"""
函数说明：数据归一化
Parameters:
    dataMatrix - 数据矩阵
Returns:
    matNormalized - 归一化后的数据矩阵
    ranges - 每一维数据 max-min 的值
    mins- 1*14矩阵，存储数据对应维的最小值
"""
    def dataNormalization(dataMatrix):
         mins= dataMatrix.min(0)
         maxs= datMatrix.max(0)
         ranges=maxs-mins
         matNormalized=np.zeros(up.shape(dataMatrix))
         m=dataMatrix.shape[0]
         matNormalized = dataMatrix - np.tile(mins,(m,1))
         matNormalized = matNormalized / np.tile(ranges,(m,1))
         return matNormalized,ranges,mins


"""
函数说明：对单个数据单元进行分类
Parameters:
    testUnit - 1*14矩阵，单个测试数据
    traingSet - 训练集
    labels - 标签列表
    k - KNN参数
Returns:
    sortedClassCountDict[0][0] - 预测分类结果
"""
def unitclassify(testUnit,traingSet,labels,k):
    traingSetSize = traingSet.shape[0]
    diffMatrix = np.tile(testUnit,(traingSetSize,1)) - traingSet
    sqMatrix = diffMatrix * * 2
    distanceMatrix = sqMatrix.sum(axis=1)
    distances = distanceMatrix * * 0.5
    sortedLabels = distances.argsort()
    classCountDict = {}
    for i in range(k):
      label = labels[sortedLabels[i]]
      classCountDict[label] = classCountDict.get(label,0)+1
    sortedClassCountDict = sorted(classCountDict.items(),key=lambda unit:unit[1],reverse=True)
    return sortedClassCountDict[0][0]