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)