def test_pop(self):
# pop selected item - return value
pq = minpq(A=5, B=8, C=1)
value = pq.pop('B')
self.assertEqual(value, 8)
pq.pop('A')
pq.pop('C')
self.assertRaises(KeyError, pq.pop, 'A')
self.assertRaises(KeyError, pq.pop, 'does_not_exist')
# no args and empty - throws
self.assertRaises(KeyError, pq.pop) # pq is now empty
# no args - return top key
pq = minpq(A=5, B=8, C=1)
self.assertEqual(pq.pop(), 'C')
def test_pop(self):
# pop selected item - return value
pq = minpq(A=5, B=8, C=1)
value = pq.pop('B')
self.assertEqual(value, 8)
pq.pop('A')
pq.pop('C')
self.assertRaises(KeyError, pq.pop, 'A')
self.assertRaises(KeyError, pq.pop, 'does_not_exist')
# no args and empty - throws
self.assertRaises(KeyError, pq.pop) # pq is now empty
# no args - return top key
pq = minpq(A=5, B=8, C=1)
self.assertEqual(pq.pop(), 'C')
def dijkstra(self, vi0, dist_func):
"""Calculate shortest path from |vi0| to all other verts.
dist_func: callable that takes two adjacent vertex indices and
returns a number indicating the distance between
them.
Returns a list of |DijkstraResult|.
Adapted from:
http://pqdict.readthedocs.io/en/latest/examples.html
"""
result = []
queue = minpq()
for vi1 in range(len(self._verts)):
queue[vi1] = float('inf')
result.append(self.DijkstraResult())
queue[vi0] = 0
for vi2, min_dist in queue.popitems():
result[vi2].dist = min_dist
for vi3 in self.adj_vert_vert(vi2):
if vi3 in queue:
new_score = result[vi2].dist + dist_func(vi2, vi3)
if new_score < queue[vi3]:
# pqdict update is O(log n)
queue[vi3] = new_score
result[vi3].prev = vi2
return result
def dijkstra(self, vi0, dist_func):
"""Calculate shortest path from |vi0| to all other verts.
dist_func: callable that takes two adjacent vertex indices and
returns a number indicating the distance between
them.
Returns a list of |DijkstraResult|.
Adapted from:
http://pqdict.readthedocs.io/en/latest/examples.html
"""
result = []
queue = minpq()
for vi1 in range(len(self._verts)):
queue[vi1] = float('inf')
result.append(self.DijkstraResult())
queue[vi0] = 0
for vi2, min_dist in queue.popitems():
result[vi2].dist = min_dist
for vi3 in self.adj_vert_vert(vi2):
if vi3 in queue:
new_score = result[vi2].dist + dist_func(vi2, vi3)
if new_score < queue[vi3]:
# pqdict update is O(log n)
queue[vi3] = new_score
result[vi3].prev = vi2
return result
def lcv_calculation(self, cell):
row_num = cell.row
col_num = cell.column
block_num = self.get_block_num(row_num, col_num)
cell.value_queue = minpq()
for value in cell.domain:
value_constraint = 0
for other_cell in self.rows[row_num].cells:
if other_cell != cell:
if value in other_cell.value_queue:
value_constraint += 1
for other_cell in self.columns[col_num].cells:
if other_cell != cell:
if value in other_cell.value_queue:
value_constraint += 1
for other_cell in self.blocks[block_num].cells:
if other_cell != cell:
if value in other_cell.value_queue:
value_constraint += 1
if value in cell.value_queue:
cell.value_queue.updateitem(value, (value_constraint, cell.get_order_val(value)))
else:
cell.value_queue.additem(value, (value_constraint, cell.get_order_val(value)))
def test_repair(self):
mutable_value = [3]
pq = minpq(A=[1], B=[2], C=mutable_value)
self.assertEqual(pq[pq.top()], [1])
mutable_value[0] = 0
self.assertEqual(pq[pq.top()], [1])
pq.heapify('C')
self.assertEqual(pq[pq.top()], [0])
def test_swap_priority(self):
pq = minpq(A=5, B=8, C=1)
pq.swap_priority('A', 'C')
self._check_index(pq)
