A plain C library for machine learning on edge devices.

Micro-LM currently implements the following ML algorithms:

• Linear SVM
• Decision Tree
• K-NN
• TripleES, Holt-Winters Triple Exponential Smoothing for time series

Each algorithm provides both classification and regression, for binary and multiclass problems. SVM supports only ordinal multiclass classification. Holt-Winters Triple Exponential Smoothing supports only regression.

We are extending the library to other algorithms, also unsupervised. Your voluntary contribution is welcome.

The library is optimized for memory footprint. Desk-LM performs model training and cross-validation, and creates .c and .h files that store the best model's parameters. These files must be compiled together with the Micro-LM files available in this repository.

As an alternative, we are working for a dynamic configuration of Micro-LM through .json files produced by Desk-LM.

You can compile the code as an executable or as a static library, using gcc/g++ for a Microcontroller or a desktop (e.g., thorugh Eclipse CDT or Visual Studio Code).

The Micro-LM files need to be compiled together with the .c and .h files produced by Desk-LM.

The program must be configured in ELM.h, where the user has to specify some #define, such as:

• The algorithm: SVM, DT, KNN or TripleES
• DS_TEST, if you want to test performance in a dataset, instead of doing one shot estimations. Used only by: knn, decisionTree, svm.
• REGRESSION, if you want to perform a regression. Default is classification (no regression). Used only by: knn, decisionTree, svm. TES performs only regression

ELM.h exposes the following functions:

• preprocess(X), where X is the sample vector
• algo_classification(X), where X is the sample vector. Algo is: knn, decisionTree, svm.
• algo_regression(X), where X is the sample vector. Algo is: knn, decisionTree, svm.
• algo_test_dataset(isRegression), for (whole or minimal) dataset testing. Algo is: knn, decisionTree, svm.
• HW_TripleExpoSmoothing(int arrayD[], int vlen, double alpha, double beta, double gamma,int slen, int n_preds, double scaling_factor), for Holt-Winters time series

float 32 data are used

This project is licensed under the MIT License - see the LICENSE.md file for details

Please see CONTRIBUTING.md

F., Sakr, F., Bellotti, R., Berta, A., De Gloria, "Machine Learning on Mainstream Microcontrollers," Sensors 2020, 20, 2638. https://www.mdpi.com/1424-8220/20/9/2638

https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-9-time-series-analysis-in-python-a270cb05e0b3