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はじめに
インストール/設定
例
SVM — The SVM class
- SVM::__construct — SVM オブジェクトを新規構築
- SVM::crossvalidate — Test training params on subsets of the training data
- SVM::getOptions — 現在の訓練パラメータを返す
- SVM::setOptions — 訓練パラメータを設定
- SVM::train — Create a SVMModel based on training data
SVMModel — SVMModel クラス
- SVMModel::checkProbabilityModel — モデルが確率情報を持つ場合 TRUE を返す
- SVMModel::__construct — SVMModel を新規構築
- SVMModel::getLabels — モデルが訓練されたラベルを取得
- SVMModel::getNrClass — モデルが訓練されたクラスの個数を返す
- SVMModel::getSvmType — モデルが訓練された SVM の種類を取得
- SVMModel::getSvrProbability — 回帰モデルに対するσ（シグマ）値を取得
- SVMModel::load — 保管された SVM モデルを読み込み
- SVMModel::predict_probability — Return class probabilities for previous unseen data
- SVMModel::predict — Predict a value for previously unseen data
- SVMModel::save — モデルをファイルに保管

The SVM class

はじめに

クラス概要

SVM

class SVM {

/* 定数 */

const int SVM::C_SVC = 0;

const int SVM::NU_SVC = 1;

const int SVM::ONE_CLASS = 2;

const int SVM::EPSILON_SVR = 3;

const int SVM::NU_SVR = 4;

const int SVM::KERNEL_LINEAR = 0;

const int SVM::KERNEL_POLY = 1;

const int SVM::KERNEL_RBF = 2;

const int SVM::KERNEL_SIGMOID = 3;

const int SVM::KERNEL_PRECOMPUTED = 4;

const int SVM::OPT_TYPE = 101;

const int SVM::OPT_KERNEL_TYPE = 102;

const int SVM::OPT_DEGREE = 103;

const int SVM::OPT_SHRINKING = 104;

const int SVM::OPT_PROPABILITY = 105;

const int SVM::OPT_GAMMA = 201;

const int SVM::OPT_NU = 202;

const int SVM::OPT_EPS = 203;

const int SVM::OPT_P = 204;

const int SVM::OPT_COEF_ZERO = 205;

const int SVM::OPT_C = 206;

const int SVM::OPT_CACHE_SIZE = 207;

/* メソッド */

public __construct()

public float svm::crossvalidate(array $problem, int $number_of_folds)

public array getOptions()

public bool setOptions(array $params)

public SVMModel svm::train(array $problem, array $weights = ?)

}

定義済み定数

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

SVMModel クラス

はじめに

SVMModel は、訓練処理の結果です。それは、以前は目に見えなかったデータを分類するために使用できます。

クラス概要

SVMModel

class SVMModel {

/* メソッド */

public __construct(string $filename = ?)

public bool checkProbabilityModel()

public array getLabels()

public int getNrClass()

public int getSvmType()

public float getSvrProbability()

public bool load(string $filename)

public float predict_probability(array $data)

public float predict(array $data)

public bool save(string $filename)

}