ssrmlp2_train {sisireg} | R Documentation |
2-layer MLP with partial sum optimization - reworked
Description
Calculates the weights of a 2-layer MLP with respect to the partial sums critereon
Usage
ssrmlp2_train(X, Y, std=TRUE, opt='ps', hl=NULL, W=NULL,
k=NULL, fn=NULL, eta=0.5, accept = 10, maxIter=1000, alpha=NULL, beta=NULL)
Arguments
X |
matrix with n-dimensional coordinates. |
Y |
array with observations. |
std |
optional: standardizing values if TRUE. |
opt |
optional: optimizing function ('simple', 'ps', 'lse'). |
hl |
optional: array tupel with number of perceptrons in each layer. |
W |
optional: previously calculates weights for refining the model. |
k |
optional: number of neighbors per quadrant. |
fn |
optional: quantile for partial sums. |
eta |
optional: constant step width of the gradient algorithm (eta=0.0 for Armijo). |
accept |
optional: percentage of acceptable deviations from Partial Sum critereon regarding number and quantile. |
maxIter |
optional: number of iterations for the numeric solver (maxIter=1000). |
alpha |
optional: weight parameter for function to be minimized. |
beta |
optional: weight parameter for side condition. |
Value
W |
List with weight matrices. |
Author(s)
Dr. Lars Metzner
References
Dr. Lars Metzner (2021) Adäquates Maschinelles Lernen. Independently Published.
Examples
# generate data
set.seed(42)
x <- rnorm(300)
y <- rnorm(300)
z <- rnorm(300) + atan2(x, y)
# coordinates
X <- matrix(cbind(x,y), ncol = 2)
Y <- as.double(z)
# Training
W <- ssrmlp2_train(X, Y)