def launch_extras(controller, visualizations=True, **kwargs):
'''
Launch post optimization extras. Including visualizations.
Keyword Args:
visualizations (Optional [bool]): If true run default visualizations for the controller. Default false.
'''
if visualizations:
mlv.show_all_default_visualizations(controller)
def launch_extras(controller,visualizations=True, **kwargs):
'''
Launch post optimization extras. Including visualizations.
Keyword Args:
visualizations (Optional [bool]): If true run default visualizations for the controller. Default false.
'''
if visualizations:
mlv.show_all_default_visualizations(controller)
def main():
#M-LOOP can be run with three commands
#First create your interface
interface = CustomInterface()
#Next create the controller. Provide it with your interface and any options you want to set
n = 10
d = 25
'''
para=np.linspace(0,interface.l/2-r,n+1)
low=para[0:n]
up=para[1:n+1]
'''
low = np.array([0] * n)
up = np.array([interface.l / 2 - d / 2] * n)
'''
#z=np.array([0.003, 0.009, 0.009, 0.009, 0.019, 0.021, 0.032, 0.038, 0.043, 0.061, 0.078, 0.079, 0.1, 0.103, 0.103])
z=np.array([0.0, 0.008, 0.019, 0.034, 0.035, 0.042, 0.063, 0.089, 0.099, 0.099])
half=0.03
low=z-half
low=np.maximum(0,low)
up=z+half
up=np.minimum(interface.l/200-r,up)
'''
controller = mlc.create_controller(
interface,
#training_type='random',
controller_type='neural_net',
max_num_runs=2000,
#target_cost = 0,
num_params=n,
min_boundary=low.tolist(),
max_boundary=up.tolist(),
keep_prob=0.8)
#To run M-LOOP and find the optimal parameters just use the controller method optimize
controller.optimize()
#The results of the optimization will be saved to files and can also be accessed as attributes of the controller.
print('Best parameters found:')
print(controller.best_params)
#You can also run the default sets of visualizations for the controller with one command
mlv.show_all_default_visualizations(controller, max_parameters_per_plot=2)
'''
mlv.create_neural_net_learner_visualizations(controller.ml_learner.total_archive_filename,
file_type=controller.ml_learner.learner_archive_file_type,
plot_cross_sections=True)
plt.show()
'''
cal_grad(params=controller.best_params, l=interface.l, r=interface.r)
cal_grad(params=controller.predicted_best_parameters,
l=interface.l,
r=interface.r)
def mloop():
#First create your interface
interface = CustomInterface()
#Next create the controller, provide it with your controller and any options you want to set
controller = mlc.create_controller(interface,
max_num_runs=50,
target_cost=-100,
num_params=1,
min_boundary=[3],
max_boundary=[68])
#To run M-LOOP and find the optimal parameters just use the controller method optimize
controller.optimize()
#The results of the optimization will be saved to files and can also be accessed as attributes of the controller.
print('Best parameters found:')
print(controller.best_params)
#You can also run the default sets of visualizations for the controller with one command
mlv.show_all_default_visualizations(controller)
def main():
#M-LOOP can be run with three commands
#First create your interface
interface = CustomInterface()
#Next create the controller, provide it with your controller and any options you want to set
controller = mlc.create_controller(interface,
max_num_runs = 1000,
target_cost = -2.99,
num_params = 3,
min_boundary = [-2,-2,-2],
max_boundary = [2,2,2])
#To run M-LOOP and find the optimal parameters just use the controller method optimize
controller.optimize()
#The results of the optimization will be saved to files and can also be accessed as attributes of the controller.
