public SIMONManager()
{
SimonUtility = SIMONUtility.GetInstance();
SimonObjectCollections = new SIMONCollection();
SimonDataManager = new SIMONDataManager();
SimonDefinitionObjects = new Dictionary<string, SIMONObject>();
SimonFunctions = new Dictionary<string, SIMONFunction>();
IntelligenceManager = new SIMONIntelligence();
LearningCount = SIMONConstants.DEFAULT_LEARNING_COUNT;
LearningPoint = SIMONConstants.DEFAULT_LEARNING_POINT;
CreateWorkPath();
}
public SIMONManager(int learningCount, int learningPoint)
{
SimonUtility = SIMONUtility.GetInstance();
SimonObjectCollections = new SIMONCollection();
SimonDataManager = new SIMONDataManager();
SimonDefinitionObjects = new Dictionary<string, SIMONObject>();
SimonFunctions = new Dictionary<string, SIMONFunction>();
IntelligenceManager = new SIMONIntelligence();
this.LearningCount = learningCount;
this.LearningPoint = learningPoint;
CreateWorkPath();
}
/// <summary>
/// Property에 대한 학습을 비동기적으로 구현합니다.
/// </summary>
/// <param name="ObjectCollection">Property 학습을 수행할 대상 Group입니다.</param>
/// <param name="SimonFunctions">학습에 사용될 SIMONFunction ADT입니다.</param>
/// <param name="learningRate">학습률입니다.</param>
/// <returns>비동기 결과객체입니다.</returns>
public IAsyncResult LearnPropertyAsync(SIMONCollection ObjectCollection, Dictionary<string, SIMONFunction> SimonFunctions, double learningRate)
{
isLearning = true;
return AlgorithmLearnPropInvoker.BeginInvoke(ObjectCollection, SimonFunctions, learningRate, LearnPropertyAsyncCallback, null);
}
/// <summary>
/// SIMONObject들의 Property 학습을 수행하는 별도의 알고리즘 구현을 제공합니다.
/// </summary>
/// <param name="ObjectCollection">Property 학습을 수행할 대상 Group입니다.</param>
/// <param name="SimonFunctions">학습에 사용될 SIMONFunction ADT입니다.</param>
public void LearnProperty(SIMONCollection ObjectCollection, Dictionary<string, SIMONFunction> SimonFunctions)
{
try
{
isLearning = true;
LearnResult = AlgorithmPerformer.Implementation(ObjectCollection, SimonFunctions);
isLearning = false;
}
catch (Exception exp)
{
throw new SIMONFramework.LearningFaultException(SIMONConstants.EXP_LEARNING_FAULT);
}
}
/// <summary>
/// SIMONAlgorithm의 구현을 담당하는 Interface의 기능을 호출합니다.
/// </summary>
/// <param name="PropertyCollection">진화에 대한 구현을 담당하는 2차원 SIMONCollection입니다.</param>
/// <param name="SimonFunctions">SIMONFunction 집합입니다.</param>
/// <returns>구현에 대한 결과값입니다.</returns>
public object Implementation(SIMONCollection GroupCollection, Dictionary<string, SIMONFunction> SimonFunctions)
{
List<List<SIMONGene>> selectedDNAPool = Selection(GroupCollection, SimonFunctions);
List<List<SIMONGene>> crossedDNAPool = CrossOver(selectedDNAPool);
List<List<SIMONGene>> mutatedDNAPool = Mutation(crossedDNAPool);
Evolution(GroupCollection, mutatedDNAPool);
UpdateObjectsPropertyDNA(GroupCollection);
return null;
}
public void SIMONLearn(SIMONCollection Group){
SimonManager.LearnRoutine(Group);
}
/// <summary>
/// 매개변수로 전달받은 SIMONObject 독립된 객체에 관한 학습 동작을 수행합니다.
/// </summary>
/// <param name="sObject">학습을 진행시킬 SIMONObject 단일객체.</param>
public void TeachObject(SIMONObject sObject)
{
SIMONCollection subCollection = new SIMONCollection();
subCollection.Add(sObject.ObjectID, sObject);
SIMONCollection subActionMap = CreateActionMap(subCollection);
IntelligenceManager.Learn(subCollection, subActionMap, SimonFunctions);
IntelligenceManager.LearnProperty(subCollection, SimonFunctions);
}
/// <summary>
/// SimonCollection들에 대한 History를 이용해서 학습 루틴을 구현합니다.
