Regression (Jenetics 5.1.0)

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.getRandom().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.getBestPhenotype() .getGenotype() .getGene(); final TreeNode<Op<Double>> tree = program.toTreeNode(); MathExpr.rewrite(tree); // Simplify result program. System.out.println("Generations: " + result.getTotalGenerations()); System.out.println("Function: " + new MathExpr(tree)); System.out.println("Error: " + REGRESSION.error(tree)); } }

Since:

5.0

Version:

5.0

Author:

Franz Wilhelmstötter

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and 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<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, Iterable<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, Sample<T>... samples)` Create a new regression problem instance with the given parameters.
`List<Sample<T>>`	`samples()` Return an immutable list of the symbolic regression's points.

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
decode, fitness, fitness, of

- Method Detail
  - 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>
  - samples
```
public List<Sample<T>> samples()
```
    Return an immutable list of the symbolic regression's points.
    
    Returns:
    
    the sample points
  - error
```
public double error(Tree<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,
                                   Iterable<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 Detail

fitness

codec

samples

error

of

of

codecOf

codecOf