added comment to letter_recog.py sample (adopted from c++ version)

diff --git a/samples/python2/letter_recog.py b/samples/python2/letter_recog.py
index aabc6b9..4e99a66 100644 (file)
--- a/samples/python2/letter_recog.py
+++ b/samples/python2/letter_recog.py
@@ -1,3 +1,28 @@
+'''\r
+The sample demonstrates how to train Random Trees classifier\r
+(or Boosting classifier, or MLP, or Knearest, or Support Vector Machines) using the provided dataset.\r
+\r
+We use the sample database letter-recognition.data\r
+from UCI Repository, here is the link:\r
+\r
+Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).\r
+UCI Repository of machine learning databases\r
+[http://www.ics.uci.edu/~mlearn/MLRepository.html].\r
+Irvine, CA: University of California, Department of Information and Computer Science.\r
+\r
+The dataset consists of 20000 feature vectors along with the\r
+responses - capital latin letters A..Z.\r
+The first 10000 samples are used for training\r
+and the remaining 10000 - to test the classifier.\r
+======================================================\r
+USAGE: \r
+  letter_recog.py [--model <model>] \r
+                  [--data <data fn>] \r
+                  [--load <model fn>] [--save <model fn>]\r
+\r
+  Models: RTrees, KNearest, Boost, SVM, MLP\r
+'''\r
+\r
 import numpy as np\r
 import cv2\r
 \r
@@ -77,7 +102,6 @@ class Boost(LetterStatModel):
 \r
 \r
 class SVM(LetterStatModel):\r
-    train_ratio = 0.1\r
     def __init__(self):\r
         self.model = cv2.SVM()\r
 \r
@@ -118,12 +142,11 @@ if __name__ == '__main__':
     import getopt\r
     import sys\r
 \r
+    print  __doc__\r
+\r
     models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes\r
     models = dict( [(cls.__name__.lower(), cls) for cls in models] )\r
 \r
-    print 'USAGE: letter_recog.py [--model <model>] [--data <data fn>] [--load <model fn>] [--save <model fn>]'\r
-    print 'Models: ', ', '.join(models)\r
-    print\r
     \r
     args, dummy = getopt.getopt(sys.argv[1:], '', ['model=', 'data=', 'load=', 'save='])\r
     args = dict(args)\r