Package io.jenetics.prog.regression

Class Regression<T>

  • Type Parameters:
    T - the operation type
    All Implemented Interfaces:
    Problem<Tree<Op<T>,​?>,​ProgramGene<T>,​Double>
    public final class Regression<T>
extends Object
implements Problem<Tree<Op<T>,​?>,​ProgramGene<T>,​Double>
    This class implements a symbolic regression problem. The example below shows a typical usage of the Regression class.
    public class SymbolicRegression { private static final ISeq<Op<Double>> OPERATIONS = ISeq.of(MathOp.ADD, MathOp.SUB, MathOp.MUL); private static final ISeq<Op<Double>> TERMINALS = ISeq.of( Var.of("x", 0), EphemeralConst.of(() -> (double)RandomRegistry.random().nextInt(10)) ); private static final Regression<Double> REGRESSION = Regression.of( Regression.codecOf(OPERATIONS, TERMINALS, 5), Error.of(LossFunction::mse), Sample.ofDouble(-1.0, -8.0000), // ... Sample.ofDouble(0.9, 1.3860), Sample.ofDouble(1.0, 2.0000) ); public static void main(final String[] args) { final Engine<ProgramGene<Double>, Double> engine = Engine .builder(REGRESSION) .minimizing() .alterers( new SingleNodeCrossover<>(0.1), new Mutator<>()) .build(); final EvolutionResult<ProgramGene<Double>, Double> result = engine.stream() .limit(Limits.byFitnessThreshold(0.01)) .collect(EvolutionResult.toBestEvolutionResult()); final ProgramGene<Double> program = result.bestPhenotype() .genotype() .gene(); final TreeNode<Op<Double>> tree = program.toTreeNode(); MathExpr.rewrite(tree); // Simplify result program. System.out.println("Generations: " + result.totalGenerations()); System.out.println("Function: " + new MathExpr(tree)); System.out.println("Error: " + REGRESSION.error(tree)); } }
    Since:
    5.0
    Version:
    6.0
    See Also:
    SampleBuffer, Sampling