def test_predict(self):
"""
Test prediction for dataset. Returns a list of np.arrays.
"""
# defining model
layerSizes = [2,2,1]
nn = MultilayerPerceptron(layerSizes)
# setting up nn
parameters = []
parameters.append(np.ones((3,2)))
parameters.append(np.ones((3,1)))
nn.set_params(parameters)
# preparing input NumericalDataSet
inputSet = np.array([[2,2]])
inputVec = np.array([[2,2]])
nrObs = 10
for _ in range(nrObs-1):
inputSet = np.vstack((inputSet,inputVec))
dataSet = NumericalDataSet(inputSet, None)
# run function
predictions = nn.predict(dataSet)
# check nr of observations
self.assertEqual(len(predictions), nrObs, "number of observations mismatch")
for prediction in predictions:
assert_equal(prediction, np.array([[2.9866142981514305]]), "wrong output")
def test_digits_prediction(self):
training_data = np.loadtxt('../../data/pendigits-training.txt')[:500, :]
testing_data = np.loadtxt('../../data/pendigits-testing.txt')
layer_sizes = [16, 16, 10]
update_method = Rprop(layer_sizes, init_step=0.01)
nn = MultilayerPerceptron(layer_sizes, iterations=100, do_classification=True,
update_method=update_method,
batch_update_size=30,
activation_function=nputils.rectifier,
deriv_activation_function=nputils.rectifier_deriv)
training_targets = nputils.convert_targets(training_data[:, -1], 10)
training_input = training_data[:, 0:-1]
maxs = np.max(training_input, axis=0)
mins = np.min(training_input, axis=0)
normalized_training_input = np.array(
[(r - mins) / (maxs - mins) for r in training_input])
training_data_set = NumericalDataSet(normalized_training_input,
training_targets)
testing_targets = nputils.convert_targets(testing_data[:, -1], 10)
testing_input = testing_data[:, 0:-1]
maxs = np.max(testing_input, axis=0)
mins = np.min(testing_input, axis=0)
normalized_testing_input = np.array(
[(r - mins) / (maxs - mins) for r in testing_input])
testing_data_set = NumericalDataSet(normalized_testing_input, testing_targets)
nn.train(training_data_set)
predictions = nn.predict(testing_data_set)
predictions = [np.argmax(p) for p in predictions]
conf_matrix = np.zeros((10, 10))
conf_matrix = np.concatenate(([np.arange(0, 10)], conf_matrix), axis=0)
conf_matrix = np.concatenate((np.transpose([np.arange(-1, 10)]), conf_matrix), axis=1)
targets = testing_data[:, -1]
for i in range(len(targets)):
conf_matrix[targets[i] + 1, predictions[i] + 1] += 1
print("Detection rate: " + str(np.sum(np.diagonal(conf_matrix[1:, 1:])) / len(targets)))
print(str(conf_matrix))