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)