update doc

aksnzhy · Dec 8, 2017 · 15ceef7 · 15ceef7
1 parent 7f67f88
commit 15ceef7
Show file tree

Hide file tree

Showing 7 changed files with 66 additions and 67 deletions.
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
@@ -1,16 +1,16 @@
 xLearn Command Line Guide
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Once you build xLearn successfully, you will get two executable files ``xlearn_train``
-and ``xlearn_predict`` in your ``build`` directory. Now you can use these two executable 
-files to perform training task and prediction task.
+Once you build xLearn from source code successfully, you will get two executable files 
+``xlearn_train`` and ``xlearn_predict`` in your ``build`` directory. Now you can use these 
+two executable files to perform training task and prediction task.
 
 Quick Start
 ----------------------------------------
 
-Make sure that you are in the build path of xLearn, and you will find the ``small_test.txt``
-and ``small_train.txt`` in this directory. Now you can type the following command to train a
-model ::
+Make sure that you are in the build path of xLearn, and you will find the demo data 
+``small_test.txt`` and ``small_train.txt`` in this directory. Now you can type the following 
+command to train a model ::
 
     ./xlearn_train ./small_train.txt
 
@@ -30,47 +30,49 @@ Here we print a portion of the output ::
 
 On default, xLearn will use the logistic regression (LR) to train our model for 10 epoch.
 
-We can see that there is a new file called ``small_train.txt.model`` in current directory. This 
-is the trainned model checkpoint file. We can use the model file to make prediction like this ::
+We can see that a new file called ``small_train.txt.model`` has been generated in current directory. 
+This file stores the trainned model checkpoint, and we can use this model file to make prediction ::
 
     ./xlearn_predict ./small_test.txt ./small_train.txt.model
 
-Then we can get a new file called ``small_test.txt.out``. This is output prediction result. 
-Let's see the first ten lines of result by using the following command ::
+Then we can get a new file called ``small_test.txt.out`` in current directory. This is output 
+prediction result. Let's see the first five lines of output by using the following command ::
     
-    head -n 10 ./small_test.txt.out
+    head -n 5 ./small_test.txt.out
 
     -1.9872
     -0.0707959
     -0.456214
     -0.170811
     -1.28986
-    -0.618315
-    -1.60154
-    -1.50124
-    -0.347515
-    -1.12173
-
-This is the score for every example in test set. The negative data represents a negative example and 
-positive data represents the positive example. You can convert the score to 0~1 by using ``--sigmoid`` option,
-or you can convert your result to bianry result (0 and 1) by using ``--sign`` option ::
-
-   ./xlearn_predict ./small_test.txt ./small_train.txt.model --sigmoid
-   head -n 10 ./small_test.txt.out
-
-   0.120553
-   0.482308
-   0.387884
-   0.457401
-   0.215877
-   0.350165
-   0.167766
-   0.182241
-   0.413985
-   0.245691
+
+The ten lines of data is the score for every example in test set. The negative data represents the 
+negative example and positive data represents the positive example. You can convert the score to (0-1) 
+by using ``--sigmoid`` option, or you can convert your result to bianry result (0 and 1) by using 
+``--sign`` option ::
+
+    ./xlearn_predict ./small_test.txt ./small_train.txt.model --sigmoid
+    head -n 5 ./small_test.txt.out
+
+    0.120553
+    0.482308
+    0.387884
+    0.457401
+    0.215877
+
+    ./xlearn_predict ./small_test.txt ./small_train.txt.model --sign
+    head -n 5 ./small_test.txt.out
+
+    0
+    0
+    0
+    0
+    0
 
 Choose Machine Learning Model
 ----------------------------------------
 
+For now, xLearn can support three different machine learning model, including LR, FM and FFM.
+
  .. toctree::
    :hidden:
diff --git a/_build/html/command_line.html b/_build/html/command_line.html
@@ -167,14 +167,14 @@
 
   <div class="section" id="xlearn-command-line-guide">
 <h1>xLearn Command Line Guide<a class="headerlink" href="#xlearn-command-line-guide" title="Permalink to this headline">¶</a></h1>
-<p>Once you build xLearn successfully, you will get two executable files <code class="docutils literal"><span class="pre">xlearn_train</span></code>
-and <code class="docutils literal"><span class="pre">xlearn_predict</span></code> in your <code class="docutils literal"><span class="pre">build</span></code> directory. Now you can use these two executable
-files to perform training task and prediction task.</p>
+<p>Once you build xLearn from source code successfully, you will get two executable files
+<code class="docutils literal"><span class="pre">xlearn_train</span></code> and <code class="docutils literal"><span class="pre">xlearn_predict</span></code> in your <code class="docutils literal"><span class="pre">build</span></code> directory. Now you can use these
+two executable files to perform training task and prediction task.</p>
 <div class="section" id="quick-start">
 <h2>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to this headline">¶</a></h2>
-<p>Make sure that you are in the build path of xLearn, and you will find the <code class="docutils literal"><span class="pre">small_test.txt</span></code>
-and <code class="docutils literal"><span class="pre">small_train.txt</span></code> in this directory. Now you can type the following command to train a
-model</p>
+<p>Make sure that you are in the build path of xLearn, and you will find the demo data
+<code class="docutils literal"><span class="pre">small_test.txt</span></code> and <code class="docutils literal"><span class="pre">small_train.txt</span></code> in this directory. Now you can type the following
+command to train a model</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>
 </pre></div>
 </div>
@@ -193,48 +193,49 @@ <h2>Quick Start<a class="headerlink" href="#quick-start" title="Permalink to thi
 </pre></div>
 </div>
 <p>On default, xLearn will use the logistic regression (LR) to train our model for 10 epoch.</p>
-<p>We can see that there is a new file called <code class="docutils literal"><span class="pre">small_train.txt.model</span></code> in current directory. This
-is the trainned model checkpoint file. We can use the model file to make prediction like this</p>
+<p>We can see that a new file called <code class="docutils literal"><span class="pre">small_train.txt.model</span></code> has been generated in current directory.
+This file stores the trainned model checkpoint, and we can use this model file to make prediction</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">xlearn_predict</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</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">model</span>
 </pre></div>
 </div>
-<p>Then we can get a new file called <code class="docutils literal"><span class="pre">small_test.txt.out</span></code>. This is output prediction result.
-Let’s see the first ten lines of result by using the following command</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">head</span> <span class="o">-</span><span class="n">n</span> <span class="mi">10</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span><span class="o">.</span><span class="n">out</span>
+<p>Then we can get a new file called <code class="docutils literal"><span class="pre">small_test.txt.out</span></code> in current directory. This is output
+prediction result. Let’s see the first five lines of output by using the following command</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">head</span> <span class="o">-</span><span class="n">n</span> <span class="mi">5</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span><span class="o">.</span><span class="n">out</span>
 
