New translations easy.py (Chinese Simplified)

lang/zh/gklearn/gedlib/lib/libsvm.3.22/tools/easy.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python
+
+import sys
+import os
+from subprocess import *
+
+if len(sys.argv) <= 1:
+	print('Usage: {0} training_file [testing_file]'.format(sys.argv[0]))
+	raise SystemExit
+
+# svm, grid, and gnuplot executable files
+
+is_win32 = (sys.platform == 'win32')
+if not is_win32:
+	svmscale_exe = "../svm-scale"
+	svmtrain_exe = "../svm-train"
+	svmpredict_exe = "../svm-predict"
+	grid_py = "./grid.py"
+	gnuplot_exe = "/usr/bin/gnuplot"
+else:
+        # example for windows
+	svmscale_exe = r"..\windows\svm-scale.exe"
+	svmtrain_exe = r"..\windows\svm-train.exe"
+	svmpredict_exe = r"..\windows\svm-predict.exe"
+	gnuplot_exe = r"c:\tmp\gnuplot\binary\pgnuplot.exe"
+	grid_py = r".\grid.py"
+
+assert os.path.exists(svmscale_exe),"svm-scale executable not found"
+assert os.path.exists(svmtrain_exe),"svm-train executable not found"
+assert os.path.exists(svmpredict_exe),"svm-predict executable not found"
+assert os.path.exists(gnuplot_exe),"gnuplot executable not found"
+assert os.path.exists(grid_py),"grid.py not found"
+
+train_pathname = sys.argv[1]
+assert os.path.exists(train_pathname),"training file not found"
+file_name = os.path.split(train_pathname)[1]
+scaled_file = file_name + ".scale"
+model_file = file_name + ".model"
+range_file = file_name + ".range"
+
+if len(sys.argv) > 2:
+	test_pathname = sys.argv[2]
+	file_name = os.path.split(test_pathname)[1]
+	assert os.path.exists(test_pathname),"testing file not found"
+	scaled_test_file = file_name + ".scale"
+	predict_test_file = file_name + ".predict"
+
+cmd = '{0} -s "{1}" "{2}" > "{3}"'.format(svmscale_exe, range_file, train_pathname, scaled_file)
+print('Scaling training data...')
+Popen(cmd, shell = True, stdout = PIPE).communicate()	
+
+cmd = '{0} -svmtrain "{1}" -gnuplot "{2}" "{3}"'.format(grid_py, svmtrain_exe, gnuplot_exe, scaled_file)
+print('Cross validation...')
+f = Popen(cmd, shell = True, stdout = PIPE).stdout
+
+line = ''
+while True:
+	last_line = line
+	line = f.readline()
+	if not line: break
+c,g,rate = map(float,last_line.split())
+
+print('Best c={0}, g={1} CV rate={2}'.format(c,g,rate))
+
+cmd = '{0} -c {1} -g {2} "{3}" "{4}"'.format(svmtrain_exe,c,g,scaled_file,model_file)
+print('Training...')
+Popen(cmd, shell = True, stdout = PIPE).communicate()
+
+print('Output model: {0}'.format(model_file))
+if len(sys.argv) > 2:
+	cmd = '{0} -r "{1}" "{2}" > "{3}"'.format(svmscale_exe, range_file, test_pathname, scaled_test_file)
+	print('Scaling testing data...')
+	Popen(cmd, shell = True, stdout = PIPE).communicate()	
+
+	cmd = '{0} "{1}" "{2}" "{3}"'.format(svmpredict_exe, scaled_test_file, model_file, predict_test_file)
+	print('Testing...')
+	Popen(cmd, shell = True).communicate()	
+
+	print('Output prediction: {0}'.format(predict_test_file))