Find-S Algorithm in Python

-
def find_s_algorithm(data, target):
    # 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)

 

Comments

Post a Comment

Popular posts from this blog

About me

-
Image
  Associate Professor Anil Neerukonda Institute of Technology & Sciences Sangivalsa, Visakhapatnam, A.P., INDIA. VIDWAN | ORCID | SCOPUS | RESEARCH ID | GOOGLE ID
Read more

A set of documents that need to be classified, use the Naive Bayesian Classifier

-
The Naive Bayes Classifier is a probabilistic machine learning model widely used for classification tasks, including document classification. Based on Bayes' Theorem, it assumes that the features (in this case, words or terms in the documents) are conditionally independent given the class label. Despite this "naive" assumption, it often performs well in practice, especially for text classification. Steps to Perform Document Classification Using Naive Bayes 1.  Prepare the Dataset Documents : Assume you have a set of documents, each labeled with a category (e.g., "Sports", "Politics", "Technology"). Preprocessing : Tokenize the text into words. Remove stop words (e.g., "the", "is", "and"). Perform stemming or lemmatization to reduce words to their base forms. Convert text into a numerical representation, such as a  bag-of-words  or  TF-IDF  vector. 2.  Split the Dataset Divide the dataset into a  training set  and...
Read more

Keras

-
  Understanding Keras Models: Your Neural Network Blueprint Think of a Keras model as the blueprint or architecture for building neural networks. Keras is designed to be simple and lets you create models quickly, while running on powerful engines like TensorFlow in the background. The Three Ways to Build a Keras Model 1. Sequential API: The Straightforward Stack What it is: The easiest way to build a model, like stacking Lego blocks in a single, straight line. Best for: Standard networks where data flows directly from one layer to the next with no fancy detours. Limitation: It's too rigid for complex designs that need multiple inputs, outputs, or layers that share connections. 2. Functional API: The Flexible Designer What it is: A more powerful approach that lets you create complex, branch-like architectures. Best for: Advanced designs with multiple inputs/outputs, skip connections (like shortcuts in the network), or layers that are shared. Example: Building sophisticated models li...
Read more