ruv-swarm/src/neural-models/neural-presets-complete.js

High-performance neural network swarm orchestration in WebAssembly

/**
 * Complete Neural Model Presets Integration
 * 27+ Production-Ready Neural Network Architectures with Cognitive Patterns
 */

import { CognitivePatternEvolution } from '../cognitive-pattern-evolution.js';
import { MetaLearningFramework } from '../meta-learning-framework.js';

// Comprehensive neural model presets with cognitive patterns
export const COMPLETE_NEURAL_PRESETS = {
  // 1. Transformer Models
  transformer: {
    bert_base: {
      name: 'BERT Base',
      description: 'Bidirectional encoder for language understanding',
      model: 'transformer',
      config: {
        dimensions: 768,
        heads: 12,
        layers: 12,
        ffDimensions: 3072,
        dropoutRate: 0.1,
        maxSequenceLength: 512,
        vocabSize: 30522,
      },
      cognitivePatterns: ['convergent', 'systems', 'abstract'],
      performance: {
        expectedAccuracy: '92-95%',
        inferenceTime: '15ms',
        memoryUsage: '420MB',
        trainingTime: '4 days on 16 TPUs',
      },
      useCase: 'Text classification, sentiment analysis, named entity recognition',
    },
    gpt_small: {
      name: 'GPT Small',
      description: 'Generative pre-trained transformer for text generation',
      model: 'transformer',
      config: {
        dimensions: 768,
        heads: 12,
        layers: 12,
        ffDimensions: 3072,
        dropoutRate: 0.1,
        maxSequenceLength: 1024,
        vocabSize: 50257,
      },
      cognitivePatterns: ['divergent', 'lateral', 'abstract'],
      performance: {
        expectedAccuracy: '88-92%',
        inferenceTime: '20ms',
        memoryUsage: '510MB',
        trainingTime: '2 weeks on 8 V100s',
      },
      useCase: 'Text generation, creative writing, code completion',
    },
    t5_base: {
      name: 'T5 Base',
      description: 'Text-to-text transformer for unified NLP tasks',
      model: 'transformer',
      config: {
        dimensions: 768,
        heads: 12,
        encoderLayers: 12,
        decoderLayers: 12,
        ffDimensions: 3072,
        dropoutRate: 0.1,
      },
      cognitivePatterns: ['systems', 'convergent', 'critical'],
      performance: {
        expectedAccuracy: '90-94%',
        inferenceTime: '25ms',
        memoryUsage: '850MB',
        trainingTime: '3 weeks on 32 TPUs',
      },
      useCase: 'Translation, summarization, question answering',
    },
  },

  // 2. CNN Models
  cnn: {
    efficientnet_b0: {
      name: 'EfficientNet-B0',
      description: 'Efficient convolutional network for image classification',
      model: 'cnn',
      config: {
        inputShape: [224, 224, 3],
        convLayers: [
          { filters: 32, kernelSize: 3, stride: 2, padding: 'same' },
          { filters: 16, kernelSize: 3, stride: 1, padding: 'same' },
          { filters: 24, kernelSize: 3, stride: 2, padding: 'same' },
          { filters: 40, kernelSize: 3, stride: 2, padding: 'same' },
          { filters: 80, kernelSize: 3, stride: 1, padding: 'same' },
          { filters: 112, kernelSize: 3, stride: 1, padding: 'same' },
          { filters: 192, kernelSize: 3, stride: 2, padding: 'same' },
          { filters: 320, kernelSize: 3, stride: 1, padding: 'same' },
        ],
        outputSize: 1000,
      },
      cognitivePatterns: ['critical', 'convergent', 'abstract'],
      performance: {
        expectedAccuracy: '77.1% top-1',
        inferenceTime: '4.9ms',
        memoryUsage: '5.3MB',
        trainingTime: '23 hours on 8 TPUs',
      },
      useCase: 'Image classification, feature extraction',
    },
    yolov5_small: {
      name: 'YOLOv5 Small',
      description: 'Real-time object detection network',
      model: 'cnn',
      config: {
        inputShape: [640, 640, 3],
        backbone: 'CSPDarknet',
        neck: 'PANet',
        head: 'YOLOv5Head',
        anchors: [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]],
      },
      cognitivePatterns: ['systems', 'critical', 'convergent'],
      performance: {
        expectedAccuracy: '37.4% mAP',
        inferenceTime: '6.4ms',
        memoryUsage: '16MB',
        trainingTime: '3 days on 1 V100',
      },
      useCase: 'Real-time object detection, autonomous driving',
    },
  },

