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)

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

PyTorch kernel-regression library for spectrum cartography.

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.

My implementation of some algorithms

Analytic Bandwidth Selector for NW and KDE Based on CV Hessian

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).

Topological Hashing Registry for Chaos Resilience.

Nonparametric regression examples with R and Python

Fast Gaussian kernel smoothers with gradients.

Cart-pole control from learned dynamics: periodic-kernel regression, differentiable JAX rollouts and gradient-based policy search, stress-tested under noise.

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

Konut fiyatlarında çekirdek regresyon, spline, GAM ve MARS karşılaştırması (R)