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