rlibkriging
Kriging Models using the 'libKriging' Library

Package index
Search the rlibkriging package
Vignettes
Functions
375
Source code
80
Man pages
73
Home
/
CRAN
/
rlibkriging
/
src/libK/README.md

src/libK/README.md
In rlibkriging: Kriging Models using the 'libKriging' Library

Build and tests Code Analysis Coverage Status License

Table of contents

If you want to contribute read Contribution guide.

Installation from pre-built packages

For the most common target {Python, R, Octave} x {Linux, macOS, Windows} x { x86-64 }, you can use released binaries.

pylibkriging for Python

pip3 install pylibkriging

or for pre-release packages (according to your OS and Python version)

pip3 install https://github.com/libKriging/libKriging/releases/download/v0.5.0-alpha/pylibkriging-0.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Usage example here

👆The sample code below should give you a taste. Please refer to the reference file linked above for a CI certified example.

import numpy as np

X = [0.0, 0.25, 0.5, 0.75, 1.0]
f = lambda x: (1 - 1 / 2 * (np.sin(12 * x) / (1 + x) + 2 * np.cos(7 * x) * x ** 5 + 0.7))
y = [f(xi) for xi in X]

import pylibkriging as lk

k_py = lk.Kriging(y, X, "gauss")
print(k_py.summary())
# you can also check logLikelhood using:
# def ll(t): return k_py.logLikelihoodFun(t,False,False)[0]
# t = np.arange(0,1,1/99); pyplot.figure(1); pyplot.plot(t, [ll(ti) for ti in t]); pyplot.show()

x = np.arange(0, 1, 1 / 99)
p = k_py.predict(x, True, False)
p = {"mean": p[0], "stdev": p[1], "cov": p[2]}  # This should be done by predict

import matplotlib.pyplot as pyplot

pyplot.figure(1)
pyplot.plot(x, [f(xi) for xi in x])
pyplot.scatter(X, [f(xi) for xi in X])

pyplot.plot(x, p['mean'], color='blue')
pyplot.fill(np.concatenate((x, np.flip(x))),
            np.concatenate((p['mean'] - 2 * p['stdev'], np.flip(p['mean'] + 2 * p['stdev']))), color='blue',
            alpha=0.2)
pyplot.show()

s = k_py.simulate(10, 123, x)

pyplot.figure(2)
pyplot.plot(x, [f(xi) for xi in x])
pyplot.scatter(X, [f(xi) for xi in X])
for i in range(10):
    pyplot.plot(x, s[:, i], color='blue', alpha=0.2)
pyplot.show()

Note for older versions (< 0.5) NB: On Windows, it should require extra DLL not (yet) embedded in the python package. To load them into Python's search PATH, use:

import os
os.environ['PATH'] = 'c:\\Users\\User\\Path\\to\\dlls' + os.pathsep + os.environ['PATH']
import pylibkriging as lk

rlibkriging for R

Download the archive from libKriging releases (CRAN repo will come soon...)

# in R
install.packages("Rcpp")
install.packages("https://github.com/libKriging/libKriging/releases/download/v0.5.1/rlibkriging_0.5.1_Linux-x86_64.tgz", repos=NULL)

Usage example here

👆The sample code below should give you a taste. Please refer to the reference file linked above for a CI certified example.

X <- as.matrix(c(0.0, 0.25, 0.5, 0.75, 1.0))
f <- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
y <- f(X)

library(rlibkriging)
k_R <- Kriging(y, X, "gauss")
print(k_R)
# you can also check logLikelhood using:
# ll = function(t) logLikelihoodFun(k_R,t)$logLikelihood; plot(ll)
x <- as.matrix(seq(0, 1, , 100))
p <- predict(k_R, x, TRUE, FALSE)

plot(f)
points(X, y)
lines(x, p$mean, col = 'blue')
polygon(c(x, rev(x)), c(p$mean - 2 * p$stdev, rev(p$mean + 2 * p$stdev)), border = NA, col = rgb(0, 0, 1, 0.2))

s <- simulate(k_R,nsim = 10, seed = 123, x=x)

plot(f)
points(X,y)
matplot(x,s,col=rgb(0,0,1,0.2),type='l',lty=1,add=T)

mlibkriging for Octave and MATLAB

⚠️ Matlab binary package are done on request (GitHub Action does not support all required Operating Systems)

