def __init__(self, simulationParameterObj, actionParameterObj, Bad_Action_Penalty, nbrReaches=REACHES, habitatSize=HABITATS, fixedStartState=False,
discountFactor=0.9, seed=None):
"""
:param simulationParameterObj (SimulationParameterClass), contains all the parameters for the domain
:param actionParameterObj (ActionParameterClass), contains all the parameters for the actions
:param Bad_Action_Penalty (float), a negative value which will be returned as the consequence of over-budget
action or non-allowable action on a state
:param nbrReaches (int), number of reaches in the river network
:param habitatSize (int), number of habitat in each reach
:param fixedStartState (bool), indicates using a random starting state or fixed starting state
:param discountFactor (float), discount factor
:param seed (int), seed for random number generator (default=None)
"""
self.seed = seed
self.fixedStartState = fixedStartState
self.discountFactor = discountFactor
self.Bad_Action_Penalty=Bad_Action_Penalty
if not self.seed is None:
self.randGenerator = random.Random(self.seed)
else:
self.randGenerator = random.Random()
if simulationParameterObj != None:
self.simulationParameterObj = simulationParameterObj
self.actionParameterObj = actionParameterObj
self.dispertionTable = []
self.germinationObj = None
else:
#upstream rate
upStreamRate = 0.1
#downstream rate
downStreamRate = 0.5
#exogenous arrival indicator
exogenousArrivalIndicator = SimulationParameterClass.ExogenousArrivalOn
#competiton parameter
competitionFactor = 1
#there is the same number of
reachArrivalRates = array([[random.randint(100, 1000) for i in xrange(2)] for i in xrange(nbrReaches)])
reachArrivalProbs = array([[random.random() for i in xrange(2)] for i in xrange(nbrReaches)])
#first value is for native and the second one for tamarisk
prodRate = [200, 200]
#first value is for native and the second one for tamarisk
deathRate = [0.2, 0.2]
graph = InvasiveUtility.createRandomGraph(nbrReaches + 1, balanced=True,randGenerator=self.randGenerator)
self.simulationParameterObj = SimulationParameterClass(nbrReaches, habitatSize, prodRate, deathRate,
exogenousArrivalIndicator, reachArrivalRates, reachArrivalProbs, upStreamRate, downStreamRate,
competitionFactor, graph)
self.actionParameterObj = ActionParameterClass(costPerTree=0.1, eradicationCost=0.5, restorationCost=0.9,
eradicationRate=1, restorationRate=1,
costPerReach=10, emptyCost=0.05, varEradicationCost=0.4, varInvasiveRestorationCost=0.8,
varEmptyRestorationCost=0.4, budget=100)
def __init__(self, simulationParameterObj, actionParameterObj, Bad_Action_Penalty, nbrReaches=7, habitatSize=4, fixedStartState=False,
discountFactor=0.9, seed=None):
"""
:param simulationParameterObj (SimulationParameterClass), contains all the parameters for the domain
:param actionParameterObj (ActionParameterClass), contains all the parameters for the actions
:param Bad_Action_Penalty (float), a negative value which will be returned as the consequence of over-budget
action or non-allowable action on a state
:param nbrReaches (int), number of reaches in the river network
:param habitatSize (int), number of habitat in each reach
:param fixedStartState (bool), indicates using a random starting state or fixed starting state
:param discountFactor (float), discount factor
:param seed (int), seed for random number generator (default=None)
"""
self.seed = seed
self.fixedStartState = fixedStartState
self.discountFactor = discountFactor
self.Bad_Action_Penalty=Bad_Action_Penalty
if not self.seed is None:
self.randGenerator = random.Random(self.seed)
else:
self.randGenerator = random.Random()
if simulationParameterObj != None:
self.simulationParameterObj = simulationParameterObj
self.actionParameterObj = actionParameterObj
self.dispertionTable = []
self.germinationObj = None
else:
#upstream rate
upStreamRate = 0.1
#downstream rate
downStreamRate = 0.5
#exogenous arrival indicator
exogenousArrivalIndicator = SimulationParameterClass.ExogenousArrivalOn
#competiton parameter
competitionFactor = 1
#there is the same number of
reachArrivalRates = array([[random.randint(100, 1000) for i in xrange(2)] for i in xrange(nbrReaches)])
reachArrivalProbs = array([[random.random() for i in xrange(2)] for i in xrange(nbrReaches)])
