機械学習・深層学習による状況認識¶

// 状況分類のための教師あり学習システム
interface SituationFeature {
  timestamp: Date;
  sensorValues: number[];
  contextualData: any;
  environmentalFactors: number[];
  historicalPattern: number[];
}

interface SituationLabel {
  category: 'NORMAL' | 'WARNING' | 'CRITICAL' | 'EMERGENCY';
  subCategory: string;
  confidenceLevel: number;
  actionRequired: boolean;
}

interface TrainingExample {
  features: SituationFeature;
  label: SituationLabel;
  metadata: {
    source: string;
    timestamp: Date;
    validator: string;
  };
}

class SupervisedSituationClassifier {
  private model: MLModel;
  private featureExtractor: FeatureExtractor;
  private preprocessor: DataPreprocessor;
  private validator: ModelValidator;

  constructor() {
    this.featureExtractor = new AdvancedFeatureExtractor();
    this.preprocessor = new DataPreprocessor();
    this.validator = new CrossValidator();
    this.initializeModel();
  }

  // モデル初期化
  private initializeModel(): void {
    // アンサンブル学習による堅牢な分類器
    this.model = new EnsembleClassifier({
      baseModels: [
        new RandomForestClassifier({
          nEstimators: 100,
          maxDepth: 10,
          minSamplesLeaf: 5
        }),
        new GradientBoostingClassifier({
          nEstimators: 50,
          learningRate: 0.1,
          maxDepth: 6
        }),
        new SupportVectorMachine({
          kernel: 'rbf',
          C: 1.0,
          gamma: 'scale'
        })
      ],
      votingStrategy: 'soft',
      weights: [0.4, 0.4, 0.2]
    });
  }

  // 訓練データからの学習
  async trainModel(trainingData: TrainingExample[]): Promise<TrainingResult> {
    // データ前処理
    const processedData = await this.preprocessTrainingData(trainingData);

    // 特徴量抽出
    const features = await this.extractFeatures(processedData);

    // 訓練・検証分割
    const { trainSet, validationSet } = this.splitData(features, 0.8);

    // モデル訓練
    const trainingMetrics = await this.model.fit(
      trainSet.features,
      trainSet.labels,
      {
        epochs: 100,
        batchSize: 32,
        validationSplit: 0.2,
        callbacks: [
          new EarlyStopping({ patience: 10 }),
          new ReduceLROnPlateau({ factor: 0.5, patience: 5 })
        ]
      }
    );

    // モデル評価
    const validationMetrics = await this.evaluateModel(validationSet);

    // 交差検証
    const crossValidationResults = await this.validator.crossValidate(
      this.model, features, 5
    );

    return {
      trainingMetrics,
      validationMetrics,
      crossValidationResults,
      modelSummary: this.model.summary(),
      featureImportance: this.getFeatureImportance()
    };
  }

  // 状況分類の実行
  async classifySituation(
    currentSituation: SituationFeature
  ): Promise<ClassificationResult> {
    // 前処理
    const processedSituation = await this.preprocessor.transform([currentSituation]);

    // 特徴量抽出
    const features = await this.featureExtractor.extract(processedSituation);

    // 予測実行
    const prediction = await this.model.predict(features);

    // 信頼度計算
    const confidence = await this.model.predictProba(features);

    // 不確実性推定
    const uncertainty = await this.estimateUncertainty(features);

    // 説明生成
    const explanation = await this.generateExplanation(features, prediction);

    return {
      predictedCategory: prediction.category,
      subCategory: prediction.subCategory,
      confidence: confidence.max(),
      uncertainty: uncertainty,
      explanation: explanation,
      recommendedActions: this.getRecommendedActions(prediction),
      alertLevel: this.determineAlertLevel(prediction, confidence)
    };
  }

  // 特徴量重要度分析
  private getFeatureImportance(): FeatureImportance[] {
    return this.model.getFeatureImportances().map((importance, index) => ({
      featureName: this.featureExtractor.getFeatureNames()[index],
      importance: importance,
      category: this.categorizeFeature(index)
    })).sort((a, b) => b.importance - a.importance);
  }

  // リアルタイム学習（オンライン学習）
  async updateModelOnline(newExample: TrainingExample): Promise<void> {
    // 新サンプルの前処理
    const processedExample = await this.preprocessTrainingData([newExample]);

