RotMatRand {ODRF} | R Documentation |
Random Rotation Matrix
Description
Generate rotation matrices by different distributions, and it comes from the library rerf
.
Usage
RotMatRand(
dimX,
randDist = "Binary",
numProj = ceiling(sqrt(dimX)),
dimProj = "Rand",
sparsity = ifelse(dimX >= 10, 3/dimX, 1/dimX),
prob = 0.5,
lambda = 1,
catLabel = NULL,
...
)
Arguments
dimX |
The number of dimensions. |
randDist |
The probability distribution of the random projection direction, including "Binary": |
numProj |
The number of projection directions (default ceiling(sqrt( |
dimProj |
Number of variables to be projected, default dimProj="Rand": random from 1 to |
sparsity |
A real number in |
prob |
A probability in |
lambda |
Parameter of the Poisson distribution (default 1). |
catLabel |
A category labels of class |
... |
Used to handle superfluous arguments passed in using paramList. |
Value
A random matrix to use in running ODT
.
Variable: Variables to be projected.
Number: Number of projections.
Coefficient: Coefficients of the projection matrix.
References
Tomita, T. M., Browne, J., Shen, C., Chung, J., Patsolic, J. L., Falk, B., ... & Vogelstein, J. T. (2020). Sparse projection oblique randomer forests. Journal of machine learning research, 21(104).
See Also
Examples
set.seed(1)
paramList <- list(dimX = 8, numProj = 3, sparsity = 0.25, prob = 0.5)
(RotMat <- do.call(RotMatRand, paramList))
paramList <- list(dimX = 8, numProj = 3, sparsity = "pois")
(RotMat <- do.call(RotMatRand, paramList))
paramList <- list(dimX = 8, randDist = "Norm", dimProj = 5)
(RotMat <- do.call(RotMatRand, paramList))