https://replica.wiki.extremist.software/index.php?action=history&feed=atom&title=Machine_Learning%2Feasy.py 2026-04-04T07:11:38Z Revision history for this page on the wiki MediaWiki 1.39.13 https://replica.wiki.extremist.software/index.php?title=Machine_Learning/easy.py&diff=11318&oldid=prev 2010-05-22T05:46:07Z

<p>Created page with &#039;&lt;pre&gt; #!/usr/bin/env python import sys import os from subprocess import * if len(sys.argv) &lt;= 1: print(&#039;Usage: %s training_file [testing_file]&#039; % sys.argv[0]) raise SystemExi…&#039;</p> <p><b>New page</b></p><div>&lt;pre&gt;<br /> #!/usr/bin/env python<br /> <br /> import sys<br /> import os<br /> from subprocess import *<br /> <br /> if len(sys.argv) &lt;= 1:<br /> print(&#039;Usage: %s training_file [testing_file]&#039; % sys.argv[0])<br /> raise SystemExit<br /> <br /> # svm, grid, and gnuplot executable files<br /> <br /> is_win32 = (sys.platform == &#039;win32&#039;)<br /> if not is_win32:<br /> svmscale_exe = &quot;../svm-scale&quot;<br /> svmtrain_exe = &quot;../svm-train&quot;<br /> svmpredict_exe = &quot;../svm-predict&quot;<br /> grid_py = &quot;./grid.py&quot;<br /> else:<br /> # example for windows<br /> svmscale_exe = r&quot;..\windows\svm-scale.exe&quot;<br /> svmtrain_exe = r&quot;..\windows\svm-train.exe&quot;<br /> svmpredict_exe = r&quot;..\windows\svm-predict.exe&quot;<br /> grid_py = r&quot;.\grid.py&quot;<br /> <br /> assert os.path.exists(svmscale_exe),&quot;svm-scale executable not found&quot;<br /> assert os.path.exists(svmtrain_exe),&quot;svm-train executable not found&quot;<br /> assert os.path.exists(svmpredict_exe),&quot;svm-predict executable not found&quot;<br /> assert os.path.exists(grid_py),&quot;grid.py not found&quot;<br /> <br /> train_pathname = sys.argv[1]<br /> assert os.path.exists(train_pathname),&quot;training file not found&quot;<br /> file_name = os.path.split(train_pathname)[1]<br /> scaled_file = file_name + &quot;.scale&quot;<br /> model_file = file_name + &quot;.model&quot;<br /> range_file = file_name + &quot;.range&quot;<br /> <br /> if len(sys.argv) &gt; 2:<br /> test_pathname = sys.argv[2]<br /> file_name = os.path.split(test_pathname)[1]<br /> assert os.path.exists(test_pathname),&quot;testing file not found&quot;<br /> scaled_test_file = file_name + &quot;.scale&quot;<br /> predict_test_file = file_name + &quot;.predict&quot;<br /> <br /> cmd = &#039;%s -s &quot;%s&quot; &quot;%s&quot; &gt; &quot;%s&quot;&#039; % (svmscale_exe, range_file, train_pathname, scaled_file)<br /> print(&#039;Scaling training data...&#039;)<br /> Popen(cmd, shell = True, stdout = PIPE).communicate() <br /> <br /> cmd = &#039;%s -svmtrain &quot;%s&quot; &quot;%s&quot;&#039; % (grid_py, svmtrain_exe, scaled_file)<br /> print(&#039;Cross validation...&#039;)<br /> f = Popen(cmd, shell = True, stdout = PIPE).stdout<br /> <br /> line = &#039;&#039;<br /> while True:<br /> last_line = line<br /> line = f.readline()<br /> if not line: break<br /> c,g,rate = map(float,last_line.split())<br /> <br /> print(&#039;Best c=%s, g=%s CV rate=%s&#039; % (c,g,rate))<br /> <br /> cmd = &#039;%s -c %s -g %s &quot;%s&quot; &quot;%s&quot;&#039; % (svmtrain_exe,c,g,scaled_file,model_file)<br /> print(&#039;Training...&#039;)<br /> Popen(cmd, shell = True, stdout = PIPE).communicate()<br /> <br /> print(&#039;Output model: %s&#039; % model_file)<br /> if len(sys.argv) &gt; 2:<br /> cmd = &#039;%s -r &quot;%s&quot; &quot;%s&quot; &gt; &quot;%s&quot;&#039; % (svmscale_exe, range_file, test_pathname, scaled_test_file)<br /> print(&#039;Scaling testing data...&#039;)<br /> Popen(cmd, shell = True, stdout = PIPE).communicate() <br /> <br /> cmd = &#039;%s &quot;%s&quot; &quot;%s&quot; &quot;%s&quot;&#039; % (svmpredict_exe, scaled_test_file, model_file, predict_test_file)<br /> print(&#039;Testing...&#039;)<br /> Popen(cmd, shell = True).communicate() <br /> <br /> print(&#039;Output prediction: %s&#039; % predict_test_file)<br /> <br /> &lt;/pre&gt;</div>

ThomasLotze