    // 特徴量抽出
    const features = await this.extractFeatures(processedExample);

    // インクリメンタル学習
    await this.model.partialFit(features[0].features, features[0].label);

    // モデル性能のモニタリング
    await this.monitorModelPerformance();
  }

  // モデル性能継続監視
  private async monitorModelPerformance(): Promise<PerformanceMetrics> {
    const recentPredictions = await this.getRecentPredictions(100);
    const actualOutcomes = await this.getActualOutcomes(recentPredictions);

    const currentMetrics = this.calculateMetrics(recentPredictions, actualOutcomes);

    // 性能劣化検知
    if (currentMetrics.accuracy < this.performanceThreshold.accuracy) {
      await this.triggerModelRetraining();
    }

    return currentMetrics;
  }

  // モデル説明生成
  private async generateExplanation(
    features: ProcessedFeatures,
    prediction: Prediction
  ): Promise<Explanation> {
    // SHAP値による説明
    const shapValues = await this.calculateSHAPValues(features);

    // 特徴量寄与度の可視化データ
    const contributionVisualization = this.createContributionVisualization(shapValues);

    // 自然言語での説明生成
    const naturalLanguageExplanation = this.generateNaturalExplanation(
      shapValues, prediction
    );

    return {
      shapValues,
      contributionVisualization,
      naturalLanguageExplanation,
      confidenceInterval: this.calculateConfidenceInterval(features),
      alternativePredictions: this.getAlternativePredictions(features)
    };
  }
}

// 結果インターフェース
interface ClassificationResult {
  predictedCategory: string;
  subCategory: string;
  confidence: number;
  uncertainty: number;
  explanation: Explanation;
  recommendedActions: Action[];
  alertLevel: AlertLevel;
}

interface TrainingResult {
  trainingMetrics: Metrics;
  validationMetrics: Metrics;
  crossValidationResults: CrossValidationMetrics;
  modelSummary: ModelSummary;
  featureImportance: FeatureImportance[];
}

// 教師なし学習による状況パターン分析システム
class UnsupervisedSituationAnalyzer {
  private clusteringModel: ClusteringModel;
  private anomalyDetector: AnomalyDetectionModel;
  private dimensionalityReducer: DimensionalityReduction;
  private patternMiner: FrequentPatternMiner;

  constructor() {
    this.initializeModels();
  }

  private initializeModels(): void {
    // 階層クラスタリング + K-means のハイブリッド
    this.clusteringModel = new HybridClustering({
      primaryMethod: new DBSCAN({
        eps: 0.5,
        minSamples: 5,
        metric: 'euclidean'
      }),
      secondaryMethod: new KMeans({
        nClusters: 'auto',
        initMethod: 'k-means++',
        maxIterations: 300
      }),
      validationMethod: new SilhouetteAnalysis()
    });

    // 異常検知（複数手法のアンサンブル）
    this.anomalyDetector = new AnomalyEnsemble({
      methods: [
        new IsolationForest({ contamination: 0.1 }),
        new LocalOutlierFactor({ nNeighbors: 20 }),
        new OneClassSVM({ kernel: 'rbf', gamma: 'scale' })
      ],
      votingStrategy: 'majority'
    });

    // 次元削減
    this.dimensionalityReducer = new AdaptiveDimensionReduction({
      methods: ['PCA', 'UMAP', 't-SNE'],
      selectionCriteria: 'explained_variance'
    });
  }

  // 状況パターンの発見
  async discoverSituationPatterns(
    situationData: SituationFeature[]
  ): Promise<PatternDiscoveryResult> {
    // 前処理とスケーリング
    const preprocessedData = await this.preprocessData(situationData);

    // 次元削減
    const reducedData = await this.dimensionalityReducer.fitTransform(
      preprocessedData
    );

    // クラスタリング実行
    const clusteringResult = await this.clusteringModel.fitPredict(reducedData);

    // 各クラスターの特徴分析
    const clusterProfiles = await this.analyzeClusterProfiles(
      preprocessedData,
      clusteringResult.labels
    );

    // 異常パターン検出
    const anomalies = await this.anomalyDetector.detectAnomalies(
      preprocessedData
    );

