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decent gradients for most parameters

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Nicolo Fusi 2013-01-28 15:55:40 +00:00
parent eb3061a9f0
commit 8b6e244cf1
5 changed files with 20 additions and 20 deletions
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7
GPy/models/sparse_GP_regression.py
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@ -37,7 +37,7 @@ class sparse_GP_regression(GP_regression):
"""
def __init__(self,X,Y,kernel=None, X_uncertainty=None, beta=100., Z=None,Zslices=None,M=10,normalize_X=False,normalize_Y=False):
self.scale_factor = 1e1
self.scale_factor = 10.0
self.beta = beta
if Z is None:
self.Z = np.random.permutation(X.copy())[:M]
@ -70,7 +70,8 @@ class sparse_GP_regression(GP_regression):
self.psi0 = self.kern.psi0(self.Z,self.X, self.X_uncertainty).sum()
self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty).T
self.psi2 = self.kern.psi2(self.Z,self.X, self.X_uncertainty)
raise NotImplementedError, "scale psi2 (in kern?)"
# raise NotImplementedError, "scale psi2 (in kern?)"
self.psi2_beta_scaled = self.psi2*(self.beta/self.scale_factor**2)
else:
self.psi0 = self.kern.Kdiag(self.X,slices=self.Xslices).sum()
self.psi1 = self.kern.K(self.Z,self.X)
@ -292,5 +293,3 @@ class sgp_debugE(sparse_GP_regression):
tmp = mdot(self.LBi.T, self.LLambdai, self.psi1V)
dE_dbeta = (np.sum(np.square(self.C)) - 0.5 * np.sum(self.A * np.dot(tmp, tmp.T)))/self.beta
return np.squeeze(dE_dbeta)