Replaced _slope_lstsq by _linear_regression (added intercept)

README.rst

@@ -19,7 +19,8 @@
 .. figure::  https://github.com/raphaelvallat/entropy/blob/master/docs/pictures/logo.png
    :align:   center
 
-EntroPy is a Python 3 package for computing several complexity metrics of one-dimensional time series.
+EntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of one-dimensional time series.
+It can be used for example to extract features from EEG signals.
 
 Documentation
 =============

docs/build/html/_modules/entropy/fractal.html

@@ -90,7 +90,7 @@ <h1>Source code for entropy.fractal</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">numba</span> <span class="k">import</span> <span class="n">jit</span>
 <span class="kn">from</span> <span class="nn">math</span> <span class="k">import</span> <span class="n">log</span><span class="p">,</span> <span class="n">floor</span>
 
-<span class="kn">from</span> <span class="nn">.utils</span> <span class="k">import</span> <span class="n">_slope_lstsq</span>
+<span class="kn">from</span> <span class="nn">.utils</span> <span class="k">import</span> <span class="n">_linear_regression</span>
 
 <span class="nb">all</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;petrosian_fd&#39;</span><span class="p">,</span> <span class="s1">&#39;katz_fd&#39;</span><span class="p">,</span> <span class="s1">&#39;higuchi_fd&#39;</span><span class="p">]</span>
 
@@ -238,7 +238,7 @@ <h1>Source code for entropy.fractal</h1><div class="highlight"><pre>
         <span class="n">lk</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">m_lm</span>
         <span class="n">x_reg</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">log</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="n">k</span><span class="p">)</span>
         <span class="n">y_reg</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">log</span><span class="p">(</span><span class="n">m_lm</span><span class="p">)</span>
-    <span class="n">higuchi</span> <span class="o">=</span> <span class="n">_slope_lstsq</span><span class="p">(</span><span class="n">x_reg</span><span class="p">,</span> <span class="n">y_reg</span><span class="p">)</span>
+    <span class="n">higuchi</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_linear_regression</span><span class="p">(</span><span class="n">x_reg</span><span class="p">,</span> <span class="n">y_reg</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">higuchi</span>
 
 

docs/build/html/_modules/entropy/others.html

@@ -89,7 +89,7 @@ <h1>Source code for entropy.others</h1><div class="highlight"><pre>
 <span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">numba</span> <span class="k">import</span> <span class="n">jit</span>
 
-<span class="kn">from</span> <span class="nn">.utils</span> <span class="k">import</span> <span class="n">_slope_lstsq</span><span class="p">,</span> <span class="n">_log_n</span>
+<span class="kn">from</span> <span class="nn">.utils</span> <span class="k">import</span> <span class="n">_linear_regression</span><span class="p">,</span> <span class="n">_log_n</span>
 
 <span class="nb">all</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;detrended_fluctuation&#39;</span><span class="p">]</span>
 
@@ -107,20 +107,15 @@ <h1>Source code for entropy.others</h1><div class="highlight"><pre>
     <span class="k">for</span> <span class="n">i_n</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">nvals</span><span class="p">):</span>
         <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">walk</span><span class="p">[:</span><span class="n">N</span> <span class="o">-</span> <span class="p">(</span><span class="n">N</span> <span class="o">%</span> <span class="n">n</span><span class="p">)],</span> <span class="p">(</span><span class="n">N</span> <span class="o">//</span> <span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
         <span class="n">ran_n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">na</span><span class="p">)</span> <span class="k">for</span> <span class="n">na</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)])</span>
-        <span class="n">ran_n_mean</span> <span class="o">=</span> <span class="n">ran_n</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
         <span class="n">d_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
         <span class="n">slope</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">d_len</span><span class="p">)</span>
         <span class="n">intercept</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">d_len</span><span class="p">)</span>
         <span class="n">trend</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">d_len</span><span class="p">,</span> <span class="n">ran_n</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">d_len</span><span class="p">):</span>
-            <span class="n">sl</span> <span class="o">=</span> <span class="n">_slope_lstsq</span><span class="p">(</span><span class="n">ran_n</span><span class="p">,</span> <span class="n">d</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-            <span class="n">di_mean</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-            <span class="n">inter</span> <span class="o">=</span> <span class="n">di_mean</span> <span class="o">-</span> <span class="n">sl</span> <span class="o">*</span> <span class="n">ran_n_mean</span>
-            <span class="n">slope</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">sl</span>
-            <span class="n">intercept</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">inter</span>
+            <span class="n">slope</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">intercept</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">_linear_regression</span><span class="p">(</span><span class="n">ran_n</span><span class="p">,</span> <span class="n">d</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
             <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">ran_n</span><span class="p">)</span>
             <span class="c1"># Equivalent to np.polyval function</span>
-            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="p">[</span><span class="n">sl</span><span class="p">,</span> <span class="n">inter</span><span class="p">]:</span>
+            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="p">[</span><span class="n">slope</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">intercept</span><span class="p">[</span><span class="n">i</span><span class="p">]]:</span>
                 <span class="n">y</span> <span class="o">=</span> <span class="n">y</span> <span class="o">*</span> <span class="n">ran_n</span> <span class="o">+</span> <span class="n">p</span>
             <span class="n">trend</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">y</span>
         <span class="c1"># calculate standard deviation (fluctuation) of walks in d around trend</span>
@@ -136,7 +131,7 @@ <h1>Source code for entropy.others</h1><div class="highlight"><pre>
         <span class="c1"># all fluctuations are zero =&gt; we cannot fit a line</span>
         <span class="n">dfa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="n">dfa</span> <span class="o">=</span> <span class="n">_slope_lstsq</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">nvals</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">fluctuations</span><span class="p">))</span>
+        <span class="n">dfa</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_linear_regression</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">nvals</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">fluctuations</span><span class="p">))</span>
     <span class="k">return</span> <span class="n">dfa</span>
 
 

docs/build/html/_sources/index.rst.txt

@@ -19,7 +19,8 @@
 .. figure::  https://raw.githubusercontent.com/raphaelvallat/entropy/master/docs/pictures/logo.png
    :align:   center
 
-EntroPy is a Python 3 package for computing several complexity metrics of one-dimensional time series.
+EntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of one-dimensional time series.
+It can be used for example to extract features from EEG signals.
 
 Installation
 ============

docs/build/html/index.html

@@ -96,7 +96,8 @@
 <div class="figure align-center">
 <img alt="https://raw.githubusercontent.com/raphaelvallat/entropy/master/docs/pictures/logo.png" src="https://raw.githubusercontent.com/raphaelvallat/entropy/master/docs/pictures/logo.png" />
 </div>
-<p>EntroPy is a Python 3 package for computing several complexity metrics of one-dimensional time series.</p>
+<p>EntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of one-dimensional time series.
+It can be used for example to extract features from EEG signals.</p>
 <div class="section" id="installation">
 <h1>Installation</h1>
 <p><strong>Develop mode</strong></p>