    // 頻出パターン抽出
    const frequentPatterns = await this.patternMiner.minePatterns(
      situationData,
      { minSupport: 0.05, minConfidence: 0.8 }
    );

    // パターンの可視化データ生成
    const visualizationData = await this.generateVisualizationData(
      reducedData,
      clusteringResult,
      anomalies
    );

    return {
      clusters: clusterProfiles,
      anomalies: anomalies,
      frequentPatterns: frequentPatterns,
      visualization: visualizationData,
      insights: await this.generateInsights(clusterProfiles, anomalies),
      recommendations: await this.generateRecommendations(clusterProfiles)
    };
  }

  // クラスタープロファイル分析
  private async analyzeClusterProfiles(
    data: PreprocessedData,
    labels: number[]
  ): Promise<ClusterProfile[]> {
    const uniqueLabels = [...new Set(labels)];

    return Promise.all(
      uniqueLabels.map(async (label) => {
        const clusterData = data.filter((_, index) => labels[index] === label);

        // 統計的特徴
        const statistics = this.calculateClusterStatistics(clusterData);

        // 代表的サンプル
        const representatives = this.findRepresentativeSamples(clusterData, 5);

        // 特徴的パターン
        const characteristicPatterns = await this.extractCharacteristicPatterns(
          clusterData
        );

        // 時系列パターン分析
        const temporalPatterns = await this.analyzeTemporalPatterns(clusterData);

        return {
          clusterId: label,
          size: clusterData.length,
          statistics: statistics,
          representatives: representatives,
          characteristicPatterns: characteristicPatterns,
          temporalPatterns: temporalPatterns,
          description: this.generateClusterDescription(statistics, characteristicPatterns)
        };
      })
    );
  }

  // リアルタイム状況分析
  async analyzeCurrentSituation(
    currentSituation: SituationFeature
  ): Promise<RealTimeAnalysisResult> {
    // 前処理
    const processedSituation = await this.preprocessData([currentSituation]);

    // どのクラスターに属するか判定
    const clusterAssignment = await this.clusteringModel.predict(
      processedSituation[0]
    );

    // 異常度判定
    const anomalyScore = await this.anomalyDetector.decisionFunction(
      processedSituation[0]
    );

    // 類似状況の検索
    const similarSituations = await this.findSimilarSituations(
      processedSituation[0],
      10
    );

    // 状況の新規性評価
    const noveltyScore = await this.assessNovelty(processedSituation[0]);

    return {
      assignedCluster: clusterAssignment,
      clusterProfile: await this.getClusterProfile(clusterAssignment),
      anomalyScore: anomalyScore,
      isAnomalous: anomalyScore > this.anomalyThreshold,
      similarSituations: similarSituations,
      noveltyScore: noveltyScore,
      interpretation: this.interpretAnalysisResult(
        clusterAssignment,
        anomalyScore,
        noveltyScore
      )
    };
  }

  // パターン進化の追跡
  async trackPatternEvolution(
    timeWindow: TimeWindow
  ): Promise<PatternEvolutionResult> {
    // 時間窓ごとのデータ取得
    const timeSeriesData = await this.getTimeSeriesData(timeWindow);

    // 各時点でのパターン分析
    const temporalPatterns = await Promise.all(
      timeSeriesData.map(async (windowData) => {
        return await this.discoverSituationPatterns(windowData.situations);
      })
    );

    // パターンの出現・消失・変化を追跡
    const patternEvolution = this.trackPatternChanges(temporalPatterns);

    // 新興パターンの検出
    const emergingPatterns = this.detectEmergingPatterns(temporalPatterns);

    // 消失パターンの検出
    const disappearingPatterns = this.detectDisappearingPatterns(temporalPatterns);

    return {
      evolutionTimeline: patternEvolution,
      emergingPatterns: emergingPatterns,
      disappearingPatterns: disappearingPatterns,
      stabilityAnalysis: this.analyzePatternStability(temporalPatterns),
      trendAnalysis: this.analyzeTrends(patternEvolution)
    };
  }
}

