setting up first machine learning repo

This commit is contained in:
Morten Hjorth-Jensen
2017-09-18 22:16:45 +02:00
parent 075470eef5
commit 1b886d7e4b
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Executable
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# compiled files:
*.o
*.so
*.a
# temporary files:
*.pyc
*.bak
*.swp
*~
.*~
*.old
tmp*
temp*
.#*
\#*
# tex files:
*.log
*.dvi
*.aux
*.blg
*.idx
*.nav
*.out
*.toc
*.snm
*.vrb
# eclipse files:
*.cproject
*.project
# misc:
.DS_Store
Trash
# doconce:
.*_html_file_collection
*.copyright
*.copyright
*.exerinfo
doc/Projects/2016/Project2/.out.copyright
doc/Projects/2016/Project2/.project2_2016.copyright
*.exerinfo
*.dlog
*.user
doc/Programs/ParallelizationMPI/dill10
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#!/bin/sh
doconce clean
rm -rf *.pdf *.tex ipynb*.tar.gz *.html ._*.html *~ reveal.js Trash README.txt
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// This function computes the autocorrelation function for
// the standard c++ random number generator
#include <fstream>
#include <iomanip>
#include <iostream>
#include <cmath>
using namespace std;
// output file as global variable
ofstream ofile;
// Main function begins here
int main(int argc, char* argv[])
{
int n;
char *outfilename;
cin >> n;
double MCint = 0.; double MCintsqr2=0.;
double invers_period = 1./RAND_MAX; // initialise the random number generator
srand(time(NULL)); // This produces the so-called seed in MC jargon
// Compute the variance and the mean value of the uniform distribution
// Compute also the specific values x for each cycle in order to be able to
// the covariance and the correlation function
// Read in output file, abort if there are too few command-line arguments
if( argc <= 2 ){
cout << "Bad Usage: " << argv[0] <<
" read also output file and number of cycles on same line" << endl;
exit(1);
}
else{
outfilename=argv[1];
}
ofile.open(outfilename);
// Get the number of Monte-Carlo samples
n = atoi(argv[2]);
double *X;
X = new double[n];
for (int i = 0; i < n; i++){
double x = double(rand())*invers_period;
X[i] = x;
MCint += x;
MCintsqr2 += x*x;
}
double Mean = MCint/((double) n );
MCintsqr2 = MCintsqr2/((double) n );
double STDev = sqrt(MCintsqr2-Mean*Mean);
double Variance = MCintsqr2-Mean*Mean;
// Write mean value and standard deviation
cout << " Standard deviation= " << STDev << " Integral = " << Mean << endl;
// Now we compute the autocorrelation function, setting the distance d between two
// to a most 1/4 of the total number of cycles
double *autocor; autocor = new double[n];
for (int j = 0; j < n; j++){
double sum = 0.0;
for (int k = 0; k < (n-j); k++){
sum += (X[k]-Mean)*(X[k+j]-Mean);
}
autocor[j] = sum/Variance/((double) n );
ofile << setiosflags(ios::showpoint | ios::uppercase);
ofile << setw(15) << setprecision(8) << j;
ofile << setw(15) << setprecision(8) << autocor[j] << endl;
}
ofile.close(); // close output file
return 0;
} // end of main program
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// This function computes the autocorrelation function for
// the standard c++ random number generator
#include <fstream>
#include <iomanip>
#include <iostream>
#include <cmath>
#include <random>
using namespace std;
// output file as global variable
ofstream ofile;
// Main function begins here
int main(int argc, char* argv[])
{
int n;
char *outfilename;
cin >> n;
double MCint = 0.; double MCintsqr2=0.;
// Initialize the seed and call the Mersienne algo
std::random_device rd;
std::mt19937_64 gen(rd());
// Set up the uniform distribution for x \in [[0, 1]
std::uniform_real_distribution<double> RandomNumberGenerator(0.0,1.0);
// Compute the variance and the mean value of the uniform distribution
// Compute also the specific values x for each cycle in order to be able to
// the covariance and the correlation function
// Read in output file, abort if there are too few command-line arguments
if( argc <= 2 ){
cout << "Bad Usage: " << argv[0] <<
" read also output file and number of cycles on same line" << endl;
exit(1);
}
else{
outfilename=argv[1];
}
ofile.open(outfilename);
// Get the number of Monte-Carlo samples
n = atoi(argv[2]);
double *X;
X = new double[n];
for (int i = 0; i < n; i++){
double x = RandomNumberGenerator(gen);
X[i] = x;
MCint += x;
MCintsqr2 += x*x;
}
double Mean = MCint/((double) n );
MCintsqr2 = MCintsqr2/((double) n );
double STDev = sqrt(MCintsqr2-Mean*Mean);
double Variance = MCintsqr2-Mean*Mean;
// Write mean value and standard deviation
cout << " Standard deviation= " << STDev << " Integral = " << Mean << endl;
// Now we compute the autocorrelation function, setting the distance d
double *autocor; autocor = new double[n];
for (int j = 0; j < n; j++){
double sum = 0.0;
for (int k = 0; k < (n-j); k++){
sum += (X[k]-Mean)*(X[k+j]-Mean);
}
autocor[j] = sum/Variance/((double) n );
ofile << setiosflags(ios::showpoint | ios::uppercase);
ofile << setw(15) << setprecision(8) << j;
ofile << setw(15) << setprecision(8) << autocor[j] << endl;
}
ofile.close(); // close output file
return 0;
} // end of main program
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import numpy as np
from matplotlib import pyplot as plt
# Load in data file
data = np.loadtxt("autocor.dat")
data1 = np.loadtxt("automersenne.dat")
# Make arrays containing x-axis and binding energies as function of A
x = data[:,0]
corr = data[:,1]
corr2 = data1[:,1]
plt.plot(x, corr ,'ro', x, corr2, 'b')
plt.axis([0,1000,-0.2, 1.1])
plt.xlabel(r'$d$')
plt.ylabel(r'$C_d$')
plt.title(r'autocorrelation function for RNG')
plt.savefig('autocorr.pdf')
plt.show()
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#!/bin/sh
set -x
function system {
"$@"
if [ $? -ne 0 ]; then
echo "make.sh: unsuccessful command $@"
echo "abort!"
