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Fixes in Bidirectional A* (#2020)

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* implement bidirectional astar

* add type hints

* add wikipedia url

* format with black

* changes from review

* fix collision check

* Add testmod()

* # doctest: +NORMALIZE_WHITESPACE

* Codespell: euclidean

* Codespell: coordinates

* Codespell: traversal

* Codespell: remaining

Co-authored-by: John Law <johnlaw.po@gmail.com>
Co-authored-by: Christian Clauss <cclauss@me.com>
This commit is contained in:
Erwin Lejeune 2020-05-21 21:50:52 +02:00 committed by GitHub
parent dc596d23a9
commit 21ed8968c0
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4 changed files with 86 additions and 48 deletions
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128
graphs/bidirectional_a_star.py
View File

@ -3,8 +3,12 @@ https://en.wikipedia.org/wiki/Bidirectional_search
"""
import time
from math import sqrt
from typing import List, Tuple
# 1 for manhattan, 0 for euclidean
HEURISTIC = 0
grid = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
@ -20,12 +24,12 @@ delta = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class Node:
"""
>>> k = Node(0, 0, 4, 5, 0, None)
>>> k = Node(0, 0, 4, 3, 0, None)
>>> k.calculate_heuristic()
9
5.0
>>> n = Node(1, 4, 3, 4, 2, None)
>>> n.calculate_heuristic()
2
2.0
>>> l = [k, n]
>>> n == l[0]
False
@ -47,18 +51,35 @@ class Node:
def calculate_heuristic(self) -> float:
"""
The heuristic here is the Manhattan Distance
Could elaborate to offer more than one choice
Heuristic for the A*
"""
dy = abs(self.pos_x - self.goal_x)
dx = abs(self.pos_y - self.goal_y)
return dx + dy
dy = self.pos_x - self.goal_x
dx = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(dx) + abs(dy)
else:
return sqrt(dy ** 2 + dx ** 2)
def __lt__(self, other):
def __lt__(self, other) -> bool:
return self.f_cost < other.f_cost
class AStar:
"""
>>> astar = AStar((0, 0), (len(grid) - 1, len(grid[0]) - 1))
>>> (astar.start.pos_y + delta[3][0], astar.start.pos_x + delta[3][1])
(0, 1)
>>> [x.pos for x in astar.get_successors(astar.start)]
[(1, 0), (0, 1)]
>>> (astar.start.pos_y + delta[2][0], astar.start.pos_x + delta[2][1])
(1, 0)
>>> astar.retrace_path(astar.start)
[(0, 0)]
>>> astar.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (1, 0), (2, 0), (2, 1), (2, 2), (2, 3), (3, 3),
(4, 3), (4, 4), (5, 4), (5, 5), (6, 5), (6, 6)]
"""
def __init__(self, start, goal):
self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
@ -68,10 +89,7 @@ class AStar:
self.reached = False
self.path = [(self.start.pos_y, self.start.pos_x)]
self.costs = [0]
def search(self):
def search(self) -> List[Tuple[int]]:
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
@ -79,8 +97,7 @@ class AStar:
if current_node.pos == self.target.pos:
self.reached = True
self.path = self.retrace_path(current_node)
break
return self.retrace_path(current_node)
self.closed_nodes.append(current_node)
successors = self.get_successors(current_node)
@ -101,7 +118,7 @@ class AStar:
self.open_nodes.append(better_node)
if not (self.reached):
print("No path found")
return [(self.start.pos)]
def get_successors(self, parent: Node) -> List[Node]:
"""
@ -111,21 +128,22 @@ class AStar:
for action in delta:
pos_x = parent.pos_x + action[1]
pos_y = parent.pos_y + action[0]
if not (0 < pos_x < len(grid[0]) - 1 and 0 < pos_y < len(grid) - 1):
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
node_ = Node(
pos_x,
pos_y,
self.target.pos_y,
self.target.pos_x,
parent.g_cost + 1,
parent,
