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fixed bug in kernel_tests for gradients_XX

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alessandratosi 2016-04-25 14:53:00 +01:00
parent 401bfbf20c
commit 1cf77c1051
3 changed files with 4 additions and 99 deletions
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97
GPy/testing/kernel_tests.py
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@ -120,25 +120,8 @@ class Kern_check_d2K_dXdX_cov(Kern_check_model):
grads = self.kernel.gradients_XX(self.dL_dK, self.X, self.X2, cov=True)
self.X.gradient[:] = grads.sum(-1).sum(1)
class Kern_check_d2K_dXdX_no_cov(Kern_check_model):
"""This class allows gradient checks for the secondderivative of a kernel with respect to X. """
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
self.X = Param('X',X)
self.link_parameter(self.X)
def log_likelihood(self):
return np.sum(self.kernel.gradients_X(self.dL_dK,self.X, self.X2))
def parameters_changed(self):
#if self.kernel.name == 'rbf':
# import ipdb;ipdb.set_trace()
grads = self.kernel.gradients_XX(self.dL_dK, self.X, self.X2, cov=False)
self.X.gradient[:] = grads.sum(1)
class Kern_check_d2Kdiag_dXdX_cov(Kern_check_model):
"""This class allows gradient checks for the secondderivative of a kernel with respect to X. """
"""This class allows gradient checks for the second derivative of a kernel with respect to X. """
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
self.X = Param('X',X)
@ -151,23 +134,6 @@ class Kern_check_d2Kdiag_dXdX_cov(Kern_check_model):
grads = self.kernel.gradients_XX_diag(self.dL_dK.diagonal(), self.X, cov=True)
self.X.gradient[:] = grads.sum(-1)
class Kern_check_d2Kdiag_dXdX_no_cov(Kern_check_model):
"""This class allows gradient checks for the secondderivative of a kernel with respect to X. """
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
self.X = Param('X',X)
self.link_parameter(self.X)
def log_likelihood(self):
return np.sum(self.kernel.gradients_X_diag(self.dL_dK.diagonal(),self.X))
def parameters_changed(self):
grads = self.kernel.gradients_XX_diag(self.dL_dK.diagonal(), self.X, cov=False)
self.X.gradient[:] = grads
# class Kern_check_d2Kdiag_dXdX(Kern_check_model):
# """This class allows gradient checks for the secondderivative of a kernel diagonal with respect to X. """
def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verbose=False, fixed_X_dims=None):
"""
This function runs on kernels to check the correctness of their
@ -304,46 +270,6 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
assert(result)
return False
if verbose:
print("Checking gradients of dK(X, X) wrt X.")
try:
testmodel = Kern_check_d2K_dXdX_no_cov(kern, X=X, X2=None)
if fixed_X_dims is not None:
testmodel.X[:,fixed_X_dims].fix()
result = testmodel.checkgrad(verbose=verbose)
except NotImplementedError:
result=True
if verbose:
print(("gradients_X not implemented for " + kern.name))
if result and verbose:
print("Check passed.")
if not result:
print(("Gradient of dK(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:"))
testmodel.checkgrad(verbose=True)
assert(result)
pass_checks = False
return False
if verbose:
print("Checking gradients of dK(X, X2) wrt X.")
try:
testmodel = Kern_check_d2K_dXdX_no_cov(kern, X=X, X2=X2)
if fixed_X_dims is not None:
testmodel.X[:,fixed_X_dims].fix()
result = testmodel.checkgrad(verbose=verbose)
except NotImplementedError:
result=True
if verbose:
print(("gradients_X not implemented for " + kern.name))
if result and verbose:
print("Check passed.")
if not result:
print(("Gradient of dK(X, X2) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:"))
testmodel.checkgrad(verbose=True)
assert(result)
pass_checks = False
return False
if verbose:
print("Checking gradients of dK(X, X) wrt X with full cov in dimensions")
try:
@ -384,27 +310,6 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
pass_checks = False
return False
if verbose:
print("Checking gradients of dKdiag(X, X) wrt X.")
try:
testmodel = Kern_check_d2Kdiag_dXdX_no_cov(kern, X=X, X2=None)
if fixed_X_dims is not None:
testmodel.X[:,fixed_X_dims].fix()
result = testmodel.checkgrad(verbose=verbose)
except NotImplementedError:
result=True
if verbose:
print(("gradients_X not implemented for " + kern.name))
if result and verbose:
print("Check passed.")
if not result:
print(("Gradient of dKdiag(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:"))
testmodel.checkgrad(verbose=True)
assert(result)
pass_checks = False
return False
if verbose:
print("Checking gradients of dKdiag(X, X) wrt X with cov in dimensions")
try: