return np.array(delta_W_ext) * -self.learning_rate
def predict(self, X):
X = self.data_normalizer.transform(X)
return self.predict_(X)
def predict_(self, X):
O_s = self.get_O_s(X)
results = O_s[-1]
return self.unique_labels[results.argmax(1)]
def plot_accuracies(self, file_name=None):
plt.figure(figsize=(12, 7))
plt.plot(self.history)
plt.xlabel("Iterations")
plt.ylabel("accuracy")
if file_name is None:
plt.show()
else:
plt.savefig(file_name)
X_train, X_test, y_train, y_test = get_data_set('digits.data')
nn = NeuralNetwork()
nn.fit(X_train, y_train)
nn.plot_accuracies('./2_hidden_layers_20_15_with_7_batches.png')
print('Score train: {}'.format(nn.score(X_train, y_train)))
print('Score: {}'.format(nn.score(X_test, y_test)))
# best_seed = 0
# worst_seed = 0
# for i in range(1000):
# X_train, X_test, y_train, y_test = get_data_set(i)
# classifier = FisherClassifier(X_train, y_train)
# score = classifier.score(X_test, y_test)
# if score > max_score:
# max_score = score
# best_seed = i
# if score < min_score:
# min_score = score
# worst_seed = i
#
# print('Best score for seed={}: {}'.format(best_seed, max_score))
# print('Worst score for seed{}: {}'.format(worst_seed, min_score))
X_train, X_test, y_train, y_test = get_data_set(879)
classifier = FisherClassifier(X_train, y_train)
score = classifier.score(X_test, y_test)
print('Score: {}'.format(classifier.score(X_test, y_test)))
lm = LinearRegression()
y_train_modified = list(map(lambda x: 1 if x == 1 else -1, y_train))
lm.fit(X_train, y_train_modified)
prediction = np.array(list(map(lambda x: 1
if x > 0 else 0, lm.predict(X_test))),
dtype=np.float64)
score = np.mean(prediction == np.array(y_test, dtype=np.float64))
print('Score with linear regression: {}'.format(score))