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* Multiple training data sets allowed * Tests with multiple training data sets * Updating docs according to #38 Documenting all models which predictions will be based on all training data provided. Some models already supported multiple training data sets.
50 lines
1.8 KiB
Markdown
50 lines
1.8 KiB
Markdown
# Support Vector Classification
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Classifier implementing Support Vector Machine 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::LINEAR)
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* $cost (float) - parameter C of C-SVC (default 1.0)
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* $degree (int) - degree of the Kernel::POLYNOMIAL function (default 3)
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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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* $probabilityEstimates (bool) - whether to enable probability estimates (default false)
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```
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$classifier = new SVC(Kernel::LINEAR, $cost = 1000);
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$classifier = new SVC(Kernel::RBF, $cost = 1000, $degree = 3, $gamma = 6);
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```
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### Train
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To train a classifier simply provide train samples and labels (as `array`). Example:
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```
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use Phpml\Classification\SVC;
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use Phpml\SupportVectorMachine\Kernel;
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$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
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$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
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$classifier = new SVC(Kernel::LINEAR, $cost = 1000);
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$classifier->train($samples, $labels);
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```
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You can train the classifier using multiple data sets, predictions will be based on all the training data.
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### Predict
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To predict sample label use `predict` method. You can provide one sample or array of samples:
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```
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$classifier->predict([3, 2]);
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// return 'b'
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$classifier->predict([[3, 2], [1, 5]]);
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// return ['b', 'a']
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```
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