KNN算法

KNN算法python实现

from operator import itemgetter

import numpy as np


# 获取训练样本和样本标签
def createDataSet():
    groups = np.array(([1, 1.1], [1, 1], [0, 0], [0, 0.1]))
    labels = np.array(['A', 'A', 'B', 'B'])
    return groups, labels

# inx表示要判断的样本
# dataSet表示训练数据集
# labels表示样本类型
# k表示使用前k个来判断
def classify(inx, dataSet, labels, k):
    # 获取训练集中的第一维数字
    dataSetSize = dataSet.shape[0]
    diffMat = np.tile(inx, (dataSetSize, 1)) - dataSet
    sqDiffMat = diffMat ** 2
    # 每行的元素相加
    sqDistance = sqDiffMat.sum(axis=1)
    # 开方
    distance = sqDistance ** 0.5
    # 返回排序索引
    sortedDistIndics = distance.argsort()
    classCount = {}
    for i in range(k):
        voteLabel = labels[sortedDistIndics[i]]
        classCount[voteLabel] = classCount.get(voteLabel, 0) + 1
    sortedClasCount = sorted(classCount.items(), key=itemgetter(1), reverse=True)
    return sortedClasCount[0][0]


# 这就是动态扩展语法糖
# inxo = [float(s) if re.match(re.compile(r'd+'), s) else s for s in sys.argv if not s.endswith('.py')]
# inx = np.array(inxo)
# print()
# dataSet, labels = createDataSet()
# ftypes = classify(inx, dataSet, labels, 3)
# print(ftypes)

# 将文本记录转换Numpy
def filematrix(filename):
    fr = open(filename)
    arrayOLines = fr.readlines()
    numberOfLines = len(arrayOLines)
    returnMat = np.zeros((numberOfLines, 3))
    classLabelVector = []
    index = 0
    for line in arrayOLines:
        line = line.strip()
        listFromLine = line.split('	')
        returnMat[index, :] = listFromLine[0:3]
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat, classLabelVector


# 归一化操作
def autoNorm(dataSet):
    # 获得数据集中每一列的最小值
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    # 范围
    rangs = maxVals - minVals
    normDataSet = np.zeros(np.shape(dataSet))
    m = dataSet.shape[0]
    normDataSet = dataSet - np.tile(minVals, (m, 1))
    normDataSet = normDataSet / np.tile(rangs, (m, 1))
    return normDataSet, rangs, minVals


def datingClassTest():
    # 设置训练数据和测试数据比例
    hoRatio = 0.10
    # 读取文件数据
    datingDataMat, datingLabels = filematrix('datingTestSet.txt')
    # 归一化操作
    normMat, rangs, minVals = autoNorm(datingDataMat)
    # 获得当前矩阵的行数
    m = normMat.shape[0]
    # 设置多少个数据用来测试
    numTestVecs = int(m * hoRatio)
    # 统计错误率
    errorCount = 0.0
    for i in range(numTestVecs):
        # 获取测试数据的每个预测类型
        classifierResult = classify(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)

        print('the classifier came back with: %d, the real answer is:%d' % (classifierResult, datingLabels[i]))
        # 如果预测类型和实际类型不相同
        if (classifierResult != datingLabels[i]):
            errorCount += 1
    print('the total error rate is:%f' % (errorCount / float(numTestVecs)))


datingClassTest()
# import matplotlib.pyplot as plt
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2])
# ax.scatter(datingDataMat[:, 0], datingDataMat[:, 2], 15.0 * np.array(datingLabels), 15.0 * np.array(datingLabels))
# plt.show()