IBSPopulation<Continuous, IBSMCPopulation>

IBSMCPopulation

Direct Known Subclasses:: IBSCPopulation

public class IBSMCPopulation extends IBSPopulation<Continuous, IBSMCPopulation>

The core class for individual based simulations with multiple continuous traits. Manages the traits of the population, while delegating the management of the population and individual fitness as well as simulation steps to super. Note that some further optimizations and simplifications are possible in the special case of a single continuous trait, which is handled in the subclass IBSCPopulation.

Author:

Christoph Hauert

See Also:

Field Summary

Fields

Modifier and Type

Field

Description

protected Model.HasIBS.MCGroups

groupmodule

For group interaction modules module==groupmodule holds and null otherwise.

protected IBSC.Init

init

Type of initial configuration.

private double[]

meantrait

The array for calculating and storing the mean traits and their standard deviation.

(package private) double[]

myTraits

Temporary storage for the traits of the focal individual.

(package private) double[]

oldScores

Temporary storage for the scores of each participant prior to group interactions.

(package private) double[]

oldTraits

Temporary storage for the traits of the focal individual before the update.

protected Model.HasIBS.MCPairs

pairmodule

For pairwise interaction modules module==pairmodule holds and null otherwise.

(package private) double[]

smallTrait

Temporary storage for traits of individuals in small sub-group interactions.

(package private) double[]

tmpGroup

Temporary storage for traits of individuals in group interactions.

protected double[]

traitRangeMax

The array with the maximal values for each trait.

protected double[]

traitRangeMin

The array with the minimal values for each trait.

(package private) double[]

traits

The array of individual traits.

(package private) double[]

traitsNext

The array for temporarily storing traits during updates.

Fields inherited from class IBSPopulation
adjustScores, competition, compGroup, consistencyCheckRequested, debugFocal, debugModel, debugModels, debugNModels, debugSame, distrMigrants, engine, fitness, groupScores, hasLookupTable, interaction, interactions, interGroup, isMultispecies, isNeutral, logger, map2fit, maxEffScoreIdx, maxFitness, maxScore, migrationType, minFitness, minScore, module, mutation, nIssues, nMixedInter, nPopulation, nTraits, opponent, optimizeHomo, pAddwire, playerScoreAveraged, playerScoring, playerUpdate, pMigration, populationUpdate, pRewire, rng, scores, smallScores, staticmodule, sumFitness, syncFraction, tags, typeFitness, typeScores, vacantIdx
Constructor Summary

Constructors

Constructor

Description

IBSMCPopulation(EvoLudo engine, Continuous module)

Creates a population of individuals with multiple continuous traits for IBS simulations.
Method Summary

Modifier and Type

Method

Description

void

addState(double[] state)

Adds the traits of all individuals to the array state.

void

adjustPairGameScoresAt(int me)

Adjusts scores of focal individual with index me and its neighbors after me changed trait.

void

adjustScoreAt(int index, double adjust)

Adjust score of individual with index index by adjust and update all applicable helper variables, e.g.

void

adjustScoreAt(int index, double before, double after)

Adjust score of individual with index index from before to after and update all applicable helper variables, e.g.

boolean

check()

Check all model parameters for consistency and adjust if necessary (and feasible).

boolean

checkConvergence()

Check if population has converged.

void

commitTraitAt(int me)

The change of a trait of the player at index is stored in a temporary variable and must be committed before proceeding.

void

commitTraits()

After a synchronous update step the new state must be copied back to become the current state.

private double

deltaTraits(int a, int b)

Measure the (Cartesian) distance between traits at a and b

protected boolean

doAdjustScores()

Check if scores can be adjusted rather than recalculated after an individual changed its trait.

void

encodeTraits(StringBuilder plist)

Encode the traits of all individuals in the IBS model in a plist inspired XML string.

private void

gatherPlayers(IBSGroup group)

Gather the traits of all individuals in the interaction group group.

void

get2DTraitHistogramData(double[] bins, int trait1, int trait2)

Creates 2D histogram for traits trait1 and trait2.

IBSC.Init

getInit()

Gets the type of the initial configuration and its arguments.

double[]

getMaxTraits()

Gets the maximal value of each trait in the population and stores it in the array max.

double[]

getMaxTraits(double[] max)

Gets the maximal value of each trait in the population and stores it in the array max.

double[]

getMeanFitness(double[] mean)

Returns the mean fitness of this population in the array mean.

double[]

getMeanTraits(double[] mean)

Returns the mean trait(s) of this population in the array mean.

double[]

getMinTraits()

Gets the minimal value of each trait in the population and stores it in the array min.

double[]

getMinTraits(double[] min)

Gets the minimal value of each trait in the population and stores it in the array min.

