python实现KNN近邻算法

示例：《电影类型分类》

获取数据来源

电影名称 打斗次数 接吻次数 电影类型 California Man 3 104 Romance He’s Not Really into Dudes 8 95 Romance Beautiful Woman 1 81 Romance Kevin Longblade 111 15 Action Roob Slayer 3000 99 2 Action Amped II 88 10 Action Unknown 18 90 unknown

数据显示：肉眼判断电影类型unknown是什么

from matplotlib import pyplot as plt​# 用来正常显示中文标签plt.rcParams['font.sans-serif'] = ['SimHei']# 电影名称names = ['California Man', 'He’s Not Really into Dudes', 'Beautiful Woman', 'Kevin Longblade', 'Robo Slayer 3000', 'Amped II', 'Unknown']# 类型标签labels = ['Romance', 'Romance', 'Romance', 'Action', 'Action', 'Action', 'Unknown']colors = ['darkblue', 'red', 'green']colorDict = {label: color for (label, color) in zip(set(labels), colors)}print(colorDict)# 打斗次数，接吻次数X = [3, 8, 1, 111, 99, 88, 18]Y = [104, 95, 81, 15, 2, 10, 88]​plt.title('通过打斗次数和接吻次数判断电影类型', fontsize=18)plt.xlabel('电影中打斗镜头出现的次数', fontsize=16)plt.ylabel('电影中接吻镜头出现的次数', fontsize=16)​# 绘制数据for i in range(len(X)): # 散点图绘制 plt.scatter(X[i], Y[i], color=colorDict[labels[i]])​# 每个点增加描述信息for i in range(0, 7): plt.text(X[i]+2, Y[i]-1, names[i], fontsize=14)​plt.show()问题分析：根据已知信息分析电影类型unknown是什么

核心思想：

未标记样本的类别由距离其最近的K个邻居的类别决定

距离度量：

一般距离计算使用欧式距离（用勾股定理计算距离），也可以采用曼哈顿距离（水平上和垂直上的距离之和）、余弦值和相似度（这是距离的另一种表达方式）。相比于上述距离，马氏距离更为精确，因为它能考虑很多因素，比如单位，由于在求协方差矩阵逆矩阵的过程中，可能不存在，而且若碰见3维及3维以上，求解过程中极其复杂，故可不使用马氏距离

知识扩展

马氏距离概念：表示数据的协方差距离 方差：数据集中各个点到均值点的距离的平方的平均值 标准差：方差的开方 协方差cov(x, y)：E表示均值，D表示方差，x，y表示不同的数据集，xy表示数据集元素对应乘积组成数据集

cov(x, y) = E(xy) - E(x)*E(y)

cov(x, x) = D(x)

cov(x1+x2, y) = cov(x1, y) + cov(x2, y)

cov(ax, by) = abcov(x, y)

协方差矩阵：根据维度组成的矩阵，假设有三个维度，a，b，c

∑ij = [cov(a, a) cov(a, b) cov(a, c) cov(b, a) cov(b,b) cov(b, c) cov(c, a) cov(c, b) cov(c, c)]

算法实现：欧氏距离

编码实现

# 自定义实现 mytest1.pyimport numpy as np​# 创建数据集def createDataSet(): features = np.array([[3, 104], [8, 95], [1, 81], [111, 15], [99, 2], [88, 10]]) labels = ['Romance', 'Romance', 'Romance', 'Action', 'Action', 'Action'] return features, labels​def knnClassify(testFeature, trainingSet, labels, k): ''' KNN算法实现，采用欧式距离 :param testFeature: 测试数据集，ndarray类型，一维数组 :param trainingSet: 训练数据集，ndarray类型，二维数组 :param labels: 训练集对应标签，ndarray类型，一维数组 :param k: k值，int类型 :return: 预测结果，类型与标签中元素一致 ''' dataSetsize = trainingSet.shape[0] ''' 构建一个由dataSet[i] - testFeature的新的数据集diffMat diffMat中的每个元素都是dataSet中每个特征与testFeature的差值（欧式距离中差） ''' testFeatureArray = np.tile(testFeature, (dataSetsize, 1)) diffMat = testFeatureArray - trainingSet # 对每个差值求平方 sqDiffMat = diffMat ** 2 # 计算dataSet中每个属性与testFeature的差的平方的和 sqDistances = sqDiffMat.sum(axis=1) # 计算每个feature与testFeature之间的欧式距离 distances = sqDistances ** 0.5​ ''' 排序，按照从小到大的顺序记录distances中各个数据的位置 如distance = [5, 9, 0, 2] 则sortedStance = [2, 3, 0, 1] ''' sortedDistances = distances.argsort()​ # 选择距离最小的k个点 classCount = {} for i in range(k): voteiLabel = labels[list(sortedDistances).index(i)] classCount[voteiLabel] = classCount.get(voteiLabel, 0) + 1 # 对k个结果进行统计、排序，选取最终结果，将字典按照value值从大到小排序 sortedclassCount = sorted(classCount.items(), key=lambda x: x[1], reverse=True) return sortedclassCount[0][0]​testFeature = np.array([100, 200])features, labels = createDataSet()res = knnClassify(testFeature, features, labels, 3)print(res)# 使用python包实现 mytest2.pyfrom sklearn.neighbors import KNeighborsClassifierfrom .mytest1 import createDataSet​features, labels = createDataSet()k = 5clf = KNeighborsClassifier(k_neighbors=k)clf.fit(features, labels)​# 样本值my_sample = [[18, 90]]res = clf.predict(my_sample)print(res)示例：《交友网站匹配效果预测》

