main.py 文件源码

def variationalInference(docs, d, gamma, phi):
    phisum = 0
    oldphi = np.zeros([K])
    digamma_gamma = np.zeros([K])

    for z in range(0, K):
        gamma[d][z] = alpha + docs[d].wordCount * 1.0 / K
        digamma_gamma[z] = psi(gamma[d][z])
        for w in range(0, len(docs[d].itemIdList)):
            phi[w, z] = 1.0 / K

    for iteration in range(0, iterInference):
        for w in range(0, len(docs[d].itemIdList)):
            phisum = 0
            for z in range(0, K):
                oldphi[z] = phi[w, z]
                phi[w, z] = digamma_gamma[z] + varphi[z, docs[d].itemIdList[w]]
                if z > 0:
                    phisum = math.log(math.exp(phisum) + math.exp(phi[w, z]))
                else:
                    phisum = phi[w, z]
            for z in range(0, K):
                phi[w, z] = math.exp(phi[w, z] - phisum)
                gamma[d][z] =  gamma[d][z] + docs[d].itemCountList[w] * (phi[w, z] - oldphi[z])
                digamma_gamma[z] = psi(gamma[d][z])


# calculate the gamma parameter of new document