def tryP0(areaPoly=None, t=60):
fig = plt.figure()
lst = [0.5, 0.6, 0.7, 0.8]
data = []
best = None
globalOrigin = (596250, 6727996)
Rini = gr.SqGridGraph(areaPoly=areaPoly, globalOrigin=globalOrigin)
R = copy.deepcopy(Rini)
ref = simplified_bruteForce(R)
print "cost:", cf.totalCost(ref)
for index, p0 in enumerate(lst):
R = copy.deepcopy(Rini)
R, tries = stochastic_several(R, t=t / float(len(lst)), probListGen=ProbListGen(p0, 15))
if not best or R.cost < best.cost:
best = R
data.append(tries)
mi = min([min(tries) for tries in data] + [ref.cost])
ma = max([max(tries) for tries in data] + [ref.cost])
for index, p0 in enumerate(lst):
ax = fig.add_subplot("%d1%d" % (len(lst), index + 1))
ax.plot(data[index], "o")
ax.plot([0, len(data[index])], [ref.cost, ref.cost], "r") # reference
ax.set_title("p0=%.1f" % p0)
ax.set_ylabel("cost")
ax.set_ylim(mi - 10, ma + 10)
ax.set_xticklabels([])
fig = plt.figure()
ax = fig.add_subplot(111)
print "best cost:", best.cost
best.draw(ax=ax)
def best(areaPoly=None, t=60):
"""
time - how long we should look for a better solution
"""
globalOrigin = 596250, 6727996
R = gr.SqGridGraph(areaPoly=areaPoly, globalOrigin=globalOrigin)
# R=simplified_bruteForce(R,aCap=0.2, anim=True)
R, tries = stochastic_several(R, t=t)
fig = plt.figure()
ax = fig.add_subplot(111)
print "best cost:", R.cost
R.draw(ax=ax)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(tries, "o")
ax.set_ylim(0, max(tries) + 100)