def main(): import numpy import matplotlib import matplotlib.pyplot as plt import kNN logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s', datefmt='%a, %d %b %Y %H:%M:%S', filename='./test.log', filemode='w') # group,labels = kNN.createDataSet() # print kNN.classify0([0, 0], group, labels, 3) # datingDataMat,datingLabels=kNN.file2matrix('datingTestSet2.txt') # fig = plt.figure() # ax = fig.add_subplot(111) # ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*numpy.array(datingLabels),15.0*numpy.array(datingLabels)) # fig = plt.figure() # ax = fig.add_subplot(111) # ax.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*numpy.array(datingLabels),15.0*numpy.array(datingLabels)) # plt.show() #kNN.classifyPerson() # testVector = kNN.img2vector('testDigits/0_13.txt') # print testVector[0, 0:31] # print testVector[0, 32:63] kNN.handwritingClassTest()
def main(): # 2.1 # print(classify0([1.2,1.2],group,labels,4)) # 2.2 # matplot() # datingClassTest() #2.3 # print(listdir('machinelearninginaction\Ch02\digits\\trainingDigits')) kNN.handwritingClassTest()
def main(): # group, labels = kNN.createDataSet() # print kNN.classify0([0,0], group, labels, 3) # datingDataMat, datingLabels = kNN.file2matrix('datingTestSet2.txt') # #print datingLabels # #print datingDataMat # # showData(datingDataMat, datingLabels) # normMat, ranges, minVals = kNN.autoNorm(datingDataMat) # print normMat # print ranges # print minVals # kNN.datingClassTest() # kNN.classifyPerson() # def showData(datingDataMat, datingLabels): # fig = plt.figure() # ax = fig.add_subplot(111) # # x,y color marker # ax.scatter(datingDataMat[:,0], datingDataMat[:,1], 15*array(datingLabels), # 15.0*array(datingLabels)) # plt.show() # returnVect = kNN.img2vector('digits/testDigits/0_0.txt') kNN.handwritingClassTest()
# -*- coding: utf-8 -*- # os.chdir('D:\www\IdeaProject\MLiA_SourceCode\machinelearninginaction') # print os.getcwd() from numpy.ma import zeros, array if __name__ == '__main__': print 'hello' # from numpy import * # import operator import kNN # group ,lables = kNN.createDataSet() # print kNN.classify0([0,0],group,lables,3) # dataMat ,dataLables = kNN.file2matrix('datingTestSet2.txt') # print dataMat # import matplotlib # import matplotlib.pyplot as plt # fig = plt.figure() # ax = fig.add_subplot(211) # ax.scatter(dataMat[:,1],dataMat[:,2],15.0*array(dataLables),15.0*array(dataLables)) # ax = fig.add_subplot(212) # ax.scatter(dataMat[:,0],dataMat[:,1],15.0*array(dataLables),15.0*array(dataLables)) # plt.show() # # kNN.datingClassTest() kNN.handwritingClassTest()
import kNN kNN.handwritingClassTest()
[ 0.76626481, 0.44109859, 0.88192528], [ 0.0975718 , 0.02096883, 0.02443895]]) >>> ranges array([ 8.78430000e+04, 2.02823930e+01, 1.69197100e+00]) >>> minVals array([ 0. , 0. , 0.001818]) """ KNN.datingClassTest() """ output: the total error rate is: 0.080000 16.0 """ KNN.classifyPerson() """ output: percentage of time spent playing video games?4 frequent flier miles earned per year?5569 liters of ice cream consumed per year?1.213192 You will probably like this person: in small doses """ KNN.handwritingClassTest() """ output: the total number of errors is: 10 the total error rate is: 0.010571 """
def test_handwriting(self): kNN.handwritingClassTest()
def digitTests(): kNN.handwritingClassTest()
def main7(): ''' 手写数字识别系统 ''' testVector = kNN.img2vector('testDigits/0_0.txt') kNN.handwritingClassTest()
result = kNN.classify0([0, 0.1], group, label, 3) print("the KNN classify result is %s" % result) #test knn classify simple end #test dating classify ###datingDatMat,datingLabels = kNN.file2matrix('datingTestSet.txt') ####print("datingDatMat is %s" % datingDatMat) ####show the seconde,third column of the matrix start ###fig = plt.figure(); ###ax = fig.add_subplot(111) ####ax.scatter(datingDatMat[:,1],datingDatMat[:,2]) #no color ###ax.scatter(datingDatMat[:,0],datingDatMat[:,1],15.0 * array(datingLabels),15.0 * array(datingLabels)) #has color ###plt.show() #show the seconde,third column of the matrix start ###数值归一化处理 ###datingDatMat,datingLabels = kNN.file2matrix('datingTestSet.txt') ###normMat,ranges,minVals = kNN.autoNorm(datingDatMat); ###约会网站测试调用 # kNN.datingClassTest() #kNN.classifyPersion() #手写数字识别系统测试条用 ''' time_start = time.time() kNN.handwritingClassTest() time_end = time.time() print ("the program spend %d s" % (time_end - time_start)) '''
import kNN import matplotlib import matplotlib.pyplot as plt from numpy import * #group,labels = kNN.createDataSet() #kNN.classify0([0,0], group,labels,3) datingDataMat, datingLabels = kNN.file2matrix('datingTestSet2.txt') """ fig = plt.figure() #表示绘制一个图 ax = fig.add_subplot(111) #将画布分割成1行1列,图像画在从左到右从上到下的第1块 ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels)) #scatter生成散点图函数。使用datingDataMat矩阵的第二、三列数据 #datingDataMat[:,1]意思是所有行的第2列(从0开始) #后面第一个数字参数对应左边两种颜色的点的半径大小,第二个数字试了下没什么变化 plt.show() """ normMat, ranges, minVals = kNN.autoNorm(datingDataMat) #print kNN.datingClassTest() #print kNN.classifyPerson() #testVector = kNN.img2vector('testDigits/0_13.txt') #print testVector[0,32:63] print kNN.handwritingClassTest()
import kNN print(kNN.handwritingClassTest())