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SVI now implemented without natural natural gradients or batches

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This commit is contained in:
Alan Saul 2014-12-22 13:35:56 +00:00
parent b642360ede
commit a8b0d60c3e
4 changed files with 16 additions and 17 deletions
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6
GPy/likelihoods/gaussian.py
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@ -318,9 +318,9 @@ class Gaussian(Likelihood):
def variational_expectations(self, Y, m, v, gh_points=None):
lik_var = float(self.variance)
F = -0.5*np.log(2*np.pi) -0.5*np.log(lik_var) - 0.5*(np.square(Y) + np.square(m) + v - 2*m.dot(Y))/lik_var
F = -0.5*np.log(2*np.pi) -0.5*np.log(lik_var) - 0.5*(np.square(Y) + np.square(m) + v - 2*m*Y)/lik_var
dF_dmu = (Y - m)/lik_var
dF_dv = -0.5/lik_var
dF_dlik_var = -0.5/lik_var + 0.5(np.square(Y) + np.square(m) + v - 2*m.dot(Y))/(lik_var**2)
dF_dv = np.ones_like(v)*(-0.5/lik_var)
dF_dlik_var = np.sum(-0.5/lik_var + 0.5*(np.square(Y) + np.square(m) + v - 2*m*Y)/(lik_var**2))
dF_dtheta = [dF_dlik_var]
return F, dF_dmu, dF_dv, dF_dtheta