Prac-01

1a. Write a program to implement depth first search algorithm.

graph1 = {
'A': set(['B', 'C']),
'B': set(['A', 'D', 'E']),
'C': set(['A', 'F']),
'D': set(['B']),
'E': set(['B', 'F']),
'F': set(['C', 'E'])
}
def dfs(graph, node, visited):
if node not in visited:
visited.append(node)
for n in graph[node]:
dfs(graph,n, visited)
return visited
visited = dfs(graph1,'A', [])
print(visited)

1b. Write a program to implement breadth first search algorithm.

graph = {'A': set(['B', 'C']),
'B': set(['A', 'D', 'E']),
'C': set(['A', 'F']),
'D': set(['B']),
'E': set(['B', 'F']),
'F': set(['C', 'E'])
}
#Implement Logic of BFS
def bfs(start):
 queue = [start]
levels={} #This Dict Keeps track of levels
levels[start]=0 #Depth of start node is 0
visited = set(start)
while queue:
node = queue.pop(0)
neighbours=graph[node]
for neighbor in neighbours:
if neighbor not in visited:
queue.append(neighbor)
visited.add(neighbor)
levels[neighbor]= levels[node]+1
print(levels) #print graph level
return visited
print(str(bfs('A'))) #print graph node
#For Finding Breadth First Search Path
def bfs_paths(graph, start, goal):
queue = [(start, [start])]
while queue:
(vertex, path) = queue.pop(0)
for next in graph[vertex] - set(path):
if next == goal:
yield path + [next]
else:
queue.append((next, path + [next]))
result=list(bfs_paths(graph, 'A', 'F'))
print(result)# [['A', 'C', 'F'], ['A', 'B', 'E', 'F']]
#For finding shortest path
def shortest_path(graph, start, goal):
try:
return next(bfs_paths(graph, start, goal))
except StopIteration:
return None
result1=shortest_path(graph, 'A', 'F')
print(result1)# ['A', 'C', 'F']
Prac2
2a. Write a program to simulate 4-Queen / N-Queen problem.
class QueenChessBoard:
def __init__(self, size):
# board has dimensions size x size
self.size = size
# columns[r] is a number c if a queen is placed at row r and column c.
# columns[r] is out of range if no queen is place in row r.
# Thus after all queens are placed, they will be at positions
# (columns[0], 0), (columns[1], 1), ... (columns[size - 1], size - 1)
self.columns = []
def place_in_next_row(self, column):
self.columns.append(column)
def remove_in_current_row(self):
return self.columns.pop()
def is_this_column_safe_in_next_row(self, column):
# index of next row
row = len(self.columns)
# check column
for queen_column in self.columns:
if column == queen_column:
return False
# check diagonal
for queen_row, queen_column in enumerate(self.columns):
if queen_column - queen_row == column - row:
return False
# check other diagonal
for queen_row, queen_column in enumerate(self.columns):
if ((self.size - queen_column) - queen_row
== (self.size - column) - row):
return False
return True
def display(self):
for row in range(self.size):
for column in range(self.size):
if column == self.columns[row]:
print('Q', end=' ')
else:
print('.', end=' ')
print()
def solve_queen(size):
"""Display a chessboard for each possible configuration of placing n
queens on an n x n chessboard and print the number of such
configurations."""
 board = QueenChessBoard(size)
number_of_solutions = 0

row = 0
column = 0
# iterate over rows of board
while True:
# place queen in next row
while column < size:
if board.is_this_column_safe_in_next_row(column):
board.place_in_next_row(column)
row += 1
column = 0
break
else:
column += 1