String

getStatus()

Gets the status of the as a formatted string.

double

getTraitAt(int idx)

Get the trait of individual with index idx.

double

getTraitAt(int idx, int d)

Get trait d of individual with index idx.

<T> void

getTraitData(T[] colors, ColorMap<T> colorMap)

Returns the traits of all individuals in this population coded as colors in the array colors using the map colorMap.

void

getTraitHistogramData(double[][] bins)

Creates a histogram for each trait separately (if there are multiple) and returns the result in the array bins where the first index denotes the trait and the second refers to the bin.

void

getTraitHistogramData(double[] bins, int trait)

Creates a histogram for the trait with index trait and returns the result in the array bins.

String

getTraitNameAt(int index)

Gets the formatted name of the trait of the individual at site index.

double[]

getTraitRangeMax()

Get the maxima for all traits.

double[]

getTraitRangeMin()

Get the minima for all traits.

String

getTraits(int digits)

Gets all traits of all individuals.

double[]

getTraitsAt(int idx)

Get the trait array of individual with index idx.

double[]

getTraitsAt(int idx, double[] idxtraits)

Get the trait array of individual with index idx.

boolean

haveSameTrait(int a, int b)

Check if individuals with index a and index b have the same traits.

void

init()

Initialize the model.

(package private) void

initGaussian()

Initialize traits by sampling from a (possibly truncated) Gaussian distribution per trait.

(package private) void

initMono()

Initialize all individuals with the same (monomorphic) trait value.

(package private) void

initMutant()

Initialize a monomorphic resident population with a single mutant.

(package private) void

initUniform()

Initialize each trait uniformly at random in its allowed range.

boolean

isSameTrait(int a)

Check if individual with index a has switched traits.

protected boolean

maybeMutateAt(int focal, boolean switched)

Consider mutating the trait of the focal individual with index focal.

protected void

maybeMutateMoran(int source, int dest)

Consider mutating the trait of the parent individual with index source.

boolean

mouseHitNode(int hit, boolean alt)

Called from GUI if node/individual with index idx received a mouse click or tap and indicates whether the alt-key had been pressed.

int

mutateAt(int focal)

Mutate the trait of the focal individual with index focal.

private boolean

mutateAt(int focal, boolean switched)

Mutate all traits of the focal individual with index focal if mutate == true.

void

playGroupGameAt(IBSGroup group)

Play a group interaction with the individuals in group.

(package private) void

playGroupOnceAt(int me, IBSGroup group)

Evaluate a single group interaction with the sampled opponents.

(package private) void

playGroupSequentiallyAt(int me, IBSGroup group, int nGroup)

Evaluate sequential group interactions when not enough opponents are available simultaneously.

(package private) void

playNoGameAt(int me)

Handle the case where a focal player has no opponents and should not play a game.

void

playPairGameAt(IBSGroup group)

Play a pairwise interaction with the individuals in group.

boolean

preferredPlayerBest(int me, int best, int sample)

For deterministic updating with multiple traits (more than two), it must be specified which trait is the preferred one.

void

prepareTraits()

Prior to a synchronous update step the current state must be duplicated in preparation for processing the next step.

void

reset()

Reset the model.

boolean

restoreTraits(Plist plist)

Restore the traits of all individuals encoded in the plist inspired map of key, value-pairs.

void

setInit(IBSC.Init init)

Sets the type of the initial configuration and any accompanying arguments.

void

setOpponentPop(IBSPopulation<?,?> opponent)

Set the interaction partner/opponent of this population.

void

setTraitAt(int idx, double trait)

Set the trait of individual with index idx to trait.

void

setTraitAt(int idx, int d, double trait)

Set trait d of the individual with index idx.

void

setTraitsAt(int idx, double[] idxtraits)

Set the trait array of individual with index idx to array idxtraits.

void

swapTraits(int a, int b)

Swap traits of individuals with index a and index b.

void

updateFromModelAt(int index, int modelPlayer)

Update individual with index me and adopt the trait of individual with index you.

void

yalpGroupGameAt(IBSGroup group)

Counterpart of IBSPopulation.playGroupGameAt(IBSGroup), IBSPopulation.playGameAt(int) and/or IBSPopulation.playGameSyncAt(int).

