def learn(self, source_data, target_data, kernel=None):
"""Launch learning process."""
if kernel is None: kernel = Kernel(kernel_str='linear')
if self.verbose: print('Building kernel matrix.')
data_matrix = np.vstack(( source_data.X, target_data.X) )
label_vector = np.hstack(( source_data.Y, np.zeros(target_data.get_nb_examples()) ))
kernel_matrix = kernel.create_matrix(data_matrix)
alpha_vector = self.learn_on_kernel_matrix(kernel_matrix, label_vector)
return KernelClassifier(kernel, data_matrix, alpha_vector)
def learn(self, source_data, target_data, kernel=None):
"""Launch learning process."""
if kernel is None: kernel = Kernel(kernel_str='linear')
if self.verbose: print('Building kernel matrix.')
data_matrix = np.vstack((source_data.X, target_data.X))
label_vector = np.hstack(
(source_data.Y, np.zeros(target_data.get_nb_examples())))
kernel_matrix = kernel.create_matrix(data_matrix)
alpha_vector = self.learn_on_kernel_matrix(kernel_matrix, label_vector)
return KernelClassifier(kernel, data_matrix, alpha_vector)
