/**
* @time
* @l
*/
int DatastoreDelayTestProbe::outputState(double timed) {
HyPerLayer * l = getTargetLayer();
InterColComm * icComm = l->getParent()->icCommunicator();
const int rcvProc = 0;
if( icComm->commRank() != rcvProc ) {
return PV_SUCCESS;
}
int status = PV_SUCCESS;
int numDelayLevels = l->getParent()->getLayer(0)->getNumDelayLevels();
pvdata_t correctValue = numDelayLevels*(numDelayLevels+1)/2;
if( timed >= numDelayLevels+2 ) {
pvdata_t * V = l->getV();
for( int k=0; k<l->getNumNeuronsAllBatches(); k++ ) {
if( V[k] != correctValue ) {
fprintf(outputstream->fp, "Layer \"%s\": timef = %f, neuron %d: value is %f instead of %d\n", l->getName(), timed, k, V[k], (int) correctValue);
status = PV_FAILURE;
}
}
if( status == PV_SUCCESS) {
fprintf(outputstream->fp, "Layer \"%s\": timef = %f, all neurons have correct value %d\n", l->getName(), timed, (int) correctValue);
}
}
assert(status == PV_SUCCESS);
return PV_SUCCESS;
}
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 StochasticReleaseTestProbe::outputState(double timed) {
// Set conn. Can't do that in initStochasticReleaseTestProbe because we need to search for a conn with the given post, and connections' postLayerName is not necessarily set.
if (conn==NULL) {
HyPerCol * hc = getTargetLayer()->getParent();
int numconns = hc->numberOfConnections();
for (int c=0; c<numconns; c++) {
if (!strcmp(hc->getConnection(c)->getPostLayerName(),getTargetLayer()->getName())) {
assert(conn==NULL); // Only one connection can go to this layer for this probe to work
BaseConnection * baseConn = hc->getConnection(c);
conn = dynamic_cast<HyPerConn *>(baseConn);
}
}
assert(conn!=NULL);
}
assert(conn->numberOfAxonalArborLists()==1);
assert(conn->xPatchSize()==1);
assert(conn->yPatchSize()==1);
assert(conn->getNumDataPatches()==conn->fPatchSize());
int status = StatsProbe::outputState(timed);
assert(status==PV_SUCCESS);
HyPerLayer * l = getTargetLayer();
HyPerCol * hc = l->getParent();
int nf = l->getLayerLoc()->nf;
if (timed>0.0) {
for (int f=0; f < nf; f++) {
if (computePValues(hc->getCurrentStep(), f)!=PV_SUCCESS) status = PV_FAILURE;
}
assert(status == PV_SUCCESS);
if (hc->columnId()==0 && hc->simulationTime()+hc->getDeltaTime()/2>=hc->getStopTime()) {
// This is the last timestep
// sort the p-values and apply Holm-Bonferroni method since there is one for each timestep and each feature.
long int num_steps = hc->getFinalStep() - hc->getInitialStep();
long int N = num_steps * nf;
qsort(pvalues, (size_t) N, sizeof(*pvalues), compar);
while(N>0 && isnan(pvalues[N-1])) {
N--;
}
for (long int k=0; k<N; k++) {
if (pvalues[k]*(N-k)<0.05) {
fprintf(stderr, "layer \"%s\" FAILED: p-value %ld out of %ld (ordered by size) with Holm-Bonferroni correction = %f\n", getTargetLayer()->getName(), k, N, pvalues[k]*(N-k));
status = PV_FAILURE;
}
}
}
}
assert(status==PV_SUCCESS);
return status;
}
