normalized → MATCH
Expected: EF28EA082C882A3F9379A57E05C929D76E98899E151A6746B07D8D899644372F
Actual: EF28EA082C882A3F9379A57E05C929D76E98899E151A6746B07D8D899644372F
kmeans → MATCH
Expected: DA96D0505BCB1A5A2B826CEB1AA7C34073CB88CB29AE1236006FA4B0F0D74C46
Actual: DA96D0505BCB1A5A2B826CEB1AA7C34073CB88CB29AE1236006FA4B0F0D74C46
Hashcheck PASSED — outputs match golden hashes.
---------
Next step is probably benchmarking this against sklearn? Accuracy comparison and performance hit from all the rounding operations.
Anyone here working in maritime auditing, medical data, or other regulated stuff - would you actually use something like this? Trying to figure out if crypto- verifiable analytics is solving a real problem or just a cool technical exercise.
=== Prima Veritas OSS — Hash Check (iris) ===
normalized → MATCH Expected: EF28EA082C882A3F9379A57E05C929D76E98899E151A6746B07D8D899644372F Actual: EF28EA082C882A3F9379A57E05C929D76E98899E151A6746B07D8D899644372F
kmeans → MATCH Expected: DA96D0505BCB1A5A2B826CEB1AA7C34073CB88CB29AE1236006FA4B0F0D74C46 Actual: DA96D0505BCB1A5A2B826CEB1AA7C34073CB88CB29AE1236006FA4B0F0D74C46
Hashcheck PASSED — outputs match golden hashes.
---------
Next step is probably benchmarking this against sklearn? Accuracy comparison and performance hit from all the rounding operations. Anyone here working in maritime auditing, medical data, or other regulated stuff - would you actually use something like this? Trying to figure out if crypto- verifiable analytics is solving a real problem or just a cool technical exercise.