KNN入门实例

原理请移步上一篇博客
数据集：链接：https://pan.baidu.com/s/18UZD2p3PwTwjVy-_S53flw 提取码：KNN1

代码如下：

import numpy as np
import pandas as pd
import operator
from os import listdir
from collections import Counter
from numpy import *
def classify0(inX,dataSet,labels,k):
    #lineLen 是数据的个数
    lineLen = dataSet.shape[0]
    #inX代表还未知分类的一个数据。将它减去dataSet对应的每一项
    diffMAt = tile(inX,(lineLen,1)) - dataSet
    #取平方（欧氏距离）
    sqDiffMat = diffMAt**2
    #对每一行取sum
    distances = sqDiffMat.sum(axis=1)
    #开方后获得距离distances
    distances = distances**0.5
    #将值对应大小排序，但是返回的数组中的值是其对应的下标值
    sortedDistances = distances.argsort()
    #定义一个分类的字典
    classCount = {
     }
    for i in range(k):
        #获得该下标值所在的分类，并作为未知分类inX的中间分类结果
        visLabel = labels[sortedDistances[i]]
        #看看classCount之前是否有过该分类，如果有就返回有过该分类的次数，否则返回0
        classCount[visLabel] = classCount.get(visLabel,0) + 1
    #对clasCount.items()中的第二个值进行逆排
    b = sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)
    #返回分类结果
    return b[0][0]
def file2matrix(filename):
    """
    将数据的feature和label划分开
    """
    fr = open(filename)
    numberOfLines = len(fr.readlines())
    returnMat = np.zeros((numberOfLines,3))
    classLabelVector = []
    index = 0
    fr = open(filename)
    for i in fr.readlines():
        line = i.strip()
        listFromLine = line.split('\t')
        #该数据的前三列为feature 第四列为lable
        returnMat[index,:] = listFromLine[0:3]
        #将最后一类lable加入到列表中
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat,classLabelVector
def autoNorm(dataSet):
    """
    归一化特征值，消除属性之间量级不同导致的影响
    :param dataSet: 数据集
    :return: 归一化后的数据集normDataSet,ranges和minVals即最小值与范围，并没有用到
    归一化公式:
        Y = (X-Xmin)/(Xmax-Xmin)
        其中的 min 和 max 分别是数据集中的最小特征值和最大特征值。该函数可以自动将数字特征值转化为0到1的区间。
    """
    #获取数据集没一列的最大值 最小值
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    #每一列最大值和最小值之差
    rangs = maxVals - minVals
    #m为dataSet的行数，也就是数据的个数
    m = dataSet.shape[0]
    #normDataSet是归一化后的数据集
    normDataSet = np.zeros(dataSet.shape)
    #将原数据集的每一项减去其对应列的最小值
    normDataSet = dataSet - tile(minVals,(m,1))
    #归一化
    normDataSet = normDataSet / tile(rangs,(m,1))
    return normDataSet,rangs,minVals
def datingClassTest():
    hoRatio = 0.1
    #datingDataMat是feature datingLables是lable
    datingDataMat,datingLabels = file2matrix('2.KNN/datingTestSet2.txt')
    #normMat是归一化后的结果，其他两项没用到
    normMat,rangs,minVals = autoNorm(datingDataMat)
    #数据的总个数
    m = normMat.shape[0]
    #取百分之十的数据集为未知分类的数据集 剩下百分之90就是已知分类的数据集 （对应86行进行观察
    numTestVecs = int(m*hoRatio)
    #errorCount是错误数
    errorCount = 0.0
    for i in range(numTestVecs):
        classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)
        print("the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i]))
        #如果预测的分类和实际的分类不一样errorCount++
        if (classifierResult != datingLabels[i]): errorCount += 1.0
    print("the total error rate is: %f" % (errorCount / float(numTestVecs)))
    print(errorCount)

if __name__ == '__main__':
     datingClassTest()

运行结果：

the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 1
the total error rate is: 0.050000
5.0