def tryOneParameter(self, key, fitnessFunctionName, args=(), nbIndividuals=30): #only makes one parameters vary for a range of value
firstValue = self.initialIndividual.inputParametersVariable[key]['value']*(1-self.initialIndividual.inputParametersVariable[key]['variation']/100)
lastValue = self.initialIndividual.inputParametersVariable[key]['value']*(1+self.initialIndividual.inputParametersVariable[key]['variation']/100)
step = (lastValue - firstValue)/nbIndividuals
xaxis = [firstValue]*nbIndividuals
yaxis = [None]*nbIndividuals
newIndiv = copy.deepcopy(self.initialIndividual)
newIndiv.inputParametersVariable[key]['value'] = firstValue
with Timer.LoggerTimer("{} {} | Fitness : {} | Computation Time ".format(newIndiv.inputParametersVariable[key]['name'], newIndiv.inputParametersVariable[key]['value'], newIndiv.fitness)): #bug, la fitness affichée est la fitness d'avant le calcul
newIndiv.calc(getattr(newIndiv, fitnessFunctionName), args)
yaxis[0] = newIndiv.fitness
for i in range(nbIndividuals - 1):
newIndiv.inputParametersVariable[key]['value'] = newIndiv.inputParametersVariable[key]['value'] + step
with Timer.LoggerTimer("{} {} | Fitness : {} | Computation Time ".format(newIndiv.inputParametersVariable[key]['name'], newIndiv.inputParametersVariable[key]['value'], newIndiv.fitness)):
newIndiv.calc(getattr(newIndiv, fitnessFunctionName), args)
xaxis[i+1] = newIndiv.inputParametersVariable[key]['value']
yaxis[i+1] = newIndiv.fitness
if (newIndiv.fitness < self.bestIndividual.fitness):
self.bestIndividual = newIndiv
return (xaxis, yaxis)
def testBetaBetaX(population, testName, fitnessFunctionName, args = (), testSize = 41):
nbFigures = -1
beta_x_first = population.initialIndividual.inputParametersVariable['beta_x']['value'] - population.initialIndividual.inputParametersVariable['beta_x']['value']*population.initialIndividual.inputParametersVariable['beta_x']['variation']/100
beta_x_last = population.initialIndividual.inputParametersVariable['beta_x']['value'] + population.initialIndividual.inputParametersVariable['beta_x']['value']*population.initialIndividual.inputParametersVariable['beta_x']['variation']/100
beta_x_step = (beta_x_last - beta_x_first)/10 #10 = nbValues of beta_x we want to try
beta_x_first = beta_x_first + beta_x_step
print("{} {} {}".format(beta_x_first, beta_x_last, beta_x_step))
beta_init_first = population.initialIndividual.inputParametersVariable['beta']['value'] - population.initialIndividual.inputParametersVariable['beta']['value']*population.initialIndividual.inputParametersVariable['beta']['variation']/100
xaxis = [beta_init_first]*(testSize+1)
yaxis = [None]*(testSize+1)
newIndiv = copy.deepcopy(population.initialIndividual)
pathName = "../Results/VariationTests/" + testName
os.makedirs(pathName, exist_ok=True)
while(beta_x_first <= beta_x_last):
i=0
fileNameEnd = testName + "__{}_{}".format('beta_x', round(beta_x_first,2))
newIndiv.inputParametersVariable['beta_x']['value'] = round(beta_x_first,2)
newIndiv.inputParametersVariable['beta']['value'] = round(beta_init_first,2)
beta_first = beta_init_first
beta_last = beta_x_first
beta_step = (beta_last - beta_first) / testSize
while(i<testSize+1 and beta_first <= beta_x_first): # and beta_first <= beta_x_first ?
