def proccess_data(FILE_, DATA, NUM_SAMPLES, LABEL): # Remove header, Nan and trash data = np.load('DB_1d/' + FILE_ + '.npy') Xc_1 = data[:10000, 0] Xc_2 = data[10000:, 0] print '\nXc_1 cleaned shape ', Xc_1.shape print 'Xc_2 cleaned shape ', Xc_2.shape #Create temporal serie NUM_SAMPLES = 50 #NUM_COLS = Xc_1.shape[1] Xc_1 = mls.generate_envelope(Xc_1, NUM_SAMPLES) Xc_2 = mls.generate_envelope(Xc_2, NUM_SAMPLES) #Labeling the type of movement C = (np.ones(len(Xc_1)) * 0).reshape((len(Xc_1), 1)) Xc_1 = np.hstack((Xc_1.reshape(Xc_1.shape), C.reshape((len(Xc_1), 1)))) C = (np.ones(len(Xc_2)) * 1).reshape((len(Xc_2), 1)) Xc_2 = np.hstack((Xc_2.reshape(Xc_2.shape), C.reshape((len(Xc_2), 1)))) print 'Xc labeled shape ', Xc_1.shape print 'Xc labeled shape ', Xc_2.shape # Salving in file on the folder <classifier_data> np.save('./preprocessed_data/' + FILE_ + 'op', Xc_1, allow_pickle=False) print FILE_ + 'op.npy' np.save('./preprocessed_data/' + FILE_ + 'pp', Xc_2, allow_pickle=False) print FILE_ + 'pp.npy' DATA.append(FILE_ + 'op.npy') DATA.append(FILE_ + 'pp.npy')
def proccess_data(FILE_, DATA, NUM_SAMPLES, LABEL): Xc = np.genfromtxt('DB_nd/' + FILE_ + '.txt', delimiter=",", usecols=(1, 2, 3, 4)) print '\nXc shape ', Xc.shape #Create temporal serie Xc = mls.generate_envelope(Xc, NUM_SAMPLES) print 'Xc temporal-serie shape ', Xc.shape #Labeling the type of movement C = (np.ones(len(Xc)) * LABEL).reshape((len(Xc), 1)) Xc = np.hstack((Xc.reshape(Xc.shape), C.reshape((len(Xc), 1)))) print 'Xc labeled shape ', Xc.shape np.save('./preprocessed_data/' + FILE_, Xc, allow_pickle=False) print FILE_ + '.npy' DATA.append(FILE_ + '.npy')
def test_generate_envelope(self): v1 = np.array([1, 2, 3]) r1 = generate_envelope(v1, 2) expeted = np.array([[1, 2], [2, 3]]) self.assertTrue((r1 == expeted).all())
def test_generate_evnelop_two_channels(self): v1 = np.array([[1, 2], [3, 4], [5, 6]]) r1 = generate_envelope(v1, 2) #expected = np.array([[1,3, 2, 4],[3,5, 4,6]]) expected = np.array([[1, 2, 3, 4], [3, 4, 5, 6]]) self.assertTrue((r1 == expected).all())