# if could not find column to place in or if board is full

if (column == size or row == size):
# if board is full, we have a solution
if row == size:
board.display()
print()
number_of_solutions += 1
# small optimization:
# In a board that already has queens placed in all rows except
# the last, we know there can only be at most one position in
# the last row where a queen can be placed. In this case, there
# is a valid position in the last row. Thus we can backtrack two
# times to reach the second last row.
board.remove_in_current_row()
row -= 1
# now backtrack
try:
prev_column = board.remove_in_current_row()
except IndexError:
# all queens removed
# thus no more possible configurations
break
# try previous row again
row -= 1
# start checking at column = (1 + value of column in previous row)
column = 1 + prev_column
print('Number of solutions:', number_of_solutions)
n = int(input('Enter n: '))
solve_queen
2b. Write a program to solve tower of Hanoi problem.
def moveTower(height,fromPole, toPole, withPole):
if height >= 1:
moveTower(height-1,fromPole,withPole,toPole)
moveDisk(fromPole,toPole)
moveTower(height-1,withPole,toPole,fromPole)
def moveDisk(fp,tp):
print("moving disk from",fp,"to",tp)
moveTower(3,"A","B","C")
Prac3
3a. Write a program to implement alpha beta search.
tree = [[[5, 1, 2], [8, -8, -9]], [[9, 4, 5], [-3, 4, 3]]]
root = 0
pruned = 0

def children(branch, depth, alpha, beta):

global tree
global root
global pruned
i=0
for child in branch:
if type(child) is list:
(nalpha, nbeta) = children(child, depth + 1, alpha, beta)
if depth % 2 == 1:
beta = nalpha if nalpha < beta else beta
else:
alpha = nbeta if nbeta > alpha else alpha
branch[i] = alpha if depth % 2 == 0 else beta
i += 1
else:
if depth % 2 == 0 and alpha < child:
alpha = child
if depth % 2 == 1 and beta > child:
beta = child
if alpha >= beta:
pruned += 1
break
if depth == root:
tree = alpha if root == 0 else beta
return (alpha, beta)

def alphabeta(in_tree=tree, start=root, upper=-15, lower=15):

global tree
global pruned
global root

(alpha, beta) = children(tree, start, upper, lower)

if __name__ == "__main__":
print ("(alpha, beta): ", alpha, beta)
print ("Result: ", tree)
print ("Times pruned: ", pruned)

return (alpha, beta, tree, pruned)

if __name__ == "__main__":
alphabeta(None)

3b. Write a program for Hill climbing problem.

import math
increment = 0.1
startingPoint = [1, 1]
point1 = [1,5]
point2 = [6,4]
point3 = [5,2]
point4 = [2,1]
def distance(x1, y1, x2, y2):
dist = math.pow(x2-x1, 2) + math.pow(y2-y1, 2)
return dist
def sumOfDistances(x1, y1, px1, py1, px2, py2, px3, py3, px4, py4):
d1 = distance(x1, y1, px1, py1)
d2 = distance(x1, y1, px2, py2)
d3 = distance(x1, y1, px3, py3)
d4 = distance(x1, y1, px4, py4)
return d1 + d2 + d3 + d4

def newDistance(x1, y1, point1, point2, point3, point4):

d1 = [x1, y1]
d1temp = sumOfDistances(x1, y1, point1[0],point1[1], point2[0],point2[1],
point3[0],point3[1], point4[0],point4[1] )
d1.append(d1temp)
return d1
minDistance = sumOfDistances(startingPoint[0], startingPoint[1],
point1[0],point1[1], point2[0],point2[1],
point3[0],point3[1], point4[0],point4[1] )
flag = True
def newPoints(minimum, d1, d2, d3, d4):
if d1[2] == minimum:
return [d1[0], d1[1]]
elif d2[2] == minimum:
return [d2[0], d2[1]]
elif d3[2] == minimum:
return [d3[0], d3[1]]
elif d4[2] == minimum:
return [d4[0], d4[1]]
i=1
while flag:
d1 = newDistance(startingPoint[0]+increment, startingPoint[1], point1, point2,
point3, point4)
d2 = newDistance(startingPoint[0]-increment, startingPoint[1], point1, point2,
point3, point4)
d3 = newDistance(startingPoint[0], startingPoint[1]+increment, point1, point2,
point3, point4)
d4 = newDistance(startingPoint[0], startingPoint[1]-increment, point1, point2,
point3, point4)
print (i,' ', round(startingPoint[0], 2), round(startingPoint[1], 2))
minimum = min(d1[2], d2[2], d3[2], d4[2])
if minimum < minDistance:
startingPoint = newPoints(minimum, d1, d2, d3, d4)
minDistance = minimum
#print i,' ', round(startingPoint[0], 2), round(startingPoint[1], 2)
i+=1

else:
flag = False
Prac4
4a. Write a program to implement A* algorithm.
from simpleai.search import SearchProblem, astar

