added datafiles
This commit is contained in:
Morten Hjorth-Jensen
@@ -0,0 +1,90 @@
|
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3.3773726001100143E-005, 3.1715032621225665E-002
|
||||
2.7018980800880114E-004, 0.25379346864959029
|
||||
9.1189060202970370E-004, 0.85691856357595775
|
||||
2.1615184640704091E-003, 2.0322700794292126
|
||||
4.2217157501375172E-003, 3.9716946611295496
|
||||
7.2951248162376296E-003, 6.8678138410008760
|
||||
1.1584388018377344E-002, 10.913973030767592
|
||||
1.7292147712563273E-002, 16.304871533080519
|
||||
2.4621046254802003E-002, 23.235226466525493
|
||||
3.3773726001100138E-002, 31.902701432416372
|
||||
4.4952829307464297E-002, 42.504284679798289
|
||||
5.8360998529901037E-002, 55.243642496340065
|
||||
7.4200876024417023E-002, 70.325036648868448
|
||||
9.2675104147018753E-002, 87.967299710326188
|
||||
0.11398632525371297, 108.39264632432710
|
||||
0.13833718170050618 , 131.84282952216495
|
||||
0.16593031584340495 , 158.57124952965228
|
||||
0.19696837003841602 , 188.82820513039681
|
||||
0.203199995458126059, 195.042764094891368
|
||||
0.211199995279312130, 202.937172130123315
|
||||
0.219199995100498229, 210.852580165355278
|
||||
0.227199994921684245, 218.789988200587175
|
||||
0.235199994742870316, 226.748396235819115
|
||||
0.243199994564056388, 234.740804271051104
|
||||
0.251199994385242487, 242.757212306283037
|
||||
0.259199994206428530, 250.797620341515000
|
||||
0.267199994027614574, 258.874028376746878
|
||||
0.275199993848800673, 266.977436411978829
|
||||
0.283199993669986716, 275.092844447210780
|
||||
0.291199993491172815, 283.248252482442751
|
||||
0.299199993312358858, 291.445660517674696
|
||||
0.307199993133544902, 299.655068552906641
|
||||
0.315199992954731001, 307.909476588138546
|
||||
0.323199992775917044, 316.192884623370503
|
||||
0.339199992418289187, 332.846700693834407
|
||||
0.355199992060661329, 349.620516764298316
|
||||
0.371199991703033416, 366.537332834762140
|
||||
0.387199991345405559, 383.604148905225998
|
||||
0.403199990987777701, 400.801964975689941
|
||||
0.419199990630149844, 418.158781046153820
|
||||
0.435199990272521986, 435.624597116617792
|
||||
0.451199989914894073, 453.288413187081574
|
||||
0.467199989557266215, 471.062229257545425
|
||||
0.483199989199638358, 489.048045328009380
|
||||
0.499199988842010500, 507.146861398473277
|
||||
0.515199988484382643, 525.392677468937222
|
||||
0.531199988126754730, 543.829493539401028
|
||||
0.531999988108873390, 544.796634342924222
|
||||
0.550079987704753859, 565.860416502548446
|
||||
0.567999987304210641, 586.865970501467928
|
||||
0.585599986910820047, 607.703068178978242
|
||||
0.603199986517429343, 628.645165856488575
|
||||
0.620639986127614951, 649.578035373294142
|
||||
0.637919985741376872, 670.496676729395176
|
||||
0.655199985355138792, 691.549318085496111
|
||||
0.672479984968900713, 712.766959441597237
|
||||
0.689599984586238834, 733.981372636993456
|
||||
0.706879984200000755, 755.521013993094471
|
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0.724159983813762675, 777.228655349195492
|
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0.741439983427524596, 799.117296705296553
|
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0.758719983041286516, 821.191938061397536
|
||||
0.775999982655048326, 843.460579417498366
|
||||
0.793439982265233934, 866.080448934304059
|
||||
0.810879981875419542, 888.907318451109631
|
||||
0.828479981482028949, 912.100416128620054
|
||||
0.846239981085061932, 935.664741966834868
|
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0.864159980684518825, 959.607295965754474
