matplotlib.rcParams.update({'axes.labelsize': 12, '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]
return json.dumps(chunk_dict) ''' Updater: dtype (e.g. wav) tr_folder_path (containing files of format classlabel_XXXXX.dtype) D_atoms alpha beta storage (e.g. 'mini' or 'half') output_path chunk_size (size in bytes, -1 for no chunks) ''' args = json.loads(sys.argv[1]) storage = name_to_storage(args['storage']) KMeans_tr_size = 200000 X, Y, X_normal = read_dataset(args['tr_folder_path'], args['dtype']) pipe = pscgen.Pipeline(100, 12) pipe.fit(X, Y, args['D_atoms'], args['alpha'], args['beta'], storage) cl1, cl2, cl3 = [], [], [] for i in xrange(len(X)): x = util.bow(pipe.nnu.index(X[i]), args['D_atoms']) cl1.append(pipe.svm.predict(x)[0]) cl2.append(pipe.svm.classes_[classify(x, pipe.svm.coef_, pipe.svm.intercept_, 13)]) cl3.append(pipe.classify(X_normal[i]))