private$loss <- loss
},
#' @description Fit an agghoo model.
- #' @param CV List describing cross-validation to run. Slots:
- #' - type: 'vfold' or 'MC' for Monte-Carlo (default: MC)
- #' - V: number of runs (default: 10)
+ #' @param CV List describing cross-validation to run. Slots: \cr
+ #' - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr
+ #' - V: number of runs (default: 10) \cr
#' - test_size: percentage of data in the test dataset, for MC
- #' (irrelevant for V-fold). Default: 0.2.
+ #' (irrelevant for V-fold). Default: 0.2. \cr
#' - shuffle: wether or not to shuffle data before V-fold.
#' Irrelevant for Monte-Carlo; default: TRUE
fit = function(
return (invisible(NULL))
}
V <- length(private$pmodels)
- if (length(private$pmodels[[1]]$model(X[1,])) >= 2)
+ oneLineX <- t(as.matrix(X[1,]))
+ if (length(private$pmodels[[1]]$model(oneLineX)) >= 2)
# Soft classification:
return (Reduce("+", lapply(private$pmodels, function(m) m$model(X))) / V)
n <- nrow(X)