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資源簡(jiǎn)介

用SVM解決兵王問(wèn)題,是比較好的機(jī)器學(xué)習(xí)svm入門(mén)項(xiàng)目,包括訓(xùn)練測(cè)試數(shù)據(jù)集、libsvm包以及程序代碼。其中數(shù)據(jù)集總樣本數(shù)為28056個(gè),取其中5000個(gè)作為訓(xùn)練樣本,其余的為測(cè)試樣本。libsvm包中有各個(gè)屬性的使用說(shuō)明,代碼為python版本,測(cè)試的準(zhǔn)確率為99.36%

資源截圖

代碼片段和文件信息

import?sys
import?os
import?datetime
import?pickle
import?numpy?as?np
import?scipy.io?as?sio
import?matplotlib
import?matplotlib.pyplot?as?plt

sys.path.append(‘.\libsvm-master\python‘)


from?svmutil?import?*
from?numpy?import?*

tStart?=?datetime.datetime.now()
#?處理數(shù)據(jù)
rawdata?=?‘krkopt.data‘
rd?=?open(rawdata)
arrayOLines?=?rd.readlines()
del?arrayOLines[0]
numbersOfLines?=?len(arrayOLines)
featureDimension?=?6
data?=?zeros((numbersOfLines?featureDimension))
label?=?zeros(numbersOfLines)
for?i?in?range(len(arrayOLines)):
????line?=?arrayOLines[i]
????listFromLine?=?line.split(‘‘)
????data[i?0]?=?ord(listFromLine[0])?-?96
????data[i?1]?=?ord(listFromLine[1])?-?48
????data[i?2]?=?ord(listFromLine[2])?-?96
????data[i?3]?=?ord(listFromLine[3])?-?48
????data[i?4]?=?ord(listFromLine[4])?-?96
????data[i?5]?=?ord(listFromLine[5])?-?48
????if?listFromLine[6]?==?‘draw\n‘:
????????label[i]?=?1
????else:
????????label[i]?=?-1
#?隨機(jī)取5000個(gè)數(shù)據(jù)作為訓(xùn)練樣本?剩下數(shù)據(jù)作為測(cè)試樣本
permutatedData?=?zeros((numbersOfLines?featureDimension))
permutatedLabel?=?zeros(numbersOfLines)

p?=?random.permutation(numbersOfLines)

for?i?in?range(numbersOfLines):
????permutatedData[i?:]?=?data[p[i]?:]
????permutatedLabel[i]?=?label[p[i]]

numbersOfTrainData?=?5000
xTrain?=?permutatedData[:numbersOfTrainData]
yTrain?=?permutatedLabel[:numbersOfTrainData]
xTest?=?permutatedData[numbersOfTrainData:]
yTest?=?permutatedLabel[numbersOfTrainData:]

#?樣本均一化
averageData?=?zeros((1?featureDimension))
for?i?in?range(len(xTrain)):
????averageData?+=?xTrain[i?:]
averageData?=?averageData?/?len(xTrain)
standardDiviation?=?zeros((1?featureDimension))
for?i?in?range(len(xTrain)):
????standardDiviation?=?standardDiviation?+?(xTrain[i]?-?averageData)?**?2
standardDiviation?=?(standardDiviation/(len(xTrain)-1))**0.5

for?i?in?range(len(xTrain)):
????xTrain[i]?=?(xTrain[i]?-?averageData)/standardDiviation
for?i?in?range(len(xTest)):
????xTest[i]?=?(xTest[i]?-?averageData)/standardDiviation

