v1.5.4

New nnodely version

In this new version of nnodely:

README completely revised that evidence, documentation, case-studies and applications
Extensively expanded documentation
Partial integration of neuralODE
Minor other fix

v1.5.2

This new version 1.5.2 of nnodely, featuring several updates:

1. Fixes many bugs
2. Improved the recurrent plot in matplotlib

v1.5.1

This new version 1.5.1 of nnodely, featuring several updates:

1. Improved clarity of documentation and examples.
2. Support for managing multi-dataset features is now available.
3. DataFrames can now be used to create datasets.
4. Datasets can now be resampled.
5. Random data training has been fixed for both classic and recurrent training.
6. The State variable has been removed.
7. It is now possible to add or remove a connection or a closed loop.
8. Partial models can now be exported.
9. The trainModel function and the resultAnalysis have been separated.
10. A new function, trainAndAnalyse, is now available.
11. The report now works across all network types.
12. The training function code has been reorganized.
13. New plotting function for network structure

v1.3.1

Here's the new version of nnodely!
In this new version, you can:

1. derive and integrate streams;
2. create a time or sample window on the stream;
3. create the new equation learner layer.
4. call easly the parametric function using a simplified inputs that improve the flexibility and reliability.

In addition, countless bugs have been fixed and the warning and error system has been hardened.
Moreover the domumentation is improved and it has been included in the tests.

v1.0.0

It’s time to build—nnodely 1.0.0 is officially here!

The main functionalities of this first version are:

1. Build your forward neural models using the basic building blocks: Activation Trigonometric and Aritmetic functions, Linear and Fir layers, Fuzzy layers and Local Models, and custom Parametric Function.
2. Build your neural controllers using Connect and CloseLoop operations.
3. Load multiple datasets from different files.
4. Train from a network's piece to the whole network, including loops and connections.
5. Export your controller or your estimator in stand-alone Pytorch or Onnx.

v0.25.0

Merge pull request #35 from tonegas/features/1-setup-the-documentation

Modified the version

v0.24.0

Merge pull request #19 from tonegas/fixes/1-test

New version for test check push

v0.23.0

Merge pull request #18 from tonegas/fixes/1-test

New version after clean the repository files

v0.22.13

Merge pull request #16 from tonegas/features/14-test-autorelease-on-p…

v0.16.0

Test the auto pypi