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Check for sympy

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Alan Saul 2014-03-31 18:22:16 +01:00
parent 292e076a9a
commit 970e133bca
1 changed files with 18 additions and 14 deletions
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32
GPy/likelihoods/symbolic.py
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@ -2,8 +2,12 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt) # Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np import numpy as np
import sympy as sym try:
from sympy.utilities.lambdify import lambdify import sympy as sym
sympy_available=True
from sympy.utilities.lambdify import lambdify
except ImportError:
sympy_available=False
import link_functions import link_functions
from scipy import stats, integrate from scipy import stats, integrate
from scipy.special import gammaln, gamma, erf, polygamma from scipy.special import gammaln, gamma, erf, polygamma
@ -56,7 +60,7 @@ class Symbolic(Likelihood):
# these are arguments for computing derivatives. # these are arguments for computing derivatives.
derivative_arguments = self._sp_f + self._sp_theta derivative_arguments = self._sp_f + self._sp_theta
# Do symbolic work to compute derivatives. # Do symbolic work to compute derivatives.
self._log_likelihood_derivatives = {theta.name : sym.diff(self._sp_log_likelihood,theta).simplify() for theta in derivative_arguments} self._log_likelihood_derivatives = {theta.name : sym.diff(self._sp_log_likelihood,theta).simplify() for theta in derivative_arguments}
self._log_likelihood_second_derivatives = {theta.name : sym.diff(self._log_likelihood_derivatives['f'],theta).simplify() for theta in derivative_arguments} self._log_likelihood_second_derivatives = {theta.name : sym.diff(self._log_likelihood_derivatives['f'],theta).simplify() for theta in derivative_arguments}
@ -78,7 +82,7 @@ class Symbolic(Likelihood):
self.log_concave = log_concave self.log_concave = log_concave
# initialise code arguments # initialise code arguments
self._arguments = {} self._arguments = {}
# generate the code for the likelihood and derivatives # generate the code for the likelihood and derivatives
self._gen_code() self._gen_code()
@ -95,7 +99,7 @@ class Symbolic(Likelihood):
setattr(self, '_first_derivative_code', {key: lambdify(self.arg_list, self._log_likelihood_derivatives[key], func_modules) for key in self._log_likelihood_derivatives.keys()}) setattr(self, '_first_derivative_code', {key: lambdify(self.arg_list, self._log_likelihood_derivatives[key], func_modules) for key in self._log_likelihood_derivatives.keys()})
setattr(self, '_second_derivative_code', {key: lambdify(self.arg_list, self._log_likelihood_second_derivatives[key], func_modules) for key in self._log_likelihood_second_derivatives.keys()}) setattr(self, '_second_derivative_code', {key: lambdify(self.arg_list, self._log_likelihood_second_derivatives[key], func_modules) for key in self._log_likelihood_second_derivatives.keys()})
setattr(self, '_third_derivative_code', {key: lambdify(self.arg_list, self._log_likelihood_third_derivatives[key], func_modules) for key in self._log_likelihood_third_derivatives.keys()}) setattr(self, '_third_derivative_code', {key: lambdify(self.arg_list, self._log_likelihood_third_derivatives[key], func_modules) for key in self._log_likelihood_third_derivatives.keys()})
# TODO: compute EP code parts based on logZ. We need dlogZ/dmu, d2logZ/dmu2 and dlogZ/dtheta # TODO: compute EP code parts based on logZ. We need dlogZ/dmu, d2logZ/dmu2 and dlogZ/dtheta
def parameters_changed(self): def parameters_changed(self):
@ -157,7 +161,7 @@ class Symbolic(Likelihood):
:type inv_inv_link_f: Nx1 array :type inv_inv_link_f: Nx1 array
:param y: data :param y: data
:type y: Nx1 array :type y: Nx1 array
:param Y_metadata: Y_metadata :param Y_metadata: Y_metadata
:returns: likelihood evaluated for this point :returns: likelihood evaluated for this point
:rtype: float :rtype: float
@ -175,12 +179,12 @@ class Symbolic(Likelihood):
:type inv_inv_link_f: Nx1 array :type inv_inv_link_f: Nx1 array
:param y: data :param y: data
:type y: Nx1 array :type y: Nx1 array
:param Y_metadata: Y_metadata :param Y_metadata: Y_metadata
:returns: gradient of likelihood with respect to each point. :returns: gradient of likelihood with respect to each point.
:rtype: Nx1 array :rtype: Nx1 array
""" """
assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape
self._arguments_update(inv_link_f, y) self._arguments_update(inv_link_f, y)
return self._first_derivative_code['f'](**self._arguments) return self._first_derivative_code['f'](**self._arguments)
@ -204,28 +208,28 @@ class Symbolic(Likelihood):
distribution for y_i depends only on link(f_i) not on distribution for y_i depends only on link(f_i) not on
link(f_(j!=i)) link(f_(j!=i))
""" """
assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape
self._arguments_update(inv_link_f, y) self._arguments_update(inv_link_f, y)
return self._second_derivative_code['f'](**self._arguments) return self._second_derivative_code['f'](**self._arguments)
def d3logpdf_dlink3(self, inv_link_f, y, Y_metadata=None): def d3logpdf_dlink3(self, inv_link_f, y, Y_metadata=None):
assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape
self._arguments_update(inv_link_f, y) self._arguments_update(inv_link_f, y)
return self._third_derivative_code['f'](**self._arguments) return self._third_derivative_code['f'](**self._arguments)
raise NotImplementedError raise NotImplementedError
def dlogpdf_link_dtheta(self, inv_link_f, y, Y_metadata=None): def dlogpdf_link_dtheta(self, inv_link_f, y, Y_metadata=None):
assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape
self._arguments_update(inv_link_f, y) self._arguments_update(inv_link_f, y)
return np.hstack([self._first_derivative_code[theta.name](**self._arguments) for theta in self._sp_theta]).sum(0) return np.hstack([self._first_derivative_code[theta.name](**self._arguments) for theta in self._sp_theta]).sum(0)
def dlogpdf_dlink_dtheta(self, inv_link_f, y, Y_metadata=None): def dlogpdf_dlink_dtheta(self, inv_link_f, y, Y_metadata=None):
assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape
self._arguments_update(inv_link_f, y) self._arguments_update(inv_link_f, y)
return np.hstack([self._second_derivative_code[theta.name](**self._arguments) for theta in self._sp_theta]) return np.hstack([self._second_derivative_code[theta.name](**self._arguments) for theta in self._sp_theta])
def d2logpdf_dlink2_dtheta(self, inv_link_f, y, Y_metadata=None): def d2logpdf_dlink2_dtheta(self, inv_link_f, y, Y_metadata=None):
assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape assert np.atleast_1d(inv_link_f).shape == np.atleast_1d(y).shape
self._arguments_update(inv_link_f, y) self._arguments_update(inv_link_f, y)
return np.hstack([self._third_derivative_code[theta.name](**self._arguments) for theta in self._sp_theta]) return np.hstack([self._third_derivative_code[theta.name](**self._arguments) for theta in self._sp_theta])