Implement a **Naive Bayes** classifier from scratch (you may use NumPy). Write a class with: - `fit(X, y)`: estimate class priors and feature likelihood parameters. - `predict(X)`: compute posteriors (or unnormalized log-posteriors) and output the most likely class. Specify which variant you implement (e.g., **Multinomial Naive Bayes** for count features, **Bernoulli NB** for binary features, or **Gaussian NB** for continuous features). Your implementation should clearly compute: - prior \(P(y=c)\) - likelihood parameters for \(P(x\mid y=c)\) - posterior scoring using Bayes’ rule with numerical stability (typically log-probabilities).