/// </summary>
public void LearnRoutine(SIMONCollection targetGroup)
{
/********************************** 학습 **************************************/
SIMONCollection newActionMap = CreateActionMap(targetGroup);
IntelligenceManager.Learn(targetGroup, newActionMap, SimonFunctions);
IntelligenceManager.LearnProperty(targetGroup, SimonFunctions);
/***************************************************************************************/
}
/// <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 집합내의 Property DNA를 진화시키는 함수를 제공합니다.
/// </summary>
/// <param name="PropertyCollection">2차원의 대상 SIMON Collection 입니다.</param>
/// <param name="nextDNA">진화시킬 다음 세대의 2차원 DNA입니다.</param>
/// <param name="learningRate">학습률을 적용합니다.</param>
private void Evolution(SIMONCollection PropertyCollection, List<List<SIMONGene>> nextDNA, double learningRate)
{
if (PropertyCollection.Count <= 0 || nextDNA == null || nextDNA.Count <= 0)
return;
int objectCount = PropertyCollection.Count;
Random rand = new Random();
for (int i = 0; i < nextDNA.Count; i++)
{
int nextGeneElementCount = nextDNA[i].Count;
for (int j = 0; j < nextGeneElementCount; j++)
nextDNA[i][j].ElementWeight *= learningRate;
}
for (int i = 0; i < objectCount; i++)
{
int selectDNAIdx = rand.Next(0, nextDNA.Count - 1);
((SIMONObject)PropertyCollection.ValueOfIndex(i)).PropertyDNA = nextDNA[selectDNAIdx];
}
}
/// <summary>
/// SIMONObjectCollection 집합내의 Property DNA를 진화시키는 함수를 제공합니다.
/// </summary>
/// <param name="PropertyCollection">2차원의 대상 SIMON Collection 입니다.</param>
/// <param name="nextDNA">진화시킬 다음 세대의 2차원 DNA입니다.</param>
private void Evolution(SIMONCollection PropertyCollection, List<List<SIMONGene>> nextDNA)
{
if (PropertyCollection.Count <= 0 || nextDNA == null || nextDNA.Count <= 0)
return;
int objectCount = PropertyCollection.Count;
Random rand = new Random();
for (int i = 0; i < objectCount; i++)
{
int selectDNAIdx = rand.Next(0, nextDNA.Count - 1);
for (int j = 0; j < nextDNA[selectDNAIdx].Count; j++)
{
for (int k = 0; k < ((SIMONObject)PropertyCollection.ValueOfIndex(i)).PropertyDNA.Count; k++)
{
if (((SIMONObject)PropertyCollection.ValueOfIndex(i)).PropertyDNA[k].ElementName == nextDNA[selectDNAIdx][j].ElementName)
{
((SIMONObject)PropertyCollection.ValueOfIndex(i)).PropertyDNA[k] = nextDNA[selectDNAIdx][j];
}
}
}
}
}
/// <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;
}
/// <summary>
/// History로부터 읽어온 데이터를 바탕으로, 유전 선택 알고리즘을 수행합니다. 우성 : 열성이 3:1 비율이 되도록 집단에서 우성 행동들과 열성 행동의 2차원 배열값을 반환합니다.
/// </summary>
/// <param name="PropertyCollection">2차원으로 구성된 Selection 대상 ObjectCollection 입니다.</param>
/// <param name="SimonFunctions">Selection 연산에 사용될 SimonFunction 집합입니다.</param>
/// <returns>선택된 2차원 Property DNA 배열입니다.</returns>
public List<List<SIMONGene>> Selection(SIMONCollection PropertyCollection, Dictionary<string, SIMONFunction> SimonFunctions)
{
List<List<SIMONGene>> selectedDNA = new List<List<SIMONGene>>();
int dnaObjectCount = PropertyCollection.Count;
GeneValue[] propertyGenes = new GeneValue[dnaObjectCount];
int[] divIndex = new int[SIMONConstants.GENE_SUM_RATING];
for (int i = 0; i < dnaObjectCount; i++)
{
SIMONObject elementObject = (SIMONObject)PropertyCollection.ValueOfIndex(i);
SIMONObject[] otherObjects = new SIMONObject[dnaObjectCount - 1];
int otherObjectsCnt = 0;
propertyGenes[i] = new GeneValue();
//otherObject 리스트 추가시키면됨.
