Description Usage Arguments Value Author(s) Examples

View source: R/DistributionOptimization.R

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

1 2 3 4 5 6 7 8 | ```
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)
``` |

`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 |

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

Florian Lerch

Jorn Lotsch

Alfred Ultsch

1 2 3 4 | ```
## Not run:
DistributionOptimization(c(rnorm(200),rnorm(200,3), 2))
## End(Not run)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.