php-ml/docs/machine-learning/classification/svc.md

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2016-05-07 21:53:42 +00:00
# Support Vector Classification
Classifier implementing Support Vector Machine based on libsvm.
### Constructor Parameters
* $kernel (int) - kernel type to be used in the algorithm (default Kernel::LINEAR)
* $cost (float) - parameter C of C-SVC (default 1.0)
* $degree (int) - degree of the Kernel::POLYNOMIAL function (default 3)
* $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)
* $probabilityEstimates (bool) - whether to enable probability estimates (default false)
```
$classifier = new SVC(Kernel::LINEAR, $cost = 1000);
$classifier = new SVC(Kernel::RBF, $cost = 1000, $degree = 3, $gamma = 6);
```
### Train
To train a classifier simply provide train samples and labels (as `array`). Example:
```
use Phpml\Classification\SVC;
use Phpml\SupportVectorMachine\Kernel;
$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
$classifier = new SVC(Kernel::LINEAR, $cost = 1000);
$classifier->train($samples, $labels);
```
### Predict
To predict sample label use `predict` method. You can provide one sample or array of samples:
```
$classifier->predict([3, 2]);
// return 'b'
$classifier->predict([[3, 2], [1, 5]]);
// return ['b', 'a']
```