  // 3. RNN Models (LSTM/GRU)
  lstm: {
    bilstm_sentiment: {
      name: 'BiLSTM Sentiment Analyzer',
      description: 'Bidirectional LSTM for sentiment analysis',
      model: 'lstm',
      config: {
        inputSize: 300,
        hiddenSize: 256,
        numLayers: 2,
        outputSize: 3,
        bidirectional: true,
        dropoutRate: 0.3,
      },
      cognitivePatterns: ['convergent', 'systems', 'critical'],
      performance: {
        expectedAccuracy: '89-91%',
        inferenceTime: '8ms',
        memoryUsage: '45MB',
        trainingTime: '4 hours on 1 GPU',
      },
      useCase: 'Sentiment analysis, emotion detection',
    },
    lstm_timeseries: {
      name: 'LSTM Time Series Predictor',
      description: 'LSTM for multi-step time series forecasting',
      model: 'lstm',
      config: {
        inputSize: 10,
        hiddenSize: 128,
        numLayers: 3,
        outputSize: 1,
        sequenceLength: 100,
        returnSequence: false,
      },
      cognitivePatterns: ['systems', 'convergent', 'abstract'],
      performance: {
        expectedAccuracy: '92% R²',
        inferenceTime: '5ms',
        memoryUsage: '25MB',
        trainingTime: '2 hours on 1 GPU',
      },
      useCase: 'Stock prediction, weather forecasting, demand prediction',
    },
  },

  // 4. GRU Models
  gru: {
    gru_translator: {
      name: 'GRU Neural Translator',
      description: 'GRU-based sequence-to-sequence translator',
      model: 'gru',
      config: {
        inputSize: 512,
        hiddenSize: 512,
        numLayers: 4,
        outputSize: 10000,
        bidirectional: true,
        attention: true,
      },
      cognitivePatterns: ['systems', 'abstract', 'convergent'],
      performance: {
        expectedAccuracy: '32.4 BLEU',
        inferenceTime: '15ms',
        memoryUsage: '120MB',
        trainingTime: '5 days on 4 GPUs',
      },
      useCase: 'Machine translation, text summarization',
    },
  },

  // 5. Autoencoder Models
  autoencoder: {
    vae_mnist: {
      name: 'VAE for MNIST',
      description: 'Variational autoencoder for digit generation',
      model: 'vae',
      config: {
        inputSize: 784,
        encoderLayers: [512, 256],
        latentDimensions: 20,
        decoderLayers: [256, 512],
        betaKL: 1.0,
      },
      cognitivePatterns: ['divergent', 'abstract', 'lateral'],
      performance: {
        expectedAccuracy: '98% reconstruction',
        inferenceTime: '2ms',
        memoryUsage: '8MB',
        trainingTime: '30 minutes on 1 GPU',
      },
      useCase: 'Digit generation, anomaly detection',
    },
    dae_denoising: {
      name: 'Denoising Autoencoder',
      description: 'Autoencoder for image denoising',
      model: 'autoencoder',
      config: {
        inputSize: 4096,
        encoderLayers: [2048, 1024, 512],
        bottleneckSize: 256,
        denoisingNoise: 0.3,
        activation: 'relu',
      },
      cognitivePatterns: ['convergent', 'critical', 'systems'],
      performance: {
        expectedAccuracy: '28.5 PSNR',
        inferenceTime: '4ms',
        memoryUsage: '32MB',
        trainingTime: '2 hours on 1 GPU',
      },
      useCase: 'Image denoising, feature extraction',
    },
  },

  // 6. GNN Models
  gnn: {
    gcn_citation: {
      name: 'GCN Citation Network',
      description: 'Graph convolutional network for citation networks',
      model: 'gnn',
      config: {
        nodeDimensions: 1433,
        hiddenDimensions: 16,
        outputDimensions: 7,
        numLayers: 2,
        dropoutRate: 0.5,
      },
      cognitivePatterns: ['systems', 'abstract', 'lateral'],
      performance: {
        expectedAccuracy: '81.5%',
        inferenceTime: '10ms',
        memoryUsage: '50MB',
        trainingTime: '10 minutes on 1 GPU',
      },
      useCase: 'Citation network classification, social network analysis',
    },
    gat_molecular: {
      name: 'GAT Molecular Property',
      description: 'Graph attention network for molecular property prediction',
      model: 'gat',
      config: {
        nodeDimensions: 64,
        attentionHeads: 8,
        hiddenUnits: 256,
        numLayers: 3,
        outputDimensions: 1,
      },
      cognitivePatterns: ['critical', 'systems', 'convergent'],
      performance: {
        expectedAccuracy: '89% R²',
        inferenceTime: '12ms',
        memoryUsage: '75MB',
        trainingTime: '8 hours on 2 GPUs',
      },
      useCase: 'Drug discovery, molecular property prediction',
    },
  },

