perceptron

class Perceptron:
    def __init__(self, eta=.1, n_iter=10, model_w=[.0, .0], model_b=.0):
        self.eta = eta
        self.n_iter = n_iter
        self.model_w = model_w
        self.model_b = model_b

    def predict(self, x):
        if np.dot(self.model_w, x) + self.model_b >= 0:
            return 1
        else:
            return -1

    def update_weights(self, idx, model_w, model_b):
        w = model_w
        b = model_b
        w += self.eta * y_train[idx] * x_train[idx]
        b += self.eta * y_train[idx]
        return w, b

    def fit(self, x, y):
        if len(x) != len(y):
            print('error')
            return False
        for i in range(self.n_iter):
            for idx in range(len(x)):
                if y[idx] != self.predict(x[idx]):
                    self.model_w, self.model_b = self.update_weights(idx,
                                            self.model_w, self.model_b)