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")
weightsList = utils.fromList_convertWeightMatrix(weightsMatrix, delayPNAN)
# utils.printSeparator()
# print 'weightsList[',pn,',',an,']',weightsList
utils.saveListToFile(weightsList, getWeightsFilename(settings, "PNAN", pn, an))
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")
weightsList = utils.fromList_convertWeightMatrix(weightsMatrix, delayPNAN)
#utils.printSeparator()
#print 'weightsList[',pn,',',an,']',weightsList
utils.saveListToFile(weightsList, getWeightsFilename(settings,'PNAN',pn, an))
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))
