algorithm

Documentation

Overview

Package algorithm implements optimization algorithms to triangulate an image

Index

• func NewModifiedGenetic(newPointGroup func() normgeom.NormPointGroup, size int, cutoff int, ...) *modifiedGenetic
• func NewSimple(newPointGroup func() normgeom.NormPointGroup, size int, cutoff int, ...) *simple
• type Algorithm
• type FitnessData
• type MutationsData
  • func (m *MutationsData) Clear()
  • func (m MutationsData) Count() int
• type Stats

Functions

func NewModifiedGenetic

func NewModifiedGenetic(newPointGroup func() normgeom.NormPointGroup, size int, cutoff int,
	newEvaluators func(n int) evaluator.Evaluator, mutator mutation.Method) *modifiedGenetic

NewModifiedGenetic returns a new modifiedGenetic algorithm.

func NewSimple

func NewSimple(newPointGroup func() normgeom.NormPointGroup, size int, cutoff int,
	newEvaluators func(n int) evaluator.Evaluator, mutator mutation.Method) *simple

NewSimple returns a new Simple algorithm.

Types

type Algorithm

type Algorithm interface {
	// Step runs one generation of the algorithm.
	Step()

	// Best returns the point group with the highest fitness.
	Best() normgeom.NormPointGroup

	// Stats returns statistics relating to the algorithm.
	Stats() Stats
}

An Algorithm is an iterative algorithm for optimizing a group of points.

type FitnessData

type FitnessData struct {
	Fitness float64
	I       int // The index of the point group in an Algorithm.
}

FitnessData stores the fitness of a point group.

type MutationsData

type MutationsData struct {
	Mutations []mutation.Mutation
	Indexes   []int // The index of the member where the mutation came from. Indexes[i] has the mutation Mutations[i].
}

MutationsData stores a set of mutations and the index of the member which each mutation came from.

func (*MutationsData) Clear

func (m *MutationsData) Clear()

Clear clears all data from the struct.

func (MutationsData) Count

func (m MutationsData) Count() int

Count returns the number of mutations stored.

type Stats

type Stats struct {
	BestFitness float64
	Generation  int
	TimeForGen  time.Duration // The time taken for the last generation.
}

Stats contains the basic statistics of an Algorithm.