47 lines
1.6 KiB
Markdown
47 lines
1.6 KiB
Markdown
# Support Vector Regression
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Class implementing Epsilon-Support Vector Regression based on libsvm.
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### Constructor Parameters
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* $kernel (int) - kernel type to be used in the algorithm (default Kernel::RBF)
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* $degree (int) - degree of the Kernel::POLYNOMIAL function (default 3)
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* $epsilon (float) - epsilon in loss function of epsilon-SVR (default 0.1)
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* $cost (float) - parameter C of C-SVC (default 1.0)
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* $gamma (float) - kernel coefficient for ‘Kernel::RBF’, ‘Kernel::POLYNOMIAL’ and ‘Kernel::SIGMOID’. If gamma is ‘null’ then 1/features will be used instead.
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* $coef0 (float) - independent term in kernel function. It is only significant in ‘Kernel::POLYNOMIAL’ and ‘Kernel::SIGMOID’ (default 0.0)
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* $tolerance (float) - tolerance of termination criterion (default 0.001)
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* $cacheSize (int) - cache memory size in MB (default 100)
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* $shrinking (bool) - whether to use the shrinking heuristics (default true)
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```
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$regression = new SVR(Kernel::LINEAR);
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$regression = new SVR(Kernel::LINEAR, $degree = 3, $epsilon=10.0);
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```
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### Train
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To train a model, simply provide train samples and targets values (as `array`). Example:
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```
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use Phpml\Regression\SVR;
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use Phpml\SupportVectorMachine\Kernel;
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$samples = [[60], [61], [62], [63], [65]];
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$targets = [3.1, 3.6, 3.8, 4, 4.1];
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$regression = new SVR(Kernel::LINEAR);
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$regression->train($samples, $targets);
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```
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You can train the model using multiple data sets, predictions will be based on all the training data.
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### Predict
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To predict sample target value, use the `predict` method. You can provide one sample or array of samples:
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```
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$regression->predict([64])
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// return 4.03
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```
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