GOAL = 'HELLO WORLD'

class HelloProblem(SearchProblem):
def actions(self, state):
if len(state) < len(GOAL):
return list(' ABCDEFGHIJKLMNOPQRSTUVWXYZ')
else:
return []

def result(self, state, action):

return state + action
 def is_goal(self, state):
return state == GOAL

def heuristic(self, state):

# how far are we from the goal?

wrong = sum([1 if state[i] != GOAL[i] else 0

for i in range(len(state))])
missing = len(GOAL) - len(state)
return wrong + missing

problem = HelloProblem(initial_state='')
result = astar(problem)
print(result.state)
print(result.path())

Prac5
5a. Write a program to solve water jug problem.
# 3 water jugs capacity -> (x,y,z) where x>y>z
# initial state (12,0,0)
# final state (6,6,0)
capacity = (12,8,5)
# Maximum capacities of 3 jugs -> x,y,z
x = capacity[0]
y = capacity[1]
z = capacity[2]
# to mark visited states
memory = {}
# store solution path
ans = []
def get_all_states(state):
# Let the 3 jugs be called a,b,c
a = state[0]
b = state[1]
c = state[2]
if(a==6 and b==6):
ans.append(state)
return True
# if current state is already visited earlier
if((a,b,c) in memory):
return False
memory[(a,b,c)] = 1
#empty jug a
if(a>0):
#empty a into b
if(a+b<=y):
if( get_all_states((0,a+b,c)) ):
ans.append(state)
return True
else:
if( get_all_states((a-(y-b), y, c)) ):
ans.append(state)
return True
#empty a into c
if(a+c<=z):
if( get_all_states((0,b,a+c)) ):
ans.append(state)
return True
else:
if( get_all_states((a-(z-c), b, z)) ):
ans.append(state)
return True
#empty jug b
if(b>0):
#empty b into a
if(a+b<=x):
if( get_all_states((a+b, 0, c)) ):
ans.append(state)
return True
else:
if( get_all_states((x, b-(x-a), c)) ):
ans.append(state)
return True
#empty b into c
if(b+c<=z):
if( get_all_states((a, 0, b+c)) ):
 ans.append(state)
return True
else:
if( get_all_states((a, b-(z-c), z)) ):
ans.append(state)
return True
#empty jug c
if(c>0):
#empty c into a
if(a+c<=x):
if( get_all_states((a+c, b, 0)) ):
ans.append(state)
return True
else:
if( get_all_states((x, b, c-(x-a))) ):
ans.append(state)
return True
#empty c into b
if(b+c<=y):
if( get_all_states((a, b+c, 0)) ):
ans.append(state)
return True
else:
if( get_all_states((a, y, c-(y-b))) ):
ans.append(state)
return True
return False
initial_state = (12,0,0)
print("Starting work...\n")
get_all_states(initial_state)
ans.reverse()
for i in ans:
print(i)
5b. Design the simulation of TIC – TAC –TOE game using min-max
algorithm.
import os

import time

board = [' ',' ',' ',' ',' ',' ',' ',' ',' ',' ']

player = 1

########win Flags##########

Win = 1

Draw = -1

Running = 0

Stop = 1

###########################

Game = Running
Mark = 'X'

#This Function Draws Game Board

def DrawBoard():

print(" %c | %c | %c " % (board[1],board[2],board[3]))

print("___|___|___")

print(" %c | %c | %c " % (board[4],board[5],board[6]))

print("___|___|___")

print(" %c | %c | %c " % (board[7],board[8],board[9]))

print(" | | ")

#This Function Checks position is empty or not

def CheckPosition(x):

if(board[x] == ' '):

return True

else:

return False

#This Function Checks player has won or not

def CheckWin():

global Game

#Horizontal winning condition

if(board[1] == board[2] and board[2] == board[3] and board[1] != ' '):

Game = Win

elif(board[4] == board[5] and board[5] == board[6] and board[4] != ' '):

