AFL.agent.Metric module#
- class AFL.agent.Metric.Delaunay(data_variable='data', params=None)#
Bases:
Similarity- calculate(X)#
Computes the Delaunay triangulation of the given points :param x: array of shape (num_nodes, 2) :return: the computed adjacency matrix
- class AFL.agent.Metric.Distance(data_variable='data', params=None, name=None, constrain_same=None, constrain_different=None)#
Bases:
Metric- calculate(dataset, **params)#
- class AFL.agent.Metric.Dummy(data_variable='data', params=None, name=None, constrain_same=None, constrain_different=None)#
Bases:
Metric- calculate(dataset, **params)#
- class AFL.agent.Metric.Metric(data_variable='data', params=None, name=None, constrain_same=None, constrain_different=None)#
Bases:
object- apply_constraints()#
- calculate(*args, **kwargs)#
- copy()#
- normalize1()#
- normalize2()#
- to_dict()#
- class AFL.agent.Metric.MultiMetric(metrics, data_variable='data', combine_by='prod', combine_by_powers=None, combine_by_coeffs=None, constrain_same=None, constrain_different=None, **params)#
Bases:
Metric- calculate(dataset)#
- prod(data_list)#
- sum(data_list)#
- to_dict()#
- update_name()#
- class AFL.agent.Metric.Similarity(data_variable='data', params=None, name=None, constrain_same=None, constrain_different=None)#
Bases:
Metric- calculate(dataset, **params)#
- AFL.agent.Metric.listify(obj)#