class RidgeClassifierCVImpl():
def __init__(self, alphas=[0.1, 1.0, 10.0], fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight='balanced', store_cv_values=False):
self._hyperparams = {
'alphas': alphas,
'fit_intercept': fit_intercept,
'normalize': normalize,
'scoring': scoring,
'cv': cv,
'class_weight': class_weight,
'store_cv_values': store_cv_values}
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
def decision_function(self, X):
return self._wrapped_model.decision_function(X)
