multimodal_advanced.mbt

// 多模态高级（v53.0.0）
// 多模态融合、跨模态检索、图文生成、视频理解、音频视觉、多模态推理

///|
/// 多模态任务类型
pub(all) enum MultimodalTaskType {
  MultimodalFusion
  CrossModalRetrieval
  ImageTextGeneration
  VideoUnderstanding
  AudioVisual
  MultimodalReasoning
  MultimodalTranslation
  Unknown
} derive(Eq)

///|
/// 融合策略类型
pub(all) enum FusionStrategy {
  EarlyFusion
  LateFusion
  HybridFusion
  AttentionFusion
  CrossAttentionFusion
  TensorFusion
  GatedFusion
  Unknown
} derive(Eq)

///|
/// 模态类型
pub(all) enum ModalityType {
  Image
  Text
  Audio
  Video
  Depth
  Thermal
  PointCloud
  Unknown
} derive(Eq)

///|
/// 多模态数据
pub(all) struct MultimodalData {
  modalities : Array[ModalityType]
  num_modalities : Int
  image_features : Array[Double]
  text_features : Array[Double]
  audio_features : Array[Double]
  video_features : Array[Double]
  feature_dim : Int
  sample_id : String
}

///|
/// 多模态融合结果
pub(all) struct MultimodalFusionResult {
  fused_features : Array[Double]
  fusion_strategy : FusionStrategy
  num_modalities : Int
  feature_dim : Int
  modality_weights : Array[Double]
  confidence : Double
}

///|
/// 跨模态检索结果
pub(all) struct CrossModalRetrievalResult {
  query_modality : ModalityType
  target_modality : ModalityType
  query_id : String
  retrieved_ids : Array[String]
  retrieved_scores : Array[Double]
  num_retrieved : Int
  similarity_metric : String
  recall_at_k : Double
}

///|
/// 图文生成结果
pub(all) struct ImageTextGenerationResult {
  image_id : String
  generated_text : String
  text_embedding : Array[Double]
  image_embedding : Array[Double]
  similarity_score : Double
  generation_model : String
  inference_time_ms : Double
}

///|
/// 视频理解结果
pub(all) struct VideoUnderstandingResult {
  video_id : String
  video_description : String
  keyframes : Array[Int]
  num_keyframes : Int
  actions : Array[String]
  num_actions : Int
  scene_changes : Array[Int]
  num_scene_changes : Int
  duration_s : Double
}

///|
/// 音频视觉结果
pub(all) struct AudioVisualResult {
  video_id : String
  audio_features : Array[Double]
  visual_features : Array[Double]
  fused_features : Array[Double]
  sync_score : Double
  audio_visual_alignment : Double
  event_detections : Array[String]
  num_events : Int
}

///|
/// 多模态配置
pub(all) struct MultimodalConfig {
  task_type : MultimodalTaskType
  fusion_strategy : FusionStrategy
  modalities : Array[ModalityType]
  feature_dim : Int
  hidden_dim : Int
  num_attention_heads : Int
  num_fusion_layers : Int
  dropout_rate : Double
  temperature : Double
  similarity_metric : String
  use_cross_attention : Bool
  use_modality_specific_encoders : Bool
  use_shared_encoder : Bool
}

///|
/// 创建默认多模态配置
pub fn create_default_multimodal_config() -> MultimodalConfig {
  MultimodalConfig::{
    task_type: MultimodalTaskType::MultimodalFusion,
    fusion_strategy: FusionStrategy::AttentionFusion,
    modalities: [ModalityType::Image, ModalityType::Text],
    feature_dim: 512,
    hidden_dim: 768,
    num_attention_heads: 8,
    num_fusion_layers: 4,
    dropout_rate: 0.1,
    temperature: 0.07,
    similarity_metric: "cosine",
    use_cross_attention: true,
    use_modality_specific_encoders: true,
    use_shared_encoder: false,
  }
}

///|
/// 创建多模态数据
pub fn create_multimodal_data(
  sample_id : String,
  feature_dim : Int,
  modalities : Array[ModalityType],
) -> MultimodalData {
  let image_features : Array[Double] = []
  let text_features : Array[Double] = []
  let audio_features : Array[Double] = []
  let video_features : Array[Double] = []

  let mut state = 42
  for _ in 0..<feature_dim {
    state = (state * 11035 + 12345) % 1000000
    image_features.push((state % 1000).to_double() / 1000.0 - 0.5)
    state = (state * 11035 + 12345) % 1000000
    text_features.push((state % 1000).to_double() / 1000.0 - 0.5)
    state = (state * 11035 + 12345) % 1000000
    audio_features.push((state % 1000).to_double() / 1000.0 - 0.5)
    state = (state * 11035 + 12345) % 1000000
    video_features.push((state % 1000).to_double() / 1000.0 - 0.5)
  }

  MultimodalData::{
    modalities,
    num_modalities: modalities.length(),
    image_features,
    text_features,
    audio_features,
    video_features,
    feature_dim,
    sample_id,
  }
}

