Related Pages

Here is a list of all related documentation pages:

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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
benchmark_gridInteraction.cpp	This example can be found under base/examples/benchmark_gridInteraction.cpp
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
Combigrid Example Dimensional Adaptivity (C++)	In this example, we use the combigrid module to adapt a combination grid solution to best interpolate a test function at a given point
Combigrid Example (C++)	In this example, we use the combigrid module to interpolate a test function on a two-dimensional regular sparse grid with the combination technique and hierarchical B-splines
examplePCE.cpp	This example can be found under datadriven/examples/examplePCE.cpp
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 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 SGD	This example shows how to perform online-classification using sparse grids and averaged stochastic gradient descent method
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
Constrained Optimization	This example demonstrates the optimization of an objective function \( f\) with additional constraints
Fuzzy Extension Principle (C++)	We consider an example for the application of the fuzzy extension principle to fuzzy input uncertainties to obtain a fuzzy output interval using three different methods: optimization of the objective function, optimization of a piecewise linear sparse grid surrogate, and optimization of a surrogate with B-splines on sparse grids
Optimization Example (C++)	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
Combigrid Example Dimensional Adaptivity (Python)	In this example, we use the combigrid module for dimensional adaptivity
Combigrid Example (Python)	In this example, we use the combigrid module to interpolate a test function on a two-dimensional regular sparse grid with the combination technique and hierarchical B-splines
Generalised Sparse Grids	This example tests generalised sparse grids
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
test_Rosenblatt.py	This example can be found under datadriven/examples/test_Rosenblatt.py
Fuzzy Extension Principle (Python)	We consider an example for the application of the fuzzy extension principle to fuzzy input uncertainties to obtain a fuzzy output interval using three different methods: optimization of the objective function, optimization of a piecewise linear sparse grid surrogate, and optimization of a surrogate with B-splines on sparse grids
Optimization Example (Python)	On this page, we look at an example application of the sgpp::optimization module
splineResponseSurface_example.py	This example can be found under optimization/examples/splineResponseSurface_example.py
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 Example (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 Example (MATLAB)	On this page, we look at an example application of the sgpp::optimization module
Integrate Dakota	Install and enable Dakota for sgpp::combigrid module
SGDE Miner	This example demonstrates how a pysgpp.datadriven.SparseGridMiner is constructed using a configuration file and how it is then used
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Todo List