def __init__(self, start_node, goal_node, grid):
self.grid = grid
self.position = start_node.xy
self.start_node = start_node
self.goal_node = goal_node
self.open_list = Heap()
self.start_node.set_val("g", 0)
self.start_node.set_val("f", start_node.h_val)
self.open_list.add(start_node, start_node.f_val)
self.search()
def dijkstra(self, s):
sdist = dict(((n, 1E9) for n in self.graph.nodes()))
sdist[s] = 0
pathprev = {s: None}
marked = {}
pq = [[sdist[n], n] for n in self.graph.nodes()]
Ref = dict([(pq[i][1], i) for i in range(len(pq))])
heap = Heap(pq, Ref)
heap.heapify()
#print pq
#print Ref
while pq:
node_distance, node = heap.heappop()
marked[node] = True
for nei in self.graph.get_neighbours(node):
if nei not in marked:
nei_new_dist = node_distance + self.graph.get_weight(
nei, node)
if sdist[nei] > nei_new_dist:
#print pq
heap.decrease_key(Ref[nei], nei_new_dist)
#print pq
sdist[nei] = nei_new_dist
pathprev[nei] = node
return sdist, pathprev
def __init__(self, start_node, goal_node, grid):
self.grid = grid
self.position = start_node.xy
self.start_node = start_node
self.goal_node = goal_node
self.open_list = Heap()
self.start_node.set_val("g", 0)
self.start_node.set_val("f", start_node.h_val)
self.open_list.add(start_node, start_node.f_val)
self.search()
def run(self):
while True:
if self.need_to_exit:
break
if self.idle:
sleep(TIME_TO_SLEEP_SECS)
continue
self.start_new_search = False
first_step = Solution(self.board, 0, heuristic(self.board), [])
all_options = Heap()
all_options.push(first_step)
all_options.push(first_step)
while len(all_options) > 1 and not self.start_new_search:
# something left at the pool
# and no solution has been found yet
cur_solution = all_options.pop()
if cur_solution.board.is_winning_board():
self.return_results(cur_solution.moves_list)
self.idle = True
break
elif cur_solution.board.is_losing_board():
continue
else:
for n_board, move in cur_solution.board.get_successors():
n_moves_list = cur_solution.moves_list[:] + [move]
n_walked_so_far = cur_solution.distance_so_far + 1
n_heuristic_v = heuristic(n_board)
all_options.push(Solution(n_board, n_walked_so_far, n_heuristic_v, n_moves_list))
# didn't find any solution
self.idle = True
continue
print("ai daemon dies now")
def dijkstra(self, s):
sdist = dict( ((n, 1E9) for n in self.graph.nodes()) )
sdist[s] = 0
pathprev = { s:None }
marked = {}
pq = [ [sdist[n], n] for n in self.graph.nodes() ]
Ref = dict([ (pq[i][1],i) for i in range(len(pq)) ])
heap = Heap(pq, Ref)
heap.heapify()
#print pq
#print Ref
while pq:
node_distance, node = heap.heappop()
marked[node] = True
for nei in self.graph.get_neighbours(node):
if nei not in marked:
nei_new_dist = node_distance + self.graph.get_weight(nei, node)
if sdist[nei] > nei_new_dist:
#print pq
heap.decrease_key(Ref[nei], nei_new_dist)
#print pq
sdist[nei] = nei_new_dist
pathprev[nei] = node
return sdist, pathprev
class SearchAgent(object):
def __init__(self, start_node, goal_node, grid):
self.grid = grid
self.position = start_node.xy
self.start_node = start_node
self.goal_node = goal_node
self.open_list = Heap()
self.start_node.set_val("g", 0)
self.start_node.set_val("f", start_node.h_val)
self.open_list.add(start_node, start_node.f_val)
self.search()
def __repr__(self):
return "agent at - ( " + str(self.position) + " )"
def expand(self, node):
if node == self.goal_node:
print "Success we got to the goal"
self.success()
return
next_nodes = node.get_neighbours()
node.visited = True
for s_next in next_nodes:
new_g_val = node.g_val + s_next.get_cost()
if s_next.g_val == None:
# First time g val setting
s_next.set_val("g", new_g_val)
f_val = new_g_val + s_next.h_val
s_next.set_val("f", f_val)
# Set this node as parent to retrace from goal
s_next.parent = node
self.open_list.add(s_next, f_val)
elif s_next.g_val > new_g_val:
