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SV % 以ä¸ä¸º⽀æåé
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4 ï¼é⽤交åéªè¯éæ©æä½³åæ° C ä¸ g
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ä¸ï¼ï¼å° gnuplot 解åãå®è£ 解åå®æ¯åï¼è¿⼊ /libsvm/tools ⽬å½ä¸ï¼⽤⽂æ¬ç¼è¾å¨ï¼è®°äº
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åï¼å° grid.py å C:/Python ⽬å½ä¸ç python.exe ⽂件æ·è´å° libsvm/windows ⽬å½ä¸ï¼é®⼊以ä¸å½ä»¤ï¼ $ python grid.py
train.1.scale æ§⾏åï¼å³å¯å¾å°æä¼åæ° c å g ã
å¦å¤ï¼⾄äºä¸ libsvm å python çæ¥⼝çé®é¢ï¼å¨ libsvm2. ä¸æ⽼å¸å·²ç»å¸®å©æ们解å³ï¼å¨/libsvm/windows/python ⽬å½ä¸⾃å¸¦äº svmc.pyd è¿ä¸ª⽂件ï¼å°è¯¥⽂件⽂件å¤å¶å°
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python svm_test.py
å¦æè½çå°ç¨åºæ§⾏ç»æï¼è¯´æ libsvm å python ä¹é´çæ¥⼝å·²ç»é ç½®å®æï¼ä»¥åå°±å¯ä»¥ç´æ¥å¨python ç¨åº⾥è°⽤ libsvm çå½æ°äºï¼
5 ï¼ é⽤æä½³åæ° C ä¸ g 对æ´ä¸ªè®ç»éè¿⾏è®ç»è·å⽀æåéæºæ¨¡å
$ svmtrain âc x âg x âv x training_set_file [model_file]
x 为ä¸è¿°å¾å°çæä¼åæ° c å g çå¼ï¼ v çå¼⼀è¬å 5 ã
6 ï¼å©⽤è·åç模åè¿⾏æµè¯ä¸é¢æµ
使⽤ Svmtrain è®ç»å¥½ç模åè¿⾏æµè¯ãè¾⼊æ°ç X å¼ï¼ç»åº SVM é¢æµåºç Y å¼
$ Svmpredict test_file model_file output_file
å¦ï¼ ./svm-predict heart_scale heart_scale.model heart_scale.out
Accuracy = .% (/) (classification)
è¿⾥æ¾⽰çæ¯ç»æ
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以 libsvm ä¸ç heart_scale ä½ä¸ºè®ç»æ°æ®åæµè¯æ°æ®ï¼åæ¶å·²ç»å° python å®è£ ⾄ c çï¼å¹¶å°grid.py ⽂件ä¸å ³äº gnuplot è·¯å¾çé»è®¤å¼ä¿®æ¹ä¸ºå®é 解å缩åçè·¯å¾ï¼å°
heart_scale ã grid.py å python.exe æ·è´⾄ /libsvm/windows ⽂件夹ä¸ã
./svm-train heart_scale
optimization finished,人人校园源码 #iter =
nu = 0.
obj = -., rho = 0.
nSV = , nBSV =
Total nSV =
æ¤æ¶ï¼å·²ç»å¾å° heart_scale.model ï¼è¿⾏é¢æµï¼
./svm-predict heart_scale heart_scale.model heart_scale.out
Accuracy = .% (/) (classification)
æ£ç¡®ç为 Accuracy = .% ã
./python grid.py heart_scale
å¾å°æä¼åæ° c= ï¼ g=0..
./svm-train -c -g 0. heart_scale å¾å° model åï¼ç± ./svm-predict heart_scale heart_scale.model
heart_scale.out å¾å°çæ£ç¡®
ç为 Accuracy = .%.è¿åè¿æç¹è¿·æï¼ä¸ºä»ä¹æ£ç¡®çéä½äºï¼
å½ç¶ä¹å¯ä»¥ç»åsubset.py å easy.py å®ç°⾃å¨åè¿ç¨ã
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::@ echo off
cls
:: split the data and output the results
for /L %%i in (1,1,) do python subset.py b.txt b(%%i).in8 b(%%i).out2
for /L %%i in (1,1,) do python easy.py b(%%i).in8 b(%%i).out2 >> result.txt
è¿æ®µæ¹å¤ç代ç ⾸å è°⽤subset.py对⽂件b.txtæ§⾏次åå±éæºæ½æ ·(对æ°æ®è¿⾏-%åå²)ç¶åè°⽤easy.py è¿⾏次åæ°å¯»ä¼ï¼æè®°å½ç»æåå°result.txtä¸
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