Added docs for Hurst exponent

hosseinmoein · Sep 30, 2020 · 1d9eafd · 1d9eafd
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diff --git a/README.md b/README.md
@@ -41,6 +41,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 This is a C++ statistical library that provides an interface similar to Pandas package in Python.<BR>
 <B>A DataFrame can have one index column and many data columns of any built-in or user-defined type</B>.<BR>
 You could slice the data in many different ways. You could join, merge, group-by the data. You could run various statistical, summarization and ML algorithms on the data. You could add your custom algorithms easily. You could multi-column sort, custom pick and delete the data. And more …<BR>
+DataFrame also includes a large collection of analytical routines in form of visitors -- see documentation below. These are from basic stats such as <I>Mean, Std Deviation, Return, …</I> to more involved analysis such as <I>Affinity Propagation, Polynomial Fit, Hurst Exponent, …</I> -- See documentation below for a complete list with code samples, and how you can add your custom algorithms.<BR>
 
 <B>I have followed a few principles in this library:</B><BR>
 

diff --git a/docs/HTML/DataFrame.html b/docs/HTML/DataFrame.html
@@ -488,7 +488,7 @@ <H2 style="text-align:center">Table of Features (with Code Samples)</H2>
     <tr onmouseover="this.style.backgroundColor='#ffff66';" onmouseout="this.style.backgroundColor='#d4e3e5';">
       <td title="Sets the Spin Lock"><a href="https://htmlpreview.github.io/?https://github.com/hosseinmoein/DataFrame/blob/master/docs/HTML/remove_lock.html">set_lock</a>( )</td>
       <td></td>
-      <td></td>
+      <td title="Calculates Hurst exponent">struct <a href="https://htmlpreview.github.io/?https://github.com/hosseinmoein/DataFrame/blob/master/docs/HTML/HurstExponentVisitor.html">HurstExponentVisitor</a>{  }</td>
       <td></td>
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diff --git a/docs/HTML/HurstExponentVisitor.html b/docs/HTML/HurstExponentVisitor.html
@@ -0,0 +1,107 @@
+<!--
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+<!DOCTYPE html>
+<html>
+<body>
+
+  <table align="center" border="1">
+
+    <tr bgcolor="lightblue">
+      <th>Signature</th> <th>Description</th> <th>Parameters</th>
+    </tr>
+    <tr bgcolor="lightgrey">
+      <td bgcolor="maroon"> <font color="white">
+        <PRE><B>
+template&lt;typename T,
+         typename I = unsigned long,
+         typename =
+             typename std::enable_if&lt;std::is_arithmetic&lt;T&gt;::value, T&gt;::type&gt;
+struct HurstExponentVisitor;
+        </B></PRE></font>
+      </td>
+      <td>
+        This is a “single action visitor”, meaning it is passed the whole data vector in one call and you must use the single_action_visit() interface.<BR>
+        This functor calculates the Hurst exponent for the given column.<BR>
+        A hurst exponent, H, between 0 to 0.5 is said to correspond to a mean reverting process (anti-persistent), H=0.5 corresponds to Geometric Brownian Motion (Random Walk), while H >= 0.5 corresponds to a process which is trending (persistent).<BR><BR>
+        <I>explicit HurstExponentVisitor(std::vector&lt;size_t&gt; &amp;&amp;ranges)</I><BR>
+		<B>ranges</B> is a vector of column length divisors. For example, {1, 2, 4 } means calculate Hurst exponent in 3 steps.  It divides the time-series column to 1 chunk, 2 chunks and 4 chunks.
+      </td>
+      <td width="12%">
+        <B>T</B>: Column data type.<BR>
+        <B>I</B>: Index type.
