mirror of
https://github.com/Llewellynvdm/php-ml.git
synced 2024-11-29 00:06:31 +00:00
45 lines
1.5 KiB
Markdown
45 lines
1.5 KiB
Markdown
# 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
|
||
```
|