f7e9f59ba8ab154067565560cc5040f31df928f5,mlxtend/classifier/perceptron.py,Perceptron,fit,#Perceptron#,47

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 (np.all(np.array([0, 1]) == self.classes_) or
                np.all(np.array([-1, 1]) == self.classes_)):
            raise ValueError("Only supports binary"

After Change

                self.w_[0] += update
                errors += int(update != 0.0)

            if self.print_progress:
                self._print_progress(epoch=i+1, cost=errors)
            self.cost_.append(errors)
        return self

    def _init_weights(self, shape):

Instances