double conv(size_t i, Matrix *y, Matrix *w) {
double acc = 0.0;
for(size_t ni=0; ni<w->nrow; ni++) {
// printf("%zu: ", ni);
size_t wi=0;
int yi=i;
for(; (wi<w->ncol) && (yi>=0); yi--, wi++) {
// printf("y:%d:%3.3f w:%d:%3.3f, ", yi, wi, getMatrixElement(y, ni, yi), getMatrixElement(w, ni, wi));
acc += getMatrixElement(y, ni, yi) * getMatrixElement(w, ni, wi);
}
// printf("\n");
}
return acc;
}
void fillSparseMatrix(SparseMatrix *sm, Matrix *m, size_t col_offset) {
for(size_t i = 0; i < m->nrow; i++ ) {
for(size_t j = 0; j < m->ncol; j++) {
setSparseMatrixElement(sm, i, j+col_offset, getMatrixElement(m, i, j) );
}
}
}
void writeMatrix(const char * const fileName, const Matrix * const M)
{
int i, j, rows, cols;
FILE * outFile;
double value;
outFile = fopen(fileName, "w");
if(NULL == outFile)
{
changeColor("magenta");
printf("=[ I can't find %s\n", fileName);
return;
}
fprintf(outFile, "%i %i\n", M->rowSize, M->colSize);
for(i = 0; i < M->rowSize; i++)
{
for(j = 0; j < M->colSize; j++)
{
fprintf(outFile, "%lg", getMatrixElement(M, i, j));
if(j < M->colSize - 1)
fprintf(outFile, " ");
}
fprintf(outFile, "\n");
}
fclose(outFile);
}
Matrix* calcErrorGrad(Matrix *y, Matrix *w, doubleVector *target, int jobs) {
assert(y->ncol == target->size);
pthread_t *threads = (pthread_t *) malloc( jobs * sizeof( pthread_t ) );
FiltWorker *workers = (FiltWorker*) malloc( jobs * sizeof(FiltWorker) );
for(size_t wi=0; wi < jobs; wi++) {
int points_per_thread = (y->ncol + jobs - 1) / jobs;
workers[wi].first = min( wi * points_per_thread, y->ncol );
workers[wi].last = min( (wi+1) * points_per_thread, y->ncol );
workers[wi].y = y;
workers[wi].w = w;
workers[wi].target = target;
}
for( int i = 0; i < jobs; i++ ) {
P( pthread_create( &threads[i], NULL, error_grad_routine, &workers[i]) );
}
for( int i = 0; i < jobs; i++ ) {
P( pthread_join( threads[i], NULL) );
}
Matrix *dedw = createZeroMatrix(w->nrow, w->ncol);
for( size_t wi=0; wi< jobs; wi++) {
for(size_t i=0; i<dedw->nrow; i++) {
for(size_t j=0; j<dedw->ncol; j++) {
incMatrixElement(dedw, i, j, getMatrixElement(workers[wi].dedw, i, j));
}
}
}
for(size_t i=0; i<dedw->nrow; i++) {
for(size_t j=0; j<dedw->ncol; j++) {
double dedw_acc = getMatrixElement(dedw, i, j);
setMatrixElement(dedw, i, j, dedw_acc/target->size);
}
}
free(threads);
free(workers);
return dedw;
}
static int readFile(FILE *file, pmatrix_t *matrix, index_t rows, index_t columns) {
index_t x, y;
initMatrix(matrix, rows, columns);
if (matrix == NULL)
return ERRORCODE_CANT_MALLOC;
for(y = 0; y < rows; y++) {
for(x = 0; x < columns; x++)
if (fscanf(file, UNIT_SPECIFIER, getMatrixElement(*matrix, y, x)) != 1)
return ERRORCODE_PARSER_ERROR;
}
return ERRORCODE_NOERRORS;
}
void *error_grad_routine(void *args) {
FiltWorker *fw = (FiltWorker*)args;
fw->dedw = createZeroMatrix(fw->w->nrow, fw->w->ncol);
for(size_t i=fw->first; i<fw->last; i++) {
double gr_val = -2*(fw->target->array[i] - conv(i, fw->y, fw->w));
for(size_t wi=0; wi < fw->dedw->nrow; wi++) {
int yi=i;
size_t wj=0;
for(; (wj < fw->dedw->ncol) && (yi>=0); wj++, yi--) {
