Python实现朴素贝叶斯的学习与分类过程解析

import copyclass native_bayes_t:    def __init__(self, character_vec_, class_vec_):    """    构造的时候需要传入特征向量的值，以数组方式传入    参数1 character_vec_ 格式为 [("character_name",["","",""])]    参数2 为包含所有类别的数组 格式为["class_X", "class_Y"]    """    self.class_set = {}    # 记录该类别下各个特征值的条件概率    character_condition_per = {}    for character_name in character_vec_:      character_condition_per[character_name[0]] = {}      for character_value in character_name[1]:        character_condition_per[character_name[0]][character_value] = {          'num'      : 0, # 记录该类别下该特征值在训练样本中的数量,          'condition_per' : 0.0 # 记录该类别下各个特征值的条件概率        }    for class_name in class_vec:      self.class_set[class_name] = {        'num'           : 0, # 记录该类别在训练样本中的数量,        'class_per'        : 0.0, # 记录该类别在训练样本中的先验概率,        'character_condition_per' : copy.deepcopy(character_condition_per),      }    #print("init", character_vec_, self.class_set) #for debug  def learn(self, sample_):    """    learn 参数为训练的样本，格式为    [      {        'character' : {'character_A':'A1'}, #特征向量        'class_name' : 'class_X'       #类别名称      }    ]    """    for each_sample in sample:      character_vec = each_sample['character']      class_name   = each_sample['class_name']      data_for_class = self.class_set[class_name]      data_for_class['num'] += 1      # 各个特质值数量加1      for character_name in character_vec:        character_value = character_vec[character_name]        data_for_character = data_for_class['character_condition_per'][character_name][character_value]        data_for_character['num'] += 1    # 数量计算完毕， 计算最终的概率值    sample_num = len(sample)    for each_sample in sample:      character_vec = each_sample['character']      class_name  = each_sample['class_name']      data_for_class = self.class_set[class_name]      # 计算类别的先验概率      data_for_class['class_per'] = float(data_for_class['num']) / sample_num      # 各个特质值的条件概率      for character_name in character_vec:        character_value = character_vec[character_name]                data_for_character = data_for_class['character_condition_per'][character_name][character_value]        data_for_character['condition_per'] = float(data_for_character['num']) / data_for_class['num']    from pprint import pprint    pprint(self.class_set) #for debug  def classify(self, input_):    """      对输入进行分类，输入input的格式为    {      "character_A":"A1",      "character_B":"B3",    }    """    best_class = ''    max_per  = 0.0    for class_name in self.class_set:      class_data = self.class_set[class_name]      per = class_data['class_per']      # 计算各个特征值条件概率的乘积      for character_name in input_:        character_per_data = class_data['character_condition_per'][character_name]        per = per * character_per_data[input_[character_name]]['condition_per']      print(class_name, per)      if per >= max_per:        best_class = class_name    return best_classcharacter_vec = [("character_A",["A1","A2","A3"]), ("character_B",["B1","B2","B3"])]class_vec   = ["class_X", "class_Y"]bayes = native_bayes_t(character_vec, class_vec)sample = [      {        'character' : {'character_A':'A1', 'character_B':'B1'}, #特征向量        'class_name' : 'class_X'       #类别名称      },      {        'character' : {'character_A':'A3', 'character_B':'B1'}, #特征向量        'class_name' : 'class_X'       #类别名称      },      {        'character' : {'character_A':'A3', 'character_B':'B3'}, #特征向量        'class_name' : 'class_X'       #类别名称      },      {        'character' : {'character_A':'A2', 'character_B':'B2'}, #特征向量        'class_name' : 'class_X'       #类别名称      },      {        'character' : {'character_A':'A2', 'character_B':'B2'}, #特征向量        'class_name' : 'class_Y'       #类别名称      },      {        'character' : {'character_A':'A3', 'character_B':'B1'}, #特征向量        'class_name' : 'class_Y'       #类别名称      },      {        'character' : {'character_A':'A1', 'character_B':'B3'}, #特征向量        'class_name' : 'class_Y'       #类别名称      },      {        'character' : {'character_A':'A1', 'character_B':'B3'}, #特征向量        'class_name' : 'class_Y'       #类别名称      },          ]input_data ={  "character_A":"A1",  "character_B":"B3",}bayes.learn(sample)print(bayes.classify(input_data))