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prod now seems to work for sparse

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This commit is contained in:
James Hensman 2014-02-19 22:23:07 +00:00
parent 92d71384b7
commit de51ad638a
6 changed files with 34 additions and 41 deletions
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16
GPy/kern/_src/linear.py
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@ -43,16 +43,16 @@ class Linear(Kern):
assert variances.size == self.input_dim, "bad number of variances, need one ARD variance per input_dim"
else:
variances = np.ones(self.input_dim)
self.variances = Param('variances', variances, Logexp())
self.variances.gradient = np.zeros(self.variances.shape)
#TODO: remove?self.variances.gradient = np.zeros(self.variances.shape)
self.add_parameter(self.variances)
self.variances.add_observer(self, self.update_variance)
# initialize cache
self._Z, self._mu, self._S = np.empty(shape=(3, 1))
self._X, self._X2 = np.empty(shape=(2, 1))
def update_variance(self, v):
self.variances2 = np.square(self.variances)
@ -62,7 +62,7 @@ class Linear(Kern):
def update_gradients_full(self, dL_dK, X):
self.variances.gradient[:] = 0
self._param_grad_helper(dL_dK, X, None, self.variances.gradient)
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
tmp = dL_dKdiag[:, None] * X ** 2
if self.ARD:
@ -71,7 +71,7 @@ class Linear(Kern):
self.variances.gradient = tmp.sum()
self._param_grad_helper(dL_dKmm, Z, None, self.variances.gradient)
self._param_grad_helper(dL_dKnm, X, Z, self.variances.gradient)
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
self._psi_computations(Z, mu, S)
# psi0:
@ -87,7 +87,7 @@ class Linear(Kern):
#from Kmm
self._K_computations(Z, None)
self._param_grad_helper(dL_dKmm, Z, None, self.variances.gradient)
def K(self, X, X2, target):
if self.ARD:
XX = X * np.sqrt(self.variances)
@ -224,7 +224,7 @@ class Linear(Kern):
weave_options = {'headers' : ['<omp.h>'],
'extra_compile_args': ['-fopenmp -O3'], #-march=native'],
'extra_link_args' : ['-lgomp']}
N,num_inducing,input_dim,mu = mu.shape[0],Z.shape[0],mu.shape[1],param_to_array(mu)
weave.inline(code, support_code=support_code, libraries=['gomp'],
arg_names=['N','num_inducing','input_dim','mu','AZZA','AZZA_2','target_mu','target_S','dL_dpsi2'],
@ -281,7 +281,7 @@ class Linear(Kern):
self._X2 = None
else:
self._X2 = X2.copy()
self._dot_product = np.dot(param_to_array(X), param_to_array(X2.T))
self._dot_product = np.dot(param_to_array(X), param_to_array(X2.T))
def _psi_computations(self, Z, mu, S):
# here are the "statistics" for psi1 and psi2