Python remove_result_cluster示例

示例#1

文件： bibauthorid_tortoise.py 项目： AlbertoPeon/invenio

def wedge_and_store(cluster_set):
    bibs = cluster_set.num_all_bibs
    expected = bibs * (bibs - 1) / 2
    bibauthor_print("Start working on %s. Total number of bibs: %d, "
                    "maximum number of comparisons: %d"
                    % (cluster_set.last_name, bibs, expected))

    wedge(cluster_set)
    remove_result_cluster(cluster_set.last_name)
    cluster_set.store()
    return True

示例#2

def wedge_and_store(cluster_set):
    bibs = cluster_set.num_all_bibs
    expected = bibs * (bibs - 1) / 2
    bibauthor_print("Start working on %s. Total number of bibs: %d, "
                    "maximum number of comparisons: %d" %
                    (cluster_set.last_name, bibs, expected))

    wedge(cluster_set)
    remove_result_cluster(cluster_set.last_name)
    cluster_set.store()
    return True

示例#3

文件： bibauthorid_tortoise.py 项目： AlbertoPeon/invenio

def _collect_statistics_lname_coeff(params):
    lname = params[0]
    coeff = params[1]

    clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
    idx = lnames.index(lname)
    cluster = clusters[idx]
    size = sizes[idx]
    bibauthor_print("Found, %s. Total number of bibs: %d." % (lname, size))
    cluster_set = cluster()
    create_matrix(cluster_set, False)

    bibs = cluster_set.num_all_bibs
    expected = bibs * (bibs - 1) / 2
    bibauthor_print("Start working on %s. Total number of bibs: %d, "
                    "maximum number of comparisons: %d"
                    % (cluster_set.last_name, bibs, expected))

    wedge(cluster_set, True, coeff)
    remove_result_cluster(cluster_set.last_name)

示例#4

def _collect_statistics_lname_coeff(params):
    lname = params[0]
    coeff = params[1]

    clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
    idx = lnames.index(lname)
    cluster = clusters[idx]
    size = sizes[idx]
    bibauthor_print("Found, %s. Total number of bibs: %d." % (lname, size))
    cluster_set = cluster()
    create_matrix(cluster_set, False)

    bibs = cluster_set.num_all_bibs
    expected = bibs * (bibs - 1) / 2
    bibauthor_print("Start working on %s. Total number of bibs: %d, "
                    "maximum number of comparisons: %d" %
                    (cluster_set.last_name, bibs, expected))

    wedge(cluster_set, True, coeff)
    remove_result_cluster(cluster_set.last_name)