def greedy_algorithm(G):
CG = construct_cluster_graph(G)
CG_copy = CG.copy()
print "done constructing"
valid_nodes = CG.nodes()
selected_clusters = []
pre_penalty = 0
while len(valid_nodes) != 0:
# node_selected = find_max_penalty(valid_nodes, CG)
node_selected = find_min_weighted_degree(valid_nodes, CG)
pre_penalty += CG.node[node_selected]['penalty']
selected_clusters.append(node_selected)
list_edges = CG.edges(node_selected, False)
nodes_to_remove = set()
nodes_to_remove.add(node_selected)
for edge in list_edges:
nodes_to_remove.add(edge[0])
nodes_to_remove.add(edge[1])
for node in nodes_to_remove:
valid_nodes.remove(node)
CG.remove_node(node)
list_cycles = []
for cluster in selected_clusters:
list_cycles.append(CG_copy.node[cluster]['nodes'])
penalty = find_total_penalty(G) - pre_penalty
output_string = format_output_cycles(list_cycles)
print(find_total_penalty(G), pre_penalty)
return [output_string, penalty]
def algorithm(graph):
CG = construct_cluster_graph(graph)
valid_nodes = CG.nodes()
adults = []
start = valid_nodes.pop()
selected_nodes = [start]
pre_penalty = 0
cycles = []
current_cycle = []
while selected_nodes != []:
node = selected_nodes.pop(0)
edges = CG.edges(node, False)
if node in current_cycle:
cycles.append(current_cycle)
for n in current_cycle:
pre_penalty += n['penalty']
valid_nodes.remove(n)
current_cycle = []
current_cycle.append(node)
for inner, outer in edges:
neighbor = CG.node[outer]
child_cycles = []
if neighbor['penalty'] == 2:
child_cycles.append(neighbor)
selected_nodes.append(neighbor)
else:
adults.append(neighbor)
if child_cycles == []:
for adult in adults:
selected_nodes.append(adult)
child_cycles = []
adults = []
penalty = find_total_penalty(G) - pre_penalty
output_string = format_output_cycles(cycles)
return [output_string, penalty]
def algorithm(graph):
CG = construct_cluster_graph(graph)
valid_nodes = CG.nodes()
adults = []
start = valid_nodes.pop()
selected_nodes = [start]
pre_penalty = 0
cycles = []
current_cycle = []
while selected_nodes != []:
node = selected_nodes.pop(0)
edges = CG.edges(node, False)
if node in current_cycle:
cycles.append(current_cycle)
for n in current_cycle:
pre_penalty += n['penalty']
valid_nodes.remove(n)
current_cycle = []
current_cycle.append(node)
for inner, outer in edges:
neighbor = CG.node[outer]
child_cycles = []
if neighbor['penalty'] == 2:
child_cycles.append(neighbor)
selected_nodes.append(neighbor)
else:
adults.append(neighbor)
if child_cycles == []:
for adult in adults:
selected_nodes.append(adult)
child_cycles = []
adults = []
penalty = find_total_penalty(G) - pre_penalty
output_string = format_output_cycles(cycles)
return [output_string, penalty]
def greedy_algorithm(G): CG = construct_cluster_graph(G) CG_copy = CG.copy() print "done constructing" valid_nodes = CG.nodes() selected_clusters = [] pre_penalty = 0 # cg_penalty = 0 while len(valid_nodes) != 0: # node_selected = find_max_penalty(valid_nodes, CG) node_selected = find_min_weighted_degree(valid_nodes, CG) # node_selected = find_min_degree(valid_nodes, CG) # node_selected = find_random(valid_nodes, CG) pre_penalty += CG_copy.node[node_selected]['penalty'] selected_clusters.append(node_selected) list_edges = CG.edges(node_selected, False) nodes_to_remove = set() nodes_to_remove.add(node_selected) # print len(list_edges) for edge in list_edges: assert (edge[0] == node_selected) nodes_to_remove.add(edge[1]) for node in nodes_to_remove: valid_nodes.remove(node) # cg_penalty += CG_copy.node[node]['penalty'] CG.remove_node(node) list_cycles = [] for cluster in selected_clusters: list_cycles.append(CG_copy.node[cluster]['nodes']) penalty = find_total_penalty(G) - pre_penalty output_string = format_output_cycles(list_cycles) # print(find_total_penalty(G), pre_penalty, cg_penalty, penalty) print [output_string, penalty] return [output_string, penalty]
