def evaluateClassifierPerf(ADDSObj, testRun):
	# Negative results below are only set to be measured after a full spike interval has passed and had the opportunity to have created a spike
	# Only evaluate results for enough epochs to test each char in input (3 spike interv per char * 4 char = 12 spike intervals total)
	# the +1 in (totalSpikeIntervals+1) is to allow a last refractoryPointSwitch triggered negative spike evaluation to occur.
	# * A change was made to the scoring to not count the 0.0-0.1 second period because the input spike generator does not start until .1
	# seconds and the first occurences of output spikes should be monitored looking at .2 seconds to see if any occured in seconds .1-.2 .	
	if timeAndRefrac.refractoryPointSwitch == True:
		if (timeAndRefrac.spikeIntervalCounter >= 2) and ((totalSpikeIntervals+1) <= timeAndRefrac.spikeIntervalCounter):
			for neuronIndex in range(dictionaryLongitude):				
				if (ADDSObj.UmSpikeFired[neuronIndex] == 1*mV):
					if (testRun.correctSpikes[neuronIndex][(timeAndRefrac.spikeIntervalCounter-1)] == 1):
						testRun.truePositiveSpikeResults = testRun.truePositiveSpikeResults + 1	
						print 'TP found\t','self.testSpikeIntervalCounter-1\t',timeAndRefrac.spikeIntervalCounter-1,'neuronIndex\t',neuronIndex
					else:
						testRun.falsePositiveSpikeResults = testRun.falsePositiveSpikeResults + 1	
				elif (ADDSObj.UmSpikeFired[neuronIndex] == 0*mV):
					if (testRun.correctSpikes[neuronIndex][(timeAndRefrac.spikeIntervalCounter-1)] == 1):
						testRun.falseNegativeSpikeResults = testRun.falseNegativeSpikeResults + 1		
					else:
						testRun.trueNegativeSpikeResults = testRun.trueNegativeSpikeResults + 1	
		for neuronIndex in range(dictionaryLongitude):											
			ADDSObj.UmSpikeFired[neuronIndex] = 0*mV

	return ADDSObj.UmSpikeFired, testRun
dataset/ETHPy150Open tartavull/snn-rl/furtherFormulas/testingProcesses.py/evaluateClassifierPerf/swapped operands of `<=`