4.2. Compute Mapper Graph of a Point Cloud

The computeMapper module provides functionality to compute the Maper graph of a point cloud.

cereeberus.compute.computemapper.computeMapper(pointcloud, lensfunction, cover, clusteralgorithm)

Computes the mapper graph of an input function.

The point cloud should be given as a list of tuples or as a numpy array.

The lens function should be given as either a list of numbers with the same length as the number of points; or as a callable function where \(f(point) = ext{value}\) so long as the function can be determined from the coordinate values of the point.

The cover should be given as a list of intervals. This can be done, for example, using the ‘cereeberus.cover’ function in this module, which takes in a minimum, maximum, number of covers, and percentage of overlap to create a cover.

The clustering algorithm should be given as a callable that takes in a point cloud and outputs cluster labels (for example, sklearn.cluster.DBSCAN(min_samples=2,eps=0.3).fit).

Parameters:

• pointcloud (A)

• function (A lens)

• cover (A)

• algorithm (A clustering)

Returns:

A MapperGraph object representing the mapper graph of the input data and lens function.

cereeberus.compute.computemapper.cover(min=-1, max=1, numcovers=10, percentoverlap=0.5)

Creates a cover to be used for inputs in the computeMapper function

Parameters:

• min – the minimum for the range of the covering sets

• max – the maximum for the range of the covering sets

• numcovers – number of covers to create

• percentoverlap – percentage (from 0 to 1) of overlap between covers

Returns:

An array of intervals