for (int j = 0; j < dnaObjectCount; j++)
if (elementObject != (SIMONObject)PropertyCollection.ValueOfIndex(j))
otherObjects[otherObjectsCnt++] = (SIMONObject)PropertyCollection.ValueOfIndex(j);
propertyGenes[i].dna = elementObject.PropertyDNA;
double fitnessValue = (double)SimonFunctions[elementObject.ObjectFitnessFunctionName].Invoke(elementObject, otherObjects);
//Upper Boundary와 Lower Boundary 경계 내의 Fitness Value를 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);
}
propertyGenes[i].fitnessValue = fitnessValue;
}
QuickSort(propertyGenes, 0, dnaObjectCount - 1);
for (int i = 0; i < SIMONConstants.GENE_SUM_RATING; i++)
{
int divNum = (int)(((i + 1) * dnaObjectCount) / SIMONConstants.GENE_SUM_RATING);
if (divNum == 0)
divNum = -1;
else
divNum--;
divIndex[i] = divNum;
}
List<GeneValue> recessiveGroup = new List<GeneValue>();
List<GeneValue> dominionGroup = new List<GeneValue>();
List<GeneValue> selectedGene = new List<GeneValue>();
for (int i = divIndex[SIMONConstants.GENE_RECESSIVE_RATING - 1]; i >= 0; i--)
{
recessiveGroup.Add(propertyGenes[i]);
}
selectedGene.AddRange(RouletteWheel(recessiveGroup, GeneSelectionLaw.RECESSIVE));
for (int i = divIndex[SIMONConstants.GENE_DOMINION_RATING]; i >= divIndex[SIMONConstants.GENE_RECESSIVE_RATING - 1] + 1; i--)
{
dominionGroup.Add(propertyGenes[i]);
}
selectedGene.AddRange(RouletteWheel(dominionGroup, GeneSelectionLaw.DOMINION));
int selectingCount = SIMONConstants.GENE_REAL_SELECT_NUM;
if (selectedGene.Count < selectingCount)
selectingCount = selectedGene.Count;
int[] selectedIdxTable = new int[selectingCount];
int selectedIdx = 0;
if (selectedGene.Count <= 0)
return selectedDNA;
while (selectedIdx < selectingCount)
{
Random rand = new Random();
int selectIdx = rand.Next(0, selectedGene.Count);
bool retryFlag = false;
for (int i = 0; i < selectedIdx; i++)
{
if (selectedIdxTable[i] == selectIdx)
{
retryFlag = true;
break;
}
}
if (retryFlag)
continue;
selectedDNA.Add(selectedGene[selectIdx].dna);
selectedIdxTable[selectedIdx++] = selectIdx;
}
return selectedDNA;
}
/// <summary>
/// SIMONAlgorithm의 구현을 담당하는 Interface의 기능을 호출합니다.
/// </summary>
/// <param name="GroupCollection">Algorithm의 적용 대상 Collection입니다.</param>
/// <param name="ActionMap">Algorithm의 적용 대상 Action에 대한 매핑 테이블을 선언합니다.</param>
/// <param name="SimonFunctions">Algorithm에 사용되는 함수를 링크합니다.</param>
/// <param name="learningRate">알고리즘의 학습률을 지정합니다.</param>
/// <returns>구현에 대한 결과값입니다.</returns>
public object Implementation(SIMONCollection GroupCollection, SIMONCollection ActionMap, Dictionary<string, SIMONFunction> SimonFunctions, double learningRate)
{
List<List<List<SIMONGene>>> selectedDNAPool = Selection(GroupCollection, ActionMap, SimonFunctions);
List<List<List<SIMONGene>>> crossedDNAPool = CrossOver(selectedDNAPool);
List<List<List<SIMONGene>>> mutatedDNAPool = Mutation(crossedDNAPool);
Evolution(GroupCollection, ActionMap, mutatedDNAPool, learningRate);
return null;
}
/// <summary>
/// Group의 모든 내용을 비웁니다.
/// </summary>
/// <param name="Group">비울 대상 Group Collection입니다.</param>
public void CleanGroup(SIMONCollection Group)
{
Group.Clear();
}
/// <summary>
/// 그룹에 대한 ActionMap을 구현합니다.
/// </summary>
/// <param name="group">ActionMap을 구조할 group입니다.</param>
/// <returns>만들어진 ActionMap SIMONCollection을 반환합니다.</returns>
private SIMONCollection CreateActionMap(SIMONCollection group)
{
SIMONCollection actionMap = new SIMONCollection();
for (int i = 0; i < group.Count; i++)
{
SIMONObject element = (SIMONObject)group.ValueOfIndex(i);
for (int j = 0; j < element.Actions.Count; j++)
{
if (!actionMap.Contains(element.Actions[j].ActionName))
{
actionMap.Add(element.Actions[j].ActionName, new List<SIMONObject>());
}
DictionaryEntry val = (DictionaryEntry)actionMap[element.Actions[j].ActionName];
var mapElement = val.Value;
((List<SIMONObject>)mapElement).Add(element);
actionMap[element.Actions[j].ActionName] = mapElement;
}
}
return actionMap;
}
/// <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>
/// 학습률을 적용해서 SIMONCollection들에 대한 History를 이용해서 학습 효과를 조절할 수 있는 학습 루틴을 구현합니다.
