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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Ricardo 2013-05-13 19:41:10 +01:00
commit 225c35d55d
7 changed files with 118 additions and 68 deletions
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2
GPy/models/GPLVM.py
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@ -45,7 +45,7 @@ class GPLVM(GP):
return np.hstack((self.X.flatten(), GP._get_params(self)))
def _set_params(self,x):
self.X = x[:self.X.size].reshape(self.N,self.Q).copy()
self.X = x[:self.N*self.Q].reshape(self.N,self.Q).copy()
GP._set_params(self, x[self.X.size:])
def _log_likelihood_gradients(self):

7
GPy/models/sparse_GP.py
View file

@ -3,17 +3,12 @@
import numpy as np
import pylab as pb
from ..util.linalg import mdot, jitchol, tdot, symmetrify
from ..util.linalg import mdot, jitchol, tdot, symmetrify, backsub_both_sides
from ..util.plot import gpplot
from .. import kern
from GP import GP
from scipy import linalg
def backsub_both_sides(L, X):
""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
tmp, _ = linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
return linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T
class sparse_GP(GP):
"""
Variational sparse GP model

11
GPy/models/warped_GP.py
View file

@ -23,6 +23,7 @@ class warpedGP(GP):
self.warping_function = TanhWarpingFunction_d(warping_terms)
self.warping_params = (np.random.randn(self.warping_function.n_terms*3+1,) * 1)
Y = self._scale_data(Y)
self.has_uncertain_inputs = False
self.Y_untransformed = Y.copy()
self.predict_in_warped_space = False
@ -30,6 +31,14 @@ class warpedGP(GP):
GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
def _scale_data(self, Y):
self._Ymax = Y.max()
self._Ymin = Y.min()
return (Y-self._Ymin)/(self._Ymax-self._Ymin) - 0.5
def _unscale_data(self, Y):
return (Y + 0.5)*(self._Ymax - self._Ymin) + self._Ymin
def _set_params(self, x):
self.warping_params = x[:self.warping_function.num_parameters]
Y = self.transform_data()
@ -79,5 +88,5 @@ class warpedGP(GP):
if self.predict_in_warped_space:
mu = self.warping_function.f_inv(mu, self.warping_params)
var = self.warping_function.f_inv(var, self.warping_params)
mu = self._unscale_data(mu)
return mu, var