#first value is for native and the second one for tamarisk
prodRate = [200, 200]
#first value is for native and the second one for tamarisk
deathRate = [0.2, 0.2]
graph = InvasiveUtility.createRandomGraph(nbrReaches + 1, balanced=True,randGenerator=self.randGenerator)
self.simulationParameterObj = SimulationParameterClass(nbrReaches, habitatSize, prodRate, deathRate,
exogenousArrivalIndicator, reachArrivalRates, reachArrivalProbs, upStreamRate, downStreamRate,
competitionFactor, graph)
self.actionParameterObj = ActionParameterClass(costPerTree=0.1, eradicationCost=0.5, restorationCost=0.9,
eradicationRate=1, restorationRate=1,
costPerReach=1, emptyCost=0, varEradicationCost=0.5, varInvasiveRestorationCost=0.1,
varEmptyRestorationCost=0, budget=100)
def __init__(self, simulationParameterObj, actionParameterObj, Bad_Action_Penalty, nbrReaches=7, habitatSize=4, fixedStartState=False,
discountFactor=0.9, seed=None):
"""
:param simulationParameterObj (SimulationParameterClass), contains all the parameters for the domain
:param actionParameterObj (ActionParameterClass), contains all the parameters for the actions
:param Bad_Action_Penalty (float), a negative value which will be returned as the consequence of over-budget
action or non-allowable action on a state
:param nbrReaches (int), number of reaches in the river network
:param habitatSize (int), number of habitat in each reach
:param fixedStartState (bool), indicates using a random starting state or fixed starting state
:param discountFactor (float), discount factor
:param seed (int), seed for random number generator (default=None)
"""
self.seed = seed
self.fixedStartState = fixedStartState
self.discountFactor = discountFactor
self.Bad_Action_Penalty=Bad_Action_Penalty
if not self.seed is None:
self.randGenerator = random.Random(self.seed)
else:
self.randGenerator = random.Random()
if simulationParameterObj != None:
self.simulationParameterObj = simulationParameterObj
self.actionParameterObj = actionParameterObj
self.dispersionTable = []
self.germinationObj = None
else:
# ============================ PARAMETERS =====================================
#upstream rate
upStreamRate = 0.1
#downstream rate
downStreamRate = 0.5
#exogenous arrival indicator
exogenousArrivalIndicator = SimulationParameterClass.ExogenousArrivalOn
#exogenousArrivalIndicator = SimulationParameterClass.ExogenousArrivalOff
#competiton parameter
competitionFactor = 1
#there is the same number of
reachArrivalRates = array([[random.randint(100, 1000) for i in xrange(2)] for i in xrange(nbrReaches)])
reachArrivalProbs = array([[random.random() for i in xrange(2)] for i in xrange(nbrReaches)])
#first value is for native and the second one for tamarisk
prodRate = [200, 200]
#first value is for native and the second one for tamarisk
deathRate = [0.2, 0.2]
# ============================ PARAMETERS =====================================
print "arrival rates (N, T)"
print reachArrivalRates[0]
print "arrival probs (N,T)"
print reachArrivalProbs[0]
print "downstreamrate"
print downStreamRate
print "prodRate"
print prodRate
print "deathRate"
print deathRate
graph = InvasiveUtility.createRandomGraph(nbrReaches + 1, balanced=True,randGenerator=self.randGenerator)
self.simulationParameterObj = SimulationParameterClass(nbrReaches, habitatSize, prodRate, deathRate,
exogenousArrivalIndicator, reachArrivalRates, reachArrivalProbs, upStreamRate, downStreamRate,
competitionFactor, graph)
# ============================ PARAMETERS =====================================
self.actionParameterObj = ActionParameterClass(costPerTree=0.1, eradicationCost=0.5, restorationCost=0.9,
eradicationRate=0.85, restorationRate=0.65,
costPerReach=10, emptyCost=0.5, varEradicationCost=0.4, varInvasiveRestorationCost=0.8,
varEmptyRestorationCost=0.4, budget=100)
# ============================ PARAMETERS =====================================
testResults = open("dispersionMatrixAnalysis.txt", "a+")
testResults.write("\n Number of reaches: "+str(nbrReaches)+"\n");
testResults.write(" Number of slots: "+str(habitatSize)+"\n");
testResults.write(" Upstram-Rate: "+str(upStreamRate)+"\n");
testResults.write(" Downstream-Rate: "+str(downStreamRate)+"\n \n");
testResults.close();
