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#' @title keras_r2
#'
#' @description This function is to calculate rsquared value for regression models.
#'
#' @param y_true : tensor of ground truth
#'
#' @param y_pred: tensor of predicted values
#'
#' @return keras tensor, double for rsquared
#'
#' @export keras_r2
keras_r2=function(y_true, y_pred) {
SS_res =keras::k_sum(keras::k_square(y_true-y_pred ))
SS_tot =keras::k_sum(keras::k_square(( y_true - keras::k_mean(y_true))))
return ( 1 - SS_res/(SS_tot + keras::k_epsilon()))
}
#' @title r2_squ
#'
#' @description This function is to calculate rsquared value for regression models.
#'
#' @param obs : vector, observed values
#'
#' @param res: residual (observed values-predicted values)
#'
#' @return double for rsquared
#'
#' @export r2_squ
r2_squ=function (obs, res){
yy = obs - matrix(mean(obs), nrow = nrow(array(obs)))
r2 = 1 - (t(res) %*% res)/(t(yy) %*% yy)
return(r2[1,1])
}
#' @title rmse
#'
#' @description This function is to calculate rsquared value for regression models.
#'
#' @param obs : vector, observed values
#'
#' @param res: residual (observed values-predicted values)
#'
#' @return double for rmse
#'
#' @export rmse
rmse=function (obs, pre) {
error = obs - pre
ret = sqrt(mean(error^2))
return(ret)
}
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