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Changelog

v0.2.1 (2026-03-21)

Fixed

  • Benchmarks now include passthrough=True variants — tree-based models show proper performance
  • Binary encoding data leakage fixed in benchmark script (MinMaxScaler was fitting on test data)
  • Visualization module __init__.py now properly exports plot_maya_number, plot_maya_grid, render_maya_text
  • Fixed plot_maya_number / plot_maya_grid return type handling in notebook examples

Added

  • "Results at a Glance" section in README and docs homepage with benchmark tables
  • Synthetic data fallback in fraud detection notebook (works without Kaggle credentials)
  • Matplotlib visualization demos in VFD deep dive notebook
  • Benchmark notebook rewritten with 5 encoding strategies × 4 models + MCE temporal analysis
  • All 6 example notebooks listed in README

v0.2.0 (2026-03-12)

Features

  • VFDEncoder: passthrough mode — New passthrough=True parameter keeps original input features alongside VFD-encoded features. This improves performance with tree-based models (RandomForest, GradientBoosting, XGBoost) that benefit from both the raw signal and multi-scale VFD decomposition.

Fixes

  • Fixed gregorian_to_jdn() usage in example notebooks (was passing 3 args instead of 1)
  • Fixed Haab' and Long Count output format in notebook examples
  • Fixed docs URL in notebook 01
  • Removed compromised polyfill.io CDN from MkDocs config

v0.1.0 (2026-03-12)

Initial release.

Features

  • VFDEncoder: Vigesimal Feature Decomposition transformer
  • Three component modes: full, lite, bars_dots
  • Auto-detection of vigesimal levels
  • Normalization support
  • Negative number handling (abs_sign, shift, error)
  • Float handling (scale, round, integer_part)
  • Inverse transform support
  • MayaCalendarEncoder: Maya Calendar temporal encoding
  • Tzolk'in (260-day sacred calendar) features
  • Haab' (365-day solar calendar) features
  • Long Count (linear day count) features
  • Cyclical sin/cos encoding
  • Wayeb' binary flag
  • Configurable epoch
  • Core functions: to_vigesimal, from_vigesimal, to_bars_dots, maya_decompose
  • Calendar functions: Gregorian ↔ JDN ↔ Maya calendar conversions
  • Visualization: Text and matplotlib rendering of Maya numbers
  • Full scikit-learn compatibility (Pipeline, clone, get_params)
  • 118 tests passing across Python 3.9-3.12