self.assertEqual(pq['A'], 1)
self.assertEqual(pq['C'], 5)
self.assertEqual(pq.top(), 'A')
self.assertRaises(KeyError, pq.swap_priority, 'A', 'Z')
def test_repair():
mutable_value = [3]
pq = minpq(A=[1], B=[2], C=mutable_value)
assert pq[pq.top()] == [1]
mutable_value[0] = 0
assert pq[pq.top()] == [1]
pq.heapify("C")
assert pq[pq.top()] == [0]
def test_swap_priority(self):
pq = minpq(A=5, B=8, C=1)
pq.swap_priority('A', 'C')
self._check_index(pq)
self.assertEqual(pq['A'], 1)
self.assertEqual(pq['C'], 5)
self.assertEqual(pq.top(), 'A')
self.assertRaises(KeyError, pq.swap_priority, 'A', 'Z')
def test_repair(self):
mutable_value = [3]
pq = minpq(A=[1], B=[2], C=mutable_value)
self.assertEqual(pq[pq.top()], [1])
mutable_value[0] = 0
self.assertEqual(pq[pq.top()], [1])
pq.heapify('C')
self.assertEqual(pq[pq.top()], [0])
def test_pop():
# pop selected item - return value
pq = minpq(A=5, B=8, C=1)
value = pq.pop("B")
assert value == 8
pq.pop("A")
pq.pop("C")
with pytest.raises(KeyError):
pq.pop("A")
with pytest.raises(KeyError):
pq.pop("does_not_exist")
# no args and empty - throws
with pytest.raises(KeyError):
pq.pop() # pq is now empty
# no args - return top key
pq = minpq(A=5, B=8, C=1)
assert pq.pop() == "C"
def test_swap_priority():
pq = minpq(A=5, B=8, C=1)
pq.swap_priority("A", "C")
_check_index(pq)
assert pq["A"] == 1
assert pq["C"] == 5
assert pq.top() == "A"
with pytest.raises(KeyError):
pq.swap_priority("A", "Z")
def test_replace_key(self):
pq = minpq(A=5, B=8, C=1)
pq.replace_key('A', 'Alice')
pq.replace_key('B', 'Bob')
self._check_index(pq)
self.assertEqual(pq['Alice'], 5)
self.assertEqual(pq['Bob'], 8)
self.assertRaises(KeyError, pq.__getitem__, 'A')
self.assertRaises(KeyError, pq.__getitem__, 'B')
self.assertRaises(KeyError, pq.replace_key, 'C', 'Bob')
def test_replace_key(self):
pq = minpq(A=5, B=8, C=1)
pq.replace_key('A', 'Alice')
pq.replace_key('B', 'Bob')
self._check_index(pq)
self.assertEqual(pq['Alice'], 5)
self.assertEqual(pq['Bob'], 8)
self.assertRaises(KeyError, pq.__getitem__, 'A')
self.assertRaises(KeyError, pq.__getitem__, 'B')
self.assertRaises(KeyError, pq.replace_key, 'C', 'Bob')
def test_constructor(self):
# sequence of pairs
pq0 = pqdict([('A', 5), ('B', 8), ('C', 7), ('D', 3), ('E', 9),
('F', 12), ('G', 1)])
pq1 = pqdict(
zip(['A', 'B', 'C', 'D', 'E', 'F', 'G'], [5, 8, 7, 3, 9, 12, 1]))
# dictionary
pq2 = pqdict({'A': 5, 'B': 8, 'C': 7, 'D': 3, 'E': 9, 'F': 12, 'G': 1})
# keyword arguments
pq3 = minpq(A=5, B=8, C=7, D=3, E=9, F=12, G=1)
self.assertTrue(pq0 == pq1 == pq2 == pq3)
def test_constructor(self):
# sequence of pairs
pq0 = pqdict(
[('A',5), ('B',8), ('C',7), ('D',3), ('E',9), ('F',12), ('G',1)])
pq1 = pqdict(
zip(['A', 'B', 'C', 'D', 'E', 'F', 'G'], [5, 8, 7, 3, 9, 12, 1]))
# dictionary
pq2 = pqdict({'A': 5, 'B': 8, 'C': 7, 'D': 3, 'E': 9, 'F': 12, 'G': 1})
# keyword arguments
pq3 = minpq(A=5, B=8, C=7, D=3, E=9, F=12, G=1)
self.assertTrue(pq0 == pq1 == pq2 == pq3)
def test_constructor():
# sequence of pairs
pq0 = pqdict([("A", 5), ("B", 8), ("C", 7), ("D", 3), ("E", 9), ("F", 12),
("G", 1)])
pq1 = pqdict(
zip(["A", "B", "C", "D", "E", "F", "G"], [5, 8, 7, 3, 9, 12, 1]))
# dictionary
pq2 = pqdict({"A": 5, "B": 8, "C": 7, "D": 3, "E": 9, "F": 12, "G": 1})
# keyword arguments
pq3 = minpq(A=5, B=8, C=7, D=3, E=9, F=12, G=1)
assert pq0 == pq1 == pq2 == pq3
def test_replace_key():
pq = minpq(A=5, B=8, C=1)
pq.replace_key("A", "Alice")
