I will show you how to implement an A* (Astar) search algorithm in this tutorial, the algorithm will be used solve a grid problem and a graph problem by using Python. The A* search algorithm uses the full path cost as the heuristic, the cost to the starting node plus the estimated cost to the goal node.

A* is an informed algorithm as it uses an heuristic to guide the search. The algorithm starts from an initial start node, expands neighbors and updates the full path cost of each neighbor. It selects the neighbor with the lowest cost and continues until it finds a goal node, this can be implemented with a priority queue or by sorting the list of open nodes in ascending order. It is important to select a good heuristic to make A* fast in searches, a good heuristic should be close to the actual cost but should not be higher than the actual cost.

A* is complete and optimal, it will find the shortest path to the goal. A good heuristic can make the search very fast, but it may take a long time and consume a lot of memory in a large search space. The time complexity is **O(n)** in a grid and **O(b^d)** in a graph/tree with a branching factor (b) and a depth (d). The branching factor is the average number of neighbor nodes that can be expanded from each node and the depth is the average number of levels in a graph/tree.

## Grid problem (maze)

I have created a simple maze (download it) with walls, a start (@) and a goal ($). The goal of the A* algorithm is to find the shortest path from the starting point to the goal point as fast as possible. The full path cost (f) for each node is calculated as the distance to the starting node (g) plus the distance to the goal node (h). Distances is calculated as the manhattan distance (taxicab geometry) between nodes.

```
# This class represents a node
class Node:
# Initialize the class
def __init__(self, position:(), parent:()):
self.position = position
self.parent = parent
self.g = 0 # Distance to start node
self.h = 0 # Distance to goal node
self.f = 0 # Total cost
# Compare nodes
def __eq__(self, other):
return self.position == other.position
# Sort nodes
def __lt__(self, other):
return self.f < other.f
# Print node
def __repr__(self):
return ('({0},{1})'.format(self.position, self.f))
# Draw a grid
def draw_grid(map, width, height, spacing=2, **kwargs):
for y in range(height):
for x in range(width):
print('%%-%ds' % spacing % draw_tile(map, (x, y), kwargs), end='')
print()
# Draw a tile
def draw_tile(map, position, kwargs):
# Get the map value
value = map.get(position)
# Check if we should print the path
if 'path' in kwargs and position in kwargs['path']: value = '+'
# Check if we should print start point
if 'start' in kwargs and position == kwargs['start']: value = '@'
# Check if we should print the goal point
if 'goal' in kwargs and position == kwargs['goal']: value = '$'
# Return a tile value
return value
# A* search
def astar_search(map, start, end):
# Create lists for open nodes and closed nodes
open = []
closed = []
# Create a start node and an goal node
start_node = Node(start, None)
goal_node = Node(end, None)
# Add the start node
open.append(start_node)
# Loop until the open list is empty
while len(open) > 0:
# Sort the open list to get the node with the lowest cost first
open.sort()
# Get the node with the lowest cost
current_node = open.pop(0)
# Add the current node to the closed list
closed.append(current_node)
# Check if we have reached the goal, return the path
if current_node == goal_node:
path = []
while current_node != start_node:
path.append(current_node.position)
current_node = current_node.parent
#path.append(start)
# Return reversed path
return path[::-1]
# Unzip the current node position
(x, y) = current_node.position
# Get neighbors
neighbors = [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]
# Loop neighbors
for next in neighbors:
# Get value from map
map_value = map.get(next)
# Check if the node is a wall
if(map_value == '#'):
continue
# Create a neighbor node
neighbor = Node(next, current_node)
# Check if the neighbor is in the closed list
if(neighbor in closed):
continue
# Generate heuristics (Manhattan distance)
neighbor.g = abs(neighbor.position[0] - start_node.position[0]) + abs(neighbor.position[1] - start_node.position[1])
neighbor.h = abs(neighbor.position[0] - goal_node.position[0]) + abs(neighbor.position[1] - goal_node.position[1])
neighbor.f = neighbor.g + neighbor.h
# Check if neighbor is in open list and if it has a lower f value
if(add_to_open(open, neighbor) == True):
# Everything is green, add neighbor to open list
open.append(neighbor)
