9260d6d1b6189335cca81903361befd32b4f739a,ParamSklearn/components/classification/sgd.py,SGD,fit,#SGD#Any#Any#,32

Before Change

    def fit(self, X, Y):
        // TODO: maybe scale training data that its norm becomes 1?
        // http://scikit-learn.org/stable/modules/sgd.html//id1
        self.alpha = float(self.alpha)
        self.fit_intercept = bool(self.fit_intercept)
        self.n_iter = int(self.n_iter)
        if self.class_weight == "None":
            self.class_weight = None
        self.l1_ratio = float(self.l1_ratio)
        self.epsilon = float(self.epsilon)
        self.eta0 = float(self.eta0)
        self.power_t = float(self.power_t)

        self.estimator = SGDClassifier(loss=self.loss,
                                       penalty=self.penalty,
                                       alpha=self.alpha,
                                       fit_intercept=self.fit_intercept,
                                       n_iter=self.n_iter,
                                       learning_rate=self.learning_rate,
                                       class_weight=self.class_weight,
                                       l1_ratio=self.l1_ratio,
                                       epsilon=self.epsilon,
                                       eta0=self.eta0,
                                       power_t=self.power_t,
                                       shuffle=True,
                                       random_state=self.random_state)
        self.estimator.fit(X, Y)
        return self

    def predict(self, X):
        if self.estimator is None:

After Change

        self.random_state = random_state
        self.estimator = None

    def fit(self, X, y):
        while not self.configuration_fully_fitted():
            self.iterative_fit(X, y, n_iter=1)

        return self

    def iterative_fit(self, X, y, n_iter=1, refit=False):
        if refit:

Instances