successors.append(
Node(
pos_x,
pos_y,
self.target.pos_y,
self.target.pos_x,
parent.g_cost + 1,
parent,
)
)
successors.append(node_)
return successors
def retrace_path(self, node: Node) -> List[Tuple[int]]:
@ -142,13 +160,24 @@ class AStar:
class BidirectionalAStar:
"""
>>> bd_astar = BidirectionalAStar((0, 0), (len(grid) - 1, len(grid[0]) - 1))
>>> bd_astar.fwd_astar.start.pos == bd_astar.bwd_astar.target.pos
True
>>> bd_astar.retrace_bidirectional_path(bd_astar.fwd_astar.start,
... bd_astar.bwd_astar.start)
[(0, 0)]
>>> bd_astar.search() # doctest: +NORMALIZE_WHITESPACE
[(0, 0), (0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (2, 4),
(2, 5), (3, 5), (4, 5), (5, 5), (5, 6), (6, 6)]
"""
def __init__(self, start, goal):
self.fwd_astar = AStar(start, goal)
self.bwd_astar = AStar(goal, start)
self.reached = False
self.path = self.fwd_astar.path
def search(self):
def search(self) -> List[Tuple[int]]:
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
@ -157,8 +186,9 @@ class BidirectionalAStar:
if current_bwd_node.pos == current_fwd_node.pos:
self.reached = True
self.retrace_bidirectional_path(current_fwd_node, current_bwd_node)
break
return self.retrace_bidirectional_path(
current_fwd_node, current_bwd_node
)
self.fwd_astar.closed_nodes.append(current_fwd_node)
self.bwd_astar.closed_nodes.append(current_bwd_node)
@ -189,30 +219,38 @@ class BidirectionalAStar:
else:
astar.open_nodes.append(better_node)
if not self.reached:
return [self.fwd_astar.start.pos]
def retrace_bidirectional_path(
self, fwd_node: Node, bwd_node: Node
) -> List[Tuple[int]]:
fwd_path = self.fwd_astar.retrace_path(fwd_node)
bwd_path = self.bwd_astar.retrace_path(bwd_node)
fwd_path.reverse()
bwd_path.pop()
bwd_path.reverse()
path = fwd_path + bwd_path
return path
# all coordinates are given in format [y,x]
init = (0, 0)
goal = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
start_time = time.time()
a_star = AStar(init, goal)
a_star.search()
end_time = time.time() - start_time
print(f"AStar execution time = {end_time:f} seconds")
doctest.testmod()
init = (0, 0)
goal = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
bd_start_time = time.time()
bidir_astar = BidirectionalAStar(init, goal)
bidir_astar.search()
bd_end_time = time.time() - bd_start_time
print(f"BidirectionalAStar execution time = {bd_end_time:f} seconds")
start_time = time.time()
a_star = AStar(init, goal)
path = a_star.search()
end_time = time.time() - start_time
print(f"AStar execution time = {end_time:f} seconds")
bd_start_time = time.time()
bidir_astar = BidirectionalAStar(init, goal)
path = bidir_astar.search()
bd_end_time = time.time() - bd_start_time
print(f"BidirectionalAStar execution time = {bd_end_time:f} seconds")

2
machine_learning/astar.py
View File

@ -17,7 +17,7 @@ class Cell(object):
"""
Class cell represents a cell in the world which have the property
position : The position of the represented by tupleof x and y
co-ordinates initially set to (0,0)
coordinates initially set to (0,0)
parent : This contains the parent cell object which we visited
before arrinving this cell
g,h,f : The parameters for constructing the heuristic function

2
scheduling/shortest_job_first_algorithm.py
View File

@ -1,5 +1,5 @@
"""
Shortest job remainig first
Shortest job remaining first
Please note arrival time and burst
Please use spaces to separate times entered.
"""

2
sorts/tree_sort.py
View File

@ -29,7 +29,7 @@ class node:
def inorder(root, res):
# Recursive travesal
# Recursive traversal
if root:
inorder(root.left, res)
res.append(root.val)