incMatrixElement(fw->dedw, wi, wj, gr_val * getMatrixElement(fw->y, wi, yi));
}
}
}
return(NULL);
}
void matrixToEulerZXY(const float matrix[3*3],
float &thetaX, float &thetaY, float& thetaZ)
{
thetaX = asin( getMatrixElement(matrix, 2, 1));
if ( thetaX < M_PI_2 ) {
if ( thetaX > -M_PI_2 ) {
thetaZ = atan2( -getMatrixElement(matrix, 0, 1),
getMatrixElement(matrix, 1, 1) );
thetaY = atan2( -getMatrixElement(matrix, 2, 0),
getMatrixElement(matrix, 2, 2) );
} else {
// Not a unique solution
thetaZ = -atan2( getMatrixElement(matrix, 0, 2),
getMatrixElement(matrix, 0, 0) );
thetaY = 0.0;
}
} else {
// Not a unique solution
thetaZ = atan2( getMatrixElement(matrix, 0, 2),
getMatrixElement(matrix, 0, 0) );
thetaY = 0.0;
}
}
Matrix* processThroughReceptiveFields(Matrix *ts, const double *centers, const Constants *c) {
Matrix *ts_out = createMatrix(c->M, ts->ncol);
// printf("%d %d %d", c->M, ts->nrow, c->M % ts->nrow);
assert(c->M % ts->nrow == 0);
size_t neurons_per_ts = c->M / ts->nrow;
size_t i, ri;
for(i=0, ri=0; (i < c->M) && (ri < ts->nrow); i+=neurons_per_ts, ri++) {
for(size_t ni=i; ni<(i+neurons_per_ts); ni++) {
for(size_t vi=0; vi < ts->ncol; vi++) {
double val = getMatrixElement(ts, ri, vi);
double val_field = c->preproc->gain * exp( - ((centers[ni] - val) * (centers[ni] - val))/c->preproc->sigma );
setMatrixElement(ts_out, ni, vi, val_field);
}
}
}
return(ts_out);
}
Matrix *runLbfgsOptim(Matrix *y, doubleVector *target, int L, int jobs, double epsilon) {
int M = y->nrow;
Matrix *w = createZeroMatrix(M, L);
lbfgsfloatval_t fx;
lbfgsfloatval_t *x = lbfgs_malloc(w->nrow*w->ncol);
lbfgs_parameter_t param;
if (x == NULL) {
printf("ERROR: Failed to allocate a memory block for variables.\n");
exit(1);
}
/* Initialize the variables. */
for (size_t i = 0; i < w->nrow; i++) {
for (size_t j = 0; j < w->ncol;j++) {
x[j*w->nrow + i] = getMatrixElement(w, i, j);
}
}
lbfgs_parameter_init(¶m);
param.epsilon = epsilon;
param.m = 20;
LbfgsInput inp;
inp.target = target;
inp.y = y;
inp.jobs = jobs;
inp.L = w->ncol;
int ret = lbfgs(w->nrow*w->ncol, x, &fx, evaluate, progress, (void*)&inp, ¶m);
printf("L-BFGS optimization terminated with status code = %d\n", ret);
printf(" fx = %f\n", fx);
Matrix *w_opt = formMatrixFromFloatVal(x, w->nrow, w->ncol);
lbfgs_free(x);
return(w_opt);
}
pMatrixVector* processTimeSeriesSet(pMatrixVector *v, const Constants *c) {
double area_max = DBL_MIN;
double area_min = DBL_MAX;
for(size_t i=0; i < v->size; i++) {
Matrix *ts = v->array[i];
for(size_t ts_i=0; ts_i < ts->nrow; ts_i++) {
for(size_t vi=0; vi < ts->ncol; vi++) {
double val = getMatrixElement(ts, ts_i, vi);
if(val > area_max) area_max = val;
if(val < area_min) area_min = val;
}
}
}
double centers[c->M];
for(size_t ni=0; ni<c->M; ni++) {
centers[ni] = area_min + (ni+0.5)*(area_max-area_min)/c->M;
}
pMatrixVector *out = TEMPLATE(createVector,pMatrix)();
for(size_t i=0; i<v->size; i++) {
TEMPLATE(insertVector,pMatrix)(out, processThroughReceptiveFields(v->array[i], centers, c));
}
return(out);
}
int main(int argc, char **argv) {
ArgOptionsPrep a = parsePrepOptions(argc, argv);