#?粗略搜素最佳的C、gamma參數(shù)
CScale?=?list(range(-5?16?2))
gammaScale?=?list(range(-15?4?2))
maxRecognitionRate?=?0
arr?=?np.array(xTrain)
newX?=?arr.tolist()
arr?=?np.array(yTrain)
newY?=?arr.tolist()
for?i?in?range(len(CScale)):
????testC?=?2?**?CScale[i]
????for?j?in?range(len(gammaScale)):
????????cmd?=?‘-t?2?-c?‘
????????cmd?+=?str(testC)
????????cmd?+=?‘?-g?‘
????????testGamma?=?2**gammaScale[j]
????????cmd?+=?str(testGamma)
????????cmd?+=?‘?-v?5‘
????????cmd?+=?‘?-h?0‘
????????print(‘rude?search‘)
????????recognitionRate?=?svm_train(newY?newX?cmd)
????????if?recognitionRate?>?maxRecognitionRate:
????????????maxRecognitionRate?=?recognitionRate
????????????print(maxRecognitionRate)
????????????maxCIndex?=?i
????????????maxGammaIndex?=?j
#精確搜索C、gamma
n?=?10
minCScale?=?0.5?*?(CScale[max(0?maxCIndex?-?1)]+CScale[maxCIndex])
maxCScale?=?0.5?*?(CScale[min(len(CScale)?-?1?maxCIndex?+?1)]?+?CScale[maxCIndex])
newCScale?=?

?屬性????????????大小?????日期????時(shí)間???名稱(chēng)
-----------?---------??----------?-----??----
?????目錄???????????0??2020-07-29?21:58??SVM\
?????文件???????????0??2020-07-27?23:22??SVM\__init__.py
?????文件??????184648??2020-07-29?21:58??SVM\decisionValues.mat
?????文件??????559878??2020-03-02?17:56??SVM\krkopt.data
?????目錄???????????0??2020-07-28?20:23??SVM\libsvm-master\
?????文件????????1497??2018-07-15?22:15??SVM\libsvm-master\COPYRIGHT
?????文件???????83238??2018-07-15?22:15??SVM\libsvm-master\FAQ.html
?????文件???????27670??2018-07-15?22:15??SVM\libsvm-master\heart_scale
?????目錄???????????0??2020-07-28?20:23??SVM\libsvm-master\java\
?????文件???????55181??2018-07-15?22:15??SVM\libsvm-master\java\libsvm.jar
?????目錄???????????0??2020-07-28?20:23??SVM\libsvm-master\java\libsvm\
?????文件???????64084??2018-07-15?22:15??SVM\libsvm-master\java\libsvm\svm.java
?????文件???????63281??2018-07-15?22:15??SVM\libsvm-master\java\libsvm\svm.m4
?????文件?????????868??2018-07-15?22:15??SVM\libsvm-master\java\libsvm\svm_model.java
?????文件?????????115??2018-07-15?22:15??SVM\libsvm-master\java\libsvm\svm_node.java
?????文件????????1285??2018-07-15?22:15??SVM\libsvm-master\java\libsvm\svm_parameter.java
?????文件??????????87??2018-07-15?22:15??SVM\libsvm-master\java\libsvm\svm_print_interface.java
?????文件?????????136??2018-07-15?22:15??SVM\libsvm-master\java\libsvm\svm_problem.java
?????文件?????????659??2018-07-15?22:15??SVM\libsvm-master\java\Makefile
?????文件????????4945??2018-07-15?22:15??SVM\libsvm-master\java\svm_predict.java
?????文件????????8937??2018-07-15?22:15??SVM\libsvm-master\java\svm_scale.java
?????文件???????12262??2018-07-15?22:15??SVM\libsvm-master\java\svm_toy.java
?????文件????????8354??2018-07-15?22:15??SVM\libsvm-master\java\svm_train.java
?????文件??????????81??2018-07-15?22:15??SVM\libsvm-master\java\test_applet.html
?????文件?????????732??2018-07-15?22:15??SVM\libsvm-master\Makefile
?????文件????????1135??2018-07-15?22:15??SVM\libsvm-master\Makefile.win
?????目錄???????????0??2020-07-28?20:23??SVM\libsvm-master\matlab\
?????文件????????4060??2018-07-15?22:15??SVM\libsvm-master\matlab\libsvmread.c
?????文件????????2326??2018-07-15?22:15??SVM\libsvm-master\matlab\libsvmwrite.c
?????文件?????????888??2018-07-15?22:15??SVM\libsvm-master\matlab\make.m
?????文件????????1240??2018-07-15?22:15??SVM\libsvm-master\matlab\Makefile
............此處省略52個(gè)文件信息

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