def run(self, ngen=10, population=None,startfile=None, checkpoint=None, cp_freq=1):
"""Run the genetic algorithm, updating the population over ngen number of generations.
Keywork arguments:
ngen -- number of generations to run the genetic algorithm.
startfile -- File containing existing population (default None)
checkpoint -- File containing existing checkpoint (default None)
Output:
population -- Resulting population after ngen generations.
"""
if startfile:
try:
print(f"Reading in {startfile}")
population = self.readpop(startfile)
except FileNotFoundError:
print("WARNING: Start file not found")
except:
raise
if not population:
print(f"Initializing a new random population")
population = self.newpopulation()
if checkpoint:
self.writepop(population, filename=f"{checkpoint}")
for cur_g in range(0, ngen+1):
print(f"Generation {cur_g} of population size {len(population)}")
histogram = Segmentors.popCounts(population)
print(f"#HIST - {histogram}")
_, population = self.popfitness(population)
bestsofar = self.hof[0]
seg = Segmentors.algoFromParams(bestsofar)
mask = seg.evaluate(self.img)
fitness = Segmentors.FitnessFunction(mask, self.mask)
print(f"#BEST - {fitness} - {bestsofar}")
if checkpoint and cur_g%cp_freq == 0:
print(f"Writing Checkpoint file - {checkpoint}")
copyfile(f"{checkpoint}", f"{checkpoint}.prev")
self.writepop(population, filename=f"{checkpoint}")
for cur_p in range(len(population)):
logging.getLogger().info(population[cur_p])
if cur_g < ngen+1:
if bestsofar.fitness.values[0] >= 0.95:
population = self.newpopulation()
# if the best fitness value is at or above the
# threshold of 0.95, discard the entire current
# population and randomly select a new population
# for the next generation
# note: setting keep_prob = 0 and mutate_prob = 1
# as mutate arguments
# should have same result as self.new_population()
else:
population = self.mutate(population)
# if the best fitness value is below this threshold,
# proceed as normal, mutating the current population
# to get the next generation
if checkpoint:
print(f"Writing Checkpoint file - {checkpoint}")
copyfile(f"{checkpoint}", f"{checkpoint}.prev")
self.writepop(population, filename=f"{checkpoint}")
for cur_p in range(len(population)):
logging.getLogger().info(population[cur_p])
return population
def run(self,
ngen=10,
population=None,
startfile=None,
checkpoint=None,
cp_freq=1):
"""Run the genetic algorithm, updating the population over ngen number of generations.
Keywork arguments:
ngen -- number of generations to run the genetic algorithm.
startfile -- File containing existing population (default None)
checkpoint -- File containing existing checkpoint (default None)
Output:
population -- Resulting population after ngen generations.
"""
if startfile:
try:
print(f"Reading in {startfile}")
population = self.readpop(startfile)
except FileNotFoundError:
print("WARNING: Start file not found")
except:
raise
if not population:
print(f"Initializing a new random population")
population = self.newpopulation()
if checkpoint:
self.writepop(population, filename=f"{checkpoint}")
for cur_g in range(0, ngen + 1):
print(f"Generation {cur_g} of population size {len(population)}")
histogram = Segmentors.popCounts(population)
print(f"#HIST - {histogram}")
_, population = self.popfitness(population)
bestsofar = self.hof[0]
seg = Segmentors.algoFromParams(bestsofar)
mask = seg.evaluate(self.img)
fitness = Segmentors.FitnessFunction(mask, self.mask)
print(f"#BEST - {fitness} - {bestsofar}")
if checkpoint and cur_g % cp_freq == 0:
print(f"Writing Checkpoint file - {checkpoint}")
copyfile(f"{checkpoint}", f"{checkpoint}.prev")
self.writepop(population, filename=f"{checkpoint}")
for cur_p in range(len(population)):
logging.getLogger().info(population[cur_p])
if cur_g < ngen + 1:
population = self.mutate(population)
if checkpoint:
print(f"Writing Checkpoint file - {checkpoint}")
copyfile(f"{checkpoint}", f"{checkpoint}.prev")
self.writepop(population, filename=f"{checkpoint}")
for cur_p in range(len(population)):
logging.getLogger().info(population[cur_p])
return population