From bad7dffe0213aa5dd130f083f1b3069bdd1cba6b Mon Sep 17 00:00:00 2001
From: Drew Herren <drewherrenopensource@gmail.com>
Date: Thu, 31 Oct 2024 02:18:01 -0400
Subject: [PATCH] Added R method

---
 R/forest.R | 17 ++++++++++++++++-
 1 file changed, 16 insertions(+), 1 deletion(-)

diff --git a/R/forest.R b/R/forest.R
index 3843192d..c2a2b177 100644
--- a/R/forest.R
+++ b/R/forest.R
@@ -101,7 +101,7 @@ ForestSamples <- R6::R6Class(
         #' @param forest_dataset `ForestDataset` R class
         #' @param forest_num Index of the forest sample within the container
         #' @return matrix of predictions with as many rows as in forest_dataset 
-        #' and as many columns as samples in the `ForestContainer`
+        #' and as many columns as dimensions in the leaves of trees in `ForestContainer`
         predict_raw_single_forest = function(forest_dataset, forest_num) {
             stopifnot(!is.null(forest_dataset$data_ptr))
             # Unpack dimensions
@@ -113,6 +113,21 @@ ForestSamples <- R6::R6Class(
             return(output)
         }, 
         
+        #' @description
+        #' Predict "raw" leaf values (without being multiplied by basis) for a specific tree in a specific forest on every observation in `forest_dataset`
+        #' @param forest_dataset `ForestDataset` R class
+        #' @param forest_num Index of the forest sample within the container
+        #' @param tree_num Index of the tree to be queried
+        #' @return matrix of predictions with as many rows as in `forest_dataset` 
+        #' and as many columns as dimensions in the leaves of trees in `ForestContainer`
+        predict_raw_single_tree = function(forest_dataset, forest_num, tree_num) {
+            stopifnot(!is.null(forest_dataset$data_ptr))
+
+            # Predict leaf values from forest
+            output <- predict_forest_raw_single_tree_cpp(self$forest_container_ptr, forest_dataset$data_ptr, forest_num, tree_num)
+            return(output)
+        }, 
+        
         #' @description
         #' Set a constant predicted value for every tree in the ensemble. 
         #' Stops program if any tree is more than a root node.