// 結果インターフェース
interface PatternDiscoveryResult {
  clusters: ClusterProfile[];
  anomalies: AnomalyInfo[];
  frequentPatterns: FrequentPattern[];
  visualization: VisualizationData;
  insights: Insight[];
  recommendations: Recommendation[];
}

interface RealTimeAnalysisResult {
  assignedCluster: number;
  clusterProfile: ClusterProfile;
  anomalyScore: number;
  isAnomalous: boolean;
  similarSituations: SimilarSituation[];
  noveltyScore: number;
  interpretation: string;
}

// CNN による画像状況認識システム
class CNNSituationRecognizer {
  private model: CNNModel;
  private preprocessor: ImagePreprocessor;
  private postprocessor: ResultPostprocessor;
  private explainer: CNNExplainer;

  constructor() {
    this.initializeModel();
    this.preprocessor = new ImagePreprocessor();
    this.postprocessor = new ResultPostprocessor();
    this.explainer = new CNNExplainer();
  }

  private initializeModel(): void {
    // ResNet + Attention メカニズムのハイブリッドモデル
    this.model = new HybridCNNModel({
      backbone: new ResNet50({
        pretrained: true,
        freezeBackbone: false
      }),
      attentionModule: new SpatialAttention({
        channelReduction: 16
      }),
      classifier: new MultiLevelClassifier({
        levels: ['scene', 'objects', 'activities', 'anomalies'],
        hiddenSizes: [512, 256, 128]
      }),
      auxiliaryTasks: [
        new ObjectDetectionHead(),
        new SemanticSegmentationHead(),
        new DepthEstimationHead()
      ]
    });
  }

  // 画像からの状況認識
  async recognizeSituation(
    imageData: ImageData | ImageData[]
  ): Promise<SituationRecognitionResult> {
    // バッチ処理対応
    const images = Array.isArray(imageData) ? imageData : [imageData];

    // 前処理
    const preprocessedImages = await this.preprocessor.process(images, {
      resize: [224, 224],
      normalize: true,
      augmentation: false
    });

    // CNN推論実行
    const modelOutput = await this.model.predict(preprocessedImages, {
      returnAttention: true,
      returnIntermediateFeatures: true
    });

    // 結果の後処理
    const processedResults = await this.postprocessor.process(modelOutput);

    // 信頼度・不確実性評価
    const confidenceScores = this.calculateConfidenceScores(modelOutput);
    const uncertaintyEstimate = await this.estimateUncertainty(modelOutput);

    // 説明可能性の生成
    const explanations = await this.explainer.generateExplanations(
      preprocessedImages,
      modelOutput,
      {
        method: 'grad-cam',
        layerName: 'conv5_block3_out'
      }
    );

    return {
      predictions: processedResults,
      confidence: confidenceScores,
      uncertainty: uncertaintyEstimate,
      explanations: explanations,
      attentionMaps: modelOutput.attentionMaps,
      detectedObjects: modelOutput.objectDetections,
      sceneSegmentation: modelOutput.segmentationMasks,
      riskAssessment: this.assessRiskFromVisualContext(processedResults)
    };
  }

  // リアルタイム映像解析
  async analyzeVideoStream(
    videoStream: MediaStream
  ): Promise<Observable<StreamAnalysisResult>> {
    return new Observable<StreamAnalysisResult>(observer => {
      const videoElement = document.createElement('video');
      const canvas = document.createElement('canvas');
      const context = canvas.getContext('2d')!;

      videoElement.srcObject = videoStream;
      videoElement.play();

      // フレーム処理のタイマー
      const frameInterval = setInterval(async () => {
        try {
          // フレーム取得
          context.drawImage(videoElement, 0, 0, canvas.width, canvas.height);
          const imageData = context.getImageData(0, 0, canvas.width, canvas.height);

          // 状況認識実行
          const result = await this.recognizeSituation(imageData);

          // 時系列解析
          const temporalAnalysis = await this.analyzeTemporalPatterns(result);

          // イベント検出
          const eventDetection = this.detectEvents(result, temporalAnalysis);

          observer.next({
            frameResult: result,
            temporalAnalysis: temporalAnalysis,
            detectedEvents: eventDetection,
            timestamp: new Date()
          });
        } catch (error) {
          observer.error(error);
        }
      }, 1000 / 30); // 30 FPS

      // クリーンアップ
      return () => {
        clearInterval(frameInterval);
        videoElement.srcObject = null;
      };
    });
  }