Methods inherited from class IBSPopulation
adjustGameScoresAt, becomesVacantAt, checkCompSampling, checkConsistentFitness, checkGeometry, checkGeometryRewire, checkInteractions, checkLookupTable, checkMigration, checkNoLookupTable, checkOptimizations, checkPayoffs, debugMarkChange, debugScores, debugUpdate, debugUpdatePopulationAt, doBirthDeathMigration, doDeathBirthMigration, doDiffusionMigration, doMigration, doSyncMigration, encodeFitness, encodeGeometry, encodeInteractions, getCompetitionGeometry, getCompGroup, getFitness, getFitnessAt, getFitnessData, getFitnessHistogramData, getFitnessNameAt, getFitnessNameAt, getInteractionGeometry, getInteractionsAt, getInterGroup, getMaxScore, getMigrationProb, getMigrationType, getMinScore, getModule, getNMean, getNMixedInteractions, getPlayerScoreAveraged, getPlayerScoring, getPopulationSize, getPopulationUpdate, getScoreAt, getScoreNameAt, getScores, getSpeciesUpdateRate, getSyncFraction, getTagAt, getTagData, getTagNameAt, getTags, getTotalFitness, isConsistent, isMonomorphic, isVacantAt, logAccountingIssue, logAccountingIssue, logFitnessIssue, logMapIssue, logScoringIssue, logScoringIssue, migrateMoran, mouseHitNode, mutate, nextBinomial, pickFitFocalIndividual, pickFitFocalIndividual, pickFitNeighborAt, pickFitNeighborAt, pickFocalIndividual, pickFocalIndividual, pickFocalSite, pickFocalSite, pickNeighborSiteAt, playGameAt, playGameSyncAt, processScore, random01, random0n, randomGaussian, removeScoreAt, removeScoreAt, resetEphemeral, resetMoran, resetScoreAt, resetScores, resetTraits, restoreFitness, restoreGeometry, restoreInteractions, setAddwire, setConsistencyCheck, setGeometryNames, setMaxEffScoreIdx, setMigrationProb, setMigrationType, setPlayerScoreAveraged, setPlayerScoring, setPopulationUpdate, setRewire, setScoreAt, setSyncFraction, setTagAt, step, swapScoresAt, updateEffScoreRange, updateFitnessAt, updateMinMaxScores, updatePlayerAsync, updatePlayerAsyncAt, updatePlayerAt, updatePlayerAt, updatePlayerBest, updatePlayerBestHalf, updatePlayerBestResponse, updatePlayerEcology, updatePlayerEcologyAt, updatePlayerMoranBirthDeath, updatePlayerMoranBirthDeathAt, updatePlayerMoranDeathBirth, updatePlayerMoranDeathBirthAt, updatePlayerMoranImitate, updatePlayerMoranImitateAt, updatePlayerSwap, updateProportionalAbs, updateReplicatorHalf, updateReplicatorPlus, updateScoreAt, updateScoreAt, updateScoreAt, updateScores, updateThermal

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

Field Details
- pairmodule
  protected Model.HasIBS.MCPairs pairmodule
  
  For pairwise interaction modules module==pairmodule holds and null otherwise. Convenience field to reduce the number of (unnecessary) casts.
  
  See Also:
  
  Model.HasIBS.MCPairs
- groupmodule
  protected Model.HasIBS.MCGroups groupmodule
  
  For group interaction modules module==groupmodule holds and null otherwise. Convenience field to reduce the number of (unnecessary) casts.
  
  See Also:
  
  Model.HasIBS.MCGroups
- traitRangeMin
  protected double[] traitRangeMin
  
  The array with the minimal values for each trait. Convenience variable to reduce calls to module.
  Note: Internally traits are always scaled to [0, 1].
  
  See Also:
  
  getTraitRangeMin()
- traitRangeMax
  protected double[] traitRangeMax
  
  The array with the maximal values for each trait. Convenience variable to reduce calls to module.
  Note: Internally traits are always scaled to [0, 1].
  
  See Also:
  
  getTraitRangeMax()
- traits
  
  double[] traits
  
  The array of individual traits. The traits of individual i are stored at traits[i * nTraits] through traits[(i + 1) * nTraits - 1]
- traitsNext
  
  double[] traitsNext
  
  The array for temporarily storing traits during updates.
- tmpGroup
  
  double[] tmpGroup
  
  Temporary storage for traits of individuals in group interactions.
- smallTrait
  
  double[] smallTrait
  
  Temporary storage for traits of individuals in small sub-group interactions.
- myTraits
  
  double[] myTraits
  
  Temporary storage for the traits of the focal individual.
- oldTraits
  
  double[] oldTraits
  
  Temporary storage for the traits of the focal individual before the update. Used for adjusting scores.
- oldScores
  
  double[] oldScores
  
  Temporary storage for the scores of each participant prior to group interactions.
- meantrait
  
  private double[] meantrait
  
  The array for calculating and storing the mean traits and their standard deviation. Must be of length > 2 * nTraits.
- init
  protected IBSC.Init init
  
  Type of initial configuration.
  
