(self, paths, maxOrder=1)
Generates a hierarchy of higher-order
        models for the given path statistics,
        up to a given maximum order

        @param paths: the paths instance for which the model should be created
        @param maxOrder: the maximum order of the multi-order model
Generates a hierarchy of higher-order
        models for the given path statistics,
        up to a given maximum order
Generates a hierarchy of higher - order models for the given path statistics up to a given maximum order
def __init__(self, paths, maxOrder=1):
        """
        Generates a hierarchy of higher-order
        models for the given path statistics,
        up to a given maximum order

        @param paths: the paths instance for which the model should be created
        @param maxOrder: the maximum order of the multi-order model
        """

        ## A dictionary containing the layers of HigherOrderNetworks, where
        ## layers[k] contains the network of order k
        self.layers = {}

        ## the maximum order of this multi-order model
        self.maxOrder = maxOrder

        ## the paths object from which this multi-order model was created
        self.paths = paths

        ## a dictionary of transition matrices for all layers of the model
        self.T = {}

        for k in range(maxOrder+1):
            Log.add('Generating ' + str(k) + '-th order network layer ...')
            self.layers[k] = HigherOrderNetwork(paths, k, paths.separator, False)

            # compute transition matrices for all layers. In order to use the maximally
            # available statistics, we always use sub paths in the calculation
            self.T[k] = self.layers[k].getTransitionMatrix(includeSubPaths=True)

        Log.add('finished.')
def __init__ ( self , paths , maxOrder = 1 ) : ## A dictionary containing the layers of HigherOrderNetworks, where ## layers[k] contains the network of order k self . layers = { } ## the maximum order of this multi-order model self . maxOrder = maxOrder ## the paths object from which this multi-order model was created self . paths = paths ## a dictionary of transition matrices for all layers of the model self . T = { } for k in range ( maxOrder + 1 ) : Log . add ( 'Generating ' + str ( k ) + '-th order network layer ...' ) self . layers [ k ] = HigherOrderNetwork ( paths , k , paths . separator , False ) # compute transition matrices for all layers. In order to use the maximally # available statistics, we always use sub paths in the calculation self . T [ k ] = self . layers [ k ] . getTransitionMatrix ( includeSubPaths = True ) Log . add ( 'finished.' )