Comments (2)
We print the same as in novosparc
np.linalg.norm(T - T_prev)
, POT actually prints the value of the loss function,
see here, the abs. loss is abs(loss - loss_prev)
and the rel. loss is abs(loss - loss_prev) / abs(loss)
.
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Doing it like POT makes more sense to me, it's easier to spot convergence that way I think .
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Related Issues (20)
- Order datasets in docs HOT 2
- Unify the way `push/pull` results are saved.
- Jit the `apply` function
- Contributing guide
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- Spatial_warp not found when running Alignment problem tutorial HOT 6
- prepare method does not warn about unknown parameters HOT 3
- update check for categorical dtype HOT 1
- batch_size not working for temporal_problem.cell_transition HOT 1
- Add `.copy` method
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- `cell_transition` changed
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- symbol column key argument when using gene sets HOT 1
- adapt `moscot.plotting.pull/push` header HOT 1
- Improve code quality in tests `test_pass_arguments`
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