  See Also:
  
  IBSC.Init.clo
Constructor Details
- IBSMCPopulation
  
  public IBSMCPopulation(EvoLudo engine, Continuous module)
  
  Creates a population of individuals with multiple continuous traits for IBS simulations.
  
  Parameters:
  
  engine - the pacemaker for running the model
  
  module - the module that defines the game
Method Details
- setOpponentPop
  
  public void setOpponentPop(IBSPopulation<?,?> opponent)
  
  Description copied from class: IBSPopulation
  
  Set the interaction partner/opponent of this population.
  
  Overrides:
  
  setOpponentPop in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  opponent - the interaction partner/opponent
- checkConvergence
  
  public boolean checkConvergence()
  
  Description copied from class: IBSPopulation
  
  Check if population has converged. By default true if population is monomorphic and no (zero) mutations. However, different implementations may have different criteria for convergence.
  Note: This tends to be less restrictive than reaching an absorbing state. Typically convergence is used as a criterion to abort simulations.
  
  Overrides:
  
  checkConvergence in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Returns:
  
  true if converged.
- getTraitRangeMin
  
  public double[] getTraitRangeMin()
  
  Get the minima for all traits.
  
  Returns:
  
  the array with the trait minima
- getTraitRangeMax
  
  public double[] getTraitRangeMax()
  
  Get the maxima for all traits.
  
  Returns:
  
  the array with the trait maxima
- getTraitAt
  
  public double getTraitAt(int idx)
  
  Get the trait of individual with index idx.
  
  Parameters:
  
  idx - the index of the individual
  
  Returns:
  
  the trait of the individual
- getTraitAt
  
  public double getTraitAt(int idx, int d)
  
  Get trait d of individual with index idx.
  
  Parameters:
  
  idx - the index of the individual
  
  d - the trait index
  
  Returns:
  
  the trait of the individual
- setTraitAt
  
  public void setTraitAt(int idx, double trait)
  
  Set the trait of individual with index idx to trait.
  
  Parameters:
  
  idx - the index of the individual
  
  trait - the trait of the individual
- setTraitAt
  
  public void setTraitAt(int idx, int d, double trait)
  
  Set trait d of the individual with index idx.
  
  Parameters:
  
  idx - the index of the individual
  
  d - the trait index
  
  trait - the new trait value
- getTraitsAt
  
  public double[] getTraitsAt(int idx)
  
  Get the trait array of individual with index idx. Allocates array to store the trait values.
  
  Parameters:
  
  idx - the index of the individual
  
  Returns:
  
  the traits of the individual
- getTraitsAt
  
  public double[] getTraitsAt(int idx, double[] idxtraits)
  
  Get the trait array of individual with index idx. Stores the trait values in the array idxtraits.
  
  Parameters:
  
  idx - the index of the individual
  
  idxtraits - the array for storing the traits
  
  Returns:
  
  the traits of the individual
- setTraitsAt
  
  public void setTraitsAt(int idx, double[] idxtraits)
  
  Set the trait array of individual with index idx to array idxtraits.
  
  Parameters:
  
  idx - the index of the individual
  
  idxtraits - the traits of the individual
- updateFromModelAt
  public void updateFromModelAt(int index, int modelPlayer)
  
  Description copied from class: IBSPopulation
  
  Update individual with index me and adopt the trait of individual with index you.
  Note: method must be subclassed to deal with different data types of traits but should also include a call to super.
  
  Overrides:
  
  updateFromModelAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  index - the index of the focal individual
  
  modelPlayer - the index of the model individual to adopt trait from
  
  See Also:
  
  IBSDPopulation.updateFromModelAt(int, int)
  
  updateFromModelAt(int, int)
- haveSameTrait
  
  public boolean haveSameTrait(int a, int b)
  
  Description copied from class: IBSPopulation
  
  Check if individuals with index a and index b have the same traits.
  
  Specified by:
  
  haveSameTrait in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  a - the index of first individual
  
  b - the index of second individual
  
  Returns:
  
  true if the two individuals have the same traits
- isSameTrait
  public boolean isSameTrait(int a)
  
  Description copied from class: IBSPopulation
  
  Check if individual with index a has switched traits.
  Note: this test is only meaningful before trait are committed.
  
