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); }