Ejemplo n.º 1
0
    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)
Ejemplo n.º 2
0
    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)
Ejemplo n.º 3
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    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)
Ejemplo n.º 4
0
    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)
Ejemplo n.º 5
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    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)
Ejemplo n.º 6
0
 def __init__(self, **options):
     config.setparams(self, options)
     self.ec2 = ec2ipy1.EC2Cluster(self.config)
     self.start()
Ejemplo n.º 7
0
 def __init__(self, **options):
     config.setparams(self, options)
     self.ec2 = ec2ipy1.EC2Cluster(self.config)
     self.start()