EBD_helpers: McDowell's Evolutionary Behaviour Dynamics
In Don-Li/CAB: The Computational Analysis of Behaviour
Description
Usage
Details
Fitness functions
Response emission functions
Get the operant class
Stock reinforcement schedule
Mutation
Fitness weights
See Also
Description
A list of functions in the CAB package's implementation of McDowell's Evolutionary Behaviour Dynamics. A list of lower level utility functions, such as for converting integers in base 10 to base 2 can be found in EBD_utilities. See CAB.EBD for more details.
Usage
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EBD_WSI_fitness(max_phenotype, phenotypes, last_resp)
EBD.response_emission(preallocated_resp_index, tick, phenotypes)
EBD.get_oc(last_resp, oc_lower_bounds)
EBD.geometric_vi(inter_rft_interval, min_irt, time)
EBD.w_gaussian_mutation(tick, preallocated_mutant_change,
preallocated_mutant_index, max_phenotype, phenotypes)
EBD.geometric_fitness_weights(fitness, parental_selection_p)
Details
Stuff.
Fitness functions
Computes the fitness of parents.
EBD.wrapped_si_fitness
Wrapped specific individual fitness. The fitness value of a behaviour is max( |i-k|, m - |i-k| ), where i is the base 10 value of a behaviour in the population, k is the integer value of the reinforced behaviour, and m is the maximum base 10 value that a behaviour can take. It is wrapped because it is modularised around the domain [0,m].
Usage
EBD.wrapped_si_fitness( domain, phenotypes, last_resp )
Arguments
domainA vector specifying the minimum and maximum base 10 values that a behaviour can take.
phenotypesA numeric vector of behaviours in base 10.
Value
Returns a numeric vector of the same length as phenotyes. Indices will match.
############################################################################
Response emission functions
Emits a response.
EBD.response_emission
Emits a behaviour.
Usage
EBD.response_emission( preallocated_resp_index, n_rft, time, rft_ticks, phenotypes )
Arguments
preallocated_resp_indexA numeric value.
n_rftNumber of reinforcement that occurred during a session.
timeNumber of time ticks.
rft_ticksDuration of reinforcment delivery.
phenotypesA numeric vector of behaviours in base 10
.
Details
The way that EBD is implemented in CAB is that the index of the responses are preallocated at the start of the simulation. This reduces the number of function calls to a random number generator and hence improves the speed of the model. The response index is recovered from the number of time ticks that the simulation has been run, time, accounting for the time ticks that were taken up by reinforcement deliveries.
Value
Returns a value selected from phenotyes.
############################################################################
Get the operant class
From a behaviour in base 10, get the operant class.
EBD.get_oc
Get the operant class
Usage
EBD.get_oc( last_resp, oc_lower_bounds )
Arguments
last_respA numeric value.
oc_lower_boundsLower bounds of the operant classes.
Value
Returns the operant class that the last response belongs in.
############################################################################
Stock reinforcement schedule
Common reinforcement schedules.
EBD.geometric_vi
A VI schedule with geometrically distributed arranged inter-reinforcement intervals.
Usage
EBD.geometric_vi( inter_rft_interval, min_irt, time )
Arguments
inter_rft_intervalNumeric. In real time.
min_irtMinimum inter-response time, i.e. time ticks for the algorithm.
timeNumeric
Value
Generates a Geometrically distributed inter-reinforcement interval by doing a continuous to discrete transformation of the inter-rft-interval and adds the time to return the time at which food will be available.
EBD.shifted_geometric_vi
A VI schedule with shifted geometrically distributed arranged inter-reinforcement intervals.
Usage
EBD.geometric_vi( inter_rft_interval, min_irt, time, shift )
Arguments
inter_rft_intervalNumeric. In real time.
min_irtMinimum inter-response time, i.e. time ticks for the algorithm.
timeNumeric.
shiftNumeric. In real time.
Value
Generates a Geometrically distributed inter-reinforcement interval by doing a continuous to discrete transformation of the inter-rft-interval and adds the time to return the time at which food will be available.
############################################################################
Mutation
Mutate behaviours in the population.
EBD.w_gaussian_mutation
Mutate behaviours my resampling their phenotypes from a wrapped normal distribution.
Usage
EBD.w_gaussian_mutation( tick, preallocated_mutant_change, preallocated_mutant_index, max_phenotype, phenotypes )
Arguments
tickNumeric. The number of iterations of the algorithm.
preallocated_mutant_changeMatrix giving how much each phenotype changes.
preallocated_mutant_indexMatrix giving the phenotypes that will change.
max_phenotypeNumeric. The maximum phenotype value.
phenotypesVector.
Details
preallocated_mutant_change is a matrix that has rows the number of iterations to run the algorithm. Each column is the amount to change each phenotype to be mutated on that iteration.
preallocated_mutant_index is a matrix of corresponding dimensions. Each column is the index of the phenotype to change.
Value
Returns a list containing the new phenotype values and the indices to which they belong.
############################################################################
Fitness weights
Sampling weights for sampling fitness values.
EBD.geometric_fitness_weights
Sampling weights from a geometric distribution
Usage
EBD.geometric_fitness_weights( fitness, parental_selection_p )
Arguments
fitnessNumeric vector of fitness values
parental_selection_pParameter for the geometric distribution
Value
Returns a vector of fitness weights.
