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added priors behaviour as intended and issue #38 closed and fixed

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Max Zwiessele 2013-06-04 18:09:02 +01:00
parent 29790e327a
commit 75f4e26b23
5 changed files with 16 additions and 16 deletions
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2
GPy/inference/SCG.py
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@ -63,7 +63,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
success = True # Force calculation of directional derivs.
nsuccess = 0 # nsuccess counts number of successes.
beta = 1.0 # Initial scale parameter.
betamin = 1.0e-15 # Lower bound on scale.
betamin = 1.0e-60 # Lower bound on scale.
betamax = 1.0e100 # Upper bound on scale.
status = "Not converged"

8
GPy/inference/SGD.py
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@ -192,7 +192,7 @@ class opt_SGD(Optimizer):
if self.model.N == 0 or Y.std() == 0.0:
return 0, step, self.model.N
self.model.likelihood._bias = Y.mean()
self.model.likelihood._offset = Y.mean()
self.model.likelihood._scale = Y.std()
self.model.likelihood.set_data(Y)
# self.model.likelihood.V = self.model.likelihood.Y*self.model.likelihood.precision
@ -219,9 +219,9 @@ class opt_SGD(Optimizer):
self.restore_constraints(ci)
self.model.grads[j] = fp
# restore likelihood _bias and _scale, otherwise when we call set_data(y) on
# restore likelihood _offset and _scale, otherwise when we call set_data(y) on
# the next feature, it will get normalized with the mean and std of this one.
self.model.likelihood._bias = 0
self.model.likelihood._offset = 0
self.model.likelihood._scale = 1
return f, step, self.model.N
@ -266,7 +266,7 @@ class opt_SGD(Optimizer):
self.model.likelihood.YYT = 0
self.model.likelihood.trYYT = 0
self.model.likelihood._bias = 0.0
self.model.likelihood._offset = 0.0
self.model.likelihood._scale = 1.0
N, Q = self.model.X.shape