int StochasticReleaseTestProbe::computePValues(long int step, int f) {
int status = PV_SUCCESS;
assert(step >=0 && step < INT_MAX);
int nf = getTargetLayer()->getLayerLoc()->nf;
assert(f >= 0 && f < nf);
int idx = (step-1)*nf + f;
pvwdata_t wgt = conn->get_wDataStart(0)[f*(nf+1)]; // weights should be one-to-one weights
HyPerLayer * pre = conn->preSynapticLayer();
const pvdata_t * preactPtr = pre->getLayerData();
const PVLayerLoc * preLoc = pre->getLayerLoc();
const int numPreNeurons = pre->getNumNeurons();
bool found=false;
pvdata_t preact = 0.0f;
for (int n=f; n<numPreNeurons; n+=nf) {
int nExt = kIndexExtended(n, preLoc->nx, preLoc->ny, preLoc->nf, preLoc->halo.lt, preLoc->halo.rt, preLoc->halo.dn, preLoc->halo.up);
pvdata_t a = preactPtr[nExt];
if (a!=0.0f) {
if (found) {
assert(preact==a);
}
else {
found = true;
preact = a;
}
}
}
preact *= getParent()->getDeltaTime();
if (preact < 0.0f) preact = 0.0f;
if (preact > 1.0f) preact = 1.0f;
const PVLayerLoc * loc = getTargetLayer()->getLayerLoc();
const pvdata_t * activity = getTargetLayer()->getLayerData();
int nnzf = 0;
const int numNeurons = getTargetLayer()->getNumNeurons();
for (int n=f; n<numNeurons; n+=nf) {
int nExt = kIndexExtended(n, loc->nx, loc->ny, loc->nf, loc->halo.lt, loc->halo.rt, loc->halo.dn, loc->halo.up);
assert(activity[nExt]==0 || activity[nExt]==wgt);
if (activity[nExt]!=0) nnzf++;
}
HyPerLayer * l = getTargetLayer();
HyPerCol * hc = l->getParent();
MPI_Allreduce(MPI_IN_PLACE, &nnzf, 1, MPI_INT, MPI_SUM, hc->icCommunicator()->communicator());
if (hc->columnId()==0) {
const int neuronsPerFeature = l->getNumGlobalNeurons()/nf;
double mean = preact * neuronsPerFeature;
double stddev = sqrt(neuronsPerFeature*preact*(1-preact));
double numdevs = (nnzf-mean)/stddev;
pvalues[idx] = erfc(fabs(numdevs)/sqrt(2));
fprintf(outputstream->fp, " Feature %d, nnz=%5d, expectation=%7.1f, std.dev.=%5.1f, discrepancy of %f deviations, p-value %f\n",
f, nnzf, mean, stddev, numdevs, pvalues[idx]);
}
assert(status==PV_SUCCESS);
return status;
}
int main(int argc, char* argv[])
{
int status = 0;
PV_Init* initObj = new PV_Init(&argc, &argv);
initObj->initialize(argc, argv);
// create the managing hypercolumn
//
HyPerCol* hc = new HyPerCol("test_constant_input column", argc, argv, initObj);
// create the image
//
TestImage * image = new TestImage("test_constant_input image", hc);
// create the layers
//
HyPerLayer * retina = new Retina("test_constant_input retina", hc);
// create the connections
//
HyPerConn * conn = new HyPerConn("test_constant_input connection", hc);
const int nxp = conn->xPatchSize();
const int nyp = conn->yPatchSize();
const PVLayerLoc * imageLoc = image->getLayerLoc();
const PVLayerLoc * retinaLoc = image->getLayerLoc();
const int nfPre = imageLoc->nf;
float sumOfWeights = (float) (nxp*nyp*nfPre);
if (imageLoc->nx > retinaLoc->nx) { sumOfWeights *= imageLoc->nx/retinaLoc->nx;}
if (imageLoc->ny > retinaLoc->ny) { sumOfWeights *= imageLoc->ny/retinaLoc->ny;}
hc->run();
const int rank = hc->columnId();
#ifdef DEBUG_OUTPUT
printf("[%d]: column: ", rank);
printLoc(hc->getImageLoc());
printf("[%d]: image : ", rank);
printLoc(image->getImageLoc());
printf("[%d]: retina: ", rank);
printLoc(*retina->getLayerLoc());
printf("[%d]: l1 : ", rank);
printLoc(*l1->getLayerLoc());
#endif
status = checkLoc(hc, image->getLayerLoc());
if (status != PV_SUCCESS) {
fprintf(stderr, "[%d]: test_constant_input: ERROR in image loc\n", rank);
exit(status);
}
status = checkLoc(hc, retina->getLayerLoc());
