In this tutorial, I will implement Dijkstras algorithm to find the shortest path in a grid and a graph. Dijkstras algorithm was created by Edsger W. Dijkstra, a programmer and computer scientist from the Netherlands. Dijkstras performs a uniform-cost search as it expands nodes in order of cost from the root node.
Dijkstras is an informed algorithm in searches as it uses an heuristic (cost so far), it starts at an initial start node and updates each neighbor node with the cost so far. The algorithm selects the neighbor with the lowest cost and continues to expand nodes until it reaches the goal node, this can be implemented by using a priority queue or by sorting the list of open nodes in ascending order. The algorithm favors nodes that are close to the starting point.
Dijkstras algorithm is complete and it will find the optimal solution, 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^(c/m)) in a graph/tree with a branching factor (b), an optimal cost (c) and minimum cost (m). The branching factor is the average number of neighbor nodes that can be expanded from each node, the optimal cost is the cost of the optimal solution and the minimum cost is the lowest cost for a node.
Grid problem (maze)
I have created a simple maze (download it) with walls, a start point (@) and a goal point ($). Dijkstras algorithm is used to find the shortest path from the start node to a goal node by using the distance to the start node (g) as the heuristic.
# This class represents a node
class Node:
# Initialize the class
def __init__(self, position:(), parent:()):
self.position = position
self.parent = parent
self.g = 0 # Distance to start node
self.h = 0 # Distance to goal node
self.f = 0 # Total cost
# Compare nodes
def __eq__(self, other):
return self.position == other.position
# Sort nodes
def __lt__(self, other):
return self.f < other.f
# Print node
def __repr__(self):
return ('({0},{1})'.format(self.position, self.f))
# Draw a grid
def draw_grid(map, width, height, spacing=2, **kwargs):
for y in range(height):
for x in range(width):
print('%%-%ds' % spacing % draw_tile(map, (x, y), kwargs), end='')
print()
# Draw a tile
def draw_tile(map, position, kwargs):
# Get the map value
value = map.get(position)
# Check if we should print the path
if 'path' in kwargs and position in kwargs['path']: value = '+'
# Check if we should print start point
if 'start' in kwargs and position == kwargs['start']: value = '@'
# Check if we should print the goal point
if 'goal' in kwargs and position == kwargs['goal']: value = '$'
# Return a tile value
return value
# Dijkstra search
def dijkstra_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 = 0
neighbor.f = neighbor.g
# 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 = dijkstra_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()#################################################################################
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#+#########+#+#####.#.#.#.#.###.#####.#.#.#.#.#####.#.#########.###.###.###.#.#.#
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#+#+++++++#+#.#+++#...#.#.....#.#.#.#...#.#.....#.#.....#.#...#...#.......#...#.#
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#+###+#########+#.#.#.###.#.#####.#.#.###.###.###########.#.#####.#########.###.#
#+#..+++++++++++#.#.......#.#...#.#.#...#.#...#.#.......#.......#.#...#.....#...#
#+#.#############.#########.#.#.###.###.#.#.###.#.#####.#.#######.#.#.#.#####.#.#
#+#.#+++++++++++#.#.#.#.....#.#.....#...#.#.....#...#.#.#.#.#...#.#.#.#.#.....#.#
#+###+#########+#.#.#.#######.#######.###.#####.###.#.#.#.#.###.#.#.#.#.#####.#.#
#+++++#+++#+++++#...#.........#.....#...#.....#...#...#.#.....#.#...#.#.#.....#.#
#.#####+#+#+#######.###########.#######.#.#######.###.#.###.###.#####.#.#.#####.#
#.....#+#+#+++#...#.#+++++++#.........#.#...#.......#.#.#...#...#.....#.#.#...#.#
#######+#+###+#.###.#+#####+#.#####.###.#.#.#.#######.#.#####.###.#####.#.###.#.#
#+++++++#+#+++#.....#+#...#+#...#.#.....#.#.#.#.#.....#...#...#...#.....#...#.#.#
#+#######+#+#.#####.#+###.#+###.#.#######.#.#.#.#.#######.#.###.#.###.#####.#.#.#
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#+#.#+#####+#.#+#+#####+#.###+###.#.#.#.#.#####.#####.#.#####.#####.#########.###
#+#..+#..+++#.#+#+#+++#+++#.#+#...#...#.#.#...#.....#...#.#...#...#.....#...#.#.#
#+###+###+#.###+#+#+#+###+#.#+#.#######.#.#.#.#####.###.#.#.###.#.#####.###.#.#.#
#+++#+++#+#.#+++#+#+#+++#+#.#+#.#.......#...#.........#.#...#...#.#...#...#.#...#
#.#+###+#+#.#+###+#+###+#+#.#+#.###.###.###########.###.#.###.###.###.###.#.###.#
#.#+++#+#+#.#+++#+++#+++#+#.#+#.....#...#...#.....#.#...#.....#.....#.#...#...#.#
#.###+#+#+#####+#####+#.#+#.#+#######.###.#.#####.#.#.#############.#.#.###.#.#.#
#...#+#+++#+++#+++++#+#.#+#.#+#+++#...#.#.#.......#.#.#...#...#...#...#.#.#.#...#
###.#+#####+#+#####+#+###+#.#+#+#+#.###.#.#########.#.#.#.#.#.#.#.#####.#.#.#####
#...#+++++++#+++++++#+++++..#+++#+++++++@...........#...#...#...#.......#.......#
#################################################################################
Steps to goal: 339Graph problem
The problem formulation is to find the shortest path from a departure city to a destination city, a map has been used to create connections between cities in the graph. Dijkstras algorithm uses a Graph class, a Node class and the distance to the departure city (start) as the heuristic to guide the search.
# 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.position, self.f))
# Dijkstra search
def dijkstra_search(graph, 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 cost so far
neighbor.g = current_node.g + graph.get(current_node.name, neighbor.name)
neighbor.h = 0
neighbor.f = neighbor.g
# 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()
# Run search algorithm
path = dijkstra_search(graph, 'Frankfurt', 'Ulm')
print(path)
print()
# Tell python to run main method
if __name__ == "__main__": main()['Frankfurt: 0', 'Wurzburg: 111', 'Ulm: 294']