def analyze_track(filename, manhattan_heuristic):
(states, start_state, t) = extract_track(filename, manhattan_heuristic)
space = structured_space(states, start_state)
results = []
for k in range(1, 4): #len(states)):
space.restart_heuristic()
lrta_costs = []
while not space.have_residuals_converged(0.001):
lrta_costs.append(space.simulate_space_lrta(start_state))
space.restart_heuristic()
lrtak_costs = []
while not space.have_residuals_converged(0.001):
lrtak_costs.append(space.simulate_space_lrtak(k, start_state))
partial_results = (lrta_costs, lrtak_costs, k)
results.append(partial_results)
return results
def analyze_track(filename, manhattan_heuristic):
(states, start_state, t) = extract_track(filename, manhattan_heuristic)
space = structured_space(states, start_state)
results = []
for k in range(1, 4): # len(states)):
space.restart_heuristic()
lrta_costs = []
while not space.have_residuals_converged(0.001):
lrta_costs.append(space.simulate_space_lrta(start_state))
space.restart_heuristic()
lrtak_costs = []
while not space.have_residuals_converged(0.001):
lrtak_costs.append(space.simulate_space_lrtak(k, start_state))
partial_results = (lrta_costs, lrtak_costs, k)
results.append(partial_results)
return results
def onEnter(self):
filename = self.entry.get()
k = self.kentry.get()
### change the one below to support a flag for when to use the other heuristic
(self.states, self.start, self.display) = extract_track(filename, True)
self.focus_state = self.start
self.restart_heuristic()
if k != "":
self.k = int(self.kentry.get()) ### change this one for a second entry box or smthng
self.simulate_space_lrtak()
else:
self.simulate_space_lrta()
def analyze_lrtak_old(logfilename, tracks_path, use_manhattan, max_k,
iterations):
tracks = [f for f in listdir(tracks_path) if isfile(join(tracks_path, f))]
logfile = open(logfilename, "w")
for track in tracks:
### construct track
(states, start, display) = extract_track(
tracks_path + "/" + track, use_manhattan
) ### recall: True as argument means a manhattan distance.
print track
### test for lrtak
for k in range(1, max_k + 1):
experimental_space = structuredSpace(states, start)
total_costs = []
total_convergence_error = [] ### stores the total deviation.
for i in range(iterations):
total_convergence_error.append(
experimental_space.get_total_error())
total_costs.append(
experimental_space.simulate_space_lrta_old(
k, start)) #### lrtak old run
if i % 5 == 0:
print i
error_rate = [total_convergence_error[0]] + [
total_convergence_error[i] - total_convergence_error[i - 1]
for i in range(len(total_convergence_error))
]
### write results
logfile.write(track)
logfile.write("\n")
logfile.write(str(k))
logfile.write("\n")
logfile.write("total_costs")
logfile.write("\n")
logfile.write(str(total_costs))
logfile.write("\n")
logfile.write("error_rate")
logfile.write("\n")
logfile.write(str(error_rate))
logfile.write("\n")
logfile.write("total_convergence_error")
logfile.write("\n")
logfile.write(str(total_convergence_error))
logfile.write("\n")
logfile.close()
def analyze_lrtak_new(logfilename, tracks_path, use_manhattan, max_k, iterations):
tracks = [f for f in listdir(tracks_path) if isfile(join(tracks_path, f)) ]
logfile = open(logfilename, "w")
for track in tracks:
### construct track
(states, start, display) = extract_track(tracks_path + "/" + track, use_manhattan) ### recall: True as argument means a manhattan distance.
print track
### test for lrtak
for k in range(1, max_k+1):
experimental_space = structuredSpace(states, start)
total_costs = []
total_convergence_error = [] ### stores the total deviation.
for i in range(iterations):
total_convergence_error.append(experimental_space.get_total_error())
total_costs.append(experimental_space.simulate_space_lrtak_new(k, start)) #### lrtak new run
if i%5 == 0:
print i
error_rate = [total_convergence_error[0]] + [total_convergence_error[i] - total_convergence_error[i-1] for i in range(len(total_convergence_error))]
### write results
logfile.write(track)
logfile.write("\n")
logfile.write(str(k))
logfile.write("\n")
logfile.write("total_costs")
logfile.write("\n")
logfile.write(str(total_costs))
logfile.write("\n")
logfile.write("error_rate")
logfile.write("\n")
logfile.write(str(error_rate))
logfile.write("\n")
logfile.write("total_convergence_error")
logfile.write("\n")
logfile.write(str(total_convergence_error))
logfile.write("\n")
logfile.close()
