php-ml/docs/machine-learning/regression/svr.md
Arkadiusz Kondas 365a9baeca update docs
2016-05-07 23:53:42 +02:00

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Support Vector Regression

Class implementing Epsilon-Support Vector Regression based on libsvm.

Constructor Parameters

  • $kernel (int) - kernel type to be used in the algorithm (default Kernel::LINEAR)
  • $degree (int) - degree of the Kernel::POLYNOMIAL function (default 3)
  • $epsilon (float) - epsilon in loss function of epsilon-SVR (default 0.1)
  • $cost (float) - parameter C of C-SVC (default 1.0)
  • $gamma (float) - kernel coefficient for Kernel::RBF, Kernel::POLYNOMIAL and Kernel::SIGMOID. If gamma is null then 1/features will be used instead.
  • $coef0 (float) - independent term in kernel function. It is only significant in Kernel::POLYNOMIAL and Kernel::SIGMOID (default 0.0)
  • $tolerance (float) - tolerance of termination criterion (default 0.001)
  • $cacheSize (int) - cache memory size in MB (default 100)
  • $shrinking (bool) - whether to use the shrinking heuristics (default true)
$regression = new SVR(Kernel::LINEAR);
$regression = new SVR(Kernel::LINEAR, $degree = 3, $epsilon=10.0);

Train

To train a model simply provide train samples and targets values (as array). Example:

use Phpml\Regression\SVR;
use Phpml\SupportVectorMachine\Kernel;

$samples = [[60], [61], [62], [63], [65]];
$targets = [3.1, 3.6, 3.8, 4, 4.1];

$regression = new SVR(Kernel::LINEAR);
$regression->train($samples, $targets);

Predict

To predict sample target value use predict method. You can provide one sample or array of samples:

$regression->predict([64])
// return 4.03