SHOGUN  3.2.1
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Macros Groups Pages
Classes | Enumerations
KernelDensity.h File Reference

Go to the source code of this file.

Classes

class  CKernelDensity
 This class implements the kernel density estimation technique. Kernel density estimation is a non-parametric way to estimate an unknown pdf. The pdf at a query point given finite training samples is calculated using the following formula : \ \(pdf(x')= \frac{1}{nh} \sum_{i=1}^n K(\frac{||x-x_i||}{h})\) \ K() in the above formula is called the kernel function and is controlled by the parameter h called kernel bandwidth. Presently, this class supports only Gaussian kernel which can be used with either Euclidean distance or Manhattan distance. This class makes use of 2 tree structures KD-tree and Ball tree for fast calculation. KD-trees are faster than ball trees at lower dimensions. In case of high dimensional data, ball tree tends to out-perform KD-tree. By default, the class used is Ball tree. More...

Enumerations

enum  EEvaluationMode { EM_KDTREE_SINGLE, EM_BALLTREE_SINGLE, EM_KDTREE_DUAL, EM_BALLTREE_DUAL }

SHOGUN Machine Learning Toolbox - Documentation