小白自学机器学习.zip

import matplotlib.pyplot as plt from numpy import * from os import listdir def loadData(filename): f = open(filename) number_lines = len(f.readlines()) returnMat = zeros((number_lines, 3)) labelMat = [] f.close() f = open(filename) index = 0 # print(type(returnMat)) # print(type(labelMat)) for line in f.readlines(): line_information = line.strip().split('\t') returnMat[index, :] = line_information[0:3] labelMat.append(int(line_information[-1])) index += 1 return returnMat, labelMat # datingDataMat, datingLabels = loadData("C:/Users/ASUS\Desktop/MachineLearning/datingTestSet2.txt") def paintPicture(): fig = plt.figure() ax = fig.add_subplot(111) datingDataMat, datingLabels = loadData("C:/Users/Admin/Desktop/MachineLearning/datingTestSet2.txt") ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1], 15.0 * array(datingLabels), 15.0 * array(datingLabels)) plt.show() def autoNorm(dataSet): minvals = dataSet.min(0) maxvals = dataSet.max(0) # min(0)返回该矩阵中每一列的最小值 # min(1)返回该矩阵中每一行的最小值 # max(0)返回该矩阵中每一列的最大值 # max(1)返回该矩阵中每一行的最大值 # print(minvals) # print(maxvals) ranges = maxvals - minvals # print(ranges) normdataSet = zeros(shape(dataSet)) m = dataSet.shape[0] # shape[0]返回行数， shape[1]返回列数 normdataSet = dataSet - tile(minvals, (m, 1)) normdataSet = normdataSet / tile(ranges, (m, 1)) return normdataSet, ranges, minvals def classify(inX, dataSet, labels, k): dataSetSize = dataSet.shape[0] diffMat = tile(inX, (dataSetSize, 1)) - dataSet sqdiffMat = diffMat ** 2 sqDistance = sqdiffMat.sum(axis=1) # 当axis为0时,是压缩行,即将每一列的元素相加,将矩阵压缩为一行 # 当axis为1时,是压缩列,即将每一行的元素相加,将矩阵压缩为一列 distance = sqDistance ** 0.5 sortedDistance = distance.argsort() # 将距离从小到大排序， 返回一个列表，里面是排序后元素在未排序前所对应的索引 classCount = {} for i in range(k): voteIlabel = labels[sortedDistance[i]] classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1 # 排序并返回出现最多的那个类型 # sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True) # sorted(iterable, key=None, reverse=False) 其中参数说明：iterable：可迭代对象 # key：通过这个参数可以自定义排序逻辑 # reverse：指定排序规则，True为降序，False为升序（默认）。 # 字典的items方法作用：是可以将字典中的所有项，以列表方式返回。字典不是可迭代对象 # 例如: dict1 = {'name': 'Tom', 'age': 20, 'gender': '男'} # print(dict1.items()) # dict_items([('name', 'Tom'), ('age', 20), ('gender', '男')]) # sorted 中的第2个参数 key=operator.itemgetter(1) 这个参数的意思是先比较第几个元素 # 例如: a=[('b',2),('a',1),('c',0)] b=sorted(a,key=operator.itemgetter(1)) >>>b=[('c',0),('a',1),('b',2)] 可以看到排序是按照后边的0,1,2进行排序的，而不是a,b,c # b=sorted(a,key=operator.itemgetter(0)) >>>b=[('a',1),('b',2),('c',0)] 这次比较的是前边的a,b,c而不是0,1,2 # b=sorted(a,key=opertator.itemgetter(1,0)) >>>b=[('c',0),('a',1),('b',2)] 这个是先比较第2个元素，然后对第一个元素进行排序，形成多级排序。 # return sortedClassCount[0][0] # 利用max函数直接返回字典中value最大的key maxClassCount = max(classCount, key=classCount.get) # 按照value值来查找最大值并返回key return maxClassCount def datingClassTest(): hoRatio = 0.1 datingDataMat, datingLabels = loadData("C:/Users/Admin\Desktop/MachineLearning/datingTestSet2.txt") normMat, ranges, minvals = autoNorm(datingDataMat) m = normMat.shape[0] numTestVecs = int(hoRatio * m) print("numTestVecs = ", numTestVecs) 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))) print(errorCount) # datingClassTest() # 约会网站预测函数 def classifyPerson(): resultlist = ["not at all", "in small does", "in large dose"] percentTats = float(input("percentage of time spent playing video games ?")) ffMiles = float(input("frequent filer miles earned per year?")) iceCream = float(input("liters of ice cream consumed per year?")) datingDataMat, datingLabels = loadData("C:/Users/Admin\Desktop/MachineLearning/datingTestSet2.txt") normMat, ranges, minvals = autoNorm(datingDataMat) inArr = np.array([ffMiles, percentTats, iceCream]) classifyResult = classify((inArr - minvals) / ranges, normMat, datingLabels, 3) print("You will probably like this people: ", resultlist[classifyResult - 1]) # classifyPerson() # KNN手写数字识别 # 将图像文本数据转化成向量 def img2vector(filename): returnVect = 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 = listdir("C:/Users/Admin\Desktop/MachineLearning/trainingDigits") # print(trainingFileList) m = len(trainingFileList) trainingMat = 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("C:/Users/Admin\Desktop/MachineLearning/trainingDigits/%s" % fileNameStr) testFileList = listdir("C:/Users/Admin\Desktop\MachineLearning/testDigits") # print(testFileList) error_count = 0.0 mTest = len(testFileList) for j in range(mTest): fileNameStr = testFileList[j] fileStr = fileNameStr.split('.')[0] classNumStr = int(fileStr.split('_')[0]) vectorUnderTest = img2vector("C:/Users/Admin\Desktop/MachineLearning/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: error_count += 1.0 print("the total number of errors is : %d" % error_count) print("the total error rate is : %f" % (error_count / float(mTest))) handwritingClassTest()