def roomTemperature():
exp = Experiment()
plotting = Plotter()
plotting.initialize(filt = "median, movingAverage", skip = 500)
dimension = 20
exp.run_experiments_multiple(RoomTemperature(), {},\
[VAR(),\
ECVARMAOGD(),\
OnlineWaveFilteringParameterFree(),\
OnlineWaveFilteringParameterFree(),\
OnlineWaveFilteringParameterFree()
],\
[{'p' : 8, 'dim': dimension},\
{'p' : 8, 'dim': dimension, 'lr': 1},\
{'max_k' : 10, 'action_dim': dimension, 'out_dim': dimension, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'max_k' : 30, 'action_dim': dimension, 'out_dim': dimension, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'max_k' : 50, 'action_dim': dimension, 'out_dim': dimension, 'opt': Hedge(), 'optForSubPredictors': FTRL()}
],\
['VAR_8',\
'ECVARMA_OGD16',\
'OnlineWaveFilteringParameterFree10',\
'OnlineWaveFilteringParameterFree30',\
'OnlineWaveFilteringParameterFree50'
],\
n_runs = 20,\
plotter = plotting,\
verbose = True,
action_generator = ProblemBasedAction(RoomTemperature()))
def sp500():
exp = Experiment()
plotting = Plotter()
plotting.initialize(filt = "movingAverage", skip = 100)
exp.run_experiments_multiple(SP500(), {},\
[ARMA(),\
ARIMA(),\
OnlineWaveFilteringParameterFree(),\
OnlineWaveFilteringParameterFree(),\
OnlineWaveFilteringParameterFree()
],\
[{'p' : 64, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'p' : 16, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'max_k' : 20, 'action_dim': 1, 'out_dim': 1, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'max_k' : 50, 'action_dim': 1, 'out_dim': 1, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'max_k' : 100, 'action_dim': 1, 'out_dim': 1, 'opt': Hedge(), 'optForSubPredictors': FTRL()}
],\
['ARMA_OGD',\
'ARIMA_OGD',\
'OnlineWaveFilteringParameterFree20',\
'OnlineWaveFilteringParameterFree50',\
'OnlineWaveFilteringParameterFree100'
],\
n_runs = 20,\
plotter = plotting,\
verbose = True,
action_generator = ProblemBasedAction(SP500()))
def artif_exp_4():
exp = Experiment()
plotting = Plotter()
plotting.initialize(yscale='log',
filt="median, movingAverage",
skip=100,
ylabel="Average Squared Error",
col=['c', 'r', 'g', 'orange', 'k'])
T = 1000
ag = RandomAction(mu=0, sigma=0.3)
exp.run_experiments_multiple(Setting4(), {"timesteps" : T},\
[OnlineWaveFilteringParameterFree(),\
OnlineWaveFiltering(),\
EMKalmanFilter(),\
SSIDKalmanFilter(),\
Consistency()],\
[{'timesteps': T, 'max_k' : 30, 'action_dim': 1, 'out_dim': 1, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'timesteps': T, 'k' : 10, 'lr': 1e-2, 'action_dim': 1, 'out_dim': 1, 'R_m': 3.0},\
{'timesteps': T, 'order': 2, 'data': 100, 'iter': 500},\
{'timesteps': T, 'order': 2, 'data': 100},\
{'timesteps': T, 'out_dim': 1}],\
['OnlineWaveFilteringParameterFree',\
'OnlineWaveFiltering',\
'EM',\
'4SID',\
'Consistency'],\
n_runs = 20,\
plotter = plotting,\
action_generator = ag,\
verbose = True)
def artif_exp_2():
exp = Experiment()
plotting = Plotter()
plotting.initialize(yscale='log',
filt="median, movingAverage",
skip=100,
ylabel="Average Squared Error",
col=['c', 'r', 'g', 'orange', 'k'])
A = np.array([[0.999, 0], [0, 0.5]])
B = np.array([[1.0], [1.0]])
C = np.array([[1.0, 1.0]])
D = np.array([[0.0]])
T = 1000
ag = RandomAction(mu=0, sigma=0.3)
exp.run_experiments_multiple(LDS(), {"timesteps" : T, 'action_dim': 1, 'hidden_dim': 2, 'out_dim': 1, 'partially_observable': True,\
'system_params': {'A': A, 'B' : B, 'C': C, 'D': D, 'noise_distribution': 'normal'}},\
[OnlineWaveFilteringParameterFree(),\
