/// <summary>
/// SIMONObjectCollection 집합에 대해서 다음 세대의 DNA를 적용시켜서 진화시킵니다.
/// </summary>
/// <param name="ObjectCollection">3차원의 대상 SIMON Collection 입니다.</param>
/// <param name="ActionMap">3차원의 ActionMap 입니다.</param>
/// <param name="nextDNA">진화시킬 다음 세대의 3차원 DNA입니다.</param>
private void Evolution(SIMONCollection ObjectCollection, SIMONCollection ActionMap, List<List<List<SIMONGene>>> nextDNA)
{
if (ObjectCollection.Count <= 0 || nextDNA == null || nextDNA.Count <= 0)
return;
int numberOfDNA = nextDNA.Count;
for (int i = 0; i < ActionMap.Count; i++)
{
if (nextDNA[i].Count < 1)
continue;
Random rand = new Random();
List<SIMONObject> actionObjectList = (List<SIMONObject>)ActionMap.ValueOfIndex(i);
for (int j = 0; j < actionObjectList.Count; j++)
{
for (int k = 0; k < actionObjectList[j].Actions.Count; k++)
{
if (ActionMap.KeyOfIndex(i).Equals(actionObjectList[j].Actions[k].ActionName))
{
int geneIdx = rand.Next(0, nextDNA[i].Count);
((List<SIMONObject>)ActionMap.ValueOfIndex(i))[j].Actions[k].ActionDNA = nextDNA[i][geneIdx];
break;
}
}
}
}
}
/// <summary>
/// SIMONObjectCollection 집합에 대해서 다음 세대의 DNA를 적용시켜서 진화시킵니다.
/// </summary>
/// <param name="ObjectCollection">3차원의 대상 SIMON Collection 입니다.</param>
/// <param name="ActionMap">3차원의 ActionMap 입니다.</param>
/// <param name="nextDNA">진화시킬 다음 세대의 3차원 DNA입니다.</param>
/// <param name="learningRate">진화의 학습률입니다.</param>
private void Evolution(SIMONCollection ObjectCollection, SIMONCollection ActionMap, List<List<List<SIMONGene>>> nextDNA, double learningRate)
{
if (ObjectCollection.Count <= 0 || nextDNA == null || nextDNA.Count <= 0)
return;
int numberOfDNA = nextDNA.Count;
for (int i = 0; i < ActionMap.Count; i++)
{
if (nextDNA[i].Count < 1)
continue;
int actionObjDNACnt = nextDNA[i].Count;
for (int j = 0; j < actionObjDNACnt; j++)
{
int nextDNAGeneCnt = nextDNA[i][j].Count;
for (int k = 0; k < nextDNAGeneCnt; k++)
{
nextDNA[i][j][k].ElementWeight *= learningRate;
}
}
}
for (int i = 0; i < ActionMap.Count; i++)
{
if (nextDNA[i].Count < 1)
continue;
Random rand = new Random();
int randIdx = rand.Next(0, numberOfDNA);
List<SIMONObject> actionObjectList = (List<SIMONObject>)ActionMap.ValueOfIndex(i);
for (int j = 0; j < actionObjectList.Count; j++)
{
for (int k = 0; k < actionObjectList[j].Actions.Count; k++)
{
if (ActionMap.KeyOfIndex(i).Equals(actionObjectList[j].Actions[k].ActionName))
{
int geneIdx = rand.Next(0, nextDNA[i].Count);
((List<SIMONObject>)ActionMap.ValueOfIndex(i))[j].Actions[k].ActionDNA = nextDNA[i][geneIdx];
}
}
}
}
}
/// <summary>
/// History로부터 읽어온 데이터를 바탕으로, 유전 선택 알고리즘을 수행합니다. 우성 : 열성이 3:1 비율이 되도록 집단에서 우성 행동들과 열성 행동의 3차원 배열값을 반환합니다.
/// </summary>
/// <param name="ObjectCollection">3차원으로 구성된 Selection 대상 ObjectCollection 입니다.</param>
/// <param name="ActionMap">Selection 대상 ActionMap 입니다.</param>
/// <param name="SimonFunctions">Selection 연산에 사용될 SimonFunction 집합입니다.</param>
/// <returns>선택된 3차원 ActionDNA 배열입니다.</returns>
public List<List<List<SIMONGene>>> Selection(SIMONCollection ObjectCollection, SIMONCollection ActionMap, Dictionary<string, SIMONFunction> SimonFunctions)
{
List<List<List<SIMONGene>>> selectedDNA = new List<List<List<SIMONGene>>>();
List<GeneValue[]> recordMap = new List<GeneValue[]>(); //ObjectCollection 내 각 객체들에 대한 Action들에 대한 Record 값들을 저장하는 Map.
int ObjectCount = ObjectCollection.Count;
int ActionCount = ActionMap.Count;
for (int i = 0; i < ActionCount; i++)
{
selectedDNA.Add(new List<List<SIMONGene>>());
}
#region 현재 ActionMap 구조를 이용한 RecordMap 구조화
for (int i = 0; i < ActionCount; i++)
{
List<SIMONObject> elementList = (List<SIMONObject>)ActionMap.ValueOfIndex(i);
int actionObjectCount = elementList.Count;
GeneValue[] gene = new GeneValue[actionObjectCount];
recordMap.Add(gene);
for (int j = 0; j < actionObjectCount; j++)
{
recordMap[i][j] = new GeneValue();
SIMONObject[] otherObjectsList;
if (ObjectCount > 1)
otherObjectsList = new SIMONObject[ObjectCollection.Count - 1];
else
otherObjectsList = null;
int otherObjectCnt = 0;
for (int k = 0; k < ObjectCount; k++)
if ((otherObjectsList != null) && (!ObjectCollection.ValueOfIndex(k).Equals(elementList[j])))
otherObjectsList[otherObjectCnt++] = (SIMONObject)ObjectCollection.ValueOfIndex(k);
for (int k = 0; k < elementList[j].Actions.Count; k++)
{
if (elementList[j].Actions[k].ActionName.Equals(ActionMap.KeyOfIndex(i)))
{
recordMap[i][j].dna = elementList[j].Actions[k].ActionDNA;
double fitnessValue = (double)SimonFunctions[elementList[j].Actions[k].FitnessFunctionName].Invoke(elementList[j], otherObjectsList);
//Upper Boundary와 Lower Boundary 내의 Fitness Value들을 채택.
