nlp_advanced.mbt

// 自然语言处理高级（v49.0.0）
// 文本分类、命名实体识别、情感分析、文本摘要、机器翻译、问答系统

///|
/// NLP任务类型
pub(all) enum NLPTaskType {
  TextClassification
  NamedEntityRecognition
  SentimentAnalysis
  TextSummarization
  MachineTranslation
  QuestionAnswering
  TextGeneration
  LanguageModeling
  Unknown
} derive(Eq)

///|
/// 文本分类模型类型
pub(all) enum TextClassificationModel {
  NaiveBayes
  SVM
  LogisticRegression
  CNN
  RNN
  LSTM
  GRU
  Transformer
  BERT
  RoBERTa
  Unknown
} derive(Eq)

///|
/// NER实体类型
pub(all) enum NEREntityType {
  Person
  Organization
  Location
  Date
  Time
  Money
  Percent
  Product
  Event
  Unknown
} derive(Eq)

///|
/// 情感类型
pub(all) enum SentimentType {
  VeryPositive
  Positive
  Neutral
  Negative
  VeryNegative
  Unknown
} derive(Eq)

///|
/// 文本分类结果
pub(all) struct TextClassificationResult {
  text : String
  predicted_class : String
  confidence : Double
  all_classes : Array[String]
  all_scores : Array[Double]
  model_type : TextClassificationModel
  num_classes : Int
}

///|
/// NER结果
pub(all) struct NERResult {
  text : String
  entities : Array[String]
  entity_types : Array[NEREntityType]
  entity_spans : Array[Array[Int]]
  confidences : Array[Double]
  num_entities : Int
}

///|
/// 情感分析结果
pub(all) struct SentimentAnalysisResult {
  text : String
  sentiment : SentimentType
  sentiment_score : Double
  confidence : Double
  positive_score : Double
  negative_score : Double
  neutral_score : Double
  aspect_sentiments : Array[String]
}

///|
/// 文本摘要结果
pub(all) struct TextSummarizationResult {
  original_text : String
  summary : String
  compression_ratio : Double
  original_length : Int
  summary_length : Int
  method_type : String
  rouge_score : Double
}

///|
/// 机器翻译结果
pub(all) struct MachineTranslationResult {
  source_text : String
  target_text : String
  source_language : String
  target_language : String
  confidence : Double
  bleu_score : Double
  method_type : String
}

///|
/// 问答系统结果
pub(all) struct QuestionAnsweringResult {
  question : String
  context : String
  answer : String
  confidence : Double
  answer_start : Int
  answer_end : Int
  method_type : String
}

///|
/// 文本嵌入
pub(all) struct TextEmbedding {
  text : String
  embedding : Array[Double]
  embedding_dim : Int
  model_type : String
  token_ids : Array[Int]
  num_tokens : Int
}

///|
/// NLP配置
pub(all) struct NLPConfig {
  task_type : NLPTaskType
  classification_model : TextClassificationModel
  max_length : Int
  batch_size : Int
  learning_rate : Double
  num_epochs : Int
  use_pretrained : Bool
  pretrained_model : String
  temperature : Double
  top_p : Double
  top_k : Int
  num_beams : Int
}

///|
/// 创建默认NLP配置
pub fn create_default_nlp_config() -> NLPConfig {
  NLPConfig::{
    task_type: NLPTaskType::TextClassification,
    classification_model: TextClassificationModel::BERT,
    max_length: 512,
    batch_size: 32,
    learning_rate: 0.00002,
    num_epochs: 3,
    use_pretrained: true,
    pretrained_model: "bert-base-uncased",
    temperature: 1.0,
    top_p: 1.0,
    top_k: 50,
    num_beams: 4,
  }
}

///|
/// 文本分词（简化版）
pub fn tokenize(text : String) -> Array[String] {
  let tokens : Array[String] = []

  // 简化的分词：按空格分割
  let parts = text.split(" ")
  for part in parts {
    let part_str = part.to_owned()
    if part_str.length() > 0 {
      tokens.push(part_str)
    }
  }

  tokens
}

///|
/// 文本分类（简化版）
pub fn classify_text(
  text : String,
  classes : Array[String],
  config : NLPConfig,
) -> TextClassificationResult {
  let tokens = tokenize(text)
  let all_scores : Array[Double] = []

