Copyright | |
Developer Manual | On this page, we describe best coding practices for SG++ |
▼Usage 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++ | We compute the sparse grid interpolant of the function \( f(x) = \sin(\pi x).\) We perform spatially-dimension-adaptive refinement of the sparse grid model, which means we refine a particular grid point (locality) only in some dimensions (dimensionality) |
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 |
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 |
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 |
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 |
Classification Example | This example shows how classification specific refinement strategies are used |
Learner Classification Test | This represents a small example how to use sparse grids for classification problems |
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 |
Learner SGDE OnOff | This example shows how to perform offline/online-classification using sparse grid density estimation and matrix decomposition methods |
learner SGDE | This examples demonstrates density estimation |
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 |
Classification Example MultipleClassRefinement | Helper to create learner |
new_sgde.cpp | This example can be found under datadriven/examples/new_sgde.cpp |
optimize_kde_bandwidth.cpp | This example can be found under datadriven/examples/optimize_kde_bandwidth.cpp |
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 | We compute the sparse grid interpolant of the function \( f(x) = \sin(10x_0)+x_1.\) We perform spatially-dimension-adaptive refinement of the sparse grid model, which means we refine a particular grid point (locality) only in some dimensions (dimensionality) |
Spatially-Dimension-Adaptive Refinement in Python | We compute the sparse grid interpolant of the function \( f(x) = \sin(\pi x).\) We perform spatially-dimension-adaptive refinement of the sparse grid model, which means we refine a particular grid point (locality) only in some dimensions (dimensionality) |
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 | We compute the sparse grid interpolant of the function \( f(x) = \sin(10x_0)+x_1.\) We perform dimension-adaptive refinement of the sparse grid model, which means we add a complete hierarchical subspace in some dimensions |
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 |
learnerSGDETest.py | This example can be found under datadriven/examples/learnerSGDETest.py |
positive_density.py | This example can be found under datadriven/examples/positive_density.py |
Calculating the regularization path | This example generates a regularization path for sparsity-inducing penalties |
test_Rosenblatt.py | This example can be found under datadriven/examples/test_Rosenblatt.py |
test_sgdeLaplace.py | This example can be found under datadriven/examples/test_sgdeLaplace.py |
resumingLearningProcess.py | This is an example of how to resume the learning process from a checkpoint without recalculating the last iteration |
optimization.py | On this page, we look at an example application of the sgpp::optimization module |
LTwoDotTest.py | This example can be found under pde/examples/LTwoDotTest.py |
▼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 |
Integrate Dakota | Install and enable Dakota for sgpp::combigrid module |
Todo List | |
Deprecated List | |