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change state_xt

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mu 2013-11-27 12:55:55 +00:00
parent d0ff97f37e
commit 7247849441
1 changed files with 19 additions and 7 deletions
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26
GPy/models/state_space_xt_sep.py
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@ -1,5 +1,5 @@
# Copyright (c) 2013, Arno Solin.
# Licensed under the BSD 3-clause license (see LICENSE.txt)
# Copyright (c) 2013, Mu Niu, Arno Solin.
# Licensed under the BSD 3-clause license (see LICENSE.txt) ??
#
# This implementation of converting GPs to state space models is based on the article:
#
@ -22,19 +22,23 @@ from GPy.util.plot import gpplot, Tango, x_frame1D
import pylab as pb
class StateSpace_1(Model):
def __init__(self, X, Y, kernel=None):
def __init__(self, T,X, Y, kernel=None):
super(StateSpace_1, self).__init__()
self.num_data, input_dim = X.shape
assert input_dim==1, "State space methods for time only"
assert input_dim==2, "State space methods for time and space"
num_data_Y, self.output_dim = Y.shape
assert num_data_Y == self.num_data, "X and Y data don't match"
assert self.output_dim == 1, "State space methods for single outputs only"
# Make sure the observations are ordered in time
sort_index = np.argsort(X[:,0])
self.X = X[sort_index]
sort_index = np.argsort(T[:,0])
self.T = T[sort_index]
self.Y = Y[sort_index]
#sort_index = np.argsort(X[:,0])
#self.X = X[sort_index]
#self.Y = Y[sort_index]
# Noise variance
self.sigma2 = 1.
@ -89,7 +93,15 @@ class StateSpace_1(Model):
Y = Y[return_index]
# Get the model matrices from the kernel
(F,L,Qc,H,Pinf) = self.kern.sde()
(F1,L1,Qc1,H1,Pinf1) = self.kern.sde()
F = np.kron(eye(n),F1);
L = np.kron(eye(n),L1);
Qc = K*Qc1; #kron(K,Qc1);
H = np.kron(eye(n),H1);
Pinf = np.kron(K,Pinf1);
# Run the Kalman filter
(M, P) = self.kalman_filter(F,L,Qc,H,self.sigma2,Pinf,X.T,Y.T)