Copyright | |
Developer Manual | On this page, we describe best coding practices for SG++ |
▼User Manual | |
▼Installation and Usage | Select your operating system and compiler to get instructions |
Linux (GCC/Clang/ICC) | This page contains instructions for compiling and using SG++ with GCC, Clang or ICC under Linux |
OSX (GCC/ICC) | This page contains instructions for compiling and using SG++ with GCC or ICC under Mac OSX |
Windows (MinGW) | This page contains instructions for compiling and using SG++ with MinGW (64-bit) under Windows |
MATLAB binaries | This page explains how to install and use the binaries we provide for use with MATLAB |
▼Modules | To insure extendability and maintainability, SG++ is divided into a number of modules that implement different functionality |
Module sgpp::base | Fundamental functionality required by all other modules |
▼Module sgpp::combigrid | Combination technique functionality |
Integrate Dakota | Install and enable Dakota for sgpp::combigrid module |
Module sgpp::datadriven | Data mining and machine learning |
Module sgpp::optimization | SG++ module for optimization of smooth sparse grid interpolants |
Module sgpp::pde | Operations and functionality related to PDEs |
Module sgpp::quadrature | Stochastic and deterministic quadrature algorithms |
Module sgpp::solver | Solvers in the broadest sense: PDE, linear equations, gradient descent, etc |
▼Examples | This is a collection of examples from all modules |
▼C++ Examples | This is a list of all C++ examples |
Using the DataMatrix object | This example shows how to initialize a DataMatrix object, store it to a file and then to restore it back |
Using the DataVector object | This example shows how to initialize a DataVector object, store it to a file and then to restore it back |
Detect the configuration of OpenCL platforms | This code detects the configuration of the OpenCL platforms available on the machine and outputs it to a file |
Interaction-Term aware sparse grids. | This example shows how grids with more interaction terms differ from simpler grids |
Generalised Sparse Grids | This example creates a generalised grid |
Using JSON | This example demonstrates how to use the basic functionality of SG++ JSON API |
Spatially-Dimension-Adaptive Refinement in C++ | |
Quadrature in C++ | The following example shows how to integrate in SG++, using both direct integration of a sparse grid function and the use of Monte Carlo integration |
Refinement Example | Here we demonstrate how to refine a grid |
tutorial.cpp (Start Here) | To be able to quickly start with a toolkit, it is often advantageous (not only for the impatient users), to look at some code examples first |
Grid unserialization | In this example we show how to store a grid into a file and how to load it back into a sgpp::base::Grid object |
List of different Grid Types | This example is supposed to simply demonstrate the available grid, boundary and basis function types |
bspline_pce.cpp | This example can be found under combigrid/examples/bspline_pce.cpp |
bSplines.cpp | This example can be found under combigrid/examples/bSplines.cpp |
Stochastic Collocation with | B-Spline Combigrids |
gettingStarted.cpp (Start Here) | This tutorial contains examples with increasing complexity to introduce you to the combigrid module |
interpolation.cpp | This example can be found under combigrid/examples/interpolation.cpp |
MR_Plotting.cpp | This example can be found under combigrid/examples/MR_Plotting.cpp |
MR_trying_out_bSplineVariance.cpp | This example can be found under combigrid/examples/MR_trying_out_bSplineVariance.cpp |
PCE with Combigrids | This simple example shows how to create a Polynomial Chaos Expansion from an adaptively refined combigrid |
performance.cpp | This example can be found under combigrid/examples/performance.cpp |
Stochastic Collocation with Combigrids | This simple example shows how to create a Stochastic Collocation surrogate from a regular combigrid |
batchLearnerExample.cpp | This example can be found under datadriven/examples/batchLearnerExample.cpp |
benchmark_OrthoAdapt.cpp | This example can be found under datadriven/examples/benchmark_OrthoAdapt.cpp |
buildMats.cpp | This example can be found under datadriven/examples/buildMats.cpp |
CholBenchmark.cpp | This example can be found under datadriven/examples/CholBenchmark.cpp |
Classification Example | This example shows how classification specific refinement strategies are used |
CrossValidationExample.cpp | This example can be found under datadriven/examples/CrossValidationExample.cpp |
geomlearnerSGDEOnOffTest.cpp | This example can be found under datadriven/examples/geomlearnerSGDEOnOffTest.cpp |
iCholConvergence.cpp | This example can be found under datadriven/examples/iCholConvergence.cpp |
icholConvergenceTest.cpp | This example can be found under datadriven/examples/icholConvergenceTest.cpp |
Learner Classification Test | This represents a small example how to use sparse grids for classification problems |
