Reservoirpy

Minor changes
- Adding new feature: **Intrinsic Plasticity** ! Allow to fit a Reservoir on some data to change its activation values distribution, using the node `reservoirpy.nodes.IPReservoir`.
- Bias initializers for readouts (`Ridge` and `FORCE`).
- Using arrays of input scaling is now possible.
- Some nodes may now have several inputs (without using a `Concat` node to concatenate them).
- Big code cleanup: new private core module `_base` declares the nodes basic API, including input/output validation, train and forward procedures, Node abstract class and better feedback handlers.

Other fixes
- Fix feedback system to avoid running twice nodes in distant feedback models, yielding incorrect results.
- Fix some matrix generation problems (arrays of input scaling unsupported, some bugs with `zeros` not accepting `**kwargs`)
- Fix readouts base API to allow bias initializers.
- Improve coverage (79% -> 88% (+9pts))
- Add documentation for all nodes/observables/activation functions.

**Full Changelog**: https://github.com/reservoirpy/reservoirpy/compare/v0.3.1...v0.3.2

- Fix: apply input scalings defined as vectors to input matrices in Reservoir was not working with sparse matrices.

**Full Changelog**: https://github.com/reservoirpy/reservoirpy/compare/v0.3.1...v0.3.1.post1

- Adding teacher nodes: nodes can be used as teachers for other nodes when training online.
- Adding `warmup` parameter in for offline learning (and removing transient parameter from `Ridge`).
- Adding new matrices initializers in `mat_gen` (more flexible than the old ones).
- Adding node `dtype` parameter to change parameter type.
- Adding `bias` and `bias_scaling` in `Reservoir`.
- Fixing tutorials (51 61)

**Full Changelog**: https://github.com/reservoirpy/reservoirpy/compare/v0.3.0...v0.3.1

Changes
Almost everything changed from previous version. Note the following changes:

- Node API: everything in ReservoirPy is now a Node, which is the minimal piece of dynamical system defined in the library. It can apply a function to some input data, modify its internal state, train its parameters...
- ...and get involved in much bigger architectures through Model objects. Model objects store graphs of Nodeobjects, and can handle forward flow of data, training of online and offline nodes, and feedbacks.
- Reservoirs have now their own class ! Along with a lot of other tools defined in reservoirpy.nodes module: non linear vector auto regressive machine (NVAR), dense network with Tikhonov regression, FORCE learning, activations functions, operators... and more coming in the next releases.
- While Model objects define complex flow of data between nodes, feedback connections define complex flow of data between nodes shifted in time. This allows for more refined techniques to be applied.
- Subclassing a Node requires only a bunch of functions, and allows to quickly create new nodes and test new behaviors.
- The ESN class has not disappeared though, and is back as Model instance optimized with some parallelization (still ongoing work, parallelization might still lead to unpredicted behavior with some configurations).
- The reservoirpy.compat module keep track of everything from the last release (v0.2.4) of ReservoirPy.

Full Changelog: v0.2.4...v0.3.0-b


**Full Changelog**: https://github.com/reservoirpy/reservoirpy/compare/v0.2.4...v0.3.0

What's Changed

- 35 32 Ensure backport of `runtime_checkable` typing function for Python < 3.8 (using `typing_extensions`)
- 33 Use `collections.abc` instead of `collections` to get `Iterable` protocol
- Removed scikit-learn support in `compat` module (at least for now, will create a scikit-laern compatible API soon)
- Added all tests to test suite for CI
- Fixed "Simple example with Mackey Glass" to support v0.3