add python_lda

README.md

@@ -9,6 +9,8 @@
 ### python_spider.py: 一个很“水”的Python爬虫入门代码文件
 
 ### python_weibo.py: “史上最详细”的Python模拟登录新浪微博流程
+
+### python_lda.py: 玩点高级的--带你入门Topic模型LDA（小改进+附源码）
 ============================================================
 
 ### 您可以fork该项目,并在修改后提交Pull request

python_lda.py

@@ -0,0 +1,733 @@
+# _*_ coding: utf-8 _*_
+
+"""
+python_lda.py by xianhu
+"""
+
+import os
+import numpy
+import logging
+from collections import defaultdict
+
+# 全局变量
+MAX_ITER_NUM = 10000    # 最大迭代次数
+VAR_NUM = 20            # 自动计算迭代次数时,计算方差的区间大小
+
+
+class BiDictionary(object):
+    """
+    定义双向字典,通过key可以得到value,通过value也可以得到key
+    """
+
+    def __init__(self):
+        """
+        :key: 双向字典初始化
+        """
+        self.dict = {}            # 正向的数据字典,其key为self的key
+        self.dict_reversed = {}   # 反向的数据字典,其key为self的value
+        return
+
+    def __len__(self):
+        """
+        :key: 获取双向字典的长度
+        """
+        return len(self.dict)
+
+    def __str__(self):
+        """
+        :key: 将双向字典转化为字符串对象
+        """
+        str_list = ["%s\t%s" % (key, self.dict[key]) for key in self.dict]
+        return "\n".join(str_list)
+
+    def clear(self):
+        """
+        :key: 清空双向字典对象
+        """
+        self.dict.clear()
+        self.dict_reversed.clear()
+        return
+
+    def add_key_value(self, key, value):
+        """
+        :key: 更新双向字典,增加一项
+        """
+        self.dict[key] = value
+        self.dict_reversed[value] = key
+        return
+
+    def remove_key_value(self, key, value):
+        """
+        :key: 更新双向字典,删除一项
+        """
+        if key in self.dict:
+            del self.dict[key]
+            del self.dict_reversed[value]
+        return
+
+    def get_value(self, key, default=None):
+        """
+        :key: 通过key获取value,不存在返回default
+        """
+        return self.dict.get(key, default)
+
+    def get_key(self, value, default=None):
+        """
+        :key: 通过value获取key,不存在返回default
+        """
+        return self.dict_reversed.get(value, default)
+
+    def contains_key(self, key):
+        """
+        :key: 判断是否存在key值
+        """
+        return key in self.dict
+
+    def contains_value(self, value):
+        """
+        :key: 判断是否存在value值
+        """
+        return value in self.dict_reversed
+
+    def keys(self):
+        """
+        :key: 得到双向字典全部的keys
+        """
+        return self.dict.keys()
+
+    def values(self):
+        """
+        :key: 得到双向字典全部的values
+        """
+        return self.dict_reversed.keys()
+
+    def items(self):
+        """
+        :key: 得到双向字典全部的items
+        """
+        return self.dict.items()
+
+
+class CorpusSet(object):
+    """
+    定义语料集类,作为LdaBase的基类
+    """
+
+    def __init__(self):
+        """
+        :key: 初始化函数
+        """
+        # 定义关于word的变量
+        self.local_bi = BiDictionary()      # id和word之间的本地双向字典,key为id,value为word
+        self.words_count = 0                # 数据集中word的数量（排重之前的）
+        self.V = 0                          # 数据集中word的数量（排重之后的）
+
+        # 定义关于article的变量
+        self.artids_list = []               # 全部article的id的列表,按照数据读取的顺序存储
+        self.arts_Z = []                    # 全部article中所有词的id信息,维数为 M * art.length()
+        self.M = 0                          # 数据集中article的数量
+
+        # 定义推断中用到的变量（可能为空）
+        self.global_bi = None               # id和word之间的全局双向字典,key为id,value为word
+        self.local_2_global = {}            # 一个字典,local字典和global字典之间的对应关系
+        return
+
+    def init_corpus_with_file(self, file_name):
+        """
+        :key: 利用数据文件初始化语料集数据。文件每一行的数据格式: id[tab]word1 word2 word3......
