Lint fix on consistent assignment
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@@ -124,15 +124,15 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
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stop("xgb.cv: cannot assign two different objectives")
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if (!is.null(params$objective))
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if (class(params$objective) == 'function') {
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obj = params$objective
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params[['objective']] = NULL
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obj <- params$objective
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params[['objective']] <- NULL
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}
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# if (!is.null(params$eval_metric) && !is.null(feval))
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# stop("xgb.cv: cannot assign two different evaluation metrics")
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if (!is.null(params$eval_metric))
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if (class(params$eval_metric)=='function') {
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feval = params$eval_metric
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params[['eval_metric']] = NULL
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feval <- params$eval_metric
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params[['eval_metric']] <- NULL
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}
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# Early Stopping
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@@ -144,9 +144,9 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
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if (is.null(maximize))
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{
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if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
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maximize = FALSE
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maximize <- FALSE
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} else {
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maximize = TRUE
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maximize <- TRUE
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}
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}
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@@ -167,16 +167,16 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
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mat_pred <- FALSE
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if (!is.null(obj_type) && obj_type == 'multi:softprob')
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{
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num_class = params[['num_class']]
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num_class <- params[['num_class']]
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if (is.null(num_class))
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stop('must set num_class to use softmax')
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predictValues <- matrix(0,xgb.numrow(dtrain),num_class)
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mat_pred = TRUE
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mat_pred <- TRUE
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}
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else
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predictValues <- rep(0,xgb.numrow(dtrain))
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history <- c()
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print.every.n = max(as.integer(print.every.n), 1L)
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print.every.n <- max(as.integer(print.every.n), 1L)
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for (i in 1:nrounds) {
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msg <- list()
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for (k in 1:nfold) {
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