Machine Learning-KNN

1、定义

url:https://en.wikipedia.org/wiki...

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.[1] In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression:

In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.

In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.

2、我的理解

其实简单理解就是：经过计算新加入点与附近K个点的距离，而后寻找到距离最近的K个点，进行占比统计，找到k个点中数量占比最高的target，那么新加入的样本，它的target就是频数最高的target

3、实践

语言：python3

欧拉距离：

# -*- coding: utf-8 -*-
"""
Created on Sat Mar 17 11:17:18 2018

@author: yangzinan
"""

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from math import sqrt

from collections import Counter 


# 样本
x= [
              [3.393533211,2.331273381],
              [3.110073483,1.781539638],
              [1.343808831,3.368360954],
              [3.582294042,4.679179110],
              [2.280362439,2.866990263],
              [7.423436942,4.696522875],
              [5.745051997,3.533989803],
              [9.172168622,2.511101045],
              [7.792783481,3.424088941],
              [7.939820817,0.791637231]
            ]

y= [0,0,0,0,0,1,1,1,1,1]


x_train = np.array(x)
y_train = np.array(y)
              

# 绘图
plt.scatter(x_train[y_train==0,0],x_train[y_train==0,1],color="red")
plt.scatter(x_train[y_train==1,0],x_train[y_train==1,1],color="green")



x_point = np.array([8.093607318,3.365731514])


plt.scatter(x_point[0],x_point[1],color="blue")
plt.show()


#计算距离 欧拉距离

distances = []

for d in x_train:
    # 求出和x相差的距离
    d_sum = sqrt(np.sum(((d-x)**2)))
    distances.append(d_sum)

print(distances)

#求出最近的点
#按照从小到大的顺序，获得下标
nearest = np.argsort(distances)

#指定应该求出的个数
k = 3
topK_y = []

#求出前K个target
for i in nearest[:k]:
    topK_y.append(y_train[i])


#获得频数最高的target，那么新加入点target 就是频数最高的
predict_y = Counter(topK_y).most_common(1)[0][0]

print(predict_y)