cuda_ml_pca {cuda.ml}

Perform principal component analysis.

Description

Compute principal component(s) of the input data. Each feature from the input will be mean-centered (but not scaled) before the SVD computation takes place.

Usage

cuda_ml_pca(
  x,
  n_components = NULL,
  eig_algo = c("dq", "jacobi"),
  tol = 1e-07,
  n_iters = 15L,
  whiten = FALSE,
  transform_input = TRUE,
  cuML_log_level = c("off", "critical", "error", "warn", "info", "debug", "trace")
)

Arguments

x

The input matrix or dataframe. Each data point should be a row and should consist of numeric values only.

n_components

Number of principal component(s) to keep. Default: min(nrow(x), ncol(x)).

eig_algo

Eigen decomposition algorithm to be applied to the covariance matrix. Valid choices are "dq" (divid-and-conquer method for symmetric matrices) and "jacobi" (the Jacobi method for symmetric matrices). Default: "dq".

tol

Tolerance for singular values computed by the Jacobi method. Default: 1e-7.

n_iters

Maximum number of iterations for the Jacobi method. Default: 15.

whiten

If TRUE, then de-correlate all components, making each component have unit variance and removing multi-collinearity. Default: FALSE.

transform_input

If TRUE, then compute an approximate representation of the input data. Default: TRUE.

cuML_log_level

Log level within cuML library functions. Must be one of "off", "critical", "error", "warn", "info", "debug", "trace". Default: off.

Value

A PCA model object with the following attributes: - "components": a matrix of n_components rows containing the top principal components. - "explained_variance": amount of variance within the input data explained by each component. - "explained_variance_ratio": fraction of variance within the input data explained by each component. - "singular_values": singular values (non-negative) corresponding to the top principal components. - "mean": the column wise mean of x which was used to mean-center x first. - "transformed_data": (only present if "transform_input" is set to TRUE) an approximate representation of input data based on principal components. - "pca_params": opaque pointer to PCA parameters which will be used for performing inverse transforms.

The model object can be used as input to the inverse_transform() function to map a representation based on principal components back to the original feature space.

Examples

library(cuda.ml)

iris.pca <- cuda_ml_pca(iris[1:4], n_components = 3)
print(iris.pca)