if (status != PV_SUCCESS) {
fprintf(stderr, "[%d]: test_constant_input: ERROR in retina loc\n", rank);
exit(status);
}
status = checkInput(image->getLayerLoc(), image->getActivity(), image->getConstantVal(), true);
if (status != PV_SUCCESS) {
fprintf(stderr, "[%d]: test_constant_input: ERROR in image data\n", rank);
exit(status);
}
float retinaVal = sumOfWeights * image->getConstantVal();
status = checkInput(retina->getLayerLoc(), retina->getActivity(), retinaVal, false);
if (status != 0) {
fprintf(stderr, "[%d]: test_constant_input: ERROR in retina data\n", rank);
exit(status);
}
status = checkInput(retina->getLayerLoc(), retina->getLayerData(), retinaVal, true);
if (status != 0) {
fprintf(stderr, "[%d]: test_constant_input: ERROR in retina data\n", rank);
exit(status);
}
delete hc;
delete initObj;
return status;
}
int RescaleLayerTestProbe::outputState(double timed)
{
int status = StatsProbe::outputState(timed);
if (timed==getParent()->getStartTime()) { return PV_SUCCESS; }
float tolerance = 2.0e-5f;
InterColComm * icComm = getTargetLayer()->getParent()->icCommunicator();
bool isRoot = icComm->commRank() == 0;
RescaleLayer * targetRescaleLayer = dynamic_cast<RescaleLayer *>(getTargetLayer());
assert(targetRescaleLayer);
if (targetRescaleLayer->getRescaleMethod()==NULL) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": RescaleLayer \"%s\" does not have rescaleMethod set. Exiting.\n", name, targetRescaleLayer->getName());
status = PV_FAILURE;
}
else if (!strcmp(targetRescaleLayer->getRescaleMethod(), "maxmin")) {
if (!isRoot) { return PV_SUCCESS; }
for(int b = 0; b < parent->getNBatch(); b++){
float targetMax = targetRescaleLayer->getTargetMax();
if (fabs(fMax[b]-targetMax)>tolerance) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": RescaleLayer \"%s\" has max %f instead of target max %f\n", getName(), targetRescaleLayer->getName(), fMax[b], targetMax);
status = PV_FAILURE;
}
float targetMin = targetRescaleLayer->getTargetMin();
if (fabs(fMin[b]-targetMin)>tolerance) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": RescaleLayer \"%s\" has min %f instead of target min %f\n", getName(), targetRescaleLayer->getName(), fMin[b], targetMin);
status = PV_FAILURE;
}
// Now, check whether rescaled activity and original V are colinear.
PVLayerLoc const * rescaleLoc = targetRescaleLayer->getLayerLoc();
PVHalo const * rescaleHalo = &rescaleLoc->halo;
int nk = rescaleLoc->nx * rescaleLoc->nf;
int ny = rescaleLoc->ny;
int rescaleStrideYExtended = (rescaleLoc->nx + rescaleHalo->lt + rescaleHalo->rt) * rescaleLoc->nf;
int rescaleExtendedOffset = kIndexExtended(0, rescaleLoc->nx, rescaleLoc->ny, rescaleLoc->nf, rescaleHalo->lt, rescaleHalo->rt, rescaleHalo->dn, rescaleHalo->up);
pvadata_t const * rescaledData = targetRescaleLayer->getLayerData() + b * targetRescaleLayer->getNumExtended() + rescaleExtendedOffset;
PVLayerLoc const * origLoc = targetRescaleLayer->getOriginalLayer()->getLayerLoc();
PVHalo const * origHalo = &origLoc->halo;
assert(nk == origLoc->nx * origLoc->nf);
assert(ny == origLoc->ny);
int origStrideYExtended = (origLoc->nx + origHalo->lt + origHalo->rt) * origLoc->nf;
int origExtendedOffset = kIndexExtended(0, rescaleLoc->nx, rescaleLoc->ny, rescaleLoc->nf, rescaleHalo->lt, rescaleHalo->rt, rescaleHalo->dn, rescaleHalo->up);
pvadata_t const * origData = targetRescaleLayer->getOriginalLayer()->getLayerData() + b * targetRescaleLayer->getOriginalLayer()->getNumExtended() + origExtendedOffset;
