def __init__(
        self,
        graph="beta skeleton",
        gradient="steepest",
        max_neighbors=-1,
        beta=1.0,
        normalization=None,
        connect=False,
        aggregator=None,
        debug=False,
    ):
        """ Initialization method
            @ In, graph, an optional string specifying the type of
            neighborhood graph to use. Default is 'beta skeleton,' but
            other valid types are: 'delaunay,' 'relaxed beta skeleton,'
            'none', or 'approximate knn'
            @ In, gradient, an optional string specifying the type of
            gradient estimator to use. Currently the only available
            option is 'steepest'
            @ In, max_neighbors, an optional integer value specifying
            the maximum number of k-nearest neighbors used to begin a
            neighborhood search. In the case of graph='[relaxed] beta
            skeleton', we will begin with the specified approximate knn
            graph and prune edges that do not satisfy the empty region
            criteria.
            @ In, beta, an optional floating point value between 0 and
            2. This value is only used when graph='[relaxed] beta
            skeleton' and specifies the radius for the empty region
            graph computation (1=Gabriel graph, 2=Relative neighbor
            graph)
            @ In, normalization, an optional string specifying whether
            the inputs/output should be scaled before computing.
            Currently, two modes are supported 'zscore' and 'feature'.
            'zscore' will ensure the data has a mean of zero and a
            standard deviation of 1 by subtracting the mean and dividing
            by the variance. 'feature' scales the data into the unit
            hypercube.
            @ In, connect, an optional boolean flag for whether the
            algorithm should enforce the data to be a single connected
            component.
            @ In, aggregator, an optional string that specifies what
            type of aggregation to do when duplicates are found in the
            domain space. Default value is None meaning the code will
            error if duplicates are identified.
            @ In, debug, an optional boolean flag for whether debugging
            output should be enabled.
        """
        super(MergeTree, self).__init__(
            graph=graph,
            gradient=gradient,
            max_neighbors=max_neighbors,
            beta=beta,
            normalization=normalization,
            connect=connect,
            aggregator=aggregator,
            debug=debug,
        )
def __init__ ( self , graph = "beta skeleton" , gradient = "steepest" , max_neighbors = - 1 , beta = 1.0 , normalization = None , connect = False , aggregator = None , debug = False , ) : super ( MergeTree , self ) . __init__ ( graph = graph , gradient = gradient , max_neighbors = max_neighbors , beta = beta , normalization = normalization , connect = connect , aggregator = aggregator , debug = debug , )
Initialization method
            @ In, graph, an optional string specifying the type of
            neighborhood graph to use. Default is 'beta skeleton,' but
            other valid types are: 'delaunay,' 'relaxed beta skeleton,'
            'none', or 'approximate knn'
            @ In, gradient, an optional string specifying the type of
            gradient estimator to use. Currently the only available
            option is 'steepest'
            @ In, max_neighbors, an optional integer value specifying
            the maximum number of k-nearest neighbors used to begin a
            neighborhood search. In the case of graph='[relaxed] beta
            skeleton', we will begin with the specified approximate knn
            graph and prune edges that do not satisfy the empty region
            criteria.
            @ In, beta, an optional floating point value between 0 and
            2. This value is only used when graph='[relaxed] beta
            skeleton' and specifies the radius for the empty region
            graph computation (1=Gabriel graph, 2=Relative neighbor
            graph)
            @ In, normalization, an optional string specifying whether
            the inputs/output should be scaled before computing.
            Currently, two modes are supported 'zscore' and 'feature'.
            'zscore' will ensure the data has a mean of zero and a
            standard deviation of 1 by subtracting the mean and dividing
            by the variance. 'feature' scales the data into the unit
            hypercube.
            @ In, connect, an optional boolean flag for whether the
            algorithm should enforce the data to be a single connected
            component.
            @ In, aggregator, an optional string that specifies what
            type of aggregation to do when duplicates are found in the
            domain space. Default value is None meaning the code will
            error if duplicates are identified.
            @ In, debug, an optional boolean flag for whether debugging
            output should be enabled.