update doc

_build/html/_sources/command_line.rst.txt

@@ -1,20 +1,20 @@
 xLearn Command Line Guide
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Once you build xLearn from source code successfully, you will get two executable files 
+Once you built 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.
+two executable files to perform training and prediction task.
 
 Quick Start
 ----------------------------------------
 
-Make sure that you are in the build path of xLearn, and you will find the demo data 
+Make sure that you are in the ``build`` directory 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
 
-Here we print a portion of the output ::
+Here, we show a portion of the xLearn's output ::
 
     Epoch      Train log_loss     Time cost (sec)
         1            0.567514                0.00
@@ -28,10 +28,11 @@ Here we print a portion of the output ::
         9            0.405954                0.00
        10            0.396313                0.00
 
-On default, xLearn will use the logistic regression (LR) to train our model for 10 epoch.
+On default, xLearn will use the logistic regression (LR) to train our model within 10 epoch.
 
 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 ::
+This file stores the trainned model checkpoint, and we can use this model file to make prediction in 
+the future ::
 
     ./xlearn_predict ./small_test.txt ./small_train.txt.model
 
@@ -46,10 +47,10 @@ prediction result. Let's see the first five lines of output by using the followi
     -0.170811
     -1.28986
 
-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 ::
+These five lines of data is the prediction score calculated 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
@@ -69,6 +70,12 @@ by using ``--sigmoid`` option, or you can convert your result to bianry result (
     0
     0
 
+Users may generate many model files, so you can set the name of the model checkpoint file 
+by using ``-m`` option ::
+
+  ./xlearn_train ./small_train.txt -m model_1.bin
+  ./xlearn_train ./small_train.txt -e 5 -m model_2.bin   
+
 Choose Machine Learning Model
 ----------------------------------------
 

_build/html/_sources/install.rst.txt

@@ -47,7 +47,7 @@ and then build xLearn using the folloing commands ::
   cd xlearn
   ./build.sh
 
-The ``build.sh`` will build xLearn in ``build`` directory in current path.
+The ``build.sh`` will build xLearn in the ``build`` directory in current path.
 
 Test
 ----------------------------------

_build/html/command_line.html

@@ -167,18 +167,18 @@
 
   <div class="section" id="xlearn-command-line-guide">
 <h1>xLearn Command Line Guide<a class="headerlink" href="#xlearn-command-line-guide" title="Permalink to this headline">¶</a></h1>
-<p>Once you build xLearn from source code successfully, you will get two executable files
+<p>Once you built xLearn from source code successfully, you will get two executable files
 <code class="docutils literal"><span class="pre">xlearn_train</span></code> and <code class="docutils literal"><span class="pre">xlearn_predict</span></code> in your <code class="docutils literal"><span class="pre">build</span></code> directory. Now you can use these
-two executable files to perform training task and prediction task.</p>
+two executable files to perform training and prediction task.</p>
 <div class="section" id="quick-start">
 <h2>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this headline">¶</a></h2>
-<p>Make sure that you are in the build path of xLearn, and you will find the demo data
+<p>Make sure that you are in the <code class="docutils literal"><span class="pre">build</span></code> directory of xLearn, and you will find the demo data
 <code class="docutils literal"><span class="pre">small_test.txt</span></code> and <code class="docutils literal"><span class="pre">small_train.txt</span></code> in this directory. Now you can type the following
 command to train a model</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span>
 </pre></div>
 </div>
-<p>Here we print a portion of the output</p>
+<p>Here, we show a portion of the xLearn’s output</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">Epoch</span>      <span class="n">Train</span> <span class="n">log_loss</span>     <span class="n">Time</span> <span class="n">cost</span> <span class="p">(</span><span class="n">sec</span><span class="p">)</span>
     <span class="mi">1</span>            <span class="mf">0.567514</span>                <span class="mf">0.00</span>
     <span class="mi">2</span>            <span class="mf">0.516861</span>                <span class="mf">0.00</span>
@@ -192,9 +192,10 @@ <h2>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to thi
    <span class="mi">10</span>            <span class="mf">0.396313</span>                <span class="mf">0.00</span>
 </pre></div>
 </div>
-<p>On default, xLearn will use the logistic regression (LR) to train our model for 10 epoch.</p>
+<p>On default, xLearn will use the logistic regression (LR) to train our model within 10 epoch.</p>
 <p>We can see that a new file called <code class="docutils literal"><span class="pre">small_train.txt.model</span></code> has been generated in current directory.
-This file stores the trainned model checkpoint, and we can use this model file to make prediction</p>
+This file stores the trainned model checkpoint, and we can use this model file to make prediction in
+the future</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">xlearn_predict</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span><span class="o">.</span><span class="n">model</span>
 </pre></div>
 </div>
@@ -209,10 +210,10 @@ <h2>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to thi
 <span class="o">-</span><span class="mf">1.28986</span>
 </pre></div>
 </div>
-<p>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 <code class="docutils literal"><span class="pre">--sigmoid</span></code> option, or you can convert your result to bianry result (0 and 1) by using
-<code class="docutils literal"><span class="pre">--sign</span></code> option</p>
+<p>These five lines of data is the prediction score calculated 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 <code class="docutils literal"><span class="pre">--sigmoid</span></code> option, or you can convert your result
+to bianry result (0 and 1) by using <code class="docutils literal"><span class="pre">--sign</span></code> option</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">xlearn_predict</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span><span class="o">.</span><span class="n">model</span> <span class="o">--</span><span class="n">sigmoid</span>
 <span class="n">head</span> <span class="o">-</span><span class="n">n</span> <span class="mi">5</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span><span class="o">.</span><span class="n">out</span>
 
@@ -232,6 +233,12 @@ <h2>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to thi
 <span class="mi">0</span>
 </pre></div>
 </div>
+<p>Users may generate many model files, so you can set the name of the model checkpoint file
+by using <code class="docutils literal"><span class="pre">-m</span></code> option</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">m</span> <span class="n">model_1</span><span class="o">.</span><span class="n">bin</span>
+<span class="o">./</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">e</span> <span class="mi">5</span> <span class="o">-</span><span class="n">m</span> <span class="n">model_2</span><span class="o">.</span><span class="n">bin</span>
+</pre></div>
+</div>
 </div>
 <div class="section" id="choose-machine-learning-model">
 <h2>Choose Machine Learning Model<a class="headerlink" href="#choose-machine-learning-model" title="Permalink to this headline">¶</a></h2>

_build/html/install.html

@@ -208,7 +208,7 @@ <h2>Install xLearn from Source Code<a class="headerlink" href="#install-xlearn-f
 <span class="o">./</span><span class="n">build</span><span class="o">.</span><span class="n">sh</span>
 </pre></div>
 </div>
-<p>The <code class="docutils literal"><span class="pre">build.sh</span></code> will build xLearn in <code class="docutils literal"><span class="pre">build</span></code> directory in current path.</p>
+<p>The <code class="docutils literal"><span class="pre">build.sh</span></code> will build xLearn in the <code class="docutils literal"><span class="pre">build</span></code> directory in current path.</p>
 </div>
 <div class="section" id="test">
 <h2>Test<a class="headerlink" href="#test" title="Permalink to this headline">¶</a></h2>