if (fitnessValue < SIMONConstants.FITNESS_MIN_VALUE)
{
throw new SIMONFramework.ValueUnderflowException(SIMONConstants.EXP_VALUE_UNDERFLOW);
}
else if (fitnessValue > SIMONConstants.FITNESS_MAX_VALUE)
{
throw new SIMONFramework.ValueOverflowException(SIMONConstants.EXP_VALUE_OVERFLOW);
}
recordMap[i][j].fitnessValue = fitnessValue;
break;
}
}
}
}
#endregion
#region 현재 division 나누는 코드. Action 별로 4등분해서 나누기 때문에 2차원 배열임.
int[][] divIndex = new int[ActionCount][];
for (int i = 0; i < ActionCount; i++)
{
int numberOfActionObjects = ((List<SIMONObject>)ActionMap.ValueOfIndex(i)).Count;
divIndex[i] = new int[SIMONConstants.GENE_SUM_RATING];
for (int j = 0; j < SIMONConstants.GENE_SUM_RATING; j++)
{
int divPosition = (numberOfActionObjects * (j + 1)) / SIMONConstants.GENE_SUM_RATING;
if (divPosition == 0)
divPosition = -1;
else
divPosition--;
divIndex[i][j] = divPosition;
}
}
#endregion
//QuickSort를 통해서 각 Action별로 fitness 값들을 정렬.
for (int i = 0; i < recordMap.Count; i++)
QuickSort(recordMap[i], 0, recordMap[i].Length - 1);
List<List<GeneValue>> firstSelectGene = new List<List<GeneValue>>();
List<List<GeneValue>> lastSelectGene = new List<List<GeneValue>>();
for (int i = 0; i < recordMap.Count; i++)
{
firstSelectGene.Add(new List<GeneValue>());
lastSelectGene.Add(new List<GeneValue>());
List<GeneValue> recessiveGroup = new List<GeneValue>();
List<GeneValue> dominionGroup = new List<GeneValue>();
//열성집합 중 1만큼의 비율을 선택. 경계값부터 시작인덱스 까지 내려가면서 집합에 포함시킨다.
for (int j = divIndex[i][(SIMONConstants.GENE_RECESSIVE_RATING - 1)]; j >= 0; j--)
{
recessiveGroup.Add(recordMap[i][j]);
}
List<GeneValue> rouletteRecessive = RouletteWheel(recessiveGroup, GeneSelectionLaw.RECESSIVE); //각 Object 들의 열성 집합 중 대표값 배열을 우성 열성 비율별로 선택
if (rouletteRecessive != null)
firstSelectGene[i].AddRange(rouletteRecessive);
//우성집합 중 3만큼의 비율을 선택. 경계값부터 시작인덱스까지 내려가면서 집합에 포함시킨다.
for (int j = divIndex[i][SIMONConstants.GENE_DOMINION_RATING]; j >= divIndex[i][(SIMONConstants.GENE_RECESSIVE_RATING - 1)] + 1; j--)
{
dominionGroup.Add(recordMap[i][j]);
}
List<GeneValue> rouletteDominion = RouletteWheel(dominionGroup, GeneSelectionLaw.DOMINION); //각 Object 들의 우성 집합 중 대표값 배열을 우성 열성 비율별로 선택
if (rouletteDominion != null)
firstSelectGene[i].AddRange(rouletteDominion);
lastSelectGene[i].AddRange(RouletteWheel(firstSelectGene[i], GeneSelectionLaw.DOMINION));
}
for (int i = 0; i < ActionCount; i++)
{
int selectedCount = SIMONConstants.GENE_REAL_SELECT_NUM;
int[] selectedIdxList = new int[lastSelectGene[i].Count];
int selectedIdxListIndex = 0;
//만약 실제 유전자 숫자가 Default로 정한 유전자 추출 갯수보다 작으면 실제 유전자 갯수 만큼을 선택 횟수로 지정한다.
if (lastSelectGene[i].Count < SIMONConstants.GENE_REAL_SELECT_NUM)
selectedCount = lastSelectGene[i].Count;
if (selectedCount <= 0)
continue;
//실제 유전시킬 유전자 갯수만큼 랜덤값을 통한 추출
while (selectedIdxListIndex < selectedCount)
{
Random rand = new Random();
int selectIdx = rand.Next(0, selectedCount);
bool retryFlag = false;
for (int j = 0; j < selectedIdxListIndex; j++)
{
if (selectIdx == selectedIdxList[j])
{
retryFlag = true;
break;
}
}
if (retryFlag)
continue;
selectedDNA[i].Add(lastSelectGene[i][selectIdx].dna);
selectedIdxList[selectedIdxListIndex++] = selectIdx;
}
}
//selectedDNA 리턴.
return selectedDNA;
}