with Timer.LoggerTimer("{} {} | {} {} | Fitness : {} | Computation Time ".format(newIndiv.inputParametersVariable['beta_x']['name'], newIndiv.inputParametersVariable['beta_x']['value'], newIndiv.inputParametersVariable['beta']['name'], newIndiv.inputParametersVariable['beta']['value'], newIndiv.fitness)):
newIndiv.calc(getattr(newIndiv, fitnessFunctionName), args)
xaxis[i] = newIndiv.inputParametersVariable['beta']['value']
yaxis[i] = newIndiv.fitness
beta_first = round(beta_first + beta_step, 2)
newIndiv.inputParametersVariable['beta']['value'] = beta_first
i = i+1
nbFigures = nbFigures + 1
plt.figure(nbFigures)
#plt.subplot(2,1,nbFigures+1)
plt.plot(xaxis, yaxis, "r+", marker="+")
plt.xlabel(population.initialIndividual.inputParametersVariable['beta']['name'])
plt.ylabel(fileNameEnd)
plt.grid(True)
fileName = "{}_variationTest_{}_{}.png".format('beta', fileNameEnd, testSize)
plt.figure(nbFigures).savefig(pathName + '/' + fileName)
beta_x_first = beta_x_first + beta_x_step
def testDeadriseE(population, testName, fitnessFunctionName, args = (), testSize = 25):
#nbFigures = -1
nbFigures = 0
deadrise = 5
E = 2
stepE = (6-2)/testSize
pathName = "../Results/VariationTests/" + testName
os.makedirs(pathName, exist_ok=True)
xaxis = [E]*(testSize+1)
yaxis_effectivePower = [None]*(testSize+1)
yaxis_stability = [None]*(testSize+1)
i=0
fileNameEnd = 'EffectivePowerAndStability'
while(E <= 6):
population.initialIndividual.inputParametersVariable['e']['value']= E
with Timer.LoggerTimer("E : {} | FitnessEffectivePower : {} | FitnessStability : {}| Computation Time ".format(
population.initialIndividual.inputParametersVariable['e']['value'],
population.initialIndividual.fitnessEffectivePower, population.initialIndividual.fitnessStability)):
population.initialIndividual.calc(getattr(population.initialIndividual, fitnessFunctionName), args)
xaxis[i] = population.initialIndividual.inputParametersVariable['e']['value']
yaxis_effectivePower[i] = population.initialIndividual.fitnessEffectivePower
yaxis_stability[i] = population.initialIndividual.fitnessStability
E = E + stepE
i = i+1
#nbFigures = nbFigures + 1
fig = plt.figure(nbFigures)
ax1 = plt.subplot()
color = 'tab:red'
# color = "r+"
ax1.set_xlabel("{}".format(population.initialIndividual.inputParametersVariable['e']['name']))
ax1.set_ylabel("EffectivePower", color = color)
ax1.tick_params(axis='y', labelcolor=color)
ax1.plot(xaxis, yaxis_effectivePower, color, marker="+")
ax2 = ax1.twinx()
color = 'tab:blue'
# color = "b+"
ax2.set_ylabel("Stability", color = color)
ax2.tick_params(axis='y', labelcolor=color)
ax2.plot(xaxis, yaxis_stability, color, marker="+")
fig.tight_layout()
plt.grid(True)
fileName = "{}_variationTest_{}_{}.png".format('e', fileNameEnd, testSize)
plt.figure(nbFigures).savefig(pathName + '/' + fileName)
plt.close()
def geneticAlgorithm(self, selectionFunction, selectionFunctionArgs = ()):
for i in range(self.nbMaxGeneration):
print("\n\nGen n° {}".format(i+1))
with Timer.LoggerTimer('Next Generation '):
self.nextGeneration(selectionFunction, selectionFunctionArgs)
print(self.bestIndividual)
for i in range(self.populationSize):
print(self.population[i])
self.bestIndividual = self.population[self.populationSize-1]
print(self.bestIndividual)
