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))