def calculateScore(winningClassesByObservation, classLabels):
utils.printSeparator()
print "Correct Answers", classLabels
print "Classifier Responses", winningClassesByObservation
numObservations = len(winningClassesByObservation)
score = 0.0
for i in range(numObservations):
if winningClassesByObservation[i] == classLabels[i]:
score = score + 1.0
scorePercent = 100.0 * score / float(numObservations)
print "Score: ", int(score), "out of ", numObservations, "(", scorePercent, "%)"
utils.printSeparator()
return scorePercent
def calculateScore(winningClassesByObservation,classLabels):
utils.printSeparator()
print 'Correct Answers', classLabels
print 'Classifier Responses', winningClassesByObservation
numObservations = len(winningClassesByObservation)
score = 0.0
for i in range (numObservations):
if winningClassesByObservation[i] == classLabels[i]:
score = score + 1.0
scorePercent = 100.0 * score/float(numObservations)
print 'Score: ', int(score), 'out of ', numObservations, '(', scorePercent, '%)'
utils.printSeparator()
return scorePercent
def saveLearntWeightsPNAN(settings,params,projectionsPNAN,numPopsPN,numPopsAN):
delayPNAN = int(params['DELAY_PN_AN'])
projections = iter(projectionsPNAN)
for an in range(numPopsAN):
for pn in range(numPopsPN):
weightsMatrix = projections.next().getWeights(format="array")
#print 'weightsMatrix with NaN',weightsMatrix
weightsMatrix = np.nan_to_num(weightsMatrix) #sets NaN to 0.0 , no connection from x to y is specified as a NaN entry, may cause problem on imports
#print 'weightsMatrix without NaN',weightsMatrix
weightsList = utils.fromList_convertWeightMatrix(weightsMatrix, delayPNAN)
utils.printSeparator()
#print 'weightsList[',pn,',',an,']',weightsList
utils.saveListToFile(weightsList, getWeightsFilename(settings,'PNAN',pn, an))
def printModelConfigurationSummary(
params, populationsInput, populationsNoiseSource, populationsRN, populationsPN, populationsAN
):
totalPops = (
len(populationsInput)
+ len(populationsNoiseSource)
+ len(populationsRN)
+ len(populationsPN)
+ len(populationsAN)
)
stdMaxNeuronsPerCore = params["MAX_NEURONS_PER_CORE"]
stdpMaxNeuronsPerCore = params["MAX_STDP_NEURONS_PER_CORE"]
inputCores = utils.coresRequired(populationsInput, stdMaxNeuronsPerCore)
noiseCores = utils.coresRequired(populationsNoiseSource, stdMaxNeuronsPerCore)
rnCores = utils.coresRequired(populationsRN, stdMaxNeuronsPerCore)
pnCores = utils.coresRequired(populationsPN, stdMaxNeuronsPerCore)
anCores = utils.coresRequired(populationsAN, stdpMaxNeuronsPerCore)
utils.printSeparator()
print "Population(Cores) Summary"
utils.printSeparator()
print "Input: ", len(populationsInput), "(", inputCores, " cores)"
print "Noise: ", len(populationsNoiseSource), "(", noiseCores, " cores)"
print "RN: ", len(populationsRN), "(", rnCores, " cores)"
print "PN: ", len(populationsPN), "(", pnCores, " cores)"
print "AN: ", len(populationsAN), "(", anCores, " cores)"
print "TOTAL: ", totalPops, "(", inputCores + noiseCores + rnCores + pnCores + anCores, " cores)"
utils.printSeparator()
def printParameters(title, params):
utils.printSeparator()
print title
utils.printSeparator()
for param in params:
print param, "=", params[param]
utils.printSeparator()
def printModelConfigurationSummary(params, populationsInput,
populationsNoiseSource, populationsRN,
populationsPN, populationsAN):
totalPops = len(populationsInput) + len(populationsNoiseSource) + \
len(populationsRN) + len(populationsPN) + len(populationsAN)
stdMaxNeuronsPerCore = params['MAX_NEURONS_PER_CORE']
stdpMaxNeuronsPerCore = params['MAX_STDP_NEURONS_PER_CORE']
inputCores = utils.coresRequired(populationsInput, stdMaxNeuronsPerCore)
noiseCores = utils.coresRequired(populationsNoiseSource,
stdMaxNeuronsPerCore)
rnCores = utils.coresRequired(populationsRN, stdMaxNeuronsPerCore)
pnCores = utils.coresRequired(populationsPN, stdMaxNeuronsPerCore)
anCores = utils.coresRequired(populationsAN, stdpMaxNeuronsPerCore)
utils.printSeparator()
print 'Population(Cores) Summary'
utils.printSeparator()
print 'Input: ', len(populationsInput), '(', inputCores, ' cores)'
print 'Noise: ', len(populationsNoiseSource), '(', noiseCores, ' cores)'
print 'RN: ', len(populationsRN), '(', rnCores, ' cores)'
print 'PN: ', len(populationsPN), '(', pnCores, ' cores)'
print 'AN: ', len(populationsAN), '(', anCores, ' cores)'
print 'TOTAL: ', totalPops, '(', inputCores + noiseCores + rnCores + \
pnCores + anCores, ' cores)'
utils.printSeparator()
def printParameters(title, params):
utils.printSeparator()
print title
utils.printSeparator()
for param in params:
print param, '=', params[param]
utils.printSeparator()
def printModelConfigurationSummary(params, populationsInput, populationsNoiseSource, populationsRN, populationsPN, populationsAN):
totalPops = len(populationsInput) + len(populationsNoiseSource) + len(populationsRN) + len(populationsPN) + len(populationsAN)
stdMaxNeuronsPerCore = params['MAX_NEURONS_PER_CORE']
stdpMaxNeuronsPerCore = params['MAX_STDP_NEURONS_PER_CORE']
inputCores = utils.coresRequired(populationsInput, stdMaxNeuronsPerCore)
noiseCores = utils.coresRequired(populationsNoiseSource, stdMaxNeuronsPerCore)
rnCores = utils.coresRequired(populationsRN, stdMaxNeuronsPerCore)
pnCores = utils.coresRequired(populationsPN, stdMaxNeuronsPerCore)
anCores = utils.coresRequired(populationsAN, stdpMaxNeuronsPerCore)
utils.printSeparator()
print 'Population(Cores) Summary'
utils.printSeparator()
print 'Input: ', len(populationsInput), '(', inputCores, ' cores)'
print 'Noise: ', len(populationsNoiseSource), '(', noiseCores, ' cores)'
print 'RN: ', len(populationsRN), '(', rnCores, ' cores)'
print 'PN: ', len(populationsPN), '(', pnCores, ' cores)'
print 'AN: ', len(populationsAN), '(', anCores, ' cores)'
print 'TOTAL: ', totalPops, '(', inputCores + noiseCores + rnCores + pnCores + anCores, ' cores)'
utils.printSeparator()
