f7e9f59ba8ab154067565560cc5040f31df928f5,mlxtend/classifier/adaline.py,Adaline,fit,#Adaline#,54

Before Change

        
        // check array shape
        if not len(X.shape) == 2:
            raise ValueError("X must be a 2D array. Try X[:,np.newaxis]")

        // check if {0, 1} or {-1, 1} class labels are used
        self.classes_ = np.unique(y)
        if not len(self.classes_) == 2 \
                or not self.classes_[0] in (-1, 0) \
                or not self.classes_[1] == 1:

After Change

        // Gradient descent or stochastic gradient descent learning
        else:
            self.init_time_ = time()
            for i in range(self.epochs):

                if self.shuffle:
                    X, y = self._shuffle(X, y)

Instances