apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-06-02 14:45:15 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

fix: beiwang will add GMM in full

Browse source
This commit is contained in:
mzwiessele 2017-02-28 10:04:07 +00:00
parent e9d302c87a
commit 67b497e5df
1 changed files with 0 additions and 85 deletions
Download patch file Download diff file Expand all files Collapse all files

85
GPy/core/parameterization/variational.py
View file

@ -36,91 +36,6 @@ class NormalPrior(VariationalPrior):
variational_posterior.mean.gradient -= variational_posterior.mean
variational_posterior.variance.gradient -= (1. - (1. / (variational_posterior.variance))) * 0.5
class GmmNormalPrior(VariationalPrior):
def __init__(self, px_mu, px_var, pi, n_component, variational_pi, name="GMMNormalPrior", **kw):
super(GmmNormalPrior, self).__init__(name=name, **kw)
self.n_component = n_component
self.px_mu = Param('mu_k', px_mu)
self.px_var = Param('var_k', px_var)
# Make sure they sum to one
variational_pi = variational_pi / np.sum(variational_pi)
pi = pi / np.sum(pi)
self.pi = pi # p(x) mixing coeffients
self.variational_pi = Param('variational_pi', variational_pi) # variational mixing coefficients
self.check_all_weights()
self.link_parameter(self.px_mu)
self.link_parameter(self.px_var)
self.link_parameter(self.variational_pi)
self.variational_pi.constrain_bounded(0.0, 1.0)
self.stop = 5
def KL_divergence(self, variational_posterior):
# Lagrange multiplier maybe also needed here
# var_mean = np.square(variational_posterior.mean).sum()
# var_S = (variational_posterior.variance - np.log(variational_posterior.variance)).sum()
# return 0.5 * (var_mean + var_S) - 0.5 * variational_posterior.input_dim * variational_posterior.num_data
mu = variational_posterior.mean
S = variational_posterior.variance
pi = self.variational_pi
total_n = variational_posterior.input_dim * variational_posterior.num_data
cita = np.zeros(4)
for i in range(self.n_component):
cita[0] += (pi[i] * np.log(self.px_var[i])).sum()
cita[1] += (pi[i] * S / self.px_var[i]).sum()
cita[2] += (pi[i] * np.square(mu - self.px_mu[i]) / self.px_var[i]).sum()
cita[3] += (pi[i] * np.log(self.pi / pi[i])).sum()
return 0.5 * (cita[0] - (np.log(S)).sum() + cita[1]) + 0.5 * (cita[2] - total_n) + cita[3]
def update_gradients_KL(self, variational_posterior):
# import pdb; pdb.set_trace() # breakpoint 1
# print("Updating Gradients")
# if self.stop<1:
# return
# self.stop-=1
#dL:
#variational_posterior.mean.gradient -= variational_posterior.mean
#variational_posterior.variance.gradient -= (1. - (1. / (variational_posterior.variance))) * 0.5
mu = variational_posterior.mean
S = variational_posterior.variance
pi = self.variational_pi
cita_0 = np.zeros(mu.shape)
cita_1 = np.zeros(mu.shape)
cita_2 = np.zeros(mu.shape)
cita_3 = np.zeros(pi.shape)
for i in range(self.n_component):
cita_0 += pi.values[i] * (mu - self.px_mu.values[i]) / self.px_var.values[i]
cita_1 += (pi[i] / self.px_var[i])
cita_2 += pi[i] * (S + np.square(mu - self.px_mu[i])) / np.square(self.px_var[i])
self.px_mu[i].gradient += pi[i] * (mu - self.px_mu[i]) / self.px_var[i]
self.px_var[i].gradient += (pi[i] * (S + np.square(mu - self.px_mu[i])) / np.square(self.px_var[i]) - (pi[i] / self.px_var[i])) * 0.5
cita_3[i] = (np.log(self.px_var[i]).sum() + (S / self.px_var[i]).sum()+ (np.square(mu - self.px_mu[i]) / self.px_var[i]).sum() )* (-0.5) + np.log(self.pi[i] / pi[i]) - 1
# self.variational_pi[i].gradient += cita_3[i]
variational_posterior.mean.gradient -= cita_0
variational_posterior.variance.gradient += (1. / (S) - cita_1) * 0.5
self.variational_pi.gradient +=cita_3
def check_weights(self, weights):
assert weights.min() >= 0.0
assert weights.max() <= 1.0
assert weights.sum() == 1.0
def check_all_weights(self):
self.check_weights(self.variational_pi)
self.check_weights(self.pi)
class SpikeAndSlabPrior(VariationalPrior):
def __init__(self, pi=None, learnPi=False, variance = 1.0, group_spike=False, name='SpikeAndSlabPrior', **kw):
super(SpikeAndSlabPrior, self).__init__(name=name, **kw)