Examples
Examples
Try the starting example
- Records: unique sample ID, supplied canonical hash, split label
- A,h1,train B,h1,test C,h2,train D,h2,train E,h3,test
- Duplicate hash groups and sample IDs
- h1: A|B; h2: C|D
- Cross-split duplicate hash groups
- h1: A|B
- Distinct supplied hashes
- 3
- Redundant records beyond one per supplied hash
- 2
- Sample IDs in cross-split duplicate groups
- A, B
Try another scenario
- Records: unique sample ID, supplied canonical hash, split label
- A,h1,train B,h1,train C,h2,train D,h2,train E,h3,test
- Duplicate hash groups and sample IDs
- h1: A|B; h2: C|D
- Cross-split duplicate hash groups
- None
- Distinct supplied hashes
- 3
- Redundant records beyond one per supplied hash
- 2
- Sample IDs in cross-split duplicate groups
- None
Calculation method
Calculation method
Duplicate hash groups and sample IDs
Group exact supplied hash strings with more than one sample.
Cross-split duplicate hash groups
Duplicate hashes present in distinct literal split labels.
Distinct supplied hashes
Count distinct exact hash strings.
Redundant records beyond one per supplied hash
Record count minus distinct hash count.
Sample IDs in cross-split duplicate groups
All samples in cross-split duplicate groups.
Symbols and units
- a
- Records: unique sample ID, supplied canonical hash, split label
Use the units shown. A list keeps the order in which you enter its values. The symbol ^ means “raised to the power of”.
Assumptions & limits
Assumptions & limits
- Hashes must already be computed using one agreed canonicalization scheme. This checks literal equality; it does not fetch files, calculate hashes, infer semantic duplicates or delete samples.
Are these inputs private?
These calculations run in your browser. A shared link contains the values you choose to share. Only questions submitted to the separate AI finder are sent to OpenAI.
Further reading
OpenStax: arithmetic and geometry ↗
Background reading; this does not imply endorsement or independent review of SumFizz.