def add_vertex_and_update_sets(G, v_num, v_helpfulness, helpful_set,
set_helpfulness, big_set, partitions):
helpful_set.append({'v_num': v_num, 'helpfulness': v_helpfulness})
set_helpfulness += v_helpfulness
update_helpfulness_of_neighbours(G, v_num, big_set, partitions)
sort_vertices_helpfulness(G, big_set)
return set_helpfulness
def find_vertices_to_balance_normal(G, size_to_balance, vertices_helpfulness,
partitions, adj_partition, cur_partition):
vertices_helpfulness_to_consider, adj_set = find_vertices_adj_to_another_partition(
G, partitions, vertices_helpfulness, adj_partition, cur_partition)
vertices_to_balance = set()
set_size = 0
end = False
while not end and len(vertices_helpfulness_to_consider
) > 0 and set_size < size_to_balance:
vertex_num, vertex_helpfulness = pop_vertex(
vertices_helpfulness_to_consider)
if set_size + 1 > size_to_balance:
end = True
else:
set_size += 1
vertices_to_balance.add(vertex_num)
update_adjacent_vertices(G=G,
v_num=vertex_num,
partitions=partitions,
vertices_helpfulness=vertices_helpfulness,
vertices_helpfulness_to_consider=
vertices_helpfulness_to_consider,
adj_set=adj_set,
helpful_set=vertices_to_balance,
cur_partition=cur_partition)
update_helpfulness_of_neighbours(G, vertex_num,
vertices_helpfulness_to_consider,
partitions)
sort_vertices_helpfulness(G, vertices_helpfulness_to_consider)
return vertices_to_balance, set_size
def search_for_helpful_set(G, vertices_helpfulness, limit, partitions):
helpful_set = set()
set_helpfulness = 0
end = False
while not end and set_helpfulness < limit and len(vertices_helpfulness) > 0:
vertex_num, vertex_helpfulness = pop_vertex(vertices_helpfulness)
if vertex_helpfulness < 0:
end = True
else:
set_helpfulness += vertex_helpfulness
helpful_set.add(vertex_num)
update_helpfulness_of_neighbours(G, vertex_num, vertices_helpfulness, partitions)
sort_vertices_helpfulness(G, vertices_helpfulness)
return helpful_set, set_helpfulness
def phase_1(G, vertices_helpfulness, S_size, S_helpfulness, partitions):
set_helpfulness = 0
helpful_set = set()
end = False
while (not end and len(helpful_set) < S_size
and set_helpfulness < S_helpfulness - 1
and len(vertices_helpfulness) > 0):
vertex_data = vertices_helpfulness.pop()
vertex_helpfulness = vertex_data['helpfulness']
vertex_num = vertex_data['v_num']
if vertex_helpfulness < 0:
end = True
else:
set_helpfulness += vertex_helpfulness
helpful_set.add(vertex_num)
update_helpfulness_of_neighbours(G, vertex_num,
vertices_helpfulness, partitions)
sort_vertices_helpfulness(G, vertices_helpfulness)
return helpful_set, set_helpfulness
def phase_1_improved(G, vertices_helpfulness, vertices_helpfulness_to_consider, adj_set, S_helpfulness, max_,
cur_partition, partitions):
set_helpfulness = 0
set_weight = 0
helpful_set = set()
end = False
while (not end
and len(vertices_helpfulness_to_consider) > 0):
vertex_data = vertices_helpfulness_to_consider.pop()
vertices_helpfulness.remove(vertex_data)
vertex_helpfulness = vertex_data['helpfulness']
vertex_num = vertex_data['v_num']
vertex_weight = G.nodes[vertex_num]['data']['weight']
if (vertex_helpfulness < 0
or vertex_weight + set_weight > max_
or set_helpfulness + vertex_helpfulness >= S_helpfulness - 1):
end = True
vertices_helpfulness_to_consider.insert(0, vertex_data)
else:
set_helpfulness += vertex_helpfulness
set_weight += vertex_weight
helpful_set.add(vertex_num)
update_adjacent_vertices(G=G,
v_num=vertex_num,
partitions=partitions,
vertices_helpfulness=vertices_helpfulness,
vertices_helpfulness_to_consider=vertices_helpfulness_to_consider,
adj_set=adj_set,
helpful_set=helpful_set,
cur_partition=cur_partition)
update_helpfulness_of_neighbours_improved(G=G,
