# Load the random state
load_state(random_path)
# -----------------------------------------------------------------
# Load the parameters table
parameters = tables.from_file(parameters_path, format="ascii.ecsv")
# Load the chi squared table
chi_squared = tables.from_file(chi_squared_path, format="ascii.ecsv")
# -----------------------------------------------------------------
# Load the GA
ga = GAEngine.from_file(path)
# Check whether the chi-squared and parameter tables match
for i in range(len(parameters)):
assert parameters["Unique name"][i] == chi_squared["Unique name"][i]
# Get the scores
scores = chi_squared["Chi-squared"]
check = parameters
# Set the scores
ga.set_scores(scores, check)
# -----------------------------------------------------------------
new_generation = last_generation + 1 if last_generation is not None else 0
# Load the random state
load_state(random_path)
# -----------------------------------------------------------------
# Load the parameters table
parameters = tables.from_file(parameters_path, format="ascii.ecsv")
# Load the chi squared table
chi_squared = tables.from_file(chi_squared_path, format="ascii.ecsv")
# -----------------------------------------------------------------
# Load the GA
ga = GAEngine.from_file(path)
# Check whether the chi-squared and parameter tables match
for i in range(len(parameters)):
assert parameters["Unique name"][i] == chi_squared["Unique name"][i]
# Get the scores
scores = chi_squared["Chi-squared"]
check = parameters
# Set the scores
ga.set_scores(scores, check)
# -----------------------------------------------------------------
new_generation = last_generation + 1 if last_generation is not None else 0
# Inform the user
log.info("Creating the GA engine ...")
# Genome instance
genome = G1DList(2)
genome.setParams(rangemin=0., rangemax=50., bestrawscore=0.00, rounddecimal=2)
genome.initializator.set(initializators.G1DListInitializatorReal)
genome.mutator.set(mutators.G1DListMutatorRealGaussian)
#genome.evaluator.set(chi_squared_function)
# Inform the user
log.info("Creating the GA engine ...")
# Genetic algorithm instance
ga = GAEngine(genome)
ga.terminationCriteria.set(RawScoreCriteria)
ga.setMinimax(constants.minimaxType["minimize"])
ga.setGenerations(5)
ga.setCrossoverRate(0.5)
ga.setPopulationSize(100)
ga.setMutationRate(0.5)
# Initialize the genetic algorithm
ga.initialize()
name_column = []
par_a_column = []
par_b_column = []
seed = config.seed
prng = setup_prng(seed)
# -----------------------------------------------------------------
# Genome instance
genome = G1DList(2)
genome.setParams(rangemin=0., rangemax=50., bestrawscore=0.00, rounddecimal=2)
genome.initializator.set(initializators.G1DListInitializatorReal)
genome.mutator.set(mutators.G1DListMutatorRealGaussian)
# Set the evaluator function
genome.evaluator.set(chi_squared_function)
# Genetic algorithm instance
ga = GAEngine(genome)
ga.terminationCriteria.set(RawScoreCriteria)
ga.setMinimax(constants.minimaxType["minimize"])
ga.setGenerations(5)
ga.setCrossoverRate(0.5)
ga.setPopulationSize(100)
ga.setMutationRate(0.5)
# Evolve
#ga.evolve(freq_stats=False)
ga.evolve()
print("Final generation:", ga.currentGeneration)
# -----------------------------------------------------------------
# Inform the user
log.info("Creating the GA engine ...")
# Genome instance
genome = G1DList(2)
genome.setParams(rangemin=0., rangemax=50., bestrawscore=0.00, rounddecimal=2)
genome.initializator.set(initializators.G1DListInitializatorReal)
genome.mutator.set(mutators.G1DListMutatorRealGaussian)
#genome.evaluator.set(chi_squared_function)
# Inform the user
log.info("Creating the GA engine ...")
# Genetic algorithm instance
ga = GAEngine(genome)
ga.terminationCriteria.set(RawScoreCriteria)
ga.setMinimax(constants.minimaxType["minimize"])
ga.setGenerations(5)
ga.setCrossoverRate(0.5)
ga.setPopulationSize(100)
ga.setMutationRate(0.5)
# Initialize the genetic algorithm
ga.initialize()
name_column = []
par_a_column = []
par_b_column = []
pop = ga.internalPop
seed = config.seed
prng = setup_prng(seed)
# -----------------------------------------------------------------
# Genome instance
genome = G1DList(2)
genome.setParams(rangemin=0., rangemax=50., bestrawscore=0.00, rounddecimal=2)
genome.initializator.set(initializators.G1DListInitializatorReal)
genome.mutator.set(mutators.G1DListMutatorRealGaussian)
# Set the evaluator function
genome.evaluator.set(chi_squared_function)
# Genetic algorithm instance
ga = GAEngine(genome)
ga.terminationCriteria.set(RawScoreCriteria)
ga.setMinimax(constants.minimaxType["minimize"])
ga.setGenerations(5)
ga.setCrossoverRate(0.5)
ga.setPopulationSize(100)
ga.setMutationRate(0.5)
# Evolve
#ga.evolve(freq_stats=False)
ga.evolve()
print("Final generation:", ga.currentGeneration)
# -----------------------------------------------------------------