OnlineWaveFiltering(),\
EMKalmanFilter(),\
SSIDKalmanFilter(),\
Consistency()],\
[{'timesteps': T, 'max_k' : 30, 'action_dim': 1, 'out_dim': 1, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'timesteps': T, 'k' : 10, 'lr': 1e-3, 'action_dim': 1, 'out_dim': 1, 'R_m': 1.0},\
{'timesteps': T, 'order': 2, 'data': 100, 'iter': 500},\
{'timesteps': T, 'order': 2, 'data': 100},\
{'timesteps': T, 'out_dim': 1}],\
['OnlineWaveFilteringParameterFree',\
'OnlineWaveFiltering',\
'EM',\
'4SID',\
'Consistency'],\
n_runs = 20,\
plotter = plotting,\
action_generator = ag,\
verbose = True)
def artif_exp_5():
exp = Experiment()
plotting = Plotter()
plotting.initialize(yscale='log',
filt="median, movingAverage",
skip=100,
ylabel="Average Squared Error",
col=['c', 'r', 'g', 'k'])
A = np.diag([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
B = np.eye(10)
C = np.random.normal(size=(10, 10)) * 0.3
D = np.zeros((10, 10))
T = 1000
ag = BlockAction(prob_repeat=0.8, sigma=0.3)
exp.run_experiments_multiple(LDS(), {'action_dim': 10, 'hidden_dim': 10, 'out_dim': 10, 'partially_observable': True,\
'system_params': {'A': A, 'B' : B, 'C': C, 'D': D}},\
[OnlineWaveFilteringParameterFree(),\
OnlineWaveFiltering(),\
EMKalmanFilter(),\
Consistency()],\
[{'timesteps': T, 'max_k' : 30, 'action_dim': 10, 'out_dim': 10, 'opt': Hedge(), 'optForSubPredictors': FTRL()},\
{'timesteps': T, 'k' : 30, 'lr': 1e-4, 'action_dim': 10, 'out_dim': 10, 'R_m': 5},\
{'timesteps': T, 'order': 10, 'data': 100, 'iter': 500},\
{'timesteps': T, 'out_dim': 10}],\
['OnlineWaveFilteringParameterFree',\
'OnlineWaveFiltering',\
'EM',\
'Consistency'],\
action_generator = ag,\
n_runs = 20,\
plotter = plotting,\
verbose = True)
def sp500Tuning():
exp = Experiment()
plotting = Plotter()
plotting.initialize(filt = "movingAverage", skip = 100)
exp.run_experiments_multiple(SP500(), {},\
[ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
ARIMA(),\
],\
[{'p' : 8, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'p' : 8, 'optimizer': RealOGD(hyperparameters={'lr':1.0})},\
{'p' : 8, 'optimizer': RealOGD(hyperparameters={'lr':0.1})},\
{'p' : 8, 'optimizer': RealOGD(hyperparameters={'lr':0.01})},\
{'p' : 8, 'optimizer': RealOGD(hyperparameters={'lr':0.001})},\
{'p' : 8, 'optimizer': RealOGD(hyperparameters={'lr':0.0001})},\
{'p' : 16, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'p' : 16, 'optimizer': RealOGD(hyperparameters={'lr':1.0})},\
{'p' : 16, 'optimizer': RealOGD(hyperparameters={'lr':0.1})},\
{'p' : 16, 'optimizer': RealOGD(hyperparameters={'lr':0.01})},\
{'p' : 16, 'optimizer': RealOGD(hyperparameters={'lr':0.001})},\
{'p' : 16, 'optimizer': RealOGD(hyperparameters={'lr':0.0001})},\
{'p' : 32, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'p' : 32, 'optimizer': RealOGD(hyperparameters={'lr':1.0})},\
{'p' : 32, 'optimizer': RealOGD(hyperparameters={'lr':0.1})},\
{'p' : 32, 'optimizer': RealOGD(hyperparameters={'lr':0.01})},\
{'p' : 32, 'optimizer': RealOGD(hyperparameters={'lr':0.001})},\
{'p' : 32, 'optimizer': RealOGD(hyperparameters={'lr':0.0001})},\
{'p' : 64, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'p' : 64, 'optimizer': RealOGD(hyperparameters={'lr':1.0})},\
{'p' : 64, 'optimizer': RealOGD(hyperparameters={'lr':0.1})},\
{'p' : 64, 'optimizer': RealOGD(hyperparameters={'lr':0.01})},\
{'p' : 64, 'optimizer': RealOGD(hyperparameters={'lr':0.001})},\
{'p' : 64, 'optimizer': RealOGD(hyperparameters={'lr':0.0001})},\
