def _alt_sfa2_grad(self, x):
"""Reference grad method based on quadratic forms."""
# note that the H and f arrays are cached in the node and remain even
# after the extension has been deactivated
if not hasattr(self, "__gradient_Hs"):
quad_forms = [self.get_quadratic_form(i)
for i in range(self.output_dim)]
self.__gradient_Hs = numx.vstack((quad_form.H[numx.newaxis]
for quad_form in quad_forms))
self.__gradient_fs = numx.vstack((quad_form.f[numx.newaxis]
for quad_form in quad_forms))
grad = (numx.dot(x, self.__gradient_Hs) +
numx.repeat(self.__gradient_fs[numx.newaxis,:,:],
len(x), axis=0))
return grad
def _alt_sfa2_grad(self, x):
"""Reference grad method based on quadratic forms."""
# note that the H and f arrays are cached in the node and remain even
# after the extension has been deactivated
if not hasattr(self, "__gradient_Hs"):
quad_forms = [
self.get_quadratic_form(i) for i in range(self.output_dim)
]
self.__gradient_Hs = numx.vstack(
(quad_form.H[numx.newaxis] for quad_form in quad_forms))
self.__gradient_fs = numx.vstack(
(quad_form.f[numx.newaxis] for quad_form in quad_forms))
grad = (numx.dot(x, self.__gradient_Hs) + numx.repeat(
self.__gradient_fs[numx.newaxis, :, :], len(x), axis=0))
return grad
def _stop_training(self):
"""Calculate the class means."""
ordered_means = []
for label in self.label_means:
self.label_means[label] /= self.n_label_samples[label]
self.ordered_labels.append(label)
ordered_means.append(self.label_means[label])
self.ordered_means = numx.vstack(ordered_means)
def _stop_training(self):
"""Calculate the class means."""
ordered_means = []
for label in self.label_means:
self.label_means[label] /= self.n_label_samples[label]
self.ordered_labels.append(label)
ordered_means.append(self.label_means[label])
self.ordered_means = numx.vstack(ordered_means)