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Packages that use ModelError | |
connectionRules | |
learningRules | |
models | |
mutationRules | |
utilities |
Uses of ModelError in connectionRules |
Methods in connectionRules that throw ModelError | |
void |
NeighborReinforce.newGeneration(Individual i)
Prepares the individual for a new round of play. |
void |
NeighborReinforce.processPayoff(Individual i)
This function is run after the payoffs are updated for all individuals. |
Uses of ModelError in learningRules |
Methods in learningRules that throw ModelError | |
void |
StratReinforce.newGeneration(Individual i)
Prepares the individual for a new generation. |
void |
StratReinforce.processPayoff(Individual i)
Processes the payoff by adding the payoff to the weight of the strategy choosen on this round |
void |
SmoothReinforcement.newGeneration(Individual i)
Prepares the individual for a new generation. |
void |
SmoothReinforcement.processPayoff(Individual i)
Processes the payoff by adding the payoff to the weight of the strategy choosen on this round |
void |
SmoothReinforcement.processPayoff(Individual i,
int d)
Processes the payoff by adding the payoff to the weight of the strategy choosen on this round. |
void |
SmoothPayoffLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
SmoothPayoffLearning.processPayoff(Individual i)
This processes the payoff by updating the agents beliefs about each payoff based on the information received this round. |
void |
SmoothBGLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
SmoothBGLearning.processPayoff(Individual i)
This processes a payoff for an individual. |
void |
PayoffLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
PayoffLearning.processPayoff(Individual i)
This processes the payoff by updating the agents beliefs about each payoff based on the information received this round. |
void |
MyopicBRLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
MyopicBRLearning.processPayoff(Individual i)
Processing a payoff does nothing, since there are no beliefs to be updated. |
void |
LearningRule.newGeneration(Individual i)
A function that resets the learning rule for a new generation |
void |
LearningRule.processPayoff(Individual i)
Process the payoffs once they have played the game fully |
void |
ImitateBestLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
ImitateBestLearning.processPayoff(Individual i)
Processing a payoff does nothing, since there are no beliefs to be updated. |
void |
HybridLearning.newGeneration(Individual i)
The new generation method implements the averaging. |
void |
HybridLearning.processPayoff(Individual i)
Updates beliefs based on the payoffs of neighbors. |
void |
BgLearning.newGeneration(Individual i)
This does nothing since there is no discounting or anything. |
void |
BgLearning.processPayoff(Individual i)
This processes a payoff for an individual. |
Constructors in learningRules that throw ModelError | |
SmoothPayoffLearning(Game g,
double gam)
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PayoffLearning(Game g)
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Uses of ModelError in models |
Methods in models that throw ModelError | |
void |
StandardModel.createPlayersHigh(Individual[] inds,
int s)
Forms a new network of specified size; expects random to be set. |
void |
StandardModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
StandardModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
Game g,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
StandardModel.playGen()
Plays a generation and then resets. |
int |
StandardModel.detectConvergence(int sow)
Checks to see if individuals have converged. |
void |
SingleDPL.playGen()
Resets then plays a generation. |
void |
SingleDPL.run(com.martiansoftware.jsap.JSAPResult config)
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void |
SingleBGM.run(com.martiansoftware.jsap.JSAPResult config)
Runs the model |
void |
SimpleCondorcetModel.resetPlayers(int s,
double e)
Resets the players beliefs |
int |
SimpleCondorcetModel.detectState()
Detects if the individuals are in one of three states, unanimity on the truth (strategy 0), majority on the truth or none |
int |
SimpleCondorcetModel.countTruth()
Returns the count of individuals who believe the truth (strategy 0). |
int |
SimpleCondorcetModel.runmodel(double mr,
boolean tr,
int g,
int v)
Runs the model. |
void |
SignalingGame.run()
|
void |
RandomNetworkModel.playGen()
Resets then plays a generation. |
int |
RandomNetworkModel.detectConvergence(int optimal)
Checks to see if the population has converged either to the correct state or the bad state. |
void |
PayoffLearningModel.playGen()
Resets then plays a generation. |
int |
PayoffLearningModel.detectConvergence(int optimal)
Checks to see if the population has converged either to the correct state or the bad state. |
void |
HybridModel.createPlayersHigh(Individual[] inds,
int s)
Forms a new network of specified size; expects random to be set. |
void |
HybridModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
HybridModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
Game g,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
HybridModel.playGen()
Resets then plays a generation. |
int |
HybridModel.detectConvergence(int sow)
Checks to see if the population has converged either to the correct state or the bad state. |
void |
BiasedNetworkModel.playGen()
Resets then plays a generation. |
int |
BiasedNetworkModel.detectConvergence(int optimal)
Checks to see if the population has converged either to the correct state or the bad state. |
void |
BgModel.createPlayersHigh(Individual[] inds,