  // 7. ResNet Models
  resnet: {
    resnet50_imagenet: {
      name: 'ResNet-50 ImageNet',
      description: 'Deep residual network for image classification',
      model: 'resnet',
      config: {
        numBlocks: 16,
        blockDepth: 3,
        hiddenDimensions: 2048,
        initialChannels: 64,
        inputShape: [224, 224, 3],
        outputDimensions: 1000,
      },
      cognitivePatterns: ['convergent', 'critical', 'systems'],
      performance: {
        expectedAccuracy: '76.1% top-1',
        inferenceTime: '25ms',
        memoryUsage: '98MB',
        trainingTime: '8 days on 8 V100s',
      },
      useCase: 'Image classification, transfer learning backbone',
    },
  },

  // 8. Attention Models
  attention: {
    multihead_attention: {
      name: 'Multi-Head Attention',
      description: 'Stand-alone multi-head attention mechanism',
      model: 'attention',
      config: {
        heads: 8,
        dimensions: 512,
        dropoutRate: 0.1,
        useCausalMask: false,
      },
      cognitivePatterns: ['systems', 'abstract', 'convergent'],
      performance: {
        expectedAccuracy: 'task-dependent',
        inferenceTime: '3ms',
        memoryUsage: '15MB',
        trainingTime: 'varies',
      },
      useCase: 'Attention mechanism component, sequence modeling',
    },
  },

  // 9. Diffusion Models
  diffusion: {
    ddpm_mnist: {
      name: 'DDPM MNIST Generator',
      description: 'Denoising diffusion probabilistic model',
      model: 'diffusion',
      config: {
        timesteps: 1000,
        betaSchedule: 'cosine',
        imageSize: 28,
        channels: 1,
        modelChannels: 128,
      },
      cognitivePatterns: ['divergent', 'lateral', 'abstract'],
      performance: {
        expectedAccuracy: '3.17 FID',
        inferenceTime: '1000ms',
        memoryUsage: '200MB',
        trainingTime: '2 days on 4 GPUs',
      },
      useCase: 'Image generation, data augmentation',
    },
  },

  // 10. Neural ODE Models
  neural_ode: {
    node_dynamics: {
      name: 'Neural ODE Dynamics',
      description: 'Continuous-time dynamics modeling',
      model: 'neural_ode',
      config: {
        solverMethod: 'dopri5',
        tolerance: 1e-6,
        hiddenDimensions: 64,
        timeDimension: 1,
      },
      cognitivePatterns: ['systems', 'abstract', 'convergent'],
      performance: {
        expectedAccuracy: '95% trajectory',
        inferenceTime: '50ms',
        memoryUsage: '30MB',
        trainingTime: '6 hours on 1 GPU',
      },
      useCase: 'Physical system modeling, continuous processes',
    },
  },

  // 11. Capsule Networks
  capsnet: {
    capsnet_mnist: {
      name: 'CapsNet MNIST',
      description: 'Capsule network with dynamic routing',
      model: 'capsnet',
      config: {
        primaryCaps: 32,
        digitCaps: 10,
        routingIterations: 3,
        capsuleDimensions: 16,
      },
      cognitivePatterns: ['lateral', 'systems', 'abstract'],
      performance: {
        expectedAccuracy: '99.23%',
        inferenceTime: '15ms',
        memoryUsage: '35MB',
        trainingTime: '10 hours on 1 GPU',
      },
      useCase: 'Viewpoint-invariant recognition, part-whole relationships',
    },
  },

  // 12. Spiking Neural Networks
  snn: {
    lif_classifier: {
      name: 'LIF Spiking Classifier',
      description: 'Leaky integrate-and-fire spiking neural network',
      model: 'snn',
      config: {
        neuronModel: 'lif',
        threshold: 1.0,
        decay: 0.95,
        timeWindow: 100,
        codingScheme: 'rate',
      },
      cognitivePatterns: ['systems', 'critical', 'convergent'],
      performance: {
        expectedAccuracy: '92%',
        inferenceTime: '100ms',
        memoryUsage: '10MB',
        trainingTime: '4 hours on 1 GPU',
      },
      useCase: 'Energy-efficient inference, neuromorphic computing',
    },
  },

  // 13. Neural Turing Machines
  ntm: {
    ntm_copy: {
      name: 'NTM Copy Task',
      description: 'Neural Turing machine for sequence copying',
      model: 'ntm',
      config: {
        memorySize: [128, 20],
        controllerSize: 100,
        numHeads: 1,
        shiftRange: 3,
      },
      cognitivePatterns: ['systems', 'abstract', 'convergent'],
      performance: {
        expectedAccuracy: '99.9%',
        inferenceTime: '20ms',
        memoryUsage: '45MB',
        trainingTime: '12 hours on 1 GPU',
      },
      useCase: 'Algorithm learning, external memory tasks',
    },
  },