bool iscolinear = colinear(nk, ny, origStrideYExtended, rescaleStrideYExtended, origData, rescaledData, tolerance, NULL, NULL, NULL);
if (!iscolinear) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": Rescale layer \"%s\" data is not a linear rescaling of original membrane potential.\n", getName(), targetRescaleLayer->getName());
status = PV_FAILURE;
}
}
}
//l2 norm with a patch size of 1 (default) should be the same as rescaling with meanstd with target mean 0 and std of 1/sqrt(patchsize)
else if (!strcmp(targetRescaleLayer->getRescaleMethod(), "meanstd") || !strcmp(targetRescaleLayer->getRescaleMethod(), "l2")) {
if (!isRoot) { return PV_SUCCESS; }
for(int b = 0; b < parent->getNBatch(); b++){
float targetMean, targetStd;
if(!strcmp(targetRescaleLayer->getRescaleMethod(), "meanstd")){
targetMean = targetRescaleLayer->getTargetMean();
targetStd = targetRescaleLayer->getTargetStd();
}
else{
targetMean = 0;
targetStd = 1/sqrt((float)targetRescaleLayer->getL2PatchSize());
}
if (fabs(avg[b]-targetMean)>tolerance) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": RescaleLayer \"%s\" has mean %f instead of target mean %f\n", getName(), targetRescaleLayer->getName(), (double)avg[b], targetMean);
status = PV_FAILURE;
}
if (sigma[b]>tolerance && fabs(sigma[b]-targetStd)>tolerance) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": RescaleLayer \"%s\" has std.dev. %f instead of target std.dev. %f\n", getName(), targetRescaleLayer->getName(), (double)sigma[b], targetStd);
status = PV_FAILURE;
}
// Now, check whether rescaled activity and original V are colinear.
PVLayerLoc const * rescaleLoc = targetRescaleLayer->getLayerLoc();
PVHalo const * rescaleHalo = &rescaleLoc->halo;
int nk = rescaleLoc->nx * rescaleLoc->nf;
int ny = rescaleLoc->ny;
int rescaleStrideYExtended = (rescaleLoc->nx + rescaleHalo->lt + rescaleHalo->rt) * rescaleLoc->nf;
int rescaleExtendedOffset = kIndexExtended(0, rescaleLoc->nx, rescaleLoc->ny, rescaleLoc->nf, rescaleHalo->lt, rescaleHalo->rt, rescaleHalo->dn, rescaleHalo->up);
pvadata_t const * rescaledData = targetRescaleLayer->getLayerData() + b*targetRescaleLayer->getNumExtended() + rescaleExtendedOffset;
PVLayerLoc const * origLoc = targetRescaleLayer->getOriginalLayer()->getLayerLoc();
PVHalo const * origHalo = &origLoc->halo;
assert(nk == origLoc->nx * origLoc->nf);
assert(ny == origLoc->ny);
int origStrideYExtended = (origLoc->nx + origHalo->lt + origHalo->rt) * origLoc->nf;
int origExtendedOffset = kIndexExtended(0, rescaleLoc->nx, rescaleLoc->ny, rescaleLoc->nf, rescaleHalo->lt, rescaleHalo->rt, rescaleHalo->dn, rescaleHalo->up);
pvadata_t const * origData = targetRescaleLayer->getOriginalLayer()->getLayerData() + b*targetRescaleLayer->getOriginalLayer()->getNumExtended() + origExtendedOffset;
bool iscolinear = colinear(nk, ny, origStrideYExtended, rescaleStrideYExtended, origData, rescaledData, tolerance, NULL, NULL, NULL);
if (!iscolinear) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": Rescale layer \"%s\" data is not a linear rescaling of original membrane potential.\n", getName(), targetRescaleLayer->getName());
status = PV_FAILURE;
}
}
}
else if (!strcmp(targetRescaleLayer->getRescaleMethod(), "pointmeanstd")) {
PVLayerLoc const * loc = targetRescaleLayer->getLayerLoc();
int nf = loc->nf;
if (nf<2) { return PV_SUCCESS; }
PVHalo const * halo = &loc->halo;
float targetMean = targetRescaleLayer->getTargetMean();
float targetStd = targetRescaleLayer->getTargetStd();
int numNeurons = targetRescaleLayer->getNumNeurons();
