplementedError(
'gammaln complex c code is not implemented')
# For some reason, on the GPU, uint64 inputs don't get casted
# automatically to float64. This make the compilation crash
dtype = ""
cast = node.outputs[0].type.dtype_specs()[1]
return """%(z)s = lgamma((%(cast)s)%(x)s);""" % locals()
gammaln = GammaLn(upgrade_to_float, name='gammaln')
class Psi(UnaryScalarOp):
"""
Derivative of log gamma function.
"""
nfunc_spec = ('scipy.special.psi', 1, 1)
@staticmethod
def st_impl(x):
return scipy.special.psi(x)
def impl(self, x):
if imported_scipy_special:
return Psi.st_impl(x)
else:
super(Psi, self).impl(x)
def L_op(self, inputs, outputs, grads):
x, = inputs
gz, = grads
if x.type in complex_types:
raise NotImplementedError()
if outputs[0].type in discrete_types:
if x.type in discrete_types:
return [x.zeros_like(dtype=theano.config.floatX)]
else:
return [x.zeros_like()]
return [gz * tri_gamma(x)]
def c_support_code(self):
return (
"""
// For GPU support
#ifdef WITHIN_KERNEL
#define DEVICE WITHIN_KERNEL
#else
#define DEVICE
#endif
#ifndef ga_double
#define ga_double double
#endif
#ifndef _PSIFUNCDEFINED
#define _PSIFUNCDEFINED
DEVICE double _psi(ga_double x) {
/*taken from
Bernardo, J. M. (1976). Algorithm AS 103:
Psi (Digamma) Function. Applied Statistics. 25 (3), 315-317.
http://www.uv.es/~bernardo/1976AppStatist.pdf */
ga_double y, R, psi_ = 0;
ga_double S = 1.0e-5;
ga_double C = 8.5;
ga_double S3 = 8.333333333e-2;
ga_double S4 = 8.333333333e-3;
ga_double S5 = 3.968253968e-3;
ga_double D1 = -0.5772156649;
y = x;
if (y <= 0.0)
return psi_;
if (y <= S)
return D1 - 1.0/y;
while (y < C) {
psi_ = psi_ - 1.0 / y;
y = y + 1;
}
R = 1.0 / y;
psi_ = psi_ + log(y) - .5 * R ;
R= R*R;
psi_ = psi_ - R * (S3 - R * (S4 - R * S5));
return psi_;
}
#endif
""")
def c_code(self, node, name, inp, out, sub):
x, = inp
z, = out
if node.inputs[0].type in float_types:
return """%(z)s =
_psi(%(x)s);""" % locals()
raise NotImplementedError('only floating point is implemented')
psi = Psi(upgrade_to_float, name='psi')
class TriGamma(UnaryScalarOp):
"""
Second derivative of log gamma function.
"""
@staticmethod
def st_impl(x):
return scipy.special.polygamma(1, x)
def impl(self, x):
if imported_scipy_special:
return TriGamma.st_impl(x)
else:
super(TriGamma, self).impl(x)
def grad(self, inputs, outputs_gradients):
raise NotImplementedError()
def c_support_code(self):
# The implementation has been copied from
# http://people.sc.fsu.edu/~jburkardt/cpp_src/asa121/asa121.html
return (
"""
// For GPU support
#ifdef WITHIN_KERNEL
#define DEVICE WITHIN_KERNEL
#else
#define DEVICE
#endif
#ifndef ga_double
#define ga_double double
#endif
#ifndef _TRIGAMMAFUNCDEFINED
#define _TRIGAMMAFUNCDEFINED
DEVICE double _tri_gamma(ga_double x) {
double a = 0.0001;
double b = 5.0;
double b2 = 0.1666666667;
double b4 = -0.03333333333;
double b6 = 0.02380952381;
double b8 = -0.03333333333;
double value;
double y;
double z;
if (x <= 0) {
return 0.0;
}
if ( x <= a ) {
value = 1.0 / x / x;
return value;
}
value = 0.0;
z = x;
while ( z < b ) {
value += 1.0 / z / z;
z += 1.0;
}
y = 1.0 / z / z;
value += 0.5 * y + (1.0 + y * (b2 + y * (b4 + y * (b6 + y * b8 )))) / z;
return value;
}
#endif
""")
def c_code(self, node, name, inp, out, sub):
x, = inp
z, = out
if node.inputs[0].type in float_types:
return """%(z)s =
_tri_gamma(%(x)s);""" % locals()
raise NotImplementedError('only floating point is implemented')
tri_gamma = TriGamma(upgrade_to_float, name='tri_gamma')
class Chi2SF(BinaryScalarOp):
"""
Compute (1 - chi2_cdf(x)) ie. chi2 pvalue (chi2 'survival function').