# Return None, no path is found
return None
# Check if a neighbor should be added to open list
def add_to_open(open, neighbor):
for node in open:
if (neighbor == node and neighbor.f >= node.f):
return False
return True
# The main entry point for this module
def main():
# Get a map (grid)
map = {}
chars = ['c']
start = None
end = None
width = 0
height = 0
# Open a file
fp = open('data\\maze.in', 'r')
# Loop until there is no more lines
while len(chars) > 0:
# Get chars in a line
chars = [str(i) for i in fp.readline().strip()]
# Calculate the width
width = len(chars) if width == 0 else width
# Add chars to map
for x in range(len(chars)):
map[(x, height)] = chars[x]
if(chars[x] == '@'):
start = (x, height)
elif(chars[x] == '$'):
end = (x, height)
# Increase the height of the map
if(len(chars) > 0):
height += 1
# Close the file pointer
fp.close()
# Find the closest path from start(@) to end($)
path = astar_search(map, start, end)
print()
print(path)
print()
draw_grid(map, width, height, spacing=1, path=path, start=start, goal=end)
print()
print('Steps to goal: {0}'.format(len(path)))
print()
# Tell python to run main method
if __name__ == "__main__": main()
```

```
#################################################################################
#.#...#....$....#...................#...#.........#.......#.............#.......#
#.#.#.#.###+###.#########.#########.#.#####.#####.#####.#.#.#######.###.#.#####.#
#...#.....#+++#.#.........#.#.....#.#...#...#...#.......#.#.#.......#.#.#.#...#.#
#############+#.#.#########.#.###.#.###.#.###.#.#.#######.###.#######.#.#.#.#.#.#
#+++++++++++#+#...#.#.....#...#...#...#.#.#.#.#...#...#.......#.......#.#.#.#.#.#
#+#########+#+#####.#.#.#.#.###.#####.#.#.#.#.#####.#.#########.###.###.###.#.#.#
#+#........+#+++#...#.#.#.#...#.....#.#.#.#...#.#...#.......#.....#.#...#...#...#
#+#########+#.#+###.#.#.#####.###.#.#.#.#.#.###.#.#########.#####.#.#.###.#####.#
#+#+++++++#+#.#+++#...#.#.....#.#.#.#...#.#.....#.#.....#.#...#...#.......#...#.#
#+#+#####+#+#.###+#####.#.#####.#.#.###.#.#######.###.#.#.###.#.###########.#.#.#
#+++#+++#+#+#...#+++++#.#.......#.#.#...#.....#...#...#.....#.#.#...#...#...#...#
#####+#+#+#+#########+#.#######.#.###.#######.#.###.#########.###.#.#.#.#.#######
#+++++#+++#+#+++++++++#.......#.#...#.#.#.....#.#.....#.......#...#.#.#.#.#.....#
#+#########+#+#########.###.###.###.#.#.#.###.#.#.###.#.#######.###.#.###.#.###.#
#+++#.#+++++#+++#.....#.#.#...#.#.#.....#...#.#.#...#.#...#...#...#.#.#...#...#.#
###+#.#+#####.#+#.#.###.#.###.#.#.#####.###.###.#####.###.#.#.#.###.#.#.#####.#.#
#+++#+++#.....#+#.#.#...#...#.....#...#.#...#...........#.#.#...#...#.......#.#.#
#+###+#########+#.#.#.###.#.#####.#.#.###.###.###########.#.#####.#########.###.#
#+#..+++++++++++#.#.......#.#...#.#.#...#.#...#.#.......#.......#.#...#.....#...#
#+#.#############.#########.#.#.###.###.#.#.###.#.#####.#.#######.#.#.#.#####.#.#
#+#.#+++++++++++#.#.#.#.....#.#.....#...#.#.....#...#.#.#.#.#...#.#.#.#.#.....#.#
#+###+#########+#.#.#.#######.#######.###.#####.###.#.#.#.#.###.#.#.#.#.#####.#.#
#+++++#+++#+++++#...#.........#.....#...#.....#...#...#.#.....#.#...#.#.#.....#.#
#.#####+#+#+#######.###########.#######.#.#######.###.#.###.###.#####.#.#.#####.#
#.....#+#+#+++#...#.#+++++++#.........#.#...#.......#.#.#...#...#.....#.#.#...#.#
#######+#+###+#.###.#+#####+#.#####.###.#.#.#.#######.#.#####.###.#####.#.###.#.#
#+++++++#+#+++#.....#+#...#+#...#.#.....#.#.#.#.#.....#...#...#...#.....#...#.#.#
#+#######+#+#.#####.#+###.#+###.#.#######.#.#.#.#.#######.#.###.#.###.#####.#.#.#
#+#.#+++++#+#.#+++#.#+++#.#+++#...#.#...#.#...#.#.....#.#...#...#...#.......#...#
#+#.#+#####+#.#+#+#####+#.###+###.#.#.#.#.#####.#####.#.#####.#####.#########.###
#+#..+#..+++#.#+#+#+++#+++#.#+#...#...#.#.#...#.....#...#.#...#...#.....#...#.#.#
#+###+###+#.###+#+#+#+###+#.#+#.#######.#.#.#.#####.###.#.#.###.#.#####.###.#.#.#
#+++#+++#+#.#+++#+#+#+++#+#.#+#.#.......#...#.........#.#...#...#.#...#...#.#...#
#.#+###+#+#.#+###+#+###+#+#.#+#.###.###.###########.###.#.###.###.###.###.#.###.#
#.#+++#+#+#.#+++#+++#+++#+#.#+#.....#...#...#.....#.#...#.....#.....#.#...#...#.#
#.###+#+#+#####+#####+#.#+#.#+#######.###.#.#####.#.#.#############.#.#.###.#.#.#
#...#+#+++#+++#+++++#+#.#+#.#+#+++#...#.#.#.......#.#.#...#...#...#...#.#.#.#...#
###.#+#####+#+#####+#+###+#.#+#+#+#.###.#.#########.#.#.#.#.#.#.#.#####.#.#.#####
#...#+++++++#+++++++#+++++..#+++#+++++++@...........#...#...#...#.......#.......#
#################################################################################
Steps to goal: 339
```