for(int b = 0; b < parent->getNBatch(); b++){
pvpotentialdata_t const * originalData = targetRescaleLayer->getV() + b*targetRescaleLayer->getNumNeurons();
pvadata_t const * rescaledData = targetRescaleLayer->getLayerData() + b*targetRescaleLayer->getNumExtended();
for (int k=0; k<numNeurons; k+=nf) {
int kExtended = kIndexExtended(k, loc->nx, loc->ny, loc->nf, halo->lt, halo->rt, halo->dn, halo->up);
double pointmean = 0.0;
for (int f=0; f<nf; f++) {
pointmean += rescaledData[kExtended+f];
}
pointmean /= nf;
double pointstd = 0.0;
for (int f=0; f<nf; f++) {
double d = rescaledData[kExtended+f]-pointmean;
pointstd += d*d;
}
pointstd /= nf;
pointstd = sqrt(pointstd);
if (fabs(pointmean-targetMean)>tolerance) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": RescaleLayer \"%s\", location in rank %d, starting at restricted neuron %d, has mean %f instead of target mean %f\n",
getName(), targetRescaleLayer->getName(), getParent()->columnId(), k, pointmean, targetMean);
status = PV_FAILURE;
}
if (pointstd>tolerance && fabs(pointstd-targetStd)>tolerance) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": RescaleLayer \"%s\", location in rank %d, starting at restricted neuron %d, has std.dev. %f instead of target std.dev. %f\n",
getName(), targetRescaleLayer->getName(), getParent()->columnId(), k, pointstd, targetStd);
status = PV_FAILURE;
}
bool iscolinear = colinear(nf, 1, 0, 0, &originalData[k], &rescaledData[kExtended], tolerance, NULL, NULL, NULL);
if (!iscolinear) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": RescaleLayer \"%s\", location in rank %d, starting at restricted neuron %d, is not a linear rescaling.\n",
getName(), targetRescaleLayer->getName(), parent->columnId(), k);
status = PV_FAILURE;
}
}
}
}
else if (!strcmp(targetRescaleLayer->getRescaleMethod(), "zerotonegative")) {
int numNeurons = targetRescaleLayer->getNumNeurons();
assert(numNeurons == targetRescaleLayer->getOriginalLayer()->getNumNeurons());
PVLayerLoc const * rescaleLoc = targetRescaleLayer->getLayerLoc();
PVHalo const * rescaleHalo = &rescaleLoc->halo;
int nf = rescaleLoc->nf;
HyPerLayer * originalLayer = targetRescaleLayer->getOriginalLayer();
PVLayerLoc const * origLoc = originalLayer->getLayerLoc();
PVHalo const * origHalo = &origLoc->halo;
assert(origLoc->nf == nf);
for(int b = 0; b < parent->getNBatch(); b++){
pvadata_t const * rescaledData = targetRescaleLayer->getLayerData() + b * targetRescaleLayer->getNumExtended();
pvadata_t const * originalData = originalLayer->getLayerData() + b * originalLayer->getNumExtended();
for (int k=0; k<numNeurons; k++) {
int rescale_kExtended = kIndexExtended(k, rescaleLoc->nx, rescaleLoc->ny, rescaleLoc->nf, rescaleHalo->lt, rescaleHalo->rt, rescaleHalo->dn, rescaleHalo->up);
int orig_kExtended = kIndexExtended(k, origLoc->nx, origLoc->ny, origLoc->nf, origHalo->lt, origHalo->rt, origHalo->dn, origHalo->up);
pvadata_t observedval = rescaledData[rescale_kExtended];
pvpotentialdata_t correctval = originalData[orig_kExtended] ? observedval : -1.0;
if (observedval != correctval) {
fprintf(stderr, "RescaleLayerTestProbe \"%s\": RescaleLayer \"%s\", rank %d, restricted neuron %d has value %f instead of expected %f\n.",
this->getName(), targetRescaleLayer->getName(), parent->columnId(), k, observedval, correctval);
status = PV_FAILURE;
}
}
}
}
else {
assert(0); // All allowable rescaleMethod values are handled above.
}
if (status == PV_FAILURE) {
exit(EXIT_FAILURE);
}
return status;
}