Constants *c = createConstants(a.const_filename);
bool saveStat = false;
if(a.stat_file) {
saveStat = true;
}
pMatrixVector* ts_data = readMatrixListFromFile(a.input_file);
pMatrixVector* ts_labels = readMatrixListFromFile(a.input_labels_file);
assert(ts_labels->size == 1);
assert(ts_labels->array[0]->nrow == ts_data->size);
pMatrixVector *out_data = TEMPLATE(createVector,pMatrix)();
assert(ts_data->size > 0);
pMatrixVector *ts_data_pr = processTimeSeriesSet(ts_data, c);
indVector *ts_indices = TEMPLATE(createVector,ind)();
for(size_t ti=0; ti< ts_data_pr->size; ti++) {
TEMPLATE(insertVector,ind)(ts_indices, ti);
}
srand(time(NULL));
shuffleIndVector(ts_indices);
SpikesList *net = createSpikesList(c->M);
doubleVector *ts_labels_current = TEMPLATE(createVector,double)();
double t = 0;
doubleVector *timeline = TEMPLATE(createVector,double)();
AdExLayer *l = createAdExLayer(c->M, saveStat);
for(size_t ts_i=0; ts_i < ts_indices->size; ts_i++) {
Matrix *ts = ts_data_pr->array[ ts_indices->array[ts_i] ];
size_t nsamples = ts->ncol;
toStartValuesAdExLayer(l, c);
size_t j;
for(j = 0; j < nsamples; t+= c->preproc->dt, j++) {
for(size_t ni=0; ni < l->N; ni++) {
double I = getMatrixElement(ts, ni, j);
propagateCurrentAdExLayer(l, &ni, &I);
simulateAdExLayerNeuron(l, &ni, c);
if(l->fired[ni] == 1) {
TEMPLATE(insertVector,double)(net->list[ni], c->preproc->mult*t);
l->fired[ni] = 0;
}
}
}
t += 250/c->preproc->mult;
TEMPLATE(insertVector,double)(timeline, c->preproc->mult*t);
TEMPLATE(insertVector,double)(ts_labels_current, getMatrixElement(ts_labels->array[0], ts_indices->array[ts_i], 0) );
}
Matrix *spikes = vectorArrayToMatrix(net->list, net->size);
TEMPLATE(insertVector,pMatrix)(out_data, spikes);
Matrix *timeline_m = vectorArrayToMatrix(&timeline, 1);
transposeMatrix(timeline_m);
TEMPLATE(insertVector,pMatrix)(out_data, timeline_m);
Matrix *classes = vectorArrayToMatrix(&ts_labels_current, 1);
transposeMatrix(classes);
TEMPLATE(insertVector,pMatrix)(out_data, classes);
saveMatrixListToFile(a.output_file, out_data);
if(l->saveStat) {
pMatrixVector *stat_data = TEMPLATE(createVector,pMatrix)();
for(size_t i=0; i<ts_data_pr->size; i++) {
TEMPLATE(insertVector,pMatrix)(stat_data, copyMatrix(ts_data_pr->array[i]));
}
Matrix *Vs = vectorArrayToMatrix(l->stat_V, l->N);
TEMPLATE(insertVector,pMatrix)(stat_data, Vs);
Matrix *ws = vectorArrayToMatrix(l->stat_w, l->N);
TEMPLATE(insertVector,pMatrix)(stat_data, ws);
saveMatrixListToFile(a.stat_file, stat_data);
TEMPLATE(deleteVector,pMatrix)(stat_data);
}
TEMPLATE(deleteVector,double)(timeline);
deleteAdExLayer(l);
deleteSpikesList(net);
deleteConstants(c);
TEMPLATE(deleteVector,pMatrix)(out_data);
TEMPLATE(deleteVector,pMatrix)(ts_data_pr);
TEMPLATE(deleteVector,pMatrix)(ts_data);
return(0);
}
inline void setInMatrix(pmatrix_t matrix, index_t rows, index_t columns, unit_t value) {
*getMatrixElement(matrix, rows, columns) = value;
return;
}
inline unit_t getFromMatrix(pmatrix_t matrix, index_t rows, index_t columns) {
return *getMatrixElement(matrix, rows, columns);
}
