MLP_classification: Multilayer Perceptron

In PauloCirino/MLAT: Machine Learning Automated Software

Description

Performs a multilayer perceptron classification

Usage

MLP_classification(X_train, Y_train, X_test, n_neurons, weight_decay = 0,
  max_iter = 1000)

Arguments

X_train	A Matrix of trainning observations.
Y_train	A numeric vector of classes or values of the trainning observations
X_test	A Matrix of testing observations.
n_neurons	Number of neurons in the hidden layer.
weight_decay	Weigth decay parameter for neural network.
max_iter	Maximun number of trainning iterations.

Value

predicted values

Examples

X <- as.matrix(cbind(runif(n = 100), runif(n = 100)))
pos <- sample(100, 70)
X_train <- X[pos, ]
X_test <- X[-pos, ]
Y_train <- as.numeric( X_train[, 1] ** 2 - X_train[, 2] > 0)
Y_test <- as.numeric(X_test[, 1] ** 2 - X_test[, 2] > 0)
n_neurons <- 50
Y_predicted <- MLP_classification(X_train = X_train, Y_train = Y_train, X_test = X_test, n_neurons = n_neurons)
table(Y_test, Y_predicted)

PauloCirino/MLAT documentation built on May 13, 2019, 1:22 p.m.