def construct_proposal(self, y):
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
for ii in range(self.num_eigen):
L = sqrt(self.dwscale[ii] * self.eigvalues[ii]) * reshape(self.eigvectors[:, ii], (self.distribution.dimension, 1))
m.components[ii] = Gaussian(y, L, is_cholesky=True, ell=1)
# Z=m.sample(1000).samples
# Visualise.plot_data(Z)
return m
def fit_gmm(self, samples):
"""
Runs a couple of em instances on random starting points and returns
internal GMM representation of best instance
"""
features = RealFeatures(samples.T)
gmms = []
log_likelihoods = zeros(self.num_runs_em)
for i in range(self.num_runs_em):
# set up Shogun's GMM class and run em (corresponds to random
# initialisation)
gmm = GMM(self.num_components)
gmm.set_features(features)
log_likelihoods[i] = gmm.train_em()
gmms.append(gmm)
max_idx = log_likelihoods.argmax()
# construct Gaussian mixture components in internal representation
components = []
for i in range(self.num_components):
mu = gmms[max_idx].get_nth_mean(i)
Sigma = gmms[max_idx].get_nth_cov(i)
components.append(Gaussian(mu, Sigma))
# construct a Gaussian mixture model based on the best EM run
pie = gmms[max_idx].get_coef()
proposal = MixtureDistribution(components[0].dimension,
self.num_components, components,
Discrete(pie))
return proposal
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