pq.replace_key("B", "Bob")
_check_index(pq)
assert pq["Alice"] == 5
assert pq["Bob"] == 8
with pytest.raises(KeyError):
pq.__getitem__("A")
with pytest.raises(KeyError):
pq.__getitem__("B")
with pytest.raises(KeyError):
pq.replace_key("C", "Bob")
def AStarWithPQ(_board: BoardStateManager):
start = Node(_board.initial_vehicles, None, None)
visited = set()
pq = minpq()
# Initialize pqdict with start node
# Assign it score of 0 (which is what start.f is initialized to)
pq.additem(start, (0, start))
while pq:
curr_node, score_tup = pq.popitem()
score = score_tup[0]
vehicles = curr_node.vehicles
if goal_state_reached(vehicles[0], _board):
return compute_path(curr_node)
# So we never visit this node again
visited.add(curr_node)
valid_actions = _board.compute_valid_actions(vehicles)
successor_nodes = curr_node.generate_successors(valid_actions)
for successor in successor_nodes:
# No need to re-process visited nodes
if successor in visited:
continue
successor.g = curr_node.g + 1
h = 0
#h = compute_heuristic(successor.vehicles, _board)
successor.f = successor.g + h
# Update scores as needed
# successor is a temporary Node state, not the same as
# the one in the pq
if successor in pq:
if successor.f < pq[successor][0]:
pq[successor][1].f = successor.f
pq[successor][1].g = successor.g
pq.updateitem(successor, (successor.f, pq[successor][1]))
else:
pq.additem(successor, (successor.f, successor))
return None
def __init__(self, domain, row, column, cell_number, input_tokens, value=0):
self.value = value
self.domain = [value] if value != 0 else domain
self.set = True if value != 0 else False
self.row = row
self.column = column
self.cell_number = cell_number
self.degree = 0
self.input_tokens = input_tokens
self.row_degree = 0
self.column_degree = 0
self.block_degree = 0
self.value_queue = minpq()
self.value_dict = dict()
self.values_removed = []
self.initialize_value_queue()
def solver(maze):
r, c = maze.shape
start, end = get_start_end(maze)
directions = [
np.array([1, 0]),
np.array([0, 1]),
np.array([-1, 0]),
np.array([0, -1])
]
path = []
path_pre = {start: None}
cost = {start: 0}
frontier_queue = pqdict.minpq({0: [start]})
while frontier_queue:
priority = frontier_queue.top()
frontier = frontier_queue[priority][0]
del frontier_queue[priority][0]
if not frontier_queue[priority]:
del frontier_queue[priority]
if frontier == end:
break
for dir_neighbor in directions:
next_node = tuple(frontier + dir_neighbor)
next_cost = cost[frontier] + 1
if maze[next_node] in [0, 3, 4] and (next_node not in cost or
next_cost < cost[next_node]):
cost[next_node] = next_cost
path_pre[next_node] = frontier
heuristic = next_cost + l1_distance(next_node, end)
# print(next_node)
if heuristic in frontier_queue:
frontier_queue[heuristic].append(next_node)
else:
frontier_queue[heuristic] = [next_node]
node = end
while node is not None:
path.insert(0, node)
node = path_pre[node]
return path
def dijkstra_walk(start_id, end_id):
distTo = {}
parent = {}
pq = minpq()
for node in nodeDict:
distTo[node] = float("inf")
parent[node] = None
distTo[start_id] = 0
pq.additem(start_id, 0)
while len(pq) > 0:
curr_id, curr_dist = pq.popitem()
curr_lat, curr_lng = nodeDict[curr_id]['lat'], nodeDict[curr_id]['lng']
if curr_dist == distTo[curr_id]:
if curr_id == end_id:
break
for neigh in nodeDict[curr_id]['neighbours']:
neigh_lat, neigh_lng = nodeDict[neigh]['lat'], nodeDict[neigh][
'lng']
dist_apart = walk_time(
haversine(curr_lat, curr_lng, neigh_lat, neigh_lng))