Download and uncompress the Octave archive from libKriging releases

# example
curl -LO https://github.com/libKriging/libKriging/releases/download/v0.5.1/mLibKriging_0.5.1_Linux-x86_64.tgz

Then

octave --path /path/to/mLibKriging/installation

or inside Octave or Matlab

addpath("path/to/mLibKriging")

Usage example here

👆The sample code below should give you a taste. Please refer to the reference file linked above for a CI certified example.

X = [0.0;0.25;0.5;0.75;1.0];
f = @(x) 1-1/2.*(sin(12*x)./(1+x)+2*cos(7.*x).*x.^5+0.7)
y = f(X);
k_m = Kriging(y, X, "gauss");
disp(k_m.summary());
% you can also check logLikelhood using:
% function llt = ll (tt) global k_m; llt=k_m.logLikelihoodFun(tt); endfunction; t=0:(1/99):1; plot(t,arrayfun(@ll,t))
x = reshape(0:(1/99):1,100,1);
[p_mean, p_stdev] = k_m.predict(x, true, false);

h = figure(1)
hold on;
plot(x,f(x));
scatter(X,f(X));
plot(x,p_mean,'b')
poly = fill([x; flip(x)], [(p_mean-2*p_stdev); flip(p_mean+2*p_stdev)],'b');
set( poly, 'facealpha', 0.2);
hold off;

s = k_m.simulate(int32(10),int32(123), x);

h = figure(2)
hold on;
plot(x,f(x));
scatter(X,f(X));
for i=1:10
   plot(x,s(:,i),'b');
end
hold off;

Expected demo results

Using the previous linked examples (in Python, R, Octave or Matlab), you should obtain the following results

predict plot | simulate plot :-------------------------------------------:|:---------------------------------------------: |

Tested installation

with libKriging 0.6.0

| | Linux Ubuntu:20 | macOS 11 & 12 (x86-64) | macOS 12 (ARM) | Windows 10 | |:-------|:--------------------------------------------|:--------------------------------------------|:---------------------------------------------|:-------------------------------------------| | Python | ✔ 3.6-3.10 | ✔ 3.6-3.10 | ✔ 3.9 | ✔ 3.6-3.9 | | R | ✔ 4.2 | ✔ 4.2 | ? | ✔ 4.2 | | Octave | ✔ 7.2 | ✔ 7.2 | 7.1 | ✔ 6.2 | | Matlab | ️✔ R2022a | ✔ R2022 | ✘ R2022 | ✔ R2022** |

Compilation

Requirements (more details)

You can use standard blas and lapack, OpenBlas, MKL.

Get the code

Just clone it with its submodules:

git clone --recurse-submodules https://github.com/libKriging/libKriging.git

Helper scripts for CI

Note: calling these scripts "by hand" should produce the same results as following "Compilation and unit tests" instructions (and it should be also easier). They use the preset of options also used in CI workflow.

To configure it, you can define following environment variables (more details):

| Variable name | Default value | Useful values | Comment | |:-----------------------|:--------------|:-----------------------------------|:------------------------------------------------| |MODE | Debug | Debug, Release | | |ENABLE_OCTAVE_BINDING | AUTO | ON, OFF, AUTO (if available) | Exclusive with Matlab binding build | |ENABLE_MATLAB_BINDING | AUTO | ON, OFF, AUTO (if available) | Exclusive with Octave binding build | |ENABLE_PYTHON_BINDING | AUTO | ON, OFF, AUTO (if available) | |

Then choose your BUILD_NAME using the following rule (stops a rule matches)

| BUILD_NAME | when you want to build | available bindings | |:-----------------|:-------------------------------|:-------------------------------| | r-windows | a R binding for windows | C++, rlibkriging | | r-linux-macos | a R binding for Linux or macOS | C++, rlibkriging | | octave-windows | an Octave for windows | C++, mlibkriging | | windows | for windows | C++, mlibkriging, pylibkriging | | linux-macos | for Linux or macOS | C++, mlibkriging, pylibkriging |

Then:

Compilation and tests manually

Preamble

We assume that:

PS: ${NAME} syntax represents a word or an absolute path of your choice

Select your compilation ${MODE} between:

Following commands are made for Unix shell. To use them with Windows use git-bash or Mingw environments.

Compilation for Linux and macOS

👆 expand the details

Compilation for Windows 64bits with Visual Studio

👆 expand the details

Compilation for Linux/Mac/Windows using R toolchain

👆 expand the details

With this method, you need R ( and R-tools if you are on Windows).

We assume you have previous requirements and also make command available in your PATH.

Deployment

To deploy libKriging as an installed library, you have to add -DCMAKE_INSTALL_PREFIX:PATH=${INSTALL_PREFIX} option to first cmake configuration command.

If CMAKE_INSTALL_PREFIX variable is not set with CMake, default installation directory is ${BUILD}/installed.

For Linux and macOS

👆 expand the details

cmake -DCMAKE_BUILD_TYPE=${MODE} -DCMAKE_INSTALL_PREFIX:PATH=${INSTALL_PREFIX} ${LIBKRIGING}

and then

cmake --build . --target install

aka with classical makefiles

make install

For Windows 64bits with Visual Studio

👆 expand the details

cmake -DCMAKE_GENERATOR_PLATFORM=x64 -DEXTRA_SYSTEM_LIBRARY_PATH=${EXTRA_SYSTEM_LIBRARY_PATH} -DCMAKE_INSTALL_PREFIX:PATH=${INSTALL_PREFIX} ${LIBKRIGING}

and then

cmake --build . --target install --config ${MODE}

Assisted compilation and installation

Using pip install from GitHub

You don't need to download libKriging. pip install will do everything. To do that, you need the Compilation requirements.

python3 -m pip install "git+https://github.com/libKriging/libKriging.git"

will download, compile and install pylibkriging from master branch.

Example of build process output (~2mn)

Collecting git+https://github.com/libKriging/libKriging.git
  Cloning https://github.com/libKriging/libKriging.git to /private/var/folders/g0/56fnffpn6tjd5kplh140ysrh0000gn/T/pip-req-build-2xhzyw9g
  Running command git clone --filter=blob:none -q https://github.com/libKriging/libKriging.git /private/var/folders/g0/56fnffpn6tjd5kplh140ysrh0000gn/T/pip-req-build-2xhzyw9g
  Resolved https://github.com/libKriging/libKriging.git to commit 1a86dd69cf1f60b6dcd2b5e5b876cafc97d616e9
  Running command git submodule update --init --recursive -q
  Preparing metadata (setup.py) ... done
Building wheels for collected packages: pylibkriging
  Building wheel for pylibkriging (setup.py) ... done
  Created wheel for pylibkriging: filename=pylibkriging-0.4.8-cp39-cp39-macosx_12_0_x86_64.whl size=712572 sha256=7963a78f16628c5a7d877b368fdd73452e5acf01784cd6931d48dcdc08e62a5b
  Stored in directory: /private/var/folders/g0/56fnffpn6tjd5kplh140ysrh0000gn/T/pip-ephem-wheel-cache-o7kxij3t/wheels/52/71/b0/e534f2249e9180c596a5b785cf0bfa5471fcfd38d2987318f8
Successfully built pylibkriging
Installing collected packages: pylibkriging
Successfully installed pylibkriging-0.4.8

To get a particular version (branch or tag ≥v0.4.9), you can use:

python3 -m pip install "git+https://github.com/libKriging/libKriging.git@tag"


Try the rlibkriging package in your browser

Any scripts or data that you put into this service are public.

rlibkriging documentation built on July 9, 2023, 5:53 p.m.

R Package Documentation

rdrr.io home R language documentation Run R code online

Browse R Packages

CRAN packages Bioconductor packages R-Forge packages GitHub packages

We want your feedback!

Note that we can't provide technical support on individual packages. You should contact the package authors for that.
GitHub issue tracker
ian@mutexlabs.com
Personal blog

 
Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.

Close