DistributionOptimization: Distribution Fitting

In DistributionOptimization: Distribution Optimization

Description

Fits a Gaussian Mixture Model onto a Dataset by minimizing a fitting error through evolutionary optimization. Every individual encodes one GMM. Details over the evolutionary process itself can be taken from the 'GA' package. ga

Usage

DistributionOptimization(Data, Modes, Monitor = 1,
  SelectionMethod = "UnbiasedTournament",
  MutationMethod = "Uniform+Focused",
  CrossoverMethod = "WholeArithmetic", PopulationSize = Modes * 3 * 25,
  MutationRate = 0.7, Elitism = 0.05, CrossoverRate = 0.2,
  Iter = Modes * 3 * 200, OverlapTolerance = NULL,
  IsLogDistribution = rep(F, Modes), ErrorMethod = "chisquare",
  NoBins = NULL, Seed = NULL, ConcurrentInit = F, ParetoRad = NULL)

Arguments

Data	Data to be modelled
Modes	Number of expected Modes
Monitor	0:no monitoring, 1: status messages, 2: status messages and plots, 3: status messages, plots and calculated error-measures
SelectionMethod	1: LinearRank selection 4: UnbiasedTournament 5: FitnessProportional selection
MutationMethod	1: UniformRandom mutation 2: NonuniformRandom mutation 4: Focused mutation, alternative random mutation around solution 5: GaussMutationCust 6: TwoPhaseMutation - mutation is uniform random during the first half of iterations, and than focuses around current solution
CrossoverMethod	1: single point crossover 2: whole arithmetic crossover 3: local arithmetic crossover 4: blend crossover 5: GaussCrossover - exchange complete gaussian components 6: MultiPointCrossover - Random amount of information between mixtures get exchanged
PopulationSize	Size of the population
MutationRate	amount (0..1) of population that gets mutated
Elitism	amount of best individuals that will survive generation unchanged
CrossoverRate	amount of individuals that will be used for crossover
Iter	number of iterations of this algorithm
OverlapTolerance	ratio between Chi-Square and OverlapError (only if FitnessMethod = 4 (Chi2ValueWithOverlap))
IsLogDistribution	which gauss components should be considered as log gaussian
ErrorMethod	"pde": fitting is measured by pareto density estimation. "chisquare": fitting is measured by Chi-Square test
NoBins	Number of Bins that will be used for evaluating fitting
Seed	Random Seed for reproducible results
ConcurrentInit	If true, before initialization a number of short optimizations are done to find a good starting point for evolution
ParetoRad	Pareto Radius for Pareto Density Estimation and its plots

Value

The GA object containing the evolutionary training and a description of the final GMM consisting of means, sdevs and weights.

Author(s)

Florian Lerch

Jorn Lotsch

Alfred Ultsch

Examples

## Not run: 
DistributionOptimization(c(rnorm(200),rnorm(200,3), 2))

## End(Not run)

DistributionOptimization documentation built on Feb. 12, 2020, 5:57 p.m.