if distTo[neigh] > distTo[curr_id] + dist_apart:
distTo[neigh] = distTo[curr_id] + dist_apart
parent[neigh] = curr_id
try:
pq.additem(neigh, distTo[neigh])
except:
pq.updateitem(neigh, distTo[neigh])
totalDur = distTo[end_id]
path_id = []
path_id.append(end_id)
curr_id = end_id
while curr_id != start_id:
curr_id = parent[curr_id]
path_id.append(curr_id)
path_id.reverse()
return path_id, totalDur
def __init__(self, N, P, Q, board_values, input_tokens):
self.n = N
self.p = P
self.q = Q
self.rows = []
self.columns = []
self.blocks = []
self.board_values = board_values
self.num_cells = self.n * self. n
self.cells_solved = 0
self.solved = False
self.start_time = None
self.time_out_limit = None
self.input_tokens = input_tokens
self.nodes_created = 0
self.times_backtracked = 0
# self.cell_queue = que.PriorityQueue()
self.test_queue = minpq()
self.domain = self.get_domain()
self.check_board_params()
self.initialize_board()
def dijkstra(graph, source, target=None):
dist = {} #lengths of the shortest paths to each node
pred = {} #predecessor node in each shortest path
pq = minpq()
for node in graph:
if node == source:
pq[node] = 0
else:
pq[node] = float('inf')
for node, min_dist in pq.popitems():
dist[node] = min_dist
if node == target:
break
for neighbor in graph[node]:
if neighbor in pq:
new_score = dist[node] + graph[node][neighbor]
if new_score < pq[neighbor]:
# Updating the score of a node is O(log n) using pqdict.
pq[neighbor] = new_score
pred[neighbor] = node
return dist, pred
def dijkstra_bus(start_id, end_id):
distTo = {}
parent = {}
pq = minpq()
for stop in stopDict:
distTo[stop] = float("inf")
parent[stop] = None
distTo[start_id] = 0
pq.additem(start_id, 0)
while len(pq) > 0:
curr_id, curr_dist = pq.popitem()
if curr_dist == distTo[curr_id]:
if curr_id == end_id:
break
for neigh in stopDict[curr_id]['neighbours']:
if distTo[neigh] > distTo[curr_id] + routeDict[
(curr_id, neigh)]['dur']:
distTo[neigh] = distTo[curr_id] + routeDict[(curr_id,
neigh)]['dur']
parent[neigh] = curr_id
try:
pq.additem(neigh, distTo[neigh])
except:
pq.updateitem(neigh, distTo[neigh])
path_id = []
path_id.append(end_id)
curr_id = end_id
while curr_id != start_id:
curr_id = parent[curr_id]
path_id.append(curr_id)
path_id.reverse()
bus_path = getBus(path_id, start_id, end_id)
return path_id, bus_path
def distanceCalcul(self):
length = {}
end = '256176'
pq = minpq()
for node in nx.node_connected_component(self.graph.G, self.author):
if node == self.author:
pq[node] = 0
else:
pq[node] = float('inf')
for node, min_dist in pq.popitems():
length[node] = min_dist
if node == end:
break
for neighbor in self.graph.G.neighbors(node):
if neighbor in pq:
new_length = length[node] + self.graph.G[node][neighbor][
'weight']
if new_length < pq[neighbor]:
pq[neighbor] = new_length
return length[end]
from ways.tools import compute_distance
import numpy as np
from pqdict import minpq
# Read files
roads = load_map_from_csv(Consts.getDataFilePath("israel.csv"))
prob = BusProblem.load(Consts.getDataFilePath("TLV_5.in"))
# TODO - Fix the missing parts in the following code
# Print details of a random order
order = prob.orders[np.random.choice(np.arange(len(prob.orders)))]
# your code goes here
myHeap = minpq()
myHeap[1] = 3
myHeap[4] = 2
print("{}".format((myHeap.popitem())[1]))
print("{}".format((myHeap.popitem())[1]))
print("One of the orders is from junction #{} at ({}, {}) to #{} at ({}, {})".
