refactor: update core types and add approximate evidence support

Refactor `Kmer`, `SuperKmer`, and chunk reader into optimized, generic representations with compile-time length parameters and bitwise operations. Update the pipeline and scheduler to support batch processing, 1→N flat transformations, and multi-source merging. Introduce an approximate evidence mode using b-bit fingerprints and `.idx` files, alongside existing exact mode. Update CLI documentation, minimizer selection, and query output schema accordingly.
2026-05-26 09:12:41 +02:00
parent 88365e444c
commit 036d044291
13 changed files with 488 additions and 216 deletions
@@ -40,7 +40,7 @@ The filter computes $\hat{H}(ws)$ for each word size ws from 1 to ws_max and ret

 $$\text{entropy}(kmer) = \min_{ws=1}^{ws_{\max}} \hat{H}(ws)$$

-A value near 0 indicates low complexity (e.g. AAAA…); near 1 indicates high complexity. A kmer is rejected if $\text{entropy}(kmer) \leq \theta$, where $\theta$ is a collection parameter. The minimum across word sizes ensures that any scale of repetition is detected independently: polyA is caught at ws=1, dinucleotide repeats at ws=2, etc.
+A value near 0 indicates low complexity (e.g. AAAA…); near 1 indicates high complexity. A kmer is rejected if $\text{entropy}(kmer) < \theta$, where $\theta$ is a collection parameter (default 0.7). The minimum across word sizes ensures that any scale of repetition is detected independently: polyA is caught at ws=1, dinucleotide repeats at ws=2, etc.

 ## Interpretation as an effective number of classes