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45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
'''
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导入科学计算包numpy和运算符模块operator
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'''
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from numpy import *
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import operator
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'''
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创建数据集和标签
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调用方式
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import kNN
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group, labels = kNN.createDataSet()
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'''
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def createDataSet():
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group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
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labels = ['A','A','B','B']
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return group, labels
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def classify0(inX, dataSet, labels, k):
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'''
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inX: 用于分类的输入向量
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dataSet: 输入的训练样本集
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labels: 标签向量
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k: 选择最近邻居的数目
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注意：labels元素数目和dataSet行数相同；程序使用欧式距离公式.
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预测数据所在分类可在输入下列命令
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kNN.classify0([0,0], group, labels, 3)
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'''
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# 1. 距离计算
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dataSetSize = dataSet.shape[0]
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diffMat = tile(inX, (dataSetSize,1)) - dataSet
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sqDiffMat = diffMat**2
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sqDistances = sqDiffMat.sum(axis=1)
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distances = sqDistances**0.5
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sortedDistIndicies = distances.argsort()
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# 2. 选择距离最小的k个点
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classCount = {}
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for i in range(k):
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voteIlabel = labels[sortedDistIndicies[i]]
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classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
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# 3. 排序
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sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
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return sortedClassCount[0][0] |