  // 简化的分类：基于关键词匹配
  let keywords = [
    ["happy", "good", "great", "excellent", "love"],
    ["sad", "bad", "terrible", "awful", "hate"],
    ["neutral", "okay", "fine", "average", "normal"],
  ]

  for class_idx in 0..<classes.length() {
    let mut score = 0.0
    for token in tokens {
      let token_lower = token
      if class_idx < keywords.length() {
        for kw in keywords[class_idx] {
          if token_lower.contains(kw) {
            score += 1.0
          }
        }
      }
    }
    if tokens.length() > 0 {
      score /= tokens.length().to_double()
    }
    all_scores.push(score)
  }

  // 归一化
  let mut total_score = 0.0
  for s in all_scores {
    total_score += s
  }
  if total_score > 0.0 {
    for i in 0..<all_scores.length() {
      all_scores[i] /= total_score
    }
  }

  // 找到最佳类别
  let mut best_class = classes[0]
  let mut best_score = 0.0
  for i in 0..<classes.length() {
    if all_scores[i] > best_score {
      best_score = all_scores[i]
      best_class = classes[i]
    }
  }

  TextClassificationResult::{
    text,
    predicted_class: best_class,
    confidence: best_score,
    all_classes: classes,
    all_scores,
    model_type: config.classification_model,
    num_classes: classes.length(),
  }
}

///|
/// 命名实体识别（简化版）
pub fn recognize_entities(text : String) -> NERResult {
  let entities : Array[String] = []
  let entity_types : Array[NEREntityType] = []
  let entity_spans : Array[Array[Int]] = []
  let confidences : Array[Double] = []

  let tokens = tokenize(text)
  let mut position = 0

  for token in tokens {
    let token_len = token.length()
    let start = position
    let end = position + token_len

    // 简化的实体识别：基于首字母大写
    let mut is_entity = false
    let mut entity_type = NEREntityType::Unknown

    if token.length() > 0 {
      // 简化的首字母大写判断
      if token.length() > 1 {
        is_entity = true
        // 简化的实体类型判断
        if token.contains("Inc") ||
          token.contains("Corp") ||
          token.contains("Company") {
          entity_type = NEREntityType::Organization
        } else if token.contains("Street") ||
          token.contains("Avenue") ||
          token.contains("City") {
          entity_type = NEREntityType::Location
        } else {
          entity_type = NEREntityType::Person
        }
      }
    }

    if is_entity {
      entities.push(token)
      entity_types.push(entity_type)
      let span : Array[Int] = []
      span.push(start)
      span.push(end)
      entity_spans.push(span)
      confidences.push(0.7)
    }

    position = end + 1
  }

  NERResult::{
    text,
    entities,
    entity_types,
    entity_spans,
    confidences,
    num_entities: entities.length(),
  }
}

///|
/// 情感分析（简化版）
pub fn analyze_sentiment(text : String) -> SentimentAnalysisResult {
  let tokens = tokenize(text)

  let positive_words = [
    "good", "great", "excellent", "love", "happy", "amazing", "wonderful", "fantastic",
    "best", "perfect",
  ]
  let negative_words = [
    "bad", "terrible", "awful", "hate", "sad", "horrible", "worst", "poor", "disappointing",
    "ugly",
  ]

  let mut positive_count = 0
  let mut negative_count = 0

  for token in tokens {
    let token_lower = token
    for pw in positive_words {
      if token_lower.contains(pw) {
        positive_count += 1
      }
    }
    for nw in negative_words {
      if token_lower.contains(nw) {
        negative_count += 1
      }
    }
  }

  let total = positive_count + negative_count
  let positive_score = if total > 0 {
    positive_count.to_double() / total.to_double()
  } else {
    0.5
  }
  let negative_score = if total > 0 {
    negative_count.to_double() / total.to_double()
  } else {
    0.5
  }
  let neutral_score = 1.0 - positive_score - negative_score

  let sentiment_score = positive_score - negative_score
  let sentiment = if sentiment_score > 0.5 {
    SentimentType::VeryPositive
  } else if sentiment_score > 0.1 {
    SentimentType::Positive
  } else if sentiment_score > -0.1 {
    SentimentType::Neutral
  } else if sentiment_score > -0.5 {
    SentimentType::Negative
  } else {
    SentimentType::VeryNegative
  }

  let confidence = if total > 0 {
    (positive_score - negative_score).abs()
  } else {
    0.3
  }

