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@aksnzhy
aksnzhy committed Dec 9, 2017
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5 changes: 5 additions & 0 deletions _build/html/_sources/command_line.rst.txt
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Expand Up @@ -92,5 +92,10 @@ Users can choose different machine learning models by using ``-s`` option ::
4 -- factorization machines (FM)
5 -- field-aware factorization machines (FFM)

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.



.. toctree::
:hidden:
31 changes: 18 additions & 13 deletions _build/html/command_line.html
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Expand Up @@ -94,7 +94,9 @@
<li class="toctree-l1"><a class="reference internal" href="install.html">Installation Guide</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">xLearn Command Line Guide</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#quick-start">Quick Start</a></li>
<li class="toctree-l2"><a class="reference internal" href="#choose-machine-learning-model">Choose Machine Learning Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="#choose-machine-learning-model">Choose Machine Learning Model</a><ul class="simple">
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="python_api.html">xLearn Python API Guide</a></li>
Expand Down Expand Up @@ -242,20 +244,23 @@ <h2>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to thi
<h2>Choose Machine Learning Model<a class="headerlink" href="#choose-machine-learning-model" title="Permalink to this headline"></a></h2>
<p>For now, xLearn can support three different machine learning model, including LR, FM and FFM.
Users can choose different machine learning models by using <code class="docutils literal"><span class="pre">-s</span></code> option</p>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="o">-</span><span class="n">s</span> <span class="o">&lt;</span><span class="nb">type</span><span class="o">&gt;</span> <span class="p">:</span> <span class="n">Type</span> <span class="n">of</span> <span class="n">machine</span> <span class="n">learning</span> <span class="n">model</span> <span class="p">(</span><span class="n">default</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">for</span> <span class="n">classification</span> <span class="n">task</span><span class="p">:</span>
<span class="mi">0</span> <span class="o">--</span> <span class="n">linear</span> <span class="n">model</span> <span class="p">(</span><span class="n">GLM</span><span class="p">)</span>
<span class="mi">1</span> <span class="o">--</span> <span class="n">factorization</span> <span class="n">machines</span> <span class="p">(</span><span class="n">FM</span><span class="p">)</span>
<span class="mi">2</span> <span class="o">--</span> <span class="n">field</span><span class="o">-</span><span class="n">aware</span> <span class="n">factorization</span> <span class="n">machines</span> <span class="p">(</span><span class="n">FFM</span><span class="p">)</span>
<span class="k">for</span> <span class="n">regression</span> <span class="n">task</span><span class="p">:</span>
<span class="mi">3</span> <span class="o">--</span> <span class="n">linear</span> <span class="n">model</span> <span class="p">(</span><span class="n">GLM</span><span class="p">)</span>
<span class="mi">4</span> <span class="o">--</span> <span class="n">factorization</span> <span class="n">machines</span> <span class="p">(</span><span class="n">FM</span><span class="p">)</span>
<span class="mi">5</span> <span class="o">--</span> <span class="n">field</span><span class="o">-</span><span class="n">aware</span> <span class="n">factorization</span> <span class="n">machines</span> <span class="p">(</span><span class="n">FFM</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>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">-</span><span class="n">s</span> <span class="o">&lt;</span><span class="nb">type</span><span class="o">&gt;</span> <span class="p">:</span> <span class="n">Type</span> <span class="n">of</span> <span class="n">machine</span> <span class="n">learning</span> <span class="n">model</span> <span class="p">(</span><span class="n">default</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">for</span> <span class="n">classification</span> <span class="n">task</span><span class="p">:</span>
<span class="mi">0</span> <span class="o">--</span> <span class="n">linear</span> <span class="n">model</span> <span class="p">(</span><span class="n">GLM</span><span class="p">)</span>
<span class="mi">1</span> <span class="o">--</span> <span class="n">factorization</span> <span class="n">machines</span> <span class="p">(</span><span class="n">FM</span><span class="p">)</span>
<span class="mi">2</span> <span class="o">--</span> <span class="n">field</span><span class="o">-</span><span class="n">aware</span> <span class="n">factorization</span> <span class="n">machines</span> <span class="p">(</span><span class="n">FFM</span><span class="p">)</span>
<span class="k">for</span> <span class="n">regression</span> <span class="n">task</span><span class="p">:</span>
<span class="mi">3</span> <span class="o">--</span> <span class="n">linear</span> <span class="n">model</span> <span class="p">(</span><span class="n">GLM</span><span class="p">)</span>
<span class="mi">4</span> <span class="o">--</span> <span class="n">factorization</span> <span class="n">machines</span> <span class="p">(</span><span class="n">FM</span><span class="p">)</span>
<span class="mi">5</span> <span class="o">--</span> <span class="n">field</span><span class="o">-</span><span class="n">aware</span> <span class="n">factorization</span> <span class="n">machines</span> <span class="p">(</span><span class="n">FFM</span><span class="p">)</span>
</pre></div>
</div>
<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.</p>
<blockquote>
<div><div class="toctree-wrapper compound">
</div>
</div></blockquote>
</div>
</div>

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5 changes: 5 additions & 0 deletions command_line.rst
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Expand Up @@ -92,5 +92,10 @@ Users can choose different machine learning models by using ``-s`` option ::
4 -- factorization machines (FM)
5 -- field-aware factorization machines (FFM)

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.



.. toctree::
:hidden:

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