  // 14. Memory Networks
  memnn: {
    memnn_qa: {
      name: 'MemNN Question Answering',
      description: 'End-to-end memory network for QA',
      model: 'memnn',
      config: {
        memorySlots: 100,
        hops: 3,
        embeddingSize: 50,
        temporalEncoding: true,
      },
      cognitivePatterns: ['convergent', 'systems', 'critical'],
      performance: {
        expectedAccuracy: '95% on bAbI',
        inferenceTime: '8ms',
        memoryUsage: '25MB',
        trainingTime: '2 hours on 1 GPU',
      },
      useCase: 'Question answering, reasoning tasks',
    },
  },

  // 15. Neural Cellular Automata
  nca: {
    nca_growth: {
      name: 'NCA Pattern Growth',
      description: 'Neural cellular automata for pattern formation',
      model: 'nca',
      config: {
        channels: 16,
        updateRule: 'sobel',
        cellStates: 16,
        gridSize: [64, 64],
      },
      cognitivePatterns: ['divergent', 'lateral', 'systems'],
      performance: {
        expectedAccuracy: 'qualitative',
        inferenceTime: '5ms/step',
        memoryUsage: '15MB',
        trainingTime: '6 hours on 1 GPU',
      },
      useCase: 'Pattern generation, self-organization studies',
    },
  },

  // 16. HyperNetworks
  hypernet: {
    hypernet_adaptive: {
      name: 'Adaptive HyperNetwork',
      description: 'Network that generates weights for target network',
      model: 'hypernet',
      config: {
        hyperDim: 512,
        targetLayers: ['conv1', 'conv2', 'fc1'],
        embeddingSize: 128,
      },
      cognitivePatterns: ['abstract', 'lateral', 'systems'],
      performance: {
        expectedAccuracy: '94%',
        inferenceTime: '30ms',
        memoryUsage: '80MB',
        trainingTime: '15 hours on 2 GPUs',
      },
      useCase: 'Adaptive networks, few-shot learning',
    },
  },

  // 17. Meta-Learning Models
  maml: {
    maml_fewshot: {
      name: 'MAML Few-Shot',
      description: 'Model-agnostic meta-learning',
      model: 'maml',
      config: {
        innerLR: 0.01,
        outerLR: 0.001,
        innerSteps: 5,
        numWays: 5,
        numShots: 1,
      },
      cognitivePatterns: ['abstract', 'divergent', 'critical'],
      performance: {
        expectedAccuracy: '95% 5-way 1-shot',
        inferenceTime: '50ms',
        memoryUsage: '40MB',
        trainingTime: '24 hours on 4 GPUs',
      },
      useCase: 'Few-shot learning, rapid adaptation',
    },
  },

  // 18. Neural Architecture Search
  nas: {
    darts_cifar: {
      name: 'DARTS CIFAR-10',
      description: 'Differentiable architecture search',
      model: 'nas',
      config: {
        searchSpace: 'darts_space',
        epochs: 50,
        channels: 36,
        layers: 20,
      },
      cognitivePatterns: ['divergent', 'critical', 'systems'],
      performance: {
        expectedAccuracy: '97.24%',
        inferenceTime: '15ms',
        memoryUsage: '60MB',
        trainingTime: '4 days on 1 GPU',
      },
      useCase: 'AutoML, architecture optimization',
    },
  },

  // 19. Mixture of Experts
  moe: {
    moe_nlp: {
      name: 'MoE Language Model',
      description: 'Sparse mixture of experts for NLP',
      model: 'moe',
      config: {
        numExperts: 8,
        expertCapacity: 2,
        hiddenSize: 512,
        routerType: 'top2',
      },
      cognitivePatterns: ['systems', 'divergent', 'abstract'],
      performance: {
        expectedAccuracy: '91% perplexity',
        inferenceTime: '12ms',
        memoryUsage: '400MB',
        trainingTime: '1 week on 8 GPUs',
      },
      useCase: 'Large-scale language modeling, multi-task learning',
    },
  },

  // 20. Neural Radiance Fields
  nerf: {
    nerf_3d: {
      name: 'NeRF 3D Reconstruction',
      description: 'Neural radiance field for 3D scene reconstruction',
      model: 'nerf',
      config: {
        positionEncoding: 10,
        directionEncoding: 4,
        hiddenLayers: 8,
        hiddenSize: 256,
      },
      cognitivePatterns: ['abstract', 'systems', 'lateral'],
      performance: {
        expectedAccuracy: '30 PSNR',
        inferenceTime: '100ms/ray',
        memoryUsage: '200MB',
        trainingTime: '2 days on 1 GPU',
      },
      useCase: '3D reconstruction, novel view synthesis',
    },
  },

