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Developer Documentation
- Bug fixes for general core bugs in 3.10.x will end 8 November 2021 (12 months).
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| File Size: | 223 lines (7 kb) |
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Defines 1 class
LDA:: (8 methods):
__construct()
fit()
transform()
getLabels()
calculateMeans()
calculateClassVar()
calculateClassCov()
calculateVar()
| __construct(?float $totalVariance = null, ?int $numFeatures = null) X-Ref |
| Linear Discriminant Analysis (LDA) is used to reduce the dimensionality of the data. Unlike Principal Component Analysis (PCA), it is a supervised technique that requires the class labels in order to fit the data to a lower dimensional space. <br><br> The algorithm can be initialized by speciyfing either with the totalVariance(a value between 0.1 and 0.99) or numFeatures (number of features in the dataset) to be preserved. param: float|null $totalVariance Total explained variance to be preserved param: int|null $numFeatures Number of features to be preserved |
| fit(array $data, array $classes) X-Ref |
| Trains the algorithm to transform the given data to a lower dimensional space. |
| transform(array $sample) X-Ref |
| Transforms the given sample to a lower dimensional vector by using the eigenVectors obtained in the last run of <code>fit</code>. |
| getLabels(array $classes) X-Ref |
| Returns unique labels in the dataset |
| calculateMeans(array $data, array $classes) X-Ref |
| Calculates mean of each column for each class and returns n by m matrix where n is number of labels and m is number of columns |
| calculateClassVar(array $data, array $classes) X-Ref |
| Returns in-class scatter matrix for each class, which is a n by m matrix where n is number of classes and m is number of columns |
| calculateClassCov() X-Ref |
| Returns between-class scatter matrix for each class, which is an n by m matrix where n is number of classes and m is number of columns |
| calculateVar(array $row, array $means) X-Ref |
| Returns the result of the calculation (x - m)T.(x - m) |
