在Perceptron Learning Model的Python实现中将数组传递给numpy.d

import numpy as np    
class Perceptron(object):
    """Perceptron classifier.

    Parameters
    ------------
    eta : float
        Learning rate (between 0.0 and 1.0)
    n_iter : int
        Passes over the training dataset.

    Attributes
    -----------
    w_ : 1d-array
        Weights after fitting.
    errors_ : list
        Number of misclassifications in every epoch.

    """
    def __init__(self,eta=0.01,n_iter=10):
        self.eta = eta
        self.n_iter = n_iter

    def fit(self,X,y):
        """Fit training data.

        Parameters
        ----------
        X : {array-like},shape = [n_samples,n_features]
            Training vectors,where n_samples 
            is the number of samples and
            n_features is the number of features.
        y : array-like,shape = [n_samples]
            Target values.

        Returns
        -------
        self : object

        """
        self.w_ = np.zeros(1 + X.shape[1])
        self.errors_ = []

        for _ in range(self.n_iter):
            errors = 0
            for xi,target in zip(X,y):
                update = self.eta * (target - self.predict(xi))
                self.w_[1:] += update * xi
                self.w_[0] += update
                errors += int(update != 0.0)
            self.errors_.append(errors)
        return self

    def net_input(self,X):
        """Calculate net input"""
        return np.dot(X,self.w_[1:]) + self.w_[0]

    def predict(self,X):
        """Return class label after unit step"""
        return np.where(self.net_input(X) >= 0.0,1,-1)

我无法理解的部分是为什么我们定义net_input()和predict()来获取数组X而不仅仅是一个向量.一切都工作正常,因为我们只是在fit()函数中传递向量xi到predict()(因此也只是将向量传递给net_input()),但是定义函数的逻辑是什么呢？阵列？如果我正确理解模型,我们一次只取一个样本,计算权重向量和与样本相??关的特征向量的点积,我们永远不需要将整个数组传递给net_input()或预测( ).