Updated matrix.py and docs

matrix.py - removed overloaded __len__
jerela · Nov 4, 2023 · 3693d07 · 3693d07
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diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
@@ -5,11 +5,9 @@ name: Check tests
 
 on:
   push:
-    #branches: [ "master" ]
     branches: [ "**" ]
   pull_request:
     branches: [ "**" ]
-    #branches: [ "master" ]
 
 jobs:
   build:

diff --git a/mola.clustering.html b/mola.clustering.html
@@ -0,0 +1,61 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
+<html><head><title>Python: module mola.clustering</title>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+</head><body bgcolor="#f0f0f8">
+
+<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="heading">
+<tr bgcolor="#7799ee">
+<td valign=bottom>&nbsp;<br>
+<font color="#ffffff" face="helvetica, arial">&nbsp;<br><big><big><strong><a href="mola.html"><font color="#ffffff">mola</font></a>.clustering</strong></big></big></font></td
+><td align=right valign=bottom
+><font color="#ffffff" face="helvetica, arial"><a href=".">index</a><br><a href="file:c%3A%5Cusers%5Clavik%5Conedrive%5Cdocuments%5Cgithub%5Cpython-linear-algebra%5Cmola%5Cclustering.py">c:\users\lavik\onedrive\documents\github\python-linear-algebra\mola\clustering.py</a></font></td></tr></table>
+    <p></p>
+<p>
+<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
+<tr bgcolor="#aa55cc">
+<td colspan=3 valign=bottom>&nbsp;<br>
+<font color="#ffffff" face="helvetica, arial"><big><strong>Modules</strong></big></font></td></tr>
+
+<tr><td bgcolor="#aa55cc"><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>
+<td width="100%"><table width="100%" summary="list"><tr><td width="25%" valign=top><a href="math.html">math</a><br>
+</td><td width="25%" valign=top></td><td width="25%" valign=top></td><td width="25%" valign=top></td></tr></table></td></tr></table><p>
+<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
+<tr bgcolor="#eeaa77">
+<td colspan=3 valign=bottom>&nbsp;<br>
+<font color="#ffffff" face="helvetica, arial"><big><strong>Functions</strong></big></font></td></tr>
+
+<tr><td bgcolor="#eeaa77"><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>
+<td width="100%"><dl><dt><a name="-distance_euclidean_pow"><strong>distance_euclidean_pow</strong></a>(p1, p2)</dt><dd><tt>Return&nbsp;the&nbsp;squared&nbsp;Euclidean&nbsp;distance&nbsp;between&nbsp;two&nbsp;points.<br>
+If&nbsp;you&nbsp;want&nbsp;to&nbsp;retrieve&nbsp;the&nbsp;actual&nbsp;Euclidean&nbsp;distance,&nbsp;take&nbsp;the&nbsp;square&nbsp;root&nbsp;of&nbsp;the&nbsp;result.&nbsp;However,&nbsp;using&nbsp;this&nbsp;squared&nbsp;version&nbsp;is&nbsp;computationally&nbsp;more&nbsp;efficient.<br>
+&nbsp;<br>
+Arguments:<br>
+p1&nbsp;--&nbsp;list:&nbsp;the&nbsp;first&nbsp;point<br>
+p2&nbsp;--&nbsp;list:&nbsp;the&nbsp;second&nbsp;point</tt></dd></dl>
+ <dl><dt><a name="-distance_taxicab"><strong>distance_taxicab</strong></a>(p1, p2)</dt><dd><tt>Return&nbsp;the&nbsp;taxicab&nbsp;distance&nbsp;(also&nbsp;known&nbsp;as&nbsp;Manhattan&nbsp;distance)&nbsp;between&nbsp;two&nbsp;points.<br>
+&nbsp;<br>
+Arguments:<br>
+p1&nbsp;--&nbsp;list:&nbsp;the&nbsp;first&nbsp;point<br>
+p2&nbsp;--&nbsp;list:&nbsp;the&nbsp;second&nbsp;point</tt></dd></dl>
+ <dl><dt><a name="-find_c_means"><strong>find_c_means</strong></a>(data, num_centers=2, max_iterations=100, distance_function=&lt;function distance_euclidean_pow at 0x000002575B236E50&gt;, initial_centers=None)</dt><dd><tt>Return&nbsp;the&nbsp;cluster&nbsp;centers&nbsp;and&nbsp;the&nbsp;membership&nbsp;matrix&nbsp;of&nbsp;points&nbsp;using&nbsp;soft&nbsp;k-means&nbsp;clustering&nbsp;(also&nbsp;known&nbsp;as&nbsp;fuzzy&nbsp;c-means).<br>
