utils.py 文件源码

def get_desc_len_from_data_uni(n, n_edges, k, edgelist, mb):
    '''
        Description length difference to a randomized instance, via PRL 110, 148701 (2013).
    '''
    assert type(edgelist) is list, "[ERROR] the type of the input parameter (edgelist) should be a list"
    assert type(mb) is list, "[ERROR] the type of the input parameter (mb) should be a list"
    # First, let's compute the m_e_rs from the edgelist and mb
    m_e_rs = np.zeros((max(mb) + 1, max(mb) + 1))
    for i in edgelist:
        # Please do check the index convention of the edgelist
        source_group = int(mb[int(i[0])])
        target_group = int(mb[int(i[1])])
        m_e_rs[source_group][target_group] += 1
        m_e_rs[target_group][source_group] += 1

    # then, we compute the profile likelihood from the m_e_rs
    italic_i = 0.
    m_e_r = np.sum(m_e_rs, axis=1)
    num_edges = m_e_r.sum() / 2.
    for ind, e_val in enumerate(np.nditer(m_e_rs)):
        ind_i = int(math.floor(ind / (m_e_rs.shape[0])))
        ind_j = ind % (m_e_rs.shape[0])
        if e_val != 0.0:
            italic_i += e_val / 2. / num_edges * math.log(
                e_val / m_e_r[ind_i] / m_e_r[ind_j] * 2 * num_edges
            )
    assert m_e_rs.shape[0] == k, "[ERROR] m_e_rs dimension (={}) is not equal to k (={})!".format(
        m_e_rs.shape[0], k
    )

    # finally, we compute the description length
    desc_len_b = (n * math.log(k) - n_edges * italic_i) / n_edges
    x = float(k * (k + 1)) / 2. / n_edges
    desc_len_b += (1 + x) * math.log(1 + x) - x * math.log(x)
    desc_len_b -= (1 + 1 / n_edges) * math.log(1 + 1 / n_edges) - (1 / n_edges) * math.log(1 / n_edges)
    return desc_len_b