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added BGPLVM in parameterized

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Max Zwiessele 2013-11-06 15:15:13 +00:00
parent 3316d29341
commit 8c02e4af36
6 changed files with 103 additions and 72 deletions
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61
GPy/models/bayesian_gplvm.py
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@ -12,6 +12,7 @@ from GPy.util import plot_latent, linalg
from GPy.models.gplvm import GPLVM
from GPy.util.plot_latent import most_significant_input_dimensions
from matplotlib import pyplot
from GPy.core.variational import Normal
class BayesianGPLVM(SparseGP, GPLVM):
"""
@ -47,6 +48,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
kernel = kern.rbf(input_dim) # + kern.white(input_dim)
SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
self.q = Normal('latent space', self.X, self.X_variance)
self.add_parameter(self.q, gradient=self._dbound_dmuS, index=0)
self.ensure_default_constraints()
def getstate(self):
@ -61,32 +65,32 @@ class BayesianGPLVM(SparseGP, GPLVM):
self.init = state.pop()
SparseGP.setstate(self, state)
def _get_param_names(self):
X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
return (X_names + S_names + SparseGP._get_param_names(self))
# def _get_param_names(self):
# X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
# S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
# return (X_names + S_names + SparseGP._get_param_names(self))
#def _get_print_names(self):
# return SparseGP._get_print_names(self)
def _get_params(self):
"""
Horizontally stacks the parameters in order to present them to the optimizer.
The resulting 1-input_dim array has this structure:
# def _get_params(self):
# """
# Horizontally stacks the parameters in order to present them to the optimizer.
# The resulting 1-input_dim array has this structure:
#
# ===============================================================
# | mu | S | Z | theta | beta |
# ===============================================================
#
# """
# x = np.hstack((self.X.flatten(), self.X_variance.flatten(), SparseGP._get_params(self)))
# return x
===============================================================
| mu | S | Z | theta | beta |
===============================================================
"""
x = np.hstack((self.X.flatten(), self.X_variance.flatten(), SparseGP._get_params(self)))
return x
def _set_params(self, x, save_old=True, save_count=0):
N, input_dim = self.num_data, self.input_dim
self.X = x[:self.X.size].reshape(N, input_dim).copy()
self.X_variance = x[(N * input_dim):(2 * N * input_dim)].reshape(N, input_dim).copy()
SparseGP._set_params(self, x[(2 * N * input_dim):])
# def _set_params(self, x, save_old=True, save_count=0):
# N, input_dim = self.num_data, self.input_dim
# self.X = x[:self.X.size].reshape(N, input_dim).copy()
# self.X_variance = x[(N * input_dim):(2 * N * input_dim)].reshape(N, input_dim).copy()
# SparseGP._set_params(self, x[(2 * N * input_dim):])
def dKL_dmuS(self):
dKL_dS = (1. - (1. / (self.X_variance))) * 0.5
@ -112,14 +116,21 @@ class BayesianGPLVM(SparseGP, GPLVM):
kl = self.KL_divergence()
return ll - kl
def _log_likelihood_gradients(self):
def _dbound_dmuS(self):
dKL_dmu, dKL_dS = self.dKL_dmuS()
dL_dmu, dL_dS = self.dL_dmuS()
d_dmu = (dL_dmu - dKL_dmu).flatten()
d_dS = (dL_dS - dKL_dS).flatten()
self.dbound_dmuS = np.hstack((d_dmu, d_dS))
self.dbound_dZtheta = SparseGP._log_likelihood_gradients(self)
return np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta))
return np.hstack((d_dmu, d_dS))
# def _log_likelihood_gradients(self):
# dKL_dmu, dKL_dS = self.dKL_dmuS()
# dL_dmu, dL_dS = self.dL_dmuS()
# d_dmu = (dL_dmu - dKL_dmu).flatten()
# d_dS = (dL_dS - dKL_dS).flatten()
# self.dbound_dmuS = np.hstack((d_dmu, d_dS))
# self.dbound_dZtheta = SparseGP._log_likelihood_gradients(self)
# return np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta))
def plot_latent(self, plot_inducing=True, *args, **kwargs):
return plot_latent.plot_latent(self, plot_inducing=plot_inducing, *args, **kwargs)