C code is provided in the Theano_lgpl repository.
This make it faster.
https://github.com/Theano/Theano_lgpl.git
"""
nfunc_spec = ('scipy.stats.chi2.sf', 2, 1)
@staticmethod
def st_impl(x, k):
return scipy.stats.chi2.sf(x, k)
def impl(self, x, k):
if imported_scipy_special:
return Chi2SF.st_impl(x, k)
else:
super(Chi2SF, self).impl(x, k)
chi2sf = Chi2SF(upgrade_to_float64, name='chi2sf')
class Jv(BinaryScalarOp):
"""
Bessel function of the first kind of order v (real).
"""
nfunc_spec = ('scipy.special.jv', 2, 1)
@staticmethod
def st_impl(v, x):
return scipy.special.jv(v, x)
def impl(self, v, x):
if imported_scipy_special:
return self.st_impl(v, x)
else:
super(Jv, self).impl(v, x)
def grad(self, inputs, grads):
v, x = inputs
gz, = grads
return [grad_not_implemented(self, 0, v),
gz * (jv(v - 1, x) - jv(v + 1, x)) / 2.]
jv = Jv(upgrade_to_float, name='jv')
class J1(UnaryScalarOp):
"""
Bessel function of the first kind of order 1.
"""
nfunc_spec = ('scipy.special.j1', 1, 1)
@staticmethod
def st_impl(x):
return scipy.special.j1(x)
def impl(self, x):
if imported_scipy_special:
return self.st_impl(x)
else:
super(J1, self).impl(x)
def grad(self, inputs, grads):
x, = inputs
gz, = grads
return [gz * (j0(x) - jv(2, x)) / 2.]
def c_code(self, node, name, inp, out, sub):
x, = inp
z, = out
if node.inputs[0].type in float_types:
return """%(z)s =
j1(%(x)s);""" % locals()
raise NotImplementedError('only floating point is implemented')
j1 = J1(upgrade_to_float, name='j1')
class J0(UnaryScalarOp):
"""
Bessel function of the first kind of order 0.
"""
nfunc_spec = ('scipy.special.j0', 1, 1)
@staticmethod
def st_impl(x):
return scipy.special.j0(x)
def impl(self, x):
if imported_scipy_special:
return self.st_impl(x)
else:
super(J0, self).impl(x)
def grad(self, inp, grads):
x, = inp
gz, = grads
return [gz * -1 * j1(x)]
def c_code(self, node, name, inp, out, sub):
x, = inp
z, = out
if node.inputs[0].type in float_types:
return """%(z)s =
j0(%(x)s);""" % locals()
raise NotImplementedError('only floating point is implemented')
j0 = J0(upgrade_to_float, name='j0')
class Iv(BinaryScalarOp):
"""
Modified Bessel function of the first kind of order v (real).
"""
nfunc_spec = ('scipy.special.iv', 2, 1)
@staticmethod
def st_impl(v, x):
return scipy.special.iv(v, x)
def impl(self, v, x):
if imported_scipy_special:
return self.st_impl(v, x)
else:
super(Iv, self).impl(v, x)
def grad(self, inputs, grads):
v, x = inputs
gz, = grads
return [grad_not_implemented(self, 0, v),
gz * (iv(v - 1, x) + iv(v + 1, x)) / 2.]
iv = Iv(upgrade_to_float, name='iv')
class I1(UnaryScalarOp):
"""
Modified Bessel function of the first kind of order 1.
"""
nfunc_spec = ('scipy.special.i1', 1, 1)
@staticmethod
def st_impl(x):
return scipy.special.i1(x)
def impl(self, x):
if imported_scipy_special:
return self.st_impl(x)
else:
super(I1, self).impl(x)
def grad(self, inputs, grads):
x, = inputs
gz, = grads
return [gz * (i0(x) + iv(2, x)) / 2.]
i1 = I1(upgrade_to_float, name='i1')
class I0(UnaryScalarOp):
"""
Modified Bessel function of the first kind of order 0.
"""
nfunc_spec = ('scipy.special.i0', 1, 1)
@staticmethod
def st_impl(x):
return scipy.special.i0(x)
def impl(self, x):
if imported_scipy_special:
return self.st_impl(x)
else:
super(I0, self).impl(x)
def grad(self, inp, grads):
x, = inp
gz, = grads
return [gz * i1(x)]
i0 = I0(upgrade_to_float, name='i0')
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