def __getitem__(self, slc):
rslc, cslc = misc.extract_slices(slc)
if self.bias_type == 'row':
return GSMNode(self._value[slc].copy(), self.scale_node[slc].copy(), self.bias_type, self.bias[rslc].copy())
elif self.bias_type == 'col':
return GSMNode(self._value[slc].copy(), self.scale_node[slc].copy(), self.bias_type, self.bias[cslc].copy())
elif self.bias_type == 'scalar':
return GSMNode(self._value[slc].copy(), self.scale_node[slc].copy(), self.bias_type, self.bias)
def __getitem__(self, slc):
rslc, cslc = misc.extract_slices(slc)
if self.variance_type == 'scalar':
sigma_sq = self.sigma_sq
elif self.variance_type == 'row':
sigma_sq = self.sigma_sq[rslc].copy()
elif self.variance_type == 'col':
sigma_sq = self.sigma_sq[cslc].copy()
return GaussianNode(self._value[slc].copy(), self.variance_type, sigma_sq)
def __getitem__(self, slc):
assert len(self.children) == 2
rslc, cslc = misc.extract_slices(slc)
return ProductNode([self.children[0][rslc, :], self.children[1][:, cslc]])
def __getitem__(self, slc):
rslc, cslc = misc.extract_slices(slc)
return DataMatrix(self.observations[slc], self.row_ids[rslc], self.col_ids[cslc],
misc.slice_list(self.row_labels, rslc), misc.slice_list(self.col_labels, cslc),
self.m_orig, self.n_orig)
