elm_train: Extreme Learning Machine training function

Description Usage Arguments Details References Examples

Description

Extreme Learning Machine training function

Usage

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elm_train(x, y, nhid, actfun, init_weights = "normal_gaussian",
  bias = FALSE, moorep_pseudoinv_tol = 0.01, leaky_relu_alpha = 0,
  seed = 1, verbose = FALSE)

Arguments

x

a matrix. The columns of the input matrix should be of type numeric

y

a matrix. In case of regression the matrix should have n rows and 1 column. In case of classification it should consist of n rows and n columns, where n > 1 and equals to the number of the unique labels.

nhid

a numeric value specifying the hidden neurons. Must be >= 1

actfun

a character string specifying the type of activation function. It should be one of the following : 'sig' ( sigmoid ), 'sin' ( sine ), 'radbas' ( radial basis ), 'hardlim' ( hard-limit ), 'hardlims' ( symmetric hard-limit ), 'satlins' ( satlins ), 'tansig' ( tan-sigmoid ), 'tribas' ( triangular basis ), 'relu' ( rectifier linear unit ) or 'purelin' ( linear )

init_weights

a character string spcecifying the distribution from which the input-weights and the bias should be initialized. It should be one of the following : 'normal_gaussian' ( in the range [0,1] ), 'uniform_positive' ( in the range [0,1] ) or 'uniform_negative' ( in the range [-1,1] )

bias

either TRUE or FALSE. If TRUE then bias weights will be added to the hidden layer

moorep_pseudoinv_tol

a numeric value. See the references web-link for more details on Moore-Penrose pseudo-inverse and specifically on the pseudo inverse tolerance value

leaky_relu_alpha

a numeric value between 0.0 and 1.0. If 0.0 then a simple relu ( f(x) = 0.0 for x < 0, f(x) = x for x >= 0 ) activation function will be used, otherwise a leaky-relu ( f(x) = alpha * x for x < 0, f(x) = x for x >= 0 ). It is applicable only if actfun equals to 'relu'

seed

a numeric value specifying the random seed. Defaults to 1

verbose

a boolean. If TRUE then information will be printed in the console

Details

The input matrix should be of type numeric. This means the user should convert any character, factor or boolean columns to numeric values before using the elm_train function

References

http://arma.sourceforge.net/docs.html

https://en.wikipedia.org/wiki/Moore

https://www.kaggle.com/robertbm/extreme-learning-machine-example

http://rt.dgyblog.com/ml/ml-elm.html

Examples

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library(elmNNRcpp)

#-----------
# Regression
#-----------

data(Boston, package = 'KernelKnn')

Boston = as.matrix(Boston)
dimnames(Boston) = NULL

x = Boston[, -ncol(Boston)]
y = matrix(Boston[, ncol(Boston)], nrow = length(Boston[, ncol(Boston)]), ncol = 1)

out_regr = elm_train(x, y, nhid = 20, actfun = 'purelin', init_weights = 'uniform_negative')


#---------------
# Classification
#---------------

data(ionosphere, package = 'KernelKnn')

x_class = ionosphere[, -c(2, ncol(ionosphere))]
x_class = as.matrix(x_class)
dimnames(x_class) = NULL

y_class = as.numeric(ionosphere[, ncol(ionosphere)])

y_class_onehot = onehot_encode(y_class - 1)     # class labels should begin from 0

out_class = elm_train(x_class, y_class_onehot, nhid = 20, actfun = 'relu')

mlampros/elmNNRcpp documentation built on May 3, 2019, 10:43 p.m.