pgm 4

 #pgm4

import pandas as pd


def find_s_algorithm(data):

    print("Training data:")

    print(data)


    attributes = data.columns[:-1]

    class_label = data.columns[-1]  


    hypothesis = None


    for index, row in data.iterrows():

        if row[class_label] == 'Yes':  # only consider positive examples

            if hypothesis is None:

                hypothesis = list(row[attributes])

            else:

                for i, value in enumerate(row[attributes]):

                    if hypothesis[i] != value:

                        hypothesis[i] = '?'  # generalize the hypothesis


    if hypothesis is None:

        hypothesis = ['?' for _ in attributes]


    return hypothesis


# Sample training data

sample_data = {

    'Sky': ['Sunny', 'Sunny', 'Rainy', 'Sunny'],

    'Temperature': ['Warm', 'Warm', 'Cold', 'Warm'],

    'Humidity': ['High', 'High', 'High', 'High'],

    'Wind': ['Weak', 'Strong', 'Weak', 'Weak'],

    'PlayTennis': ['Yes', 'Yes', 'No', 'Yes']

}


data = pd.DataFrame(sample_data)


hypothesis = find_s_algorithm(data)


print("\nThe final hypothesis is:", hypothesis)


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