v0.3.2 -> v0.3.3

[nTrouvain]

New features

• Add japanese_vowels to datasets ! You can now dowload the Japanese vowels dataset to try Reservoir Computing on a well known pattern recognition task.

Major fixes

• Incorrect import of ArpackNoConvergence Exception ( ModuleNotFoundError: No module named 'scipy.sparse.linalg.eigen.arpack'; 'scipy.sparse.linalg.eigen' is not a package #67)
• Models with multiple inputs and complex branches were not created properly (Concat nodes were not created at the right place) (Problem with multiple inputs #68)
• Model.run can now be called on multiple series (using lists of arrays or arrays with ndim > 2)

Minor fixes

• Lock was used too soon in Ridge.partial_fit. Dot product is now performed in parallel.
• Better data handling with complex models.
• New Unsupervised node subclass can be used to mark a Node as unsupervised learner.
• Update random state generator to numpy.random.Generator in hyper.research.
• Other minor fixes in examples and typos.

[codecov-commenter]

Codecov Report

Merging #69 (523cd5d) into master (ce11aaf) will increase coverage by 0.81%.
The diff coverage is 94.24%.

@@            Coverage Diff             @@
##           master      #69      +/-   ##
==========================================
+ Coverage   89.09%   89.90%   +0.81%     
==========================================
  Files          31       33       +2     
  Lines        2539     2666     +127     
  Branches      606      619      +13     
==========================================
+ Hits         2262     2397     +135     
+ Misses        163      151      -12     
- Partials      114      118       +4     

Flag	Coverage Δ
unittests	89.68% <94.24%> (+0.83%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
reservoirpy/datasets/_chaos.py	81.00% <ø> (ø)
reservoirpy/nodes/ridge.py	85.41% <70.00%> (-5.29%)	⬇️
reservoirpy/datasets/_utils.py	80.00% <80.00%> (ø)
reservoirpy/nodes/esn.py	94.77% <81.25%> (+1.44%)	⬆️
reservoirpy/utils/model_utils.py	91.11% <89.79%> (+11.56%)	⬆️
reservoirpy/ops.py	83.60% <93.93%> (+0.42%)	⬆️
reservoirpy/model.py	90.41% <98.66%> (+2.00%)	⬆️
reservoirpy/_base.py	93.91% <100.00%> (+0.01%)	⬆️
reservoirpy/_version.py	100.00% <100.00%> (ø)
reservoirpy/datasets/__init__.py	86.66% <100.00%> (+0.95%)	⬆️
... and 10 more

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