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Added gpx dataset.

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This commit is contained in:
Neil Lawrence 2013-08-22 14:03:59 +02:00
parent 7cd119d2b0
commit 00a5f8e5f6
4 changed files with 123 additions and 91 deletions
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87
GPy/testing/kernel_tests.py
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@ -4,93 +4,6 @@
import unittest
import numpy as np
import GPy
from GPy.core.model import Model
class Kern_check_model(Model):
"""This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgradient() to be called independently on a kernel."""
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
num_samples = 20
num_samples2 = 10
if kernel==None:
kernel = GPy.kern.rbf(1)
if X==None:
X = np.random.randn(num_samples, kernel.input_dim)
if X2==None:
X2 = np.random.randn(num_samples2, kernel.input_dim)
if dL_dK==None:
dL_dK = np.ones((X.shape[0], X2.shape[0]))
self.kernel=kernel
self.X = X
self.X2 = X2
self.dL_dK = dL_dK
#self.constrained_indices=[]
#self.constraints=[]
Model.__init__(self)
def is_positive_definite(self):
v = np.linalg.eig(self.kernel.K(self.X))[0]
if any(v<0):
return False
else:
return True
def _get_params(self):
return self.kernel._get_params()
def _get_param_names(self):
return self.kernel._get_param_names()
def _set_params(self, x):
self.kernel._set_params(x)
def log_likelihood(self):
return (self.dL_dK*self.kernel.K(self.X, self.X2)).sum()
def _log_likelihood_gradients(self):
raise NotImplementedError, "This needs to be implemented to use the kern_check_model class."
class Kern_check_dK_dtheta(Kern_check_model):
"""This class allows gradient checks for the gradient of a kernel with respect to parameters. """
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)
def _log_likelihood_gradients(self):
return self.kernel.dK_dtheta(self.dL_dK, self.X, self.X2)
class Kern_check_dKdiag_dtheta(Kern_check_model):
"""This class allows gradient checks of the gradient of the diagonal of a kernel with respect to the parameters."""
def __init__(self, kernel=None, dL_dK=None, X=None):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
if dL_dK==None:
self.dL_dK = np.ones((self.X.shape[0]))
def log_likelihood(self):
return (self.dL_dK*self.kernel.Kdiag(self.X)).sum()
def _log_likelihood_gradients(self):
return self.kernel.dKdiag_dtheta(self.dL_dK, self.X)
class Kern_check_dK_dX(Kern_check_model):
"""This class allows gradient checks for the gradient 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)
def _log_likelihood_gradients(self):
return self.kernel.dK_dX(self.dL_dK, self.X, self.X2).flatten()
def _get_param_names(self):
names = []
for i in range(self.X.shape[0]):
for j in range(self.X.shape[0]):
names.append('X_' +str(i) + ','+str(j))
return names
def _get_params(self):
return self.X.flatten()
def _set_params(self, x):
self.X=x.reshape(self.X.shape)