learnerOnOff.cpp | This example can be found under datadriven/examples/learnerOnOff.cpp |
Regression Learner | This example demonstrates sparse grid regression learning |
Learner SGDE Online | This example shows how to perform online-classification using sparse grid density estimation and conjugate gradients method |
learnerSGDEOnOffTest.cpp | This example can be found under datadriven/examples/learnerSGDEOnOffTest.cpp |
learnerSGDEOnOffTest.cpp | This example can be found under datadriven/examples/learnerSGDEOnOffTest.cpp |
learner SGDE | This examples demonstrates density estimation |
Learner SGD | This example shows how to perform online-classification using sparse grids and averaged stochastic gradient descent method |
Learner SVM | This example shows how to perform online-classification using the support vector machine with sparse grid kernels |
MinerFromConfigFile.cpp | This example can be found under datadriven/examples/MinerFromConfigFile.cpp |
Classification Example MultipleClassRefinement | Helper to create learner |
Nearest Neighbors | This example calculates all feature-interactions that arise from an image with 64 pixels, when one only considers pixels whose \( L_2 \) distance is not larger than \( \sqrt{2} \) |
constrainedOptimization.cpp | This example demonstrates the optimization of an objective function \( f\) with additional constraints |
optimization.cpp | On this page, we look at an example application of the sgpp::optimization module |
FISTA Solver | This example demonstrates the FISTA solver for a toy dataset using using the elastic net regularization method with various regularization penalties |
▼Python Examples | This is a list of all Python examples |
Using the DataMatrix object | This example shows how to initialize a DataMatrix object, store it to a file and then to restore it back |
Using the DataVector object | This example shows how to initialize a DataVector object, store it to a file and then to restore it back |
Generalised Sparse Grids | This example creates a generalised grid |
Spatially-Dimension-Adaptive Refinement of ANOVA Components in Python | |
Spatially-Dimension-Adaptive Refinement in Python | |
Quadrature in Python | The following example shows how to integrate in SG++, using both direct integration of a sparse grid function and the use of Monte Carlo integration |
refinement.py | Here we demonstrate how to refine a grid |
Dimension-Adaptive Refinement in Python | |
tutorial.py (Start Here) | To be able to quickly start with a toolkit, it is often advantageous (not only for the impatient users), to look at some code examples first |
bSplines.py | plots anisotropic full grids that form part of the combination technique |
convergence.py | simple code that provides convergence plots for various analytic models |
example_comparison.py | |
gettingStarted.py (Start Here) | This tutorial contains examples with increasing complexity to introduce you to the combigrid module |
gridConverter.py | This tutorial contains examples on how to convert sparse grids with a hierarchical basis to a sparse grid defined on the combination of anisotropic full grids (combination technique) |
PCE with Combigrids (Python) | This simple example shows how to create a Polynomial Chaos Expansion from an adaptively refined combigrid |
plot_2d_sparse_grids.py | plots anisotropic full grids that form part of the combination technique |
Point Distributions (Python) | This simple example demonstrates the different types of 1-D point distributions available in the combigrid module |
Gaussian Weight Priors | This example compares two different Gaussian priors for sparse grid regression |
Generalised Sparse Grids | This example tests generalised sparse grids |
Interaction Terms Aware Sparse Grids | This example compares standard sparse grids with sparse grids that only contain a subset of all possible interaction terms |
learnerExample.py | This example can be found under datadriven/examples/learnerExample.py |
Calculating the regularization path | This example generates a regularization path for sparsity-inducing penalties |
optimization.py | On this page, we look at an example application of the sgpp::optimization module |
▼Java Examples | This is a list of all Java examples |
Refinement Example | Here we demonstrate how to refine a grid |
tutorial.java (Start Here) | To be able to quickly start with a toolkit, it is often advantageous (not only for the impatient users), to look at some code examples first |
Learner SGDE | This tutorial demostrates the sparse grid density estimation |
optimization.java | On this page, we look at an example application of the sgpp::optimization module |
▼MATLAB Examples | This is a list of all MATLAB examples |
tutorial.m (Start Here) | To be able to quickly start with a toolkit, it is often advantageous (not only for the impatient users), to look at some code examples first |
optimization.m | On this page, we look at an example application of the sgpp::optimization module |
Deprecated List | |