Algorithms that score how alike two sequences are on a similarity scale (higher == more alike), as opposed to the cost metrics in edit-distance. Covers Jaro, Jaro-Winkler, n-gram (Jaccard / Sørensen-Dice / cosine / overlap), Ratcliff-Obershelp, and the Monge-Elkan composite scorer.
All scorers accept bytes, str (UCS-1 / UCS-2 / UCS-4), and
NumPy ndarray. UCS-2 inputs route to a 16-bit-token SIMD path —
Chinese, Japanese and Korean strings hit the same kernel as ASCII
rather than being downcoded to bytes.
Output-processing variants
| Suffix | Signature | Return type | Use when |
|---|---|---|---|
<algo>_similarities(query, targets) |
one-to-many | ndarray[float64] in [0, 1] |
raw similarity scores per target |
<algo>_best(query, targets) |
one-to-one | (target, float_similarity) or None |
only need the closest match |
Multi-query (queries × targets), thresholded, or top-k workloads
live on the cdist family — see cdist.
Algorithms
jaro_similarities — Jaro similarity
Matching-character ratio with transposition penalty. SIMD path
computes per-text-position window state in registers (lo_v,
hi_complement_v, active_v); no scalar prologue.
import stride_align as sa
sa.jaro_similarities("dixon", ["dicksonx", "dickson"])
# array([0.79166667, 0.76666667])
jaro_winkler_similarities — Jaro-Winkler
Jaro with a prefix bonus for shared leading characters. Tunable
via prefix_weight (default 0.1), prefix_threshold
(0.7), and prefix_cap (4 — maximum prefix length to
score, per Winkler's spec).
sa.jaro_winkler_similarities(
"martha", ["marhta", "marty"],
prefix_weight=0.1, prefix_cap=4,
)
n-gram similarities: jaccard_similarities, dice_similarities, cosine_similarities, overlap_similarities
Token-set similarity over character n-grams. Tunable via
n= (default 2 — character bigrams). UCS-2 inputs use a 32-bit
n-gram representation that fits 4× more tokens per SIMD lane than
the 64-bit fallback.
| Function | Formula |
|---|---|
jaccard_similarities |
|A ∩ B| / |A ∪ B| |
dice_similarities |
2 |A ∩ B| / (|A| + |B|) |
cosine_similarities |
|A ∩ B| / sqrt(|A| · |B|) |
overlap_similarities |
|A ∩ B| / min(|A|, |B|) |
sa.dice_similarities("Stride-Align", ["StrideAlign", "Stride", "Alignment"], n=3)
ratcliff_obershelp_similarities — Ratcliff-Obershelp
Recursive longest-common-substring match ratio. Backs
difflib.SequenceMatcher.ratio() semantics.
MongeElkan — composite token-level scorer
Symmetric mean of best per-token match over two token lists. The
inner scorer is any one-to-many similarity function — pass a
stride_align _similarities function as inner= for full SIMD
speed, or a Python callable as a fallback.
from stride_align._monge_elkan import monge_elkan_similarity
score = monge_elkan_similarity(
"Department of Computer Science",
"Computer Science Department",
inner=sa.jaro_winkler_similarities,
)
Algebraic identity with Indel
indel_normalized_score(a, b) == fuzz.ratio(a, b) / 100. This
lets one bit-parallel Indel kernel back four public surfaces
(indel_normalized_*, fuzz.ratio, fuzz.QRatio, and the
rapidfuzz Indel distance class) with no kernel duplication.
SIMD dispatch
Same runtime backend selection as the edit-distance family — the widest available kernel for the current CPU is chosen once at import. See edit-distance#simd-dispatch.