 <span class="o">-</span><span class="mf">1.9872</span>
 <span class="o">-</span><span class="mf">0.0707959</span>
 <span class="o">-</span><span class="mf">0.456214</span>
 <span class="o">-</span><span class="mf">0.170811</span>
 <span class="o">-</span><span class="mf">1.28986</span>
-<span class="o">-</span><span class="mf">0.618315</span>
-<span class="o">-</span><span class="mf">1.60154</span>
-<span class="o">-</span><span class="mf">1.50124</span>
-<span class="o">-</span><span class="mf">0.347515</span>
-<span class="o">-</span><span class="mf">1.12173</span>
 </pre></div>
 </div>
-<p>This is the score for every example in test set. The negative data represents a negative example and
-positive data represents the positive example. You can convert the score to 0~1 by using <code class="docutils literal"><span class="pre">--sigmoid</span></code> option,
-or you can convert your result to bianry result (0 and 1) by using <code class="docutils literal"><span class="pre">--sign</span></code> option</p>
+<p>The ten lines of data is the score for every example in test set. The negative data represents the
+negative example and positive data represents the positive example. You can convert the score to (0-1)
+by using <code class="docutils literal"><span class="pre">--sigmoid</span></code> option, or you can convert your result to bianry result (0 and 1) by using
+<code class="docutils literal"><span class="pre">--sign</span></code> option</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">xlearn_predict</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</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">model</span> <span class="o">--</span><span class="n">sigmoid</span>
-<span class="n">head</span> <span class="o">-</span><span class="n">n</span> <span class="mi">10</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span><span class="o">.</span><span class="n">out</span>
+<span class="n">head</span> <span class="o">-</span><span class="n">n</span> <span class="mi">5</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span><span class="o">.</span><span class="n">out</span>
 
 <span class="mf">0.120553</span>
 <span class="mf">0.482308</span>
 <span class="mf">0.387884</span>
 <span class="mf">0.457401</span>
 <span class="mf">0.215877</span>
-<span class="mf">0.350165</span>
-<span class="mf">0.167766</span>
-<span class="mf">0.182241</span>
-<span class="mf">0.413985</span>
-<span class="mf">0.245691</span>
+
+<span class="o">./</span><span class="n">xlearn_predict</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</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">model</span> <span class="o">--</span><span class="n">sign</span>
+<span class="n">head</span> <span class="o">-</span><span class="n">n</span> <span class="mi">5</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span><span class="o">.</span><span class="n">out</span>
+
+<span class="mi">0</span>
+<span class="mi">0</span>
+<span class="mi">0</span>
+<span class="mi">0</span>
+<span class="mi">0</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="choose-machine-learning-model">
 <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.</p>
 <blockquote>
 <div><div class="toctree-wrapper compound">
 </div>

diff --git a/_build/html/searchindex.js b/_build/html/searchindex.js
diff --git a/command_line.rst b/command_line.rst
@@ -72,5 +72,7 @@ by using ``--sigmoid`` option, or you can convert your result to bianry result (
 Choose Machine Learning Model
 ----------------------------------------
 
+For now, xLearn can support three different machine learning model, including LR, FM and FFM.
+
  .. toctree::
    :hidden:
diff --git a/conf.py b/conf.py
@@ -20,7 +20,6 @@
 # import sys
 # sys.path.insert(0, os.path.abspath('.'))
 
-
 # -- General configuration ------------------------------------------------
 
 # If your documentation needs a minimal Sphinx version, state it here.
@@ -142,7 +141,6 @@
      u'Chao Ma', 'manual'),
 ]
 
-
 # -- Options for manual page output ---------------------------------------
 
 # One entry per manual page. List of tuples
@@ -152,7 +150,6 @@
      [author], 1)
 ]
 
-
 # -- Options for Texinfo output -------------------------------------------
 
 # Grouping the document tree into Texinfo files. List of tuples
@@ -162,7 +159,4 @@
     (master_doc, 'xlearn_doc', u'xlearn_doc Documentation',
      author, 'xlearn_doc', 'One line description of project.',
      'Miscellaneous'),
-]
-
-
-
+]