landscapes = settings.landscapes
for nVal in settings.nList:
for kVal in settings.kList:
if kVal <= nVal:
if kVal == nVal:
realK = kVal-1
else:
realK = kVal
for [search, distance, cum] in runConfigs:
params = sim.SimInput(nVal, realK)
params.setSearchMethod(search)
params.setMutateDistance(distance)
params.setCumulativeDistance(cum)
params.generateAdjMatrix()
utils.logInitialConditions(logger, params, landscapes)
simulation = model.NK(params)
key = int(round(time.time() * 1000))
output = simulation.runSimulation(key, landscapes)
utils.logResults(logger, params, output, landscapes)
nextRow = []
nextRow.append(params.nValue())
nextRow.append(params.kValue())
nextRow.append(output.meanFitness())
nextRow.append(params.searchMethodString())
nextRow.append(params.mutateDistance())
nextRow.append(params.cumulativeDistance())
nextRow.append(output.landscapes())
for [search, distance, cum, nBar] in runConfigs:
if (params[configIndex] == None):
params[configIndex] = sim.SimInput(nVal, realK)
params[configIndex].setSearchMethod(search)
params[configIndex].setMutateDistance(distance)
params[configIndex].setCumulativeDistance(cum)
params[configIndex].setNBar(nBar)
params[configIndex].setAdjMatrix(iterationAdjMatrix)
if (savedFitnessDict != None and savedNodeConfig != None):
params[configIndex].setStartConfig(savedNodeConfig, savedFitnessDict)
else:
params[configIndex].clearStartConfig()
# Do you need to initialise the startConfig?
utils.logInitialConditions(logger, params[configIndex], landscapes)
simulation = model.NK(params[configIndex])
""" Save the nodeConfig for use in following iteration """
savedNodeConfig = simulation.nodeConfig()
# header = ["Fields", "Key", "N", "K", "A", "SearchMethod", "Distance", "CumulativeDistance"]
# header += ["Landscape", "NumberOfLandscapes", "AttemptedFlips", "AcceptedFlips", "WasFlipAccepted"]
# header += ["CurrentConfiguration", "CurrentSystemFitness", "ConsideredConfiguration"]
# for i in range(0, nVal):
# header.append("w" + str(i))
# header.append("ConsideredSystemFitness")
key = int(round(time.time() * 1000))
# transactionCSVFile = open("log/" + str(iteration) + str(configIndex) + ".csv", 'w')
# transWriter = csv.writer(transactionCSVFile)
