def sarcos_all_joints_experiment(method,
components,
sparsity_factor,
run_id,
image=None,
n_threads=1,
partition_size=3000,
optimize_stochastic=False):
"""
Run the sarcos experiment on all joints.
Parameters
----------
method : str
The method under which to run the experiment (mix1, mix2, or full).
sparsity_factor : float
The sparsity of inducing points.
run_id : int
The id of the configuration.
"""
name = 'sarcos_all_joints'
data = data_source.sarcos_all_joints_data()[run_id - 1]
kernel = get_kernels(data['train_inputs'].shape[1], 8, False)
cond_ll = likelihood.CogLL(0.1, 7, 1)
scaler = data_transformation.preprocessing.StandardScaler().fit(
data['train_inputs'])
data['train_inputs'] = scaler.transform(data['train_inputs'])
data['test_inputs'] = scaler.transform(data['test_inputs'])
transform = data_transformation.MeanStdYTransformation(
data['train_inputs'], data['train_outputs'])
return run_model.run_model(data['train_inputs'],
data['train_outputs'],
data['test_inputs'],
data['test_outputs'],
cond_ll,
kernel,
method,
components,
name,
data['id'],
sparsity_factor,
transform,
False,
False,
optimization_config={
'mog': 25,
'hyp': 10,
'll': 10,
'inducing': 6
},
max_iter=200,
partition_size=partition_size,
ftol=10,
n_threads=n_threads,
model_image_dir=image,
optimize_stochastic=optimize_stochastic)
def sarcos_inducing_experiment(method,
sparsity_factor,
run_id,
image=None,
n_threads=1,
partition_size=3000,
optimize_stochastic=False):
"""
Run the sarcos experiment on two joints.
Parameters
----------
method : str
The method under which to run the experiment (mix1, mix2, or full).
sparsity_factor : float
The sparsity of inducing points.
run_id : int
The id of the configuration.
"""
name = 'sarcos'
data = data_source.sarcos_data()[run_id - 1]
kernel = get_kernels(data['train_inputs'].shape[1], 3, False)
cond_ll = likelihood.CogLL(0.1, 2, 1)
transform = data_transformation.MeanStdYTransformation(
data['train_inputs'], data['train_outputs'])
return run_model.run_model(data['train_inputs'],
data['train_outputs'],
data['test_inputs'],
data['test_outputs'],
cond_ll,
kernel,
method,
name,
data['id'],
sparsity_factor,
transform,
True,
False,
optimization_config={
'mog': 5,
'hyp': 2,
'll': 2,
'inducing': 1
},
max_iter=200,
partition_size=partition_size,
n_threads=n_threads,
model_image_dir=image,
optimize_stochastic=optimize_stochastic)