def calculateWinnersAN(settings, populationsAN, classLabels):
nrObs = len(classLabels)
numTotClasses = len(populationsAN)
observationExposureTimeMs = settings["OBSERVATION_EXPOSURE_TIME_MS"]
# set up lists to hold highest spike count and current winning class so
# far for each observation
winningSpikeCount = [0] * nrObs
winningClass = [0] * nrObs
for cls in range(numTotClasses):
allSpikes = populationsAN[cls].getSpikes(compatible_output=True)
for observation in range(nrObs):
startMs = observation * observationExposureTimeMs
endMs = startMs + observationExposureTimeMs
observationSpikes = utils.getSpikesBetween(startMs, endMs, allSpikes)
spikeCount = observationSpikes.shape[0]
# print spikeCount
# print 'StartMs:', startMs, 'EndMs:', endMs, 'Observation:' ,
# observation, 'Class:' , cls, 'Spikes:' , spikeCount
if spikeCount > winningSpikeCount[observation] and spikeCount > 500:
winningSpikeCount[observation] = spikeCount
winningClass[observation] = cls
return winningClass, winningSpikeCount
def calculateWinnersAN(settings, populationsAN, classLabels):
nrObs = len(classLabels)
numTotClasses = len(populationsAN)
observationExposureTimeMs = settings['OBSERVATION_EXPOSURE_TIME_MS']
#set up lists to hold highest spike count and current winning class so
#far for each observation
winningSpikeCount = [0] * nrObs
winningClass = [0] * nrObs
for cls in range(numTotClasses):
allSpikes = populationsAN[cls].getSpikes(compatible_output=True)
for observation in range(nrObs):
startMs = observation * observationExposureTimeMs
endMs = startMs + observationExposureTimeMs
observationSpikes = utils.getSpikesBetween(startMs, endMs,
allSpikes)
spikeCount = observationSpikes.shape[0]
#print spikeCount
#print 'StartMs:', startMs, 'EndMs:', endMs, 'Observation:' ,
#observation, 'Class:' , cls, 'Spikes:' , spikeCount
if spikeCount > winningSpikeCount[observation] and spikeCount > 500:
winningSpikeCount[observation] = spikeCount
winningClass[observation] = cls
return winningClass, winningSpikeCount
def calculateWinnersAN(settings,populationsAN):
numClasses = len(populationsAN)
numObservations = settings['NUM_OBSERVATIONS']
observationExposureTimeMs = settings['OBSERVATION_EXPOSURE_TIME_MS']
#set up lists to hold highest spike count and current winning class so far for each observation
winningSpikeCount = [0] * numObservations
winningClass = [-1] * numObservations
for cls in range(numClasses):
allSpikes = populationsAN[cls].getSpikes(compatible_output=True)
for observation in range(numObservations):
startMs = observation * observationExposureTimeMs
endMs = startMs + observationExposureTimeMs
observationSpikes = utils.getSpikesBetween(startMs,endMs,allSpikes)
spikeCount= observationSpikes.shape[0]
#print 'StartMs:', startMs, 'EndMs:', endMs, 'Observation:' , observation, 'Class:' , cls, 'Spikes:' , spikeCount
if spikeCount > winningSpikeCount[observation]:
winningSpikeCount[observation] = spikeCount
winningClass[observation] = cls
return winningClass
def calculateWinnersAN(settings,populationsAN):
numClasses = len(populationsAN)
numObservations = settings['NUM_OBSERVATIONS']
observationExposureTimeMs = float(settings['OBSERVATION_EXPOSURE_TIME_MS'])
#classActivationExposureFraction = settings['CLASS_ACTIVATION_EXPOSURE_FRACTION']
#set up lists to hold highest spike count and current winning class so far for each observation
winningSpikeCount = [0] * numObservations
winningClass = [-1] * numObservations
spikeInputStartMs = 10000.0
#set spike count window to most , not all the exposure time
allSpikesByClass = list()
'''
This approach is faling during Test. There are much earlier ghost spikes in AN
Try setting up mini pop fed by all of spike injection neurons with strong weigting.
Any true injected spikes will trigger this pop, use it to mark start of true input
'''
#we don't know when first live input spikes will appear as the model takes a varying amount of time to startup
#assume that first spike in whole AN layer is very close to the start of input from the spike sender
#Go thorugh all AN spikes and mark the earliest
for cls in range(numClasses):
allSpikes = populationsAN[cls].getSpikes(compatible_output=True)
allSpikesByClass.append(allSpikes)
spikeTimeMs = utils.getFirstSpikeTime(allSpikes)
if spikeTimeMs < spikeInputStartMs:
spikeInputStartMs = spikeTimeMs
print 'class ' , cls, 'spikeInputStartMs updated to ' , spikeInputStartMs
presentationStartTimes = utils.loadListFromFile("PresentationTimes.txt")
print 'Loaded presentation times from file:', presentationStartTimes
for cls in range(numClasses):
allSpikes = allSpikesByClass[cls] #get ptr to spikes extracted for this class over whole duration
for observation in range(numObservations):
observationStartMs = presentationStartTimes[observation]
'''
#don't set to whole observationExposureTimeMs because first spike may not be right at the start therefore could catch spikes from next observation
observationWindowMs = float(0.9 * observationExposureTimeMs)
offsetMs = spikeInputStartMs + 0.5 * (observationExposureTimeMs-observationWindowMs)
startMs = offsetMs + (observation * observationExposureTimeMs)
'''
startMs = spikeInputStartMs + observationStartMs
endMs = startMs + observationExposureTimeMs
observationSpikes = utils.getSpikesBetween(startMs,endMs,allSpikes)
spikeCount= observationSpikes.shape[0]
print 'Observation:', observation, 'StartMs:', startMs, 'EndMs:', endMs, 'Class:' , cls, 'Spikes:' , spikeCount
if spikeCount > winningSpikeCount[observation]:
winningSpikeCount[observation] = spikeCount
winningClass[observation] = cls
print 'Winning Class for each observation:'
print winningClass
return winningClass