|
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0.882079980283975607, 983.787849964674024
|
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0.900159979879856187, 1008.36063212429838
|
||||
0.918399979472160344, 1033.33564244462718
|
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0.936639979064464612, 1058.56865276495591
|
||||
0.955199978649616255, 1084.36811940669395
|
||||
0.973919978231191585, 1110.59381420913678
|
||||
0.992799977809190715, 1137.25073717228429
|
||||
1.01183997738361353, 1164.35288829613614
|
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1.06047997629642499, 1234.41724915034661
|
||||
1.11039997518062594, 1307.72443529019392
|
||||
1.16191997402906422, 1384.74890303708753
|
||||
1.21535997283458719, 1466.00110871243692
|
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1.27071997159719463, 1551.73305231624181
|
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1.32847997030615828, 1642.68741833061654
|
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1.38895996895432461, 1739.56266307697001
|
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1.45263996753096580, 1843.32947103741640
|
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1.52031996601820008, 1955.19398301547858
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1.59295996439456933, 2077.14256797538474
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1.67167996263504026, 2211.44382304206692
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1.75855996069312082, 2361.74471430468566
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1.85647995850443825, 2533.64534865592441
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1.96959995597600934, 2734.93465827410455
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2.10543995293974895, 2980.03336671234320
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|
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,67 @@
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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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@@ -0,0 +1,76 @@
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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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File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,16 @@
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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')
|
||||
plt.axis([0,1000,-0.2, 1.1])
|
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plt.xlabel(r'$d$')
|
||||
plt.ylabel(r'$C_d$')
|
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plt.title(r'autocorrelation function for RNG')
|
||||
plt.savefig('autocorr.pdf')
|
||||
plt.show()
|
||||
@@ -0,0 +1,90 @@
|
||||
3.3773726001100143E-005, 3.1715032621225665E-002
|
||||
2.7018980800880114E-004, 0.25379346864959029
|
||||
9.1189060202970370E-004, 0.85691856357595775
|
||||
2.1615184640704091E-003, 2.0322700794292126
|
||||
4.2217157501375172E-003, 3.9716946611295496
|
||||
7.2951248162376296E-003, 6.8678138410008760
|
||||
1.1584388018377344E-002, 10.913973030767592
|
||||
1.7292147712563273E-002, 16.304871533080519
|
||||
2.4621046254802003E-002, 23.235226466525493
|
||||
3.3773726001100138E-002, 31.902701432416372
|
||||
4.4952829307464297E-002, 42.504284679798289
|
||||
5.8360998529901037E-002, 55.243642496340065
|
||||
7.4200876024417023E-002, 70.325036648868448
|
||||
9.2675104147018753E-002, 87.967299710326188
|
||||
0.11398632525371297, 108.39264632432710
|
||||
0.13833718170050618 , 131.84282952216495
