update doc

_build/html/_sources/demo.rst.txt

@@ -80,6 +80,21 @@ survived the tragedy. (`Link`__)
 
 You can find the data used in this demo in the path ``/demo/classification/titanic/``.
 
+The follow code is the Python demo:
+
+.. code-block:: python
+
+   import xlearn as xl
+
+   # Training task
+   fm_model = xl.create_fm()
+   fm_model.setTrain("./titanic_train.txt")
+   param = {'task':'binary', 'lr':0.2, 
+            'lambda':0.002, 'metric':'acc'}
+
+   # Cross-validation
+   fm_model.cv(param)
+
 House Price Prediction
 -----------------------------
 

_build/html/demo.html

@@ -238,6 +238,19 @@ <h2>Predict Survival in Titanic<a class="headerlink" href="#predict-survival-in-
 were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers
 survived the tragedy. (<a class="reference external" href="https://www.kaggle.com/c/titanic">Link</a>)</p>
 <p>You can find the data used in this demo in the path <code class="docutils literal"><span class="pre">/demo/classification/titanic/</span></code>.</p>
+<p>The follow code is the Python demo:</p>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">xlearn</span> <span class="kn">as</span> <span class="nn">xl</span>
+
+<span class="c1"># Training task</span>
+<span class="n">fm_model</span> <span class="o">=</span> <span class="n">xl</span><span class="o">.</span><span class="n">create_fm</span><span class="p">()</span>
+<span class="n">fm_model</span><span class="o">.</span><span class="n">setTrain</span><span class="p">(</span><span class="s2">&quot;./titanic_train.txt&quot;</span><span class="p">)</span>
+<span class="n">param</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;task&#39;</span><span class="p">:</span><span class="s1">&#39;binary&#39;</span><span class="p">,</span> <span class="s1">&#39;lr&#39;</span><span class="p">:</span><span class="mf">0.2</span><span class="p">,</span>
+         <span class="s1">&#39;lambda&#39;</span><span class="p">:</span><span class="mf">0.002</span><span class="p">,</span> <span class="s1">&#39;metric&#39;</span><span class="p">:</span><span class="s1">&#39;acc&#39;</span><span class="p">}</span>
+
+<span class="c1"># Cross-validation</span>
+<span class="n">fm_model</span><span class="o">.</span><span class="n">cv</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>
+</pre></div>
+</div>
 </div>
 <div class="section" id="house-price-prediction">
 <h2>House Price Prediction<a class="headerlink" href="#house-price-prediction" title="Permalink to this headline">¶</a></h2>

demo.rst

@@ -80,6 +80,21 @@ survived the tragedy. (`Link`__)
 
 You can find the data used in this demo in the path ``/demo/classification/titanic/``.
 
+The follow code is the Python demo:
+
+.. code-block:: python
+
+   import xlearn as xl
+
+   # Training task
+   fm_model = xl.create_fm()
+   fm_model.setTrain("./titanic_train.txt")
+   param = {'task':'binary', 'lr':0.2, 
+            'lambda':0.002, 'metric':'acc'}
+
+   # Cross-validation
+   fm_model.cv(param)
+
 House Price Prediction
 -----------------------------