  Specified by:
  
  isSameTrait in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  a - index of individual
  
  Returns:
  
  true if trait remained unchanged
  
  See Also:
  
  IBSPopulation.commitTraitAt(int)
- swapTraits
  public void swapTraits(int a, int b)
  
  Description copied from class: IBSPopulation
  
  Swap traits of individuals with index a and index b.
  Note: the traits still need to be committed.
  
  Specified by:
  
  swapTraits in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  a - the index of first individual
  
  b - the index of second individual
  
  See Also:
  
  IBSPopulation.commitTraitAt(int)
- mutateAt
  
  public int mutateAt(int focal)
  
  Description copied from class: IBSPopulation
  
  Mutate the trait of the focal individual with index focal. The mutated trait is committed and the scores updated.
  
  Specified by:
  
  mutateAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  focal - the index of the focal individual
  
  Returns:
  
  the number of elapsed realtime units
- maybeMutateAt
  
  protected boolean maybeMutateAt(int focal, boolean switched)
  
  Description copied from class: IBSPopulation
  
  Consider mutating the trait of the focal individual with index focal. The trait of the focal individual is stored in the array traits unless the focal individual switched trait. In that case the current trait is stored in the array traitsNext.
  Important: The trait is not committed regardless of whether a mutation occurred.
  
  Specified by:
  
  maybeMutateAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  focal - the index of the focal individual
  
  switched - true if the focal individual switched trait
  
  Returns:
  
  true if the trait of the focal individual changed
- maybeMutateMoran
  
  protected void maybeMutateMoran(int source, int dest)
  
  Description copied from class: IBSPopulation
  
  Consider mutating the trait of the parent individual with index source. The mutated trait is committed and the scores updated.
  
  Specified by:
  
  maybeMutateMoran in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  source - the index of the parent individual
  
  dest - the index of the location for the offspring placement
- mutateAt
  
  private boolean mutateAt(int focal, boolean switched)
  
  Mutate all traits of the focal individual with index focal if mutate == true. In all cases commit traits and update scores.
  
  Parameters:
  
  focal - the index of the focal individual that gets updated
  
  switched - true if focal already switched trait
  
  Returns:
  
  true if the trait has changed
- preferredPlayerBest
  
  public boolean preferredPlayerBest(int me, int best, int sample)
  
  For deterministic updating with multiple traits (more than two), it must be specified which trait is the preferred one.
  Summary: does 'me' prefer 'sample' over 'best'?
  Here we introduce the convention the trait closer to me is preferred.
  
  Specified by:
  
  preferredPlayerBest in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  me - the index of the focal individual
  
  best - the index of the best performing individual
  
  sample - the index of the sample type
  
  Returns:
  
  true if sample is preferred over best
- deltaTraits
  
  private double deltaTraits(int a, int b)
  
  Measure the (Cartesian) distance between traits at a and b
  
  Parameters:
  
  a - the index where the traits of the first individual start
  
  b - the index where the traits of the second individual start
  
  Returns:
  
  the distance between a and b
- gatherPlayers
  
  private void gatherPlayers(IBSGroup group)
  
  Gather the traits of all individuals in the interaction group group.
  
  Parameters:
  
  group - the interaction group
- doAdjustScores
  protected boolean doAdjustScores()
  
  Description copied from class: IBSPopulation
  
  Check if scores can be adjusted rather than recalculated after an individual changed its trait. This requires that individuals interact with all their neighbours and that the structure of the population is not well-mixed. Some implementations may be able to extend adjustments to other structures. For example, adjusting scores is feasible in well-mixed populations for discrete traits.
  Requirements:
  
  Group.SAMPLING_ALL
  
  individuals need to be interacting with all their neighbours (not just a randomly selected subset).
  
  AbstractGeometry.MEANFIELD
  
  interactions with everyone are not feasible (impossible to model efficiently), in general, for unstructured populations (subclasses can do better, e.g. for discrete trait it is possible, see IBSDPopulation.doAdjustScores()).
  
  playerScoreReset
  
  if scores are reset whenever an individual adopts the trait of another (regardless of whether an actual trait change occurred) then the expected number of interactions of each individual remains constant over time (even though the interaction count may differ for individuals on heterogeneous structures).
  
  Specified by:
  
  doAdjustScores in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Returns:
  
  true if adjusting scores is feasible
  
  See Also:
  
  IBS.ScoringType
  
  IBSDPopulation
- adjustScoreAt
  
  public void adjustScoreAt(int index, double before, double after)
  
  Description copied from class: IBSPopulation
  
  Adjust score of individual with index index from before to after and update all applicable helper variables, e.g. sumFitness.
  Important: Use only to adjust scores of individuals that did not change trait.
  