+        """
+        with open(file_name, "r", encoding="utf-8") as file_iter:
+            self.init_corpus_with_articles(file_iter)
+        return
+
+    def init_corpus_with_articles(self, article_list):
+        """
+        :key: 利用article的列表初始化语料集。每一篇article的格式为: id[tab]word1 word2 word3......
+        """
+        # 清理数据--word数据
+        self.local_bi.clear()
+        self.words_count = 0
+        self.V = 0
+
+        # 清理数据--article数据
+        self.artids_list.clear()
+        self.arts_Z.clear()
+        self.M = 0
+
+        # 清理数据--清理local到global的映射关系
+        self.local_2_global.clear()
+
+        # 读取article数据
+        for line in article_list:
+            frags = line.strip().split()
+            if len(frags) < 2:
+                continue
+
+            # 获取article的id
+            art_id = frags[0].strip()
+
+            # 获取word的id
+            art_wordid_list = []
+            for word in [w.strip() for w in frags[1:] if w.strip()]:
+                local_id = self.local_bi.get_key(word) if self.local_bi.contains_value(word) else len(self.local_bi)
+
+                # 这里的self.global_bi为None和为空是有区别的
+                if self.global_bi is None:
+                    # 更新id信息
+                    self.local_bi.add_key_value(local_id, word)
+                    art_wordid_list.append(local_id)
+                else:
+                    if self.global_bi.contains_value(word):
+                        # 更新id信息
+                        self.local_bi.add_key_value(local_id, word)
+                        art_wordid_list.append(local_id)
+
+                        # 更新local_2_global
+                        self.local_2_global[local_id] = self.global_bi.get_key(word)
+
+            # 更新类变量: 必须article中word的数量大于0
+            if len(art_wordid_list) > 0:
+                self.words_count += len(art_wordid_list)
+                self.artids_list.append(art_id)
+                self.arts_Z.append(art_wordid_list)
+
+        # 做相关初始计算--word相关
+        self.V = len(self.local_bi)
+        logging.debug("words number: " + str(self.V) + ", " + str(self.words_count))
+
+        # 做相关初始计算--article相关
+        self.M = len(self.artids_list)
+        logging.debug("articles number: " + str(self.M))
+        return
+
+    def save_wordmap(self, file_name):
+        """
+        :key: 保存word字典,即self.local_bi的数据
+        """
+        with open(file_name, "w", encoding="utf-8") as f_save:
+            f_save.write(str(self.local_bi))
+        return
+
+    def load_wordmap(self, file_name):
+        """
+        :key: 加载word字典,即加载self.local_bi的数据
+        """
+        self.local_bi.clear()
+        with open(file_name, "r", encoding="utf-8") as f_load:
+            for _id, _word in [line.strip().split() for line in f_load if line.strip()]:
+                self.local_bi.add_key_value(int(_id), _word.strip())
+        self.V = len(self.local_bi)
+        return
+
+
+class LdaBase(CorpusSet):
+    """
+    LDA模型的基类,相关说明:
+    》article的下标范围为[0, self.M), 下标为 m
+    》wordid的下标范围为[0, self.V), 下标为 w
+    》topic的下标范围为[0, self.K), 下标为 k 或 topic
+    》article中word的下标范围为[0, article.size()), 下标为 n
+    """
+
+    def __init__(self):
+        """
+        :key: 初始化函数
+        """
+        CorpusSet.__init__(self)
+
+        # 基础变量--1
+        self.dir_path = ""          # 文件夹路径,用于存放LDA运行的数据、中间结果等
+        self.model_name = ""        # LDA训练或推断的模型名称,也用于读取训练的结果
+        self.current_iter = 0       # LDA训练或推断的模型已经迭代的次数,用于继续模型训练过程
+        self.iters_num = 0          # LDA训练或推断过程中Gibbs抽样迭代的总次数,整数值或者"auto"
+        self.topics_num = 0         # LDA训练或推断过程中的topic的数量,即self.K值
+        self.K = 0                  # LDA训练或推断过程中的topic的数量,即self.topics_num值