|
||||
0.16593031584340495 , 158.57124952965228
|
||||
0.19696837003841602 , 188.82820513039681
|
||||
0.203199995458126059, 195.042764094891368
|
||||
0.211199995279312130, 202.937172130123315
|
||||
0.219199995100498229, 210.852580165355278
|
||||
0.227199994921684245, 218.789988200587175
|
||||
0.235199994742870316, 226.748396235819115
|
||||
0.243199994564056388, 234.740804271051104
|
||||
0.251199994385242487, 242.757212306283037
|
||||
0.259199994206428530, 250.797620341515000
|
||||
0.267199994027614574, 258.874028376746878
|
||||
0.275199993848800673, 266.977436411978829
|
||||
0.283199993669986716, 275.092844447210780
|
||||
0.291199993491172815, 283.248252482442751
|
||||
0.299199993312358858, 291.445660517674696
|
||||
0.307199993133544902, 299.655068552906641
|
||||
0.315199992954731001, 307.909476588138546
|
||||
0.323199992775917044, 316.192884623370503
|
||||
0.339199992418289187, 332.846700693834407
|
||||
0.355199992060661329, 349.620516764298316
|
||||
0.371199991703033416, 366.537332834762140
|
||||
0.387199991345405559, 383.604148905225998
|
||||
0.403199990987777701, 400.801964975689941
|
||||
0.419199990630149844, 418.158781046153820
|
||||
0.435199990272521986, 435.624597116617792
|
||||
0.451199989914894073, 453.288413187081574
|
||||
0.467199989557266215, 471.062229257545425
|
||||
0.483199989199638358, 489.048045328009380
|
||||
0.499199988842010500, 507.146861398473277
|
||||
0.515199988484382643, 525.392677468937222
|
||||
0.531199988126754730, 543.829493539401028
|
||||
0.531999988108873390, 544.796634342924222
|
||||
0.550079987704753859, 565.860416502548446
|
||||
0.567999987304210641, 586.865970501467928
|
||||
0.585599986910820047, 607.703068178978242
|
||||
0.603199986517429343, 628.645165856488575
|
||||
0.620639986127614951, 649.578035373294142
|
||||
0.637919985741376872, 670.496676729395176
|
||||
0.655199985355138792, 691.549318085496111
|
||||
0.672479984968900713, 712.766959441597237
|
||||
0.689599984586238834, 733.981372636993456
|
||||
0.706879984200000755, 755.521013993094471
|
||||
0.724159983813762675, 777.228655349195492
|
||||
0.741439983427524596, 799.117296705296553
|
||||
0.758719983041286516, 821.191938061397536
|
||||
0.775999982655048326, 843.460579417498366
|
||||
0.793439982265233934, 866.080448934304059
|
||||
0.810879981875419542, 888.907318451109631
|
||||
0.828479981482028949, 912.100416128620054
|
||||
0.846239981085061932, 935.664741966834868
|
||||
0.864159980684518825, 959.607295965754474
|
||||
0.882079980283975607, 983.787849964674024
|
||||
0.900159979879856187, 1008.36063212429838
|
||||
0.918399979472160344, 1033.33564244462718
|
||||
0.936639979064464612, 1058.56865276495591
|
||||
0.955199978649616255, 1084.36811940669395
|
||||
0.973919978231191585, 1110.59381420913678
|
||||
0.992799977809190715, 1137.25073717228429
|
||||
1.01183997738361353, 1164.35288829613614
|
||||
1.06047997629642499, 1234.41724915034661
|
||||
1.11039997518062594, 1307.72443529019392
|
||||
1.16191997402906422, 1384.74890303708753
|
||||
1.21535997283458719, 1466.00110871243692
|
||||
1.27071997159719463, 1551.73305231624181
|
||||
1.32847997030615828, 1642.68741833061654
|
||||
1.38895996895432461, 1739.56266307697001
|
||||
1.45263996753096580, 1843.32947103741640
|
||||
1.52031996601820008, 1955.19398301547858
|
||||
1.59295996439456933, 2077.14256797538474
|
||||
1.67167996263504026, 2211.44382304206692
|
||||
1.75855996069312082, 2361.74471430468566
|
||||
1.85647995850443825, 2533.64534865592441
|
||||
1.96959995597600934, 2734.93465827410455
|
||||
