def run_simulation(repo_path, gen_max):
logger.info("Running simulation with: REPO_PATH={}, GENERATION_MAX={}"
.format(repo_path, gen_max))
# Init base system
sys=System(repo_path=repo_path)
sys.setLowestVersions()
sys.dump()
# Init goal (latest version)
goal=System.latestVersion(sys)
goal.dump()
# Init population
pop=Population(DEFAULT_POP_SIZE, sys, goal)
# Init dataframe for storing results
## TODO
logger.info("Starting simulation...")
for i in range(gen_max):
'''
For each generation we need to go through all the genomes
and evolve them (mutate/crossover/rebel) and test. The more
successful a genome is (more programs installed/tests), the
more likely it is to create offspring for the next generation
'''
logger.info("Generation #{}".format(i))
for genome in pop.getGenomes():
# TODO some evaluation, score, record
logger.info("\t{}".format(genome))
#Check if objective has been met yet
if pop.isGoalMet():
# TODO
logger.info("Goal configuration has been found!")
break
pop=pop.nextGeneration()
logger.debug("System state at end of this generation")
pop.sys.dump()
#TODO do some final reporting
logger.info("Final configuration after {} generations looks like:".format(i))
pop.sys.dump()
