Opytimizer

Changelog

Description

Welcome to v1.0.3 release. We have added methods for supporting new optimizers. Additionally, we have fixed some previous implementations and improved their convergence. Everything should be appropriate now.

Some examples integrating PyTorch with Opytimizer were created as well. Ranging from linear regression to long short-term memory networks, we hope to continue improving our library to serve you well.

Again, every test is implemented, making a 100% score of coverage. Please refer to the wiki in order to running them.

Please stay tuned for our next updates and our newest integrations (Sklearn and Tensorflow)!

Includes
- optimizers.aiwpso
- optimizers.ba
- optimizers.cs
- optimizers.fa
- examples.integrations.pytorch

Changelog

Description

Welcome to v1.0.2 release. We have added methods for supporting hypercomplex representations. From math modules to new spaces, we do support any hypercomplex approach, ranging from complexes to octonions.

A History class has been added as well. It will server as the one to hold vital information from the optimization task. In the future, we will support visualization and plots graphing.

Also, internal class has been removed. All of its contents were moved to core.function module. For now, this will be our new structure (there is a slighly chance to be modified in the future to accomodate multi-objective functions).

Finally, we have tests. Every test is implemented, making a 100% score of coverage. Please refer to the wiki in order to running them.

Please stay tuned for our next updates!

Includes
- math.hypercomplex
- spaces
- utils.history
- tests (100% coverage)

Excludes
- functions

Changelog

Description

Welcome to v1.0.1 release. Essentialy, we have reworked some basic structures, added a new math distribution module and a new optimizer (Flower Pollination Algorithm). Please stay tuned for our next updates!

Includes
- math.distribution
- optimizers.fpa

Changelog

Description

This is the initial release of Opytimizer. It includes all basic modules in order to work with it. One can create an internal optimization function and apply a Particle Swarm Optimization optimizer onto it. Please check examples folder or read the docs in order to know how to use this library.

Includes
- core
- functions
- math
- optimizers
- utils