def test_util(): df = load_watch() data = util.make_ts_data(df['X'], df['side']) util.get_ts_data_parts(data) util.check_ts_data(data, df['y']) util.check_ts_data(df['X'], df['y']) util.ts_stats(df['X'], df['y'], fs=1., class_labels=df['y_labels'])
def test_util(): df = load_watch() data = TS_Data(df['X'], df['side']) Xt, Xc = util.get_ts_data_parts(data) assert np.array_equal(Xc, df['side']) assert np.all([np.array_equal(Xt[i], df['X'][i]) for i in range(len(df['X']))]) util.check_ts_data(data, df['y']) util.check_ts_data(df['X'], df['y']) util.ts_stats(df['X'], df['y'], fs=1., class_labels=df['y_labels'])
def test_watch(): df = load_watch() data = TS_Data(df['X'], df['side'])
kernel_size=conv_kernel_size, padding='valid', activation='relu', input_shape=input_shape)) model.add(LSTM(units=lstm_units, dropout=0.1, recurrent_dropout=0.1)) model.add(Dense(n_classes, activation="softmax")) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model # load the data data = load_watch() X = data['X'] y = data['y'] # temporal splitting of data splitter = TemporalKFold(n_splits=3) Xs, ys, cv = splitter.split(X, y) # create a segment learning pipeline width = 100 pipe = Pype([('seg', SegmentX(order='C')), ('crnn', KerasClassifier(build_fn=crnn_model, epochs=1, batch_size=256, verbose=0))])
def test_watch(): df = load_watch() data = TS_Data(df['X'], df['side']) assert isinstance(data, TS_Data)