print (D.shape) #least_squares y = np.array([1,2,3]) D = image_data[0][:,1] print (D.ndim) print (D) beta = sp.least_squares(D, y) print (beta) ''' ############################## D = sp.build_dictionary(image_data) y = np.random.randint(1, 71, size = 10) #print (D) #print (y) beta, indices = sp.choose_atoms(D, y) print (beta) print (indices) ##############################Built list of files to iterate through#################################################### ''' ##Big laptop #os.chdir("C:\\Users\\Jack2\\Google Drive\\URMP\\jc2\\MNIST_Load") ##Little laptop os.chdir("C:\\Users\\Jack\\Google Drive\\URMP\\jc2\\MNIST_Load")
win1 = 100 #Window for mov avg 1 win2 = 500 #Window for mov avg 2 ######################## Preprocess data - dictionary gets loaded with first num_rfields MNIST images ############################ #################################### Training data starts loading after dictionary images ######################################## dict_data = mnist.load_images(image_file, num_rfields) training_data = mnist.load_images(image_file, num_images, num_rfields) dict_data = [np.array(i, dtype=float) / 255 for i in dict_data] training_data = [np.array(i, dtype=float) / 255 for i in training_data] ######################################### Initialize network dictionary and parameters ########################################### D = sp.build_dictionary(dict_data) network = lca.r_network(D) network.set_parameters(lamb, tau, delta, u_stop, t_type) network.set_dim(dict_data[0].shape) #Save out the original dictionary network.save_dictionary(5, 10, dict1_path, line_color = 255) #################################################### Train dictionary ############################################################ network.set_alpha(alpha) network.load_ims(training_data) network.train(alpha_decay, alpha_decay_rate, alpha_decay_iters) network.plot_rmse(win1, win2) network.plot_decay()