lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

small update on book

Browse Source
This commit is contained in:
Morten Hjorth-Jensen
2024-09-03 05:59:13 +02:00
parent 48d4efe695
commit 621836e70c
12 changed files with 938 additions and 909 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/LectureNotes/_build/.doctrees/environment.pickle
BIN
View File
Binary file not shown.
doc/LectureNotes/_build/.doctrees/week36.doctree
BIN
View File
Binary file not shown.
doc/LectureNotes/_build/html/_sources/week36.ipynb
+300 -298
View File
File diff suppressed because it is too large Load Diff
doc/LectureNotes/_build/html/genindex.html
+1 -1
View File
@@ -271,7 +271,7 @@ const thebe_selector_output = ".output, .cell_output"
</li>
<li class="toctree-l1">
<a class="reference internal" href="week36.html">
Week 36: Linear Rgeression and Statistical interpretations
Week 36: Linear Regression and Statistical interpretations
</a>
</li>
</ul>
doc/LectureNotes/_build/html/intro.html
+1 -1
View File
@@ -272,7 +272,7 @@ const thebe_selector_output = ".output, .cell_output"
</li>
<li class="toctree-l1">
<a class="reference internal" href="week36.html">
Week 36: Linear Rgeression and Statistical interpretations
Week 36: Linear Regression and Statistical interpretations
</a>
</li>
</ul>
doc/LectureNotes/_build/html/objects.inv
BIN
View File
Binary file not shown.
doc/LectureNotes/_build/html/search.html
+1 -1
View File
@@ -277,7 +277,7 @@ const thebe_selector_output = ".output, .cell_output"
</li>
<li class="toctree-l1">
<a class="reference internal" href="week36.html">
Week 36: Linear Rgeression and Statistical interpretations
Week 36: Linear Regression and Statistical interpretations
</a>
</li>
</ul>
doc/LectureNotes/_build/html/searchindex.js
+1 -1
View File
File diff suppressed because one or more lines are too long
doc/LectureNotes/_build/html/week36.html
+28 -7
View File
@@ -5,7 +5,7 @@
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Week 36: Linear Rgeression and Statistical interpretations &#8212; Applied Data Analysis and Machine Learning</title>
<title>Week 36: Linear Regression and Statistical interpretations &#8212; Applied Data Analysis and Machine Learning</title>
<link href="_static/css/theme.css" rel="stylesheet">
<link href="_static/css/index.ff1ffe594081f20da1ef19478df9384b.css" rel="stylesheet">
@@ -55,6 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="Project 1 on Machine Learning, deadline October 7 (midnight), 2024" href="project1.html" />
<link rel="prev" title="Exercises week 36" href="exercisesweek36.html" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="docsearch:language" content="None">
@@ -274,7 +275,19 @@ const thebe_selector_output = ".output, .cell_output"
</li>
<li class="toctree-l1 current active">
<a class="current reference internal" href="#">
Week 36: Linear Rgeression and Statistical interpretations
Week 36: Linear Regression and Statistical interpretations
</a>
</li>
</ul>
<p aria-level="2" class="caption" role="heading">
<span class="caption-text">
Projects
</span>
</p>
<ul class="nav bd-sidenav">
<li class="toctree-l1">
<a class="reference internal" href="project1.html">
Project 1 on Machine Learning, deadline October 7 (midnight), 2024
</a>
</li>
</ul>
@@ -609,7 +622,7 @@ const thebe_selector_output = ".output, .cell_output"
<div class="col-12 col-md-9 pl-md-3 pr-md-0">
<!-- Table of contents that is only displayed when printing the page -->
<div id="jb-print-docs-body" class="onlyprint">
<h1>Week 36: Linear Rgeression and Statistical interpretations</h1>
<h1>Week 36: Linear Regression and Statistical interpretations</h1>
<!-- Table of contents -->
<div id="print-main-content">
<div id="jb-print-toc">
@@ -881,8 +894,8 @@ const thebe_selector_output = ".output, .cell_output"
<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
doconce format html week36.do.txt --no_mako -->
<!-- dom:TITLE: Week 36: Linear Rgeression and Statistical interpretations --><div class="tex2jax_ignore mathjax_ignore section" id="week-36-linear-rgeression-and-statistical-interpretations">
<h1>Week 36: Linear Rgeression and Statistical interpretations<a class="headerlink" href="#week-36-linear-rgeression-and-statistical-interpretations" title="Permalink to this headline"></a></h1>
<!-- dom:TITLE: Week 36: Linear Regression and Statistical interpretations --><div class="tex2jax_ignore mathjax_ignore section" id="week-36-linear-regression-and-statistical-interpretations">
<h1>Week 36: Linear Regression and Statistical interpretations<a class="headerlink" href="#week-36-linear-regression-and-statistical-interpretations" title="Permalink to this headline"></a></h1>
<p><strong>Morten Hjorth-Jensen</strong>, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</p>
<p>Date: <strong>September 2-6, 2024</strong></p>
<div class="section" id="plans-for-week-36">
@@ -941,6 +954,7 @@ survey of your data, with a critical assessment of them in case you need to scal
<p>If you need to scale the data, not doing so will give an <em>unfair</em>
penalization of the parameters since their magnitude depends on the
scale of their corresponding predictor.</p>
<p>The <strong>Scikit-Learn</strong> site <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section">https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section</a> has a good discussion of different ways of preprocessing data.</p>
<p>Suppose as an example that you
you have an input variable given by the heights of different persons.
