hit_rates = []
no_of_attributes = dataset.shape[1] - 1
no_of_classes = len(dataset[0, no_of_attributes])
# insert bias
no_rows = dataset.shape[0]
dataset = np.c_[-1 * np.ones(no_rows), dataset]
# perceptron = Perceptron(no_of_classes, no_of_attributes, 5, 'logistic')
for j in range(0, 20):
print("realization %d" % j)
train_X, train_y, test_X, test_y = Classifier.train_test_split(dataset)
train_X = np.array(train_X, dtype=float)
test_X = np.array(test_X, dtype=float)
hidden_units = ELM.model_training(no_of_classes, no_of_attributes, train_X,
train_y)
elm = ELM(no_of_classes, no_of_attributes, hidden_units)
elm.train(train_X, train_y)
predictions = elm.predict(test_X)
hit_rates.append(elm.evaluate(test_y, predictions))
print(elm.confusion_matrix(test_y, predictions))
# Perceptron.plot_decision_boundaries(train_X, train_y, test_X, test_y, perceptron, hidden_neurons, j)
print('hit rates: {}'.format(hit_rates))
print('accuracy: {}'.format(np.mean(hit_rates)))
print('std: {}'.format(np.std(hit_rates)))
# Perceptron.show_plot_decision_boundaries()
