update doc

_build/html/_sources/index.rst.txt

@@ -34,7 +34,7 @@ source code and build it locally. We will update the xLearn source code on pip w
     sudo pip install xlearn
 
 The installation process will take a while. And then you can type the following script in python 
-shell to check whether we install xLearn successfully:
+shell to check whether the xLearn has been installed successfully:
 
 >>> import xlearn as xl
 >>> xl.hello()
@@ -68,7 +68,7 @@ Here is a simple python demo no how to use xLearn.
 
 This example shows how to use field-aware factorizations machine (ffm) to solve a 
 simple binary classification task. You can check out the demo data 
-(``small_train.txt`` and ``small_test.txt``) from the path ``demo/classification/criteo_ctr``
+(``small_train.txt`` and ``small_test.txt``) from the path ``demo/classification/criteo_ctr``.
 
  .. __: install.html
  .. __: install.html

_build/html/index.html

@@ -188,7 +188,7 @@ <h2>Quick Install<a class="headerlink" href="#quick-install" title="Permalink to
 </pre></div>
 </div>
 <p>The installation process will take a while. And then you can type the following script in python
-shell to check whether we install xLearn successfully:</p>
+shell to check whether the xLearn has been installed successfully:</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">xl</span><span class="o">.</span><span class="n">hello</span><span class="p">()</span>
 </pre></div>
@@ -219,7 +219,7 @@ <h2>Python Demo<a class="headerlink" href="#python-demo" title="Permalink to thi
 </div>
 <p>This example shows how to use field-aware factorizations machine (ffm) to solve a
 simple binary classification task. You can check out the demo data
-(<code class="docutils literal"><span class="pre">small_train.txt</span></code> and <code class="docutils literal"><span class="pre">small_test.txt</span></code>) from the path <code class="docutils literal"><span class="pre">demo/classification/criteo_ctr</span></code></p>
+(<code class="docutils literal"><span class="pre">small_train.txt</span></code> and <code class="docutils literal"><span class="pre">small_test.txt</span></code>) from the path <code class="docutils literal"><span class="pre">demo/classification/criteo_ctr</span></code>.</p>
 <blockquote>
 <div><div class="toctree-wrapper compound">
 </div>

index.rst

@@ -34,7 +34,7 @@ source code and build it locally. We will update the xLearn source code on pip w
     sudo pip install xlearn
 
 The installation process will take a while. And then you can type the following script in python 
-shell to check whether we install xLearn successfully:
+shell to check whether the xLearn has been installed successfully:
 
 >>> import xlearn as xl
 >>> xl.hello()
@@ -68,7 +68,7 @@ Here is a simple python demo no how to use xLearn.
 
 This example shows how to use field-aware factorizations machine (ffm) to solve a 
 simple binary classification task. You can check out the demo data 
-(``small_train.txt`` and ``small_test.txt``) from the path ``demo/classification/criteo_ctr``
+(``small_train.txt`` and ``small_test.txt``) from the path ``demo/classification/criteo_ctr``.
 
  .. __: install.html
  .. __: install.html