to_one_hot
to_one_hot: 5 implementations from 6 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 4 distinct outputs across 2 buckets, one shared input per bucket.
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence. Implementations are compared only within one bucket, the positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name; each bucket's shared input is fixed by that key, so members of one bucket saw bitwise-identical inputs under their own scalar arguments. A cluster is the set of members whose recorded output digest is identical. Nothing here says which computation a paper's method intended, and nothing reproduces a paper's results.
Not compared, and not in the tables or the counts above:
- 2 more implementations of this name were examined but did not run on the shared input (AssertionError 2).
- 1 more implementation from 1 paper took no array argument and ran only on its own fixture arguments; no shared input existed for it, so it is not compared (on 1 the recorded shared-output digest is the own-fixture digest).
- Every compared output was digested.
Bucket 1 of 2: arg 1: rank 1, kind int, dtype int64
4 implementations from 5 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 3 distinct outputs, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 2 implementations 2 papers 7bcb5c10457d recorded values identical |
Core-tuning.py a1632d59 recorded label:1/int/int64; num_classes=4 astra/RuleAttentionNetwork.py a9b299e9 recorded x:1/int/int64; num_classes=4 |
[0, 0, 1, 0, 0, 1, 0, 0, …] shape [8, 4] · float32 · Tensor |
| 1 implementation 2 papers b15d214c5570 |
probmetrics/calibrators/base.py 3836d7ad recorded y:1/int/int64; n_classes=4, label_smoothing_eps=0.1 |
[0.025, 0.025, 0.925, 0.025, 0.025, 0.925, 0.025, 0.025, …] shape [8, 4] · float32 · Tensor |
| 1 implementation 1 paper ba987dd00cc2 |
BasicFunc.py dd87064d recorded labels:1/int/int64; dimension=46 |
[0, 0, 1, 0, 0, 0, 0, 0, …] shape [8, 46] · float64 · ndarray |
Bucket 2 of 2: arg 1: rank 2, kind int, dtype int64
1 implementation from 1 paper share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 1 distinct output, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 1 implementation 1 paper 60069f246d8e |
src/wavenet_vocoder/wavenet.py 09179961 recorded tensor:2/int/int64; n=4, fill_with=1.0 |
[0, 0, 1, 0, 0, 0, 0, 1, …] shape [4, 8, 4] · float32 · Tensor |
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence; where a cluster's members carry recorded values, the largest difference among them is shown under the cluster. Paper titles are the archive's archive 2025-07-28 where the paper is in the archive and the graph's where it was added by Syntology; papers with no page here are shown by their recorded paper id only. A paper count above the implementation count means one implementation (one code sha) is held from several papers' repositories and counts once. Per-sample status, licence and fingerprint records for each paper are on its paper page. JSON twin: /census/to-one-hot.json.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections