Implementing Topic Model with Python (numpy)

Try this. Sampling from a joint distribution over topics and sentiment labels just means that the entire T x S matrix should sum to 1.

docs=[[0,1],[0,0],[1,0,1]]D=len(docs)z_d_n=[[0 for _ in xrange(len(d))] for d in docs]l_d_n=[[0 for _ in xrange(len(d))] for d in docs]V=2T=2S=2n_m_j_k=numpy.zeros( (V,T,S) )n_j_k_d=numpy.zeros( (T,S,D) )n_j_k=numpy.zeros( (T,S) )n_k_d=numpy.zeros( (S,D) )n_d=numpy.zeros( (D) )beta=.1alpha=.1gamma=.1for d, doc in enumerate(docs): #d: doc id    for n, m in enumerate(doc): #i: index of the word inside document, m: id of the word in the vocabulary        # j is the topic        j = z_d_n[d][n]        # k is the sentiment        k = l_d_n[d][n]        n_m_j_k[m][j][k] += 1        n_j_k_d[j][k][d] += 1        n_j_k[j][k] += 1        n_k_d[k][d] += 1        n_d[d] += 1 for d, doc in enumerate(docs): #d: doc id    for n, m in enumerate(doc): #i: index of the word inside document, m: id of the word in the vocabulary        # j is the topic        j = z_d_n[d][n]        # k is the sentiment        k = l_d_n[d][n]        n_m_j_k[m][j][k] -= 1        n_j_k_d[j][k][d] -= 1        n_j_k[j][k] -= 1        n_k_d[k][d] -= 1        n_d[d] -= 1         # sample a new topic and sentiment label jointly        # T is the number of topics        # S is the number of sentiments        p_left = (n_m_j_k[m] + beta) / (n_j_k + V * beta) # T x S array        p_mid = (n_j_k_d[:,:,d] + alpha) / numpy.tile(n_k_d[:,d] + T * alpha, (T,1) )        p_right = numpy.tile(n_k_d[:,d] + gamma,(T,1)) /  numpy.tile(n_d[d] + S * gamma,(T,S))        p = p_left * p_mid * p_right        p /= numpy.sum(p)        new_jk = numpy.random.multinomial(1, numpy.reshape(p, (T*S) )).argmax()        j=new_jk/T        k=new_jk%T        z_d_n[d][n]=j        l_d_n[d][n]=k        n_m_j_k[m][j][k] += 1        n_j_k[j][k] += 1        n_k_d[k][d] += 1        n_d[d] += 1