基于最大熵原理的贝叶斯评定python 代码

下面是一个基于最大熵原理的贝叶斯分类器的 Python 代码，用于评估文本数据：

import math

class MaxEnt:
    def __init__(self):
        self.feats = [] # 存放特征的列表
        self.trainset = [] # 存放训练集的列表
        self.labels = set() # 存放标签的集合
        self.weights = [] # 存放权重的列表

    def load_data(self, filename):
        '''
        加载数据集
        '''
        with open(filename, 'r') as f:
            for line in f:
                fields = line.strip().split()
                # 第一个字段是标签，后面是特征
                label = fields[0]
                self.labels.add(label)
                feats = {}
                for field in fields[1:]:
                    feat, value = field.split(':')
                    feats[feat] = int(value)
                self.trainset.append((feats, label))

    def calc_empirical_expectation(self, feat):
        '''
        计算经验分布下的特征函数的期望
        '''
        count = 0
        for feats, label in self.trainset:
            if feat in feats:
                count += 1
        return float(count) / float(len(self.trainset))

    def calc_model_expectation(self, feat):
        '''
        计算模型分布下的特征函数的期望
        '''
        count = 0.0
        for label in self.labels:
            weight = self.weights[label]
            for feats, l in self.trainset:
                if l != label:
                    continue
                if feat in feats:
                    count += weight * feats[feat] / float(len(self.trainset))
        return count

    def train(self, max_iter=1000):
        '''
        训练模型
        '''
        self.feats = set()
        for feats, label in self.trainset:
            for feat in feats:
                self.feats.add(feat)
        for label in self.labels:
            self.weights.append(0.0)
        for i in range(max_iter):
            print('Iter:%d' % i)
            empirical_feats_count = [0.0] * len(self.feats)
            model_feats_count = [0.0] * len(self.feats)
            for feats, label in self.trainset:
                for j, feat in enumerate(self.feats):
                    if feat in feats:
                        empirical_feats_count[j] += 1.0
                    # 计算 p(y|x)
                    prob = self.calc_prob(feats)
                    for j, feat in enumerate(self.feats):
                        if feat in feats:
                            model_feats_count[j] += prob[label] * feats[feat]
            # 更新权重
            for j, w in enumerate(self.weights):
                delta = 1.0 / len(self.trainset) * math.log(self.calc_empirical_expectation(j) / self.calc_model_expectation(j))
                self.weights[j] += delta
            print(self.weights)

    def calc_prob(self, feats):
        '''
        计算 p(y|x)
        '''
        features = {}
        for feat in feats:
            if feat not in self.feats:
                continue
            features[feat] = feats[feat]
        prob = {}
        Z = 0.0
        for label in self.labels:
            weight = self.weights[label]
            feat_weight = 0.0
            for feat in features:
                feat_weight += features[feat] * self.weights[label]
            prob[label] = math.exp(feat_weight)
            Z += prob[label]
        for label in self.labels:
            prob[label] /= Z
        return prob

使用方法：

me = MaxEnt()
me.load_data('train.txt')
me.train()

其中，`train.txt` 是训练数据集文件，每一行代表一个训练样本，第一个字段是标签，后面是特征。你需要根据你的数据集进行相应的调整。