def __init__(self, hyperparams):
"""
Hyperparameters:
min_samples_per_cluster: Minimum samples per cluster.
max_clusters: Maximum number of clusters to fit.
max_samples: Maximum number of trajectories to use for
fitting the GMM at any given time.
strength: Adjusts the strength of the prior.
"""
config = copy.deepcopy(DYN_PRIOR_GMM)
config.update(hyperparams)
self._hyperparams = config
self.X = None
self.U = None
self.gmm = GMM()
self._min_samp = self._hyperparams['min_samples_per_cluster']
self._max_samples = self._hyperparams['max_samples']
self._max_clusters = self._hyperparams['max_clusters']
self._strength = self._hyperparams['strength']
def __init__(self, hyperparams):
"""
Hyperparameters:
min_samples_per_cluster: Minimum number of samples.
max_clusters: Maximum number of clusters to fit.
max_samples: Maximum number of trajectories to use for
fitting the GMM at any given time.
strength: Adjusts the strength of the prior.
"""
config = copy.deepcopy(POLICY_PRIOR_GMM)
config.update(hyperparams)
self._hyperparams = config
self.X = None
self.obs = None
self.gmm = GMM()
# TODO: handle these params better (e.g. should depend on N?)
self._min_samp = self._hyperparams['min_samples_per_cluster']
self._max_samples = self._hyperparams['max_samples']
self._max_clusters = self._hyperparams['max_clusters']
self._strength = self._hyperparams['strength']
def __init__(self, hyperparams):
"""
Hyperparameters:
min_samples_per_cluster: Minimum number of samples.
max_clusters: Maximum number of clusters to fit.
max_samples: Maximum number of trajectories to use for
fitting the GMM at any given time.
strength: Adjusts the strength of the prior.
"""
config = copy.deepcopy(POLICY_PRIOR_GMM)
config.update(hyperparams)
self._hyperparams = config
self.X = None
self.obs = None
self.gmm = GMM()
self._min_samp = self._hyperparams['min_samples_per_cluster']
self._max_samples = self._hyperparams['max_samples']
self._max_clusters = self._hyperparams['max_clusters']
self._strength = self._hyperparams['strength']
self._init_sig_reg = self._hyperparams['init_regularization']
self._subsequent_sig_reg = self._hyperparams['subsequent_regularization']
def __init__(self, hyperparams):
"""Initializes the dynamics.
Args:
hyperparams: Dictionary of hyperparameters.
Hyperparameters:
min_samples_per_cluster: Minimum samples per cluster.
max_clusters: Maximum number of clusters to fit.
max_samples: Maximum number of trajectories to use for fitting the GMM at any given time.
strength: Adjusts the strength of the prior.
"""
config = copy.deepcopy(DYN_PRIOR_GMM)
config.update(hyperparams)
self._hyperparams = config
self.X = None
self.U = None
self.gmm = GMM()
self._min_samp = self._hyperparams['min_samples_per_cluster']
self._max_samples = self._hyperparams['max_samples']
self._max_clusters = self._hyperparams['max_clusters']
self._strength = self._hyperparams['strength']
self.regularization = self._hyperparams.get('regularization', 0)
