kernel-regression

pwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with some tools extending numpy/scipy. It has a set of powerful parsers and data types for storing calculation data.

Machine-Learning-Regression

Calibration of an air pollution sensor monitoring network in uncontrolled environments with multiple machine learning algorithms

Code and Simulations using Bayesian Approximate Kernel Regression (BAKR)

For quick search

Tool for non-parametric curve fitting using local polynomials.

Train a neural network in feature and lazy regimes on a regression task defined on the hyper-sphere.

Implementation of various Machine Learning Algorithms and Machine Learning Concepts in Python

This is the recent work of my on the importance and application of mathematical function around its Hilbert function theory on artificial intelligence algorithms. The main motivation was the desire of improving the convergence rate and learning rate of various learning algorithms via Generalized Gaussian Radial Basis Function.

My implementation of some algorithms

Sequential Regression Extrapolation (SRE): An accurate method of extrapolation using machine learning

A novel DR visualization technique for interactive exploration of multimodal embeddings through Dynamic Kernel enhanced projection.

Implementation of standard ml algorithms for regression, classification and clustering

Nonparametric regression examples with R and Python

Assess Balance with Machine Learning

Topological Hashing Registry for Chaos Resilience.

A library of smoothing kernels in multiple languages for use in kernel regression and kernel density estimation.

My realization of kernel regression.

MATLAB implementation of a multi-scale Bayesian Residual Transform (BRT) to enhance power quality signals. It decomposes noisy signals containing power quality disturbances using Nadaraya-Watson kernel regression, applies MAD soft-thresholding for denoising, and outputs IEEE-formatted metrics (SNRI, THD, RMSE).