## Graph problem

The goal of this graph problem is to find the shortest path between a starting location and destination location. A map has been used to create a graph with actual distances between locations. The A* algorithm uses a Graph class, a Node class and heuristics to find the shortest path in a fast manner. Heuristics is calculated as straight-line distances (air-travel distances) between locations, air-travel distances will never be larger than actual distances.

```
# This class represent a graph
class Graph:
# Initialize the class
def __init__(self, graph_dict=None, directed=True):
self.graph_dict = graph_dict or {}
self.directed = directed
if not directed:
self.make_undirected()
# Create an undirected graph by adding symmetric edges
def make_undirected(self):
for a in list(self.graph_dict.keys()):
for (b, dist) in self.graph_dict[a].items():
self.graph_dict.setdefault(b, {})[a] = dist
# Add a link from A and B of given distance, and also add the inverse link if the graph is undirected
def connect(self, A, B, distance=1):
self.graph_dict.setdefault(A, {})[B] = distance
if not self.directed:
self.graph_dict.setdefault(B, {})[A] = distance
# Get neighbors or a neighbor
def get(self, a, b=None):
links = self.graph_dict.setdefault(a, {})
if b is None:
return links
else:
return links.get(b)
# Return a list of nodes in the graph
def nodes(self):
s1 = set([k for k in self.graph_dict.keys()])
s2 = set([k2 for v in self.graph_dict.values() for k2, v2 in v.items()])
nodes = s1.union(s2)
return list(nodes)
# This class represent a node
class Node:
# Initialize the class
def __init__(self, name:str, parent:str):
self.name = name
self.parent = parent
self.g = 0 # Distance to start node
self.h = 0 # Distance to goal node
self.f = 0 # Total cost
# Compare nodes
def __eq__(self, other):
return self.name == other.name
# Sort nodes
def __lt__(self, other):
return self.f < other.f
# Print node
def __repr__(self):
return ('({0},{1})'.format(self.name, self.f))
# A* search
def astar_search(graph, heuristics, start, end):
# Create lists for open nodes and closed nodes
open = []
closed = []
# Create a start node and an goal node
start_node = Node(start, None)
goal_node = Node(end, None)
# Add the start node
open.append(start_node)
# Loop until the open list is empty
while len(open) > 0:
# Sort the open list to get the node with the lowest cost first
open.sort()
# Get the node with the lowest cost
current_node = open.pop(0)
# Add the current node to the closed list
closed.append(current_node)
# Check if we have reached the goal, return the path
if current_node == goal_node:
path = []
while current_node != start_node:
path.append(current_node.name + ': ' + str(current_node.g))
current_node = current_node.parent
path.append(start_node.name + ': ' + str(start_node.g))
# Return reversed path
return path[::-1]
# Get neighbours
neighbors = graph.get(current_node.name)
# Loop neighbors
for key, value in neighbors.items():
# Create a neighbor node
neighbor = Node(key, current_node)
# Check if the neighbor is in the closed list
if(neighbor in closed):
continue
# Calculate full path cost
neighbor.g = current_node.g + graph.get(current_node.name, neighbor.name)
neighbor.h = heuristics.get(neighbor.name)
neighbor.f = neighbor.g + neighbor.h
# Check if neighbor is in open list and if it has a lower f value
if(add_to_open(open, neighbor) == True):