int s)
Forms a new network of specified size; expects random to be set. |
void |
BgModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
BgModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
Game g,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
BgModel.playGen()
Resets then plays a generation. |
int |
BgModel.detectConvergence(int sow)
Checks to see if the population has converged either to the correct state or the bad state. |
Constructors in models that throw ModelError | |
SingleDPL(com.martiansoftware.jsap.JSAPResult config)
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SingleBGM(com.martiansoftware.jsap.JSAPResult config)
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RandomNetworkModel(Individual[] i,
Game g,
LearningRule l,
double m,
double p)
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PayoffLearningModel(Individual[] i,
Game g,
ConnectionRule c,
LearningRule l,
double m)
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BiasedNetworkModel(Individual[] i,
Game g,
LearningRule l,
double m,
double min,
double max)
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Uses of ModelError in mutationRules |
Methods in mutationRules that throw ModelError | |
void |
NoMutate.setMutationRate(double m,
int s)
Does nothing |
void |
NoMutate.mutate(Individual i)
Does nothing |
void |
MutationRule.mutate(Individual i)
|
void |
MutationRule.setMutationRate(double m,
int s)
|
void |
MutateStrategy.mutate(Individual i)
This function mutates based on the mutation matrix already established by the constructor. |
void |
MutateStrategy.setMutationRate(double m,
int s)
Sets the mutation rate (equivalent to the constructor) |
void |
MutateStrategy.setMutationRate(double[][] m)
Same as constructor |
void |
MutateBeliefs.mutate(Individual i)
Mutates weights, beliefs, and alpha/beta values. |
void |
MutateBeliefs.setMutationRate(double m,
int s)
Sets the mutation rate |
Constructors in mutationRules that throw ModelError | |
MutateStrategy(double[][] m)
The constructor which takes a mutation matrix |
|
MutateStrategy(double m,
int s)
Sets the mutation matrix as uniform based on m |
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MutateBeliefs(double m)
Constructor which accepts rate and strategy number. |
Uses of ModelError in utilities |
Methods in utilities that throw ModelError | |
void |
Individual.setBeliefs(double[] b)
A function to set the Bayesian beliefs for one domain. |
void |
Individual.setBeliefs(double[][] b)
A function to set the Bayesian beliefs for multi-domain situations. |
void |
Individual.setBeliefs(double[] b,
int d)
A function to set the Bayesian beliefs for one domain in mutli-domain learning situations. |
void |
Individual.setConstraint(double c)
Sets a constraint that initializations can be constrained by. |
void |
Individual.setConstraint(double[] c)
Sets a constraint that initializations can be constrained by. |
void |
Individual.setConstraint(int d,
double c)
Sets a constraint that initializations can be constrained by. |
void |
Individual.setStratWeights(double[] s)
Sets the strategy weights to a specific value. |
void |
Individual.setStratWeights(double[][] s)
Sets the strategy weights to a specific value. |
void |
Individual.setStratWeights(int p,
double s)
Sets the strategy weight for a particular strategy to a specific value. |
void |
Individual.setStratWeights(int d,
int p,
double s)
Sets the strategy weight for a particular strategy to a specific value. |
void |
Individual.addStratWeight(int p,
double w)
Adds a specified weight to a particular strategy. |
void |
Individual.addStratWeight(int d,
int p,
double w)
Adds a specified weight to a particular strategy. |
void |
Individual.setDiscount(double d)
Sets a discount value |
void |
Individual.setMutation(double m)
Calls the mutationRule set function. |
void |
Individual.mutate()
Calls the mutationRules mutate function. |
void |
Individual.setStrat(int s)
Sets the current strategy of a user |
int |
Individual.chooseStrat()
Uses the learning rule to choose a strategy |
int |
Individual.getStrat()
Returns the current strategy, if its not set chooses one. |
void |
Individual.reset()
Resets for a new generation. |
void |
Individual.playGame()
Plays the game with each neighbor. |
void |
Individual.update()
Updates after play is complete. |
void |
Individual.initStart(int s)
The initializes the individual for single domain models by giving them random beliefs and randomizing the weights The random beliefs are constrained by the zeroconstraint. |
void |
Individual.initStart(int d,
int s)
The initializes the individual for multi domain models by giving them random beliefs and randomizes the the wieghts The random beliefs are constrained by the zeroconstraint. |
static java.util.HashSet |
GraphIO.readJobsFile(java.lang.String fn)
A function to read jobs files. |
static int[][] |
GraphIO.stringMatrix(java.lang.String s)
A function to turn a string representing an adjacency matrix into an integer matrix. |
void |
Game.setMatrix(int[][] m)
Sets the payoff matrix. |
void |
Game.setMatrix(int[][] m,
int bp)
Sets the payoff matrix. |
void |
BeliefAnimation.newFrame()
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Constructors in utilities that throw ModelError | |
Game(int[][] m,
boolean so)
Returns a game object. |
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Game(int[][] m,
boolean so,
MersenneTwister r)
Returns a game object. |
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Game(int[][] m,
boolean so,
MersenneTwister r,
boolean st)
Returns a game object |
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