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"""

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(object):

"""

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(object):

"""

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)

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