+      </td>
+    </tr>
+
+  </table>
+
+<pre style='color:#000000;background:#ffffff;'><span style='color:#800000; font-weight:bold; '>static</span> <span style='color:#800000; font-weight:bold; '>void</span> test_HurstExponentVisitor<span style='color:#808030; '>(</span><span style='color:#808030; '>)</span>  <span style='color:#800080; '>{</span>
+
+    <span style='color:#666616; '>std</span><span style='color:#800080; '>::</span><span style='color:#603000; '>cout</span> <span style='color:#808030; '>&lt;</span><span style='color:#808030; '>&lt;</span> <span style='color:#800000; '>"</span><span style='color:#0f69ff; '>\n</span><span style='color:#0000e6; '>Testing HurstExponentVisitor{ } ...</span><span style='color:#800000; '>"</span> <span style='color:#808030; '>&lt;</span><span style='color:#808030; '>&lt;</span> <span style='color:#666616; '>std</span><span style='color:#800080; '>::</span><span style='color:#603000; '>endl</span><span style='color:#800080; '>;</span>
+
+    RandGenParams<span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span>   p<span style='color:#800080; '>;</span>
+
+    p<span style='color:#808030; '>.</span>seed <span style='color:#808030; '>=</span> <span style='color:#008c00; '>123</span><span style='color:#800080; '>;</span>
+    p<span style='color:#808030; '>.</span>min_value <span style='color:#808030; '>=</span> <span style='color:#008c00; '>0</span><span style='color:#800080; '>;</span>
+    p<span style='color:#808030; '>.</span>max_value <span style='color:#808030; '>=</span> <span style='color:#008c00; '>30</span><span style='color:#800080; '>;</span>
+
+    <span style='color:#666616; '>std</span><span style='color:#800080; '>::</span><span style='color:#603000; '>vector</span><span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span> d1 <span style='color:#808030; '>=</span> gen_uniform_real_dist<span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span><span style='color:#808030; '>(</span><span style='color:#008c00; '>1024</span><span style='color:#808030; '>,</span> p<span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+    <span style='color:#666616; '>std</span><span style='color:#800080; '>::</span><span style='color:#603000; '>vector</span><span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span> d2 <span style='color:#808030; '>=</span> <span style='color:#800080; '>{</span> <span style='color:#008000; '>0.04</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.02</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.05</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.08</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.02</span><span style='color:#808030; '>,</span> <span style='color:#808030; '>-</span><span style='color:#008000; '>0.17</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.05</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.0</span> <span style='color:#800080; '>}</span><span style='color:#800080; '>;</span>
+    <span style='color:#666616; '>std</span><span style='color:#800080; '>::</span><span style='color:#603000; '>vector</span><span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span> d3 <span style='color:#808030; '>=</span> <span style='color:#800080; '>{</span> <span style='color:#008000; '>0.04</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.05</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.055</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.06</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.061</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.072</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.073</span><span style='color:#808030; '>,</span> <span style='color:#008000; '>0.8</span> <span style='color:#800080; '>}</span><span style='color:#800080; '>;</span>
+
+    MyDataFrame df<span style='color:#800080; '>;</span>
+
+    df<span style='color:#808030; '>.</span>load_index<span style='color:#808030; '>(</span><span style='color:#666616; '>std</span><span style='color:#800080; '>::</span><span style='color:#603000; '>move</span><span style='color:#808030; '>(</span>MyDataFrame<span style='color:#800080; '>::</span>gen_sequence_index<span style='color:#808030; '>(</span><span style='color:#008c00; '>0</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>1024</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>1</span><span style='color:#808030; '>)</span><span style='color:#808030; '>)</span><span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+    df<span style='color:#808030; '>.</span>load_column<span style='color:#808030; '>(</span><span style='color:#800000; '>"</span><span style='color:#0000e6; '>d1_col</span><span style='color:#800000; '>"</span><span style='color:#808030; '>,</span> <span style='color:#666616; '>std</span><span style='color:#800080; '>::</span><span style='color:#603000; '>move</span><span style='color:#808030; '>(</span>d1<span style='color:#808030; '>)</span><span style='color:#808030; '>,</span> nan_policy<span style='color:#800080; '>::</span>dont_pad_with_nans<span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+    df<span style='color:#808030; '>.</span>load_column<span style='color:#808030; '>(</span><span style='color:#800000; '>"</span><span style='color:#0000e6; '>d2_col</span><span style='color:#800000; '>"</span><span style='color:#808030; '>,</span> <span style='color:#666616; '>std</span><span style='color:#800080; '>::</span><span style='color:#603000; '>move</span><span style='color:#808030; '>(</span>d2<span style='color:#808030; '>)</span><span style='color:#808030; '>,</span> nan_policy<span style='color:#800080; '>::</span>dont_pad_with_nans<span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+    df<span style='color:#808030; '>.</span>load_column<span style='color:#808030; '>(</span><span style='color:#800000; '>"</span><span style='color:#0000e6; '>d3_col</span><span style='color:#800000; '>"</span><span style='color:#808030; '>,</span> <span style='color:#666616; '>std</span><span style='color:#800080; '>::</span><span style='color:#603000; '>move</span><span style='color:#808030; '>(</span>d3<span style='color:#808030; '>)</span><span style='color:#808030; '>,</span> nan_policy<span style='color:#800080; '>::</span>dont_pad_with_nans<span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+
