```
"""
The A* algorithm combines features of uniform-cost search and pure
heuristic search to efficiently compute optimal solutions.
A* algorithm is a best-first search algorithm in which the cost
associated with a node is f(n) = g(n) + h(n),
where g(n) is the cost of the path from the initial state to node n and
h(n) is the heuristic estimate or the cost or a path
from node n to a goal.A* algorithm introduces a heuristic into a
regular graph-searching algorithm,
essentially planning ahead at each step so a more optimal decision
is made.A* also known as the algorithm with brains
"""
import numpy as np
class Cell:
"""
Class cell represents a cell in the world which have the property
position : The position of the represented by tupleof x and y
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
which can be any function. for simplicity used line
distance
"""
def __init__(self):
self.position = (0, 0)
self.parent = None
self.g = 0
self.h = 0
self.f = 0
"""
overrides equals method because otherwise cell assign will give
wrong results
"""
def __eq__(self, cell):
return self.position == cell.position
def showcell(self):
print(self.position)
class Gridworld:
"""
Gridworld class represents the external world here a grid M*M
matrix
world_size: create a numpy array with the given world_size default is 5
"""
def __init__(self, world_size=(5, 5)):
self.w = np.zeros(world_size)
self.world_x_limit = world_size[0]
self.world_y_limit = world_size[1]
def show(self):
print(self.w)
def get_neigbours(self, cell):
"""
Return the neighbours of cell
"""
neughbour_cord = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
current_x = cell.position[0]
current_y = cell.position[1]
neighbours = []
for n in neughbour_cord:
x = current_x + n[0]
y = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
c = Cell()
c.position = (x, y)
c.parent = cell
neighbours.append(c)
return neighbours
def astar(world, start, goal):
"""
Implementation of a start algorithm
world : Object of the world object
start : Object of the cell as start position
stop : Object of the cell as goal position
>>> p = Gridworld()
>>> start = Cell()
>>> start.position = (0,0)
>>> goal = Cell()
>>> goal.position = (4,4)
>>> astar(p, start, goal)
[(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
"""
_open = []
_closed = []
_open.append(start)
while _open:
min_f = np.argmin([n.f for n in _open])
current = _open[min_f]
_closed.append(_open.pop(min_f))
if current == goal:
break
for n in world.get_neigbours(current):
for c in _closed:
if c == n:
continue
n.g = current.g + 1
x1, y1 = n.position
x2, y2 = goal.position
n.h = (y2 - y1) ** 2 + (x2 - x1) ** 2
n.f = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(n)
path = []
while current.parent is not None:
path.append(current.position)
current = current.parent
path.append(current.position)
return path[::-1]
if __name__ == "__main__":
world = Gridworld()
# stat position and Goal
start = Cell()
start.position = (0, 0)
goal = Cell()
goal.position = (4, 4)
print(f"path from {start.position} to {goal.position}")
s = astar(world, start, goal)
# Just for visual reasons
for i in s:
world.w[i] = 1
print(world.w)
```