format(order[0], roads[order[0]].lat, roads[order[0]].lon, order[1],
roads[order[1]].lat, roads[order[1]].lon))
print(
"A lower bound on the distance we need to drive for this order is: {:.2f}km"
.format(
compute_distance(roads[order[0]].coordinates,
roads[order[1]].coordinates) / 1000))
def a_star(estado_inicial): front = minpq() return generic_search(estado_inicial, front, manhattan)
def test_popitem(self):
pq = minpq(A=5, B=8, C=1)
# pop top item
key, value = pq.popitem()
self.assertEqual(key,'C')
self.assertEqual(value,1)
def test_pushpopitem(self):
pq = minpq(A=5, B=8, C=1)
self.assertEqual(pq.pushpopitem('D', 10), ('C', 1))
self.assertEqual(pq.pushpopitem('E', 5), ('E', 5))
self.assertRaises(KeyError, pq.pushpopitem, 'A', 99)
def test_popitem(self):
pq = minpq(A=5, B=8, C=1)
# pop top item
key, value = pq.popitem()
self.assertEqual(key, 'C')
self.assertEqual(value, 1)
def getBus(path_id, start_id, end_id):
bus_graph = {}
for i in range(len(path_id) - 1):
stop1_id, stop2_id = path_id[i], path_id[i + 1]
for bus in routeDict[(stop1_id, stop2_id)]['svc']:
bus_graph[stop1_id + ' ' + bus] = [stop2_id]
for j in range(i + 1, len(path_id) - 1):
if bus in routeDict[(path_id[j], path_id[j + 1])]['svc']:
bus_graph[stop1_id + ' ' + bus].append(path_id[j + 1])
distTo = {}
parent = {}
pq = minpq()
for value in bus_graph:
if value.split()[0] == start_id:
pq.additem(value, 0)
for stop_id in path_id:
distTo[stop_id] = (float("inf"))
parent[stop_id] = []
distTo[start_id] = 0
while len(pq) > 0:
curr, curr_dist = pq.popitem()
if curr_dist == distTo[curr.split()[0]]:
for neigh in bus_graph[curr]:
if distTo[neigh] > distTo[curr.split()[0]] + 1:
distTo[neigh] = distTo[curr.split()[0]] + 1
parent[neigh].append(curr)
for value in bus_graph:
if value.split()[0] == neigh:
pq.additem(value, distTo[neigh])
elif distTo[neigh] == distTo[curr.split()[0]] + 1:
parent[neigh].append(curr)
bus_path = []
while end_id != start_id:
curr_buses = []
for value in parent[end_id]:
curr_id = value.split()[0]
if value.split()[1] not in curr_buses:
curr_buses.append(value.split()[1])
bus_path.append([end_id, curr_buses, []])
end_id = curr_id
bus_path.reverse()
i, j = 0, 0
while j < len(path_id) and i < len(bus_path):
if path_id[j] != bus_path[i][0]:
bus_path[i][2].append(path_id[j])
j += 1
else:
bus_path[i][2].append(path_id[j])
i += 1
for i in range(len(bus_path)):
dur = 240
for j in range(len(bus_path[i][2][:-1])):
if j != 0:
dur += stopDict[bus_path[i][2][j]]['dur']
dur += routeDict[(bus_path[i][2][j], bus_path[i][2][j + 1])]['dur']
bus_path[i] = [['bus', dur, len(bus_path[i][2]) - 1], bus_path[i][2],
bus_path[i][1]]
return bus_path
def get_path(self):
print self.end
r, c = self.layout.shape
init_state = self.encode_state(self.sim.mario.state)
# we use path to record states, and state_action_map to map state to action
path = []
state_action_map = {init_state: None}
# state dict for previous state
path_pre = {init_state: None}
# cost to get this state, in terms of time (moves)
cost = {init_state: 0}
frontier_queue = pqdict.minpq({init_state: 0})
# astar search
closest_distance = 99999999
expansion = 0
node = None
solved = False
while frontier_queue:
# get top
raw_frontier = frontier_queue.pop()
frontier = self.decode_state(raw_frontier)
self.sim.mario.state = frontier
expansion += 1
# expand frontier
for i in [
"remains", "left_jump", "right_jump", "left_nojump",
"right_nojump", "nolr_jump"
]:
