https://github.com/root-project/root
Tip revision: a48bb7fe1e147de1fad7e8775a664518df516f81 authored by Unknown Author on 29 May 2006, 10:03:03 UTC
This commit was manufactured by cvs2svn to create tag 'v5-10-00e'.
This commit was manufactured by cvs2svn to create tag 'v5-10-00e'.
Tip revision: a48bb7f
principal.C
#include "TPrincipal.h"
#include "iomanip.h"
void principal(Int_t n=10, Int_t m=10000)
{
//
// Principal Components Analysis (PCA) example
//
// Example of using TPrincipal as a stand alone class.
//
// We create n-dimensional data points, where c = trunc(n / 5) + 1
// are correlated with the rest n - c randomly distributed variables.
//
// Here's the plot of the eigenvalues Begin_Html
// <IMG SRC="gif/principal_eigen.gif">
// End_Html
Int_t c = n / 5 + 1;
cout << "*************************************************" << endl;
cout << "* Principal Component Analysis *" << endl;
cout << "* *" << endl;
cout << "* Number of variables: " << setw(4) << n
<< " *" << endl;
cout << "* Number of data points: " << setw(8) << m
<< " *" << endl;
cout << "* Number of dependent variables: " << setw(4) << c
<< " *" << endl;
cout << "* *" << endl;
cout << "*************************************************" << endl;
// Initilase the TPrincipal object. Use the empty string for the
// final argument, if you don't wan't the covariance
// matrix. Normalising the covariance matrix is a good idea if your
// variables have different orders of magnitude.
TPrincipal* principal = new TPrincipal(n,"N");
// Use a pseudo-random number generator
TRandom* random = new TRandom;
// Make the m data-points
// Make a variable to hold our data
// Allocate memory for the data point
Double_t* data = new Double_t[n];
for (Int_t i = 0; i < m; i++) {
// First we create the un-correlated, random variables, according
// to one of three distributions
for (Int_t j = 0; j < n - c; j++) {
if (j % 3 == 0)
data[j] = random->Gaus(5,1);
else if (j % 3 == 1)
data[j] = random->Poisson(8);
else
data[j] = random->Exp(2);
}
// Then we create the correlated variables
for (Int_t j = 0 ; j < c; j++) {
data[n - c + j] = 0;
for (Int_t k = 0; k < n - c - j; k++)
data[n - c + j] += data[k];
}
// Finally we're ready to add this datapoint to the PCA
principal->AddRow(data);
}
// We delete the data after use, since TPrincipal got it by now.
delete [] data;
// Do the actual analysis
principal->MakePrincipals();
// Print out the result on
principal->Print();
// Test the PCA
principal->Test();
// Make some histograms of the orginal, principal, residue, etc data
principal->MakeHistograms();
// Make two functions to map between feature and pattern space
principal->MakeCode();
// Start a browser, so that we may browse the histograms generated
// above
TBrowser* b = new TBrowser("principalBrowser", principal);
}