def predict(self, X):
X = normalize(polynomial_features(X, degree=self.degree))
return super(ElasticNet, self).predict(X)
def fit(self, X, y):
X = normalize(polynomial_features(X, degree=self.degree))
super(ElasticNet, self).fit(X, y)
def predict(self, X):
X = normalize(polynomial_features(X, degree=self.degree))
return super(PolynomialRidgeRegression, self).predict(X)
def fit(self, X, y):
X = normalize(polynomial_features(X, degree=self.degree))
super(PolynomialRidgeRegression, self).fit(X, y)
def predict(self, X):
X = polynomial_features(X, degree=self.degree)
return super(PolynomialRegression, self).predict(X)
def fit(self, X, y):
X = polynomial_features(X, degree=self.degree)
super(PolynomialRegression, self).fit(X, y)
