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adding kernels flattening and parameters already in hierarchy

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This commit is contained in:
Max Zwiessele 2014-03-18 17:41:08 +00:00
parent 9c553ba15c
commit 6637eb7ac8
11 changed files with 624 additions and 9 deletions
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2
GPy/inference/latent_function_inference/exact_gaussian_inference.py
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@ -43,7 +43,7 @@ class ExactGaussianInference(object):
K = kern.K(X)
Ky = K.copy()
diag.add(Ky, likelihood.gaussian_variance(Y, Y_metadata))
diag.add(Ky, likelihood.gaussian_variance(Y_metadata))
Wi, LW, LWi, W_logdet = pdinv(Ky)
alpha, _ = dpotrs(LW, YYT_factor, lower=1)

2
GPy/inference/latent_function_inference/var_dtc.py
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@ -65,7 +65,7 @@ class VarDTC(object):
_, output_dim = Y.shape
#see whether we've got a different noise variance for each datum
beta = 1./np.fmax(likelihood.gaussian_variance(Y, Y_metadata), 1e-6)
beta = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6)
# VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
#self.YYTfactor = self.get_YYTfactor(Y)
#VVT_factor = self.get_VVTfactor(self.YYTfactor, beta)