ec.cgp.problems
Class ClassificationProblem

java.lang.Object
  ec.Problem
      ec.cgp.problems.ProblemCGP
          ec.cgp.problems.ClassificationProblem

All Implemented Interfaces:

ec.Prototype, ec.Setup, ec.simple.SimpleProblemForm, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

ProblemBreastCancerW, ProblemBreastCancerY, ProblemIris

public abstract class ClassificationProblem
extends ProblemCGP

An extension of CGPProblem which provides some facilities to represent and evaluate classification problems.

Author:

David Oranchak, doranchak@gmail.com, http://oranchak.com

See Also:

Serialized Form

Field Summary

(package private) static java.util.List<Record>	data storage for records
(package private) static java.lang.String	P_FILE
(package private) static java.lang.String	P_TEST
(package private) static float	test proportion of records to reserve for the testing set (example: 0.3 = 30%)
(package private) static java.util.List<Record>	testSet portion of the records used for testing/verification
(package private) static java.util.List<Record>	trainingSet portion of the records used for training

Fields inherited from class ec.cgp.problems.ProblemCGP

constantMax, constantMin, constants, numConstants, P_CONSTANT_MAX, P_CONSTANT_MIN, P_CONSTANTS

Fields inherited from class ec.Problem

P_PROBLEM

Constructor Summary

ClassificationProblem()

Method Summary

(package private) abstract boolean[]	compare(java.lang.Object[] outputs, Record r) During evaluation, outputs are passed to this method.
(package private) boolean[]	eval(ec.EvolutionState state, int threadnum, java.lang.Object[] inputs, Record rec, VectorIndividualCGP ind) Sets the inputs, runs the Cartesian Genetic Program, and returns the results of classification.
void	evaluate(ec.EvolutionState state, ec.Individual ind, int subpopulation, int threadnum) Evaluate this individual.
(package private) abstract Record	makeRecord(java.lang.String line) Your implementing class must do the grunt work of turning a line from the text file into a Record instance that is specific to your problem.
(package private) void	makeRecords(java.lang.String fileName, ec.util.MersenneTwisterFast rand) Read lines of text from the input file and turn each line into a Record instance.
(package private) abstract void	setInputs(java.lang.Object[] inputs, Record r) Your implementing class must map attributes of the given Record to items in the input vector using this method.
void	setup(ec.EvolutionState state, ec.util.Parameter base) Configure this Classification Problem.

Methods inherited from class ec.cgp.problems.ProblemCGP

describe, initConstants

Methods inherited from class ec.Problem

canEvaluate, clone, closeContacts, defaultBase, finishEvaluating, initializeContacts, prepareToEvaluate, reinitializeContacts

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

data

static java.util.List<Record> data

storage for records

trainingSet

static java.util.List<Record> trainingSet

portion of the records used for training

testSet

static java.util.List<Record> testSet

portion of the records used for testing/verification

P_FILE

static java.lang.String P_FILE

P_TEST

static java.lang.String P_TEST

test

static float test

proportion of records to reserve for the testing set (example: 0.3 = 30%)

Constructor Detail

ClassificationProblem

public ClassificationProblem()

Method Detail

setup

public void setup(ec.EvolutionState state,
                  ec.util.Parameter base)

Configure this Classification Problem.

Specified by:

setup in interface ec.Prototype

Specified by:

setup in interface ec.Setup

Overrides:

setup in class ProblemCGP

makeRecords

void makeRecords(java.lang.String fileName,
                 ec.util.MersenneTwisterFast rand)

Read lines of text from the input file and turn each line into a Record instance.

Parameters:

fileName - The full path of the input file.

rand - RNG to use.

makeRecord

abstract Record makeRecord(java.lang.String line)

Your implementing class must do the grunt work of turning a line from the text file into a Record instance that is specific to your problem.

Parameters:

line - The comma-delimited line from the data file.

Returns:

an instance of the Record.

evaluate

public void evaluate(ec.EvolutionState state,
                     ec.Individual ind,
                     int subpopulation,
                     int threadnum)

Evaluate this individual. Fitness is set to the proportion of unsuccessfully classified training instances. If there are constant values, the input vector is filled with them starting at the end. The test set is also evaluated to measure performance of the classifier.

setInputs

abstract void setInputs(java.lang.Object[] inputs,
                        Record r)

Your implementing class must map attributes of the given Record to items in the input vector using this method.

Parameters:

inputs - input array to set

r - record to set values from

compare

abstract boolean[] compare(java.lang.Object[] outputs,
                           Record r)

During evaluation, outputs are passed to this method. Your implementing class must compare the outputs to the target class(es) in the given Record. Each element of the return vector is set to true if the corresponding output represents a successfully classified instance.

Parameters:

outputs - The output vector resulting from evaluation of the CGP.

r - The record from which to compare classification results.

Returns:

A boolean vector indicating sucess of classification.

eval

boolean[] eval(ec.EvolutionState state,
               int threadnum,
               java.lang.Object[] inputs,
               Record rec,
               VectorIndividualCGP ind)

Sets the inputs, runs the Cartesian Genetic Program, and returns the results of classification.

Parameters:

state - The evolution state

threadnum - The current thread number

inputs - The input vector

rec - The current record

ind - The current individual

Returns:

boolean vector indicating successful/unsuccessful classification(s).