def construct_proposal(self, y):
"""
proposal is a mixture of normals,
centred at y and with covariance gamma^2 I + nu^2 MHaa'HM',
where a are the eigenvectors of centred kernel matrix Kc=HKH
"""
assert (len(shape(y)) == 1)
m = MixtureDistribution(self.distribution.dimension, self.num_eigen)
m.mixing_proportion = Discrete(
(self.eigvalues + 1) / (sum(self.eigvalues) + self.num_eigen))
# print "current mixing proportion: ", m.mixing_proportion.omega
M = 2 * self.kernel.gradient(y, self.Z)
H = Kernel.centring_matrix(len(self.Z))
for ii in range(self.num_eigen):
Sigma = self.gamma ** 2 * eye(len(y)) + \
self.nu2 * (M.T).dot(H.dot(outer(self.eigvectors[:, ii], self.eigvectors[:, ii]).dot(H.dot(M))))
m.components[ii] = Gaussian(y, Sigma)
return m
def construct_proposal(self, y):
"""
proposal is a mixture of normals,
centred at y and with covariance gamma^2 I + nu^2 MHaa'HM',
where a are the eigenvectors of centred kernel matrix Kc=HKH
"""
assert len(shape(y)) == 1
m = MixtureDistribution(self.distribution.dimension, self.num_eigen)
m.mixing_proportion = Discrete((self.eigvalues + 1) / (sum(self.eigvalues) + self.num_eigen))
# print "current mixing proportion: ", m.mixing_proportion.omega
M = 2 * self.kernel.gradient(y, self.Z)
H = Kernel.centring_matrix(len(self.Z))
for ii in range(self.num_eigen):
Sigma = self.gamma ** 2 * eye(len(y)) + self.nu2 * (M.T).dot(
H.dot(outer(self.eigvectors[:, ii], self.eigvectors[:, ii]).dot(H.dot(M)))
)
m.components[ii] = Gaussian(y, Sigma)
return m
def mean_and_cov_adapt(self,learn_scale):
current_1d=reshape(self.current_sample_object.samples, (self.distribution.dimension,))
difference=current_1d - self.mean_est
self.cov_est += learn_scale * (outer(difference, difference) - self.cov_est)
self.mean_est += learn_scale * (current_1d - self.mean_est)