# Everything is green, add neighbor to open list
open.append(neighbor)
# Return None, no path is found
return None
# Check if a neighbor should be added to open list
def add_to_open(open, neighbor):
for node in open:
if (neighbor == node and neighbor.f > node.f):
return False
return True
# The main entry point for this module
def main():
# Create a graph
graph = Graph()
# Create graph connections (Actual distance)
graph.connect('Frankfurt', 'Wurzburg', 111)
graph.connect('Frankfurt', 'Mannheim', 85)
graph.connect('Wurzburg', 'Nurnberg', 104)
graph.connect('Wurzburg', 'Stuttgart', 140)
graph.connect('Wurzburg', 'Ulm', 183)
graph.connect('Mannheim', 'Nurnberg', 230)
graph.connect('Mannheim', 'Karlsruhe', 67)
graph.connect('Karlsruhe', 'Basel', 191)
graph.connect('Karlsruhe', 'Stuttgart', 64)
graph.connect('Nurnberg', 'Ulm', 171)
graph.connect('Nurnberg', 'Munchen', 170)
graph.connect('Nurnberg', 'Passau', 220)
graph.connect('Stuttgart', 'Ulm', 107)
graph.connect('Basel', 'Bern', 91)
graph.connect('Basel', 'Zurich', 85)
graph.connect('Bern', 'Zurich', 120)
graph.connect('Zurich', 'Memmingen', 184)
graph.connect('Memmingen', 'Ulm', 55)
graph.connect('Memmingen', 'Munchen', 115)
graph.connect('Munchen', 'Ulm', 123)
graph.connect('Munchen', 'Passau', 189)
graph.connect('Munchen', 'Rosenheim', 59)
graph.connect('Rosenheim', 'Salzburg', 81)
graph.connect('Passau', 'Linz', 102)
graph.connect('Salzburg', 'Linz', 126)
# Make graph undirected, create symmetric connections
graph.make_undirected()
# Create heuristics (straight-line distance, air-travel distance)
heuristics = {}
heuristics['Basel'] = 204
heuristics['Bern'] = 247
heuristics['Frankfurt'] = 215
heuristics['Karlsruhe'] = 137
heuristics['Linz'] = 318
heuristics['Mannheim'] = 164
heuristics['Munchen'] = 120
heuristics['Memmingen'] = 47
heuristics['Nurnberg'] = 132
heuristics['Passau'] = 257
heuristics['Rosenheim'] = 168
heuristics['Stuttgart'] = 75
heuristics['Salzburg'] = 236
heuristics['Wurzburg'] = 153
heuristics['Zurich'] = 157
heuristics['Ulm'] = 0
# Run the search algorithm
path = astar_search(graph, heuristics, 'Frankfurt', 'Ulm')
print(path)
print()
# Tell python to run main method
if __name__ == "__main__": main()
```

`['Frankfurt: 0', 'Wurzburg: 111', 'Ulm: 294']`

Hello,

I think there’s a mistake, just before the comment “# Check if neighbor is in open list and if it has a lower f value” it should be break, not continue, and then that last line “open.append(neighbor)” should be inside a “else:”, using the syntax for;break;else.

Hi,

thank you very much for your comment. I have updated the code according to your suggestions.

Hi, great post!

Is there any restrictions or licenses upon this code? What’s the correct procedure to reproduce it on a graduation project?

Regards

Hi,

you are free to use the code however you want. If you use it in a graduation project, add a reference to this page and apply the code to other problems. You can also make some minor changes in the code in your graduation project.

Thanks, it will be referenced and I would also like to send you a copy if you want to see it!

Thank you, you can find my email address in the bottom right corner of this website.

File “final_project.py”, line 32

def __init__(self, name:str, parent:str):

^

SyntaxError: invalid syntax

Hi, i wanted to ask how can we code it if we want to input the start and end node instead of writing it in the program?