2.10543995293974895, 2980.03336671234320
|
||||
|
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,67 @@
|
||||
// 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
|
||||
@@ -0,0 +1,76 @@
|
||||
// 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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,16 @@
|
||||
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()
|
||||
@@ -0,0 +1,90 @@
|
||||
3.3773726001100143E-005, 3.1715032621225665E-002
|
||||
2.7018980800880114E-004, 0.25379346864959029
|
||||
9.1189060202970370E-004, 0.85691856357595775
|
||||
2.1615184640704091E-003, 2.0322700794292126
|
||||
4.2217157501375172E-003, 3.9716946611295496
|
||||
7.2951248162376296E-003, 6.8678138410008760
|
||||
1.1584388018377344E-002, 10.913973030767592
|
||||
1.7292147712563273E-002, 16.304871533080519
|
||||
2.4621046254802003E-002, 23.235226466525493
|
||||
3.3773726001100138E-002, 31.902701432416372
|
||||
4.4952829307464297E-002, 42.504284679798289
|
||||
5.8360998529901037E-002, 55.243642496340065
|
||||
7.4200876024417023E-002, 70.325036648868448
|
||||
9.2675104147018753E-002, 87.967299710326188
|
||||
0.11398632525371297, 108.39264632432710
|
||||
0.13833718170050618 , 131.84282952216495
|
||||
0.16593031584340495 , 158.57124952965228
|
||||
0.19696837003841602 , 188.82820513039681
|
||||
0.203199995458126059, 195.042764094891368
|
||||
0.211199995279312130, 202.937172130123315
|
||||
0.219199995100498229, 210.852580165355278
|
||||
0.227199994921684245, 218.789988200587175
|
||||
0.235199994742870316, 226.748396235819115
|
||||
0.243199994564056388, 234.740804271051104
|
||||
0.251199994385242487, 242.757212306283037
|
||||
0.259199994206428530, 250.797620341515000
|
||||
0.267199994027614574, 258.874028376746878
|
||||
0.275199993848800673, 266.977436411978829
|
||||
0.283199993669986716, 275.092844447210780
|
||||
0.291199993491172815, 283.248252482442751
|
||||
0.299199993312358858, 291.445660517674696
|
||||
0.307199993133544902, 299.655068552906641
|
||||
0.315199992954731001, 307.909476588138546
|
||||
0.323199992775917044, 316.192884623370503
|
||||
0.339199992418289187, 332.846700693834407
|
||||
0.355199992060661329, 349.620516764298316
|
||||
0.371199991703033416, 366.537332834762140
|
||||
0.387199991345405559, 383.604148905225998
|
||||
0.403199990987777701, 400.801964975689941
|
||||
0.419199990630149844, 418.158781046153820
|
||||
0.435199990272521986, 435.624597116617792
|
||||
0.451199989914894073, 453.288413187081574
|
||||
0.467199989557266215, 471.062229257545425
|
||||
0.483199989199638358, 489.048045328009380
|
||||
0.499199988842010500, 507.146861398473277
|
||||
0.515199988484382643, 525.392677468937222
|
||||
0.531199988126754730, 543.829493539401028
|
||||
0.531999988108873390, 544.796634342924222
|
||||
0.550079987704753859, 565.860416502548446
|
||||
0.567999987304210641, 586.865970501467928
|
||||
0.585599986910820047, 607.703068178978242
|
||||
0.603199986517429343, 628.645165856488575
|
||||
0.620639986127614951, 649.578035373294142
|
||||
0.637919985741376872, 670.496676729395176
|
||||
0.655199985355138792, 691.549318085496111
|
||||
0.672479984968900713, 712.766959441597237
|
||||
0.689599984586238834, 733.981372636993456
|
||||
0.706879984200000755, 755.521013993094471
|
||||
0.724159983813762675, 777.228655349195492
|
||||
0.741439983427524596, 799.117296705296553
|
||||
0.758719983041286516, 821.191938061397536
|
||||
0.775999982655048326, 843.460579417498366
|
||||
0.793439982265233934, 866.080448934304059
|
||||
0.810879981875419542, 888.907318451109631
|
||||
0.828479981482028949, 912.100416128620054