+        self.twords_num = 0         # LDA训练或推断结束后输出与每个topic相关的word的个数
+
+        # 基础变量--2
+        self.alpha = numpy.zeros(self.K)            # 超参数alpha,K维的float值,默认为50/K
+        self.beta = numpy.zeros(self.V)             # 超参数beta,V维的float值,默认为0.01
+
+        # 基础变量--3
+        self.Z = []                                 # 所有word的topic信息,即Z(m, n),维数为 M * article.size()
+
+        # 统计计数(可由self.Z计算得到)
+        self.nd = numpy.zeros((self.M, self.K))     # nd[m, k]用于保存第m篇article中第k个topic产生的词的个数,其维数为 M * K
+        self.ndsum = numpy.zeros((self.M, 1))       # ndsum[m, 0]用于保存第m篇article的总词数,维数为 M * 1
+        self.nw = numpy.zeros((self.K, self.V))     # nw[k, w]用于保存第k个topic产生的词中第w个词的数量,其维数为 K * V
+        self.nwsum = numpy.zeros((self.K, 1))       # nwsum[k, 0]用于保存第k个topic产生的词的总数,维数为 K * 1
+
+        # 多项式分布参数变量
+        self.theta = numpy.zeros((self.M, self.K))  # Doc-Topic多项式分布的参数,维数为 M * K,由alpha值影响
+        self.phi = numpy.zeros((self.K, self.V))    # Topic-Word多项式分布的参数,维数为 K * V,由beta值影响
+
+        # 辅助变量,目的是提高算法执行效率
+        self.sum_alpha = 0.0                        # 超参数alpha的和
+        self.sum_beta = 0.0                         # 超参数beta的和
+
+        # 先验知识,格式为{word_id: [k1, k2, ...], ...}
+        self.prior_word = defaultdict(list)
+
+        # 推断时需要的训练模型
+        self.train_model = None
+        return
+
+    # --------------------------------------------------辅助函数---------------------------------------------------------
+    def init_statistics_document(self):
+        """
+        :key: 初始化关于article的统计计数。先决条件: self.M, self.K, self.Z
+        """
+        assert self.M > 0 and self.K > 0 and self.Z
+
+        # 统计计数初始化
+        self.nd = numpy.zeros((self.M, self.K), dtype=numpy.int)
+        self.ndsum = numpy.zeros((self.M, 1), dtype=numpy.int)
+
+        # 根据self.Z进行更新,更新self.nd[m, k]和self.ndsum[m, 0]
+        for m in range(self.M):
+            for k in self.Z[m]:
+                self.nd[m, k] += 1
+            self.ndsum[m, 0] = len(self.Z[m])
+        return
+
+    def init_statistics_word(self):
+        """
+        :key: 初始化关于word的统计计数。先决条件: self.V, self.K, self.Z, self.arts_Z
+        """
+        assert self.V > 0 and self.K > 0 and self.Z and self.arts_Z
+
+        # 统计计数初始化
+        self.nw = numpy.zeros((self.K, self.V), dtype=numpy.int)
+        self.nwsum = numpy.zeros((self.K, 1), dtype=numpy.int)
+
+        # 根据self.Z进行更新,更新self.nw[k, w]和self.nwsum[k, 0]
+        for m in range(self.M):
+            for k, w in zip(self.Z[m], self.arts_Z[m]):
+                self.nw[k, w] += 1
+                self.nwsum[k, 0] += 1
+        return
+
+    def init_statistics(self):
+        """
+        :key: 初始化全部的统计计数。上两个函数的综合函数。
+        """
+        self.init_statistics_document()
+        self.init_statistics_word()
+        return
+
+    def sum_alpha_beta(self):
+        """
+        :key: 计算alpha、beta的和
+        """
+        self.sum_alpha = self.alpha.sum()
+        self.sum_beta = self.beta.sum()
+        return
+
+    def calculate_theta(self):
+        """
+        :key: 初始化并计算模型的theta值(M*K),用到alpha值
+        """
+        assert self.sum_alpha > 0
+        self.theta = (self.nd + self.alpha) / (self.ndsum + self.sum_alpha)
+        return
+
+    def calculate_phi(self):
+        """
+        :key: 初始化并计算模型的phi值(K*V),用到beta值
+        """
+        assert self.sum_beta > 0
+        self.phi = (self.nw + self.beta) / (self.nwsum + self.sum_beta)
+        return
+
+    # ---------------------------------------------计算Perplexity值------------------------------------------------------
+    def calculate_perplexity(self):
+        """
