def test_fit(): speech_response = loadmat(str(root / "data" / "speech_data.mat")) fs = speech_response["fs"][0][0] response = np.stack([speech_response["resp"][0:100] for _ in range(20)]) stimulus = np.stack([speech_response["stim"][0:100] for _ in range(20)]) tmin = np.random.uniform(-0.1, 0.05) tmax = np.random.uniform(0.1, 0.4) direction = np.random.choice([1, -1]) reg = np.random.uniform(0, 10) trf = TRF(direction=direction) trf.fit(stimulus, response, fs, tmin, tmax, reg) reg = [np.random.uniform(0, 10) for _ in range(randint(2, 10))] trf = TRF(direction=direction) trf.fit(stimulus, response, fs, tmin, tmax, reg)
def test_save_load(): tmpdir = Path(tempfile.gettempdir()) speech_response = loadmat(str(root / "data" / "speech_data.mat")) fs = speech_response["fs"][0][0] response = np.stack([speech_response["resp"][0:100] for _ in range(20)]) stimulus = np.stack([speech_response["stim"][0:100] for _ in range(20)]) tmin = np.random.uniform(-0.1, 0.05) tmax = np.random.uniform(0.1, 0.4) direction = np.random.choice([1, -1]) reg = np.random.uniform(0, 10) trf1 = TRF(direction=direction) trf1.fit(stimulus, response, fs, tmin, tmax, reg) trf1.save(tmpdir / "test.trf") trf2 = TRF() trf2.load(tmpdir / "test.trf") np.testing.assert_equal(trf1.weights, trf2.weights)
def test_encoding(): # load the data speech_response = loadmat(str(root / "data" / "speech_data.mat")) fs = speech_response["fs"][0][0] response = speech_response["resp"] stimuli = speech_response["stim"] # and the expected result encoder_results = loadmat(str(root / "results" / "encoder_results.mat")) # w = input features (stimuli) x times x output features (=channels) w, b, times, _, direction, kind = encoder_results["modelEncoder"][0][0] prediction1 = encoder_results["predResp"] correlation1 = encoder_results["predRespStats"]["r"][0][0][0] error1 = encoder_results["predRespStats"]["err"][0][0][0] # train the TRF model on the data trf_encoder = TRF() tmin, tmax = -0.1, 0.2 trf_encoder.train(stimuli, response, fs, tmin, tmax, 100) # use the trained TRF to predict data prediction2, correlation2, error2 = trf_encoder.predict( stimuli, response, average_features=False) # check that the results are the same as in matlab np.testing.assert_almost_equal(trf_encoder.weights, w, decimal=12) np.testing.assert_almost_equal(trf_encoder.bias, b, decimal=12) np.testing.assert_equal(trf_encoder.times, times[0] / 1e3) np.testing.assert_almost_equal(prediction1, prediction2, decimal=12) np.testing.assert_almost_equal(correlation1, correlation2, decimal=12) np.testing.assert_almost_equal(error1, error2, decimal=12) # we should get the same results if we duplicate the data and use the # fit function stimuli = np.stack([stimuli for _ in range(10)], axis=0) response = np.stack([response for _ in range(10)], axis=0) trf_encoder = TRF() tmin, tmax = -0.1, 0.2 trf_encoder.fit(stimuli, response, fs, tmin, tmax, 100) prediction2, correlation2, error2 = trf_encoder.predict( stimuli, response, average_features=False) # check that the results are the same as in matlab np.testing.assert_almost_equal(trf_encoder.weights, w, decimal=12) np.testing.assert_almost_equal(trf_encoder.bias, b, decimal=12) np.testing.assert_equal(trf_encoder.times, times[0] / 1e3) np.testing.assert_almost_equal(correlation1, correlation2, decimal=12) np.testing.assert_almost_equal(error1, error2, decimal=12)