cuml_rand_proj: Random projection for dimensionality reduction.
In cuml: R Interface for the RAPIDS cuML Suite of Libraries

Description Usage Arguments Value Examples

Generate a random projection matrix for dimensionality reduction, and optionally transform input data to a projection in a lower dimension space using the generated random matrix.

cuml_rand_proj(
  x,
  n_components = NULL,
  eps = 0.1,
  gaussian_method = TRUE,
  density = NULL,
  transform_input = TRUE,
  seed = 0L
)

`x`	The input matrix or dataframe. Each data point should be a row and should consist of numeric values only.
`n_components`	Dimensionality of the target projection space. If NULL, then the parameter is deducted using the Johnson-Lindenstrauss lemma, taking into consideration the number of samples and the `eps` parameter. Default: NULL.
`eps`	Error tolerance during projection. Default: 0.1.
`gaussian_method`	If TRUE, then use the Gaussian random projection method. Otherwise, use the sparse random projection method. See https://en.wikipedia.org/wiki/Random_projection for details. Default: TRUE.
`density`	Ratio of non-zero component in the random projection matrix. If NULL, then the value is set to the minimum density as recommended by Ping Li et al.: 1 / sqrt(n_features). Default: NULL.
`transform_input`	Whether to project input data onto a lower dimension space using the random matrix. Default: TRUE.
`seed`	Seed for the pseudorandom number generator. Default: 0L.

A context object containing GPU pointer to a random matrix that can be used as input to the cuml_transform() function. If transform_input is set to TRUE, then the context object will also contain a "transformed_data" attribute containing the lower dimensional projection of the input data.

library(cuml)
library(mlbench)

data(Vehicle)
vehicle_data <- Vehicle[order(Vehicle$Class), which(names(Vehicle) != "Class")]

ctx <- cuml_rand_proj(vehicle_data, n_components = 4)

set.seed(0L)
print(kmeans(ctx$transformed_data, centers = 4, iter.max = 1000))