print('Best parameters found:')
print(controller.best_params)
#You can also run the default sets of visualizations for the controller with one command
mlv.show_all_default_visualizations(controller)
def main():
#M-LOOP can be run with three commands
#First create your interface
interface = CustomInterface()
#Next create the controller. Provide it with your interface and any options you want to set
n = 10
para = np.linspace(0, 0.135, n + 1)
low = para[0:n]
up = para[1:n + 1]
controller = mlc.create_controller(
interface,
#training_type='random',
controller_type='neural_net',
max_num_runs=5000,
target_cost=0,
num_params=n,
min_boundary=low.tolist(),
max_boundary=up.tolist(),
keep_prob=0.9)
#To run M-LOOP and find the optimal parameters just use the controller method optimize
controller.optimize()
#The results of the optimization will be saved to files and can also be accessed as attributes of the controller.
print('Best parameters found:')
print(controller.best_params)
#You can also run the default sets of visualizations for the controller with one command
mlv.show_all_default_visualizations(controller)
mlv.create_neural_net_learner_visualizations(
controller.ml_learner.total_archive_filename,
file_type=controller.ml_learner.learner_archive_file_type,
plot_cross_sections=True)
plt.show()
cal_grad(controller.best_params)
cal_grad(controller.predicted_best_parameters)
def main():
#M-LOOP can be run with three commands
#First create your interface
interface = CustomInterface()
#Next create the controller. Provide it with your interface and any options you want to set
controller = mlc.create_controller(interface,
max_num_runs = 10000,
controller_type='neural_net',
#target_cost = -2.99,
num_params = 3,
min_boundary = [-2,-2,-2],
max_boundary = [2,2,2])
#To run M-LOOP and find the optimal parameters just use the controller method optimize
controller.optimize()
#The results of the optimization will be saved to files and can also be accessed as attributes of the controller.
print('Best parameters found:')
print(controller.best_params)
#You can also run the default sets of visualizations for the controller with one command
mlv.show_all_default_visualizations(controller)
plt.show()
def main():
# create a file folder to save today's data
today = str(datetime.date.today())
path_today = 'E:/python/code/data/' + today
if not os.path.exists(path_today):
os.makedirs(path_today)
n = 7
interface_2 = CustomInterface_2(n)
controller = mlc.create_controller(
interface_2,
#training_type='differential_evolution',
controller_type='neural_net',
max_num_runs=100,
keep_prob=0.8,
num_params=n,
min_boundary=[0.2] * n,
max_boundary=[1] * n)
# start the second optimization
controller.optimize()
# create the file folder of this optimization
start_datetime = str(controller.start_datetime)[11:19]
start_time = sf.change_colon_into_dash(start_datetime)
path_today += '/' + str(start_time)
os.makedirs(path_today)
# save the best wave
res = sf.add_zero(controller.best_params)
filename = path_today + '/best_paras_2_' + str(start_time) + '.csv'
sf.array_to_csv(filename=filename, array=res)
# save the predicted wave
res = sf.add_zero(controller.predicted_best_parameters)
filename = path_today + '/pre_best_paras_2_' + str(start_time) + '.csv'
sf.array_to_csv(filename=filename, array=res)
# save the seed light of the predicted wave
res = sf.wave_normalization(res)
filename = 'E:/python/documents/wave_opt/gausswave.csv'
sf.array_to_csv(filename=filename, array=res)
siganl = [1]
gain = sf.convey_params_get_cost(siganl)
res = res / gain * interface_2.paras_scale
filename = 'E:/python/documents/wave_opt/gausswave.csv'
sf.array_to_csv(filename=filename, array=res)
signal = [-1]
gate = sf.convey_params_get_cost(signal)
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename_ASQ = 'D:/shot noise/' + sf.today_str() + '/try/ASQ.csv'
df_SQ = pd.read_csv(filename_SQ, header=None).values
df_ASQ = pd.read_csv(filename_ASQ, header=None).values
res = df_SQ - df_ASQ
df_SQ = df_SQ[np.where(np.abs(res - np.mean(res)) < 3 * gate)]
res = res[np.where(np.abs(res - np.mean(res)) < 3 * gate)]
cost = np.var(res) / np.mean(df_SQ)
