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add queen mary DSP library
git-svn-id: svn://localhost/ardour2/branches/3.0@9029 d708f5d6-7413-0410-9779-e7cbd77b26cf
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Paul Davis
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356
libs/qm-dsp/maths/pca/pca.c
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356
libs/qm-dsp/maths/pca/pca.c
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/*********************************/
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/* Principal Components Analysis */
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/*********************************/
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/*********************************************************************/
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/* Principal Components Analysis or the Karhunen-Loeve expansion is a
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classical method for dimensionality reduction or exploratory data
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analysis. One reference among many is: F. Murtagh and A. Heck,
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Multivariate Data Analysis, Kluwer Academic, Dordrecht, 1987.
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Author:
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F. Murtagh
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Phone: + 49 89 32006298 (work)
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+ 49 89 965307 (home)
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Earn/Bitnet: fionn@dgaeso51, fim@dgaipp1s, murtagh@stsci
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Span: esomc1::fionn
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Internet: murtagh@scivax.stsci.edu
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F. Murtagh, Munich, 6 June 1989 */
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/*********************************************************************/
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#include <stdio.h>
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#include <stdlib.h>
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#include <math.h>
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#include "pca.h"
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#define SIGN(a, b) ( (b) < 0 ? -fabs(a) : fabs(a) )
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/** Variance-covariance matrix: creation *****************************/
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/* Create m * m covariance matrix from given n * m data matrix. */
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void covcol(double** data, int n, int m, double** symmat)
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{
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double *mean;
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int i, j, j1, j2;
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/* Allocate storage for mean vector */
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mean = (double*) malloc(m*sizeof(double));
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/* Determine mean of column vectors of input data matrix */
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for (j = 0; j < m; j++)
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{
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mean[j] = 0.0;
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for (i = 0; i < n; i++)
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{
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mean[j] += data[i][j];
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}
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mean[j] /= (double)n;
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}
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/*
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printf("\nMeans of column vectors:\n");
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for (j = 0; j < m; j++) {
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printf("%12.1f",mean[j]); } printf("\n");
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*/
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/* Center the column vectors. */
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for (i = 0; i < n; i++)
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{
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for (j = 0; j < m; j++)
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{
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data[i][j] -= mean[j];
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}
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}
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/* Calculate the m * m covariance matrix. */
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for (j1 = 0; j1 < m; j1++)
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{
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for (j2 = j1; j2 < m; j2++)
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{
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symmat[j1][j2] = 0.0;
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for (i = 0; i < n; i++)
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{
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symmat[j1][j2] += data[i][j1] * data[i][j2];
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}
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symmat[j2][j1] = symmat[j1][j2];
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}
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}
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free(mean);
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return;
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}
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/** Error handler **************************************************/
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void erhand(char* err_msg)
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{
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fprintf(stderr,"Run-time error:\n");
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fprintf(stderr,"%s\n", err_msg);
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fprintf(stderr,"Exiting to system.\n");
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exit(1);
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}
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/** Reduce a real, symmetric matrix to a symmetric, tridiag. matrix. */
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/* Householder reduction of matrix a to tridiagonal form.
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Algorithm: Martin et al., Num. Math. 11, 181-195, 1968.
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Ref: Smith et al., Matrix Eigensystem Routines -- EISPACK Guide
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Springer-Verlag, 1976, pp. 489-494.
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W H Press et al., Numerical Recipes in C, Cambridge U P,
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1988, pp. 373-374. */
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void tred2(double** a, int n, double* d, double* e)
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{
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int l, k, j, i;
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double scale, hh, h, g, f;
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for (i = n-1; i >= 1; i--)
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{
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l = i - 1;
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h = scale = 0.0;
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if (l > 0)
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{
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for (k = 0; k <= l; k++)
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scale += fabs(a[i][k]);
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if (scale == 0.0)
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e[i] = a[i][l];
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else
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{
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for (k = 0; k <= l; k++)
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{
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a[i][k] /= scale;
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h += a[i][k] * a[i][k];
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}
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f = a[i][l];
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g = f>0 ? -sqrt(h) : sqrt(h);
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e[i] = scale * g;
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h -= f * g;
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a[i][l] = f - g;
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f = 0.0;
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for (j = 0; j <= l; j++)
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{
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a[j][i] = a[i][j]/h;
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g = 0.0;
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for (k = 0; k <= j; k++)
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g += a[j][k] * a[i][k];
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for (k = j+1; k <= l; k++)
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g += a[k][j] * a[i][k];
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e[j] = g / h;
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f += e[j] * a[i][j];
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}
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hh = f / (h + h);
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for (j = 0; j <= l; j++)
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{
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f = a[i][j];
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e[j] = g = e[j] - hh * f;
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for (k = 0; k <= j; k++)
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a[j][k] -= (f * e[k] + g * a[i][k]);
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}
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}
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}
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else
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e[i] = a[i][l];
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d[i] = h;
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}
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d[0] = 0.0;
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e[0] = 0.0;
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for (i = 0; i < n; i++)
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{
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l = i - 1;
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if (d[i])
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{
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for (j = 0; j <= l; j++)
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{
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g = 0.0;
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for (k = 0; k <= l; k++)
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g += a[i][k] * a[k][j];
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for (k = 0; k <= l; k++)
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a[k][j] -= g * a[k][i];
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}
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}
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d[i] = a[i][i];
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a[i][i] = 1.0;
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for (j = 0; j <= l; j++)
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a[j][i] = a[i][j] = 0.0;
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}
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}
