update doc

_build/html/_sources/command_line.rst.txt

@@ -76,6 +76,13 @@ by using ``-m`` option ::
   ./xlearn_train ./small_train.txt -m model_1.bin
   ./xlearn_train ./small_train.txt -s 1 -m model_2.bin   
 
+Also, users can dump the model in txt format by using ``-t`` option. For example: ::
+
+  ./xlearn_train ./small_train.txt -t model.txt
+
+Here, we get a file called ``model.txt`` that stores the trainned model in txt format.
+For now, only bias and linear term can be wirtten to the txt file.
+
 Choose Machine Learning Model
 ----------------------------------------
 
@@ -153,7 +160,8 @@ On default, xLearn uses 5-folds cross validation, and users can set the number o
     
     ./xlearn_train ./small_train.txt -f 3 --cv
 
-The xLearn will calcluate the avergae validation loss at the end of it's message. ::
+Here, we set the number of folds to ``3``. The xLearn will calcluate the avergae validation loss at the end 
+of it's message. ::
 
     [------------] Average log_loss: 0.549417
     [ ACTION     ] Finish Cross-Validation
@@ -162,5 +170,7 @@ The xLearn will calcluate the avergae validation loss at the end of it's message
 
 
 
+
+
  .. toctree::
    :hidden:

_build/html/command_line.html

@@ -239,6 +239,12 @@ <h2>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to thi
 <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">s</span> <span class="mi">1</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>
+<p>Also, users can dump the model in txt format by using <code class="docutils literal"><span class="pre">-t</span></code> option. For example:</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">t</span> <span class="n">model</span><span class="o">.</span><span class="n">txt</span>
+</pre></div>
+</div>
+<p>Here, we get a file called <code class="docutils literal"><span class="pre">model.txt</span></code> that stores the trainned model in txt format.
+For now, only bias and linear term can be wirtten to the txt file.</p>
 </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>
@@ -316,14 +322,17 @@ <h2>Cross Validation<a class="headerlink" href="#cross-validation" title="Permal
 <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">f</span> <span class="mi">3</span> <span class="o">--</span><span class="n">cv</span>
 </pre></div>
 </div>
-<p>The xLearn will calcluate the avergae validation loss at the end of it’s message.</p>
+<p>Here, we set the number of folds to <code class="docutils literal"><span class="pre">3</span></code>. The xLearn will calcluate the avergae validation loss at the end
+of it’s message.</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>   <span class="p">[</span><span class="o">------------</span><span class="p">]</span> <span class="n">Average</span> <span class="n">log_loss</span><span class="p">:</span> <span class="mf">0.549417</span>
    <span class="p">[</span> <span class="n">ACTION</span>     <span class="p">]</span> <span class="n">Finish</span> <span class="n">Cross</span><span class="o">-</span><span class="n">Validation</span>
    <span class="p">[</span> <span class="n">ACTION</span>     <span class="p">]</span> <span class="n">Clear</span> <span class="n">the</span> <span class="n">xLearn</span> <span class="n">environment</span> <span class="o">...</span>
    <span class="p">[</span><span class="o">------------</span><span class="p">]</span> <span class="n">Total</span> <span class="n">time</span> <span class="n">cost</span><span class="p">:</span> <span class="mf">0.03</span> <span class="p">(</span><span class="n">sec</span><span class="p">)</span>
 
 
 
+
+
 <span class="o">..</span> <span class="n">toctree</span><span class="p">::</span>
   <span class="p">:</span><span class="n">hidden</span><span class="p">:</span>
 </pre></div>

command_line.rst

@@ -76,6 +76,13 @@ by using ``-m`` option ::
   ./xlearn_train ./small_train.txt -m model_1.bin
   ./xlearn_train ./small_train.txt -s 1 -m model_2.bin   
 
+Also, users can dump the model in txt format by using ``-t`` option. For example: ::
+
+  ./xlearn_train ./small_train.txt -t model.txt
+
+Here, we get a file called ``model.txt`` that stores the trainned model in txt format.
+For now, only bias and linear term can be wirtten to the txt file.
+
 Choose Machine Learning Model
 ----------------------------------------
 
@@ -153,7 +160,8 @@ On default, xLearn uses 5-folds cross validation, and users can set the number o
     
     ./xlearn_train ./small_train.txt -f 3 --cv
 
-The xLearn will calcluate the avergae validation loss at the end of it's message. ::
+Here, we set the number of folds to ``3``. The xLearn will calcluate the avergae validation loss at the end 
+of it's message. ::
 
     [------------] Average log_loss: 0.549417
     [ ACTION     ] Finish Cross-Validation
@@ -162,5 +170,7 @@ The xLearn will calcluate the avergae validation loss at the end of it's message
 
 
 
+
+
  .. toctree::
    :hidden: