Examples with GeneticAlgorithm smile.gap.GeneticAlgorithm used on opensource projects

Example 1 with GeneticAlgorithm

use of smile.gap.GeneticAlgorithm in project smile by haifengl.

the class GAFeatureSelection method learn.

/**
     * Genetic algorithm based feature selection for classification.
     * @param size the population size of Genetic Algorithm.
     * @param generation the maximum number of iterations.
     * @param trainer classifier trainer.
     * @param measure classification measure as the chromosome fitness measure.
     * @param x training instances.
     * @param y training labels.
     * @param k k-fold cross validation for the evaluation.
     * @return bit strings of last generation.
     */
public BitString[] learn(int size, int generation, ClassifierTrainer<double[]> trainer, ClassificationMeasure measure, double[][] x, int[] y, int k) {
    if (size <= 0) {
        throw new IllegalArgumentException("Invalid population size: " + size);
    }
    if (k < 2) {
        throw new IllegalArgumentException("Invalid k-fold cross validation: " + k);
    }
    if (x.length != y.length) {
        throw new IllegalArgumentException(String.format("The sizes of X and Y don't match: %d != %d", x.length, y.length));
    }
    int p = x[0].length;
    ClassificationFitness fitness = new ClassificationFitness(trainer, measure, x, y, k);
    BitString[] seeds = new BitString[size];
    for (int i = 0; i < size; i++) {
        seeds[i] = new BitString(p, fitness, crossover, crossoverRate, mutationRate);
    }
    GeneticAlgorithm<BitString> ga = new GeneticAlgorithm<>(seeds, selection);
    ga.evolve(generation);
    return seeds;
}

Example 2 with GeneticAlgorithm

use of smile.gap.GeneticAlgorithm in project smile by haifengl.

the class GAFeatureSelection method learn.

/**
     * Genetic algorithm based feature selection for regression.
     * @param size the population size of Genetic Algorithm.
     * @param generation the maximum number of iterations.
     * @param trainer regression model trainer.
     * @param measure classification measure as the chromosome fitness measure.
     * @param x training instances.
     * @param y training instance response variable.
     * @param k k-fold cross validation for the evaluation.
     * @return bit strings of last generation.
     */
public BitString[] learn(int size, int generation, RegressionTrainer<double[]> trainer, RegressionMeasure measure, double[][] x, double[] y, int k) {
    if (size <= 0) {
        throw new IllegalArgumentException("Invalid population size: " + size);
    }
    if (k < 2) {
        throw new IllegalArgumentException("Invalid k-fold cross validation: " + k);
    }
    if (x.length != y.length) {
        throw new IllegalArgumentException(String.format("The sizes of X and Y don't match: %d != %d", x.length, y.length));
    }
    int p = x[0].length;
    RegressionFitness fitness = new RegressionFitness(trainer, measure, x, y, k);
    BitString[] seeds = new BitString[size];
    for (int i = 0; i < size; i++) {
        seeds[i] = new BitString(p, fitness, crossover, crossoverRate, mutationRate);
    }
    GeneticAlgorithm<BitString> ga = new GeneticAlgorithm<>(seeds, selection);
    ga.evolve(generation);
    return seeds;
}