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/** Tridiagonal QL algorithm -- Implicit **********************/
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void tqli(double* d, double* e, int n, double** z)
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{
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int m, l, iter, i, k;
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double s, r, p, g, f, dd, c, b;
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for (i = 1; i < n; i++)
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e[i-1] = e[i];
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e[n-1] = 0.0;
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for (l = 0; l < n; l++)
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{
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iter = 0;
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do
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{
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for (m = l; m < n-1; m++)
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{
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dd = fabs(d[m]) + fabs(d[m+1]);
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if (fabs(e[m]) + dd == dd) break;
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}
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if (m != l)
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{
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if (iter++ == 30) erhand("No convergence in TLQI.");
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g = (d[l+1] - d[l]) / (2.0 * e[l]);
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r = sqrt((g * g) + 1.0);
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g = d[m] - d[l] + e[l] / (g + SIGN(r, g));
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s = c = 1.0;
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p = 0.0;
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for (i = m-1; i >= l; i--)
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{
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f = s * e[i];
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b = c * e[i];
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if (fabs(f) >= fabs(g))
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{
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c = g / f;
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r = sqrt((c * c) + 1.0);
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e[i+1] = f * r;
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c *= (s = 1.0/r);
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}
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else
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{
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s = f / g;
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r = sqrt((s * s) + 1.0);
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e[i+1] = g * r;
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s *= (c = 1.0/r);
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}
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g = d[i+1] - p;
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r = (d[i] - g) * s + 2.0 * c * b;
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p = s * r;
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d[i+1] = g + p;
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g = c * r - b;
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for (k = 0; k < n; k++)
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{
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f = z[k][i+1];
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z[k][i+1] = s * z[k][i] + c * f;
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z[k][i] = c * z[k][i] - s * f;
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}
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}
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d[l] = d[l] - p;
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e[l] = g;
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e[m] = 0.0;
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}
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} while (m != l);
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}
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}
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/* In place projection onto basis vectors */
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void pca_project(double** data, int n, int m, int ncomponents)
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{
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int i, j, k, k2;
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double **symmat, **symmat2, *evals, *interm;
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//TODO: assert ncomponents < m
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symmat = (double**) malloc(m*sizeof(double*));
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for (i = 0; i < m; i++)
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symmat[i] = (double*) malloc(m*sizeof(double));
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covcol(data, n, m, symmat);
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/*********************************************************************
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Eigen-reduction
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**********************************************************************/
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/* Allocate storage for dummy and new vectors. */
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evals = (double*) malloc(m*sizeof(double)); /* Storage alloc. for vector of eigenvalues */
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interm = (double*) malloc(m*sizeof(double)); /* Storage alloc. for 'intermediate' vector */
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//MALLOC_ARRAY(symmat2,m,m,double);
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//for (i = 0; i < m; i++) {
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// for (j = 0; j < m; j++) {
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// symmat2[i][j] = symmat[i][j]; /* Needed below for col. projections */
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// }
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//}
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tred2(symmat, m, evals, interm); /* Triangular decomposition */
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tqli(evals, interm, m, symmat); /* Reduction of sym. trid. matrix */
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/* evals now contains the eigenvalues,
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columns of symmat now contain the associated eigenvectors. */
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/*
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printf("\nEigenvalues:\n");
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for (j = m-1; j >= 0; j--) {
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printf("%18.5f\n", evals[j]); }
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printf("\n(Eigenvalues should be strictly positive; limited\n");
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printf("precision machine arithmetic may affect this.\n");
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printf("Eigenvalues are often expressed as cumulative\n");
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printf("percentages, representing the 'percentage variance\n");
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printf("explained' by the associated axis or principal component.)\n");
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printf("\nEigenvectors:\n");
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printf("(First three; their definition in terms of original vbes.)\n");
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for (j = 0; j < m; j++) {
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for (i = 1; i <= 3; i++) {
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printf("%12.4f", symmat[j][m-i]); }
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printf("\n"); }
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*/
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/* Form projections of row-points on prin. components. */
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/* Store in 'data', overwriting original data. */
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for (i = 0; i < n; i++) {
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for (j = 0; j < m; j++) {
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interm[j] = data[i][j]; } /* data[i][j] will be overwritten */
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for (k = 0; k < ncomponents; k++) {
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data[i][k] = 0.0;
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for (k2 = 0; k2 < m; k2++) {
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data[i][k] += interm[k2] * symmat[k2][m-k-1]; }
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}
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}
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/*
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printf("\nProjections of row-points on first 3 prin. comps.:\n");
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for (i = 0; i < n; i++) {
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for (j = 0; j < 3; j++) {
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printf("%12.4f", data[i][j]); }
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printf("\n"); }
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*/
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/* Form projections of col.-points on first three prin. components. */
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/* Store in 'symmat2', overwriting what was stored in this. */
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//for (j = 0; j < m; j++) {
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// for (k = 0; k < m; k++) {
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// interm[k] = symmat2[j][k]; } /*symmat2[j][k] will be overwritten*/
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// for (i = 0; i < 3; i++) {
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// symmat2[j][i] = 0.0;
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// for (k2 = 0; k2 < m; k2++) {
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// symmat2[j][i] += interm[k2] * symmat[k2][m-i-1]; }
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// if (evals[m-i-1] > 0.0005) /* Guard against zero eigenvalue */
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// symmat2[j][i] /= sqrt(evals[m-i-1]); /* Rescale */
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// else
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// symmat2[j][i] = 0.0; /* Standard kludge */
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// }
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// }
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/*
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printf("\nProjections of column-points on first 3 prin. comps.:\n");
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for (j = 0; j < m; j++) {
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for (k = 0; k < 3; k++) {
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printf("%12.4f", symmat2[j][k]); }
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printf("\n"); }
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*/
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for (i = 0; i < m; i++)
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free(symmat[i]);
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free(symmat);
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//FREE_ARRAY(symmat2,m);
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free(evals);
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free(interm);
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}
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