Package: codpy
OverviewCodpy is a kernel based, open source software library for high performance numerical computation, relying on theRKHStheory.
It contains a set of core tools that we use for machine Learning, statistics and numerical simulations. As a machine learning platform, it enjoys some interesting properties :It is a numerically efficient machine learning platform. We provide benchmark tools to compare codpy to other popular machine learning platforms.It is a white box method. Any learning machine has access to worst-error bounds computations. These allow to compute confidence levels of prediction on any test set. Moreover, reproducibility properties of kernel methods allow to fully understand and explain obtained results.Each learning machine has access to all classical differential operators. These properties allow us to use this library with any PDE (partial differential equations) approach.Each learning machine has access to optimal transport tools, much needed for statistics.Technical requirementThis version of the library is CPUs based, and is tested onwindows / amd64 platformsDirectory structureOnce installed (see below), navigate to<path\to\python39>\Lib\site-packages\codpy. The directory structure should becodpydocscodpy-book.pdf is the codpy reference manual.*.ipynb are jupyter notebooks to reproduce the example of codpy book.com: low level tools and interface.pred: Wrappers to a number of prediction machines : kernels, neural networks, and more.data: Wrappers to data set handlingproj: some examples of applicationsBTC_predictor.py    : an example of time serie prediction.clustering.py       : benchmarks of clustering methods.housing_prices.py   : benchmarks for the venerable Boston house price data set.mnist_codpy.py      : benchmarks for the MNIST data set.radon.py            : an application for medical imagery.reordering.py       : illustration of optimal transport tools.README.md        : this documentinit.py      : codpy loaderinclude.py       : called byinitInstallationNote: this installation process has been tested onwindows / amd64 platformprerequisiteMinimum installationpython3.9.7: a valid python python3.9.7 installation.NOTE: Python installation differs from one machine to another. The python root folder is denoted "<path/to/python39>" in the rest of this document. The software Everything (or another equivalent) can be of great help finding files.Dev installationsFor information, we list the softwares that we are using for our dev configuration :GitHub DesktopR: use any CRAN mirror for downloadRStudio: see the download link, then choose the free versionMiKTEX: see the download tabEverythingVisual Studio CodeThose installations should be fine using the latest (64 bits) version and the default settings for each software .NoteOnce R and RStudio are installed, open the latter.
In the console, enter "install.packages("rmarkdown")" to installRMarkdown.Cloning repoDownload the codpy repo atcodpy alphato your location <path/to/codpyrepo>InstallationprerequisiteWe suppose that there is a valid python installation on the host machine. The reader caneither use its main python environment<path/to/python39>or create a virtual python environment<path/to/venv>, a good practice that we describe in the rest of this section.First open a command shellcmd,  create a virtual environment and activate it.python -m venv .\venv
.\venv\Scripts\activateNOTE: In the rest of the installation procedure, we consider a virtual environment <path/to/venv>. One can replace with <path/to/python39> if a main environment installation is desired, for dev purposes for instance.pip install codpyOpen a command shellcmd, and pip install codpypip install codpyor from the local repositorypip install <path/to/codpyrepo>/dist/codpy-XXXX.whlThe installation procedure might take some minutes depending on your internet connection.Test codpyopen a python shell and import codpypythonimport codpyTesting with Visual Studio CodeYou can your visual studio installation.With Visual Studio Code, open the<path/to/codpyrepo>folder and select for instance the file<path/to/codpyrepo>/proj/clustering.pySelect your python interpreter (Shift+P)Hit F5. If everything works, you should have some figures.