+        :key: 计算Perplexity值,并返回
+        """
+        # 计算theta和phi值
+        self.calculate_theta()
+        self.calculate_phi()
+
+        # 开始计算
+        preplexity = 0.0
+        for m in range(self.M):
+            for w in self.arts_Z[m]:
+                preplexity += numpy.log(numpy.sum(self.theta[m] * self.phi[:, w]))
+        return numpy.exp(-(preplexity / self.words_count))
+
+    # --------------------------------------------------静态函数---------------------------------------------------------
+    @staticmethod
+    def multinomial_sample(pro_list):
+        """
+        :key: 静态函数,多项式分布抽样,此时会改变pro_list的值
+        :param pro_list: [0.2, 0.7, 0.4, 0.1],此时说明返回下标1的可能性大,但也不绝对
+        """
+        # 将pro_list进行累加
+        for k in range(1, len(pro_list)):
+            pro_list[k] += pro_list[k-1]
+
+        # 确定随机数 u 落在哪个下标值,此时的下标值即为抽取的类别（random.rand()返回: [0, 1.0)）
+        u = numpy.random.rand() * pro_list[-1]
+
+        return_index = len(pro_list) - 1
+        for t in range(len(pro_list)):
+            if pro_list[t] > u:
+                return_index = t
+                break
+        return return_index
+
+    # ----------------------------------------------Gibbs抽样算法--------------------------------------------------------
+    def gibbs_sampling(self, is_calculate_preplexity):
+        """
+        :key: LDA模型中的Gibbs抽样过程
+        :param is_calculate_preplexity: 是否计算preplexity值
+        """
+        # 计算preplexity值用到的变量
+        pp_list = []
+        pp_var = numpy.inf
+
+        # 开始迭代
+        last_iter = self.current_iter + 1
+        iters_num = self.iters_num if self.iters_num != "auto" else MAX_ITER_NUM
+        for self.current_iter in range(last_iter, last_iter+iters_num):
+            info = "......"
+
+            # 是否计算preplexity值
+            if is_calculate_preplexity:
+                pp = self.calculate_perplexity()
+                pp_list.append(pp)
+
+                # 计算列表最新VAR_NUM项的方差
+                pp_var = numpy.var(pp_list[-VAR_NUM:]) if len(pp_list) >= VAR_NUM else numpy.inf
+                info = (", preplexity: " + str(pp)) + ((", var: " + str(pp_var)) if len(pp_list) >= VAR_NUM else "")
+
+            # 输出Debug信息
+            logging.debug("\titeration " + str(self.current_iter) + info)
+
+            # 判断是否跳出循环
+            if self.iters_num == "auto" and pp_var < (VAR_NUM / 2):
+                break
+
+            # 对每篇article的每个word进行一次抽样,抽取合适的k值
+            for m in range(self.M):
+                for n in range(len(self.Z[m])):
+                    w = self.arts_Z[m][n]
+                    k = self.Z[m][n]
+
+                    # 统计计数减一
+                    self.nd[m, k] -= 1
+                    self.ndsum[m, 0] -= 1
+                    self.nw[k, w] -= 1
+                    self.nwsum[k, 0] -= 1
+
+                    if self.prior_word and (w in self.prior_word):
+                        # 带有先验知识,否则进行正常抽样
+                        k = numpy.random.choice(self.prior_word[w])
+                    else:
+                        # 计算theta值--下边的过程为抽取第m篇article的第n个词w的topic,即新的k
+                        theta_p = (self.nd[m] + self.alpha) / (self.ndsum[m, 0] + self.sum_alpha)
+
+                        # 计算phi值--判断是训练模型,还是推断模型（注意self.beta[w_g]）
+                        if self.local_2_global and self.train_model:
+                            w_g = self.local_2_global[w]
+                            phi_p = (self.train_model.nw[:, w_g] + self.nw[:, w] + self.beta[w_g]) / \
+                                    (self.train_model.nwsum[:, 0] + self.nwsum[:, 0] + self.sum_beta)
+                        else:
+                            phi_p = (self.nw[:, w] + self.beta[w]) / (self.nwsum[:, 0] + self.sum_beta)