/// </summary>
/// <param name="targetGroup">Simulate를 적용시킬 대상 Group입니다.</param>
/// <param name="learningRate">적용시킬 학습률입니다.</param>
public void LearnSimulate(SIMONCollection targetGroup, double learningRate)
{
/********************************** 학습 **************************************/
SIMONCollection newActionMap = CreateActionMap(targetGroup);
IntelligenceManager.LearnAsync(targetGroup, newActionMap, SimonFunctions, learningRate);
IntelligenceManager.LearnPropertyAsync(targetGroup, SimonFunctions, learningRate);
/***************************************************************************************/
}
/// <summary>
/// ObjectCollection에 대해서 Property DNA를 업데이트합니다.
/// </summary>
/// <param name="ObjectCollection">Update를 수행할 대상 Collection.</param>
private void UpdateObjectsPropertyDNA(SIMONCollection ObjectCollection)
{
for (int i = 0; i < ObjectCollection.Count; i++)
{
SIMONObject elementObject = (SIMONObject)ObjectCollection.ValueOfIndex(i);
for (int j = 0; j < elementObject.Properties.Count; j++)
{
if (elementObject.Properties[j].Inherit)
{
for (int k = 0; k < elementObject.PropertyDNA.Count; k++)
{
if (elementObject.PropertyDNA[k].ElementName.Equals(elementObject.Properties[j].PropertyName))
{
elementObject.Properties[j].PropertyValue = elementObject.PropertyDNA[k].ElementWeight;
}
}
}
}
}
}
/// <summary>
/// SIMONManager에 등록해서 사용할 새로운 SIMONCollection을 생성해서 반환합니다.
/// </summary>
/// <returns>SIMONCollection 객체를 반환합니다.</returns>
public SIMONCollection CreateSIMONGroup()
{
SIMONCollection newGroup = new SIMONCollection();
return newGroup;
}
/// <summary>
/// SIMONObject들의 Property 학습을 수행하는 별도의 알고리즘 구현을 제공합니다.
/// </summary>
/// <param name="ObjectCollection"></param>
/// <param name="SimonFunctions"></param>
public void LearnProperty(SIMONCollection ObjectCollection, Dictionary<string, SIMONFunction> SimonFunctions)
{
try
{
isLearning = true;
LearnResult = AlgorithmPerformer.Implementation(ObjectCollection, SimonFunctions);
isLearning = false;
}
catch (Exception exp)
{
throw new Exception("[SIMON Framework] : Exception Occurs in learning property algorithm.\n" + exp.Message, exp);
}
}
public SIMONObjectCollectionEnumerator(SIMONCollection simonCollection)
{
SIMONDictionary = new DictionaryEntry[simonCollection.Count];
Array.Copy(simonCollection.SIMONDictionary, 0, SIMONDictionary, 0, simonCollection.Count);
}
public void CleanSIMONGroup(SIMONCollection Group){
SimonManager.CleanGroup (Group);
}
public void SIMONLearnSimulate(SIMONCollection Group, double learningRate){
SimonManager.LearnSimulate(Group, learningRate);
}
/// <summary>
/// SIMONObject들의 그룹과 Action들의 Map, 학습에 이용되는 SimonFunction들에 대한 Dictionary 및 학습률을 적용시킨 비동기 학습루틴을 구현합니다.
/// </summary>
/// <param name="GroupCollection">학습 대상 SIMONObject 그룹입니다.</param>
/// <param name="ActionMap">학습 대상에 대한 ActionMap입니다.</param>
/// <param name="SimonFunctions">학습에 사용될 SIMONFunction ADT입니다.</param>
/// <param name="learningRate">학습률입니다.</param>
/// <returns>비동기 결과 객체입니다.</returns>
public IAsyncResult LearnAsync(SIMONCollection GroupCollection, SIMONCollection ActionMap, Dictionary<string, SIMONFunction> SimonFunctions, double learningRate)
{
isLearning = true;
return AlgorithmLearnInvoker.BeginInvoke(GroupCollection, ActionMap, SimonFunctions, learningRate, LearnCallbackInvoker, null);
}