{'p' : 8, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'p' : 8, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':1.0})},\
{'p' : 8, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.1})},\
{'p' : 8, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.01})},\
{'p' : 8, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.001})},\
{'p' : 8, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.0001})},\
{'p' : 16, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'p' : 16, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':1.0})},\
{'p' : 16, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.1})},\
{'p' : 16, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.01})},\
{'p' : 16, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.001})},\
{'p' : 16, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.0001})},\
{'p' : 32, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'p' : 32, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':1.0})},\
{'p' : 32, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.1})},\
{'p' : 32, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.01})},\
{'p' : 32, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.001})},\
{'p' : 32, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.0001})},\
{'p' : 64, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':10.0})},\
{'p' : 64, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':1.0})},\
{'p' : 64, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.1})},\
{'p' : 64, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.01})},\
{'p' : 64, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.001})},\
{'p' : 64, 'd' : 2, 'optimizer': RealOGD(hyperparameters={'lr':0.0001})}
],\
['ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD',\
'ARIMA_OGD'
],\
n_runs = 5,\
plotter = plotting,\
verbose = True,
action_generator = ProblemBasedAction(SP500()))
def roomTemperatureTuning():
exp = Experiment()
plotting = Plotter()
plotting.initialize(filt = "median, movingAverage", skip = 0, printLast = True)
dimension = 20
exp.run_experiments_multiple(RoomTemperature(), {},\
[VAR(),\
VAR(),\
VAR(),\
VAR(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD(),\
ECVARMAOGD()
],\
[{'p' : 8, 'dim': dimension},\
{'p' : 16, 'dim': dimension},\
{'p' : 32, 'dim': dimension},\
{'p' : 64, 'dim': dimension},\
{'p' : 8, 'dim': dimension, 'lr': 1},\
{'p' : 16, 'dim': dimension, 'lr': 1},\
{'p' : 32, 'dim': dimension, 'lr': 1},\
{'p' : 64, 'dim': dimension, 'lr': 1},\
{'p' : 8, 'dim': dimension, 'lr': 0.1},\
{'p' : 16, 'dim': dimension, 'lr': 0.1},\
{'p' : 32, 'dim': dimension, 'lr': 0.1},\
{'p' : 64, 'dim': dimension, 'lr': 0.1},\
{'p' : 8, 'dim': dimension, 'lr': 0.01},\
{'p' : 16, 'dim': dimension, 'lr': 0.01},\
{'p' : 32, 'dim': dimension, 'lr': 0.01},\
{'p' : 64, 'dim': dimension, 'lr': 0.01},\
{'p' : 8, 'dim': dimension, 'lr': 0.001},\
{'p' : 16, 'dim': dimension, 'lr': 0.001},\
{'p' : 32, 'dim': dimension, 'lr': 0.001},\
{'p' : 64, 'dim': dimension, 'lr': 0.001}
],\
['VAR_8',\
'VAR_16',\
'VAR_32',\
'VAR_64',\
'ECVARMA_OGD8_1',\
'ECVARMA_OGD16_1',\
'ECVARMA_OGD32_1',\
'ECVARMA_OGD64_1',\
'ECVARMA_OGD8_01',\
'ECVARMA_OGD16_01',\
'ECVARMA_OGD32_01',\
'ECVARMA_OGD64_01',\
'ECVARMA_OGD8_001',\
'ECVARMA_OGD16_001',\
'ECVARMA_OGD32_001',\
'ECVARMA_OGD64_001',\
'ECVARMA_OGD8_0001',\
'ECVARMA_OGD16_0001',\
'ECVARMA_OGD32_0001',\
'ECVARMA_OGD64_0001'
],\
n_runs = 5,\
plotter = plotting,\
verbose = True,
action_generator = ProblemBasedAction(RoomTemperature()))