+
+                        # multi_p为多项式分布的参数,此时没有进行标准化
+                        multi_p = theta_p * phi_p
+
+                        # 此时的topic即为Gibbs抽样得到的topic,它有较大的概率命中多项式概率大的topic
+                        k = LdaBase.multinomial_sample(multi_p)
+
+                    # 统计计数加一
+                    self.nd[m, k] += 1
+                    self.ndsum[m, 0] += 1
+                    self.nw[k, w] += 1
+                    self.nwsum[k, 0] += 1
+
+                    # 更新Z值
+                    self.Z[m][n] = k
+        # 抽样完毕
+        return
+
+    # -----------------------------------------Model数据存储、读取相关函数-------------------------------------------------
+    def save_parameter(self, file_name):
+        """
+        :key: 保存模型相关参数数据,包括: topics_num, M, V, K, words_count, alpha, beta
+        """
+        with open(file_name, "w", encoding="utf-8") as f_param:
+            for item in ["topics_num", "M", "V", "K", "words_count"]:
+                f_param.write("%s\t%s\n" % (item, str(self.__dict__[item])))
+            f_param.write("alpha\t%s\n" % ",".join([str(item) for item in self.alpha]))
+            f_param.write("beta\t%s\n" % ",".join([str(item) for item in self.beta]))
+        return
+
+    def load_parameter(self, file_name):
+        """
+        :key: 加载模型相关参数数据,和上一个函数相对应
+        """
+        with open(file_name, "r", encoding="utf-8") as f_param:
+            for line in f_param:
+                key, value = line.strip().split()
+                if key in ["topics_num", "M", "V", "K", "words_count"]:
+                    self.__dict__[key] = int(value)
+                elif key in ["alpha", "beta"]:
+                    self.__dict__[key] = numpy.array([float(item) for item in value.split(",")])
+        return
+
+    def save_zvalue(self, file_name):
+        """
+        :key: 保存模型关于article的变量,包括: arts_Z, Z, artids_list等
+        """
+        with open(file_name, "w", encoding="utf-8") as f_zvalue:
+            for m in range(self.M):
+                out_line = [str(w) + ":" + str(k) for w, k in zip(self.arts_Z[m], self.Z[m])]
+                f_zvalue.write(self.artids_list[m] + "\t" + " ".join(out_line) + "\n")
+        return
+
+    def load_zvalue(self, file_name):
+        """
+        :key: 读取模型的Z变量。和上一个函数相对应
+        """
+        self.arts_Z = []
+        self.artids_list = []
+        self.Z = []
+        with open(file_name, "r", encoding="utf-8") as f_zvalue:
+            for line in f_zvalue:
+                frags = line.strip().split()
+                art_id = frags[0].strip()
+                w_k_list = [value.split(":") for value in frags[1:]]
+                # 添加到类中
+                self.artids_list.append(art_id)
+                self.arts_Z.append([int(item[0]) for item in w_k_list])
+                self.Z.append([int(item[1]) for item in w_k_list])
+        return
+
+    def save_twords(self, file_name):
+        """
+        :key: 保存模型的twords数据,要用到phi的数据
+        """
+        self.calculate_phi()
+        out_num = self.V if self.twords_num > self.V else self.twords_num
+        with open(file_name, "w", encoding="utf-8") as f_twords:
+            for k in range(self.K):
+                words_list = sorted([(w, self.phi[k, w]) for w in range(self.V)], key=lambda x: x[1], reverse=True)
+                f_twords.write("Topic %dth:\n" % k)
+                f_twords.writelines(["\t%s %f\n" % (self.local_bi.get_value(w), p) for w, p in words_list[:out_num]])
+        return
+
+    def load_twords(self, file_name):
+        """
+        :key: 加载模型的twords数据,即先验数据
+        """
+        self.prior_word.clear()
