def gen_path(start, end, matrix, fn=val.avoid_obstacles): costs = val.value_update(start, end, matrix, fn) return plan_path(costs, start, end)
minadjv = costs[loc] for adj in adjlst: if minadjv > costs[adj]: minadjv = costs[adj] loc = adj # print loc if loc == oldloc: break pathlist.append(loc) return pathlist def gen_path(start, end, matrix, fn=val.avoid_obstacles): costs = val.value_update(start, end, matrix, fn) return plan_path(costs, start, end) matrix = np.zeros((12,12)) matrix[(0,8)] = 1 matrix[(0,9)] = 1 # matrix[(3,2)] = 1 # matrix[(3,3)] = 1 # matrix[(3,1)] = 1 # matrix[(3,0)] = 1 # matrix[(3,4)] = 1 # matrix[(1,3)] = 1 # matrix[(0,3)] = 1 costs = val.value_update((1,5), (1,11), matrix, val.avoid_obstacles) print costs print plan_path(costs, (1,5), (1,11))
