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#' Calculates the cross entropy error
#'
#' This function calculates the cross entropy error and its first order derivatives
#'
#' @param output the output value
#' @param target the target value
#'
#' @export
crossEntropyErr <- function(output, target) {
# err <- - sum(target[] * log(output[]) + (1 - target[]) * log(1 - output[]))
err <- - sum(target * log(output) + (1 - target) * log(1 - output))
err2 <- (1-target)/(1-output) - target/output
ret <- list()
ret[[1]] <- err
ret[[2]] <- err2
ret[[3]] <- "Cross Entropy Error"
return(ret)
}
#' Calculates the mean squared error
#'
#' This function calculates the mean squared error and its first order derivatives
#'
#' @param output the output value
#' @param target the target value
#'
#' @export
meanSquareErr <- function(output, target) {
err <- 1/2 * sum(output - target)^2 / dim(output)[[1]]
err2 <- (output - target)
ret <- list()
ret[[1]] <- err
ret[[2]] <- err2
ret[[3]] <- "Mean Squared Error"
return(ret)
}
#' Calculates the classification error
#'
#' This function calculates the classification error
#'
#' @param output the output of a classifier in the form of probability. Probability > 1
#' will be treated as positive (target = 1).
#' @param target the target variable
#'
#' @export
classification_error <- function(output, target) {
boolOut <- (output > 0.5) * 1
boolOutTarget <- cbind(boolOut, target)
rows <- nrow(target)
cols <- ncol(target)
classification_error <- sum(apply(boolOutTarget, 1, function(y)
{ any(y[1:cols] != y[(cols+1):(2*cols)])})) / rows * 100
ret <- list()
ret[[1]] <- classification_error
ret[[2]] <- "Classification Error"
return (ret)
}
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