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
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
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)
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)