Hi,

you can import the file in another module (import filename without .py) and remove the main method.

Hey, what do I do if I want to check the open and closed list on every iteration to check the a-star_search() status?

Hi,

you can use the print() function. Example:

It gives the following error:

Traceback (most recent call last):

File “a_star.py”, line 161, in

if __name__ == “__main__”: main()

File “a_star.py”, line 157, in main

path = astar_search(graph, heuristics, ‘Frankfurt’, ‘Ulm’)

File “a_star.py”, line 97, in astar_search

print(neighbor)

File “a_star.py”, line 49, in __repr__

return (‘({0},{1})’.format(self.position, self.f))

AttributeError: ‘Node’ object has no attribute ‘position’

Hi,

it is a bug in the Graph Node code (I will fix this). A Graph Node does not have position as attribute.

Change to

`self.name`

and test.Hey thanks for the replies so far. I have one additional doubt over here. A-star is supposed to find an optimal solution but here it stops as soon as it gets to the goal state. What is there are multiple solutions and I have to find an optimal solution out of all possible solutions?

Consider these input values:

graph.connect(‘S’, ‘A’, 6)

graph.connect(‘S’, ‘B’, 5)

graph.connect(‘S’, ‘C’, 10)

graph.connect(‘A’, ‘E’, 6)

graph.connect(‘B’, ‘E’, 6)

graph.connect(‘B’, ‘D’, 7)

graph.connect(‘C’, ‘D’, 6)

graph.connect(‘E’, ‘F’, 4)

graph.connect(‘D’, ‘F’, 6)

graph.connect(‘F’, ‘G’, 3)

and

heuristics[‘S’] = 17

heuristics[‘A’] = 10

heuristics[‘B’] = 13

heuristics[‘C’] = 4

heuristics[‘D’] = 2

heuristics[‘E’] = 4

heuristics[‘F’] = 1

heuristics[‘G’] = 0

Currently it gives solution as SAEFG with G:19

but SBEFG is an optimal solution with G:18… so how do I get it to give me an optimal solution instead of just the first solution. Many thanks!

Please help !

How can i get the following values to print please help in this !

For every iteration, you need to display

• visited school list’s value at present

• visited school state’s path

• path length

• estimated complete [h(n) plus real] path cost

Hello there,

I am student studying in the International Baccalaureate Diploma Program. I am writing this thing called the extended essay. It is a research based essay where I take topic in a subject of interest and conduct some sort of investigation/analysis. My extended essay is in computer science and in my essay I am analyzing time complexities of various pathfinding algorithms in finding the shortest path.

Would it be okay if I use this code for my essay (I will provide proper references and credits)

Yes, you are more than welcome to use the code in your essay.

neighbor.g = abs(neighbor.position[0] – start_node.position[0]) + abs(neighbor.position[1] – start_node.position[1])

This code can be problematic, since the closest to the start doesn’t necessarily mean it is the best (see on link below), it’s best to do: neighbor.g = neighbor.g + 1

since this will be the real distance to start

Image:

