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AI Practicals
Practical 1 - Random Search Algorithm
Q1
...
choice(directions)
if move_blank_tile(current_board, random_direction):
num_moves += 1
if is_goal_state(current_board, goal_state):
return current_board, num_moves
initial_board = [[2, 8, 3], [1, 6, 4], [7, 0, 5]]
goal_state = [[1, 2, 3], [8, 0, 4], [7, 6, 5]]
solved_board, num_moves = random_search(initial_board,
goal_state)
print(solved_board,"\n",num_moves)

Q2
...

import random
def quadratic_function(x): # finding the value of the function
return 2 * x**2 - 5 * x + 3
def find_minimum_random_search(max_iterations=1000): #Finding
the minimum
best_x = None
best_value = float('inf') # Initialize with positive
infinity
for _ in range(max_iterations):
x = random
...
WAPP for password cracking using random search, incorporating character combinations
import random
import string
def generate_possible_passwords(length):
characters = string
...
digits +
string
...
join(random
...
Implement the water jug problem using BFS
from collections import deque
def BFS_SEARCH(cap1, cap2, target):
closed = set()
open = deque()
open
...
popleft()
print("Current State : ", current_state)
if current_state in closed:
continue
closed
...
append((cap1,y))
#Fill in 2nd jug
if yopen
...
append((x-a,y+a))
#Pour from 2 to 1
if x+y>=cap2 and y>0:
a = min(cap2, cap1-x)
open
...
append((0,y))
#Empty 2nd jug
if y>0:
open
...
Implement Water jug problem using DFS
from collections import deque
def DFS_SEARCH(cap1, cap2, target):
closed = set()
open = deque()
open
...
pop()
print("Current State : ", current_state)
if current_state in closed:

continue
closed
...
append((cap1,y))
#Fill in 2nd jug
if yopen
...
append((x-a,y+a))
#Pour from 2 to 1
if x+y>=cap2 and y>0:
a = min(cap2, cap1-x)
open
...
append((0,y))
#Empty 2nd jug
if y>0:
open
...
Demonstrate the implementation of Uniform cost search for a small city map
from geopy
...
distance
geolocator = Nominatim(user_agent="DistanceCalculator")

def get_lat_long(city):
loc = geolocator
...
latitude, loc
...
distance
...
km
graph = {"nodes": set(), "edges": {}}
def add_locations(location1,location2):
if location1 not in graph["nodes"]:
graph["nodes"]
...
add(location2)
graph["edges"][location2] = []
print(graph)
position1 = get_lat_long(location1)
position2 = get_lat_long(location2)
print(position1, position2)
distance =
round(calculateDistance(position1[0],position1[1],position2[0]
,position2[1]),2)
print(distance)
graph["edges"][location1]
...
append((location1,distance))
def uniform_cost_search(start, goal):
priority_queue = [(0,start,[])]
while priority_queue:
(cost, current_location,path) = priority_queue
...
append((cost+distance, neighbour, path))
priority_queue
...
Implement 8-Puzzle Problem Using Best-First Search
import heapq
import copy
#Displaying the 8 puzzle:
def print_puzzle(state):
for i in range(3):
for j in range(3):
print(state[i][j],"|",end = " ")
print()
# counting misplaced value
def heuristic(current_state, goal_state):
sum = 0
for i in range(3):
for j in range(3):
if current_state[i][j]!=0 and
current_state[i][j]!=goal_state[i][j]:
sum +=1
return sum
# get neighbors
def get_neighbors(state):
neighbors = []
#get_blanktile:
for i in range(3):
for j in range(3):
if state[i][j]==0:
row, col = i, j

for dr, dc in [(1, 0), (-1, 0), (0, 1), (0, -1)]:
new_row, new_col = row + dr, col + dc
if 0 <= new_row < 3 and 0 <= new_col < 3:
new_state = copy
...
append(new_state)
return neighbors
# best_first_search algorithm
def best_first_search(initial_state, goal_state):
start_node = initial_state
goal_node = goal_state
priority_queue = [(heuristic(initial_state, goal_state),
start_node)]
while priority_queue:
h_value, current_node = heapq
...
heappush(priority_queue,(heuristic(neighbor_state,
goal_state), neighbor_state))
return "Failure"
start = [[1,2,3],
[0,5,6],
[4,7,8]]
goal = [[1,2,3],
[4,5,6],
[7,8,0]]
best_first_search(start,goal)
Q2
...
deepcopy(state)
new_state[row][col],new_state[new_row][new_col] =
new_state[new_row][new_col],new_state[row][col]
neighbors
...
heappop(priority_queue)
print_puzzle(current_node)
print("The heuristic value: ", h_value)

