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aksnzhy · Dec 9, 2017 · 3868927 · 3868927
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diff --git a/_build/doctrees/command_line.doctree b/_build/doctrees/command_line.doctree
diff --git a/_build/doctrees/environment.pickle b/_build/doctrees/environment.pickle
diff --git a/_build/html/_sources/command_line.rst.txt b/_build/html/_sources/command_line.rst.txt
@@ -95,7 +95,7 @@ Users can choose different machine learning models by using ``-s`` option ::
 For LR and FM, the input data can be ``CSV`` or ``libsvm`` data format, while for FFM, the 
 input data should be the ``libffm`` format. You can give a ``libffm`` file to LR and FM. At that 
 time, xLearn will treat this data as ``libsvm`` format. The following command shows how to use different
-machine learning model to solve the binary classification problem  ::
+machine learning model to solve the binary classification problem:  ::
 
 ./xlearn_train ./small_train.txt -s 0  # Using linear model
 ./xlearn_train ./small_train.txt -s 1  # Using factorization machine (FM)

diff --git a/_build/html/command_line.html b/_build/html/command_line.html
@@ -259,7 +259,7 @@ <h2>Choose Machine Learning Model<a class="headerlink" href="#choose-machine-lea
 <p>For LR and FM, the input data can be <code class="docutils literal"><span class="pre">CSV</span></code> or <code class="docutils literal"><span class="pre">libsvm</span></code> data format, while for FFM, the
 input data should be the <code class="docutils literal"><span class="pre">libffm</span></code> format. You can give a <code class="docutils literal"><span class="pre">libffm</span></code> file to LR and FM. At that
 time, xLearn will treat this data as <code class="docutils literal"><span class="pre">libsvm</span></code> format. The following command shows how to use different
-machine learning model to solve the binary classification problem</p>
+machine learning model to solve the binary classification problem:</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">s</span> <span class="mi">0</span>  <span class="c1"># Using linear model</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">s</span> <span class="mi">1</span>  <span class="c1"># Using factorization machine (FM)</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">s</span> <span class="mi">2</span>  <span class="c1"># Using field-awre factorization machine (FFM)</span>

diff --git a/command_line.rst b/command_line.rst
@@ -95,7 +95,7 @@ Users can choose different machine learning models by using ``-s`` option ::
 For LR and FM, the input data can be ``CSV`` or ``libsvm`` data format, while for FFM, the 
 input data should be the ``libffm`` format. You can give a ``libffm`` file to LR and FM. At that 
 time, xLearn will treat this data as ``libsvm`` format. The following command shows how to use different
-machine learning model to solve the binary classification problem  ::
+machine learning model to solve the binary classification problem:  ::
 
 ./xlearn_train ./small_train.txt -s 0  # Using linear model
 ./xlearn_train ./small_train.txt -s 1  # Using factorization machine (FM)