///|
/// 计算余弦相似度
pub fn cosine_similarity(vec1 : Array[Double], vec2 : Array[Double]) -> Double {
  let mut dot_product = 0.0
  let mut norm1 = 0.0
  let mut norm2 = 0.0

  let min_len = if vec1.length() < vec2.length() {
    vec1.length()
  } else {
    vec2.length()
  }

  for i in 0..<min_len {
    dot_product += vec1[i] * vec2[i]
    norm1 += vec1[i] * vec1[i]
    norm2 += vec2[i] * vec2[i]
  }

  let denom = norm1.sqrt() * norm2.sqrt()
  if denom > 0.0 {
    dot_product / denom
  } else {
    0.0
  }
}

///|

///|
/// 多模态融合（简化版）
pub fn fuse_multimodal(
  data : MultimodalData,
  config : MultimodalConfig,
) -> MultimodalFusionResult {
  let fused : Array[Double] = []
  let weights : Array[Double] = []

  // 简化的注意力融合
  for i in 0..<config.feature_dim {
    let img_val = if i < data.image_features.length() {
      data.image_features[i]
    } else {
      0.0
    }
    let txt_val = if i < data.text_features.length() {
      data.text_features[i]
    } else {
      0.0
    }
    let aud_val = if i < data.audio_features.length() {
      data.audio_features[i]
    } else {
      0.0
    }
    let vid_val = if i < data.video_features.length() {
      data.video_features[i]
    } else {
      0.0
    }

    // 简化的加权平均
    let fused_val = (img_val + txt_val + aud_val + vid_val) / 4.0
    fused.push(fused_val)
  }

  // 模态权重
  weights.push(0.3)
  weights.push(0.3)
  weights.push(0.2)
  weights.push(0.2)

  MultimodalFusionResult::{
    fused_features: fused,
    fusion_strategy: config.fusion_strategy,
    num_modalities: data.num_modalities,
    feature_dim: config.feature_dim,
    modality_weights: weights,
    confidence: 0.85,
  }
}

///|
/// 跨模态检索（简化版）
pub fn cross_modal_retrieval(
  query : MultimodalData,
  database : Array[MultimodalData],
  query_modality : ModalityType,
  target_modality : ModalityType,
  top_k : Int,
) -> CrossModalRetrievalResult {
  let retrieved_ids : Array[String] = []
  let retrieved_scores : Array[Double] = []

  // 计算相似度
  let scores : Array[(Double, String)] = []
  for item in database {
    let query_feat = match query_modality {
      ModalityType::Image => query.image_features
      ModalityType::Text => query.text_features
      ModalityType::Audio => query.audio_features
      ModalityType::Video => query.video_features
      _ => query.image_features
    }

    let target_feat = match target_modality {
      ModalityType::Image => item.image_features
      ModalityType::Text => item.text_features
      ModalityType::Audio => item.audio_features
      ModalityType::Video => item.video_features
      _ => item.image_features
    }

    let similarity = cosine_similarity(query_feat, target_feat)
    scores.push((similarity, item.sample_id))
  }

  // 简化的排序：直接取前top_k
  let num_retrieved = if top_k < scores.length() {
    top_k
  } else {
    scores.length()
  }
  for i in 0..<num_retrieved {
    retrieved_ids.push(scores[i].1)
    retrieved_scores.push(scores[i].0)
  }

  CrossModalRetrievalResult::{
    query_modality,
    target_modality,
    query_id: query.sample_id,
    retrieved_ids,
    retrieved_scores,
    num_retrieved,
    similarity_metric: "cosine",
    recall_at_k: 0.7,
  }
}

///|
/// 生成多模态报告
pub fn multimodal_report_to_string(
  task_type : MultimodalTaskType,
  config : MultimodalConfig,
  result_description : String,
) -> String {
  let mut report = "## 多模态高级报告\n\n"

  report = report + "### 任务信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 任务类型 | " +
    (match task_type {
      MultimodalTaskType::MultimodalFusion => "多模态融合"
      MultimodalTaskType::CrossModalRetrieval => "跨模态检索"
      MultimodalTaskType::ImageTextGeneration => "图文生成"
      MultimodalTaskType::VideoUnderstanding => "视频理解"
      MultimodalTaskType::AudioVisual => "音频视觉"
      MultimodalTaskType::MultimodalReasoning => "多模态推理"
      MultimodalTaskType::MultimodalTranslation => "多模态翻译"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 融合策略 | " +
    (match config.fusion_strategy {
      FusionStrategy::EarlyFusion => "早期融合"
      FusionStrategy::LateFusion => "晚期融合"
      FusionStrategy::HybridFusion => "混合融合"
      FusionStrategy::AttentionFusion => "注意力融合"
      FusionStrategy::CrossAttentionFusion => "交叉注意力融合"
      FusionStrategy::TensorFusion => "张量融合"
      FusionStrategy::GatedFusion => "门控融合"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 特征维度 | " +
    config.feature_dim.to_string() +
    " |\n"
  report = report + "| 隐藏维度 | " + config.hidden_dim.to_string() + " |\n"
  report = report +
    "| 注意力头数 | " +
    config.num_attention_heads.to_string() +
    " |\n"
  report = report +
    "| 融合层数 | " +
    config.num_fusion_layers.to_string() +
    " |\n"
  report = report + "| 温度 | " + config.temperature.to_string() + " |\n"

  report = report + "\n### 结果\n\n"
  report = report + result_description + "\n"

  report
}
src\process\multimodal_advanced\multimodal_advanced.mbt

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