Creates new PCA (Principal Component Analysis) from the dataset
dataset or covariance matrix.
true if the dataset is a covariance matrix.
select which method to use: SVD (default), covarianceMatrix or NIPALS.
number of components to be computed with NIPALS.
should the data be centered (subtract the mean).
should the data be scaled (divide by the standard deviation).
ignore columns with zero variance if scale is true.
scale
true
Optional
Returns the cumulative proportion of variance
Returns the Eigenvalues (on the diagonal)
Returns the Eigenvectors of the covariance matrix
Returns the proportion of variance for each component
Returns the loadings matrix
Returns the standard deviations of the principal components
Calculates the inverse PCA transform
dataset projected in the PCA space
Project the dataset into the PCA space
Export the current model to a JSON object
model
Static
Load a PCA model from JSON
Creates new PCA (Principal Component Analysis) from the dataset
Param
dataset or covariance matrix.
Param
Param
true if the dataset is a covariance matrix.
Param
select which method to use: SVD (default), covarianceMatrix or NIPALS.
Param
number of components to be computed with NIPALS.
Param
should the data be centered (subtract the mean).
Param
should the data be scaled (divide by the standard deviation).
Param
ignore columns with zero variance if
scaleistrue.