+&nbsp;<br>
+This&nbsp;algorithm&nbsp;is&nbsp;well-suited&nbsp;to&nbsp;cluster&nbsp;data&nbsp;that&nbsp;is&nbsp;not&nbsp;clearly&nbsp;separable&nbsp;into&nbsp;distinct&nbsp;clusters.<br>
+&nbsp;&nbsp;&nbsp;&nbsp;<br>
+Arguments:<br>
+data&nbsp;--&nbsp;Matrix:&nbsp;the&nbsp;data&nbsp;containing&nbsp;the&nbsp;points&nbsp;to&nbsp;be&nbsp;clustered<br>
+num_centers&nbsp;--&nbsp;int:&nbsp;the&nbsp;number&nbsp;of&nbsp;cluster&nbsp;centers&nbsp;to&nbsp;be&nbsp;found&nbsp;(default&nbsp;2)<br>
+max_iterations&nbsp;--&nbsp;int:&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;iterations&nbsp;where&nbsp;cluster&nbsp;centers&nbsp;are&nbsp;updated&nbsp;(default&nbsp;100)<br>
+distance_function&nbsp;--&nbsp;function:&nbsp;the&nbsp;distance&nbsp;function&nbsp;to&nbsp;be&nbsp;used&nbsp;(default&nbsp;Euclidean&nbsp;distance);&nbsp;options&nbsp;are&nbsp;squared&nbsp;Euclidean&nbsp;distance&nbsp;(distance_euclidean_pow)&nbsp;and&nbsp;taxicab&nbsp;distance&nbsp;(distance_taxicab)<br>
+initial_centers&nbsp;--&nbsp;Matrix:&nbsp;the&nbsp;initial&nbsp;cluster&nbsp;centers;&nbsp;if&nbsp;not&nbsp;specified,&nbsp;they&nbsp;are&nbsp;initialized&nbsp;randomly&nbsp;(default&nbsp;None)</tt></dd></dl>
+ <dl><dt><a name="-find_k_means"><strong>find_k_means</strong></a>(data, num_centers=2, max_iterations=100, distance_function=&lt;function distance_euclidean_pow at 0x000002575B236E50&gt;, initial_centers=None)</dt><dd><tt>Return&nbsp;the&nbsp;cluster&nbsp;centers&nbsp;using&nbsp;hard&nbsp;k-means&nbsp;clustering.<br>
+&nbsp;<br>
+Note&nbsp;that&nbsp;there&nbsp;is&nbsp;no&nbsp;guarantee&nbsp;that&nbsp;the&nbsp;algorithm&nbsp;converges.&nbsp;This&nbsp;is&nbsp;why&nbsp;you&nbsp;should&nbsp;use&nbsp;several&nbsp;restarts&nbsp;or&nbsp;fuzzy&nbsp;k-means&nbsp;(function&nbsp;<a href="#-find_c_means">find_c_means</a>()&nbsp;in&nbsp;this&nbsp;module).<br>
+&nbsp;<br>
+Arguments:<br>
+data&nbsp;--&nbsp;Matrix:&nbsp;the&nbsp;data&nbsp;containing&nbsp;the&nbsp;points&nbsp;to&nbsp;be&nbsp;clustered<br>
+num_centers&nbsp;--&nbsp;int:&nbsp;the&nbsp;number&nbsp;of&nbsp;centers&nbsp;to&nbsp;be&nbsp;found&nbsp;(default&nbsp;2)<br>
+max_iterations&nbsp;--&nbsp;int:&nbsp;the&nbsp;maximum&nbsp;number&nbsp;of&nbsp;iterations&nbsp;where&nbsp;cluster&nbsp;centers&nbsp;are&nbsp;updated&nbsp;(default&nbsp;100)<br>
+distance_function&nbsp;--&nbsp;function:&nbsp;the&nbsp;distance&nbsp;function&nbsp;to&nbsp;be&nbsp;used&nbsp;(default&nbsp;Euclidean&nbsp;distance);&nbsp;options&nbsp;are&nbsp;squared&nbsp;Euclidean&nbsp;distance&nbsp;(distance_euclidean_pow)&nbsp;and&nbsp;taxicab&nbsp;distance&nbsp;(distance_taxicab)<br>
+initial_centers&nbsp;--&nbsp;Matrix:&nbsp;the&nbsp;initial&nbsp;cluster&nbsp;centers;&nbsp;if&nbsp;not&nbsp;specified,&nbsp;they&nbsp;are&nbsp;initialized&nbsp;randomly&nbsp;(default&nbsp;None)</tt></dd></dl>
+ <dl><dt><a name="-random"><strong>random</strong></a>()<font color="#909090"><font face="helvetica, arial"> method of <a href="random.html#Random">random.Random</a> instance</font></font></dt><dd><tt><a href="#-random">random</a>()&nbsp;-&gt;&nbsp;x&nbsp;in&nbsp;the&nbsp;interval&nbsp;[0,&nbsp;1).</tt></dd></dl>
+</td></tr></table>
+</body></html>
diff --git a/mola.regression.html b/mola.regression.html
@@ -17,7 +17,7 @@
 <font color="#ffffff" face="helvetica, arial"><big><strong>Functions</strong></big></font></td></tr>
 
