def evaluate_individual(individual):
""" Wrapper to call Simulation which will evaluate an individual.
Args:
individual: arguments to pass to the simulation
Returns:
fitness of an individual
"""
simulation = Simulation(log_frames=args.log_frames, run_num=args.run_num, eval_time=args.eval_time, dt=.02, n=4)
return simulation.evaluate_individual_wrap(individual)
args = parser.parse_args()
running = True
eval_time = args.eval_time
output_path = args.output_path
run_num = args.run_num
# Seed only the evolutionary runs.
random.seed(run_num)
if args.debug_runtime:
# Initialize the Simulation component
simulation = Simulation(log_frames=args.log_frames,
run_num=args.run_num,
eval_time=args.eval_time,
dt=.02,
n=4)
simulation.debug_validator()
elif args.validator:
# Initialize the Simulation component
simulation = Simulation(log_frames=args.log_frames,
run_num=args.run_num,
eval_time=args.eval_time,
dt=.02,
n=4)
# Ensure that the file exists before trying to open to avoid hanging.
NEAT_file = output_path + "best_individuals/Evo_NEAT_Mus_run_" + str(
args.run_num) + "_best_gen_" + str(args.gens) + ".dat"
parser.add_argument("--no_periodic",action="store_true",help="Whether we're including a periodic signal or not.")
parser.add_argument("--sym_mus_groups",action="store_true",help="If true, we use half the number of muscle groups enforcing a left/right symmetry in the muscle groups.")
args = parser.parse_args()
running = True
eval_time = args.eval_time
output_path = args.output_path
run_num = args.run_num
# Seed only the evolutionary runs.
random.seed(run_num)
if args.debug_runtime:
# Initialize the Simulation component
simulation = Simulation(log_frames=args.log_frames, run_num=args.run_num, eval_time=args.eval_time, dt=.02, n=4)
simulation.debug_validator()
elif args.validator:
# Initialize the Simulation component
simulation = Simulation(log_frames=args.log_frames, run_num=args.run_num, eval_time=args.eval_time, dt=.02, n=4)
# Ensure that the file exists before trying to open to avoid hanging.
NEAT_file = output_path+"best_individuals/Evo_NEAT_Mus_run_"+str(args.run_num)+"_best_gen_"+str(args.gens)+".dat"
if not os.path.isfile(NEAT_file):
print("NEAT Genome file doesn't exist! "+NEAT_file)
exit()
Mus_Net_File = output_path+"/best_individuals/Evo_NEAT_Mus_run_"+str(run_num)+"_best_mn_gen_"+str(args.gens)+".dat"
if not os.path.isfile(Mus_Net_File):
print("Muscle Network file doesn't exist! "+Mus_Net_File)
args = parser.parse_args()
running = True
eval_time = args.eval_time
output_path = args.output_path
run_num = args.run_num
# Seed only the evolutionary runs.
random.seed(run_num)
if args.validator:
# Initialize the Simulation component
simulation = Simulation(log_frames=args.log_frames,
run_num=args.run_num,
eval_time=args.eval_time,
dt=.02,
n=4,
con_type="ind")
# Ensure that the file exists before trying to open to avoid hanging.
NEAT_file = output_path + "best_individuals/Evo_NEAT_Mus_run_" + str(
args.run_num) + "_best_gen_" + str(args.gens) + ".dat"
if not os.path.isfile(NEAT_file):
print("NEAT Genome file doesn't exist! " + NEAT_file)
exit()
Mus_Net_File = output_path + "/best_individuals/Evo_NEAT_Mus_run_" + str(
run_num) + "_best_mn_gen_" + str(args.gens) + ".dat"
if not os.path.isfile(Mus_Net_File):
print("Muscle Network file doesn't exist! " + Mus_Net_File)
exit()
random.seed(run_num)
# Set the fitness function to use in the experiment.
if "Distance" in str(args.fit_func): #str(args.fit_func)[0] == "D":
fitness_function = 1
else:
fitness_function = 2
print("Distance Metric is: " + str(fitness_function) + " Argument is: " +
str(args.fit_func))
sys.stdout.flush()
if args.debug_runtime:
# Initialize the Simulation component
simulation = Simulation(log_frames=args.log_frames,
run_num=args.run_num,
eval_time=args.eval_time,
dt=.02,
n=4)
ann_activations = simulation.validator("")
elif args.validator:
# Initialize the Simulation component
simulation = Simulation(log_frames=args.log_frames,
run_num=args.run_num,
eval_time=args.eval_time,
dt=.02,
n=4)
# Ensure that the file exists before trying to open to avoid hanging.
NEAT_file = output_path + "best_individuals/Evo_NEAT_run_" + str(
parser.add_argument("--config_string",type=str,default="",help="Use a string instead of file for configuration.")
parser.add_argument("--no_periodic",action="store_true",help="Whether we're including a periodic signal or not.")
args = parser.parse_args()
running = True
eval_time = args.eval_time
output_path = args.output_path
run_num = args.run_num
# Seed only the evolutionary runs.
random.seed(run_num)
if args.debug_runtime:
# Initialize the Simulation component
simulation = Simulation(log_frames=args.log_frames, run_num=args.run_num, eval_time=args.eval_time, dt=.02, n=4)
simulation.debug_validator()
elif args.validator:
# Initialize the Simulation component
simulation = Simulation(log_frames=args.log_frames, run_num=args.run_num, eval_time=args.eval_time, dt=.02, n=4)
# Ensure that the file exists before trying to open to avoid hanging.
NEAT_file = output_path+"best_individuals/Evo_NEAT_Mus_run_"+str(args.run_num)+"_best_gen_"+str(args.gens)+".dat"
if not os.path.isfile(NEAT_file):
print("NEAT Genome file doesn't exist! "+NEAT_file)
exit()
simulation.validator(NEAT_file)
else:
# Ensure that the necessary folders are created for recording data.