Python 纯手写 实现KNN

实现《统计学习方法》P39 例3.1

输入：数据集，实例X，K值，以及计算距离的方法

输出：距离最近的K个数据，以及最近距离

例3.1：

首先定义三种计算距离的方法：欧氏距离，曼哈顿距离，以及各个坐标值的最大值

对传入的实例X，计算再不同的距离计算方法下的最近距离，及对应的最近的坐标值

Python代码：

import numpy as np
import math
x = np.array([[5,1],[4,4],[2,1],[3,3],[4,5],[7,2]])
xx = [1,1]



def Euclidean_distance(xi,yi):
    """
    欧式距离计算
    :param xi:
    :param yi:
    :return:
    """
    sum_distance = 0
    for i in range(len(xi)):
        sum_distance += pow(abs(xi[i] - yi[i]) , 2)
    return math.sqrt(sum_distance)


def Manhattan_distance(xi,yi):
    """
    曼哈顿距离
    :param xi:
    :param yi:
    :return:
    """
    sum_distance = 0
    for i in range(len(xi)):
        sum_distance += abs(xi[i] - yi[i])
    return sum_distance


def Max_distance(xi,yi):
    """
    各个坐标距离的最大值
    :param xi:
    :param yi:
    :return:
    """
    sum_distance = 0
    for i in range(len(xi)):
        sum_distance = max(sum_distance , abs(xi[i] - yi[i]))
    return sum_distance

def nearest_neighbr(xx , x , k , functin):
    nearest_dict = {}
    max_list = []
    nearest_list = []
    for i in range(len(x)):

        distance = functin(xx,x[i])
        max_list.append(distance)
        if distance not in nearest_dict:
            nearest_dict[distance] = [x[i].tolist()]
        else:
            nearest_dict[distance].append(x[i].tolist())

    max_list.sort()
    for i in max_list[:k]:
        nearest_list += [z for z in nearest_dict[i]]

    return max_list[:k]  , nearest_list[:k]


E , E_list = nearest_neighbr(xx , x , 2 , Euclidean_distance)
M , M_list = nearest_neighbr(xx , x , 2 , Manhattan_distance)
Ma , Ma_list = nearest_neighbr(xx , x , 2 , Max_distance)

print(E)
print(E_list)
print(M)
print(M_list)
print(Ma)
print(Ma_list)

当后期需要实现KNN分类时

可将最近k个样本点的label值，取多数原则，对X实例进行类别划分即可