next_cost = cost[raw_frontier]
prev_state = self.sim.mario.state
self.sim.advance_frame(action=i)
next_cost += l2_distance(prev_state[0:2, 0],
self.sim.mario.state[0:2, 0])
# upsample runs
for j in range(self.interval - 1):
prev_state = self.sim.mario.state
self.sim.advance_frame(action="remains")
next_cost += l2_distance(prev_state[0:2, 0],
self.sim.mario.state[0:2, 0])
next_state = self.sim.mario.state
raw_next_state = self.encode_state(next_state)
# if new state is legal
if not np.array_equal(
next_state, frontier
) and next_state[0][0] > 0 and next_state[1][0] > 0 and (
raw_next_state not in cost
or next_cost < cost[raw_next_state]):
cost[raw_next_state] = next_cost
path_pre[raw_next_state] = raw_frontier
state_action_map[raw_next_state] = i
# task end test
distance_to_end = l1_distance(self.end, next_state[0:2, 0])
closest_distance = min(closest_distance, distance_to_end)
# print i, next_state[0:2, 0], next_cost, closest_distance
if closest_distance < 0.5:
node = raw_next_state
solved = True
break
heuristic = next_cost + distance_to_end
frontier_queue[raw_next_state] = heuristic
self.sim.mario.state = frontier
if closest_distance < 0.5:
solved = True
break
action_path = []
if solved:
while node:
for i in range(self.interval - 1):
action_path.insert(0, "remains")
action_path.insert(0, state_action_map[node])
node = path_pre[node]
for i in range(self.interval):
action_path.pop(0)
print expansion
return action_path
def a_star(layout, simulation, init_pos, end_pos, actions, interval=5, config=None):
"""
A* search for path-finding
:param layout: 2D numpy array layout
:param simulation: Physics simulation object
:param init_pos: 3D vector of the initial position
:param end_pos: 3D vector of the target position for search
:param actions: Possible actions
:param interval: Frame interval between two actions
:return: Action sequence list
"""
epsilon = 0.001 if config is None or "epsilon" not in config else config["epsilon"]
empty_action = "remains" if config is None or "empty_action" not in config else config["empty_action"]
bound = -1, -1, layout.shape[0] + 8, layout.shape[1] + 8
simulation.mario.state[0:3, 0] = init_pos
init_state = encode_state(simulation)
end_state = None
state_pre = {init_state: None}
state_preaction_map = {}
cost = {init_state: 0}
reversed_action_path = []
frontier_queue = pqdict.minpq({init_state: heuristic(get_state_pos(init_state), end_pos)})
expansion = 0
greatest_x = 0
max_q = l2_distance(init_pos, end_pos) * 20
pruned_num = 0
while frontier_queue and not end_state:
frontier = frontier_queue.pop()
expansion += 1
if frontier[0] > greatest_x:
greatest_x = frontier[0]
# print frontier[0], frontier[3], pruned_num
# Expand frontier
for act in actions:
simulation = decode_state(frontier, simulation)
simulation.advance_frame(act)
next_cost = l2_distance(get_state_pos(frontier), get_state_pos(simulation)) + cost[frontier] + 1 * interval
# Downsample actions
for i in range(interval - 1):
mid_state = encode_state(simulation)
simulation.advance_frame(empty_action)
next_cost += l2_distance(get_state_pos(mid_state), get_state_pos(simulation))
next_state = encode_state(simulation)
if in_bound(next_state, bound) and (next_state not in cost or next_cost < cost[next_state]):
cost[next_state] = next_cost
state_pre[next_state] = frontier
state_preaction_map[next_state] = act
h = heuristic(get_state_pos(next_state), end_pos) + next_cost
if h < max_q:
frontier_queue[next_state] = h
