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.
0.0719690322876
6.30275511742
Processing /mnt/vdb/lsf/workspace/root-makedoc-v608/rootspi/rdoc/src/v6-08-00-patches/tutorials/math/principal.C...
*************************************************
* Principal Component Analysis *
* *
* Number of variables: 10 *
* Number of data points: 10000 *
* Number of dependent variables: 3 *
* *
*************************************************
Variable # | Mean Value | Sigma | Eigenvalue
-------------+------------+------------+------------
0 | 5.008 | 1.005 | 0.3851
1 | 7.998 | 2.861 | 0.1107
2 | 1.967 | 1.956 | 0.1036
3 | 5.016 | 1.005 | 0.1015
4 | 8.009 | 2.839 | 0.1008
5 | 2.013 | 1.973 | 0.09962
6 | 4.992 | 1.014 | 0.09864
7 | 35 | 5.156 | 5.979e-16
8 | 30.01 | 5.049 | 2.525e-16
9 | 28 | 4.649 | 5.658e-16
Writing on file "pca.C" ... done
{
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;
for (
Int_t i = 0; i <
m; i++) {
for (
Int_t j = 0; j < n -
c; j++) {
if (j % 3 == 0) data[j] = randumNum->
Gaus(5,1);
else if (j % 3 == 1) data[j] = randumNum->
Poisson(8);
else data[j] = randumNum->
Exp(2);
}
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];
}
}
delete [] data;
}
- Authors
- Rene Brun, Christian Holm Christensen
Definition in file principal.C.