# MIKE — AI / ML dataset benchmark pack

Real files. Real tools. Run it yourself — no trust required.

    python3 bench.py

Every result is CRC-verified after a full **compress → decompress** round-trip.
Missing competitors (zip / rar / 7z / xz / zstd) are skipped automatically;
MIKE is compared against whatever you have installed.

## The datasets — what each one *is*, and why it matters

| File | What it is | Real ML use-case | MIKE result |
|------|------------|------------------|-------------|
| `htru2-features.f32`        | 17,898 × 9 **float32 feature tensor** (raw `np.save`-style array) | Feature stores, cached model inputs, embeddings shipped between training runs | 🏆 **smallest of all tools** |
| `california-housing.csv`    | 20,640-row numeric **tabular dataset** | The bread-and-butter of ML: business/finance tables, model training data | 🏆 **smallest of all tools** |
| `htru2.csv`                 | 17,898-row **scientific measurement table** (pulsar candidates) | Sensor logs, telemetry, scientific/experimental datasets | 🏆 **smallest of all tools** |
| `winequality-red.csv`       | 1,599-row numeric table | Small tabular datasets, lab results, IoT batches | 🏆 **smallest of all tools** |
| `mnist-t10k-images.uint8`   | 10,000 × 784 **uint8 image tensor** | Image datasets stored as raw pixel arrays | 2nd — beats 7z/zip/rar/zstd, ~0.7% behind xz |
| `bert-tiny-weights.f32.bin` | Trained **float32 model weights** | Shipping/versioning model checkpoints | ~tie — **trained weights are near-random; nobody compresses them (~8%). We say so.** |

## The takeaway

On **ML datasets** — the raw numeric kind: feature tensors, tabular data,
scientific/sensor tables — MIKE's numeric modelling produces the **smallest
file of every tool tested**, and *crushes* the everyday tools (on the float32
tensor it saves ~25% where zip manages 17%).

On **trained model weights**, which are statistically close to random, *nobody*
wins — MIKE included. We don't pretend otherwise. That honesty is the point.

Trade-off: MIKE spends more time (numeric analysis + a full SAFE verify pass).
You buy ratio with seconds.

## Provenance

- HTRU2 — UCI Machine Learning Repository (Lyon et al.). `htru2-features.f32`
  is the 9 numeric columns packed little-endian float32.
- California housing — StatLib / Pace & Barry (1997).
- Wine Quality — UCI (Cortez et al., 2009).
- MNIST — LeCun, Cortes, Burges (test split, raw IDX pixel bytes).
- bert-tiny — prajjwal1/bert-tiny (HuggingFace, MIT).

All are publicly available research datasets, included here unmodified (except
the float32 packing noted above) purely so you can reproduce the numbers.
