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KernelPCA:: (8 methods):
__construct()
fit()
transform()
calculateKernelMatrix()
centerMatrix()
getKernel()
getDistancePairs()
projectSample()
__construct(int $kernel = self::KERNEL_RBF, ?float $totalVariance = null, ?int $numFeatures = null, ?float $gamma = null) X-Ref |
Kernel principal component analysis (KernelPCA) is an extension of PCA using techniques of kernel methods. It is more suitable for data that involves vectors that are not linearly separable<br><br> Example: <b>$kpca = new KernelPCA(KernelPCA::KERNEL_RBF, null, 2, 15.0);</b> will initialize the algorithm with an RBF kernel having the gamma parameter as 15,0. <br> This transformation will return the same number of rows with only <i>2</i> columns. param: float $totalVariance Total variance to be preserved if numFeatures is not given param: int $numFeatures Number of columns to be returned param: float $gamma Gamma parameter is used with RBF and Sigmoid kernels |
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 variables obtained during the last run of <code>fit</code>. |
calculateKernelMatrix(array $data, int $numRows) X-Ref |
Calculates similarity matrix by use of selected kernel function<br> An n-by-m matrix is given and an n-by-n matrix is returned |
centerMatrix(array $matrix, int $n) X-Ref |
Kernel matrix is centered in its original space by using the following conversion: Kâ² = K â N.K â K.N + N.K.N where N is n-by-n matrix filled with 1/n |
getKernel() X-Ref |
Returns the callable kernel function |
getDistancePairs(array $sample) X-Ref |
No description |
projectSample(array $pairs) X-Ref |
No description |