Game = Win

elif(board[7] == board[8] and board[8] == board[9] and board[7] != ' '):

 Game = Win

#Vertical Winning Condition

elif(board[1] == board[4] and board[4] == board[7] and board[1] != ' '):

Game = Win

elif(board[2] == board[5] and board[5] == board[8] and board[2] != ' '):

Game = Win

elif(board[3] == board[6] and board[6] == board[9] and board[3] != ' '):

Game=Win

#Diagonal Winning Condition

elif(board[1] == board[5] and board[5] == board[9] and board[5] != ' '):

Game = Win

elif(board[3] == board[5] and board[5] == board[7] and board[5] != ' '):

Game=Win

#Match Tie or Draw Condition

elif(board[1]!=' ' and board[2]!=' ' and board[3]!=' ' and board[4]!=' ' and

board[5]!=' ' and board[6]!=' ' and board[7]!=' ' and board[8]!=' ' and board[9]!=' '):

Game=Draw

else:

Game=Running

print("Tic-Tac-Toe Game")

print("Player 1 [X] --- Player 2 [O]\n")

print()

print()

print("Please Wait...")

time.sleep(1)
while(Game == Running):

os.system('cls')

DrawBoard()

if(player % 2 != 0):

print("Player 1's chance")

Mark = 'X'

else:

print("Player 2's chance")

Mark = 'O'

choice = int(input("Enter the position between [1-9] where you want to mark :"))

if(CheckPosition(choice)):

board[choice] = Mark

player+=1

CheckWin()

os.system('cls')

DrawBoard()

if(Game==Draw):

print("Game Draw")

elif(Game==Win):

player-=1

if(player%2!=0):

print("Player 1 Won")

else:

print("Player 2 Won")
Prac6
6a. Write a program to solve Missionaries and Cannibals problem.
import math
# Missionaries and Cannibals Problem

class State():
def __init__(self, cannibalLeft, missionaryLeft, boat, cannibalRight,missionaryRight):
self.cannibalLeft = cannibalLeft
self.missionaryLeft = missionaryLeft
self.boat = boat
self.cannibalRight = cannibalRight
self.missionaryRight = missionaryRight
self.parent = None

def is_goal(self):
if self.cannibalLeft == 0 and self.missionaryLeft == 0:
return True
else:
return False
def is_valid(self):
if self.missionaryLeft >= 0 and self.missionaryRight >= 0 \
and self.cannibalLeft >= 0 and self.cannibalRight >= 0 \
and (self.missionaryLeft == 0 or self.missionaryLeft >=
self.cannibalLeft) \
and (self.missionaryRight == 0 or self.missionaryRight >=self.cannibalRight):
return True
else:
return False

def __eq__(self, other):

return self.cannibalLeft == other.cannibalLeft and self.missionaryLeft==
other.missionaryLeft \
and self.boat == other.boat and self.cannibalRight == other.cannibalRight \
and self.missionaryRight == other.missionaryRight

def __hash__(self):
return hash((self.cannibalLeft, self.missionaryLeft, self.boat, self.cannibalRight,
self.missionaryRight))

def successors(cur_state):
children = [];
if cur_state.boat == 'left':
new_state = State(cur_state.cannibalLeft, cur_state.missionaryLeft -2, 'right',
cur_state.cannibalRight, cur_state.missionaryRight + 2)