  Specified by:
  
  adjustScoreAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  index - the index of the individual
  
  before - the score before adjustments
  
  after - the score after adjustments
- adjustScoreAt
  
  public void adjustScoreAt(int index, double adjust)
  
  Description copied from class: IBSPopulation
  
  Adjust score of individual with index index by adjust and update all applicable helper variables, e.g. sumFitness.
  
  Specified by:
  
  adjustScoreAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  index - the index of the individual
  
  adjust - the score adjustment
- playPairGameAt
  public void playPairGameAt(IBSGroup group)
  
  Play a pairwise interaction with the individuals in group.
  Continuous modules with a single trait never get here.
  
  Specified by:
  
  playPairGameAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  group - the group of individuals interacting in pairs
  
  See Also:
  
  IBSCPopulation.playPairGameAt(IBSGroup)
- adjustPairGameScoresAt
  public void adjustPairGameScoresAt(int me)
  
  Adjusts scores of focal individual with index me and its neighbors after me changed trait. Only works if adjustScores==true.
  Important: new trait must not yet have been committed.
  Continuous modules with a single trait never get here.
  
  Specified by:
  
  adjustPairGameScoresAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  me - the index of the focal individual
  
  See Also:
  
  IBSCPopulation.adjustPairGameScoresAt(int)
- playGroupGameAt
  public void playGroupGameAt(IBSGroup group)
  
  Play a group interaction with the individuals in group.
  Continuous modules with a single trait never get here.
  
  Specified by:
  
  playGroupGameAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  group - the group of interacting individuals
  
  See Also:
  
  IBSCPopulation.playGroupGameAt(IBSGroup)
- playNoGameAt
  
  void playNoGameAt(int me)
  
  Handle the case where a focal player has no opponents and should not play a game.
  
  Parameters:
  
  me - focal player index
- playGroupSequentiallyAt
  
  void playGroupSequentiallyAt(int me, IBSGroup group, int nGroup)
  
  Evaluate sequential group interactions when not enough opponents are available simultaneously.
  
  Parameters:
  
  me - focal player index
  
  group - sampled opponent group
  
  nGroup - number of players participating in each interaction
- playGroupOnceAt
  
  void playGroupOnceAt(int me, IBSGroup group)
  
  Evaluate a single group interaction with the sampled opponents.
  
  Parameters:
  
  me - focal player index
  
  group - sampled opponent group
- yalpGroupGameAt
  public void yalpGroupGameAt(IBSGroup group)
  
  Counterpart of IBSPopulation.playGroupGameAt(IBSGroup), IBSPopulation.playGameAt(int) and/or IBSPopulation.playGameSyncAt(int). Removes the payoffs of group interactions.
  Continuous modules with a single trait never get here.
  
  Specified by:
  
  yalpGroupGameAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  group - the interaction group
  
  See Also:
  
  IBSCPopulation.yalpGroupGameAt(IBSGroup)
- prepareTraits
  public void prepareTraits()
  
  Description copied from class: IBSPopulation
  
  Prior to a synchronous update step the current state must be duplicated in preparation for processing the next step.
  
  Specified by:
  
  prepareTraits in class IBSPopulation<Continuous, IBSMCPopulation>
  
  See Also:
  
  IBSPopulation.commitTraits()
- commitTraits
  public void commitTraits()
  
  Description copied from class: IBSPopulation
  
  After a synchronous update step the new state must be copied back to become the current state.
  
  Specified by:
  
  commitTraits in class IBSPopulation<Continuous, IBSMCPopulation>
  
  See Also:
  
  IBSPopulation.prepareTraits()
- commitTraitAt
  
  public void commitTraitAt(int me)
  
  Description copied from class: IBSPopulation
  
  The change of a trait of the player at index is stored in a temporary variable and must be committed before proceeding.
  
  Specified by:
  
  commitTraitAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  me - the index of the player that needs to have its new trait committed
- getTraitData
  
  public <T> void getTraitData(T[] colors, ColorMap<T> colorMap)
  
  Description copied from class: IBSPopulation
  
  Returns the traits of all individuals in this population coded as colors in the array colors using the map colorMap. Used by GUI to visualize the current state of this IBS model. Colors are coded in different data types <T> depending on the runtime environment (GWT or JRE) as well as the graph (e.g. PopGraph2D or PopGraph3D).
  
  Specified by:
  
  getTraitData in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Type Parameters:
  
  T - the type of color data (String or MeshLambertMaterial for GWT and Color for JRE).
  
  Parameters:
  
  colors - the array where the colors of all nodes are stored
  
  colorMap - the map that converts traits into colors
- getTraitHistogramData
  
  public void getTraitHistogramData(double[][] bins)
  
  Creates a histogram for each trait separately (if there are multiple) and returns the result in the array bins where the first index denotes the trait and the second refers to the bin.
  
