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Random Rotation Matrix

Source: R/RotMat.R
RotMatRand.Rd

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": \(B\{-1,1\}\) binomial distribution (default), "Norm":\(N(0,1)\) normal distribution, "Uniform": \(U(-1,1)\) uniform distribution.

numProj

The number of projection directions (default ceiling(sqrt(dimX))).

dimProj

Number of variables to be projected, default dimProj="Rand": random from 1 to dimX.

sparsity

A real number in \((0,1)\) that specifies the distribution of non-zero elements in the random matrix. When sparsity="pois" means that non-zero elements are generated by the p(lambda) Poisson distribution.

prob

A probability in \((0,1)\) used for sampling from \({-1,1}\) where prob = 0 will only sample -1 and prob = 1 will only sample 1.

lambda

Parameter of the Poisson distribution (default 1).

catLabel

A category labels of class list in predictors. (default NULL, for details see Examples of ODT)

...

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

RotMatPPO RotMatRF RotMatMake

Examples

set.seed(1)
paramList <- list(dimX = 8, numProj = 3, sparsity = 0.25, prob = 0.5)
(RotMat <- do.call(RotMatRand, paramList))
#>      Variable Number Coefficient
#> [1,]        1      1           1
#> [2,]        2      1           1
#> [3,]        4      1           1
#> [4,]        7      1          -1
#> [5,]        3      2           1
#> [6,]        6      2          -1
paramList <- list(dimX = 8, numProj = 3, sparsity = "pois")
(RotMat <- do.call(RotMatRand, paramList))
#>      Variable Number Coefficient
#> [1,]        2      1           1
#> [2,]        7      2           1
#> [3,]        1      3           1
#> [4,]        7      3           1
#> [5,]        5      3          -1
#> [6,]        6      3           1
paramList <- list(dimX = 8, randDist = "Norm", dimProj = 5)
(RotMat <- do.call(RotMatRand, paramList))
#>       Variable Number Coefficient
#>  [1,]        2      1  0.80418951
#>  [2,]        5      1 -0.05710677
#>  [3,]        1      1  0.50360797
#>  [4,]        4      1  1.08576936
#>  [5,]        3      1 -0.69095384
#>  [6,]        1      2 -1.28459935
#>  [7,]        4      2  0.04672617
#>  [8,]        3      2 -0.23570656
#>  [9,]        2      2 -0.54288826
#> [10,]        5      2 -0.43331032
#> [11,]        2      3 -0.64947165
#> [12,]        5      3  0.72675075
#> [13,]        3      3  1.15191175
#> [14,]        4      3  0.99216037
#> [15,]        1      3 -0.42951311