def reassign_partitions(graph, partitioning, k, min_score):
repartitioning = partitioning.copy()
for loops in range(3):
reassigned = 0
for v1 in range(graph.num_vertices):
votes = [0] * k
for v2 in graph.edge_dict[v1]:
votes[partitioning[v2]] += 1
if votes.index(max(votes)) != repartitioning[v1]:
reassigned += 1
repartitioning[v1] = votes.index(max(votes))
score = score_partitioning(graph, repartitioning)
if score < min_score:
min_score = score
partitioning = repartitioning.copy()
print("Reassigned: ", reassigned)
print(score)
return partitioning
for v in range(graph.num_vertices):
print(v)
print(min_score)
partition = list()
partitioning = np.zeros(graph.num_vertices, dtype=int)
partitioning[v] = 1
partition.append(v)
# search for a one cut
for i in range(2):
additions = list()
for j in range(len(partition)):
additions += graph.edge_dict[partition[j]]
partition += list(additions)
for p in partition:
partitioning[p] = 1
score = score_partitioning(graph, partitioning)
if score < min_score:
min_score = score
min_where = v
final_partitioning = partitioning
print(score_partitioning(graph, final_partitioning))
score
print(min_score)
print(min_where)
final_score = score_partitioning(graph, final_partitioning)
print(final_score)
print(np.where(final_partitioning==1))
# write_file('web_', graph, final_partitioning, 2)
output_num = int(input("Output number:"))
output_name = DIRs[output_num]
filename_output = "outputs/" + output_name
# Input prompt for filename
DIRs = [x for x in os.listdir("data/")]
i = 0
for DIR in DIRs:
print(i, DIR)
i += 1
data_num = int(input("Output number:"))
data_name = DIRs[data_num]
graph, _ = read_file(data_name)
partitioning = []
with open(filename_output, "r") as f:
for line in f.readlines():
if line[0] == "#" or line[0] == " ":
continue
line = line.split(" ")
partitioning.append(int(line[1]))
print("Score:", score_partitioning(graph, partitioning))
return partition
start = datetime.datetime.now()
#
# init_vertex = 0
# p = partitions[init_vertex]
# while p != 0:
# init_vertex += 1
# p = partitions[init_vertex]
for k in range(num_partitions):
init_vertex = 0
while partitioning[init_vertex] != -1:
init_vertex += 1
partition = create_partition(graph, partitioning, init_vertex, 100, 10000)
print(partition)
print(len(set(partition)))
for v in partition:
partitioning[v] = k
for v in range(graph.num_vertices):
if partitioning[v] == -1:
partitioning[v] = random.randint(0, num_partitions - 1)
print(score_partitioning(graph, partitioning))
end = datetime.datetime.now()
print("Crawled in ", (end - start).total_seconds(), "seconds")
from utils import read_file, write_file, Graph, score_partitioning, random_partition
import numpy as np
import random
import datetime
import pickle as p
graph = read_file()
k = 5
partitioning = random_partition(graph, k)
curr_score = score_partitioning(graph, partitioning)
for i in range(k):
print("Num", i, " - ", len([x for x in partitioning if x == i]))
vertex = graph.get_random_vertex()
curr_cluster = int(partitioning[vertex])
alpha = 0.1
jumps = 1000
max_cluster_size = (graph.num_vertices - (graph.num_vertices /
(k * 2))) / (k - 1)
iterations = 999999
for it in range(iterations):
counts = np.zeros((k, graph.num_vertices))
jump = 0
while jump < jumps:
if random.random() > alpha:
vertex = graph.get_connected_vertex(vertex)
from utils import read_file, write_file, Graph, score_partitioning, random_partition
import numpy as np
import random
import datetime
graph = read_file('ca-GrQc.txt')
num_partitions = 2
print("Random Partition Score: ",
score_partitioning(graph, random_partition(graph, num_partitions)))
def one_crawl(graph, v, steps):
vertices_reached = list()
for i in range(steps):
v = graph.get_connected_vertex(v)
vertices_reached.append(v)
return vertices_reached
def init_vertices(graph, init_vert=0):
visit_count = np.zeros(graph.num_vertices)
init_verts = list()
init_verts.append(init_vert)
for k in range(num_partitions - 1):
print(k + 1, "vertices initialized out of ", num_partitions)
for i in range(10000):
verts = one_crawl(graph, init_vert, 40)
for v in verts:
visit_count[v] += 1