+        topic = -1
+        with open(file_name, "r", encoding="utf-8") as f_twords:
+            for line in f_twords:
+                if line.startswith("Topic"):
+                    topic = int(line.strip()[6:-3])
+                else:
+                    word_id = self.local_bi.get_key(line.strip().split()[0].strip())
+                    self.prior_word[word_id].append(topic)
+        return
+
+    def save_tag(self, file_name):
+        """
+        :key: 输出模型最终给数据打标签的结果,用到theta值
+        """
+        self.calculate_theta()
+        with open(file_name, "w", encoding="utf-8") as f_tag:
+            for m in range(self.M):
+                f_tag.write("%s\t%s\n" % (self.artids_list[m], " ".join([str(item) for item in self.theta[m]])))
+        return
+
+    def save_model(self):
+        """
+        :key: 保存模型数据
+        """
+        name_predix = "%s-%05d" % (self.model_name, self.current_iter)
+
+        # 保存训练结果
+        self.save_parameter(os.path.join(self.dir_path, "%s.%s" % (name_predix, "param")))
+        self.save_wordmap(os.path.join(self.dir_path, "%s.%s" % (name_predix, "wordmap")))
+        self.save_zvalue(os.path.join(self.dir_path, "%s.%s" % (name_predix, "zvalue")))
+
+        #保存额外数据
+        self.save_twords(os.path.join(self.dir_path, "%s.%s" % (name_predix, "twords")))
+        self.save_tag(os.path.join(self.dir_path, "%s.%s" % (name_predix, "tag")))
+        return
+
+    def load_model(self):
+        """
+        :key: 加载模型数据
+        """
+        name_predix = "%s-%05d" % (self.model_name, self.current_iter)
+
+        # 加载训练结果
+        self.load_parameter(os.path.join(self.dir_path, "%s.%s" % (name_predix, "param")))
+        self.load_wordmap(os.path.join(self.dir_path, "%s.%s" % (name_predix, "wordmap")))
+        self.load_zvalue(os.path.join(self.dir_path, "%s.%s" % (name_predix, "zvalue")))
+        return
+
+
+class LdaModel(LdaBase):
+    """
+    LDA模型定义,主要实现训练、继续训练、推断的过程
+    """
+
+    def init_train_model(self, dir_path, model_name, current_iter, iters_num=None, topics_num=10, twords_num=200,
+                         alpha=-1.0, beta=0.01, data_file="", prior_file=""):
+        """
+        :key: 初始化训练模型,根据参数current_iter（是否等于0）决定是初始化新模型,还是加载已有模型
+        :key: 当初始化新模型时,除了prior_file先验文件外,其余所有的参数都需要,且current_iter等于0
+        :key: 当加载已有模型时,只需要dir_path, model_name, current_iter（不等于0）, iters_num, twords_num即可
+        :param iters_num: 可以为整数值或者“auto”
+        """
+        if current_iter == 0:
+            logging.debug("init a new train model")
+
+            # 初始化语料集
+            self.init_corpus_with_file(data_file)
+
+            # 初始化部分变量
+            self.dir_path = dir_path
+            self.model_name = model_name
+            self.current_iter = current_iter
+            self.iters_num = iters_num
+            self.topics_num = topics_num
+            self.K = topics_num
+            self.twords_num = twords_num
+
+            # 初始化alpha和beta
+            self.alpha = numpy.array([alpha if alpha > 0 else (50.0/self.K) for k in range(self.K)])
+            self.beta = numpy.array([beta if beta > 0 else 0.01 for w in range(self.V)])
+
+            # 初始化Z值,以便统计计数
+            self.Z = [[numpy.random.randint(self.K) for n in range(len(self.arts_Z[m]))] for m in range(self.M)]
+        else:
+            logging.debug("init an existed model")
+
+            # 初始化部分变量
+            self.dir_path = dir_path
+            self.model_name = model_name
+            self.current_iter = current_iter
+            self.iters_num = iters_num
+            self.twords_num = twords_num
+
+            # 加载已有模型
+            self.load_model()
+
+        # 初始化统计计数
