BnB

class pyoptex.analysis.estimators.sams.bnb.bnb.BnB

Base branch-and-bround class to retain ntop models (instead of only the best). It is a maximization algorithm.

__init__()

Methods

BnB.branches(node)	Function to generates the branches from a node
BnB.init_queue(top_results, top_scores)	Initializes the branches queue from the top results and scores.
BnB.initialize(nfit)	Initialize a guess for the optimal nodes.
BnB.leaf(node)	Checks whether a node is a leaf.
BnB.loop(top_results, top_scores)	Loops through the branch-and-bound algorithm keeping topn results.
BnB.node_in_results(node, results)	Check whether the node is already in the results.
BnB.postloop(top_results, top_scores)	Callback to run after the branch-and-bound algorithm has run.
BnB.postnew(old, new, top)	Function defining what to do after finding a new optimal node and adding it to the top.
BnB.preloop(top_results, top_scores)	Callback to run before starting the branch-and-bound algorithm (but after intialization).
BnB.prenew(old, new, top)	Function defining what to do after finding a new optimal node and before adding it to the top.
BnB.top(nfit)	Returns the top nfit results using the branch-and-bound algorithm.
BnB.upperbound(node)	Function to compute the upperbound for the maximization branch-and-bound algorithm.