数据来源：略

数据显示

import pandas as pdimport numpy as npfrom matplotlib import pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D​# 数据加载def loadDatingData(file): datingData = pd.read_table(file, header=None) datingData.columns = ['FlightDistance', 'PlaytimePreweek', 'IcecreamCostPreweek', 'label'] datingTrainData = np.array(datingData[['FlightDistance', 'PlaytimePreweek', 'IcecreamCostPreweek']]) datingTrainLabel = np.array(datingData['label']) return datingData, datingTrainData, datingTrainLabel​# 3D图显示数据def dataView3D(datingTrainData, datingTrainLabel): plt.figure(1, figsize=(8, 3)) plt.subplot(111, projection='3d') plt.scatter(np.array([datingTrainData[x][0] for x in range(len(datingTrainLabel)) if datingTrainLabel[x] == 'smallDoses']), np.array([datingTrainData[x][1] for x in range(len(datingTrainLabel)) if datingTrainLabel[x] == 'smallDoses']), np.array([datingTrainData[x][2] for x in range(len(datingTrainLabel)) if datingTrainLabel[x] == 'smallDoses']), c='red') plt.scatter(np.array([datingTrainData[x][0] for x in range(len(datingTrainLabel)) if datingTrainLabel[x] == 'didntLike']), np.array([datingTrainData[x][1] for x in range(len(datingTrainLabel)) if datingTrainLabel[x] == 'didntLike']), np.array([datingTrainData[x][2] for x in range(len(datingTrainLabel)) if datingTrainLabel[x] == 'didntLike']), c='green') plt.scatter(np.array([datingTrainData[x][0] for x in range(len(datingTrainLabel)) if datingTrainLabel[x] == 'largeDoses']), np.array([datingTrainData[x][1] for x in range(len(datingTrainLabel)) if datingTrainLabel[x] == 'largeDoses']), np.array([datingTrainData[x][2] for x in range(len(datingTrainLabel)) if datingTrainLabel[x] == 'largeDoses']), c='blue') plt.xlabel('飞行里程数', fontsize=16) plt.ylabel('视频游戏耗时百分比', fontsize=16) plt.clabel('冰淇凌消耗', fontsize=16) plt.show() datingData, datingTrainData, datingTrainLabel = loadDatingData(FILEPATH1)datingView3D(datingTrainData, datingTrainLabel)问题分析：抽取数据集的前10%在数据集的后90%进行测试

编码实现

# 自定义方法实现import pandas as pdimport numpy as np​# 数据加载def loadDatingData(file): datingData = pd.read_table(file, header=None) datingData.columns = ['FlightDistance', 'PlaytimePreweek', 'IcecreamCostPreweek', 'label'] datingTrainData = np.array(datingData[['FlightDistance', 'PlaytimePreweek', 'IcecreamCostPreweek']]) datingTrainLabel = np.array(datingData['label']) return datingData, datingTrainData, datingTrainLabel​# 数据归一化def autoNorm(datingTrainData): # 获取数据集每一列的最值 minValues, maxValues = datingTrainData.min(0), datingTrainData.max(0) diffValues = maxValues - minValues # 定义形状和datingTrainData相似的最小值矩阵和差值矩阵 m = datingTrainData.shape(0) minValuesData = np.tile(minValues, (m, 1)) diffValuesData = np.tile(diffValues, (m, 1)) normValuesData = (datingTrainData-minValuesData)/diffValuesData return normValuesData​# 核心算法实现def KNNClassifier(testData, trainData, trainLabel, k): m = trainData.shape(0) testDataArray = np.tile(testData, (m, 1)) diffDataArray = (testDataArray - trainData) ** 2 sumDataArray = diffDataArray.sum(axis=1) ** 0.5 # 对结果进行排序 sumDataSortedArray = sumDataArray.argsort() classCount = {} for i in range(k): labelName = trainLabel[list(sumDataSortedArray).index(i)] classCount[labelName] = classCount.get(labelName, 0)+1 classCount = sorted(classCount.items(), key=lambda x: x[1], reversed=True) return classCount[0][0] ​# 数据测试def datingTest(file): datingData, datingTrainData, datingTrainLabel = loadDatingData(file) normValuesData = autoNorm(datingTrainData) errorCount = 0 ratio = 0.10 total = datingTrainData.shape(0) numberTest = int(total * ratio) for i in range(numberTest): res = KNNClassifier(normValuesData[i], normValuesData[numberTest:m], datingTrainLabel, 5) if res != datingTrainLabel[i]: errorCount += 1 print('The total error rate is : {}n'.format(error/float(numberTest)))​if __name__ == '__main__': FILEPATH = './datingTestSet1.txt' datingTest(FILEPATH)# python 第三方包实现import pandas as pdimport numpy as npfrom sklearn.neighbors import KNeighborsClassifier​if __name__ == '__main__': FILEPATH = './datingTestSet1.txt' datingData, datingTrainData, datingTrainLabel = loadDatingData(FILEPATH) normValuesData = autoNorm(datingTrainData) errorCount = 0 ratio = 0.10 total = normValuesData.shape[0] numberTest = int(total * ratio) k = 5 clf = KNeighborsClassifier(n_neighbors=k) clf.fit(normValuesData[numberTest:total], datingTrainLabel[numberTest:total]) for i in range(numberTest): res = clf.predict(normValuesData[i].reshape(1, -1)) if res != datingTrainLabel[i]: errorCount += 1 print('The total error rate is : {}n'.format(errorCount/float(numberTest)))

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