.. _quick_start: ############### Getting Started ############### Installation ============ From PyPI (Recommended) ----------------------- Once available on PyPI, install scikit-bayes with pip: .. prompt:: bash $ pip install scikit-bayes From Source ----------- To install the development version directly from GitHub: .. prompt:: bash $ pip install git+https://github.com/ptorrijos99/scikit-bayes.git Quick Examples ============== MixedNB: Handling Mixed Data Types ---------------------------------- :class:`skbayes.MixedNB` automatically detects and handles datasets with Gaussian (continuous), Categorical, and Bernoulli (binary) features: .. code-block:: python import numpy as np from skbn import MixedNB # Data: [Gaussian, Bernoulli, Categorical] X = np.array([ [0.5, 0, 0], # continuous, binary, categories 0-2 [-1.2, 1, 1], [0.6, 1, 2], [-0.1, 0, 0], ]) y = np.array([0, 1, 1, 0]) clf = MixedNB() clf.fit(X, y) print(clf.predict([[-0.5, 1, 1]])) # Output: [1] AnDE: Solving Problems Naive Bayes Cannot ----------------------------------------- :class:`skbayes.AnDE` relaxes the independence assumption of Naive Bayes, allowing it to capture feature dependencies like the XOR problem: .. code-block:: python import numpy as np from skbayes import AnDE # XOR problem: class depends on interaction of features X = np.array([[-1, -1], [-1, 1], [1, -1], [1, 1]]) y = np.array([0, 1, 1, 0]) # XOR: same sign → 0, different → 1 # Naive Bayes fails (~50% accuracy), AnDE succeeds clf = AnDE(n_dependence=1, n_bins=2) clf.fit(X, y) print(clf.predict(X)) # Output: [0, 1, 1, 0] ✓ Development Setup ================= This project uses `pixi `_ for environment management. Install pixi ------------ Follow the instructions at https://pixi.sh/latest/#installation Common Commands --------------- Run tests: .. prompt:: bash $ pixi run test Run linter: .. prompt:: bash $ pixi run lint Build documentation: .. prompt:: bash $ pixi run build-doc Activate development environment: .. prompt:: bash $ pixi shell -e dev This activates an environment with all dependencies for testing, linting, and building documentation. What's Next? ============ * :ref:`User Guide ` - Detailed documentation of all estimators * :ref:`API Reference ` - Complete API documentation * :ref:`Examples ` - Gallery of usage examples