def testDeadriseLCG(population, testName, fitnessFunctionName, args = (), testSize = 41):
#nbFigures = -1
nbFigures = 0
deadrise = 5
LCG = 24
stepLCG = (30-24)/testSize
pathName = "../Results/VariationTests/" + testName
os.makedirs(pathName, exist_ok=True)
xaxis = [LCG]*(testSize+1)
yaxis_effectivePower = [None]*(testSize+1)
yaxis_stability = [None]*(testSize+1)
while (deadrise <= 25):
i=0
LCG = 24
population.initialIndividual.inputParametersVariable['beta']['value'] = deadrise
population.initialIndividual.inputParametersVariable['beta_x']['value'] = deadrise
fileNameEnd = testName + "__{}_{}".format('Deadrise', deadrise)
while(LCG <= 30):
population.initialIndividual.inputParametersVariable['LCG']['value']= LCG
with Timer.LoggerTimer("{} {} |{} {} | {} | FitnessEffectivePower : {} | FitnessStability : {}| Computation Time ".format(population.initialIndividual.inputParametersVariable['beta_x']['name'], population.initialIndividual.inputParametersVariable['beta_x']['value'], population.initialIndividual.inputParametersVariable['beta']['name'], population.initialIndividual.inputParametersVariable['beta']['value'],
population.initialIndividual.inputParametersVariable['LCG']['value'],
population.initialIndividual.fitnessEffectivePower, population.initialIndividual.fitnessStability)):
population.initialIndividual.calc(getattr(population.initialIndividual, fitnessFunctionName), args)
xaxis[i] = population.initialIndividual.inputParametersVariable['LCG']['value']
yaxis_effectivePower[i] = population.initialIndividual.fitnessEffectivePower
yaxis_stability[i] = population.initialIndividual.fitnessStability
LCG = LCG + stepLCG
i = i+1
#nbFigures = nbFigures + 1
fig = plt.figure(nbFigures)
ax1 = plt.subplot()
color = 'tab:red'
# color = "r+"
ax1.set_xlabel("LCG with set Deadrise Angle of {}°".format(deadrise))
ax1.set_ylabel("EffectivePower", color = color)
ax1.tick_params(axis='y', labelcolor=color)
ax1.plot(xaxis, yaxis_effectivePower, color, marker="+")
ax2 = ax1.twinx()
color = 'tab:blue'
# color = "b+"
ax2.set_ylabel("Stability", color = color)
ax2.tick_params(axis='y', labelcolor=color)
ax2.plot(xaxis, yaxis_stability, color, marker="+")
fig.tight_layout()
plt.grid(True)
fileName = "{}_variationTest_{}_{}.png".format('beta', fileNameEnd, testSize)
plt.figure(nbFigures).savefig(pathName + '/' + fileName)
plt.close()
deadrise = deadrise + 1
# population.tryOneParameter('LCG')
# with Timer.LoggerTimer('\nVariation Test Stability '):
# # variationTestMultiple(population, ["Drag"], ["calcFitnessDrag"])
# for key in ['e']:
# variationTest(population, "EffectivePower", "calcFitnessEffectivePower", (), key)
# with Timer.LoggerTimer('\nVariation Test Stability '):
# variationTest(population, "Stability", "calcFitnessStability", (), 'LCG', 25)
#
with Timer.LoggerTimer('\nVariation Test Stability '):
variationTest(population, "Stability", "calcFitnessStability", (), 'VCG', 25)
with Timer.LoggerTimer('\nVariation Test Stability '):
variationTest(population, "Stability", "calcFitnessStability", (), 'e', 25)
# with Timer.LoggerTimer('\nTest e'):
# testDeadriseE(population, 'EffectivePowerAndStability', 'calcFitnessEffectivePower', ())
#
#
# with Timer.LoggerTimer('\nTest VCG'):
# testDeadriseVCG(population, 'EffectivePowerAndStability', 'calcFitnessEffectivePower', ())
