update doc

command_line.rst

@@ -1,16 +1,16 @@
 xLearn Command Line Guide
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Once you build xLearn successfully, you will get two executable files ``xlearn_train``
-and ``xlearn_predict`` in your ``build`` directory. Now you can use these two executable 
-files to perform training task and prediction task.
+Once you build xLearn from source code successfully, you will get two executable files 
+``xlearn_train`` and ``xlearn_predict`` in your ``build`` directory. Now you can use these 
+two executable files to perform training task and prediction task.
 
 Quick Start
 ----------------------------------------
 
-Make sure that you are in the build path of xLearn, and you will find the ``small_test.txt``
-and ``small_train.txt`` in this directory. Now you can type the following command to train a
-model ::
+Make sure that you are in the build path of xLearn, and you will find the demo data 
+``small_test.txt`` and ``small_train.txt`` in this directory. Now you can type the following 
+command to train a model ::
 
     ./xlearn_train ./small_train.txt
 
@@ -30,44 +30,44 @@ Here we print a portion of the output ::
 
 On default, xLearn will use the logistic regression (LR) to train our model for 10 epoch.
 
-We can see that there is a new file called ``small_train.txt.model`` in current directory. This 
-is the trainned model checkpoint file. We can use the model file to make prediction like this ::
+We can see that a new file called ``small_train.txt.model`` has been generated in current directory. 
+This file stores the trainned model checkpoint, and we can use this model file to make prediction ::
 
     ./xlearn_predict ./small_test.txt ./small_train.txt.model
 
-Then we can get a new file called ``small_test.txt.out``. This is output prediction result. 
-Let's see the first ten lines of result by using the following command ::
+Then we can get a new file called ``small_test.txt.out`` in current directory. This is output 
+prediction result. Let's see the first five lines of output by using the following command ::
     
-    head -n 10 ./small_test.txt.out
+    head -n 5 ./small_test.txt.out
 
     -1.9872
     -0.0707959
     -0.456214
     -0.170811
     -1.28986
-    -0.618315
-    -1.60154
-    -1.50124
-    -0.347515
-    -1.12173
-
-This is the score for every example in test set. The negative data represents a negative example and 
-positive data represents the positive example. You can convert the score to 0~1 by using ``--sigmoid`` option,
-or you can convert your result to bianry result (0 and 1) by using ``--sign`` option ::
-
-   ./xlearn_predict ./small_test.txt ./small_train.txt.model --sigmoid
-   head -n 10 ./small_test.txt.out
-
-   0.120553
-   0.482308
-   0.387884
-   0.457401
-   0.215877
-   0.350165
-   0.167766
-   0.182241
-   0.413985
-   0.245691
+
+The ten lines of data is the score for every example in test set. The negative data represents the 
+negative example and positive data represents the positive example. You can convert the score to (0-1) 
+by using ``--sigmoid`` option, or you can convert your result to bianry result (0 and 1) by using 
+``--sign`` option ::
+
+    ./xlearn_predict ./small_test.txt ./small_train.txt.model --sigmoid
+    head -n 5 ./small_test.txt.out
+
+    0.120553
+    0.482308
+    0.387884
+    0.457401
+    0.215877
+
+    ./xlearn_predict ./small_test.txt ./small_train.txt.model --sign
+    head -n 5 ./small_test.txt.out
+
+    0
+    0
+    0
+    0
+    0
 
 Choose Machine Learning Model
 ----------------------------------------