Find S- Algorithm

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 def find_s_algorithm(data, target):
    """
    Implements the Find-S algorithm for concept learning.
    Parameters:
    - data: List of examples (list of lists).
    - target: List of target values (list of strings, e.g., 'Yes' or 'No').
    Returns:
    - The most specific hypothesis.
    """
    # Step 1: Initialize the most specific hypothesis
    hypothesis = ["Φ"] * len(data[0])
    # Step 2: Iterate over the dataset
    for i, instance in enumerate(data):
        if target[i] == "Yes":  # Process only positive examples
            for j in range(len(instance)):
                if hypothesis[j] == "Φ":  # Initialize to the first positive example
                    hypothesis[j] = instance[j]
                elif hypothesis[j] != instance[j]:  # Generalize if there's a mismatch
                    hypothesis[j] = "?"
    
    return hypothesis

# Example Dataset
attributes = [
    ["Sunny", "Warm", "Normal", "Strong", "Warm", "Same"],
    ["Sunny", "Warm", "High", "Strong", "Warm", "Same"],
    ["Rainy", "Cold", "High", "Strong", "Warm", "Change"],
    ["Sunny", "Warm", "High", "Weak", "Warm", "Same"],
    ["Sunny", "Cool", "Normal", "Weak", "Warm", "Change"]
]
target = ["Yes", "Yes", "No", "Yes", "No"]
# Run Find-S Algorithm
final_hypothesis = find_s_algorithm(attributes, target)
# Display the results
print("Final Hypothesis:", final_hypothesis)

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