def lauchCMAESForListOfPoints(target_size, rs, save, points):
p = ThreadPool(processes=len(points))
posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
p.map(
partial(launchCMAESForSpecificTargetSizeAndSpecificPointMulti,
target_size, rs, save), [[i, posIni[i]] for i in points])
p.close()
p.join()
def launchCMAESForAllTargetSizesMulti(rs):
'''
Launch in parallel (on differents processor) the cmaes optimization for each target size
'''
#initializes setup variables
#initializes a pool of worker, ie multiprocessing
p = ThreadPool(processes=4)
#run cmaes on each targets size on separate processor
p.map(partial(launchCMAESForSpecificTargetSize, rs=rs, save=False), rs.target_size)
p.close()
p.join()
def launchCMAESForAllTargetSizesMulti(rs):
'''
Launch in parallel (on differents processor) the cmaes optimization for each target size
'''
#initializes setup variables
#initializes a pool of worker, ie multiprocessing
p = ThreadPool(processes=4)
#run cmaes on each targets size on separate processor
p.map(partial(launchCMAESForSpecificTargetSize, rs=rs, save=False),
rs.target_size)
p.close()
p.join()
def launch(rs):
all_points = []
for target_size in [0.005, 0.01, 0.02, 0.04]:
for i in range(15):
if not check_if_theta_file_exists(target_size, i):
all_points.append([i, target_size])
p = ThreadPool(processes=len(all_points))
posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
p.map(partial(launchCMAESMissing, rs, True, 6), [[point[0], posIni[point[0]], point[1]] for point in all_points])
p.close()
p.join()
def launch(rs):
all_points = []
for target_size in [0.005, 0.01, 0.02, 0.04]:
for i in range(15):
if not check_if_theta_file_exists(target_size, i):
all_points.append([i, target_size])
p = ThreadPool(processes=len(all_points))
posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
p.map(partial(launchCMAESMissing, rs, True, 6),
[[point[0], posIni[point[0]], point[1]] for point in all_points])
p.close()
p.join()
def launchCMAESForAllPoint(rs, target_size, save, noise=None):
"""
Launch in parallel (on differents processor) the cmaes optimization for each point
input:
rs: setup file
target_size: size of the target
save: for save experience log
noise: noise on muscle, if None, defalt noise from muscle setup
"""
p = ThreadPool(processes=15)
#run cmaes on each targets size on separate processor
posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
p.map(partial(launchCMAESForSpecificTargetSizeAndSpecificPoint, target_size, rs, save, noise=noise), enumerate(posIni))
p.close()
p.join()
def launchCMAESForAllPoint(rs, target_size, save, noise=None):
"""
Launch in parallel (on differents processor) the cmaes optimization for each point
input:
rs: setup file
target_size: size of the target
save: for save experience log
noise: noise on muscle, if None, defalt noise from muscle setup
"""
p = ThreadPool(processes=15)
#run cmaes on each targets size on separate processor
posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
p.map(
partial(launchCMAESForSpecificTargetSizeAndSpecificPoint,
target_size,
rs,
save,
noise=noise), enumerate(posIni))
p.close()
p.join()
def lauchCMAESForListOfPoints(target_size, rs, save, points):
p = ThreadPool(processes=len(points))
posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
p.map(partial(launchCMAESForSpecificTargetSizeAndSpecificPointMulti, target_size, rs, save), [[i, posIni[i]] for i in points])
p.close()
p.join()
