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机器学习之KNN算法学习笔记

时间：2018-08-17 08:50:05

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机器学习之KNN算法学习笔记

1. 综述

1.1 Cover和Hart在1968年提出了最初的邻近算法 1.2 分类(classification)算法|回归算法，这里讨论的是分类算法 1.3 输入基于实例的学习(instance-based learning), 懒惰学习(lazy learning)

2.1 步骤

为了判断未知实例的类别，以所有已知类别的实例作为参照选择参数K 计算未知实例与所有已知实例的距离选择最近K个已知实例根据少数服从多数的投票法则(majority-voting)，让未知实例归类为K个最邻近样本中最多数的类别

2.2 细节:

关于K 关于距离的衡量方法: 2.2.1 Euclidean Distance 定义其他距离衡量：余弦值（cos）, 相关度（correlation）, 曼哈顿距离（Manhattandistance）

2.3 举例

3. 例子

未知电影属于什么类型？

4、python代码实现

利用Python的机器学习库sklearn: SkLearnExample.py

import csvimport randomimport mathimport operatordef loadDataset(filename, split, trainingSet = [], testSet = []):'''导入数据:param filename::param split: 将数据总集以split为界限分成训练集和测试集:param trainingSet::param testSet::return:'''with open(filename, 'rt') as csvfile: # 以逗号为分隔符lines = csv.reader(csvfile) # 读取所有行dataset = list(lines)for x in range(len(dataset)-1):for y in range(4):dataset[x][y] = float(dataset[x][y])if random.random() < split:trainingSet.append(dataset[x])else:testSet.append(dataset[x])def euclideanDistance(instance1, instance2, length):'''计算euclideanDistance:param instance1::param instance2::param length: 维度:return:'''distance = 0for x in range(length):distance += pow((instance1[x]-instance2[x]), 2)return math.sqrt(distance)def getNeighbors(trainingSet, testInstance, k):'''返回最近的k个邻居:param trainingSet: 训练集:param testInstance: 一个测试实例:param k: 参数k:return:'''distances = []length = len(testInstance)-1for x in range(len(trainingSet)):#testinstancedist = euclideanDistance(testInstance, trainingSet[x], length)distances.append((trainingSet[x], dist))#distances.append(dist)distances.sort(key=operator.itemgetter(1))neighbors = []for x in range(k):neighbors.append(distances[x][0])return neighborsdef getResponse(neighbors):'''以距离排序，返回最近的几个点:param neighbors::return:'''classVotes = {}for x in range(len(neighbors)):response = neighbors[x][-1]if response in classVotes:classVotes[response] += 1else:classVotes[response] = 1sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True) # python3 里的.items()返回的是列表，.iteritems()返回的是一个迭代器return sortedVotes[0][0]def getAccuracy(testSet, predictions):'''预测值和实际值的准确率:param testSet::param predictions::return:'''correct = 0for x in range(len(testSet)):if testSet[x][-1] == predictions[x]:correct += 1return (correct/float(len(testSet)))*100.0def main():#prepare datatrainingSet = []testSet = []split = 0.67loadDataset(r'irisdata.txt', split, trainingSet, testSet)print('Train set: ' + repr(len(trainingSet)))print('Test set: ' + repr(len(testSet)))#generate predictionspredictions = []k = 3for x in range(len(testSet)):# trainingsettrainingSet[x]neighbors = getNeighbors(trainingSet, testSet[x], k)result = getResponse(neighbors)predictions.append(result)print ('>predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))accuracy = getAccuracy(testSet, predictions)print('Accuracy: ' + repr(accuracy)+ '%')if __name__ == '__main__':main()