KNN算法python实现

KNN算法python实现。

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原理

• 可参考其它地方

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优点

• 算法简单，精度比较高
• 易于理解，易于实现，不需要训练

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缺点

• 计算复杂度高，空间复杂度高
• 可解释性比较差

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代码

import numpy as np
import operator

def createDataSet():
    group = array([[1.0, 1.2], [1.1, 1.0], [0.1, 0.2], [0.0, 0.1]])
    labels = ['A', 'A', 'B', 'B']
    return group, labels
    
def knn(inx, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    diffMat = tile(inx, (dataSetSize, 1)) - dataSet
    sqDiffMat = diffMat**2
    sqDistances = sqDiffMat.sum(1)
    distances = sqDistances**0.5
    sortedDistIndicies = distances.argsort()
    classCount = {}
    for i in range(k):
        voteLabel = labels[sortedDistIndicies[i]]
        if voteLabel not in classCount.keys():
            classCount[voteLabel] = 0
        classCount[voteLabel] += 1
    sortedClassCount = sorted(classCount.items(), key = operator.itemgetter(1), reverse = True)
    return sortedClassCount[0][0]

测试

group,labels = createDataSet()
knn([0.1,0.2],group,labels,3)

结果

'B'