  // 21. WaveNet
  wavenet: {
    wavenet_tts: {
      name: 'WaveNet TTS',
      description: 'WaveNet for text-to-speech synthesis',
      model: 'wavenet',
      config: {
        dilationChannels: 32,
        residualChannels: 32,
        skipChannels: 512,
        dilationDepth: 10,
        dilationRepeat: 3,
      },
      cognitivePatterns: ['convergent', 'systems', 'critical'],
      performance: {
        expectedAccuracy: '4.5 MOS',
        inferenceTime: '500ms/second',
        memoryUsage: '150MB',
        trainingTime: '1 week on 8 GPUs',
      },
      useCase: 'Speech synthesis, audio generation',
    },
  },

  // 22. PointNet
  pointnet: {
    pointnet_seg: {
      name: 'PointNet++ Segmentation',
      description: 'Point cloud segmentation network',
      model: 'pointnet',
      config: {
        pointFeatures: 3,
        globalFeatures: 1024,
        numClasses: 50,
        samplingGroups: 3,
      },
      cognitivePatterns: ['systems', 'critical', 'abstract'],
      performance: {
        expectedAccuracy: '85.1% mIoU',
        inferenceTime: '40ms',
        memoryUsage: '90MB',
        trainingTime: '20 hours on 2 GPUs',
      },
      useCase: '3D point cloud analysis, robotics',
    },
  },

  // 23. World Models
  world_model: {
    world_model_rl: {
      name: 'World Model RL',
      description: 'World model for reinforcement learning',
      model: 'world_model',
      config: {
        visionModel: 'vae',
        memoryModel: 'mdn_rnn',
        latentSize: 32,
        hiddenSize: 256,
      },
      cognitivePatterns: ['systems', 'abstract', 'divergent'],
      performance: {
        expectedAccuracy: '900 score',
        inferenceTime: '10ms',
        memoryUsage: '120MB',
        trainingTime: '3 days on 4 GPUs',
      },
      useCase: 'Model-based RL, environment simulation',
    },
  },

  // 24. Normalizing Flows
  flow: {
    realvp_generation: {
      name: 'RealNVP Generation',
      description: 'Real-valued non-volume preserving flow',
      model: 'normalizing_flow',
      config: {
        flowType: 'real_nvp',
        couplingLayers: 8,
        hiddenUnits: 512,
        numBlocks: 2,
      },
      cognitivePatterns: ['divergent', 'abstract', 'lateral'],
      performance: {
        expectedAccuracy: '3.49 bits/dim',
        inferenceTime: '20ms',
        memoryUsage: '100MB',
        trainingTime: '2 days on 4 GPUs',
      },
      useCase: 'Density estimation, generative modeling',
    },
  },

  // 25. Energy-Based Models
  ebm: {
    ebm_generation: {
      name: 'EBM Generator',
      description: 'Energy-based generative model',
      model: 'ebm',
      config: {
        energyFunction: 'mlp',
        samplingSteps: 100,
        stepSize: 10,
        noise: 0.005,
      },
      cognitivePatterns: ['divergent', 'critical', 'systems'],
      performance: {
        expectedAccuracy: '7.85 FID',
        inferenceTime: '200ms',
        memoryUsage: '80MB',
        trainingTime: '3 days on 2 GPUs',
      },
      useCase: 'Generative modeling, density estimation',
    },
  },

  // 26. Neural Processes
  neural_process: {
    cnp_regression: {
      name: 'CNP Regression',
      description: 'Conditional neural process for regression',
      model: 'neural_process',
      config: {
        latentDim: 128,
        contextPoints: 10,
        encoderHidden: [128, 128],
        decoderHidden: [128, 128],
      },
      cognitivePatterns: ['abstract', 'systems', 'convergent'],
      performance: {
        expectedAccuracy: '0.15 MSE',
        inferenceTime: '5ms',
        memoryUsage: '30MB',
        trainingTime: '4 hours on 1 GPU',
      },
      useCase: 'Few-shot regression, uncertainty estimation',
    },
  },

  // 27. Set Transformer
  set_transformer: {
    set_anomaly: {
      name: 'Set Anomaly Detection',
      description: 'Set transformer for anomaly detection',
      model: 'set_transformer',
      config: {
        inducingPoints: 32,
        dimensions: 128,
        numHeads: 4,
        numBlocks: 4,
      },
      cognitivePatterns: ['critical', 'systems', 'convergent'],
      performance: {
        expectedAccuracy: '95% AUC',
        inferenceTime: '15ms',
        memoryUsage: '50MB',
        trainingTime: '6 hours on 1 GPU',
      },
      useCase: 'Anomaly detection on sets, point cloud analysis',
    },
  },
};

/**
 * Cognitive Pattern Selector
 * Automatically selects cognitive patterns based on model and task
 */
export class CognitivePatternSelector {
  constructor() {
    this.patternEvolution = new CognitivePatternEvolution();
    this.metaLearning = new MetaLearningFramework();
  }

