private static List <int> TestLearningThreshold(SimParams par, Population pop,
List <int> notVacant)
{
/*Get birds with a threshold below the age keepign in mind
* the vertical learning threshold*/
float ModifiedThresh;
List <int> Learners = new List <int> {
};
for (int i = 0; i < notVacant.Count; i++)
{
ModifiedThresh = pop.LearningThreshold[notVacant[i]] >= 1?
pop.LearningThreshold[notVacant[i]]:
(pop.LearningThreshold[notVacant[i]] - par.VerticalLearningCutOff) / (1 - par.VerticalLearningCutOff);
if (pop.Age[notVacant[i]] + 1 <= ModifiedThresh)//full learn to learn
{
Learners.Add(notVacant[i]);
}
else if (pop.Age[notVacant[i]] < ModifiedThresh) //Chance to learn during partical year
{
if (par.NextFloat() < ModifiedThresh - pop.Age[notVacant[i]])
{
Learners.Add(notVacant[i]);
}
}
}
return(Learners);
}
//Learning Accessory functions
private static ConsensusResults ConsensusLearning(SimParams par, Population pop,
List <int>[] tutors, List <int> learners)
{
//Create a consensus based on songs from several males
List <int>[] ConsensusSongs = new List <int> [learners.Count];
List <int>[] AddSyls = new List <int> [learners.Count];
List <int>[] AllSongs;
List <int> CollapsedSongs;
float Conform;
for (int i = 0; i < learners.Count; i++)
{
ConsensusSongs[i] = new List <int> {
};
AddSyls[i] = new List <int> {
};
AllSongs = ListeningTest(par, pop, tutors[i].ToArray(), par.ListeningThreshold);
CollapsedSongs = AllSongs.SelectMany(x => x).ToList();
ConsensusSongs[i] = CollapsedSongs.Distinct().ToList();
for (int j = 0; j < ConsensusSongs[i].Count(); j++)
{
Conform = ConsensusCalc(par, ConsensusSongs[i][j], CollapsedSongs);
if (par.NextFloat() < Conform)
{
AddSyls[i].Add(ConsensusSongs[i][j]);
}
}
}
ConsensusResults Returnable = new ConsensusResults(AddSyls, ConsensusSongs);
return(Returnable);
}
private static List <int> CheckEncounter(SimParams par, List <int> capable)
{
for (int i = capable.Count - 1; i >= 0; i--)
{
if (par.NextFloat() > par.EncounterSuccess)
{
capable.RemoveAt(i);
}
}
return(capable);
}
/*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);
}
private static List <int> DropSyllables(SimParams par, List <int> song,
List <int> droppableSyls, float chanceForget)
{
//Remove syllables not heard from tutor based on ChanceForget
for (int i = 0; i < droppableSyls.Count; i++)
{
if (par.NextFloat() < chanceForget)
{
song.Remove(droppableSyls[i]);
}
}
return(song);
}
//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> GenerateNovelSong(SimParams par)
{
//Test whether each syllable is learned
List <int> Song = new List <int>();
while (Song.Count == 0)
{
for (int i = 0; i < par.SongCore.Count; i++)
{
if (par.NextFloat() < par.SongCore[i])
{
Song.Add(i);
}
}
}
return(Song);
}
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);
}
//Death
private static int[] AgeDeath(SimParams par, Population pop)
{
//Send birds to the circus based on retirement age
List <int> DeadIndex = new List <int> {
};
float[] PickChance = LearningPenalty(par, pop.LearningThreshold);
int[] AgePool;
float Dead;
int MustDie;
float ChanceDie;
int[] Chosen;
int[] ChosenInd;
float[] PickChanceSubSet;
for (int i = 0; i < par.MaxAge; i++)
{
//which birds are age i
AgePool = Enumerable.Range(0, pop.Age.Length)
.Where(x => pop.Age[x] == i).ToArray();
if (AgePool.Length == 0)//No birds of a given age
{
continue;
}
Dead = AgePool.Length * (1 - pop.SurvivalChance[i]);
MustDie = (int)Math.Floor(Dead);
ChanceDie = Dead % 1;
if (par.NextFloat() < ChanceDie)
{
MustDie += 1;
}
if (MustDie == 0)
{
continue;
}
Chosen = new int[MustDie];
if (MustDie >= AgePool.Length)
{
Chosen = AgePool;
}
else
{
PickChanceSubSet = new float[AgePool.Length];
for (int j = 0; j < AgePool.Length; j++)
{
PickChanceSubSet[j] = PickChance[AgePool[j]];
}
ChosenInd = par.RandomSampleUnequal(PickChanceSubSet, MustDie, false);
for (int j = 0; j < ChosenInd.Length; j++)
{
Chosen[j] = AgePool[ChosenInd[j]];
}
}
DeadIndex.AddRange(Chosen);
}
DeadIndex.AddRange(Enumerable.Range(0, pop.Age.Length).Where(x => pop.Age[x] == par.MaxAge).ToArray());//birds of max age must die
/*double[] GetIndexer = new double[DeadIndex.Count];
* for(int i=0;i<DeadIndex.Count;i++){
* GetIndexer[i] = PickChance[DeadIndex[i]];
* }
* Console.WriteLine(PickChance.Average());
* //Console.WriteLine(GetIndexer.Average());*/
return(DeadIndex.ToArray());
}