 <tr><td bgcolor="#eeaa77"><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>
-<td width="100%"><dl><dt><a name="-fit_nonlinear"><strong>fit_nonlinear</strong></a>(independent_values, dependent_values, h, J, initial=None, max_iters=100.0)</dt><dd><tt>Returns&nbsp;the&nbsp;estimated&nbsp;parameters&nbsp;of&nbsp;a&nbsp;nonlinear&nbsp;model&nbsp;using&nbsp;the&nbsp;Gauss-Newton&nbsp;iteration&nbsp;algorithm.<br>
+<td width="100%"><dl><dt><a name="-fit_nonlinear"><strong>fit_nonlinear</strong></a>(independent_values, dependent_values, h, J, initial=None, max_iters=100)</dt><dd><tt>Returns&nbsp;the&nbsp;estimated&nbsp;parameters&nbsp;of&nbsp;a&nbsp;nonlinear&nbsp;model&nbsp;using&nbsp;the&nbsp;Gauss-Newton&nbsp;iteration&nbsp;algorithm.<br>
 &nbsp;<br>
 Arguments:<br>
 independent_values&nbsp;--&nbsp;Matrix:&nbsp;the&nbsp;matrix&nbsp;of&nbsp;independent&nbsp;values<br>

diff --git a/mola.utils.html b/mola.utils.html
@@ -33,6 +33,11 @@
 precision&nbsp;--&nbsp;float:&nbsp;the&nbsp;maximum&nbsp;allowed&nbsp;difference&nbsp;between&nbsp;matching&nbsp;elements&nbsp;(default&nbsp;1e-12)<br>
 &nbsp;<br>
 Raises&nbsp;an&nbsp;exception&nbsp;if&nbsp;'left'&nbsp;and&nbsp;'right'&nbsp;have&nbsp;different&nbsp;dimensions.</tt></dd></dl>
+ <dl><dt><a name="-get_mean"><strong>get_mean</strong></a>(data, along='col')</dt><dd><tt>Return&nbsp;the&nbsp;mean&nbsp;of&nbsp;a&nbsp;matrix.<br>
+&nbsp;<br>
+Arguments:<br>
+data&nbsp;--&nbsp;Matrix:&nbsp;the&nbsp;matrix&nbsp;whose&nbsp;mean&nbsp;is&nbsp;to&nbsp;be&nbsp;calculated<br>
+along&nbsp;--&nbsp;string:&nbsp;the&nbsp;dimension&nbsp;along&nbsp;which&nbsp;the&nbsp;mean&nbsp;is&nbsp;to&nbsp;be&nbsp;calculated&nbsp;(default&nbsp;'col')</tt></dd></dl>
  <dl><dt><a name="-identity"><strong>identity</strong></a>(rows, cols=None)</dt><dd><tt>Return&nbsp;a&nbsp;square&nbsp;identity&nbsp;matrix.<br>
 &nbsp;<br>
 Arguments:<br>
@@ -56,6 +61,14 @@
 delimiter&nbsp;--&nbsp;character:&nbsp;specifies&nbsp;the&nbsp;delimiter&nbsp;that&nbsp;separates&nbsp;data&nbsp;values&nbsp;in&nbsp;the&nbsp;text&nbsp;file&nbsp;(default&nbsp;,)<br>
 &nbsp;<br>
 If&nbsp;no&nbsp;delimiter&nbsp;is&nbsp;given,&nbsp;the&nbsp;file&nbsp;is&nbsp;assumed&nbsp;to&nbsp;be&nbsp;in&nbsp;comma-separated&nbsp;values&nbsp;format.</tt></dd></dl>
+ <dl><dt><a name="-transpose_list"><strong>transpose_list</strong></a>(data)</dt><dd><tt>Return&nbsp;the&nbsp;transpose&nbsp;of&nbsp;a&nbsp;2D&nbsp;list.<br>
+&nbsp;<br>
+Arguments:<br>
+data&nbsp;--&nbsp;list:&nbsp;the&nbsp;2D&nbsp;list&nbsp;to&nbsp;be&nbsp;transposed</tt></dd></dl>
+ <dl><dt><a name="-uniques"><strong>uniques</strong></a>(data)</dt><dd><tt>Return&nbsp;the&nbsp;unique&nbsp;rows&nbsp;of&nbsp;a&nbsp;2D&nbsp;matrix&nbsp;or&nbsp;list.<br>
+&nbsp;<br>
+Arguments:<br>
+data&nbsp;--&nbsp;Matrix&nbsp;or&nbsp;list:&nbsp;the&nbsp;matrix&nbsp;or&nbsp;list&nbsp;whose&nbsp;unique&nbsp;rows&nbsp;are&nbsp;to&nbsp;be&nbsp;returned</tt></dd></dl>
  <dl><dt><a name="-write_matrix_to_file"><strong>write_matrix_to_file</strong></a>(matrix, file_name, delimiter=',')</dt><dd><tt>Write&nbsp;a&nbsp;matrix&nbsp;to&nbsp;a&nbsp;text&nbsp;file.<br>
 &nbsp;<br>
 Arguments:<br>

diff --git a/mola/matrix.py b/mola/matrix.py
@@ -370,9 +370,6 @@ def get(self,i,j):
         """Get the element at specified position."""
         return self.data[i][j]
 
-    def __len__(self):
-        return self.n_rows
-
     # define what happens when the matrix is converted to a string, such as when print(Matrix) is called
     def __str__(self, precision = 4):
         """Define the string representation of Matrix.