  SentimentAnalysisResult::{
    text,
    sentiment,
    sentiment_score,
    confidence,
    positive_score,
    negative_score,
    neutral_score,
    aspect_sentiments: [],
  }
}

///|
/// 文本摘要（简化版：提取式摘要）
pub fn summarize_text(
  text : String,
  max_sentences : Int,
) -> TextSummarizationResult {
  // 简化的句子分割：按句号分割
  let sentences : Array[String] = []
  let parts = text.split(".")
  for part in parts {
    let current_sentence = part.to_owned() + "."
    if current_sentence.length() > 10 {
      sentences.push(current_sentence)
    }
  }

  // 简化的句子评分：基于长度
  let scored_sentences : Array[(Double, String)] = []
  for sentence in sentences {
    let score = sentence.length().to_double()
    scored_sentences.push((score, sentence))
  }

  // 选择Top-N句子
  let num_selected = if max_sentences < sentences.length() {
    max_sentences
  } else {
    sentences.length()
  }

  let mut summary = ""
  for i in 0..<num_selected {
    summary = summary + scored_sentences[i].1
  }

  let original_length = text.length()
  let summary_length = summary.length()
  let compression_ratio = if original_length > 0 {
    1.0 - summary_length.to_double() / original_length.to_double()
  } else {
    0.0
  }

  TextSummarizationResult::{
    original_text: text,
    summary,
    compression_ratio,
    original_length,
    summary_length,
    method_type: "extractive",
    rouge_score: 0.5,
  }
}

///|
/// 生成文本嵌入（简化版）
pub fn embed_text(text : String, embedding_dim : Int) -> TextEmbedding {
  let tokens = tokenize(text)
  let embedding : Array[Double] = []
  let token_ids : Array[Int] = []

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

  for _ in tokens {
    state = (state * 11035 + 12345) % 1000000
    token_ids.push(state % 30000)
  }

  TextEmbedding::{
    text,
    embedding,
    embedding_dim,
    model_type: "word2vec",
    token_ids,
    num_tokens: tokens.length(),
  }
}

///|
/// 生成NLP报告
pub fn nlp_report_to_string(
  task_type : NLPTaskType,
  config : NLPConfig,
  result_description : String,
) -> String {
  let mut report = "## 自然语言处理高级报告\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 {
      NLPTaskType::TextClassification => "文本分类"
      NLPTaskType::NamedEntityRecognition => "命名实体识别"
      NLPTaskType::SentimentAnalysis => "情感分析"
      NLPTaskType::TextSummarization => "文本摘要"
      NLPTaskType::MachineTranslation => "机器翻译"
      NLPTaskType::QuestionAnswering => "问答系统"
      NLPTaskType::TextGeneration => "文本生成"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 分类模型 | " +
    (match config.classification_model {
      TextClassificationModel::NaiveBayes => "朴素贝叶斯"
      TextClassificationModel::SVM => "SVM"
      TextClassificationModel::CNN => "CNN"
      TextClassificationModel::RNN => "RNN"
      TextClassificationModel::LSTM => "LSTM"
      TextClassificationModel::Transformer => "Transformer"
      TextClassificationModel::BERT => "BERT"
      TextClassificationModel::RoBERTa => "RoBERTa"
      _ => "未知"
    }) +
    " |\n"
  report = report + "| 最大长度 | " + config.max_length.to_string() + " |\n"
  report = report + "| 批次大小 | " + config.batch_size.to_string() + " |\n"
  report = report + "| 学习率 | " + config.learning_rate.to_string() + " |\n"
  report = report + "| 训练轮数 | " + config.num_epochs.to_string() + " |\n"
  report = report + "| 预训练模型 | " + config.pretrained_model + " |\n"

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

  report
}
src\process\nlp_advanced\nlp_advanced.mbt

87.05%
