v1.1.1

• Update seed value in all 4 classes to ensure reproducibility of your results
• Add mode, n_workers, and termination parameter in model.fit() of MhaElmRegressor and MhaElmClassifier classes
  • These parameters are derived from Mealpy library
  • With mode parameter, you can speed your training model
  • With n_workers, you can set the number of threads or CPUs to speed up the training process
  • With termination, you can set early stopping strategy for your model.
• Update docs, examples, and tests.

v1.1.0

• Update core modules to fit upgraded version of Mealpy>=3.0.1, PerMetrics>=2.0.0, Scikit-Learn>=1.2.1
• IntelELM no longer support Python 3.7. Only support Python >= 3.8
• Update docs and add examples

v1.0.3

• Fix bug lb and ub in BaseMhaElm class
• Update docs and add example

v1.0.2

• Fix bug in DataTransformer class
• Fix bug in LabelEncoder class
• Add more activation functions
• Update documents and examples

v1.0.1

• Add "evaluate" function to all Estimators (ElmRegressor, ElmClassifier, MhaElmRegressor, MhaElmClassifier)
• Add new module "scaler"
• Our scaler can be utilized with multiple methods.
• Add "save_loss_train" and "save_metrics" functions to all Estimators
• Add "save_model" and "load_model" functions to all Estimators
• Add "save_y_predicted" function to all Estimators
• Update all examples and documents

v1.0.0

• Add supported information for each classes.
• Restructure intelelm module to based_elm module and model subpackage that includes mha_elm and standard_elm modules.
• Add traditional/standard ELM models (ElmRegressor and ElmClassifier classes) to standard_elm module.
• Add examples and tests for traditional models
• Add score and scores functions to all classes.
• Fix bug calculate metrics and objective in ELM-based models.
• Add examples with real-world datasets and examples with GridsearchCV to tune hyper-parameters of ELM-based models.
• Add documents

v0.0.1

Setup Zenodo