GPU-aware principal component analysis

Usage

cuda_pca(
  x,
  n_components = 2L,
  center = TRUE,
  scale. = FALSE,
  device = c("auto", "cuda", "cpu")
)

Arguments

x: A matrix or cudasparse object with observations in rows and features in columns.
n_components: Number of components to return.
center: Whether to centre features.
scale.: Whether to scale features to unit variance.
device: One of "auto", "cuda", or "cpu".

Value

A cuda_pca object with scores in x, loadings in rotation, standard deviations, centring/scaling values, and actual device. Observation names, feature names, and stable PC1, PC2, ... component names are preserved on every backend.

Details

A native CUDA cudatensor selected on the same backend is validated on the device and passed directly into preprocessing and cuSOLVER without downloading its input matrix. Float32 and integer tensors are converted to float64 on the device. PCA scores retain shared native storage for direct composition with native distance and kNN operations. Sparse inputs transfer directly from their stable COO mirror. Constant features are scanned from that mirror only when scale. = TRUE; unscaled sparse PCA does not build or scan an intermediate Matrix object.

Examples

fit <- cuda_pca(iris[, 1:4], n_components = 2, device = "cpu")
fit
#> <cuda_pca components=2 device=cpu compute=cpu backend=stats>
#>                      PC1         PC2
#> Sepal.Length  0.36138659 -0.65658877
#> Sepal.Width  -0.08452251 -0.73016143
#> Petal.Length  0.85667061  0.17337266
#> Petal.Width   0.35828920  0.07548102