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freeswitch/libs/libcodec2/unittest/vqtrainsp.c

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/*--------------------------------------------------------------------------*\
FILE........: vqtrainsp.c
AUTHOR......: David Rowe
DATE CREATED: 7 August 2012
This program trains sparse amplitude vector quantisers.
Modified from vqtrainph.c
\*--------------------------------------------------------------------------*/
/*
Copyright (C) 2012 David Rowe
All rights reserved.
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License version 2, as
published by the Free Software Foundation. This program is
distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
License for more details.
You should have received a copy of the GNU Lesser General Public License
along with this program; if not, see <http://www.gnu.org/licenses/>.
*/
/*-----------------------------------------------------------------------*\
INCLUDES
\*-----------------------------------------------------------------------*/
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <ctype.h>
#include <assert.h>
typedef struct {
float real;
float imag;
} COMP;
/*-----------------------------------------------------------------------* \
DEFINES
\*-----------------------------------------------------------------------*/
#define DELTAQ 0.01 /* quiting distortion */
#define MAX_STR 80 /* maximum string length */
/*-----------------------------------------------------------------------*\
FUNCTION PROTOTYPES
\*-----------------------------------------------------------------------*/
void zero(float v[], int d);
void acc(float v1[], float v2[], int d);
void norm(float v[], int k, int n[]);
int quantise(float cb[], float vec[], int d, int e, float *se);
void print_vec(float cb[], int d, int e);
void split(float cb[], int d, int b);
int gain_shape_quantise(float cb[], float vec[], int d, int e, float *se, float *best_gain);
/*-----------------------------------------------------------------------* \
MAIN
\*-----------------------------------------------------------------------*/
int main(int argc, char *argv[]) {
int d,e; /* dimension and codebook size */
float *vec; /* current vector */
float *cb; /* vector codebook */
float *cent; /* centroids for each codebook entry */
int *n; /* number of vectors in this interval */
int J; /* number of vectors in training set */
int ind; /* index of current vector */
float se; /* total squared error for this iteration */
float var; /* variance */
float var_1; /* previous variance */
float delta; /* improvement in distortion */
FILE *ftrain; /* file containing training set */
FILE *fvq; /* file containing vector quantiser */
int ret;
int i,j, finished, iterations;
float sd;
int var_n, bits, b, levels;
/* Interpret command line arguments */
if (argc < 5) {
printf("usage: %s TrainFile D(dimension) B(number of bits) VQFile [error.txt file]\n", argv[0]);
exit(1);
}
/* Open training file */
ftrain = fopen(argv[1],"rb");
if (ftrain == NULL) {
printf("Error opening training database file: %s\n",argv[1]);
exit(1);
}
/* determine k and m, and allocate arrays */
d = atoi(argv[2]);
bits = atoi(argv[3]);
e = 1<<bits;
printf("\n");
printf("dimension D=%d number of bits B=%d entries E=%d\n", d, bits, e);
vec = (float*)malloc(sizeof(float)*d);
cb = (float*)malloc(sizeof(float)*d*e);
cent = (float*)malloc(sizeof(float)*d*e);
n = (int*)malloc(sizeof(int)*d*e);
if (cb == NULL || cb == NULL || cent == NULL || vec == NULL) {
printf("Error in malloc.\n");
exit(1);
}
/* determine size of training set */
J = 0;
var_n = 0;
while(fread(vec, sizeof(float), d, ftrain) == (size_t)d) {
for(j=0; j<d; j++)
if (vec[j] != 0.0)
var_n++;
J++;
}
printf("J=%d sparse vectors in training set, %d non-zero values\n", J, var_n);
/* set up initial codebook from centroid of training set */
//#define DBG
zero(cent, d);
for(j=0; j<d; j++)
n[j] = 0;
rewind(ftrain);
#ifdef DBG
printf("initial codebook...\n");
#endif
for(i=0; i<J; i++) {
ret = fread(vec, sizeof(float), d, ftrain);
#ifdef DBG
print_vec(vec, d, 1);
#endif
acc(cent, vec, d);
for(j=0; j<d; j++)
if (vec[j] != 0.0)
n[j]++;
}
norm(cent, d, n);
memcpy(cb, cent, d*sizeof(float));
#ifdef DBG
printf("\n");
print_vec(cb, d, 1);
#endif
/* main loop */
printf("\n");
printf("bits Iteration delta var std dev\n");
printf("---------------------------------------\n");
for(b=1; b<=bits; b++) {
levels = 1<<b;
iterations = 0;
finished = 0;
delta = 0;
var_1 = 0.0;
split(cb, d, levels/2);
//print_vec(cb, d, levels);
do {
/* zero centroids */
for(i=0; i<levels; i++) {
zero(&cent[i*d], d);
for(j=0; j<d; j++)
n[i*d+j] = 0;
}
//#define DBG
#ifdef DBG
printf("cb...\n");
print_vec(cb, d, levels);
printf("\n\nquantise...\n");
#endif
/* quantise training set */
se = 0.0;
rewind(ftrain);
for(i=0; i<J; i++) {
ret = fread(vec, sizeof(float), d, ftrain);
ind = quantise(cb, vec, d, levels, &se);
//ind = gain_shape_quantise(cb, vec, d, levels, &se, &best_gain);
