| 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 | |
1.8.13