|
||||
0.846239981085061932, 935.664741966834868
|
||||
0.864159980684518825, 959.607295965754474
|
||||
0.882079980283975607, 983.787849964674024
|
||||
0.900159979879856187, 1008.36063212429838
|
||||
0.918399979472160344, 1033.33564244462718
|
||||
0.936639979064464612, 1058.56865276495591
|
||||
0.955199978649616255, 1084.36811940669395
|
||||
0.973919978231191585, 1110.59381420913678
|
||||
0.992799977809190715, 1137.25073717228429
|
||||
1.01183997738361353, 1164.35288829613614
|
||||
1.06047997629642499, 1234.41724915034661
|
||||
1.11039997518062594, 1307.72443529019392
|
||||
1.16191997402906422, 1384.74890303708753
|
||||
1.21535997283458719, 1466.00110871243692
|
||||
1.27071997159719463, 1551.73305231624181
|
||||
1.32847997030615828, 1642.68741833061654
|
||||
1.38895996895432461, 1739.56266307697001
|
||||
1.45263996753096580, 1843.32947103741640
|
||||
1.52031996601820008, 1955.19398301547858
|
||||
1.59295996439456933, 2077.14256797538474
|
||||
1.67167996263504026, 2211.44382304206692
|
||||
1.75855996069312082, 2361.74471430468566
|
||||
1.85647995850443825, 2533.64534865592441
|
||||
1.96959995597600934, 2734.93465827410455
|
||||
2.10543995293974895, 2980.03336671234320
|
||||
|
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,67 @@
|
||||
// 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
|
||||
@@ -0,0 +1,76 @@
|
||||
// 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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,16 @@
|
||||
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()
|
||||
@@ -0,0 +1,90 @@
|
||||
3.3773726001100143E-005, 3.1715032621225665E-002
|
||||
2.7018980800880114E-004, 0.25379346864959029
|
||||
9.1189060202970370E-004, 0.85691856357595775
|
||||
2.1615184640704091E-003, 2.0322700794292126
|
||||
4.2217157501375172E-003, 3.9716946611295496
|
||||
7.2951248162376296E-003, 6.8678138410008760
|
||||
1.1584388018377344E-002, 10.913973030767592
|
||||
1.7292147712563273E-002, 16.304871533080519
|
||||
2.4621046254802003E-002, 23.235226466525493
|
||||
3.3773726001100138E-002, 31.902701432416372
|
||||
4.4952829307464297E-002, 42.504284679798289
|
||||
5.8360998529901037E-002, 55.243642496340065
|
||||
7.4200876024417023E-002, 70.325036648868448
|
||||
9.2675104147018753E-002, 87.967299710326188
|
||||
0.11398632525371297, 108.39264632432710
|
||||
0.13833718170050618 , 131.84282952216495
|
||||
0.16593031584340495 , 158.57124952965228
|
||||
0.19696837003841602 , 188.82820513039681
|
||||
0.203199995458126059, 195.042764094891368
|
||||
0.211199995279312130, 202.937172130123315
|
||||
0.219199995100498229, 210.852580165355278
|
||||
0.227199994921684245, 218.789988200587175
|
||||
0.235199994742870316, 226.748396235819115
|
||||
0.243199994564056388, 234.740804271051104
|
||||
0.251199994385242487, 242.757212306283037
|
||||
0.259199994206428530, 250.797620341515000
|
||||
0.267199994027614574, 258.874028376746878
|
||||
0.275199993848800673, 266.977436411978829
|
||||
0.283199993669986716, 275.092844447210780
|
||||
0.291199993491172815, 283.248252482442751
|
||||
0.299199993312358858, 291.445660517674696
|
||||
0.307199993133544902, 299.655068552906641
|
||||
0.315199992954731001, 307.909476588138546
|
||||
0.323199992775917044, 316.192884623370503
|
||||
0.339199992418289187, 332.846700693834407
|
||||
0.355199992060661329, 349.620516764298316
|
||||
0.371199991703033416, 366.537332834762140
|
||||
0.387199991345405559, 383.604148905225998
|
||||
0.403199990987777701, 400.801964975689941
|
||||
0.419199990630149844, 418.158781046153820
|
||||
0.435199990272521986, 435.624597116617792