//for(j=0; j<d; j++)
// if (vec[j] != 0.0)
// vec[j] += best_gain;
#ifdef DBG
print_vec(vec, d, 1);
printf(" ind %d se: %f\n", ind, se);
#endif
acc(&cent[ind*d], vec, d);
for(j=0; j<d; j++)
if (vec[j] != 0.0)
n[ind*d+j]++;
}
#ifdef DBG
printf("cent...\n");
print_vec(cent, d, e);
printf("\n");
#endif
/* work out stats */
var = se/var_n;
sd = sqrt(var);
iterations++;
if (iterations > 1) {
if (var > 0.0) {
delta = (var_1 - var)/var;
}
else
delta = 0;
if (delta < DELTAQ)
finished = 1;
}
if (!finished) {
/* determine new codebook from centroids */
for(i=0; i<levels; i++) {
norm(&cent[i*d], d, &n[i*d]);
memcpy(&cb[i*d], &cent[i*d], d*sizeof(float));
}
}
#ifdef DBG
printf("new cb ...\n");
print_vec(cent, d, e);
printf("\n");
#endif
printf("%2d %2d %4.3f %6.3f %4.3f\r",b,iterations, delta, var, sd);
fflush(stdout);
var_1 = var;
} while (!finished);
printf("\n");
}
//print_vec(cb, d, 1);
/* save codebook to disk */
fvq = fopen(argv[4],"wt");
if (fvq == NULL) {
printf("Error opening VQ file: %s\n",argv[4]);
exit(1);
}
fprintf(fvq,"%d %d\n",d,e);
for(j=0; j<e; j++) {
for(i=0; i<d; i++)
fprintf(fvq,"% 7.3f ", cb[j*d+i]);
fprintf(fvq,"\n");
}
fclose(fvq);
/* optionally dump error file for multi-stage work */
if (argc == 6) {
FILE *ferr = fopen(argv[5],"wt");
assert(ferr != NULL);
rewind(ftrain);
for(i=0; i<J; i++) {
ret = fread(vec, sizeof(float), d, ftrain);
ind = quantise(cb, vec, d, levels, &se);
for(j=0; j<d; j++) {
if (vec[j] != 0.0)
vec[j] -= cb[ind*d+j];
fprintf(ferr, "%f ", vec[j]);
}
fprintf(ferr, "\n");
}
}
return 0;
}
/*-----------------------------------------------------------------------*\
FUNCTIONS
\*-----------------------------------------------------------------------*/
void print_vec(float cb[], int d, int e)
{
int i,j;
for(j=0; j<e; j++) {
printf(" ");
for(i=0; i<d; i++)
printf("% 7.3f ", cb[j*d+i]);
printf("\n");
}
}
/*---------------------------------------------------------------------------*\
FUNCTION....: zero()
AUTHOR......: David Rowe
DATE CREATED: 23/2/95
Zeros a vector of length d.
\*---------------------------------------------------------------------------*/
void zero(float v[], int d)
{
int i;
for(i=0; i<d; i++) {
v[i] = 0.0;
}
}
/*---------------------------------------------------------------------------*\
FUNCTION....: acc()
AUTHOR......: David Rowe
DATE CREATED: 23/2/95
Adds d dimensional vectors v1 to v2 and stores the result back
in v1.
An unused entry in a sparse vector is set to zero so won't
affect the accumulation process.
\*---------------------------------------------------------------------------*/
void acc(float v1[], float v2[], int d)
{
int i;
for(i=0; i<d; i++)
v1[i] += v2[i];
}
/*---------------------------------------------------------------------------*\
FUNCTION....: norm()
AUTHOR......: David Rowe
DATE CREATED: 23/2/95
Normalises each element in d dimensional vector.
\*---------------------------------------------------------------------------*/
void norm(float v[], int d, int n[])
{
int i;
for(i=0; i<d; i++) {
if (n[i] != 0)
v[i] /= n[i];
}
}
/*---------------------------------------------------------------------------*\
FUNCTION....: quantise()
AUTHOR......: David Rowe
DATE CREATED: 23/2/95
Quantises vec by choosing the nearest vector in codebook cb, and
returns the vector index. The squared error of the quantised vector
is added to se.
Unused entries in sparse vectors are ignored.
\*---------------------------------------------------------------------------*/
int quantise(float cb[], float vec[], int d, int e, float *se)
{
float error; /* current error */
int besti; /* best index so far */
float best_error; /* best error so far */
int i,j;
float diff;
besti = 0;
best_error = 1E32;
for(j=0; j<e; j++) {
error = 0.0;
for(i=0; i<d; i++) {
if (vec[i] != 0.0) {
diff = cb[j*d+i] - vec[i];
error += diff*diff;
}
}
if (error < best_error) {
best_error = error;
besti = j;
}
}
*se += best_error;
return(besti);
}
int gain_shape_quantise(float cb[], float vec[], int d, int e, float *se, float *best_gain)
{
float error; /* current error */
int besti; /* best index so far */
float best_error; /* best error so far */
int i,j,m;
float diff, metric, best_metric, gain, sumAm, sumCb;
besti = 0;
best_metric = best_error = 1E32;
for(j=0; j<e; j++) {
/* compute optimum gain */
sumAm = sumCb = 0.0;
m = 0;
for(i=0; i<d; i++) {
if (vec[i] != 0.0) {
m++;
sumAm += vec[i];
sumCb += cb[j*d+i];
}
}
gain = (sumAm - sumCb)/m;
/* compute error */
metric = error = 0.0;
for(i=0; i<d; i++) {
if (vec[i] != 0.0) {
diff = vec[i] - cb[j*d+i] - gain;
error += diff*diff;
metric += diff*diff;
}
}
if (metric < best_metric) {
best_error = error;
best_metric = metric;
*best_gain = gain;
besti = j;
}
}
*se += best_error;
return(besti);
}
void split(float cb[], int d, int levels)
{
int i,j;
for (i=0;i<levels;i++) {
for (j=0;j<d;j++) {
float delta = .01*(rand()/(float)RAND_MAX-.5);
cb[i*d+j] += delta;
cb[(i+levels)*d+j] = cb[i*d+j] - delta;
}
}
}
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