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Developer Documentation
- Bug fixes for general core bugs in 3.11.x will end 14 Nov 2022 (12 months plus 6 months extension).
- Bug fixes for security issues in 3.11.x will end 13 Nov 2023 (18 months plus 12 months extension).
- PHP version: minimum PHP 7.3.0 Note: minimum PHP version has increased since Moodle 3.10. PHP 7.4.x is supported too.
(no description)
| File Size: | 131 lines (4 kb) |
| Included or required: | 0 times |
| Referenced: | 0 times |
| Includes or requires: | 0 files |
Defines 1 class
PCA:: (5 methods):
__construct()
fit()
transform()
calculateMeans()
normalize()
| __construct(?float $totalVariance = null, ?int $numFeatures = null) X-Ref |
| PCA (Principal Component Analysis) used to explain given data with lower number of dimensions. This analysis transforms the data to a lower dimensional version of it by conserving a proportion of total variance within the data. It is a lossy data compression technique.<br> param: float $totalVariance Total explained variance to be preserved param: int $numFeatures Number of features to be preserved |
| fit(array $data) X-Ref |
| Takes a data and returns a lower dimensional version of this data while preserving $totalVariance or $numFeatures. <br> $data is an n-by-m matrix and returned array is n-by-k matrix where k <= m |
| 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>. |
| calculateMeans(array $data, int $n) X-Ref |
| No description |
| normalize(array $data, int $n) X-Ref |
| Normalization of the data includes subtracting mean from each dimension therefore dimensions will be centered to zero |