############################################################################
See Also
CAB.EBD
Don-Li/CAB documentation built on May 6, 2019, 2:52 p.m.
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Description Usage Details Fitness functions Response emission functions Get the operant class Stock reinforcement schedule Mutation Fitness weights See Also
Description
A list of functions in the CAB package's implementation of McDowell's Evolutionary Behaviour Dynamics. A list of lower level utility functions, such as for converting integers in base 10 to base 2 can be found in EBD_utilities. See CAB.EBD for more details.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 | EBD_WSI_fitness(max_phenotype, phenotypes, last_resp)
EBD.response_emission(preallocated_resp_index, tick, phenotypes)
EBD.get_oc(last_resp, oc_lower_bounds)
EBD.geometric_vi(inter_rft_interval, min_irt, time)
EBD.w_gaussian_mutation(tick, preallocated_mutant_change,
preallocated_mutant_index, max_phenotype, phenotypes)
EBD.geometric_fitness_weights(fitness, parental_selection_p)
|
Details
Stuff.
Fitness functions
Computes the fitness of parents.
EBD.wrapped_si_fitness
Wrapped specific individual fitness. The fitness value of a behaviour is max( |i-k|, m - |i-k| ), where i is the base 10 value of a behaviour in the population, k is the integer value of the reinforced behaviour, and m is the maximum base 10 value that a behaviour can take. It is wrapped because it is modularised around the domain [0,m].
Usage
EBD.wrapped_si_fitness( domain, phenotypes, last_resp )
Arguments
domainA vector specifying the minimum and maximum base 10 values that a behaviour can take.
phenotypesA numeric vector of behaviours in base 10.
Value
Returns a numeric vector of the same length as phenotyes. Indices will match.
############################################################################
Response emission functions
Emits a response.
EBD.response_emission
Emits a behaviour.
Usage
EBD.response_emission( preallocated_resp_index, n_rft, time, rft_ticks, phenotypes )
Arguments
preallocated_resp_indexA numeric value.
n_rftNumber of reinforcement that occurred during a session.
timeNumber of time ticks.
rft_ticksDuration of reinforcment delivery.
phenotypesA numeric vector of behaviours in base 10
.
Details
The way that EBD is implemented in CAB is that the index of the responses are preallocated at the start of the simulation. This reduces the number of function calls to a random number generator and hence improves the speed of the model. The response index is recovered from the number of time ticks that the simulation has been run, time, accounting for the time ticks that were taken up by reinforcement deliveries.
Value
Returns a value selected from phenotyes.
############################################################################
Get the operant class
From a behaviour in base 10, get the operant class.
EBD.get_oc
Get the operant class
Usage
EBD.get_oc( last_resp, oc_lower_bounds )
Arguments
last_respA numeric value.
oc_lower_boundsLower bounds of the operant classes.
Value
Returns the operant class that the last response belongs in.
############################################################################
Stock reinforcement schedule
Common reinforcement schedules.
EBD.geometric_vi
A VI schedule with geometrically distributed arranged inter-reinforcement intervals.
Usage
EBD.geometric_vi( inter_rft_interval, min_irt, time )
Arguments
inter_rft_intervalNumeric. In real time.
min_irtMinimum inter-response time, i.e. time ticks for the algorithm.
timeNumeric
Value
Generates a Geometrically distributed inter-reinforcement interval by doing a continuous to discrete transformation of the inter-rft-interval and adds the time to return the time at which food will be available.
EBD.shifted_geometric_vi
A VI schedule with shifted geometrically distributed arranged inter-reinforcement intervals.
Usage
EBD.geometric_vi( inter_rft_interval, min_irt, time, shift )
Arguments
inter_rft_intervalNumeric. In real time.
min_irtMinimum inter-response time, i.e. time ticks for the algorithm.
timeNumeric.
shiftNumeric. In real time.
Value
Generates a Geometrically distributed inter-reinforcement interval by doing a continuous to discrete transformation of the inter-rft-interval and adds the time to return the time at which food will be available.
############################################################################
Mutation
Mutate behaviours in the population.
EBD.w_gaussian_mutation
Mutate behaviours my resampling their phenotypes from a wrapped normal distribution.
Usage
EBD.w_gaussian_mutation( tick, preallocated_mutant_change, preallocated_mutant_index, max_phenotype, phenotypes )
Arguments
tickNumeric. The number of iterations of the algorithm.
preallocated_mutant_changeMatrix giving how much each phenotype changes.
preallocated_mutant_indexMatrix giving the phenotypes that will change.
max_phenotypeNumeric. The maximum phenotype value.
phenotypesVector.
Details
preallocated_mutant_change is a matrix that has rows the number of iterations to run the algorithm. Each column is the amount to change each phenotype to be mutated on that iteration.
preallocated_mutant_index is a matrix of corresponding dimensions. Each column is the index of the phenotype to change.
Value
Returns a list containing the new phenotype values and the indices to which they belong.
############################################################################
Fitness weights
Sampling weights for sampling fitness values.
EBD.geometric_fitness_weights
Sampling weights from a geometric distribution
Usage
EBD.geometric_fitness_weights( fitness, parental_selection_p )
Arguments
fitnessNumeric vector of fitness values
parental_selection_pParameter for the geometric distribution
Value
Returns a vector of fitness weights.
############################################################################
See Also
CAB.EBD
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