cuda_ml_tsvd {cuda.ml}

Truncated SVD.

Description

Dimensionality reduction using Truncated Singular Value Decomposition.

Usage

cuda_ml_tsvd(
  x,
  n_components = 2L,
  eig_algo = c("dq", "jacobi"),
  tol = 1e-07,
  n_iters = 15L,
  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

Desired dimensionality of output data. Must be strictly less than ncol(x) (i.e., the number of features in input data). Default: 2.

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.

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 TSVD model object with the following attributes: - "components": a matrix of n_components rows to be used for dimensionalitiy reduction on new data points. - "explained_variance": (only present if "transform_input" is set to TRUE) amount of variance within the input data explained by each component. - "explained_variance_ratio": (only present if "transform_input" is set to TRUE) fraction of variance within the input data explained by each component. - "singular_values": The singular values corresponding to each component. The singular values are equal to the 2-norms of the n_components variables in the lower-dimensional space. - "tsvd_params": opaque pointer to TSVD parameters which will be used for performing inverse transforms.

Examples

library(cuda.ml)

iris.tsvd <- cuda_ml_tsvd(iris[1:4], n_components = 2)
print(iris.tsvd)