print('predicted best cost_2 is:', controller.predicted_best_cost)
print('actual cost_2 is:', 10 * np.log10(cost))
filename_seedlight = 'D:/shot noise/' + sf.today_str() + '/try2/seed.csv'
filename = path_today + '/seedlight_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_seedlight, header=None)
df.to_csv(filename, header=0, index=0)
# save the excited stokes light
filename_excited = 'D:/shot noise/' + sf.today_str() + '/try/excited.csv'
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename = path_today + '/excited_light_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_excited, header=None)
df.to_csv(filename, header=0, index=0)
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename = path_today + '/SQ_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_SQ, header=None)
df.to_csv(filename, header=0, index=0)
# save the anti-stokes light
filename_excited = 'D:/shot noise/' + sf.today_str(
) + '/try/antistokes.csv'
filename = path_today + '/antistokes_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_excited, header=None)
df.to_csv(filename, header=0, index=0)
filename_ASQ = 'D:/shot noise/' + sf.today_str() + '/try/ASQ.csv'
filename = path_today + '/ASQ_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_ASQ, header=None)
df.to_csv(filename, header=0, index=0)
# show visualizations
mlv.show_all_default_visualizations(controller)
def main():
# create a file folder to save today's data
today = str(datetime.date.today())
path_today = 'E:/python/code/data/' + today
if not os.path.exists(path_today):
os.makedirs(path_today)
# interface to optimize gain
interface_1 = CustomInterface_1(nums_params=7)
controller = mlc.create_controller(
interface_1,
#training_type='random',
controller_type='neural_net',
max_num_runs=100,
# target_cost = -2.99,
keep_prob=0.8,
num_params=interface_1.nums_params,
min_boundary=[0.2] * interface_1.nums_params,
max_boundary=[1] * interface_1.nums_params)
# To run M-LOOP and find the optimal parameters just use the controller method optimize
controller.optimize()
# create the file folder of this optimization
start_datetime = str(controller.start_datetime)[11:19]
start_time = sf.change_colon_into_dash(start_datetime)
path_today += '/' + str(start_time)
os.makedirs(path_today)
# save the best wave
res = sf.add_zero(controller.best_params)
filename = path_today + '/best_paras_' + str(start_time) + '.csv'
sf.array_to_csv(filename=filename, array=res)
# save the predicted wave
res = sf.add_zero(controller.predicted_best_parameters)
filename = path_today + '/pre_best_paras_' + str(start_time) + '.csv'
sf.array_to_csv(filename=filename, array=res)
# save the seed light of the predicted wave
filename = 'E:/python/documents/wave_opt/gausswave.csv'
sf.array_to_csv(filename=filename, array=res)
signal = [1]
gain = sf.convey_params_get_cost(signal)
print('predicted best cost is:', controller.predicted_best_cost)
print('actual cost is:', 10 * np.log10(gain))
filename_seedlight = 'D:/shot noise/' + sf.today_str() + '/try2/seed.csv'
filename = path_today + '/seedlight_' + str(start_time) + '.csv'
df = pd.read_csv(filename_seedlight, header=None)
df.to_csv(filename, header=0, index=0)
# save the excited stokes light
filename_excited = 'D:/shot noise/' + sf.today_str() + '/try/excited.csv'
filename = path_today + '/excited_light_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_excited, header=None)
df.to_csv(filename, header=0, index=0)
filename_SQ = 'D:/shot noise/' + sf.today_str() + '/try/SQ.csv'
filename = path_today + '/SQ_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_SQ, header=None)
df.to_csv(filename, header=0, index=0)
# save the anti-stokes light
filename_excited = 'D:/shot noise/' + sf.today_str(
) + '/try/antistokes.csv'
filename = path_today + '/antistokes_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_excited, header=None)
df.to_csv(filename, header=0, index=0)
filename_ASQ = 'D:/shot noise/' + sf.today_str() + '/try/ASQ.csv'
filename = path_today + '/ASQ_2_' + str(start_time) + '.csv'
df = pd.read_csv(filename_ASQ, header=None)
df.to_csv(filename, header=0, index=0)
# show visualizations
mlv.show_all_default_visualizations(controller)
'''
