rgayler / vecsymr Goto Github PK

The vsa_mk_atom_bipolar() unit tests crank the random number generator - so alter .Random.seed
Look at using {withr} to cleanup after tests
https://ropensci.slack.com/archives/C026GCWKC/p1665308114322969?thread_ts=1665305988.911269&cid=C026GCWKC

expect_error will catch any error, not necessarily the {checkmate} errors I am looking for.
Look at using a regex to ensure the errors trapped come from {checkmate}
https://ropensci.slack.com/archives/C026GCWKC/p1665308114322969?thread_ts=1665305988.911269&cid=C026GCWKC

Check repeated calls with and without seed set to see that generated atoms are identical when intended.

The argument checking via {checkmate} is tediously repetitive to test.
Also, the VSA arguments to the operators will be all of the same "type".

Look at {patrick} and {autotest} as possible ways to simplify/automate testing.

https://ropensci.slack.com/archives/C026GCWKC/p1665368771849799?thread_ts=1665305988.911269&cid=C026GCWKC

https://ropensci.slack.com/archives/C026GCWKC/p1665382314171099?thread_ts=1665305988.911269&cid=C026GCWKC

The currently defined (bipolar) functions have single hypervectors as the arguments and outputs.
Consider whether it would be useful to generalise these functions to work with collections (lists or vectors) of hypervectors.

The VSA_altitude_hold::run_simulation() function returns a data frame with list columns of hypervectors.

Torchhd generator functions return arrays of hypervectors (tensors), presumably fro compatibility with pytorch.

List columns of hypervectors are probably more idiomatic for R analyses, but it might eventually be useful for this package to be based on tensorflow for performance, in which case a tensor representation might be needed.