机器学习实战第二章 KNN算法的实现

from numpy import *
import operator

def readfile(filename):
    fr =open(filename)
    arrayOLines = fr.readlines()
    numbersOFLines = len(arrayOLines)
    returnMat = zeros((numbersOFLines, 3))
    classLabelVector = []
    index = 0
    for line in arrayOLines:
        line = line.strip()
        listFromLine = line.split('\t')
        returnMat[index, :] = listFromLine[0:3]
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat,classLabelVector

def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet  = zeros(shape(dataSet))
    m = dataSet.shape[0]
    normDataSet = dataSet - tile(minVals,(m,1))
    normDataSet = normDataSet/tile(ranges,(m,1))
    return normDataSet,ranges,minVals

A,B =readfile('datingTestSet2.txt')
print(A,B)
A,C,D = autoNorm(A)   #A 为normmat   C为ranges  D为minvals

import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(221)
ax.scatter(A[:,1],A[:,2])
ax = fig.add_subplot(222)
ax.scatter(A[:,1],A[:,2],15.0*array(B),15.0*array(B))
ax = fig.add_subplot(223)
ax.scatter(A[:,0],A[:,1])
ax = fig.add_subplot(224)
ax.scatter(A[:,0],A[:,1],15.0*array(B),15.0*array(B))
plt.show()
print('-----------------------------')
def classify0(inX, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    diffMat = tile(inX, (dataSetSize,1)) - dataSet
    sqDiffMat = diffMat**2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances**0.5
    sortedDistIndicies = distances.argsort()
    classCount={}
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

def datingClassTest():
    hoRatio = 0.10
    datingDataMat,datingLables = readfile('datingTestSet2.txt')
    normMat , ranges , minVals = autoNorm(datingDataMat)
    m = normMat.shape[0]
    numTestVecs = int(m*hoRatio)
    errorCount = 0.0
    for i in range(numTestVecs):
        classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLables[numTestVecs:m],3)
        print('分类器反馈：%d，实际是：%d'%(classifierResult,datingLables[i]))
        if (classifierResult != datingLables[i]) : errorCount += 1.0
    print('总的差错率是：%f'%(errorCount/float(numTestVecs)))

datingClassTest()

以上是具体代码，书上给的是基于python2，本人给的是基于python3。

下面写点总结：

KNN代码的核心部分是定义的classify0函数，其伪代码如下：

对未知类别属性的数据集中的每个点依次执行以下操作：

（1）计算已知数据集中的点与当前点之间的距离

（2）按照距离递增次序排序

（3）选取与当前点距离最小的k个点

（4）确定前k个点所在类别的出现的概率

（5）返回前k个点出现频率最高的类别作为当前点的预测分类

代码中inx是用于分类的数据向量，dataset 是数据集，labels是数据集对应的标签，k是分类数

机器学习实战第二章 KNN算法的实现

猜你喜欢