https://imgur.com/a/SjMesHF

please tell me the line number of this code

import pandas as pd

data={

‘1’: {‘START’:’OSLO’, ‘END’:’Helsinki’,’DIST’:970 },

‘2’: {‘START’:’Rome’, ‘END’:’Milan’,’DIST’: 681 },

‘3’:{‘START’:’Madrid’ , ‘END’: ‘Barcelona’,’DIST’: 628},

‘4’:{‘START’:’Helsinki’ , ‘END’: ‘Stockholm’,’DIST’: 400 },

‘5’: {‘START’:’Milan’ , ‘END’: ‘Budapest’,’DIST’: 789 },

‘6’: {‘START’:’Madrid’ , ‘END’: ‘Lisbon’,’DIST’: 638},

‘7’: {‘START’:’Oslo’ , ‘END’: ‘Stockholm’,’DIST’: 570},

‘8’: {‘START’:’Vienna’ , ‘END’: ‘Budapest’,’DIST’: 217},

‘9’: {‘START’:’Lisbon’ , ‘END’: ‘London’,’DIST’: 2210},

’10’: {‘START’:’Stockholm’ , ‘END’: ‘Copenhagen’,’DIST’: 522 },

’11’: {‘START’:’Vienna’ , ‘END’: ‘Munich’,’DIST’: 458 },

’12’: {‘START’:’Barcelona’ , ‘END’: ‘Lyon’,’DIST’: 644},

’13’: {‘START’:’Copenhagen’ , ‘END’: ‘Warsaw’,’DIST’: 668},

’14’: {‘START’:’Prague’ , ‘END’: ‘Vienna’,’DIST’: 312 },

’15’: {‘START’:’Paris’ , ‘END’: ‘London’,’DIST’: 414},

’16’: {‘START’:’Warsaw’ , ‘END’: ‘Bucharest’,’DIST’: 946 },

’17’: {‘START’:’Prague’ , ‘END’: ‘Berlin’,’DIST’: 354 },

’18’: {‘START’:’London’ , ‘END’: ‘Dublin’,’DIST’: 463},

’19’: {‘START’:’Bucharest’ , ‘END’: ‘Athens’,’DIST’: 1300 },

’20’: {‘START’:’Berlin’ , ‘END’: ‘Copenhagen’,’DIST’: 743 },

’21’: {‘START’:’London’ , ‘END’: ‘Glasgow’,’DIST’: 667},

’22’: {‘START’:’Budapest’ , ‘END’: ‘Bucharest’,’DIST’: 900 },

’23’: {‘START’:’Berlin’ , ‘END’: ‘Amsterdam’,’DIST’: 648 },

’24’: {‘START’:’Glasgow’ , ‘END’: ‘Amsterdam’,’DIST’: 711},

’25’: {‘START’:’Budapest’ , ‘END’: ‘Belgrade’,’DIST’: 316 },

’26’:{‘START’:’Munich’ , ‘END’: ‘Lyon’,’DIST’: 753 },

’27’: {‘START’:’Budapest’ , ‘END’: ‘Prague’,’DIST’:443},

’28’: {‘START’:’Belgrade’ , ‘END’: ‘Sofia’,’DIST’:330 },

’29’: {‘START’:’Lyon’ , ‘END’: ‘Paris’,’DIST’:481 },

’30’: {‘START’:’Barcelona’ , ‘END’: ‘Rome’,’DIST’:1471},

’31’: {‘START’:’Rome’ , ‘END’: ‘Palermo’,’DIST’:1043 },

’32’: {‘START’:’Lyon’ , ‘END’: ‘Bordeaux’,’DIST’:542 },

’33’: {‘START’:’Paris’ , ‘END’: ‘Bordeaux’,’DIST’:579},

’34’: {‘START’:’Palermo’ , ‘END’: ‘Athens’,’DIST’:907 },

’35’: {‘START’:’Glasgow’ , ‘END’: ‘Dublin’,’DIST’:306},

}

df=pd.DataFrame(data=data).T

g_func = []

for row in df.iterrows():

g_func.append((row[1][0], row[1][1], row[1][2]))

print(g_func)

data1={

‘1’: {‘START’:’Amsterdam’,’DIST’: 2280},

‘2’: {‘START’:’Copenhagen’,’DIST’: 2250},

‘3’: {‘START’:’Madrid’,’DIST’: 3300},

‘4’: {‘START’:’Rome’,’DIST’: 1140},

‘5’: {‘START’:’Athens’,’DIST’: 1300},

‘6’: {‘START’:’Dublin’,’DIST’: 2530 },

‘7’: {‘START’:’Milan’,’DIST’: 1750 },

‘8’: {‘START’:’Sofia’,’DIST’: 390},

‘9’: {‘START’:’Barcelona’,’DIST’: 2670 },

’10’: {‘START’:’Glasgow’,’DIST’: 2470 },

’11’: {‘START’:’Munich’,’DIST’: 1600 },

’12’: {‘START’:’Stockholm’,’DIST’: 2890},

’13’: {‘START’:’Belgrade’,’DIST’: 630 },

’14’: {‘START’:’Helsinki’,’DIST’: 2820 },

’15’: {‘START’:’Oslo’,’DIST’: 2870 },

’16’: {‘START’:’Vienna’,’DIST’: 1150},

’17’: {‘START’:’Berlin’,’DIST’: 1800 },

’18’: {‘START’:’Lisbon’,’DIST’: 3950 },

’19’: {‘START’:’Palermo’,’DIST’: 1280 },

’20’: {‘START’:’Warsaw’,’DIST’: 946},

’21’: {‘START’:’Bordeaux’,’DIST’: 2100},

’22’: {‘START’:’London’,’DIST’: 2590 },

’23’: {‘START’:’Paris’,’DIST’: 2970 },

’24’: {‘START’:’Budapest’,’DIST’: 900 },

’25’: {‘START’:’Lyon’,’DIST’: 1660 },

’26’: {‘START’:’Prague’,’DIST’: 1490 },

}

df=pd.DataFrame(data=data1).T

f_func = []

for row in df.iterrows():

f_func.append((row[1][0], row[1][1]))

print(f_func);

