//Adult Learning-Specific Accessory functions
private static List <int>[] ListeningTest(SimParams par, Population pop, int[] tutors, float lisThresh)
{
/*Get syls and test whether Repsize larger than listening threshold,
* if so, randomly remove extra syllables.*/
List <int>[] TutorSyls = new List <int> [tutors.Length];
for (int i = 0; i < tutors.Length; i++)
{
TutorSyls[i] = pop.MaleSong[tutors[i]].ToList();
}
if (lisThresh >= .999f & lisThresh < 1)
{
return(TutorSyls);
}
if (lisThresh % 1 == 0)//Absolute number of sylls learned
{
int Thresh = (int)lisThresh;
int Remove;
for (int i = 0; i < TutorSyls.Length; i++)
{
if (TutorSyls[i].Count > Thresh)
{
Remove = TutorSyls[i].Count - Thresh;
par.RandomSampleEqualNoReplace(TutorSyls[i], Remove);
}
}
}
else //percentage of sylls learned
{
float Learnable;
float Remove;
for (int i = 0; i < TutorSyls.Length; i++)
{
Learnable = (TutorSyls[i].Count - par.MinLearnedSyllables) * lisThresh + par.MinLearnedSyllables;
if (Learnable < TutorSyls[i].Count)
{
Learnable = par.NextFloat() < Learnable % 1?
(float)Math.Ceiling(Learnable):(float)Math.Floor(Learnable);
Remove = TutorSyls[i].Count - Learnable;
if (Remove > 0)
{
par.RandomSampleEqualNoReplace(TutorSyls[i], (int)Remove);
}
}
}
}
return(TutorSyls);
}
private static List <int>[] ChooseMultipleTutors(SimParams par, Population pop,
List <int> learners, List <int> notVacant, int numTutors)
{
/*remove chicks and songless birds + any Misc, mark all excluded
* birds as unavailable*/
HashSet <int> PotentialTutorsTemp = new HashSet <int>(notVacant.Where(x => pop.Age[x] > 0));
PotentialTutorsTemp.ExceptWith(PotentialTutorsTemp.Where(x => pop.SyllableRepertoire[x] == 0).ToArray());
List <int>[] Tutors = new List <int> [learners.Count];
if (par.LocalTutor)
{
HashSet <int> Unavailable = new HashSet <int>(Enumerable.Range(0, par.NumBirds));
Unavailable.ExceptWith(PotentialTutorsTemp);
for (int i = 0; i < learners.Count; i++)
{
if (par.SocialCues)
{
Tutors[i] = Locations.GetLocalBirds(par, pop, learners[i], Unavailable, numTutors, pop.Bred).ToList();
}
else
{
Tutors[i] = Locations.GetLocalBirds(par, pop, learners[i], Unavailable, numTutors).ToList();
}
}
}
else
{
List <int> PotentialTutors;
for (int i = 0; i < learners.Count; i++)
{
PotentialTutors = PotentialTutorsTemp.ToList();
PotentialTutors.Remove(learners[i]);
if (par.SocialCues)
{
float[] TutorProbs = new float[PotentialTutors.Count];
for (int j = 0; j < TutorProbs.Length; j++)
{
TutorProbs[j] = pop.Bred[PotentialTutors[j]];
}
int[] ChosenIndex = par.RandomSampleUnequal(TutorProbs, numTutors);
for (int j = 0; j < numTutors; j++)
{
ChosenIndex[j] = PotentialTutors[ChosenIndex[j]];
}
Tutors[i] = ChosenIndex.ToList();
}
else
{
Tutors[i] = par.RandomSampleEqualNoReplace(PotentialTutors, numTutors).ToList();
}
}
}
return(Tutors);
}
/*Functions for decideing how birds learn and
* allowing them to do so*/
//Main process
public static List <int> CoreLearningProcess(SimParams par, Population pop,
int learner, List <int> learnerSong, List <int> tutorSong)
{
//Copy Tutor Syllables
List <float> Rolls = new List <float> {
};
float AccRoll;
for (int i = 0; i < tutorSong.Count; i++)
{
AccRoll = par.NextFloat();
if (AccRoll < pop.Accuracy[learner])//Correct
{
learnerSong.Add(tutorSong[i]);
}
else
{
Rolls.Add(AccRoll);
}
}
//Innovation
if (Rolls.Count > 0)
{
int Innovation = 0;
float InventThresh = 1 - ((1 - pop.Accuracy[learner]) * pop.ChanceInvent[learner]);
for (int i = 0; i < Rolls.Count; i++)
{
if (Rolls[i] >= InventThresh)
{
Innovation += 1;
}
}
if (Innovation > 0)
{
int[] NewSyls;
HashSet <int> AvailableSyllables = new HashSet <int>(par.AllSyls);
AvailableSyllables.ExceptWith(Enumerable.Concat(learnerSong, tutorSong));
if (Innovation >= AvailableSyllables.Count)//Not enough syls for sampling
{
NewSyls = AvailableSyllables.ToArray();
}
else // Enough syls for sampling
{
NewSyls = par.RandomSampleEqualNoReplace(AvailableSyllables.ToList(), Innovation);
}
learnerSong.AddRange(NewSyls);
} //Otherwise no innovation occured
} //Otherwise no mistakes were made
return(learnerSong);
}