v_num=vertex_num,
vertices_helpfulness=vertices_helpfulness,
vertices_helpfulness_to_consider=vertices_helpfulness_to_consider,
partitions=partitions)
sort_vertices_helpfulness(G, vertices_helpfulness_to_consider)
return helpful_set, set_helpfulness, set_weight
def search_for_helpful_set_improved(G, vertices_helpfulness, limit, s_max, partitions, adj_partition, cur_partition, draw_):
helpful_set = set()
set_weight = 0
set_helpfulness = 0
end = False
vertices_helpfulness_to_consider, adj_set = find_vertices_adj_to_another_partition(G,
partitions,
vertices_helpfulness,
adj_partition,
cur_partition)
while not end and set_helpfulness < limit and len(vertices_helpfulness_to_consider) > 0 and len(
helpful_set) < s_max:
# draw_helpfulness(G, vertices_helpfulness, '')
# draw_helpfulness(G, vertices_helpfulness_to_consider, '')
vertex_num, vertex_helpfulness = pop_vertex_improved(vertices_helpfulness_to_consider, vertices_helpfulness)
if vertex_helpfulness < 0:
end = True
else:
# draw_vertices(G, {vertex_num}, 1, '')
set_helpfulness += vertex_helpfulness
set_weight += G.nodes[vertex_num]['data']['weight']
helpful_set.add(vertex_num)
update_adjacent_vertices(G=G,
v_num=vertex_num,
partitions=partitions,
vertices_helpfulness=vertices_helpfulness,
vertices_helpfulness_to_consider=vertices_helpfulness_to_consider,
adj_set=adj_set,
helpful_set=helpful_set,
cur_partition=cur_partition)
update_helpfulness_of_neighbours_improved(G, vertex_num, vertices_helpfulness,
vertices_helpfulness_to_consider, partitions)
sort_vertices_helpfulness(G, vertices_helpfulness_to_consider)
if draw_:
draw_helpful_set(G, helpful_set=helpful_set, color=3, title='helpfulset')
return helpful_set, set_helpfulness, set_weight
def add_vert_with_helpfulness_gte_zero(G, _2_set, _2_set_helpfulness, _2_set_weight, S_dash_weight,
big_set, max_, partitions):
end = False
while (_2_set_helpfulness < 0
and not end
and contains_diff_values_greater_than_0(big_set)):
v_num, v_helpfulness, v_weight = pop_vertex_improved(G, big_set)
if S_dash_weight + _2_set_weight + v_weight <= max_:
_2_set_helpfulness, _2_set_weight = add_vertex_and_update_sets(G=G,
v_num=v_num,
v_helpfulness=v_helpfulness,
v_weight=v_weight,
helpful_set=_2_set,
set_helpfulness=_2_set_helpfulness,
set_weight=_2_set_weight,
big_set=big_set,
partitions=partitions)
else:
end = True
big_set.insert(0, {'v_num': v_num, 'helpfulness': v_helpfulness})
sort_vertices_helpfulness(G, big_set)
return _2_set_helpfulness, _2_set_weight
def find_vertices_to_balance_weighted(G, weight_to_balance,
vertices_helpfulness, partitions,
adj_partition, cur_partition):
vertices_helpfulness_to_consider, adj_set = find_vertices_adj_to_another_partition(
G, partitions, vertices_helpfulness, adj_partition, cur_partition)
vertices_to_balance = set()
set_weight = 0
end = False
while not end and len(vertices_helpfulness_to_consider
) > 0 and set_weight < weight_to_balance:
vertex_num, vertex_helpfulness = pop_vertex(
vertices_helpfulness_to_consider)
vertex_weight = G.nodes[vertex_num]['data']['weight']
if set_weight + vertex_weight > weight_to_balance:
end = True
else:
set_weight += G.nodes[vertex_num]['data']['weight']
vertices_to_balance.add(vertex_num)
update_adjacent_vertices(G=G,
v_num=vertex_num,
partitions=partitions,
vertices_helpfulness=vertices_helpfulness,
vertices_helpfulness_to_consider=
vertices_helpfulness_to_consider,
adj_set=adj_set,
helpful_set=vertices_to_balance,
cur_partition=cur_partition)
update_helpfulness_of_neighbours(G, vertex_num,
vertices_helpfulness_to_consider,
partitions)
sort_vertices_helpfulness(G, vertices_helpfulness_to_consider)
return vertices_to_balance, set_weight