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

89 lines
2.7 KiB
Markdown
Raw Normal View History

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::RBF)
2016-05-07 21:53:42 +00:00
* $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);
```
You can train the classifier using multiple data sets, predictions will be based on all the training data.
2016-05-07 21:53:42 +00:00
### Predict
To predict sample label use `predict` method. You can provide one sample or array of samples:
```
$classifier->predict([3, 2]);
// return 'b'
$classifier->predict([[3, 2], [1, 5]]);
// return ['b', 'a']
```
### Probability estimation
To predict probabilities you must build a classifier with `$probabilityEstimates` set to true. 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, // $kernel
1.0, // $cost
3, // $degree
null, // $gamma
0.0, // $coef0
0.001, // $tolerance
100, // $cacheSize
true, // $shrinking
true // $probabilityEstimates, set to true
);
$classifier->train($samples, $labels);
```
Then use `predictProbability` method instead of `predict`:
```
$classifier->predictProbability([3, 2]);
// return ['a' => 0.349833, 'b' => 0.650167]
$classifier->predictProbability([[3, 2], [1, 5]]);
// return [
// ['a' => 0.349833, 'b' => 0.650167],
// ['a' => 0.922664, 'b' => 0.0773364],
// ]
```