exit 1
fi
}
if [ $# -eq 0 ]; then
echo 'bash make.sh slides1|slides2'
exit 1
fi
name=$1
rm -f *.tar.gz
opt="--encoding=utf-8"
# Note: Makefile examples contain constructions like ${PROG} which
# looks like Mako constructions, but they are not. Use --no_mako
# to turn off Mako processing.
opt="--no_mako"
rm -f *.aux
html=${name}-reveal
system doconce format html $name --pygments_html_style=perldoc --keep_pygments_html_bg --html_links_in_new_window --html_output=$html $opt
system doconce slides_html $html reveal --html_slide_theme=beige
# Plain HTML documents
html=${name}-solarized
system doconce format html $name --pygments_html_style=perldoc --html_style=solarized3 --html_links_in_new_window --html_output=$html $opt
system doconce split_html $html.html --method=space10
html=${name}
system doconce format html $name --pygments_html_style=default --html_style=bloodish --html_links_in_new_window --html_output=$html $opt
system doconce split_html $html.html --method=space10
# Bootstrap style
html=${name}-bs
system doconce format html $name --html_style=bootstrap --pygments_html_style=default --html_admon=bootstrap_panel --html_output=$html $opt
system doconce split_html $html.html --method=split --pagination --nav_button=bottom
# IPython notebook
system doconce format ipynb $name $opt
# LaTeX Beamer slides
beamertheme=red_plain
system doconce format pdflatex $name --latex_title_layout=beamer --latex_table_format=footnotesize $opt
system doconce ptex2tex $name envir=minted
# Add special packages
doconce subst "% Add user's preamble" "\g<1>\n\\usepackage{simplewick}" $name.tex
system doconce slides_beamer $name --beamer_slide_theme=$beamertheme
system pdflatex -shell-escape ${name}
system pdflatex -shell-escape ${name}
cp $name.pdf ${name}-beamer.pdf
cp $name.tex ${name}-beamer.tex
# Handouts
system doconce format pdflatex $name --latex_title_layout=beamer --latex_table_format=footnotesize $opt
system doconce ptex2tex $name envir=minted
# Add special packages
doconce subst "% Add user's preamble" "\g<1>\n\\usepackage{simplewick}" $name.tex
system doconce slides_beamer $name --beamer_slide_theme=red_shadow --handout
system pdflatex -shell-escape $name
pdflatex -shell-escape $name
pdflatex -shell-escape $name
pdfnup --nup 2x3 --frame true --delta "1cm 1cm" --scale 0.9 --outfile ${name}-beamer-handouts2x3.pdf ${name}.pdf
rm -f ${name}.pdf
# Ordinary plain LaTeX document
rm -f *.aux # important after beamer
system doconce format pdflatex $name --minted_latex_style=trac --latex_admon=paragraph $opt
system doconce ptex2tex $name envir=minted
# Add special packages
doconce subst "% Add user's preamble" "\g<1>\n\\usepackage{simplewick}" $name.tex
doconce replace 'section{' 'section*{' $name.tex
pdflatex -shell-escape $name
pdflatex -shell-escape $name
mv -f $name.pdf ${name}-minted.pdf
cp $name.tex ${name}-plain-minted.tex
# Publish
dest=../../pub
if [ ! -d $dest/$name ]; then
mkdir $dest/$name
mkdir $dest/$name/pdf
mkdir $dest/$name/html
mkdir $dest/$name/ipynb
fi
cp ${name}*.pdf $dest/$name/pdf
cp -r ${name}*.html ._${name}*.html reveal.js $dest/$name/html
# Figures: cannot just copy link, need to physically copy the files
if [ -d fig-${name} ]; then
if [ ! -d $dest/$name/html/fig-$name ]; then
mkdir $dest/$name/html/fig-$name
fi
cp -r fig-${name}/* $dest/$name/html/fig-$name
fi
cp ${name}.ipynb $dest/$name/ipynb
ipynb_tarfile=ipynb-${name}-src.tar.gz
if [ ! -f ${ipynb_tarfile} ]; then
cat > README.txt <<EOF
This IPython notebook ${name}.ipynb does not require any additional
programs.
EOF
tar czf ${ipynb_tarfile} README.txt
fi
cp ${ipynb_tarfile} $dest/$name/ipynb