|
||||
0.451199989914894073, 453.288413187081574
|
||||
0.467199989557266215, 471.062229257545425
|
||||
0.483199989199638358, 489.048045328009380
|
||||
0.499199988842010500, 507.146861398473277
|
||||
0.515199988484382643, 525.392677468937222
|
||||
0.531199988126754730, 543.829493539401028
|
||||
0.531999988108873390, 544.796634342924222
|
||||
0.550079987704753859, 565.860416502548446
|
||||
0.567999987304210641, 586.865970501467928
|
||||
0.585599986910820047, 607.703068178978242
|
||||
0.603199986517429343, 628.645165856488575
|
||||
0.620639986127614951, 649.578035373294142
|
||||
0.637919985741376872, 670.496676729395176
|
||||
0.655199985355138792, 691.549318085496111
|
||||
0.672479984968900713, 712.766959441597237
|
||||
0.689599984586238834, 733.981372636993456
|
||||
0.706879984200000755, 755.521013993094471
|
||||
0.724159983813762675, 777.228655349195492
|
||||
0.741439983427524596, 799.117296705296553
|
||||
0.758719983041286516, 821.191938061397536
|
||||
0.775999982655048326, 843.460579417498366
|
||||
0.793439982265233934, 866.080448934304059
|
||||
0.810879981875419542, 888.907318451109631
|
||||
0.828479981482028949, 912.100416128620054
|
||||
0.846239981085061932, 935.664741966834868
|
||||
0.864159980684518825, 959.607295965754474
|
||||
0.882079980283975607, 983.787849964674024
|
||||
0.900159979879856187, 1008.36063212429838
|
||||
0.918399979472160344, 1033.33564244462718
|
||||
0.936639979064464612, 1058.56865276495591
|
||||
0.955199978649616255, 1084.36811940669395
|
||||
0.973919978231191585, 1110.59381420913678
|
||||
0.992799977809190715, 1137.25073717228429
|
||||
1.01183997738361353, 1164.35288829613614
|
||||
1.06047997629642499, 1234.41724915034661
|
||||
1.11039997518062594, 1307.72443529019392
|
||||
1.16191997402906422, 1384.74890303708753
|
||||
1.21535997283458719, 1466.00110871243692
|
||||
1.27071997159719463, 1551.73305231624181
|
||||
1.32847997030615828, 1642.68741833061654
|
||||
1.38895996895432461, 1739.56266307697001
|
||||
1.45263996753096580, 1843.32947103741640
|
||||
1.52031996601820008, 1955.19398301547858
|
||||
1.59295996439456933, 2077.14256797538474
|
||||
1.67167996263504026, 2211.44382304206692
|
||||
1.75855996069312082, 2361.74471430468566
|
||||
1.85647995850443825, 2533.64534865592441
|
||||
1.96959995597600934, 2734.93465827410455
|
||||
2.10543995293974895, 2980.03336671234320
|
||||
|
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,67 @@
|
||||
// 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
|
||||
@@ -0,0 +1,76 @@
|
||||
// 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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,16 @@
|
||||
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()
|
||||
@@ -13,6 +13,7 @@ DATE: today
|
||||
!split
|
||||
===== Thursday September 17 =====
|
||||
|
||||
"Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember17.mp4?vrtx=view-as-webpage" and "link to handwritten notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember17.pdf".
|
||||
|
||||
!split
|
||||
===== Ridge and LASSO Regression, reminder =====
|
||||
@@ -919,6 +920,9 @@ other models for all values of $\lambda$.
|
||||
!split
|
||||
===== Friday September 18: Intro to Logistic Regression =====
|
||||
|
||||
"Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureSeptember18.mp4?vrtx=view-as-webpage" and "link to handwritten notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesSeptember18.pdf".
|
||||
|
||||
|
||||
|
||||
!split
|
||||
===== Logistic Regression =====
|
||||
|
||||
Reference in New Issue
Block a user