else:
pruned_num += 1
# Reach the end and exit
if l1_distance(get_state_pos(next_state), end_pos) <= 1:
end_state = next_state
break
# No solution found
if not end_state:
return []
# Generate action sequences
node = end_state
while node in state_preaction_map:
reversed_action_path.extend(itertools.repeat(empty_action, interval - 1))
reversed_action_path.append(state_preaction_map[node])
node = state_pre[node]
return list(reversed(reversed_action_path))
def test_pushpopitem():
pq = minpq(A=5, B=8, C=1)
assert pq.pushpopitem("D", 10) == ("C", 1)
assert pq.pushpopitem("E", 5) == ("E", 5)
with pytest.raises(KeyError):
pq.pushpopitem("A", 99)
def test_popitem():
pq = minpq(A=5, B=8, C=1)
# pop top item
key, value = pq.popitem()
assert key == "C"
assert value == 1
def test_minpq(self):
pq = minpq(A=5, B=8, C=7, D=3, E=9, F=12, G=1)
self.assertEqual(list(pq.popvalues()), [1, 3, 5, 7, 8, 9, 12])
self.assertEqual(pq.precedes, operator.lt)
def dijkstra_combined(start_id, end_id):
distTo = {}
parent = {}
pq = minpq()
for node in nodeDict:
distTo[node] = float("inf")
parent[node] = None
distTo[start_id] = 0
pq.additem(start_id, 0)
while len(pq) > 0:
curr_id, curr_dist = pq.popitem()
curr_lat, curr_lng = nodeDict[curr_id]['lat'], nodeDict[curr_id]['lng']
if curr_dist == distTo[curr_id]:
if curr_id == end_id:
break
for neigh in nodeDict[curr_id]['neighbours']:
neigh_lat, neigh_lng = nodeDict[neigh]['lat'], nodeDict[neigh][
'lng']
dist_apart = walk_time(
haversine(curr_lat, curr_lng, neigh_lat, neigh_lng))
if distTo[neigh] > distTo[curr_id] + dist_apart:
distTo[neigh] = distTo[curr_id] + dist_apart
parent[neigh] = (curr_id, 'walk')
try:
pq.additem(neigh, distTo[neigh])
except:
pq.updateitem(neigh, distTo[neigh])
if nodeDict[curr_id]['type'] == 'Bus Stop':
for neigh in stopDict[curr_id]['neighbours']:
if distTo[neigh] > distTo[curr_id] + routeDict[
(curr_id, neigh)]['dur']:
distTo[neigh] = distTo[curr_id] + routeDict[
(curr_id, neigh)]['dur']
parent[neigh] = (curr_id, 'bus')
try:
pq.additem(neigh, distTo[neigh])
except:
pq.updateitem(neigh, distTo[neigh])
path_id = []
curr_id = end_id
temp = []
temp.append(curr_id)
transport_type = parent[curr_id][1]
while curr_id != start_id:
curr_id = parent[curr_id][0]
temp.append(curr_id)
if parent[curr_id] != None and parent[curr_id][1] != transport_type:
temp.reverse()
path_id.append([transport_type, temp])
temp = [curr_id]
transport_type = parent[curr_id][1]
temp.reverse()
path_id.append([transport_type, temp])
path_id.reverse()
path = []
for segment in path_id:
if segment[0] == 'bus':
path.extend(getBus(segment[1], segment[1][0], segment[1][-1]))
else:
dur = 0
for i in range(len(segment[1]) - 1):
dur += walk_time(
haversine(nodeDict[segment[1][i]]['lat'],
nodeDict[segment[1][i]]['lng'],
nodeDict[segment[1][i + 1]]['lat'],
nodeDict[segment[1][i + 1]]['lng']))
path.append([['walk', dur], segment[1]])
return path
def test_minpq(self):
pq = minpq(A=5, B=8, C=7, D=3, E=9, F=12, G=1)
self.assertEqual(
list(pq.popvalues()),
[1, 3, 5, 7, 8, 9, 12])
self.assertEqual(pq.precedes, operator.lt)
def test_minpq():
pq = minpq(A=5, B=8, C=7, D=3, E=9, F=12, G=1)
assert list(pq.popvalues()) == [1, 3, 5, 7, 8, 9, 12]
assert pq.precedes == operator.lt
def test_pushpopitem(self):
pq = minpq(A=5, B=8, C=1)
self.assertEqual(pq.pushpopitem('D', 10), ('C', 1))
self.assertEqual(pq.pushpopitem('E', 5), ('E', 5))
self.assertRaises(KeyError, pq.pushpopitem, 'A', 99)