Machine_Learning_in_Action02 - K-Nearest Neighbors

K-Nearest Neighbors

KNN algorithm

从文件读数据

画散点图

归一化数值型数据

目录

• K-Nearest Neighbors
  • 根据距离分类
    • 算法描述
    • knn实现
    • 画散点图
  • 案例分析
  • 手写数字分类
  • 完整代码

根据距离分类

• 优点
  • 精度高
  • 对奇异点不敏感
  • 对数据没有假设
• 缺点
  • 计算量大
  • 需要大量内存
• 数据类型
  • 数值型，名词性数值

算法描述

没有训练过程，直接进行分类。当获取了新的数据，遍历已有数据（训练集），计算新数据与每个训练数据的距离，然后按照距离顺序排列，取前k的数据，然后根据这k个数据进行投票，哪个类获得的投票多，那就将新数据分为这一类。

knn实现

import numpy as np
import operator

def createDataSet():
    group = np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
    labels = ['A','A','B','B']
    return group, labels

def classify(intX, dataSet, labels, k):
    diff = np.array(dataSet)-np.array(intX)
    diff_2 = diff**2
    diff_sum = np.sum(diff_2, axis=1)
    dist = diff_sum**0.5
    sort_idx = np.argsort(dist)

    label_count = {}
    for i in range(k):
        idx = sort_idx[i]
        label = labels[idx]
        label_count[label] = label_count.get(label, 0) + 1
    label_count_sort = sorted(label_count.items(), key=lambda a: a[1], reverse=True)
    return label_count_sort[0][0]

if __name__ == '__main__':
    dataSet, labels = createDataSet()

    x = [0.8,0.2]
    target = classify(x, dataSet, labels, k=3)
    print('target class:', target)

画散点图

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

def file2matrix(file_name):
    with open(file_name) as file:
        lines = file.readlines()
    lines_num = len(lines)
    mat = np.zeros((lines_num, 3))
    labels = []

    idx = 0
    for line in lines:
        line = line.strip()
        line_split = line.split('\t')
        mat[idx, :] = line_split[0:3]
        labels.append(int(line_split[-1]))
        idx += 1

    return mat, labels

if __name__ == '__main__':
    mat, labels = file2matrix('datingTestSet2.txt')
    print(mat, labels)

    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(mat[:,1], mat[:,2])
    plt.show()

• 结果

案例分析

def datingClassTest():
    hoRatio = 0.10
    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDataMat)
    m = normMat.shape[0]
    numTestVecs = int(m*hoRatio)
    errorCount = 0.0
    for i in range(numTestVecs):
        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]))
        if (classifierResult != datingLabels[i]): errorCount += 1.0
    print("the total error rate is: %f" % (errorCount/float(numTestVecs)))

if __name__ == '__main__':
    datingClassTest()

手写数字分类

def handwritingClassTest():
    hwLabels = []
    trainingFileList = os.listdir('digits/trainingDigits')
    m = len(trainingFileList)
    trainingMat = np.zeros((m,1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        hwLabels.append(classNumStr)
        trainingMat[i,:] = img2vector('digits/trainingDigits/%s' % fileNameStr)
    testFileList = os.listdir('digits/testDigits')
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2vector('digits/testDigits/%s' % fileNameStr)
        classifierResult = classify(vectorUnderTest, trainingMat, hwLabels, 3)
        print("the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr))
        if (classifierResult != classNumStr): errorCount += 1.0
    print("\nthe total number of errors is: %d" % errorCount)
    print("\nthe total error rate is: %f" % (errorCount/float(mTest)))

---

完整代码

import os
import numpy as np
import operator
import matplotlib
import matplotlib.pyplot as plt

def createDataSet():
    group = np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
    labels = ['A','A','B','B']
    return group, labels

def classify(intX, dataSet, labels, k):
    diff = np.array(dataSet)-np.array(intX)
    diff_2 = diff**2
    diff_sum = np.sum(diff_2, axis=1)
    dist = diff_sum**0.5
    sort_idx = np.argsort(dist)

    label_count = {}
    for i in range(k):
        idx = sort_idx[i]
        label = labels[idx]
        label_count[label] = label_count.get(label, 0) + 1
    label_count_sort = sorted(label_count.items(), key=lambda a: a[1], reverse=True)
    return label_count_sort[0][0]


def classify_test():
    dataSet, labels = createDataSet()

    x = [0.8,0.2]
    target = classify(x, dataSet, labels, k=3)
    print('target class:', target)


def file2matrix(file_name):
    with open(file_name) as file:
        lines = file.readlines()
    lines_num = len(lines)
    mat = np.zeros((lines_num, 3))
    labels = []

    idx = 0
    for line in lines:
        line = line.strip()
        line_split = line.split('\t')
        mat[idx, :] = line_split[0:3]
        labels.append(int(line_split[-1]))
        idx += 1

    return mat, labels


def scatter_test():
    mat, labels = file2matrix('datingTestSet2.txt')
    print(mat, labels)

    fig = plt.figure()
    ax = fig.add_subplot(111)
    # ax.scatter(mat[:,1], mat[:,2])
    ax.scatter(mat[:,1], mat[:,2],
15.0*np.array(labels), 15.0*np.array(labels))
    plt.show()


def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet = np.zeros(np.shape(dataSet))
    m = dataSet.shape[0]
    normDataSet = dataSet - minVals
    normDataSet = normDataSet/ranges
    return normDataSet, ranges, minVals


def norm_test():
    dataSet, labels = createDataSet()
    print(autoNorm(dataSet))


def datingClassTest():
    hoRatio = 0.10
    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDataMat)
    m = normMat.shape[0]
    numTestVecs = int(m*hoRatio)
    errorCount = 0.0
    for i in range(numTestVecs):
        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]))
        if (classifierResult != datingLabels[i]): errorCount += 1.0
    print("the total error rate is: %f" % (errorCount/float(numTestVecs)))


def classifyPerson():
    resultList = ['not at all','in small doses', 'in large doses']
    percentTats = float(input("percentage of time spent playing video games?"))
    ffMiles = float(input("frequent flier miles earned per year?"))
    iceCream = float(input("liters of ice cream consumed per year?"))
    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDataMat)
    inArr = np.array([ffMiles, percentTats, iceCream])
    classifierResult = classify((inArr-minVals)/ranges,normMat,datingLabels,3)
    print("You will probably like this person: ", resultList[classifierResult - 1])


## handwritten digits
def img2vector(filename):
    returnVect = np.zeros((1,1024))
    fr = open(filename)
    for i in range(32):
        lineStr = fr.readline()
        for j in range(32):
            returnVect[0,32*i+j] = int(lineStr[j])
    return returnVect


def handwritingClassTest():
    hwLabels = []
    trainingFileList = os.listdir('digits/trainingDigits')
    m = len(trainingFileList)
    trainingMat = np.zeros((m,1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        hwLabels.append(classNumStr)
        trainingMat[i,:] = img2vector('digits/trainingDigits/%s' % fileNameStr)
    testFileList = os.listdir('digits/testDigits')
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2vector('digits/testDigits/%s' % fileNameStr)
        classifierResult = classify(vectorUnderTest, trainingMat, hwLabels, 3)
        print("the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr))
        if (classifierResult != classNumStr): errorCount += 1.0
    print("\nthe total number of errors is: %d" % errorCount)
    print("\nthe total error rate is: %f" % (errorCount/float(mTest)))


if __name__ == '__main__':
    # classify_test()
    # scatter_test()
    # norm_test()
    # datingClassTest()
    # classifyPerson()
    handwritingClassTest()