update doc

_build/html/_sources/index.rst.txt

@@ -32,22 +32,25 @@ source code and build it locally. We will update the xLearn source code on pip w
 
     sudo pip install --index-url https://test.pypi.org/simple/ xlearnn 
 
-If you want to build the lastest code, or you want to use the xLearn command line
-interface, you can see how to build xLearn from source code from `Installation Guide`__.
+If you want to build the lastest code on github, or you want to use the xLearn command line, 
+you can see how to build xLearn from source code from `Installation Guide`__.
 
 Python Demo
 ----------------------------------
 
+Here is a simple python demo no how to use xLearn. Now type **python** and get started!
+
 >>> import xlearn as xl
->>> ffm_model = xl.create_ffm() # Create FFM model
->>> ffm_model.setTrain("./small_train.txt") # Training set
->>> ffm_model.setValidate("./small_test.txt") # Validation set
->>> param = { 'task':'binary', 'lr':0.2, 'lambda':0.002, 'metric':'auc'} # Set parameter
->>> ffm_model.fit(param, "./model.out") # Train model
->>> ffm_model.setTest("./small_test.txt") # Test set
->>> ffm_model.predict("./model.out", "./output.txt") # Predict
-
-This example shows how to use xlearn to solve a binary classification task.
+>>> ffm_model = xl.create_ffm()  # create ffm model
+>>> ffm_model.setTrain("./small_train.txt")  # set training data
+>>> ffm_model.setValidate("./small_test.txt") # Validation data
+>>> param = { 'task':'binary', 'lr':0.2, 'lambda':0.002, 'metric':'auc'}  # set parameters
+>>> ffm_model.fit(param, "./model.out")  # train model
+>>> ffm_model.setTest("./small_test.txt")  # set test data
+>>> ffm_model.predict("./model.out", "./output.txt")  # predict
+
+This example shows how to use xlearn to solve a simple binary classification task. 
+You can find the demo data **small_train.txt** and **small_test.txt** from the **demo/classification/criteo_ctr/**
 