Human height might be measured in inches or meters or
@@ -1142,7 +1156,7 @@ Note also that we do not split the data into training and test.</p>
<span class="n">intercept</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">y_offset</span> <span class="o">-</span> <span class="n">X_offset</span> <span class="o">@</span> <span class="n">beta</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Manual intercept: </span><span class="si">{</span><span class="n">intercept</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Fitted beta (wiothout intercept): </span><span class="si">{</span><span class="n">beta</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Fitted beta (without intercept): </span><span class="si">{</span><span class="n">beta</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Sklearn intercept: </span><span class="si">{</span><span class="n">skl</span><span class="o">.</span><span class="n">intercept_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Sklearn fitted beta (without intercept): </span><span class="si">{</span><span class="n">skl</span><span class="o">.</span><span class="n">coef_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">ypredictOwn</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">beta</span>
@@ -1170,7 +1184,7 @@ MSE with intercept column
MSE with intercept column from SKL
0.004113634617443147
Manual intercept: 2.083766322923899
Fitted beta (wiothout intercept): [0.19569961 3.97898392]
Fitted beta (without intercept): [0.19569961 3.97898392]
Sklearn intercept: 2.0837663229239043
Sklearn fitted beta (without intercept): [0.19569961 3.97898392]
MSE with Manual intercept
@@ -2903,6 +2917,13 @@ C(\boldsymbol{\beta})=(4-2\beta_0)^2+(2-\beta_1)^2+\lambda(\vert\beta_0\vert+\ve
<p class="prev-next-title">Exercises week 36</p>
</div>
</a>
<a class='right-next' id="next-link" href="project1.html" title="next page">
<div class="prev-next-info">
<p class="prev-next-subtitle">next</p>
<p class="prev-next-title">Project 1 on Machine Learning, deadline October 7 (midnight), 2024</p>
</div>
<i class="fas fa-angle-right"></i>
</a>
</div>
</div>
doc/LectureNotes/_build/jupyter_execute/week36.ipynb
+301 -299
View File
File diff suppressed because it is too large Load Diff
doc/LectureNotes/_build/jupyter_execute/week36.py
+5 -3
View File
@@ -3,9 +3,9 @@
# <!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
# doconce format html week36.do.txt --no_mako -->
# <!-- dom:TITLE: Week 36: Linear Rgeression and Statistical interpretations -->
# <!-- dom:TITLE: Week 36: Linear Regression and Statistical interpretations -->
# # Week 36: Linear Rgeression and Statistical interpretations
# # Week 36: Linear Regression and Statistical interpretations
# **Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
#
# Date: **September 2-6, 2024**
@@ -73,6 +73,8 @@
# penalization of the parameters since their magnitude depends on the
# scale of their corresponding predictor.
#
# The **Scikit-Learn** site <https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section> has a good discussion of different ways of preprocessing data.
#
# Suppose as an example that you
# you have an input variable given by the heights of different persons.
# Human height might be measured in inches or meters or
@@ -292,7 +294,7 @@ beta = fit_beta(X - X_offset, y - y_offset)
intercept = np.mean(y_offset - X_offset @ beta)
print(f"Manual intercept: {intercept}")
print(f"Fitted beta (wiothout intercept): {beta}")
print(f"Fitted beta (without intercept): {beta}")
print(f"Sklearn intercept: {skl.intercept_}")
print(f"Sklearn fitted beta (without intercept): {skl.coef_}")
ypredictOwn = X @ beta
doc/LectureNotes/week36.ipynb
+300 -298
View File
File diff suppressed because it is too large Load Diff