# Old parent to this node is not the shortest path make new one
s_next.set_val("g", new_g_val)
f_val = new_g_val + s_next.h_val
s_next.set_val("f", f_val)
# Change parent
s_next.parent = node
if s_next.i == None:
# Not in the heap yet
self.open_list.add(s_next, f_val)
else:
# update key in heap
self.open_list.update_key(s_next.i, f_val)
# self.grid.root.after(800, self.search)
self.search()
def search(self):
node = self.open_list.pop()
if node.f_val > self.goal_node.f_val:
print "Search Failed, path can not be reached"
self.grid.root.destroy()
return None
self.expand(node)
def success(self):
path_list = []
node = self.goal_node
path_list.append(node)
while True:
# node.fill_box("yellow")
node = node.parent
path_list.append(node)
if node == self.start_node:
break
self.move_agent(path_list)
def move_agent(self, path):
# Move agent along the path provided
# Check for changing cost
if len(path) == 0:
self.grid.root.destroy()
return None
next_node = path.pop()
self.start_node.clear_agent()
self.start_node = next_node
next_node.place_agent()
self.grid.root.after(800, lambda: self.move_agent(path))
def median_maintenance(nums):
min_heap = Heap(min)
max_heap = Heap(max)
for num in nums:
if max_heap.root() is None:
max_heap.add(num)
elif min_heap.root() is None:
min_heap.add(num)
elif num < max_heap.root():
max_heap.add(num)
else:
min_heap.add(num)
if (len(min_heap) - len(max_heap)) > 1:
max_heap.add(min_heap.extract_root())
elif (len(max_heap) - len(min_heap)) > 1:
min_heap.add(max_heap.extract_root())
if len(max_heap) > len(min_heap):
result = max_heap.root()
elif len(min_heap) > len(max_heap):
result = min_heap.root()
else:
result = (max_heap.root() + min_heap.root()) / 2.0
return result
class SearchAgent(object):
def __init__(self, start_node, goal_node, grid):
self.grid = grid
self.position = start_node.xy
self.start_node = start_node
self.goal_node = goal_node
self.open_list = Heap()
self.start_node.set_val("g", 0)
self.start_node.set_val("f", start_node.h_val)
self.open_list.add(start_node, start_node.f_val)
self.search()
def __repr__(self):
return "agent at - ( " + str(self.position) + " )"
def expand(self, node):
if node == self.goal_node:
print "Success we got to the goal"
self.success()
return
next_nodes = node.get_neighbours()
node.visited = True
for s_next in next_nodes:
new_g_val = node.g_val + s_next.get_cost()
if s_next.g_val == None:
# First time g val setting
s_next.set_val("g", new_g_val)
f_val = new_g_val + s_next.h_val
s_next.set_val("f", f_val)
# Set this node as parent to retrace from goal
s_next.parent = node
self.open_list.add(s_next, f_val)
elif s_next.g_val > new_g_val:
# Old parent to this node is not the shortest path make new one
s_next.set_val("g", new_g_val)
f_val = new_g_val + s_next.h_val
s_next.set_val("f", f_val)
# Change parent
s_next.parent = node
if s_next.i == None:
# Not in the heap yet
self.open_list.add(s_next, f_val)
else:
# update key in heap
self.open_list.update_key(s_next.i, f_val)
# self.grid.root.after(800, self.search)
self.search()
def search(self):
node = self.open_list.pop()
if node.f_val > self.goal_node.f_val:
print "Search Failed, path can not be reached"
self.grid.root.destroy()
return None
self.expand(node)
def success(self):
path_list = []
node = self.goal_node
path_list.append(node)
while True:
# node.fill_box("yellow")
node = node.parent
path_list.append(node)
if node == self.start_node:
break
self.move_agent(path_list)
def move_agent(self,path):
# Move agent along the path provided
# Check for changing cost
if len(path) == 0:
self.grid.root.destroy()
return None
next_node = path.pop()
self.start_node.clear_agent()
self.start_node = next_node
next_node.place_agent()
self.grid.root.after(800, lambda:self.move_agent(path))
from utils import Heap heap = Heap([3, 1, 2]) heap.push(1) heap.push(2) a = [i for i in heap] assert (a == [1, 1, 2, 2, 3])