  .. __: install
  .. __: commandline

_build/html/index.html

@@ -187,22 +187,24 @@ <h2>Quick Install<a class="headerlink" href="#quick-install" title="Permalink to
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">sudo</span> <span class="n">pip</span> <span class="n">install</span> <span class="o">--</span><span class="n">index</span><span class="o">-</span><span class="n">url</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">test</span><span class="o">.</span><span class="n">pypi</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="n">simple</span><span class="o">/</span> <span class="n">xlearnn</span>
 </pre></div>
 </div>
-<p>If you want to build the lastest code, or you want to use the xLearn command line
-interface, you can see how to build xLearn from source code from <a class="reference external" href="install">Installation Guide</a>.</p>
+<p>If you want to build the lastest code on github, or you want to use the xLearn command line,
+you can see how to build xLearn from source code from <a class="reference external" href="install">Installation Guide</a>.</p>
 </div>
 <div class="section" id="python-demo">
 <h2>Python Demo<a class="headerlink" href="#python-demo" title="Permalink to this headline">¶</a></h2>
+<p>Here is a simple python demo no how to use xLearn. Now type <strong>python</strong> and get started!</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">xlearn</span> <span class="k">as</span> <span class="nn">xl</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span> <span class="o">=</span> <span class="n">xl</span><span class="o">.</span><span class="n">create_ffm</span><span class="p">()</span> <span class="c1"># Create FFM model</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span><span class="o">.</span><span class="n">setTrain</span><span class="p">(</span><span class="s2">&quot;./small_train.txt&quot;</span><span class="p">)</span> <span class="c1"># Training set</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span><span class="o">.</span><span class="n">setValidate</span><span class="p">(</span><span class="s2">&quot;./small_test.txt&quot;</span><span class="p">)</span> <span class="c1"># Validation set</span>
-<span class="gp">&gt;&gt;&gt; </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;auc&#39;</span><span class="p">}</span> <span class="c1"># Set parameter</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="s2">&quot;./model.out&quot;</span><span class="p">)</span> <span class="c1"># Train model</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span><span class="o">.</span><span class="n">setTest</span><span class="p">(</span><span class="s2">&quot;./small_test.txt&quot;</span><span class="p">)</span> <span class="c1"># Test set</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="s2">&quot;./model.out&quot;</span><span class="p">,</span> <span class="s2">&quot;./output.txt&quot;</span><span class="p">)</span> <span class="c1"># Predict</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span> <span class="o">=</span> <span class="n">xl</span><span class="o">.</span><span class="n">create_ffm</span><span class="p">()</span>  <span class="c1"># create ffm model</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span><span class="o">.</span><span class="n">setTrain</span><span class="p">(</span><span class="s2">&quot;./small_train.txt&quot;</span><span class="p">)</span>  <span class="c1"># set training data</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span><span class="o">.</span><span class="n">setValidate</span><span class="p">(</span><span class="s2">&quot;./small_test.txt&quot;</span><span class="p">)</span> <span class="c1"># Validation data</span>
+<span class="gp">&gt;&gt;&gt; </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;auc&#39;</span><span class="p">}</span>  <span class="c1"># set parameters</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="s2">&quot;./model.out&quot;</span><span class="p">)</span>  <span class="c1"># train model</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span><span class="o">.</span><span class="n">setTest</span><span class="p">(</span><span class="s2">&quot;./small_test.txt&quot;</span><span class="p">)</span>  <span class="c1"># set test data</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ffm_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="s2">&quot;./model.out&quot;</span><span class="p">,</span> <span class="s2">&quot;./output.txt&quot;</span><span class="p">)</span>  <span class="c1"># predict</span>
 </pre></div>
 </div>
-<p>This example shows how to use xlearn to solve a binary classification task.</p>
+<p>This example shows how to use xlearn to solve a simple binary classification task.
+You can find the demo data <strong>small_train.txt</strong> and <strong>small_test.txt</strong> from the <strong>demo/classification/criteo_ctr/</strong></p>
 <blockquote>
 <div></div></blockquote>
 <div class="toctree-wrapper compound">

index.rst

@@ -32,22 +32,25 @@ source code and build it locally. We will update the xLearn source code on pip w
 
     sudo pip install --index-url https://test.pypi.org/simple/ xlearnn 
 
-If you want to build the lastest code, or you want to use the xLearn command line
-interface, you can see how to build xLearn from source code from `Installation Guide`__.
+If you want to build the lastest code on github, or you want to use the xLearn command line, 
+you can see how to build xLearn from source code from `Installation Guide`__.
 
 Python Demo
 ----------------------------------
 
+Here is a simple python demo no how to use xLearn. Now type **python** and get started!
+
 >>> import xlearn as xl
->>> ffm_model = xl.create_ffm() # Create FFM model
->>> ffm_model.setTrain("./small_train.txt") # Training set
->>> ffm_model.setValidate("./small_test.txt") # Validation set
->>> param = { 'task':'binary', 'lr':0.2, 'lambda':0.002, 'metric':'auc'} # Set parameter
->>> ffm_model.fit(param, "./model.out") # Train model
->>> ffm_model.setTest("./small_test.txt") # Test set
->>> ffm_model.predict("./model.out", "./output.txt") # Predict
-
-This example shows how to use xlearn to solve a binary classification task.
+>>> ffm_model = xl.create_ffm()  # create ffm model
+>>> ffm_model.setTrain("./small_train.txt")  # set training data
+>>> ffm_model.setValidate("./small_test.txt") # Validation data
+>>> param = { 'task':'binary', 'lr':0.2, 'lambda':0.002, 'metric':'auc'}  # set parameters
+>>> ffm_model.fit(param, "./model.out")  # train model
+>>> ffm_model.setTest("./small_test.txt")  # set test data
+>>> ffm_model.predict("./model.out", "./output.txt")  # predict
+
+This example shows how to use xlearn to solve a simple binary classification task. 
+You can find the demo data **small_train.txt** and **small_test.txt** from the **demo/classification/criteo_ctr/**
 
  .. __: install
  .. __: commandline