Release notes¶

Version 1.4¶

Add a linear_transform method to the OPU and OPUMap classes, that adds linearity to the transform method.
Provide encoder and decoder directly in the transform and linean_transform arguments

See linear_transform for more details, as well as the What is an OPU? page.

lightonml is publicly available on PyPi and Github.
The OPU class, previously in lightonopu, has been moved in lightonml. This allows easier installation for using a simulated OPU on a local machine, by simply running pip install lightonml.

Online transform mode allows a large speedup when running transform on single vectors, or small batches (<100 vectors). To use it, add online=True to the new OPU fit1d/fit2d methods. On single vectors the speedup is more than 70x with regards to online=False.
Add fit/transform methods. The new fit1d/fit2d methods allow the OPU transform to be fit first before transform. They accept in parameters either the number of features, or input vectors.
The transform method is called without 1d/2d variants, the choice for number of input dimensions going to the fit1d/fit2d methods.
Add a fit to OPUMap objects in lightonml.projections.sklearn and lightonml.projections.torch.

Context metadata with information on the transform is now an attribute of the output array
Internal optimizations speed up transform by ~5 %
Add new verbose level (0 to 3, 3 being the most verbose), and set it at a global level with lightonopu.set_verbose_level()
Allow transform settings to be overridden in the fit1d/fit2d methods (for advanced usage)