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mhjensen
2019-01-14 20:20:56 -05:00
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doc/pub/How2ReadData/html/How2ReadData.html
+27 -22
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@@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta name="description" content="Data Analysis and Machine Learning: Getting started, our first data and Machine Learning encounters">
<title>Data Analysis and Machine Learning: Getting started, our first data and Machine Learning encounters</title>
@@ -68,22 +69,23 @@ div { text-align: justify; text-justify: inter-word; }
'sections': [('Introduction', 2, None, '___sec0'),
('Software and needed installations', 2, None, '___sec1'),
('Python installers', 2, None, '___sec2'),
('Installing R, C++, cython or Julia', 2, None, '___sec3'),
('Installing R, C++, cython, Numba etc', 2, None, '___sec4'),
('Useful Python packages', 2, None, '___sec3'),
('Installing R, C++, cython or Julia', 2, None, '___sec4'),
('Installing R, C++, cython, Numba etc', 2, None, '___sec5'),
('Simple linear regression model using _scikit-learn_',
2,
None,
'___sec5'),
('Non-Linear Least squares in R', 2, None, '___sec6'),
('Examples', 2, None, '___sec7'),
('Ecoli lab experiment', 3, None, '___sec8'),
('Predator-Prey model from ecology', 3, None, '___sec9'),
('Simulating financial transactions', 3, None, '___sec10'),
'___sec6'),
('Non-Linear Least squares in R', 2, None, '___sec7'),
('Examples', 2, None, '___sec8'),
('Ecoli lab experiment', 3, None, '___sec9'),
('Predator-Prey model from ecology', 3, None, '___sec10'),
('Simulating financial transactions', 3, None, '___sec11'),
('Particle in one dimension and velocity distribution',
3,
None,
'___sec11'),
('Random walk model', 3, None, '___sec12')]}
'___sec12'),
('Random walk model', 3, None, '___sec13')]}
end of tocinfo -->
<body>
@@ -125,7 +127,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>May 30, 2018</h4></center> <!-- date -->
<center><h4>Jan 14, 2019</h4></center> <!-- date -->
<br>
<h2 id="___sec0">Introduction </h2>
@@ -237,7 +239,10 @@ distribution for scientific and analytic computing distribution and
analysis environment, available for free and under a commercial
license.
<h2 id="___sec3">Installing R, C++, cython or Julia </h2>
<h2 id="___sec3">Useful Python packages </h2>
Here we list several useful Python packages.
<h2 id="___sec4">Installing R, C++, cython or Julia </h2>
<p>
You will also find it convenient to utilize R. Although we will mainly
@@ -255,7 +260,7 @@ texts.
To install <b>R</b> with Jupyter notebook
<a href="https://mpacer.org/maths/r-kernel-for-ipython-notebook" target="_blank">follow the link here</a>
<h2 id="___sec4">Installing R, C++, cython, Numba etc </h2>
<h2 id="___sec5">Installing R, C++, cython, Numba etc </h2>
<p>
For the C++ aficionados, Jupyter/IPython notebook allows you also to
@@ -289,7 +294,7 @@ Finally, if you wish to use the light mark-up language
<a href="https://github.com/hplgit/doconce" target="_blank">doconce</a> you can convert a standard ascii text file into various HTML
formats, ipython notebooks, latex files, pdf files etc with minimal edits.
<h2 id="___sec5">Simple linear regression model using <b>scikit-learn</b> </h2>
<h2 id="___sec6">Simple linear regression model using <b>scikit-learn</b> </h2>
<p>
We start with perhaps our simplest possible example, using <b>scikit-learn</b> to perform linear regression analysis on a data set produced by us.
@@ -577,7 +582,7 @@ plt<span style="color: #666666">.</span>show()
Similarly, using <b>R</b>, we can perform similar studies. The following <b>R</b> code illustrates this.
(more details on <b>R</b> will be inserted later).
<h2 id="___sec6">Non-Linear Least squares in R </h2>
<h2 id="___sec7">Non-Linear Least squares in R </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
@@ -627,7 +632,7 @@ data_pandas <span style="color: #666666">=</span> pd<span style="color: #666666"
display(data_pandas)
</pre></div>
<h2 id="___sec7">Examples </h2>
<h2 id="___sec8">Examples </h2>
<p>
We present here several examples, with pertinent Python codes that we
@@ -644,7 +649,7 @@ Another model from the life sciences is the so-called predator-prey model from e
a simple model for financial transactions before moving to a random walk model and ending with
the simulation of velocities of a non-interacting atom or molecule confined to move in a one-dimensional region.
<h3 id="___sec8">Ecoli lab experiment </h3>
<h3 id="___sec9">Ecoli lab experiment </h3>
<p>
A typical pattern seen in population models is that the population grows faster and faster. <a href="http://www.zo.utexas.edu/courses/Thoc/PopGrowth.html" target="_blank">Why? Is there an underlying (general) mechanism</a>?
@@ -764,7 +769,7 @@ plt<span style="color: #666666">.</span>show()
We can then change the parameter \( r \) in the program and play around to make a better fit. By now we know that this
'search bythe eye' approach is not the most optimal one.
<h3 id="___sec9">Predator-Prey model from ecology </h3>
<h3 id="___sec10">Predator-Prey model from ecology </h3>
<p>
The population dynamics of a simple predator-prey system is a
@@ -1060,7 +1065,7 @@ confint(linearMod)
predict(linearMod,<span style="color: #B00040">data.frame</span>(Year<span style="color: #666666">=</span><span style="color: #B00040">c</span>(<span style="color: #666666">1910</span>,<span style="color: #666666">1914</span>,<span style="color: #666666">1920</span>)),interval<span style="color: #666666">=</span><span style="color: #BA2121">&quot;confidence&quot;</span>)
</pre></div>
<h3 id="___sec10">Simulating financial transactions </h3>
<h3 id="___sec11">Simulating financial transactions </h3>
<p>
The aim here is to simulate financial transactions among financial agents
@@ -1235,7 +1240,7 @@ $$
equilibrium distributions and compare these with the Gibbs distribution. We will use this model to
extract a parametrization of the above curves, see for example <a href="http://www.sciencedirect.com/science/article/pii/S0378437104004327" target="_blank">Patriarca and collaborators</a>.
<h3 id="___sec11">Particle in one dimension and velocity distribution </h3>
<h3 id="___sec12">Particle in one dimension and velocity distribution </h3>
<p>
@@ -1285,7 +1290,7 @@ plt<span style="color: #666666">.</span>grid(<span style="color: #008000">True</
plt<span style="color: #666666">.</span>show()
</pre></div>
<h3 id="___sec12">Random walk model </h3>
<h3 id="___sec13">Random walk model </h3>
<p>
@@ -1385,7 +1390,7 @@ plt<span style="color: #666666">.</span>show()
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
<!-- copyright --> &copy; 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
</center>