def predict(self, inputs, normalized=True):
polynomial_inputs = polynomial_features(inputs, degree=self.degree)
if normalized:
polynomial_inputs = normalize(polynomial_inputs)
return super(PolynomialRegression, self).predict(polynomial_inputs)
def fit(self, inputs, targets, verbose=False, normalized=True):
polynomial_inputs = PolynomialFeatures(
degree=self.degree).fit_transform(inputs)
if normalized:
polynomial_inputs = normalize(polynomial_inputs)
fit_stats = super(ElasticNetRegression,
self).fit(polynomial_inputs, targets, verbose)
return fit_stats
def predict(self, inputs, normalized=True):
polynomial_inputs = PolynomialFeatures(
degree=self.degree).fit_transform(inputs)
if normalized:
polynomial_inputs = normalize(polynomial_inputs)
return super(ElasticNetRegression, self).predict(polynomial_inputs)
def fit(self, inputs, targets, verbose=False, normalized=True):
polynomial_inputs = polynomial_features(inputs, degree=self.degree)
if normalized:
polynomial_inputs = normalize(polynomial_inputs)
fit_stats = super(PolynomialRegression,
self).fit(polynomial_inputs, targets, verbose)
return fit_stats