def plot_graph(graph=None, path=None, dist=None, best=None, best_dist=None,
save=False, name=None, title=None):
"""
Plot a TSP graph.
Parameters
----------
graph: An XML TSP graph. If None, use the default graph from parse_xml_graph.
path: An ordered list of node indices. If given, plot the path. Otherwise, plot
the underlying graph.
dist: Distances of the paths in path.
best: Empirical best path.
best_dist: Empirical best path distance.
save: If True, saves the graph as name.png. Otherwise, draws the graph.
name: Graph is saved as name.png
title: Caption of the graph.
"""
# Check input parameters
if save:
assert (name is not None), 'If saving graph, must provide name'
# Initialize graph
if graph is not None:
g = parse_xml_graph(graph)
else:
g = parse_xml_graph()
G = nx.from_numpy_matrix(g)
# Plot path, if applicable
edges = list()
edge_colors = list()
if path is not None:
edges.extend([(path[i], path[i+1]) for i in range(len(path)-1)])
edges.append((path[-1], path[0]))
edge_colors.extend(['r' for i in range(len(path))])
if best is not None:
edges.extend([(best[i], best[i+1]) for i in range(len(best)-1)])
edges.append((best[-1], best[0]))
edge_colors.extend(['b' for i in range(len(best))])
if path is None and best is None:
edges = G.edges()
plt.clf()
fig = plt.figure(figsize=(14, 5.5))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
nx.draw_graphviz(G, edgelist=edges, edge_color=edge_colors, with_labels=None,
node_color='k', node_size=100, ax=ax1)
ax1.set_title(title)
ax2.plot(np.arange(1, len(dist)+1), dist, color='r', alpha=0.9, label='Best found path')
ax2.hlines(best_dist, 0, len(dist)+1, color='b', label='Best path')
ax2.set_xlim(1, max(len(dist), 2));
ax2.legend()
if not save:
plt.show()
else:
plt.savefig('temp/{}.png'.format(name))
fig.clf()
from annealing_helper_functions import distance, changepath, simulated_annealing
from parallel_tempering import serial_parallel_tempering, parallel_parallel_tempering
from utils.plotting import get_plots
from utils.timer import Timer
from utils.xml_parse import parse_xml_graph
if __name__ == '__main__':
# Necessary to make multiprocessing work on Windows:
freeze_support()
# Uncomment and/or move this to generate plots
# get_plots(history, graph=None, best=bestpath,
# best_dist=distance(graph, bestpath))
graph = parse_xml_graph('resources/fri26.xml')
# Square matrix
assert graph.shape[0] == graph.shape[1]
size = graph.shape[0]
# Hardcoded best path to validate distance calculations, zero-indexed
bestpath = np.asarray([x-1 for x in [1, 25, 24, 23, 26, 22, 21, 17, 18,
20, 19, 16, 11, 12, 13, 15, 14, 10,
9, 8, 7, 5, 6, 4, 3, 2]])
print "Best path: " + str(bestpath)
print "Best path length: " + str(distance(graph, bestpath)) + "\n"
# Initial values, probably need to be tuned
initial_path = np.random.permutation(size)
print "Initial path: " + str(initial_path)
for i in history:
if i[0] < mindist:
mindist = i[0] # Update lowest value in history
dist_hist.append(mindist)
time_hist.append(i[3] - starttime) # Update elapsed time
return dist_hist, time_hist
if __name__ == "__main__":
# Necessary to make multiprocessing work on Windows:
freeze_support()
# Load graph
graph = parse_xml_graph("resources/fri26.xml")
# Square matrix
assert graph.shape[0] == graph.shape[1]
size = graph.shape[0]
# Hardcoded best path to validate distance calculations, zero-indexed
bestpath = np.asarray(
[x - 1 for x in [1, 25, 24, 23, 26, 22, 21, 17, 18, 20, 19, 16, 11, 12, 13, 15, 14, 10, 9, 8, 7, 5, 6, 4, 3, 2]]
)
optimum = distance(graph, bestpath)
# Initial values for parallel tempering
num_processes = 4
ratio = np.sqrt(5) # Ratio of temperatures between processes
iterr = 50000
