def doSimAnneal(env, model):
print(
"You are about to run a hill climber starting with a random starting model."
)
print(
"Within the parameters you can choose if you want to run the hill climber with option 1: constraints (checking if the model is valid) or option 2: without constraints (returned model is not valid)"
)
print(
"Another choice to make is what kind of mutation you want. Option 1: Move 1 house to another battery. Option 2: switch two houses between random batteries"
)
print(
"The last choice of this algorithm is the cooling scheme. option 1 for a linear cooling scheme, option 2 for an exponential cooling scheme or option 3 for a sigmoid cooling scheme"
)
print(
"Please fill in the following parameters separated by a space: Amount of iterations, constraint option 1 or 2, mutation option 1 or 2, amount of moves before checking, cooling scheme option 1, 2 or 3"
)
iterations, chooseConstraint, mutation, moves, coolingSchedule = input(
).split()
model = simAnneal(env, model, int(iterations), int(chooseConstraint),
int(mutation), int(moves), int(coolingSchedule))
name = str("SimAnneal " + str(iterations) + " " + str(chooseConstraint) +
" " + str(mutation) + " " + str(moves) + " " +
str(coolingSchedule))
model.setName(name, 0)
model.printResult()
model.write(env)
visVillage(env, model)
def doRandom(env, choice):
print("How many times would you like to perform a random sampling?")
iterations = int(input())
costs = []
for i in range(0, iterations):
model = runRandom(env)
model.setName("random sampling", i)
model.printResult()
costs.append(model.cost)
visVillage(env, model)
plotHistMultiple([costs], ["random sampling"], choice, iterations,
env.village)
def doKmeans(env):
print(
"You are about to run a K-means algorithm. Please fill in the following parameter: iteration"
)
iteration = int(input())
kMeansTuple = kMeans(env, iteration)
kMeansEnv = kMeansTuple[0]
kMeansModel = kMeansTuple[1]
kMeansModel.calculateCosts(kMeansEnv.distanceTable)
name = str("K-means " + str(iteration))
kMeansModel.setName(name, 0)
kMeansModel.printResult()
kMeansModel.write(kMeansEnv)
visVillage(kMeansEnv, kMeansModel)
def doEvolution(env):
print(
"You are about to run an evolution algorithm. Make sure you have read the readme to know exactly what the parameters do."
)
print(
"Please fill in the following parameters separated by a space: maximum amount of generations, population size, probability of mutation, crossover probability"
)
maximumGenerations, populationSize, mutationProbability, crossoverProbability = input(
).split()
evoModel = evolution(env, int(maximumGenerations), int(populationSize), 1,
1, 0.5, float(mutationProbability),
float(crossoverProbability), True)
name = str("Evolution " + str(maximumGenerations) + " " +
str(populationSize) + " " + str(mutationProbability) + " " +
str(crossoverProbability))
evoModel.setName(name, 0)
evoModel.printResult
evoModel.write(env)
visVillage(env, evoModel)
def doHillClimber(env, model):
print(
"You are about to run a hill climber starting with a random starting model."
)
print(
"Within the parameters you can choose if you want to run the hill climber with option 1: constraints (checking if the model is valid) or option 2: without constraints (returned model is not valid)"
)
print(
"Another choice to make is what kind of mutation you want. Option 1: Move 1 house to another battery. Option 2: switch two houses between random batteries"
)
print(
"Please fill in the following parameters separated by a space: Amount of iterations, constraint option 1 or 2, mutation option 1 or 2, amount of moves before checking"
)
iterations, chooseConstraints, mutation, moves = input().split()
hcModel = hillClimber(env, model, int(iterations), int(chooseConstraints),
int(mutation), int(moves))
name = str("Hill climber " + str(iterations) + " " +
str(chooseConstraints) + " " + str(mutation) + " " + str(moves))
hcModel.setName(name, 0)
hcModel.printResult()
hcModel.write(env)
visVillage(env, hcModel)
def doDepthFirst(env):
model = depthFirstBnB(env)
model.setName("Depth First", 0)
model.printResult()
visVillage(env, model)