a3

#program 3
#Importing necessary libraries
# import numpy as np
import pandas as pd
# Reading the dataset
data = pd.read_csv('P3Data.csv')
concepts = data.iloc[:, :-1].values
target = data.iloc[:, -1].values
print(concepts)
print(target)
# Candidate Elimination algorithm
def learn(concepts, target):
 specific_h = concepts[0].copy() # Initialize specific hypothesis
 general_h = [["?" for _ in range(len(specific_h))] for _ in
range(len(specific_h))]
 print("\nInitialization of specific_h and general_h")
 print("\nSpecific hypothesis: ", specific_h)
 print("\nGeneric hypothesis: \n", general_h)
 for i, h in enumerate(concepts):
 print("\nInstance", i+1, "is ", h)
 print("Instance is Positive")
 if target[i] == "yes": # Positive instance
 specific_h = ['?' if specific_h[x] != h[x] else
specific_h[x] for x in range(len(specific_h))]

 else: # Negative instance
 print("Instance is Negative")
 for x in range(len(specific_h)):
 if specific_h[x] != h[x]:
 general_h[x][x] = specific_h[x]
 print("Specific hypothesis after ", i+1, "Instance is ",
specific_h)
 print("Generic hypothesis after ", i+1, "Instance is ",
general_h)
 print("\n")
 # Remove redundant general hypotheses
 general_h = [gh for gh in general_h if gh != ['?' for _ in
range(len(specific_h))]]
 return specific_h, general_h
# Train and display results
s_final, g_final = learn(concepts, target)
print("Final Specific_h:\n", s_final)
print("Final General_h:", g_final)