Changelog

All notable changes to PyHDC are documented here. The project follows Semantic Versioning and Keep a Changelog conventions.

The source is CHANGELOG.md on GitHub.

v2.0.0: 2026-06-12

Added

  • Dimension-first (D, N) batched hypervectors. enc.generate(size=(D, N)) returns one Hypervector wrapping a (D, N) array whose columns are hypervectors. bundle() collapses a (D, N) batch to a single (D,) prototype; bind() / unbind operate per column.

  • select(): select hypervectors (columns) from a (D, N) batch by index, on both the NumPy and PyTorch backends.

  • stack(): backend-agnostic combine of hypervectors/batches into one (D, N) batch along the batch axis (a (D,) vector becomes a column).

  • Global backend/device defaults: prefer_torch(), prefer_cuda(), prefer_numpy(), prefer_cpu(), get_default_backend(), get_default_device(). Encodings created without an explicit backend / device inherit these.

  • Multi-mode similarity: a single (D, N) batch returns column 0 against each remaining column; two (D, N) batches return per-column pairs; a (D,) vector against a (D, N) batch broadcasts.

  • BSDC_THIN is now exported at the top level.

Changed (breaking)

  • Hypervector batches are now dimension-first (D, N) (each column is a hypervector), not batch-first (N, D). enc.generate(size=N) with an integer now returns a single N-dimensional vector; use enc.generate(size=(D, N)) for a batch of N vectors.

  • Batched similarity() is column-wise over (D, N) instead of per-row over (N, D): similarity(A, B) returns per-column pairs, and similarity(batch) returns column 0 vs each remaining column.

Migration guide:

# A batch of N vectors was (N, D) in 1.1.0; make or transpose it to (D, N).
batch = enc.generate(size=(10_000, 50))   # was enc.generate(size=50)

# Batched similarity now indexes columns, not rows.
sims   = enc.similarity(batch_a, batch_b)   # sims[i] = sim(batch_a[:, i], batch_b[:, i])
member = batch[:, i]                         # was batch[i]

Fixed

  • Batched generation is order-reproducible: generate(size=(D, N)) yields the same vectors as N successive generate() calls under a fixed seed, and works for every generator (a 2-D size previously mis-ordered the columns or failed).

v1.1.0: 2026-05-24

Added

  • BSDC_THIN encoding: sparse binary with post-bundling random thinning to enforce a density constraint. Uses Shifting / InverseShifting for binding.

  • DisjunctionThinned bundling function in pyhdc.components.bundling: bitwise OR followed by random thinning to a target density.

  • similarity_remap parameter on all encoding classes: optional callable applied to every similarity result before returning.

  • remap_to_unit in pyhdc.components.similarity: maps [-1, 1] → [0, 1]. Works on scalars, NumPy arrays, and PyTorch tensors.

  • PyTorch support for all four similarity functions (CosineSimilarity, HammingDistance, Overlap, AngleDistance).

  • Batched similarity calling conventions: (a, b) both 2-D returns per-row similarities; (arr,) single 2-D returns row 0 vs. rows 1+.

Changed (breaking)

  • HammingDistance now returns [-1, 1] instead of [0, 1].

  • Overlap now returns [-1, 1] instead of [0, 1].

Migration guide: any code comparing HammingDistance or Overlap output against thresholds in [0, 1] must be updated. The easiest fix:

from pyhdc.components.similarity import remap_to_unit

# Option A: remap manually
sim = hv1.similarity(hv2)
sim_01 = remap_to_unit(sim)

# Option B: remap automatically at the encoding level
enc = pyhdc.BSC(dimension=10_000, similarity_remap=remap_to_unit)
sim_01 = hv1.similarity(hv2)   # always in [0, 1]

Fixed

  • MAP_I_Bits integer overflow on Python 3.9.

  • All similarity functions now handle PyTorch tensors without falling back to NumPy.

v1.0.1: 2026-05-23

Changed

  • Added README.md with badges, installation instructions, and a quickstart example (omitted from the v1.0.0 tag; this patch ensures it appears on the PyPI release page).

v1.0.0: 2026-05-23

Added

  • Unit test suite covering all 14 encoding types, all 7 generator families, all components, and the hypervector API.

  • Performance benchmark suite (pytest-benchmark).

  • mypy static type checking configuration.

  • Pre-commit hooks: autoflake, isort, black, pylint, mypy.

  • CONTRIBUTING.md with developer setup and PR process.

  • SECURITY.md with vulnerability reporting guidance.

  • Codecov integration.

  • TestPyPI and PyPI publish workflows with OIDC Trusted Publishing.

Fixed

  • All internal imports changed from hdc. to pyhdc. namespace.

  • DefaultGenerator._next_word integer overflow for word_size >= 32.

  • MBAT.bind incorrectly storing tuple as hypervector data.

  • MAP_I_Bits wrong keyword argument names in ElementAdditionBits.

  • FeistelCounterGenerator non-deterministic round key generation.

v0.0.1: 2024-01-01

Initial template release to PyPI.

Added

  • Core encoding types: MAP_C, MAP_I, MAP_I_Bits, MAP_B, HRR, HRR_NoNorm, HRR_ConstNorm, FHRR, VTB, MBAT, BSC, BSDC_CDT, BSDC_S, BSDC_SEG

  • Random number generator families: LCG, DLFSR, LFSR, LCA, PCG, Xorshift, ShiftedCounter

  • Recovery algorithm framework (not yet public API)

  • NumPy backend; PyTorch optional

  • GitHub Actions CI: lint, test, PyPI publish workflows