def __init__(self,
data_,
network_,
prior_=None,
localscore_cache=None,
**options):
"""Create a network evaluator for use with missing values.
This evaluator uses a Gibb's sampler for sampling over the space of
possible completions for the missing values.
For more information about Gibb's sampling, consult:
1. http://en.wikipedia.org/wiki/Gibbs_sampling
2. D. Heckerman. A Tutorial on Learning with Bayesian Networks.
Microsoft Technical Report MSR-TR-95-06, 1995. p.21-22.
Any config param for 'gibbs' can be passed in via options.
Use just the option part of the parameter name.
"""
super(MissingDataNetworkEvaluator,
self).__init__(data_, network_, prior_)
self._localscore = None # no cache w/ missing data
config.setparams(self, options)
def __init__(self, **options):
"""Create a XGridController.
Any config param for 'xgrid' can be passed in via options.
Use just the option part of the parameter name.
"""
config.setparams(self, options)
def __init__(self, **options):
"""Create a XGridController.
Any config param for 'xgrid' can be passed in via options.
Use just the option part of the parameter name.
"""
config.setparams(self, options)
def __init__(self, data_=None, prior_=None, **options):
"""Create a Simulated Aneaaling learner.
For more information about Simulated Annealing algorithms, consult:
1. http://en.wikipedia.org/wiki/Simulated_annealing
2. D. Heckerman. A Tutorial on Learning with Bayesian Networks.
Microsoft Technical Report MSR-TR-95-06, 1995. p.35-36.
Any config param for 'simanneal' can be passed in via options.
Use just the option part of the parameter name.
"""
super(SimulatedAnnealingLearner,self).__init__(data_, prior_)
config.setparams(self, options)
if not isinstance(self.seed, network.Network):
self.seed = network.Network(self.data.variables, self.seed)
def __init__(self, data_, network_, prior_=None, localscore_cache=None,
**options):
"""Create a network evaluator for use with missing values.
This evaluator uses a Gibb's sampler for sampling over the space of
possible completions for the missing values.
For more information about Gibb's sampling, consult:
1. http://en.wikipedia.org/wiki/Gibbs_sampling
2. D. Heckerman. A Tutorial on Learning with Bayesian Networks.
Microsoft Technical Report MSR-TR-95-06, 1995. p.21-22.
Any config param for 'gibbs' can be passed in via options.
Use just the option part of the parameter name.
"""
super(MissingDataNetworkEvaluator, self).__init__(data_, network_,
prior_)
self._localscore = None # no cache w/ missing data
config.setparams(self, options)
def __init__(self, **options):
config.setparams(self, options)
self.ec2 = ec2ipy1.EC2Cluster(self.config)
self.start()
def __init__(self, **options):
config.setparams(self, options)
self.ec2 = ec2ipy1.EC2Cluster(self.config)
self.start()