  // マルチスケール状況認識
  async multiScaleRecognition(
    imageData: ImageData
  ): Promise<MultiScaleResult> {
    const scales = [0.5, 0.75, 1.0, 1.25, 1.5];

    // 各スケールでの認識実行
    const scaleResults = await Promise.all(
      scales.map(async (scale) => {
        const scaledImage = await this.preprocessor.rescale(imageData, scale);
        return await this.recognizeSituation(scaledImage);
      })
    );

    // スケール間の結果統合
    const integratedResult = this.integrateMultiScaleResults(scaleResults);

    // スケール不変特徴の抽出
    const scaleInvariantFeatures = this.extractScaleInvariantFeatures(scaleResults);

    return {
      perScaleResults: scaleResults,
      integratedResult: integratedResult,
      scaleInvariantFeatures: scaleInvariantFeatures,
      optimalScale: this.determineOptimalScale(scaleResults)
    };
  }

  // 対話的説明生成
  async generateInteractiveExplanation(
    imageData: ImageData,
    recognitionResult: SituationRecognitionResult,
    userQuery: string
  ): Promise<InteractiveExplanation> {
    // ユーザークエリの解析
    const queryAnalysis = await this.analyzeUserQuery(userQuery);

    // 該当領域のハイライト
    const relevantRegions = await this.identifyRelevantRegions(
      imageData,
      recognitionResult,
      queryAnalysis
    );

    // 段階的説明の生成
    const layeredExplanation = await this.generateLayeredExplanation(
      recognitionResult,
      queryAnalysis,
      relevantRegions
    );

    // 反実例の生成
    const counterfactualExamples = await this.generateCounterfactuals(
      imageData,
      recognitionResult,
      queryAnalysis
    );

    return {
      queryAnalysis: queryAnalysis,
      relevantRegions: relevantRegions,
      layeredExplanation: layeredExplanation,
      counterfactualExamples: counterfactualExamples,
      interactionSuggestions: this.generateInteractionSuggestions(queryAnalysis)
    };
  }
}

// 結果インターフェース
interface SituationRecognitionResult {
  predictions: SituationPrediction[];
  confidence: ConfidenceScores;
  uncertainty: UncertaintyEstimate;
  explanations: Explanation[];
  attentionMaps: AttentionMap[];
  detectedObjects: ObjectDetection[];
  sceneSegmentation: SegmentationMask;
  riskAssessment: RiskAssessment;
}

interface StreamAnalysisResult {
  frameResult: SituationRecognitionResult;
  temporalAnalysis: TemporalAnalysis;
  detectedEvents: Event[];
  timestamp: Date;
}

// 強化学習による適応的状況認識システム
class ReinforcementLearningSituationAgent {
  private agent: RLAgent;
  private environment: SituationEnvironment;
  private replayBuffer: ExperienceReplayBuffer;
  private explorationStrategy: ExplorationStrategy;

  constructor(environmentConfig: EnvironmentConfig) {
    this.environment = new SituationEnvironment(environmentConfig);
    this.initializeAgent();
    this.replayBuffer = new PrioritizedExperienceReplay({
      capacity: 100000,
      alpha: 0.6,
      beta: 0.4
    });
    this.explorationStrategy = new EpsilonGreedyStrategy({
      epsilonStart: 1.0,
      epsilonEnd: 0.01,
      epsilonDecay: 0.995
    });
  }

  private initializeAgent(): void {
    // Double DQN + Dueling Network のハイブリッド
    this.agent = new HybridRLAgent({
      network: new DuelingDQN({
        stateSize: this.environment.getStateSize(),
        actionSize: this.environment.getActionSize(),
        hiddenLayers: [512, 256, 128],
        advantageStream: [256, 128],
        valueStream: [256, 1]
      }),
      targetNetwork: new DuelingDQN({
        // 同じ構成
      }),
      optimizer: new AdamOptimizer({
        learningRate: 0.001,
        beta1: 0.9,
        beta2: 0.999
      }),
      updateFrequency: 4,
      targetUpdateFrequency: 1000
    });
  }

