'text.fontsize': 10, 'legend.fontsize': 10, 'xtick.labelsize': 10, 'ytick.labelsize': 10, 'text.usetex': False, 'figure.figsize': [4.5, 4.5]}) import pscgen def num_ops(N, M): return N * (2 * M - 1) args = json.loads(sys.argv[1]) storage = pscgen.name_to_storage(args['storage']) comp_scheme = pscgen.name_to_comp_scheme('pca') X, Y, X_flat = util.wav_to_np(args['tr_folder_path'], window_size=50) assert False num_folds = 5 acc = 0.0 max_atoms = 1000 sss = StratifiedShuffleSplit(Y, num_folds, test_size=0.7, random_state=0) alphas = [1, 2, 3, 4, 5, 5, 5, 5, 5, 10, 10, 10, 15, 20, 20, 20, 20, 25] betas = [1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 7, 10, 10, 10, 12, 15, 20, 20] Ns = [1, 2, 3, 4, 5, 10, 15, 20, 25, 50, 70, 100, 150, 200, 240, 300, 400, 500] alphas = [1, 2, 3, 4, 5, 5, 5, 5, 5, 10, 10, 10, 15, 20, 20] betas = [1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 7, 10, 10, 10, 12] Ns = [1, 2, 3, 4, 5, 10, 15, 20, 25, 50, 70, 100, 150, 200, 240] accs = {}
for s in sets: result = result.intersection(s) return result N = 25 M = 10000 KMeans_tr_size = 200000 D_atoms = 500 zoom_dim = 20 data_folder = sys.argv[1] output_folder = sys.argv[2] if output_folder[-1] != '/': output_folder += '/' X, Y = util.wav_to_np(data_folder) X = [util.sliding_window(x, 40, 20) for x in X] X = np.vstack(X) X = X[np.random.permutation(len(X))] X_Kmeans = X[:KMeans_tr_size] D = KMeans(n_clusters=D_atoms, init_size=D_atoms*3) D.fit(X_Kmeans) D = D.cluster_centers_ D = util.normalize(D) X = util.normalize(X) D_mean = np.mean(D, axis=0) D = D - D_mean X = X - D_mean U, S_D, V = np.linalg.svd(D)
predictions = np.array(predictions) return self.class_labels[np.argmax(np.sum(predictions, axis=0), axis=1)] linewidth = 2 fig_name = 'boosted_dim' KMeans_tr_size = 200000 D_atoms = 500 ws = 50 subsample_pcts = [1., 1., 1., 0.5] X_flat, Y = util.wav_to_np('/home/brad/data/robot/') X = [] num_folds = 10 sss = StratifiedShuffleSplit(Y, num_folds, test_size=0.7, random_state=0) for x in X_flat: X.append(util.sliding_window(x, ws, 5)) plt_dict = {} features = [2, 5, 10, 20] nodes = [1, 2, 5, 10, 15, 20] for f in features: plt_dict[f] = [] for n in nodes: