Table of contents
- Installation from pre-built packages
- Compilation
If you want to contribute read Contribution guide.
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 pylibkrigingor 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.whlUsage 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](https://github.com/libKriging/libKriging/releases/download/v0.4.2/extra_dlls_for_python_on_windows.zip) not (yet) embedded in the python package. To load them into Python's search PATH, use: ```python 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
# in R
install.packages("https://github.com/libKriging/rlibkriging/releases/download/0.8-0.1/rlibkriging_0.8-0_R_x86_64-pc-linux-gnu.tar.gz", 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)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.tgzThen
octave --path /path/to/mLibKriging/installationor 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;Using the previous linked examples (in Python, R, Octave or Matlab), you should obtain the following results
predict plot |
simulate plot |
|---|---|
 |
 |
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** |
-
* : requires extra DLLs. See python installation
-
** : no pre-built package nor CI
-
? : requires manual verification (not updated since previous release)
Requirements (more details)
-
CMake ≥ 3.13
-
C++ Compiler with C++17 support
-
Linear algebra packages providing blas and lapack functions.
You can use standard blas and lapack, OpenBlas, MKL.
-
Python ≥ 3.6 (optional)
-
Octave ≥ 6.0 (optional)
-
Matlab ≥ R2021 (optional)
-
R ≥ 3.6 (optional)
Just clone it with its submodules:
git clone --recurse-submodules https://github.com/libKriging/libKriging.git
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:
-
Go into
libKrigingroot directorycd libKriging -
Prepare your environment (Once, for your first compilation)
.travis-ci/${BUILD_NAME}/install.sh -
Build
.travis-ci/${BUILD_NAME}/build.shNB: It will create a
builddirectory. -
Test
.travis-ci/${BUILD_NAME}/test.sh
We assume that:
- libKriging code is available locally in directory
${LIBKRIGING}(could be a relative path like..) - you have built a fresh new directory
${BUILD}(should be an absolute path) - following commands are executed in
${BUILD}directory
PS: ${NAME} syntax represents a word or an absolute path of your choice
Select your compilation ${MODE} between:
Release: produce an optimized codeDebug(default) : produce a debug codeCoverage: for code coverage analysis (not yet tested with Windows)
Following commands are made for Unix shell. To use them with Windows use git-bash or Mingw environments.
👆 expand the details
- Configure
cmake -DCMAKE_BUILD_TYPE=${MODE} ${LIBKRIGING}
- Build
cmake --build . - Run tests
ctest
- Build documentation (requires doxygen)
cmake --build . --target doc - if you have selected
MODE=Coveragemode, you can generate code coverage analysis over all tests usingorcmake --build . --target coverage --config Coverageto produce a html report located incmake --build . --target coverage-report --config Coverage${BUILD}/coverage/index.html
👆 expand the details
- Configure
where
cmake -DCMAKE_GENERATOR_PLATFORM=x64 -DEXTRA_SYSTEM_LIBRARY_PATH=${EXTRA_SYSTEM_LIBRARY_PATH} ${LIBKRIGING}
EXTRA_SYSTEM_LIBRARY_PATHis an extra path where libraries (e.g. OpenBLAS) can be found. - Build
cmake --build . --target ALL_BUILD --config ${MODE}
- Run tests
export PATH=${BUILD}/src/lib/${MODE}:$PATH ctest -C ${MODE}
👆 expand the details
With this method, you need R ( and R-tools if you are on Windows).
We assume you have previous requirements and also make command available in your PATH.
- Configure
CC=$(R CMD config CC) CXX=$(R CMD config CXX) cmake -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=${MODE} ${LIBKRIGING}
- Build
cmake --build . - Run tests
ctest
To deploy libKriging as an installed library, you have to add -DCMAKE_INSTALL_PREFIX:PATH=${INSTALL_PREFIX} option to
first cmake configuration command.
If CMAKE_INSTALL_PREFIX variable is not set with CMake, default installation directory is ${BUILD}/installed.
👆 expand the details
cmake -DCMAKE_BUILD_TYPE=${MODE} -DCMAKE_INSTALL_PREFIX:PATH=${INSTALL_PREFIX} ${LIBKRIGING}and then
cmake --build . --target installaka with classical makefiles
make install👆 expand the details
cmake -DCMAKE_GENERATOR_PLATFORM=x64 -DEXTRA_SYSTEM_LIBRARY_PATH=${EXTRA_SYSTEM_LIBRARY_PATH} -DCMAKE_INSTALL_PREFIX:PATH=${INSTALL_PREFIX} ${LIBKRIGING} and then
cmake --build . --target install --config ${MODE}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"
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