The SVM class
The SVM class
Introduction
(PECL svm >= 0.1.0)
Class synopsis
class SVM {
const int C_SVC
= 0;
const int NU_SVC
= 1;
const int ONE_CLASS
= 2;
const int EPSILON_SVR
= 3;
const int NU_SVR
= 4;
const int KERNEL_LINEAR
= 0;
const int KERNEL_POLY
= 1;
const int KERNEL_RBF
= 2;
const int KERNEL_SIGMOID
= 3;
const int KERNEL_PRECOMPUTED
= 4;
const int OPT_TYPE
= 101;
const int OPT_KERNEL_TYPE
= 102;
const int OPT_DEGREE
= 103;
const int OPT_SHRINKING
= 104;
const int OPT_PROPABILITY
= 105;
const int OPT_GAMMA
= 201;
const int OPT_NU
= 202;
const int OPT_EPS
= 203;
const int OPT_P
= 204;
const int OPT_COEF_ZERO
= 205;
const int OPT_C
= 206;
const int OPT_CACHE_SIZE
= 207;
/* Methods */
public __construct()
public svm::crossvalidate(array $problem, int $number_of_folds): float
public getOptions(): array
public setOptions(array $params): bool
public svm::train(array $problem, array $weights = ?): SVMModel
}
Predefined Constants
SVM Constants
SVM::C_SVC
- The basic C_SVC SVM type. The default, and a good starting point
SVM::NU_SVC
- The NU_SVC type uses a different, more flexible, error weighting
SVM::ONE_CLASS
- One class SVM type. Train just on a single class, using outliers as negative examples
SVM::EPSILON_SVR
- A SVM type for regression (predicting a value rather than just a class)
SVM::NU_SVR
- A NU style SVM regression type
SVM::KERNEL_LINEAR
- A very simple kernel, can work well on large document classification problems
SVM::KERNEL_POLY
- A polynomial kernel
SVM::KERNEL_RBF
- The common Gaussian RBD kernel. Handles non-linear problems well and is a good default for classification
SVM::KERNEL_SIGMOID
- A kernel based on the sigmoid function. Using this makes the SVM very similar to a two layer sigmoid based neural network
SVM::KERNEL_PRECOMPUTED
- A precomputed kernel - currently unsupported.
SVM::OPT_TYPE
- The options key for the SVM type
SVM::OPT_KERNEL_TYPE
- The options key for the kernel type
SVM::OPT_DEGREE
SVM::OPT_SHRINKING
- Training parameter, boolean, for whether to use the shrinking heuristics
SVM::OPT_PROBABILITY
- Training parameter, boolean, for whether to collect and use probability estimates
SVM::OPT_GAMMA
- Algorithm parameter for Poly, RBF and Sigmoid kernel types.
SVM::OPT_NU
- The option key for the nu parameter, only used in the NU_ SVM types
SVM::OPT_EPS
- The option key for the Epsilon parameter, used in epsilon regression
SVM::OPT_P
- Training parameter used by Episilon SVR regression
SVM::OPT_COEF_ZERO
- Algorithm parameter for poly and sigmoid kernels
SVM::OPT_C
- The option for the cost parameter that controls tradeoff between errors and generality - effectively the penalty for misclassifying training examples.
SVM::OPT_CACHE_SIZE
- Memory cache size, in MB
Table of Contents
- SVM::__construct — Construct a new SVM object
- SVM::crossvalidate — Test training params on subsets of the training data
- SVM::getOptions — Return the current training parameters
- SVM::setOptions — Set training parameters
- SVM::train — Create a SVMModel based on training data
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Licensed under the Creative Commons Attribution License v3.0 or later.
https://www.php.net/manual/en/class.svm.php
/* Constants */