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started sorting out some tests

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James Hensman 2014-02-25 08:58:51 +00:00
parent da4686dd3c
commit 4eac0bd6db
7 changed files with 42 additions and 43 deletions
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8
GPy/kern/_src/add.py
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@ -67,13 +67,13 @@ class Add(Kern):
return sum([p.Kdiag(X[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)])
def psi0(self, Z, mu, S):
def psi0(self, Z, variational_posterior):
return np.sum([p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)],0)
def psi1(self, Z, mu, S):
def psi1(self, Z, variational_posterior):
return np.sum([p.psi1(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
def psi2(self, Z, mu, S):
def psi2(self, Z, variational_posterior):
psi2 = np.sum([p.psi2(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
# compute the "cross" terms
@ -101,7 +101,7 @@ class Add(Kern):
raise NotImplementedError, "psi2 cannot be computed for this kernel"
return psi2
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, variational_posterior, Z):
from white import White
from rbf import RBF
#from rbf_inv import RBFInv

32
GPy/kern/_src/static.py
View file

@ -18,26 +18,26 @@ class Static(Kern):
ret[:] = self.variance
return ret
def gradients_X(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2=None):
return np.zeros(X.shape)
def gradients_X_diag(self, dL_dKdiag, X, target):
def gradients_X_diag(self, dL_dKdiag, X):
return np.zeros(X.shape)
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
return np.zeros(Z.shape)
def gradients_muS_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
return np.zeros(mu.shape), np.zeros(S.shape)
def gradients_muS_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
return np.zeros(variational_posterior.shape), np.zeros(variational_posterior.shape)
def psi0(self, Z, mu, S):
return self.Kdiag(mu)
def psi0(self, Z, variational_posterior):
return self.Kdiag(variational_posterior.mean)
def psi1(self, Z, mu, S, target):
return self.K(mu, Z)
def psi1(self, Z, variational_posterior):
return self.K(variational_posterior.mean, Z)
def psi2(Z, mu, S):
K = self.K(mu, Z)
def psi2(self, Z, variational_posterior):
K = self.K(variational_posterior.mean, Z)
return K[:,:,None]*K[:,None,:] # NB. more efficient implementations on inherriting classes
@ -51,8 +51,8 @@ class White(Static):
else:
return np.zeros((X.shape[0], X2.shape[0]))
def psi2(self, Z, mu, S, target):
return np.zeros((mu.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
def psi2(self, Z, variational_posterior):
return np.zeros((variational_posterior.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
def update_gradients_full(self, dL_dK, X):
self.variance.gradient = np.trace(dL_dK)
@ -60,7 +60,7 @@ class White(Static):
def update_gradients_diag(self, dL_dKdiag, X):
self.variance.gradient = dL_dKdiag.sum()
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
self.variance.gradient = np.trace(dL_dKmm) + dL_dpsi0.sum()
@ -80,11 +80,11 @@ class Bias(Static):
def update_gradients_diag(self, dL_dKdiag, X):
self.variance.gradient = dL_dK.sum()
def psi2(self, Z, mu, S, target):
def psi2(self, Z, variational_posterior):
ret = np.empty((mu.shape[0], Z.shape[0], Z.shape[0]), dtype=np.float64)
ret[:] = self.variance**2
return ret
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
self.variance.gradient = dL_dKmm.sum() + dL_dpsi0.sum() + dL_dpsi1.sum() + 2.*self.variance*dL_dpsi2.sum()