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Alans change to checkgrad

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James Hensman 2014-03-13 17:05:46 +00:00
parent 433b213165
commit f50b121d4d
1 changed files with 5 additions and 6 deletions
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11
GPy/core/model.py
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@ -15,7 +15,7 @@ import itertools
class Model(Parameterized): class Model(Parameterized):
_fail_count = 0 # Count of failed optimization steps (see objective) _fail_count = 0 # Count of failed optimization steps (see objective)
_allowed_failures = 10 # number of allowed failures _allowed_failures = 10 # number of allowed failures
def __init__(self, name): def __init__(self, name):
super(Model, self).__init__(name) # Parameterized.__init__(self) super(Model, self).__init__(name) # Parameterized.__init__(self)
self.optimization_runs = [] self.optimization_runs = []
@ -27,7 +27,7 @@ class Model(Parameterized):
def _log_likelihood_gradients(self): def _log_likelihood_gradients(self):
return self.gradient return self.gradient
def _getstate(self): def _getstate(self):
""" """
Get the current state of the class. Get the current state of the class.
@ -231,7 +231,7 @@ class Model(Parameterized):
raise RuntimeError, "Cannot optimize, when everything is fixed" raise RuntimeError, "Cannot optimize, when everything is fixed"
if self.size == 0: if self.size == 0:
raise RuntimeError, "Model without parameters cannot be minimized" raise RuntimeError, "Model without parameters cannot be minimized"
if optimizer is None: if optimizer is None:
optimizer = self.preferred_optimizer optimizer = self.preferred_optimizer
@ -301,9 +301,8 @@ class Model(Parameterized):
denominator = (2 * np.dot(dx, gradient)) denominator = (2 * np.dot(dx, gradient))
global_ratio = (f1 - f2) / np.where(denominator==0., 1e-32, denominator) global_ratio = (f1 - f2) / np.where(denominator==0., 1e-32, denominator)
gloabl_diff = (f1 - f2) - denominator
return (np.abs(1. - global_ratio) < tolerance) or (np.abs(gloabl_diff) == 0) return np.abs(1. - global_ratio) < tolerance)
else: else:
# check the gradient of each parameter individually, and do some pretty printing # check the gradient of each parameter individually, and do some pretty printing
try: try:
@ -349,7 +348,7 @@ class Model(Parameterized):
xx[xind] -= 2.*step xx[xind] -= 2.*step
f2 = self.objective_function(xx) f2 = self.objective_function(xx)
numerical_gradient = (f1 - f2) / (2 * step) numerical_gradient = (f1 - f2) / (2 * step)
if np.all(gradient[xind]==0): ratio = (f1-f2) == gradient[xind] if np.all(gradient[xind]==0): ratio = (f1-f2) == gradient[xind]
else: ratio = (f1 - f2) / (2 * step * gradient[xind]) else: ratio = (f1 - f2) / (2 * step * gradient[xind])
difference = np.abs((f1 - f2) / 2 / step - gradient[xind]) difference = np.abs((f1 - f2) / 2 / step - gradient[xind])