## Two missionaries cross left to right.

if new_state.is_valid():
new_state.parent = cur_state
 children.append(new_state)
new_state = State(cur_state.cannibalLeft - 2, cur_state.missionaryLeft, 'right',
cur_state.cannibalRight + 2, cur_state.missionaryRight)
## Two cannibals cross left to right.
if new_state.is_valid():
new_state.parent = cur_state
children.append(new_state)
new_state = State(cur_state.cannibalLeft - 1, cur_state.missionaryLeft - 1, 'right',
cur_state.cannibalRight + 1, cur_state.missionaryRight + 1)
## One missionary and one cannibal cross left to right.
if new_state.is_valid():
new_state.parent = cur_state
children.append(new_state)
new_state = State(cur_state.cannibalLeft, cur_state.missionaryLeft -1, 'right',
cur_state.cannibalRight, cur_state.missionaryRight + 1)
## One missionary crosses left to right.
if new_state.is_valid():
new_state.parent = cur_state
children.append(new_state)
new_state = State(cur_state.cannibalLeft - 1, cur_state.missionaryLeft, 'right',
cur_state.cannibalRight + 1, cur_state.missionaryRight)
## One cannibal crosses left to right.
if new_state.is_valid():
new_state.parent = cur_state
children.append(new_state)
else:
new_state = State(cur_state.cannibalLeft, cur_state.missionaryLeft + 2, 'left',
cur_state.cannibalRight, cur_state.missionaryRight - 2)
## Two missionaries cross right to left.
if new_state.is_valid():
new_state.parent = cur_state
children.append(new_state)
new_state = State(cur_state.cannibalLeft + 2, cur_state.missionaryLeft, 'left',
cur_state.cannibalRight - 2, cur_state.missionaryRight)

## Two cannibals cross right to left.

if new_state.is_valid():
new_state.parent = cur_state
children.append(new_state)
new_state = State(cur_state.cannibalLeft + 1, cur_state.missionaryLeft + 1, 'left',
cur_state.cannibalRight - 1, cur_state.missionaryRight - 1)

## One missionary and one cannibal cross right to left.

if new_state.is_valid():
new_state.parent = cur_state
children.append(new_state)
 new_state = State(cur_state.cannibalLeft, cur_state.missionaryLeft + 1, 'left',
cur_state.cannibalRight, cur_state.missionaryRight - 1)
## One missionary crosses right to left.
if new_state.is_valid():
new_state.parent = cur_state
children.append(new_state)
new_state = State(cur_state.cannibalLeft + 1, cur_state.missionaryLeft, 'left',
cur_state.cannibalRight - 1, cur_state.missionaryRight)
## One cannibal crosses right to left.
if new_state.is_valid():
new_state.parent = cur_state
children.append(new_state)
return children

def breadth_first_search():
initial_state = State(3,3,'left',0,0)
if initial_state.is_goal():
return initial_state
frontier = list()
explored = set()
frontier.append(initial_state)
while frontier:
state = frontier.pop(0)
if state.is_goal():
return state
explored.add(state)
children = successors(state)
for child in children:
if (child not in explored) or (child not in frontier):
frontier.append(child)
return None

def print_solution(solution):
path = []
path.append(solution)
parent = solution.parent
while parent:
path.append(parent)
parent = parent.parent

for t in range(len(path)):
state = path[len(path) - t - 1]
print ("(" + str(state.cannibalLeft) + "," + str(state.missionaryLeft) \
+ "," + state.boat + "," + str(state.cannibalRight) + "," + \
str(state.missionaryRight) + ")")
def main():
solution = breadth_first_search()
print ("Missionaries and Cannibals solution:")
print ("(cannibalLeft,missionaryLeft,boat,cannibalRight,missionaryRight)")
print_solution(solution)

# if called from the command line, call main()

if __name__ == "__main__":
main()

6b. Design an application to simulate number puzzle problem.

from __future__ import print_function

from simpleai.search import astar, SearchProblem

from simpleai.search.viewers import WebViewer

GOAL = '''1-2-3

4-5-6

7-8-e'''

INITIAL = '''4-1-2

7-e-3

8-5-6'''

def list_to_string(list_):

return '\n'.join(['-'.join(row) for row in list_])

def string_to_list(string_):

return [row.split('-') for row in string_.split('\n')]

def find_location(rows, element_to_find):

'''Find the location of a piece in the puzzle.

Returns a tuple: row, column'''

for ir, row in enumerate(rows):

for ic, element in enumerate(row):

if element == element_to_find:

return ir, ic

# we create a cache for the goal position of each piece, so we don't have to

# recalculate them every time

goal_positions = {}

rows_goal = string_to_list(GOAL)

for number in '12345678e':

goal_positions[number] = find_location(rows_goal, number)

class EigthPuzzleProblem(SearchProblem):

def actions(self, state):

'''Returns a list of the pieces we can move to the empty space.'''

rows = string_to_list(state)

row_e, col_e = find_location(rows, 'e')

actions = []

if row_e > 0:

actions.append(rows[row_e - 1][col_e])

if row_e < 2:

actions.append(rows[row_e + 1][col_e])

if col_e > 0:

actions.append(rows[row_e][col_e - 1])

if col_e < 2:

actions.append(rows[row_e][col_e + 1])

return actions

def result(self, state, action):

'''Return the resulting state after moving a piece to the empty space.

