java.lang.Object

io.jenetics.prog.regression.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:

Method Summary

Modifier and Type

Method

Description

Codec<Tree<Op<T>,?>,ProgramGene<T>>

codec()

static <T> Codec<Tree<Op<T>,?>,ProgramGene<T>>

codecOf(ISeq<Op<T>> operations, ISeq<Op<T>> terminals, int depth)

Create a new codec, usable for symbolic regression problems, with the given parameters.

static <T> Codec<Tree<Op<T>,?>,ProgramGene<T>>

codecOf(ISeq<Op<T>> operations, ISeq<Op<T>> terminals, int depth, Predicate<? super ProgramChromosome<T>> validator)

Create a new codec, usable for symbolic regression problems, with the given parameters.

double

error(Tree<? extends Op<T>,?> program)

Calculates the actual error for the given program.

Function<Tree<Op<T>,?>,Double>

fitness()

static <T> Regression<T>

of(Codec<Tree<Op<T>,?>,ProgramGene<T>> codec, Error<T> error, Sample<T>... samples)

Create a new regression problem instance with the given parameters.

static <T> Regression<T>

of(Codec<Tree<Op<T>,?>,ProgramGene<T>> codec, Error<T> error, Sampling<T> sampling)

Create a new regression problem instance with the given parameters.

static <T> Regression<T>

of(Codec<Tree<Op<T>,?>,ProgramGene<T>> codec, Error<T> error, Iterable<? extends Sample<T>> samples)

Create a new regression problem instance with the given parameters.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface io.jenetics.engine.Problem
constraint, decode, fitness, fitness

Method Details
- fitness
  
  public Function<Tree<Op<T>,?>,Double> fitness()
  
  Specified by:
  
  fitness in interface Problem<Tree<Op<T>,?>,ProgramGene<T>,Double>
- codec
  
  public Codec<Tree<Op<T>,?>,ProgramGene<T>> codec()
  
  Specified by:
  
  codec in interface Problem<Tree<Op<T>,?>,ProgramGene<T>,Double>
- error
  
  public double error(Tree<? extends Op<T>,?> program)
  
  Calculates the actual error for the given program.
  
  Parameters:
  
  program - the program to calculate the error value for
  
  Returns:
  
  the overall error value of the program
- of
  
  public static <T> Regression<T> of(Codec<Tree<Op<T>,?>,ProgramGene<T>> codec, Error<T> error, Sampling<T> sampling)
  
  Create a new regression problem instance with the given parameters.
  Type Parameters:
  
  T - the operation type
  
  Parameters:
  
  codec - the problem codec to use
  
  error - the error function
  
  sampling - the sampling function
  
  Returns:
  
  a new regression problem instance
  
  Throws:
  
  NullPointerException - if on of the arguments is null
  
  See Also:
  
  codecOf(ISeq, ISeq, int)
  
  codecOf(ISeq, ISeq, int, Predicate)
- of
  
  public static <T> Regression<T> of(Codec<Tree<Op<T>,?>,ProgramGene<T>> codec, Error<T> error, Iterable<? extends Sample<T>> samples)
  
  Create a new regression problem instance with the given parameters.
  Type Parameters:
  
  T - the operation type
  
  Parameters:
  
  codec - the problem codec to use
  
  error - the error function
  
  samples - the sample points used for regression analysis
  
  Returns:
  
  a new regression problem instance
  
  Throws:
  
  IllegalArgumentException - if the given samples is empty
  
  NullPointerException - if on of the arguments is null
  
  See Also:
  
  codecOf(ISeq, ISeq, int)
  
  codecOf(ISeq, ISeq, int, Predicate)
- of
  
  @SafeVarargs public static <T> Regression<T> of(Codec<Tree<Op<T>,?>,ProgramGene<T>> codec, Error<T> error, Sample<T>... samples)
  
  Create a new regression problem instance with the given parameters.
  Type Parameters:
  
  T - the operation type
  
  Parameters:
  
  codec - the problem codec to use
  
  error - the error function
  
  samples - the sample points used for regression analysis
  
  Returns:
  
  a new regression problem instance
  
  Throws:
  
  IllegalArgumentException - if the given samples is empty
  
  NullPointerException - if on of the arguments is null
  
  See Also:
  
  codecOf(ISeq, ISeq, int)
  
  codecOf(ISeq, ISeq, int, Predicate)
- codecOf
  
  public static <T> Codec<Tree<Op<T>,?>,ProgramGene<T>> codecOf(ISeq<Op<T>> operations, ISeq<Op<T>> terminals, int depth, Predicate<? super ProgramChromosome<T>> validator)
  
  Create a new codec, usable for symbolic regression problems, with the given parameters.
  
  Type Parameters:
  
  T - the operation type
  
  Parameters:
  
  operations - the operations used for the symbolic regression
  
  terminals - the terminal operations of the program tree
  
  depth - the maximal tree depth (height) of newly created program trees
  
  validator - the chromosome validator. A typical validator would check the size of the tree and if the tree is too large, mark it at invalid. The validator may be null.
  
  Returns:
  
  a new codec, usable for symbolic regression
  
  Throws:
  
  IllegalArgumentException - if the tree depth is not in the valid range of [0, 30)
  
  NullPointerException - if the operations or terminals are null
- codecOf
  
  public static <T> Codec<Tree<Op<T>,?>,ProgramGene<T>> codecOf(ISeq<Op<T>> operations, ISeq<Op<T>> terminals, int depth)
  
  Create a new codec, usable for symbolic regression problems, with the given parameters.
  
  Type Parameters:
  
  T - the operation type
  
  Parameters:
  
  operations - the operations used for the symbolic regression
  
  terminals - the terminal operations of the program tree
  
  depth - the maximal tree depth (height) of newly created program trees
  
  Returns:
  
  a new codec, usable for symbolic regression
  
  Throws:
  
  IllegalArgumentException - if the tree depth is not in the valid range of [0, 30)
  
  NullPointerException - if the operations or terminals are null

Class Regression<T>

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface io.jenetics.engine.Problem

Method Details

fitness

codec

error

of

of

of

codecOf

codecOf