  // 訓練ループ
  async train(episodes: number): Promise<TrainingResult> {
    const trainingMetrics: EpisodeMetrics[] = [];

    for (let episode = 0; episode < episodes; episode++) {
      let state = await this.environment.reset();
      let totalReward = 0;
      let stepCount = 0;
      const episodeExperiences: Experience[] = [];

      while (!this.environment.isDone()) {
        // 行動選択（探索 vs 活用）
        const action = await this.selectAction(state, episode);

        // 環境での行動実行
        const stepResult = await this.environment.step(action);
        const { nextState, reward, done, info } = stepResult;

        // 経験の記録
        const experience: Experience = {
          state: state,
          action: action,
          reward: reward,
          nextState: nextState,
          done: done,
          priority: this.calculatePriority(reward, info)
        };

        episodeExperiences.push(experience);
        this.replayBuffer.add(experience);

        // 学習実行（バッチサイズに達した場合）
        if (this.replayBuffer.size() >= 32) {
          await this.learn();
        }

        state = nextState;
        totalReward += reward;
        stepCount++;
      }

      // エピソード終了後の処理
      const episodeMetrics = {
        episode: episode,
        totalReward: totalReward,
        stepCount: stepCount,
        explorationRate: this.explorationStrategy.getCurrentEpsilon(),
        averageQLoss: this.agent.getAverageQLoss(),
        situationRecognitionAccuracy: this.evaluateSituationRecognition()
      };

      trainingMetrics.push(episodeMetrics);

      // 探索率の更新
      this.explorationStrategy.updateEpsilon();

      // 定期的なモデル評価
      if (episode % 100 === 0) {
        await this.evaluateAgent(episode);
      }
    }

    return {
      trainingMetrics: trainingMetrics,
      finalAgent: this.agent,
      learnedPolicy: await this.extractLearnedPolicy()
    };
  }

  // 行動選択
  private async selectAction(state: State, episode: number): Promise<Action> {
    // 探索 vs 活用の決定
    if (this.explorationStrategy.shouldExplore(episode)) {
      return this.environment.sampleRandomAction();
    } else {
      // Q値に基づく最適行動選択
      const qValues = await this.agent.predict(state);
      return this.agent.selectBestAction(qValues);
    }
  }

  // 学習実行
  private async learn(): Promise<void> {
    // 優先度付き経験リプレイからサンプリング
    const batch = this.replayBuffer.sample(32);

    // ターゲットQ値の計算
    const targetQValues = await this.calculateTargetQValues(batch);

    // 損失計算と逆伝播
    const loss = await this.agent.trainStep(batch.states, targetQValues);

    // 優先度の更新
    this.replayBuffer.updatePriorities(batch.indices, loss.itemLosses);
  }

  // 実行時の状況認識・意思決定
  async performSituationalDecisionMaking(
    currentSituation: SituationState
  ): Promise<SituationalDecision> {
    // 状況の前処理と特徴抽出
    const processedState = await this.preprocessSituation(currentSituation);

    // Q値による行動価値評価
    const actionValues = await this.agent.predict(processedState);

    // 最適行動の選択
    const optimalAction = this.agent.selectBestAction(actionValues);

    // 行動の信頼度評価
    const actionConfidence = this.calculateActionConfidence(actionValues);

    // 代替行動の評価
    const alternativeActions = this.evaluateAlternativeActions(actionValues);

    // 状況認識の説明生成
    const explanation = await this.generateDecisionExplanation(
      processedState,
      actionValues,
      optimalAction
    );

    return {
      recommendedAction: optimalAction,
      confidence: actionConfidence,
      alternativeActions: alternativeActions,
      explanation: explanation,
      situationAssessment: this.assessSituation(processedState),
      riskEvaluation: this.evaluateRisk(processedState, optimalAction)
    };
  }

  // メタ学習による迅速適応
  async adaptToNewSituation(
    newSituationType: SituationType,
    fewShotExamples: SituationExample[]
  ): Promise<AdaptationResult> {
    // メタ学習済みモデルから初期化
    const adaptedAgent = this.agent.clone();

    // 少数サンプルでの迅速適応
    const adaptationResult = await adaptedAgent.fewShotAdaptation(
      fewShotExamples,
      {
        adaptationSteps: 10,
        metaLearningRate: 0.01,
        innerLearningRate: 0.1
      }
    );

