def test_cost_function(train_data, test_data):
x, y, y_onehot, = train_data
x_test, y_test = test_data
network = NeuralNetwork(layers=[784, 25, 10])
network.fit(x, y_onehot, alpha=0.1, iterations=40)
prediction_test = network.predict(x_test)
accuracy_test = accuracy(prediction_test, y_test)
assert accuracy_test > 90
if __name__ == '__main__':
# Reading data, Inintializing classes in the workspace
hlp, ft, als = Helpers(), Features(), Analysis()
all_data = hlp.saveReadToLocal('read', '_all_av45', None, '_cache')
all_data = all_data['av45']
keys = ['normal', 'mci', 'ad', 'labels_normal', 'labels_mci', 'labels_ad']
mixed_all_data = als.getTrainingSet(all_data, 0, 0, keys, do_split=False)
print('Features dimention before normalization: ',
len(mixed_all_data[0][0]))
mixed_all_data = ft.normalizeFeatures(mixed_all_data)
print('Features dimention after normalization: ', len(mixed_all_data[0][0]))
# mixed_all_data = als._cleanDataset(mixed_all_data[0], mixed_all_data[1])
# grid_params = runAnalysis(mixed_all_data, classifier='svm')
# a = runAnalysis(mixed_all_data, params=grid_params, classifier='svm')
# print('Overall accuracy: ', np.mean(a))
input_size = len(mixed_all_data[0][0])
nn = NeuralNetwork([input_size, input_size, 40, 40, 3])
input_data = np.asarray(mixed_all_data[0]).reshape(
(input_size, len(mixed_all_data[0])))
input_labels = np.asarray(mixed_all_data[1]).reshape(
(1, len(mixed_all_data[1])))
nn.run(input_data, input_labels, 0.01, 5000)
test = np.asarray(mixed_all_data[0][0]).reshape(
(input_size, 1))
print(nn.predict(test))