if (current_node == goal_state):
print("puzzle solved!")
return True
cost += 1 #change
for neighbor_state in get_neighbors(current_node):
heapq
...
Implement tic tac toe game using Minimax algorithm
...
Create a fuzzy control system which models how you might choose to tip at a restaurant
...
You use
this to leave a tip of between 0 and 25%
...
Antecedent(np
...
Antecedent(np
...
Consequent(np
...
automf(3)
service
...
view()
# Custom membership functions for tips:
tip['low'] = fuzz
...
universe, [0, 0, 13])
tip['medium'] = fuzz
...
universe, [0, 13, 25])
tip['high'] = fuzz
...
universe, [13, 25, 25])
tip
...
Rule(quality['poor'] | service['poor'],
tip['low'])
rule2 = ctrl
...
Rule(service['good'] | quality['good'],
tip['high'])
tipping_ctrl = ctrl
...
ControlSystemSimulation(tipping_ctrl)
tipping
...
5
tipping
...
8
tipping
...
output['tip'])
tip
...
Demonstrate the use of linear discriminant analysis for a classification problem
...
pyplot as plt
from sklearn
...
model_selection import train_test_split
from sklearn
...
metrics import confusion_matrix
# Preparing dataset:
from sklearn
...
data
y = dt
...
fit_transform(X,y)
lda
...
xlabel('LD1')
plt
...
scatter(lda_t[:,0],lda_t[:,1],c=y,cmap='rainbow',edgecolor
s='black')
Practical 8 - CNN(Kmeans)
Q1
...
Perform Clustering of
MNIST dataset using CNN & K-means
# Importing necessary libraries
import numpy as np
import tensorflow as tf
from tensorflow
...
cluster import KMeans
from sklearn
...
pyplot as plt
# Load MNIST dataset
mnist = tf
...
datasets
...
load_data()
# Printing sample data images
for i in range(5):

plt
...
imshow(x_train[i], cmap='gray')
plt
...
axis('off')
plt
...
0, x_test / 255
...
expand_dims(x_train, axis=-1)
x_test = np
...
Sequential([
layers
...
MaxPooling2D(pool_size=(2, 2)),
layers
...
MaxPooling2D(pool_size=(2, 2)),
layers
...
Dense(128, activation='relu'),
layers
...
5),
layers
...
compile(optimizer='adam',
loss='sparse_categorical_crossentropy', metrics=['accuracy'])
return model
cnn_model = build_cnn(input_shape=x_train[0]
...
utils import plot_model
plot_model(cnn_model, show_shapes=True, show_layer_names=True)
cnn_model
...
Model(inputs=cnn_model
...
layers[-2]
...
predict(x_train)
features_test = feature_extractor
...
fit(features_train)
# Predict clusters for test data
cluster_labels = kmeans
...
figure(figsize=(10, 10))
for cluster_id in range(10):
cluster_indices = np
...
subplot(10, 3, cluster_id*3 + i + 1)
plt
...
reshape(28, 28),
cmap='gray')
plt
...
title(f'Cluster {cluster_id}', fontsize=10)
# Show the plot
plt
...
show()
Practical 9 - Decision Tree and KNN algorithm
Q1
...

import numpy as np
import matplotlib
...
read_csv('Social_Network_Ads
...
iloc[:, :-1]
...
iloc[:, -1]
...
model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size = 0
...
preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc
...
transform(X_test)
from sklearn
...
fit(X_train, y_train)
from sklearn
...
Develop a K-NN model to predict whether the customer will make a purchase or not
based on age and salary
...
pyplot as plt
import pandas as pd
dataset = pd
...
csv')
X = dataset
...
values
y = dataset
...
values
from sklearn
...
25, random_state = 0)
from sklearn
...
fit_transform(X_train)
X_test = sc
...
neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric =
'minkowski', p = 2)
classifier
...
predict(X_test)
print(np
...
reshape(len(y_pred),1),
y_test
...
metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred)



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Title: Artificial Intelligence Codes in python language
Description: These are codes for every method in AI and are well explained using comments within the codes. As I have also previously uploaded the theory notes for these. You can refer them for better understanding.

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