+    HurstExponentVisitor<span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span>    he_v1 <span style='color:#808030; '>(</span><span style='color:#ffffff; background:#dd0000; font-weight:bold; font-style:italic; '>{</span> <span style='color:#008c00; '>1</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>2</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>4</span> <span style='color:#ffffff; background:#dd0000; font-weight:bold; font-style:italic; '>}</span><span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+    <span style='color:#800000; font-weight:bold; '>auto</span>                            result1 <span style='color:#808030; '>=</span> df<span style='color:#808030; '>.</span>single_act_visit<span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span><span style='color:#808030; '>(</span><span style='color:#800000; '>"</span><span style='color:#0000e6; '>d2_col</span><span style='color:#800000; '>"</span><span style='color:#808030; '>,</span> he_v1<span style='color:#808030; '>)</span><span style='color:#808030; '>.</span>get_result<span style='color:#808030; '>(</span><span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+
+    assert<span style='color:#808030; '>(</span>result1 <span style='color:#808030; '>-</span> <span style='color:#008000; '>0.865926</span> <span style='color:#808030; '>&lt;</span> <span style='color:#008000; '>0.00001</span><span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+
+    HurstExponentVisitor<span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span>    he_v2 <span style='color:#808030; '>(</span><span style='color:#ffffff; background:#dd0000; font-weight:bold; font-style:italic; '>{</span> <span style='color:#008c00; '>1</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>2</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>4</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>5</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>6</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>7</span> <span style='color:#ffffff; background:#dd0000; font-weight:bold; font-style:italic; '>}</span><span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+    <span style='color:#800000; font-weight:bold; '>auto</span>                            result2 <span style='color:#808030; '>=</span> df<span style='color:#808030; '>.</span>single_act_visit<span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span><span style='color:#808030; '>(</span><span style='color:#800000; '>"</span><span style='color:#0000e6; '>d1_col</span><span style='color:#800000; '>"</span><span style='color:#808030; '>,</span> he_v2<span style='color:#808030; '>)</span><span style='color:#808030; '>.</span>get_result<span style='color:#808030; '>(</span><span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+
+    assert<span style='color:#808030; '>(</span>result2 <span style='color:#808030; '>-</span> <span style='color:#008000; '>0.487977</span> <span style='color:#808030; '>&lt;</span> <span style='color:#008000; '>0.00001</span><span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+
+    HurstExponentVisitor<span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span>    he_v3 <span style='color:#808030; '>(</span><span style='color:#ffffff; background:#dd0000; font-weight:bold; font-style:italic; '>{</span> <span style='color:#008c00; '>1</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>2</span><span style='color:#808030; '>,</span> <span style='color:#008c00; '>4</span> <span style='color:#ffffff; background:#dd0000; font-weight:bold; font-style:italic; '>}</span><span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+    <span style='color:#800000; font-weight:bold; '>auto</span>                            result3 <span style='color:#808030; '>=</span> df<span style='color:#808030; '>.</span>single_act_visit<span style='color:#800080; '>&lt;</span><span style='color:#800000; font-weight:bold; '>double</span><span style='color:#800080; '>></span><span style='color:#808030; '>(</span><span style='color:#800000; '>"</span><span style='color:#0000e6; '>d3_col</span><span style='color:#800000; '>"</span><span style='color:#808030; '>,</span> he_v3<span style='color:#808030; '>)</span><span style='color:#808030; '>.</span>get_result<span style='color:#808030; '>(</span><span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+
+    assert<span style='color:#808030; '>(</span>result3 <span style='color:#808030; '>-</span> <span style='color:#008000; '>0.903057</span> <span style='color:#808030; '>&lt;</span> <span style='color:#008000; '>0.00001</span><span style='color:#808030; '>)</span><span style='color:#800080; '>;</span>
+<span style='color:#800080; '>}</span>
+</pre>
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