for line in content_reader:
for i in range(num_methods-1):
for j in range(MAX_ROBOTS):
samples[i][j].append(-float(line[i+num_methods*j]))
with open(sys.argv[2], 'rb') as csvfile:
content_reader = csv.reader(csvfile, delimiter=',')
for line in content_reader:
samples[num_methods-1][0].append(-float(line[0]))
samples2 = samples[1:3] + samples[:1] + samples[4:] + samples[3:4]
yticklabels = [str(-x) for x in range(10)]
fig, ax, rects, means= \
graph.draw_bar_chart(samples2, ["Heuristic", "HeuristicImproved", "SingleRobot", "UCT-NSM", "UCT"],
second_level_names=('',),
yticklabels=yticklabels,
xlabel='Different Approaches',
ylabel='Normalized Reward (negated)')
plt.axhline(y=0.0, xmin=0, xmax=2, linewidth=1, color="black")
plt.axis([-rects[0][0].get_width(), (num_methods + 1)*rects[0][0].get_width(), 0, 9])
fig = plt.gcf()
fig.set_size_inches(5.5,5.5)#2.3,4)
plt.savefig('out.png',bbox_inches='tight',pad_inches=0.1,dpi=150)
plt.show()
# pylab.figure()
# for method in range(num_methods):
# pylab.hist(samples[method][4], [1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.5, 3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 20.0, 50.0, 100.0], histtype='step', label=method_names[method])
print 'Reading from: ' + sys.argv[1]
with open(sys.argv[1], 'rb') as csvfile:
content_reader = csv.reader(csvfile, delimiter=',')
for line in content_reader:
if str(line[0]) == 'heuristic':
for i in range(MAX_ROBOTS):
print 2 + 2*i
samples[0][i].append(float(line[2 + 2*i]) / shortest_distances[i])
else:
for i in range(MAX_ROBOTS):
samples[1][i].append(float(line[2 + 2*i]) / shortest_distances[i])
fig, ax, rects, means= \
graph.draw_bar_chart(samples, method_names,
('1 Robot', '2 Robots', '3 Robots', '4 Robots',
'5 Robots'),
title='Average Normalized Distance',
ylabel='Normalized Distance')
sigs = None
if num_methods == 2:
sigs = []
for robots in range(MAX_ROBOTS):
sig = graph.is_significant(samples[0][robots], samples[1][robots])
if sig:
print "For " + str(robots + 1) + " robots, diff is significant"
else:
print "For " + str(robots + 1) + " robots, diff is not significant"
sigs.append(sig)
#attach some text labels
for j in range(MAX_ROBOTS):
samples[i].append([])
first = True
print 'Reading from: ' + sys.argv[2]
with open(sys.argv[2], 'rb') as csvfile:
content_reader = csv.reader(csvfile, delimiter=',')
for line in content_reader:
for i in range(num_methods):
for j in range(MAX_ROBOTS):
samples[i][j].append(float(line[i + num_methods*j]))
fig, ax, rects, means= \
graph.draw_bar_chart(samples[:1] + samples[-1:] + samples[1:3],
method_names[:1] + method_names[-1:] + method_names[1:3],
('1 Robot', '2 Robots', '3 Robots', '4 Robots',
'5 Robots'),
ylabel='Normalized Time Taken',
xlabel='maxRobots')
sigs = []
for robots in range(MAX_ROBOTS):
sig = graph.is_significant(samples[2][robots], samples[1][robots])
if sig:
print "For " + str(robots + 1) + " robots, diff is significant"
else:
print "For " + str(robots + 1) + " robots, diff is not significant"
sigs.append(sig)
print sigs
#attach some text labels
if sigs:
for j in range(MAX_ROBOTS):
samples[i].append([])
first = True
print "Reading from: " + sys.argv[2]
with open(sys.argv[2], "rb") as csvfile:
content_reader = csv.reader(csvfile, delimiter=",")
for line in content_reader:
for i in range(num_methods):
for j in range(MAX_ROBOTS):
samples[i][j].append(float(line[i + num_methods * j]))
fig, ax, rects, means = graph.draw_bar_chart(
samples,
method_names,
("1 Robot", "2 Robots", "3 Robots", "4 Robots", "5 Robots"),
title="Average Normalized Distance",
ylabel="Normalized Distance",
)
sigs = None
if num_methods == 2:
sigs = []
for robots in range(MAX_ROBOTS):
sig = graph.is_significant(samples[0][robots], samples[1][robots])
if sig:
print "For " + str(robots + 1) + " robots, diff is significant"
else:
print "For " + str(robots + 1) + " robots, diff is not significant"
sigs.append(sig)