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X caching is not yet done, parameter caching working fine. X cache must be adjusted to update at the right times

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Max Zwiessele 2013-10-27 17:04:46 +00:00
parent d3721b76a8
commit d8151eee61
12 changed files with 249 additions and 109 deletions
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41
GPy/models/gplvm.py
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@ -13,6 +13,7 @@ from ..core import GP
from ..likelihoods import Gaussian
from .. import util
from GPy.util import plot_latent
from GPy.core.parameter import Param
class GPLVM(GP):
@ -34,7 +35,9 @@ class GPLVM(GP):
kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2))
likelihood = Gaussian(Y, normalize=normalize_Y, variance=np.exp(-2.))
GP.__init__(self, X, likelihood, kernel, normalize_X=False)
self.set_prior('.*X', Gaussian_prior(0, 1))
self.X = Param('q_mean', self.X)
self.add_parameter(self.X, self.dL_dK, 0)
#self.set_prior('.*X', Gaussian_prior(0, 1))
self.ensure_default_constraints()
def initialise_latent(self, init, input_dim, Y):
@ -50,33 +53,35 @@ class GPLVM(GP):
def setstate(self, state):
GP.setstate(self, state)
def _get_param_names(self):
return sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) + GP._get_param_names(self)
# def _get_param_names(self):
# return sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) + GP._get_param_names(self)
#
# def _get_params(self):
# return np.hstack((self.X.flatten(), GP._get_params(self)))
#
# def _set_params(self, x):
# self.X = x[:self.num_data * self.input_dim].reshape(self.num_data, self.input_dim).copy()
# GP._set_params(self, x[self.X.size:])
def _get_params(self):
return np.hstack((self.X.flatten(), GP._get_params(self)))
def _set_params(self, x):
self.X = x[:self.num_data * self.input_dim].reshape(self.num_data, self.input_dim).copy()
GP._set_params(self, x[self.X.size:])
def _log_likelihood_gradients(self):
dL_dX = self.kern.dK_dX(self.dL_dK, self.X)
return np.hstack((dL_dX.flatten(), GP._log_likelihood_gradients(self)))
def dK_dX(self):
return self.kern.dK_dX(self.dL_dK, self.X)
# def _log_likelihood_gradients(self):
# dL_dX = self.kern.dK_dX(self.dL_dK, self.X)
#
# return np.hstack((dL_dX.flatten(), GP._log_likelihood_gradients(self)))
def jacobian(self,X):
target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
for i in range(self.output_dim):
target[:,:,i]=self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
target[:,:,i]=self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
return target
def magnification(self,X):
target=np.zeros(X.shape[0])
J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
J=self.jacobian(X)
#J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
J = self.jacobian(X)
for i in range(X.shape[0]):
target[i]=np.sqrt(pb.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
target[i]=np.sqrt(pb.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
return target
def plot(self):