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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.
40 lines
1023 B
Markdown
40 lines
1023 B
Markdown
# KNearestNeighbors Classifier
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Classifier implementing the k-nearest neighbors algorithm.
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## Constructor Parameters
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* $k - number of nearest neighbors to scan (default: 3)
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* $distanceMetric - Distance object, default Euclidean (see [distance documentation](../../math/distance.md))
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```
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$classifier = new KNearestNeighbors($k=4);
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$classifier = new KNearestNeighbors($k=3, new Minkowski($lambda=4));
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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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$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
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$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
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$classifier = new KNearestNeighbors();
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$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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