+        self.init_statistics()
+
+        # 计算alpha和beta的和值
+        self.sum_alpha_beta()
+
+        # 初始化先验知识
+        if prior_file:
+            self.load_twords(prior_file)
+
+        # 返回该模型
+        return self
+
+    def begin_gibbs_sampling_train(self, is_calculate_preplexity=True):
+        """
+        :key: 训练模型,对语料集中的所有数据进行Gibbs抽样,并保存最后的抽样结果
+        """
+        # Gibbs抽样
+        logging.debug("sample iteration start, iters_num: " + str(self.iters_num))
+        self.gibbs_sampling(is_calculate_preplexity)
+        logging.debug("sample iteration finish")
+
+        # 保存模型
+        logging.debug("save model")
+        self.save_model()
+        return
+
+    def init_inference_model(self, train_model):
+        """
+        :key: 初始化推断模型
+        """
+        self.train_model = train_model
+
+        # 初始化变量: 主要用到self.topics_num, self.K
+        self.topics_num = train_model.topics_num
+        self.K = train_model.K
+
+        # 初始化变量self.alpha, self.beta,直接沿用train_model的值
+        self.alpha = train_model.alpha      # K维的float值,训练和推断模型中的K相同,故可以沿用
+        self.beta = train_model.beta        # V维的float值,推断模型中用于计算phi的V值应该是全局的word的数量,故可以沿用
+        self.sum_alpha_beta()               # 计算alpha和beta的和
+
+        # 初始化数据集的self.global_bi
+        self.global_bi = train_model.local_bi
+        return
+
+    def inference_data(self, article_list, iters_num=100, repeat_num=3):
+        """
+        :key: 利用现有模型推断数据
+        :param article_list: 每一行的数据格式为: id[tab]word1 word2 word3......
+        :param iters_num: 每一次迭代的次数
+        :param repeat_num: 重复迭代的次数
+        """
+        # 初始化语料集
+        self.init_corpus_with_articles(article_list)
+
+        # 初始化返回变量
+        return_theta = numpy.zeros((self.M, self.K))
+
+        # 重复抽样
+        for i in range(repeat_num):
+            logging.debug("inference repeat_num: " + str(i+1))
+
+            # 初始化变量
+            self.current_iter = 0
+            self.iters_num = iters_num
+
+            # 初始化Z值,以便统计计数
+            self.Z = [[numpy.random.randint(self.K) for n in range(len(self.arts_Z[m]))] for m in range(self.M)]
+
+            # 初始化统计计数
+            self.init_statistics()
+
+            # 开始推断
+            self.gibbs_sampling(is_calculate_preplexity=False)
+
+            # 计算theta
+            self.calculate_theta()
+            return_theta += self.theta
+
+        # 计算结果,并返回
+        return return_theta / repeat_num
+
+
+if __name__ == "__main__":
+    """
+    测试代码
+    """
+    logging.basicConfig(level=logging.DEBUG, format="%(asctime)s\t%(levelname)s\t%(message)s")
+
+    # train或者inference
+    test_type = "train"
+    # test_type = "inference"
+
+    # 测试新模型
+    if test_type == "train":
+        model = LdaModel()
+        # 由prior_file决定是否带有先验知识
+        model.init_train_model("data/", "model", current_iter=0, iters_num="auto", topics_num=10, data_file="corpus.txt")
+        # model.init_train_model("data/", "model", current_iter=0, iters_num="auto", topics_num=10, data_file="corpus.txt", prior_file="prior.twords")
+        model.begin_gibbs_sampling_train()
+    elif test_type == "inference":
+        model = LdaModel()
+        model.init_inference_model(LdaModel().init_train_model("data/", "model", current_iter=134))
+        data = [
+            "cn	咪咕 漫画 咪咕 漫画 漫画 更名 咪咕 漫画 资源 偷星 国漫 全彩 日漫 实时 在线看 随心所欲 登陆 漫画 资源 黑白 全彩 航海王",
+            "co	aircloud aircloud 硬件 设备 wifi 智能 手要 平板电脑 电脑 存储 aircloud 文件 远程 型号 aircloud 硬件 设备 wifi"
+        ]
+        result = model.inference_data(data)
+
+    # 退出程序
+    exit()

python_weibo.py

@@ -1,5 +1,9 @@
 # _*_ coding: utf-8 _*_
 
+"""
+python_weibo.py by xianhu
+"""
+
 import re
 import rsa
 import ssl