# -*- encoding : utf-8 -*-
class BeyesClassifier::Base
  extend BeyesClassifier::Storage::ActAsStorable
  attr_reader :name
  attr_reader :word_list
  attr_reader :category_list
  attr_reader :training_count

  attr_accessor :tokenizer
  attr_accessor :language

  attr_accessor :thresholds
  attr_accessor :min_prob


  storable :version,:word_list,:category_list,:training_count,:thresholds,:min_prob

  # opts :
  # language
  # stemming : true | false
  # weight
  # assumed_prob
  # storage
  # purge_state ?

  def initialize(name, opts={})
    @version = BeyesClassifier::VERSION

    @name = name

    # This values are nil or are loaded from storage
    @word_list = {}
    @category_list = {}
    @training_count=0

    # storage
    purge_state = opts[:purge_state]
    @storage = opts[:storage] || BeyesClassifier::Base.storage
    unless purge_state
      @storage.load_state(self)
    else
      @storage.purge_state(self)
    end

    # This value can be set during initialization or overrided after load_state
    @thresholds = opts[:thresholds] || {}
    @min_prob = opts[:min_prob] || 0.0


    @ignore_words = nil
    @tokenizer = BeyesClassifier::Tokenizer.new(opts)

  end

  def incr_word(word, category)
    @word_list[word] ||= {}

    @word_list[word][:categories] ||= {}
    @word_list[word][:categories][category] ||= 0
    @word_list[word][:categories][category] += 1

    @word_list[word][:_total_word] ||= 0
    @word_list[word][:_total_word] += 1


    # words count by categroy
    @category_list[category] ||= {}
    @category_list[category][:_total_word] ||= 0
    @category_list[category][:_total_word] += 1

  end

  def incr_cat(category)
    @category_list[category] ||= {}
    @category_list[category][:_count] ||= 0
    @category_list[category][:_count] += 1

    @training_count ||= 0
    @training_count += 1

  end

  # return number of times the word appears in a category
  def word_count(word, category)
    return 0.0 unless @word_list[word] && @word_list[word][:categories] && @word_list[word][:categories][category]
    @word_list[word][:categories][category].to_f
  end

  # return the number of times the word appears in all categories
  def total_word_count(word)
    return 0.0 unless @word_list[word] && @word_list[word][:_total_word]
    @word_list[word][:_total_word].to_f
  end

  # return the number of words in a categories
  def total_word_count_in_cat(cat)
    return 0.0 unless @category_list[cat] && @category_list[cat][:_total_word]
    @category_list[cat][:_total_word].to_f
  end

  # return the number of training item
  def total_cat_count
    @training_count
  end

  # return the number of training document for a category
  def cat_count(category)
    @category_list[category][:_count] ? @category_list[category][:_count].to_f : 0.0
  end

  # return the number of time categories in wich a word appear
  def categories_with_word_count(word)
    return 0 unless @word_list[word] && @word_list[word][:categories]
    @word_list[word][:categories].length
  end

  # return the number of categories
  def total_categories
    categories.length
  end

  # return categories list
  def categories
    @category_list.keys
  end

  # train the classifier
  def train(category, text)
    @tokenizer.each_word(text) {|w| incr_word(w, category) }
    incr_cat(category)
  end

  # classify a text
  def classify(text, default=nil)
    # Find the category with the highest probability
    max_prob = @min_prob
    best = nil

    scores = cat_scores(text)
    scores.each do |score|
      cat, prob = score
      if prob > max_prob
        max_prob = prob
        best = cat
      end
    end

    # Return the default category in case the threshold condition was
    # not met. For example, if the threshold for :spam is 1.2
    #
    #    :spam => 0.73, :ham => 0.40  (OK)
    #    :spam => 0.80, :ham => 0.70  (Fail, :ham is too close)

    return default unless best

    threshold = @thresholds[best] || 1.0

    scores.each do |score|
      cat, prob = score
      next if cat == best
      return default if prob * threshold > max_prob
    end

    return best
  end

  def save_state
    @storage.save_state(self)
  end

  class << self
    attr_writer :storage

    def storage
      @storage = BeyesClassifier::InMemoryStorage.new unless defined? @storage
      @storage
    end

    def open(name)
      inst = self.new(name)
      if block_given?
        yield inst
        inst.save_state
      else
        inst
      end
    end
  end
end

# 训练函数
def train(category, text)
  each_word(text) {|w| increment_word(w, category) }
  increment_cat(category)
end

# 使用
classifier.train :spam, "Grow your penis to 20 inches in just 1 week"
classifier.train :ham,  "I'm hungry, no I don't want your penis"

def classify(text, default=nil)
  # Find the category with the highest probability

  max_prob = 0.0
  best = nil

  scores = cat_scores(text)
  scores.each do |score|
    cat, prob = score
    if prob > max_prob
      max_prob = prob
      best = cat
    end
  end

  # Return the default category in case the threshold condition was
  # not met. For example, if the threshold for :spam is 1.2
  #
  #    :spam => 0.73, :ham => 0.40  (OK)
  #    :spam => 0.80, :ham => 0.70  (Fail, :ham is too close)

  return default unless best
  threshold = @thresholds[best] || 1.0

  scores.each do |score|
    cat, prob = score
    next if cat == best
    return default if prob * threshold > max_prob
  end
  return best
end