  Parameters:
  
  bins - the array to store the histogram(s)
- getTraitHistogramData
  
  public void getTraitHistogramData(double[] bins, int trait)
  
  Creates a histogram for the trait with index trait and returns the result in the array bins.
  
  Parameters:
  
  bins - the array to store the histogram(s)
  
  trait - the index of the trait
- get2DTraitHistogramData
  
  public void get2DTraitHistogramData(double[] bins, int trait1, int trait2)
  
  Creates 2D histogram for traits trait1 and trait2. The result is returned in the linear array bins and arranged in a way that is compatible with square lattice geometries for visualization by Distribution and PopGraph2D (GWT only).
  
  Parameters:
  
  bins - the linear array to store the 2D histogram
  
  trait1 - the index of the first trait
  
  trait2 - the index of the second trait
- getTraits
  
  public String getTraits(int digits)
  
  Gets all traits of all individuals. The traits are returned as a formatted string with an accuracy of digits decimals. With multiple traits they are listed sequentially for each individual.
  
  Parameters:
  
  digits - the number of decimals of the formatted string
  
  Returns:
  
  the formatted traits
- getTraitNameAt
  
  public String getTraitNameAt(int index)
  
  Description copied from class: IBSPopulation
  
  Gets the formatted name of the trait of the individual at site index.
  
  Specified by:
  
  getTraitNameAt in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  index - the index of the
  
  Returns:
  
  the string describing the trait
- getMeanTraits
  public double[] getMeanTraits(double[] mean)
  
  Returns the mean trait(s) of this population in the array mean. Used by GUI to visualize the current state of this IBS model.
  For continuous traits the first nTraits entries represent the mean of each trait and the second nTraits entries denote the standard deviation.
  
  Specified by:
  
  getMeanTraits in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  mean - the array for returning the trait values
  
  Returns:
  
  the array mean containing the mean trait values
  
  See Also:
  
  Model.getMeanTraits(int, double[])
- getMinTraits
  
  public double[] getMinTraits()
  
  Gets the minimal value of each trait in the population and stores it in the array min. The array must be of length >= nTraits.
  
  Returns:
  
  the array with the minimal trait values (same as input)
- getMinTraits
  
  public double[] getMinTraits(double[] min)
  
  Gets the minimal value of each trait in the population and stores it in the array min. The array must be of length >= nTraits.
  
  Parameters:
  
  min - the array to store the minimal trait values
  
  Returns:
  
  the array with the minimal trait values (same as input)
- getMaxTraits
  
  public double[] getMaxTraits()
  
  Gets the maximal value of each trait in the population and stores it in the array max.
  
  Returns:
  
  the array with the maximal trait values (same as input)
- getMaxTraits
  
  public double[] getMaxTraits(double[] max)
  
  Gets the maximal value of each trait in the population and stores it in the array max. The array must be of length >= nTraits.
  
  Parameters:
  
  max - the array to store the maximal trait values
  
  Returns:
  
  the array with the maximal trait values (same as input)
- getMeanFitness
  public double[] getMeanFitness(double[] mean)
  
  Returns the mean fitness of this population in the array mean. Used by GUI to visualize the current state of this IBS model. Returns true if data point belongs to the same time series and false if a new series was started through IBSPopulation.init() or IBSPopulation.reset().
  For continuous traits the first nTraits entries represent the mean fitness of each trait and the second nTraits entries denote their standard deviation.
  
  Specified by:
  
  getMeanFitness in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  mean - the array for storing the mean fitness values
  
  Returns:
  
  the array mean containing the mean fitness values
  
  See Also:
  
  Model.getMeanFitness(int, double[])
- addState
  
  public void addState(double[] state)
  
  Adds the traits of all individuals to the array state. The array must be of length nPopulation * nTraits.
  
  Parameters:
  
  state - the array to store the traits
- getStatus
  
  public String getStatus()
  
  Description copied from class: IBSPopulation
  
  Gets the status of the as a formatted string. This is typically used in the GUI to summarize the progress of the model.
  
  Specified by:
  
  getStatus in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Returns:
  
  the status of the population
- check
  public boolean check()
  
  Description copied from class: IBSPopulation
  
  Check all model parameters for consistency and adjust if necessary (and feasible). Returns true if adjustments require a reset. Free memory if possible and request a reset if new memory needs to be allocated.
  