  /**
   * Select optimal cognitive patterns for a neural model preset
   * @param {string} modelType - Type of neural model
   * @param {string} presetName - Name of the preset
   * @param {object} taskContext - Context about the task
   */
  selectPatternsForPreset(modelType, presetName, taskContext = {}) {
    const preset = COMPLETE_NEURAL_PRESETS[modelType]?.[presetName];
    if (!preset) {
      console.warn(`Preset not found: ${modelType}/${presetName}`);
      return ['convergent']; // Default fallback
    }

    // Start with preset's recommended patterns
    let patterns = [...preset.cognitivePatterns];

    // Adjust based on task context
    if (taskContext.requiresCreativity) {
      patterns = this.enhanceCreativity(patterns);
    }

    if (taskContext.requiresPrecision) {
      patterns = this.enhancePrecision(patterns);
    }

    if (taskContext.requiresAdaptation) {
      patterns = this.enhanceAdaptation(patterns);
    }

    if (taskContext.complexity === 'high') {
      patterns = this.handleHighComplexity(patterns);
    }

    // Ensure pattern diversity
    patterns = this.ensurePatternDiversity(patterns);

    return patterns;
  }

  /**
   * Enhance patterns for creative tasks
   */
  enhanceCreativity(patterns) {
    if (!patterns.includes('divergent')) {
      patterns.push('divergent');
    }
    if (!patterns.includes('lateral') && patterns.length < 4) {
      patterns.push('lateral');
    }
    return patterns;
  }

  /**
   * Enhance patterns for precision tasks
   */
  enhancePrecision(patterns) {
    if (!patterns.includes('convergent')) {
      patterns.push('convergent');
    }
    if (!patterns.includes('critical') && patterns.length < 4) {
      patterns.push('critical');
    }
    // Remove highly exploratory patterns for precision
    return patterns.filter(p => p !== 'divergent' || patterns.length > 2);
  }

  /**
   * Enhance patterns for adaptive tasks
   */
  enhanceAdaptation(patterns) {
    if (!patterns.includes('systems')) {
      patterns.push('systems');
    }
    if (!patterns.includes('abstract') && patterns.length < 4) {
      patterns.push('abstract');
    }
    return patterns;
  }

  /**
   * Handle high complexity tasks
   */
  handleHighComplexity(patterns) {
    // For high complexity, ensure both analytical and creative patterns
    const hasAnalytical = patterns.some(p => ['convergent', 'critical', 'systems'].includes(p));
    const hasCreative = patterns.some(p => ['divergent', 'lateral', 'abstract'].includes(p));

    if (!hasAnalytical) {
      patterns.push('systems');
    }
    if (!hasCreative) {
      patterns.push('abstract');
    }

    return patterns;
  }

  /**
   * Ensure pattern diversity
   */
  ensurePatternDiversity(patterns) {
    // Limit to maximum 4 patterns
    if (patterns.length > 4) {
      // Keep the most diverse set
      const diversity = this.calculatePatternDiversity(patterns);
      patterns = this.selectMostDiverse(patterns, diversity, 4);
    }

    // Ensure at least 2 patterns for robustness
    if (patterns.length < 2) {
      if (!patterns.includes('convergent')) {
        patterns.push('convergent');
      } else {
        patterns.push('systems');
      }
    }

    return [...new Set(patterns)]; // Remove duplicates
  }

  /**
   * Calculate diversity score for pattern combinations
   */
  calculatePatternDiversity(patterns) {
    const patternTypes = {
      analytical: ['convergent', 'critical'],
      creative: ['divergent', 'lateral'],
      systemic: ['systems', 'abstract'],
    };

    let diversityScore = 0;
    const typesCovered = new Set();

    patterns.forEach(pattern => {
      Object.entries(patternTypes).forEach(([type, typePatterns]) => {
        if (typePatterns.includes(pattern)) {
          typesCovered.add(type);
        }
      });
    });

    diversityScore = typesCovered.size / Object.keys(patternTypes).length;
    return diversityScore;
  }

  /**
   * Select most diverse pattern combination
   */
  selectMostDiverse(patterns, currentDiversity, targetCount) {
    if (patterns.length <= targetCount) {
      return patterns;
    }

    // Simple heuristic: keep patterns that maximize type coverage
    const selected = [];
    const patternTypes = {
      analytical: ['convergent', 'critical'],
      creative: ['divergent', 'lateral'],
      systemic: ['systems', 'abstract'],
    };

    // First, ensure one pattern from each type if possible
    Object.values(patternTypes).forEach(typePatterns => {
      const available = patterns.filter(p => typePatterns.includes(p));
      if (available.length > 0 && selected.length < targetCount) {
        selected.push(available[0]);
      }
    });