    // 適応後の性能評価
    const performanceMetrics = await this.evaluateAdaptedAgent(
      adaptedAgent,
      newSituationType
    );

    return {
      adaptedAgent: adaptedAgent,
      adaptationLoss: adaptationResult.loss,
      performanceMetrics: performanceMetrics,
      adaptationConfidence: this.calculateAdaptationConfidence(performanceMetrics)
    };
  }
}

// インターフェース定義
interface SituationalDecision {
  recommendedAction: Action;
  confidence: number;
  alternativeActions: AlternativeAction[];
  explanation: DecisionExplanation;
  situationAssessment: SituationAssessment;
  riskEvaluation: RiskEvaluation;
}

interface Experience {
  state: State;
  action: Action;
  reward: number;
  nextState: State;
  done: boolean;
  priority: number;
}

// モデル性能評価・監視システム
class MLPerformanceMonitor {
  private metricsCalculator: MetricsCalculator;
  private driftDetector: ConceptDriftDetector;
  private performanceTracker: PerformanceTracker;
  private alertSystem: AlertSystem;

  constructor() {
    this.metricsCalculator = new ComprehensiveMetrics();
    this.driftDetector = new MultiMethodDriftDetector();
    this.performanceTracker = new TimeSeriesTracker();
    this.alertSystem = new IntelligentAlertSystem();
  }

  // 包括的性能評価
  async evaluateModel(
    model: MLModel,
    testData: TestDataset,
    evaluationConfig: EvaluationConfig
  ): Promise<ComprehensiveEvaluation> {
    // 基本性能指標
    const basicMetrics = await this.metricsCalculator.calculateBasicMetrics(
      model, testData
    );

    // 詳細分析
    const detailedAnalysis = await this.performDetailedAnalysis(
      model, testData, basicMetrics
    );

    // 公平性評価
    const fairnessMetrics = await this.evaluateFairness(
      model, testData, evaluationConfig.protectedAttributes
    );

    // 堅牢性テスト
    const robustnessTest = await this.testRobustness(model, testData);

    // 説明可能性評価
    const explainabilityScore = await this.evaluateExplainability(model, testData);

    return {
      basicMetrics: basicMetrics,
      detailedAnalysis: detailedAnalysis,
      fairnessMetrics: fairnessMetrics,
      robustnessTest: robustnessTest,
      explainabilityScore: explainabilityScore,
      overallScore: this.calculateOverallScore({
        basicMetrics,
        fairnessMetrics,
        robustnessTest,
        explainabilityScore
      })
    };
  }

  // リアルタイム性能監視
  monitorRealTimePerformance(
    model: MLModel,
    dataStream: Observable<PredictionData>
  ): Observable<PerformanceAlert> {
    return dataStream.pipe(
      // バッファリング（時間窓）
      bufferTime(60000), // 1分間のデータをバッファ

      // 性能計算
      map(buffer => this.calculateRealTimeMetrics(buffer)),

      // ドリフト検出
      map(metrics => ({
        metrics,
        drift: this.driftDetector.detectDrift(metrics)
      })),

      // アラート判定
      filter(result => this.shouldAlert(result)),

      // アラート生成
      map(result => this.generatePerformanceAlert(result))
    );
  }

  // A/Bテスト実行
  async runABTesting(
    modelA: MLModel,
    modelB: MLModel,
    testDataset: TestDataset,
    testConfig: ABTestConfig
  ): Promise<ABTestResult> {
    // テスト分割
    const { groupA, groupB } = this.splitTestData(testDataset, testConfig.splitRatio);

    // 並行評価
    const [resultA, resultB] = await Promise.all([
      this.evaluateModel(modelA, groupA, testConfig),
      this.evaluateModel(modelB, groupB, testConfig)
    ]);

    // 統計的有意差検定
    const significanceTest = this.performSignificanceTest(resultA, resultB);

    // 実用的有意差評価
    const practicalSignificance = this.assessPracticalSignificance(
      resultA, resultB, testConfig.minEffectSize
    );

    return {
      modelAResult: resultA,
      modelBResult: resultB,
      significanceTest: significanceTest,
      practicalSignificance: practicalSignificance,
      recommendation: this.generateRecommendation(
        resultA, resultB, significanceTest, practicalSignificance
      )
    };
  }
}