Feb 16, 2021 pyproject.toml Fix sdist to include c++ files and check build with sdist. (#177) Jul 20, 2024 Fast and flexible Gaussian Process regression in Python. Releases13 v0.4.4Latest Apr 12, 2025 + 12 releases
The regression was undertaken in log-space to ensure that all estimates had values \(\ge 0\), maintaining physical accuracy, i.e. it should not be possible to estimate a negative count rate. To compute the regression, GPy a well-established package for python was used46. It was assumed ...
The results of this work were also employed by glearn, a high-performance python package for machine learning using Gaussian process regression [2]. The paper is organized as follows. In Section 2, we provide a brief overview of the generalized inverses of matrices. Our main results are ...
For the GP regression, a noisy approach was used, by adding a GP with a white noise kernel to the model, and including its noise hyperparameter to the hyperparameter optimization process. For the random generation method, dmin=5 and dmax=15 were used to limit the size of each expression ...
Python package for K2 systematics correction using Gaussian processes. Installation git clone https://github.com/OxES/k2sc.git cd k2sc python setup.py install --user Basic usage A MAST K2 light curve can be detrended by calling k2sc <filename> where <filename> is either a MAST light ...
E. (2005). A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 6, 1939–1959. MathSciNet Google Scholar Rasmussen, C. E., & Ghahramani, Z. (2002). Infinite mixtures of Gaussian process experts. Advances in neural information processing ...
ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/. For our tests, we used thegp2Scalelibrary that is part of thefvGPPython package, available from GitHub (https://github.com/lbl-camera/fvGP) and pypi (pip install fvgp). A Gaussian process is needed to analyze these data for a variety ...
The proposed GPSL models are implemented in Matlab. The GPSL Matlab programs are run on a 3.2 GHz Intel processor with 4 GB of shared main memory under Linux. The SSVM approach is implemented in C, the CRF approach is coded in C++ and the GPStruct approach is in Python. Since the im...
Updated Sep 5, 2024 Python theogf / AugmentedGaussianProcesses.jl Star 138 Code Issues Pull requests Gaussian Process package based on data augmentation, sparsity and natural gradients julia svm gps process gaussian classification gaussian-processes gpc natural-gradients Updated Apr 8, 2024 Julia...
pyGPs is a Python library for Gaussian Process (GP) Regression and Classification. Here is an onlinedocumentation, where you will find a comprehensive introduction to functionalities and demonstrations. You can also find the same doc locally in/doc/build/html/index.html. ...