    // Fill remaining slots with most unique patterns
    patterns.forEach(pattern => {
      if (!selected.includes(pattern) && selected.length < targetCount) {
        selected.push(pattern);
      }
    });

    return selected;
  }

  /**
   * Get preset recommendations based on use case
   */
  getPresetRecommendations(useCase, requirements = {}) {
    const recommendations = [];

    Object.entries(COMPLETE_NEURAL_PRESETS).forEach(([modelType, presets]) => {
      Object.entries(presets).forEach(([presetName, preset]) => {
        if (preset.useCase.toLowerCase().includes(useCase.toLowerCase())) {
          const score = this.calculatePresetScore(preset, requirements);
          recommendations.push({
            modelType,
            presetName,
            preset,
            score,
            cognitivePatterns: this.selectPatternsForPreset(modelType, presetName, requirements),
          });
        }
      });
    });

    // Sort by score
    recommendations.sort((a, b) => b.score - a.score);

    return recommendations.slice(0, 5); // Top 5 recommendations
  }

  /**
   * Calculate preset score based on requirements
   */
  calculatePresetScore(preset, requirements) {
    let score = 1.0;

    // Check performance requirements
    if (requirements.maxInferenceTime) {
      const inferenceTime = parseInt(preset.performance.inferenceTime, 10);
      if (inferenceTime <= requirements.maxInferenceTime) {
        score += 0.2;
      } else {
        score -= 0.3;
      }
    }

    if (requirements.maxMemoryUsage) {
      const memoryUsage = parseInt(preset.performance.memoryUsage, 10);
      if (memoryUsage <= requirements.maxMemoryUsage) {
        score += 0.2;
      } else {
        score -= 0.3;
      }
    }

    if (requirements.minAccuracy) {
      const accuracy = parseFloat(preset.performance.expectedAccuracy);
      if (accuracy >= requirements.minAccuracy) {
        score += 0.3;
      } else {
        score -= 0.2;
      }
    }

    // Cognitive pattern alignment
    if (requirements.cognitivePreference) {
      const hasPreferred = preset.cognitivePatterns.some(p =>
        p === requirements.cognitivePreference,
      );
      if (hasPreferred) {
        score += 0.2;
      }
    }

    return Math.max(0, Math.min(2, score));
  }
}

/**
 * Neural Adaptation Engine
 * Enables cross-session learning and adaptation
 */
export class NeuralAdaptationEngine {
  constructor() {
    this.adaptationHistory = new Map();
    this.crossSessionMemory = new Map();
    this.performanceBaselines = new Map();
  }

  /**
   * Initialize adaptation for a model preset
   */
  async initializeAdaptation(agentId, modelType, presetName) {
    const preset = COMPLETE_NEURAL_PRESETS[modelType]?.[presetName];
    if (!preset) {
      return;
    }

    this.adaptationHistory.set(agentId, {
      modelType,
      presetName,
      baselinePerformance: preset.performance,
      adaptations: [],
      sessionCount: 0,
      totalTrainingTime: 0,
      performanceGains: [],
    });

    this.performanceBaselines.set(`${modelType}/${presetName}`, preset.performance);
  }

  /**
   * Record adaptation results
   */
  async recordAdaptation(agentId, adaptationResult) {
    const history = this.adaptationHistory.get(agentId);
    if (!history) {
      return;
    }

    history.adaptations.push({
      timestamp: Date.now(),
      sessionId: history.sessionCount++,
      result: adaptationResult,
      performanceGain: this.calculatePerformanceGain(adaptationResult, history.baselinePerformance),
    });

    // Update cross-session memory
    await this.updateCrossSessionMemory(agentId, adaptationResult);
  }

  /**
   * Calculate performance gain from adaptation
   */
  calculatePerformanceGain(result, baseline) {
    const baselineAccuracy = parseFloat(baseline.expectedAccuracy) || 0;
    const currentAccuracy = result.accuracy || 0;

    return {
      accuracyGain: currentAccuracy - baselineAccuracy,
      relativeGain: baselineAccuracy > 0 ? (currentAccuracy - baselineAccuracy) / baselineAccuracy : 0,
      efficiency: result.trainingTime ? baseline.trainingTime / result.trainingTime : 1,
    };
  }

  /**
   * Update cross-session memory
   */
  async updateCrossSessionMemory(agentId, adaptationResult) {
    const memoryKey = `agent_${agentId}_adaptations`;

    if (!this.crossSessionMemory.has(memoryKey)) {
      this.crossSessionMemory.set(memoryKey, []);
    }

    const memory = this.crossSessionMemory.get(memoryKey);
    memory.push({
      timestamp: Date.now(),
      patterns: adaptationResult.cognitivePatterns || [],
      performance: adaptationResult.performance || {},
      insights: adaptationResult.insights || [],
    });

    // Keep only recent memories (last 100)
    if (memory.length > 100) {
      memory.splice(0, memory.length - 100);
    }
  }

