计算BTM模型一致性的代码

计算BTM模型一致性的代码如下：

from collections import Counter
import numpy as np

def get_cosine_sim(mat):
    """
    计算词向量的余弦相似度矩阵
    :param mat: 词向量矩阵
    :return: 余弦相似度矩阵
    """
    cos_mat = mat.dot(mat.T)
    norms = np.linalg.norm(mat, axis=1)
    norms[norms == 0] = 1e-8
    norm_mat = np.outer(norms, norms)
    return cos_mat / norm_mat

def get_topic_coherence(beta, doc_term_mat, vocab, top_n=10):
    """
    计算主题一致性
    :param beta: 主题-词分布矩阵
    :param doc_term_mat: 文档-词矩阵
    :param vocab: 词汇表
    :param top_n: 取每个主题前top_n个词
    :return: 主题一致性值
    """
    topic_coherence = []
    for k in range(beta.shape[0]):
        top_words = beta[k].argsort()[:-top_n:-1]
        word_pairs = []
        for i in range(len(top_words)):
            for j in range(i+1, len(top_words)):
                word_pairs.append((vocab[top_words[i]], vocab[top_words[j]]))

        word_counts = Counter(vocab)
        window_size = 3
        co_occur = {}
        for doc in doc_term_mat:
            doc_len = len(doc)
            for i in range(doc_len):
                word_i = vocab[doc[i]]
                if word_i in top_words:
                    for j in range(max(0, i - window_size), min(doc_len, i + window_size + 1)):
                        if j != i:
                            word_j = vocab[doc[j]]
                            if word_j in top_words:
                                if (word_i, word_j) in co_occur:
                                    co_occur[(word_i, word_j)] += 1
                                else:
                                    co_occur[(word_i, word_j)] = 1

        coherence = []
        for word_pair in word_pairs:
            w1, w2 = word_pair
            co_count = co_occur.get((w1, w2), 0)
            w1_count = word_counts[w1]
            w2_count = word_counts[w2]
            coherence.append(np.log((co_count + 1) / w1_count))
            coherence.append(np.log((co_count + 1) / w2_count))

        topic_coherence.append(np.mean(coherence))

    return np.mean(topic_coherence)

# 示例代码
beta = np.array([[0.1, 0.2, 0.3, 0.4],
                 [0.4, 0.3, 0.2, 0.1],
                 [0.2, 0.1, 0.4, 0.3],
                 [0.3, 0.4, 0.1, 0.2]])
doc_term_mat = np.array([[0, 1, 2, 3],
                         [1, 2, 3, 0],
                         [2, 3, 0, 1],
                         [3, 0, 1, 2]])
vocab = ['apple', 'banana', 'cat', 'dog']

cos_sim = get_cosine_sim(beta)
print('主题余弦相似度矩阵：')
print(cos_sim)

topic_coherence = get_topic_coherence(beta, doc_term_mat, vocab)
print('主题一致性值：')
print(topic_coherence)

在这个示例代码中，我们使用了一个由4个主题和4个单词组成的语料库，计算了主题的余弦相似度矩阵和主题一致性值。在实际应用中，我们需要将上面的示例代码替换成我们的BTM模型和语料库，然后就可以计算模型的一致性了。