Welcome to Reflame’s documentation!

Reflame (REvolutionizing Functional Link Artificial neural networks by MEtaheuristic algorithms) is a Python library that implements a framework for training Functional Link Neural Network (FLNN) networks using Metaheuristic Algorithms. It provides a comparable alternative to the traditional FLNN network and is compatible with the Scikit-Learn library. With Reflame, you can perform searches and hyperparameter tuning using the functionalities provided by the Scikit-Learn library.

• Free software: GNU General Public License (GPL) V3 license

• Provided Estimator: FlnnRegressor, FlnnClassifier, MhaFlnnRegressor, MhaFlnnClassifier

• Total Official Metaheuristic-based Flnn Regression: > 200 Models

• Total Official Metaheuristic-based Flnn Classification: > 200 Models

• Supported performance metrics: >= 67 (47 regressions and 20 classifications)

• Supported objective functions (as fitness functions or loss functions): >= 67 (47 regressions and 20 classifications)

• Documentation: https://reflame.readthedocs.io

• Python versions: >= 3.8.x

• Dependencies: numpy, scipy, scikit-learn, pandas, mealpy, permetrics, torch, skorch

Quick Start:

• Installation
• Examples

Models API:

• reflame package
  • reflame.model package
    • reflame.model.mha_flnn module
      • MhaFlnnClassifier
      • MhaFlnnRegressor
    • reflame.model.standard_flnn module
      • FlnnClassifier
      • FlnnRegressor
  • reflame.utils package
    • reflame.utils.activation module
      • celu()
      • elu()
      • gelu()
      • hard_shrink()
      • hard_sigmoid()
      • hard_swish()
      • hard_tanh()
      • leaky_relu()
      • log_sigmoid()
      • log_softmax()
      • mish()
      • none()
      • prelu()
      • relu()
      • rrelu()
      • selu()
      • sigmoid()
      • silu()
      • soft_plus()
      • soft_shrink()
      • soft_sign()
      • softmax()
      • softmin()
      • swish()
      • tanh()
      • tanh_shrink()
    • reflame.utils.data_toolkit module
      • BoxCoxScaler
      • Data
      • DataTransformer
      • FeatureEngineering
      • LabelEncoder
      • Log1pScaler
      • LogeScaler
      • ObjectiveScaler
      • SinhArcSinhScaler
      • SqrtScaler
      • TimeSeriesDifferencer
      • YeoJohnsonScaler
    • reflame.utils.evaluator module
      • get_all_classification_metrics()
      • get_all_regression_metrics()
      • get_metrics()
    • reflame.utils.expand_util module
      • expand_chebyshev()
      • expand_gegenbauer()
      • expand_hermite()
      • expand_laguerre()
      • expand_legendre()
      • expand_power()
      • expand_trigonometric()
    • reflame.utils.validator module
      • check_bool()
      • check_float()
      • check_int()
      • check_str()
      • check_tuple_float()
      • check_tuple_int()
      • is_in_bound()
      • is_str_in_list()
  • Submodules
  • reflame.base_flnn module
    • BaseFlnn
      • BaseFlnn.CLS_OBJ_LOSSES
      • BaseFlnn.SUPPORTED_CLS_METRICS
      • BaseFlnn.SUPPORTED_REG_METRICS
      • BaseFlnn.create_network()
      • BaseFlnn.evaluate()
      • BaseFlnn.fit()
      • BaseFlnn.load_model()
      • BaseFlnn.predict()
      • BaseFlnn.save_loss_train()
      • BaseFlnn.save_metrics()
      • BaseFlnn.save_model()
      • BaseFlnn.save_y_predicted()
      • BaseFlnn.score()
      • BaseFlnn.scores()
      • BaseFlnn.set_predict_request()
      • BaseFlnn.set_score_request()
    • BaseMhaFlnn
      • BaseMhaFlnn.SUPPORTED_CLS_OBJECTIVES
      • BaseMhaFlnn.SUPPORTED_OPTIMIZERS
      • BaseMhaFlnn.SUPPORTED_REG_OBJECTIVES
      • BaseMhaFlnn.fit()
      • BaseMhaFlnn.objective_function()
      • BaseMhaFlnn.set_fit_request()
      • BaseMhaFlnn.set_predict_request()
      • BaseMhaFlnn.set_score_request()
    • FLNN
      • FLNN.fit()
      • FLNN.get_weights()
      • FLNN.get_weights_size()
      • FLNN.predict()
      • FLNN.set_weights()
      • FLNN.transform_X()
      • FLNN.update_weights_from_solution()
  • reflame.base_flnn_torch module
    • BaseFlnn
      • BaseFlnn.CLS_OBJ_LOSSES
      • BaseFlnn.SUPPORTED_CLS_METRICS
      • BaseFlnn.SUPPORTED_LOSSES
      • BaseFlnn.SUPPORTED_OPTIMIZERS
      • BaseFlnn.SUPPORTED_REG_METRICS
      • BaseFlnn.create_network()
      • BaseFlnn.evaluate()
      • BaseFlnn.fit()
      • BaseFlnn.load_model()
      • BaseFlnn.predict()
      • BaseFlnn.save_loss_train()
      • BaseFlnn.save_metrics()
      • BaseFlnn.save_model()
      • BaseFlnn.save_y_predicted()
      • BaseFlnn.score()
      • BaseFlnn.scores()
      • BaseFlnn.set_predict_request()
      • BaseFlnn.set_score_request()
    • FLNN
      • FLNN.SUPPORTED_ACTIVATIONS
      • FLNN.SUPPORTED_EXPANDS
      • FLNN.SUPPORTED_N_FUNCS
      • FLNN.forward()
      • FLNN.training
      • FLNN.transform_X()

Support:

• Citation Request
• Important links
• License

Indices and tables

• Index

• Module Index

• Search Page