(the "action" parameter contains the piece to move)

'''

rows = string_to_list(state)

row_e, col_e = find_location(rows, 'e')

row_n, col_n = find_location(rows, action)

rows[row_e][col_e], rows[row_n][col_n] = rows[row_n][col_n],

rows[row_e][col_e]
return list_to_string(rows)

def is_goal(self, state):

'''Returns true if a state is the goal state.'''

return state == GOAL

def cost(self, state1, action, state2):

'''Returns the cost of performing an action. No useful on this problem, i

but needed.

'''

return 1

def heuristic(self, state):

'''Returns an *estimation* of the distance from a state to the goal.

We are using the manhattan distance.

'''

rows = string_to_list(state)

distance = 0

for number in '12345678e':

row_n, col_n = find_location(rows, number)

row_n_goal, col_n_goal = goal_positions[number]

distance += abs(row_n - row_n_goal) + abs(col_n - col_n_goal)

return distance

result = astar(EigthPuzzleProblem(INITIAL))

for action, state in result.path():

print('Move number', action)

print(state
Prac7
7a. Write a program to shuffle Deck of cards.
#first let's import random procedures since we will be shuffling
import random

#next, let's start building list holders so we can place our cards in there:
cardfaces = []
suits = ["Hearts", "Diamonds", "Clubs", "Spades"]
royals = ["J", "Q", "K", "A"]
deck = []

#now, let's start using loops to add our content:

for i in range(2,11):
cardfaces.append(str(i)) #this adds numbers 2-10 and converts them to string data

for j in range(4):

cardfaces.append(royals[j]) #this will add the royal faces to the cardbase

for k in range(4):
for l in range(13):
card = (cardfaces[l] + " of " + suits[k])
#this makes each card, cycling through suits, but first through faces
deck.append(card)
#this adds the information to the "full deck" we want to make
#now let's shuffle our deck!
random.shuffle(deck)

#now let's see the cards!

for m in range(52):
print(deck[m])

8a. Solve Constraints Satisfactions Problem.

from __future__ import print_function

from simpleai.search import CspProblem, backtrack,

min_conflicts,MOST_CONSTRAINED_VARIABLE, HIGHEST_DEGREE_VARIABLE,
LEAST_CONSTRAINING_VALUE

variables = ('WA', 'NT', 'SA', 'Q', 'NSW', 'V', 'T')

domains = dict((v, ['red', 'green', 'blue']) for v in variables)

def const_different(variables, values):

return values[0] != values[1] # expect the value of the neighbors to be different

constraints = [

(('WA', 'NT'), const_different),

(('WA', 'SA'), const_different),

(('SA', 'NT'), const_different),

(('SA', 'Q'), const_different),

(('NT', 'Q'), const_different),

(('SA', 'NSW'), const_different),

(('Q', 'NSW'), const_different),

(('SA', 'V'), const_different),

(('NSW', 'V'), const_different)

my_problem = CspProblem(variables, domains, constraints)

print(backtrack(my_problem))

print(backtrack(my_problem,

variable_heuristic=MOST_CONSTRAINED_VARIABLE))

print(backtrack(my_problem,

variable_heuristic=HIGHEST_DEGREE_VARIABLE))

print(backtrack(my_problem,

value_heuristic=LEAST_CONSTRAINING_VALUE))

print(backtrack(my_problem,

variable_heuristic=MOST_CONSTRAINED_VARIABLE,

value_heuristic=LEAST_CONSTRAINING_VALUE))

print(backtrack(my_problem,

variable_heuristic=HIGHEST_DEGREE_VARIABLE,

value_heuristic=LEAST_CONSTRAINING_VALUE))

print(min_conflicts(my_problem))