update doc

index.rst

@@ -66,25 +66,24 @@ Here is a simple Python demo no how to use xLearn for a binary classification pr
     import xlearn as xl
 
     # Training task
-    ffm_model = xl.create_ffm() # Use field-aware factorization machine
-    ffm_model.setTrain("./small_train.txt")  # Training data
-    ffm_model.setValidate("./small_test.txt")  # Validation data
+    ffm_model = xl.create_ffm()                # Use field-aware factorization machine (ffm)
+    ffm_model.setTrain("./small_train.txt")    # Path of training data
+    ffm_model.setValidate("./small_test.txt")  # Path of validation data
 
     # param:
-    #  0. binary classification
+    #  0. task: binary classification
     #  1. learning rate: 0.2
     #  2. regular lambda: 0.002
     #  3. evaluation metric: accuracy
-    param = {'task':'binary', 'lr':0.2, 
-             'lambda':0.002, 'metric':'acc'}
+    param = {'task':'binary', 'lr':0.2, 'lambda':0.002, 'metric':'acc'}
 
     # Start to train
     # The trained model will be stored in model.out
     ffm_model.fit(param, './model.out')
 
     # Prediction task
-    ffm_model.setTest("./small_test.txt")  # Test data
-    ffm_model.setSigmoid()  # Convert output to 0-1
+    ffm_model.setTest("./small_test.txt")  # Path of test data
+    ffm_model.setSigmoid()                 # Convert output to 0-1
 
     # Start to predict
     # The output result will be stored in output.txt