def random_algorithm(input_graph):
G = input_graph.copy()
cycle_list = []
pre_penalty = 0
while G.nodes():
try:
edge_cycle = nx.find_cycle(G)
vertex_cycle = []
for edge in edge_cycle:
vertex_cycle.append(edge[0])
pre_penalty += G.node[edge[0]]['penalty']
G.remove_node(edge[0])
cycle_list.append(vertex_cycle)
except:
print("No cycle found")
break
penalty = find_total_penalty(input_graph) - pre_penalty
formatted_cycle_list = format_output_cycles(cycle_list)
return [formatted_cycle_list, penalty]
def random_algorithm(input_graph):
G = input_graph.copy()
cycle_list = []
pre_penalty = 0
while G.nodes():
try:
edge_cycle = nx.find_cycle(G)
vertex_cycle = []
for edge in edge_cycle:
vertex_cycle.append(edge[0])
pre_penalty += G.node[edge[0]]['penalty']
G.remove_node(edge[0])
cycle_list.append(vertex_cycle)
except:
print("No cycle found")
break
penalty = find_total_penalty(input_graph) - pre_penalty
formatted_cycle_list = format_output_cycles(cycle_list)
return [formatted_cycle_list, penalty]
def random_algorithm_3(input_graph):
G = input_graph.copy()
cycle_list = []
pre_penalty = 0
random_result = build_randomized_graph(input_graph)
randG = random_result[0]
nodes_dict = random_result[1]
while randG.nodes():
source_node = randG.nodes()[0]
cycle = find_cycle(randG, source_node)
if cycle == []:
randG.remove_node(source_node)
else:
real_cycle = []
for node in cycle:
real_cycle.append(nodes_dict[node])
cycle_list.append(real_cycle)
for node in cycle:
real_node = nodes_dict[node]
pre_penalty += G.node[real_node]['penalty']
randG.remove_node(node)
penalty = find_total_penalty(input_graph) - pre_penalty
formatted_cycle_list = format_output_cycles(cycle_list)
return [formatted_cycle_list, penalty]
def random_algorithm_4(input_graph): G = input_graph.copy() cycle_list = [] pre_penalty = 0 random_result = build_randomized_graph(input_graph) randG = random_result[0] nodes_dict = random_result[1] while randG.nodes(): source_node = randG.nodes()[0] cycle = find_random_length_cycle(randG, source_node) if cycle == []: randG.remove_node(source_node) else: real_cycle = [] for node in cycle: real_cycle.append(nodes_dict[node]) cycle_list.append(real_cycle) for node in cycle: real_node = nodes_dict[node] pre_penalty += G.node[real_node]['penalty'] randG.remove_node(node) penalty = find_total_penalty(input_graph) - pre_penalty formatted_cycle_list = format_output_cycles(cycle_list) return [formatted_cycle_list, penalty]
from utils import find_total_penalty
#file_a = "CURRENT BEST SOLUTIONS"
#file_b = "SOLUTION_RECORDS_TONY.txt"
#combine(file_a, file_b)
#write_condensed_solutions("COMBINED SOLUTIONS")
"""G = create_graph("instances/12.in")
contains = False
edge_to_check = (119, 57)
for edge in G.edges():
if edge_to_check == edge:
contains = True
print(contains)
optimal_sol_list = do_not_test_set("COMBINED SOLUTIONS")
out_file = open("Sub-Optimal Instances", "w")
read_file = open("Condensed Solution Records", "r")
read_data = read_file.readlines()
for element in range(1, 493):
if element not in optimal_sol_list:
line_data = read_data[element - 1]
pre_penalty = line_data.split(":")
penalty = pre_penalty[1].strip()
out_file.write(str(element) + ": " + penalty + "\n") """
G = create_graph("instances/8.in")
penalty = find_total_penalty(G)
print(penalty)
#CG = construct_cluster_graph(G)
#print_cluster_graph_cycles(CG)