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
t = trackDisplay(width, length)
#print t.print_board
t.update(start_state)
#print t.print_board
#t.printdisplay()
states = []
for states_pack in index_to_states.values():
states.append(states_pack[0])
states.append(states_pack[1])
states.append(states_pack[2])
space = structured_space(states, start_state)
space.restart_heuristic()
print "number of states"
print len(states)
for s in states:
if len(s.actions) == 0:
print 'NOOOOOOOOOO'
print states_to_indexes[s]
while not space.have_residuals_converged(0.001):
print space.simulate_space_lrta(start_state, t)
space.restart_heuristic()
