朴素贝叶斯的学习与分类

import copy

class 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_class

character_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))