def main(): data = datasets.load_digits() X = normalize(data.data) y = data.target y = to_categorical(y) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4) model = Perceptron(max_iter=5000, learning_rate=0.001, penalty=l2_loss) model.fit(X_train, y_train) y_pred = np.argmax(model.predict(X_test), axis=1) y = np.argmax(y_test, axis=1) accuracy = calculate_accuracy_score(y, y_pred) print("Accuracy Score: {:.2%}".format(accuracy))
def main(): # Example 1 X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) Y = np.array([1, 1, 1, 2, 2, 2]) model = GaussianNB() model.fit(X, Y) print(model.predict([[-0.8, -1]])) # Example 2 iris = load_iris() X = normalize(iris.data) y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4) model = GaussianNB() model.fit(X_train, y_train) y_pred = model.predict(X_test) accuracy = calculate_accuracy_score(y_test, y_pred) print("Accuracy Score: {:.2%}".format(accuracy))
def predict(self, X): X = normalize(PolynomialFeatures(X, degree=self.degree)) return super(ElasticNet, self).predict(X)
def fit(self, X, y): X = normalize(PolynomialFeatures(X, degree=self.degree)) super(ElasticNet, self).fit(X, y)
def predict(self, X): X = normalize(PolynomialFeatures(X, degree=self.degree)) return super(PolynomialRidgeRegression, self).predict(X)
def fit(self, X, y): X = normalize(PolynomialFeatures(X, degree=self.degree)) super(PolynomialRidgeRegression, self).fit(X, y)