  /**
   * Get adaptation recommendations
   */
  async getAdaptationRecommendations(agentId) {
    const history = this.adaptationHistory.get(agentId);
    if (!history || history.adaptations.length < 3) {
      return null; // Need more data
    }

    const recommendations = {
      patterns: this.analyzePatternEffectiveness(history),
      hyperparameters: this.suggestHyperparameters(history),
      trainingStrategy: this.recommendTrainingStrategy(history),
    };

    return recommendations;
  }

  /**
   * Analyze pattern effectiveness from history
   */
  analyzePatternEffectiveness(history) {
    const patternPerformance = new Map();

    history.adaptations.forEach(adaptation => {
      const patterns = adaptation.result.cognitivePatterns || [];
      const gain = adaptation.performanceGain.accuracyGain;

      patterns.forEach(pattern => {
        if (!patternPerformance.has(pattern)) {
          patternPerformance.set(pattern, { totalGain: 0, count: 0 });
        }
        const stats = patternPerformance.get(pattern);
        stats.totalGain += gain;
        stats.count++;
      });
    });

    // Calculate average gain per pattern
    const effectiveness = [];
    patternPerformance.forEach((stats, pattern) => {
      effectiveness.push({
        pattern,
        avgGain: stats.totalGain / stats.count,
        frequency: stats.count,
      });
    });

    effectiveness.sort((a, b) => b.avgGain - a.avgGain);
    return effectiveness;
  }

  /**
   * Suggest hyperparameters based on history
   */
  suggestHyperparameters(history) {
    // Analyze successful adaptations
    const successfulAdaptations = history.adaptations.filter(a =>
      a.performanceGain.accuracyGain > 0,
    );

    if (successfulAdaptations.length === 0) {
      return {
        learningRate: 0.001,
        batchSize: 32,
        epochs: 10,
      };
    }

    // Extract and average successful hyperparameters
    const hyperparams = {
      learningRate: 0,
      batchSize: 0,
      epochs: 0,
    };

    successfulAdaptations.forEach(adaptation => {
      const config = adaptation.result.trainingConfig || {};
      hyperparams.learningRate += config.learningRate || 0.001;
      hyperparams.batchSize += config.batchSize || 32;
      hyperparams.epochs += config.epochs || 10;
    });

    const count = successfulAdaptations.length;
    return {
      learningRate: hyperparams.learningRate / count,
      batchSize: Math.round(hyperparams.batchSize / count),
      epochs: Math.round(hyperparams.epochs / count),
    };
  }

  /**
   * Recommend training strategy
   */
  recommendTrainingStrategy(history) {
    const recentPerformance = history.adaptations.slice(-5);
    const isImproving = recentPerformance.every((a, i) =>
      i === 0 || a.performanceGain.accuracyGain >= recentPerformance[i - 1].performanceGain.accuracyGain,
    );

    if (isImproving) {
      return {
        strategy: 'continue_current',
        description: 'Current approach is showing consistent improvement',
        recommendations: ['Maintain current learning rate', 'Consider increasing batch size'],
      };
    }
    return {
      strategy: 'explore_alternatives',
      description: 'Performance has plateaued',
      recommendations: [
        'Try different cognitive patterns',
        'Reduce learning rate',
        'Implement learning rate scheduling',
        'Consider data augmentation',
      ],
    };

  }

  /**
   * Export adaptation insights
   */
  exportAdaptationInsights() {
    const insights = {
      totalAgents: this.adaptationHistory.size,
      modelTypes: {},
      overallPerformance: {
        avgAccuracyGain: 0,
        totalAdaptations: 0,
      },
      bestPractices: [],
    };

    this.adaptationHistory.forEach((history, _agentId) => {
      const modelKey = `${history.modelType}/${history.presetName}`;

      if (!insights.modelTypes[modelKey]) {
        insights.modelTypes[modelKey] = {
          count: 0,
          avgGain: 0,
          bestGain: 0,
        };
      }

      const modelStats = insights.modelTypes[modelKey];
      modelStats.count++;

      history.adaptations.forEach(adaptation => {
        const gain = adaptation.performanceGain.accuracyGain;
        modelStats.avgGain += gain;
        modelStats.bestGain = Math.max(modelStats.bestGain, gain);
        insights.overallPerformance.avgAccuracyGain += gain;
        insights.overallPerformance.totalAdaptations++;
      });
    });

    // Calculate averages
    Object.values(insights.modelTypes).forEach(stats => {
      if (stats.count > 0) {
        stats.avgGain /= stats.count;
      }
    });

    if (insights.overallPerformance.totalAdaptations > 0) {
      insights.overallPerformance.avgAccuracyGain /= insights.overallPerformance.totalAdaptations;
    }

    return insights;
  }
}

// All components are already exported above