Machine Learn in Action(K-近邻算法)
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使用K-近邻算法将某点[0.6, 0.6]划分到某个类(A, B)中。
from numpy import * import operator def classify0(inX, dataSet, labels, k): dataSetSize = dataSet.shape[0] # 数组行数 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 # operator.itemgetter(1)根据iterable的第二个值域排序 sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True) return sortedClassCount[0][0] if __name__ == ‘__main__‘: # 定义训练集 group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]]) labels = [‘A‘, ‘A‘, ‘B‘, ‘B‘] print(classify0([0.6, 0.6], group, labels, 3))
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