  Overrides:
  
  check in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Returns:
  
  true if reset is required
  
  See Also:
  
  Model.check()
  
  Model.reset()
  
  IBSPopulation.reset()
- setInit
  
  public void setInit(IBSC.Init init)
  
  Sets the type of the initial configuration and any accompanying arguments. If either type or args are null the respective current setting is preserved.
  
  Parameters:
  
  init - the type and arguments of the initial configuration
- getInit
  
  public IBSC.Init getInit()
  
  Gets the type of the initial configuration and its arguments.
  
  Returns:
  
  the type and arguments of the initial configuration
- init
  public void init()
  
  Description copied from class: IBSPopulation
  
  Initialize the model. All parameters must be consistent. Subclasses must override this method to generate the initial trait configuration and call super.
  Note: Initialization leaves the interaction and competition structures untouched
  
  Overrides:
  
  init in class IBSPopulation<Continuous, IBSMCPopulation>
  
  See Also:
  
  IBSPopulation.check()
  
  Model.init()
- initUniform
  
  void initUniform()
  
  Initialize each trait uniformly at random in its allowed range.
- initMono
  
  void initMono()
  
  Initialize all individuals with the same (monomorphic) trait value.
- initGaussian
  
  void initGaussian()
  
  Initialize traits by sampling from a (possibly truncated) Gaussian distribution per trait.
- initMutant
  
  void initMutant()
  
  Initialize a monomorphic resident population with a single mutant.
- reset
  public void reset()
  
  Description copied from class: IBSPopulation
  
  Reset the model. All parameters must be consistent at this point. Allocate memory and initialize the interaction and competition structures. If structures include random elements, e.g. random regular graphs, a new structure is generated. Generate initial configuration. Subclasses must override this method to allocate memory for the trait and call super.
  
  Overrides:
  
  reset in class IBSPopulation<Continuous, IBSMCPopulation>
  
  See Also:
  
  IBSPopulation.check()
  
  Model.reset()
- mouseHitNode
  
  public boolean mouseHitNode(int hit, boolean alt)
  
  Description copied from class: IBSPopulation
  
  Called from GUI if node/individual with index idx received a mouse click or tap and indicates whether the alt-key had been pressed.
  
  Overrides:
  
  mouseHitNode in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  hit - the index of the node
  
  alt - true if the alt-key was pressed
  
  Returns:
  
  false if no actions taken
- encodeTraits
  public void encodeTraits(StringBuilder plist)
  
  Description copied from class: IBSPopulation
  
  Encode the traits of all individuals in the IBS model in a plist inspired XML string.
  
  Specified by:
  
  encodeTraits in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  plist - the StringBuilder to write the encoded state to
  
  See Also:
  
  Model.encodeState(StringBuilder)
- restoreTraits
  public boolean restoreTraits(Plist plist)
  
  Description copied from class: IBSPopulation
  
  Restore the traits of all individuals encoded in the plist inspired map of key, value-pairs.
  
  Specified by:
  
  restoreTraits in class IBSPopulation<Continuous, IBSMCPopulation>
  
  Parameters:
  
  plist - the map of key, value-pairs
  
  Returns:
  
  true if successful
  
  See Also:
  
  Model.restoreState(Plist)

Class IBSMCPopulation

Field Summary

Fields inherited from class IBSPopulation

Constructor Summary

Method Summary

Methods inherited from class IBSPopulation

Methods inherited from class Object

Field Details

pairmodule

groupmodule

traitRangeMin

traitRangeMax

traits

traitsNext

tmpGroup

smallTrait

myTraits

oldTraits

oldScores

meantrait

init

Constructor Details

IBSMCPopulation

Method Details

setOpponentPop

checkConvergence

getTraitRangeMin

getTraitRangeMax

getTraitAt

getTraitAt

setTraitAt

setTraitAt

getTraitsAt

getTraitsAt

setTraitsAt

updateFromModelAt

haveSameTrait

isSameTrait

swapTraits

mutateAt

maybeMutateAt

maybeMutateMoran

mutateAt

preferredPlayerBest

deltaTraits

gatherPlayers

doAdjustScores

Requirements:

adjustScoreAt

adjustScoreAt

playPairGameAt

adjustPairGameScoresAt

playGroupGameAt

playNoGameAt

playGroupSequentiallyAt

playGroupOnceAt

yalpGroupGameAt

prepareTraits

commitTraits

commitTraitAt

getTraitData

getTraitHistogramData

getTraitHistogramData

get2DTraitHistogramData

getTraits

getTraitNameAt

getMeanTraits

getMinTraits

getMinTraits

getMaxTraits

getMaxTraits

getMeanFitness

addState

getStatus

check

setInit

getInit

init

initUniform

initMono