import matplotlib.pyplot as plt from numpy import * import kNN def paintDataSet(): datingDataMat, datingLabels = kNN.file2matrix('datingTestSet.txt') fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2], 20.0*array(datingLabels), 20.0*array(datingLabels)) plt.show() if __name__ == '__main__': kNN.classifyPerson()
datingDataMat, datingLabels = kNN.file2matrix('datingTestSet2.txt') print(datingDataMat) print(datingLabels[0:20]) # scatter plot fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1], 15.0 * np.array(datingLabels), 15.0 * np.array(datingLabels)) plt.show() # normalization normMat, ranges, minVals = kNN.autoNorm(datingDataMat) print(normMat) print(ranges) print(minVals) # test error rate kNN.datingClassTest() # predict kNN.classifyPerson() # handwriting nums recognition # load daata testVector = kNN.img2vector('dataset/testDigits/0_13.txt') print(testVector[0, 0:31]) # handwriting class test 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 """
''' datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt') print(datingDataMat) print(datingLabels) fig = plt.figure() # 图片位置 ax = fig.add_subplot(111) # scatter(x,y,大小,颜色) ax.scatter(datingDataMat[:,0],datingDataMat[:,1], 20.0*array(datingLabels),15.0*array(datingLabels)) plt.show() ''' ''' datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt') normMat,ranges,minVals = kNN.autoNorm(datingDataMat) # print(normMat) # kNN.datingClassTest() kNN.classifyPerson() ''' ''' testVector = kNN.img2Vector('digits/testDigitsnumFeatures/0_13.txt') print("--------") print(testVector[0,0:31]) print(testVector[0,32:63]) '''
def test_classifyPerson(self): kNN.classifyPerson()
def main6(): ''' 约会网站测试函数 ''' kNN.classifyPerson()
from numpy import array import kNN reload(kNN) print kNN.datingClassTest() print kNN.classifyPerson()