# This class represent a graph

class Graph:

# Initialize the class

def __init__(self, graph_dict=None, directed=True):

self.graph_dict = graph_dict or {}

self.directed = directed

if not directed:

self.make_undirected()

# Create an undirected graph by adding symmetric edges

def make_undirected(self):

for a in list(self.graph_dict.keys()):

for (b, dist) in self.graph_dict[a].items():

self.graph_dict.setdefault(b, {})[a] = dist

# Add a link from A and B of given distance, and also add the inverse link if the graph is undirected

def connect(self, A, B, distance=1):

self.graph_dict.setdefault(A, {})[B] = distance

if not self.directed:

self.graph_dict.setdefault(B, {})[A] = distance

# Get neighbors or a neighbor

def get(self, a, b=None):

links = self.graph_dict.setdefault(a, {})

if b is None:

return links

else:

return links.get(b)

# Return a list of nodes in the graph

def nodes(self):

s1 = set([k for k in self.graph_dict.keys()])

s2 = set([k2 for v in self.graph_dict.values() for k2, v2 in v.items()])

nodes = s1.union(s2)

return list(nodes)

# This class represent a node

class Node:

# Initialize the class

def __init__(self, name:str, parent:str):

self.name = name

self.parent = parent

self.g = 0 # Distance to start node

self.h = 0 # Distance to goal node

self.f = 0 # Total cost

# Compare nodes

def __eq__(self, other):

return self.name == other.name

# Sort nodes

def __lt__(self, other):

return self.f 0:

# Sort the open list to get the node with the lowest cost first

open.sort()

# Get the node with the lowest cost

current_node = open.pop(0)

# Add the current node to the closed list

closed.append(current_node)

# Check if we have reached the goal, return the path

if current_node == goal_node:

path = []

while current_node != start_node:

path.append(current_node.name + ‘: ‘ + str(current_node.g))

current_node = current_node.parent

path.append(start_node.name + ‘: ‘ + str(start_node.g))

# Return reversed path

return path[::-1]

# Get neighbours

neighbors = graph.get(current_node.name)

# Loop neighbors

for key, value in neighbors.items():

# Create a neighbor node

neighbor = Node(key, current_node)

# Check if the neighbor is in the closed list

if(neighbor in closed):

continue

# Calculate full path cost

neighbor.g = current_node.g + graph.get(current_node.name, neighbor.name)

neighbor.h = heuristics.get(neighbor.name)

neighbor.f = neighbor.g + neighbor.h

# Check if neighbor is in open list and if it has a lower f value

if(add_to_open(open, neighbor) == True):

# Everything is green, add neighbor to open list

open.append(neighbor)

print(neighbor)

# Return None, no path is found

return None

# Check if a neighbor should be added to open list

def add_to_open(open, neighbor):

for node in open:

if (neighbor == node and neighbor.f > node.f):

return False

return True

# The main entry point for this module

def main():

# Create a graph

graph = Graph()

for el in g_func:

graph.connect(el[0], el[1], el[2])

# Make graph undirected, create symmetric connections

graph.make_undirected()

heuristics = {}

for el in f_func:

heuristics[el[0]] = el[1]

print(heuristics)

# Run the search algorithm

path = astar_search(graph, heuristics, ‘Paris’, ‘Bucharest’)

print(path)

print()

# Tell python to run main method

if __name__ == “__main__”: main()

This Code gives me this error:

in astar_search(graph, heuristics, start, end)

175 neighbor.g = current_node.g + graph.get(current_node.name, neighbor.name)

176 neighbor.h = heuristics.get(neighbor.name)

–> 177 neighbor.f = neighbor.g + neighbor.h

178 # Check if neighbor is in open list and if it has a lower f value

179 if(add_to_open(open, neighbor) == True):

TypeError: unsupported operand type(s) for +: ‘int’ and ‘NoneType’