public static int[] GetGlobalBirds(SimParams par, Population pop,
int learner, List <int> potentialBirds, int numBirds = 1)
{
int[] Birds;
List <int> PotentialBirds = potentialBirds.ToList();
PotentialBirds.Remove(learner);
if (numBirds == 1)
{
Birds = par.RandomSampleEqualReplace(PotentialBirds, numBirds);
}
else
{
Birds = par.RandomSampleEqualNoReplace(PotentialBirds, numBirds);
}
return(Birds);
}
public static InvasionData Invasion(SimParams par, string type, float invaderStat, int numInvaders = 1, int burnIn = 500)
{
CheckStatValue(par, type, invaderStat);
Population Pop = new Population(par);
//Get to Equilibrium
for (int i = 0; i < burnIn; i++)
{
Pop = BirthDeathCycle.Step(par, Pop);
}
//Invade
int[] InvaderIndex = par.RandomSampleEqualNoReplace(Enumerable.Range(0, par.NumBirds).ToList(), numInvaders);
for (int i = 0; i < numInvaders; i++)
{
CreateInvader(Pop, type, InvaderIndex[i], invaderStat);
//Pop.Age[InvaderIndex[i]] = 1;
//Pop.LearningThreshold[InvaderIndex[i]] = invaderStat;
}
//Postinvasion run
int Counter = 1;
int Categories = 2;
while (Categories != 1)
{
Counter += 1;
Pop = BirthDeathCycle.Step(par, Pop);
if (Counter == 400)
{
break;
}
else
{
Categories = CountCategories(Pop, type);//Pop.LearningThreshold.Distinct().Count();
}
}
InvasionData Results = new InvasionData(Counter, GetAverage(Pop, type));//Pop.LearningThreshold.Average());
return(Results);
}
//Getting local and global birds
public static int[] GetLocalBirds(SimParams par, Population pop,
int target, HashSet <int> unavailable, int numBirds = 1, float[] probs = null)
{
//pick Tutors locally
int[] Birds = new int[numBirds];
int[] UsableInd;
UsableInd = LocalSearch(par, pop, target, unavailable, numBirds).ToArray();
//return the right number of birds
if (UsableInd.Length == numBirds)//only numTutors choice(s)
{
return(UsableInd);
}
else //Multiple choices
{
if (probs != null)//unequal probabilities
{
int[] Index;
float[] Prob = new float[UsableInd.Length];
for (int j = 0; j < UsableInd.Length; j++)
{
Prob[j] = probs[UsableInd[j]];
}
Index = par.RandomSampleUnequal(Prob, numBirds, false);
for (int j = 0; j < numBirds; j++)
{
Birds[j] = UsableInd[Index[j]];
}
return(Birds);
}
if (numBirds == 1)//can use the faster with replace
{
Birds[0] = UsableInd[par.RandomSampleEqualReplace(UsableInd.ToList(), 1)[0]];
return(Birds);
}//need the slower without replace
Birds = par.RandomSampleEqualNoReplace(UsableInd.ToList(), numBirds);
return(Birds);
}
}
static int[] GetAgeGroup(SimParams par, float[] ageRates)
{
//get the number of birds that are in each age group
List <int> AgeGroup = new List <int> {
};
//Guaranteed ages
int Birds = new int {};
for (int i = 0; i <= par.MaxAge; i++)
{
Birds = (int)Math.Floor(par.NumBirds * ageRates[i]);
AgeGroup.AddRange(Enumerable.Repeat(i, Birds).ToList());
}
//Get the chance ages if there are any
int Remainder = par.NumBirds - AgeGroup.Count;
List <int> RemainderAges = new List <int> {
};
if (Remainder > 0)
{
float[] Chance = new float[Remainder];
for (int i = 0; i < Remainder; i++)
{
Chance[i] = par.NextFloat();
}
//Chance to be at each possible age
float[] CumulativeAgeProbability = new float[ageRates.Length + 1];
CumulativeAgeProbability[0] = 0;
CumulativeAgeProbability[ageRates.Length] = 1;
for (int i = 0; i < ageRates.Length - 1; i++)
{
CumulativeAgeProbability[i + 1] = ageRates[i] + CumulativeAgeProbability[i];
}
//test whether Chance belongs to a given age group
for (int i = 0; i <= par.MaxAge; i++)
{
int AgeN = Chance.Count(x => (x >= CumulativeAgeProbability[i] && x < CumulativeAgeProbability[i + 1]));
if (AgeN == 1)
{
RemainderAges.Add(i);
}
else if (AgeN > 1)
{
RemainderAges.AddRange(Enumerable.Repeat(i, AgeN));
}
}
AgeGroup.AddRange(RemainderAges);
}
//Rearrange the age groups randomly
List <int> Rearrange = Enumerable.Range(0, par.NumBirds).ToList();
int[] NewIndex = par.RandomSampleEqualNoReplace(Rearrange, par.NumBirds);
//AgeGroup = AgeGroup.OrderBy(x => par.Rand.Next()).ToList(); for generating array with rand numbers
int[] ScrambledAgeGroup = AgeGroup.ToArray();
Array.Sort(NewIndex, ScrambledAgeGroup);
return(ScrambledAgeGroup);
}
