rename pkg --> aggexp

[aggexp.git] / pkg / R / m_KnearestNeighbors.R

diff --git a/pkg/R/m_KnearestNeighbors.R b/pkg/R/m_KnearestNeighbors.R
deleted file mode 100644 (file)
index 926b22b..0000000
--- a/pkg/R/m_KnearestNeighbors.R
+++ /dev/null
@@ -1,48 +0,0 @@
-#' @include b_Algorithm.R
-
-#' @title K Nearest Neighbors Algorithm
-#'
-#' @description K Nearest Neighbors Algorithm.
-#' Inherits \code{\link{Algorithm}}
-#'
-#' @field k Number of neighbors to consider. Default: \code{n^(2/3)}
-#'
-KnearestNeighbors = setRefClass(
-       Class = "KnearestNeighbors",
-
-       fields = c(
-               k = "numeric"
-       ),
-
-       contains = "Algorithm",
-
-       methods = list(
-               predictOne = function(X, Y, x)
-               {
-                       "Find the neighbors of one row, and solve a constrained linear system to obtain weights"
-
-                       distances = sqrt(apply(X, 1, function(z)(return (sum((z-x)^2)))))
-                       rankedHistory = sort(distances, index.return=TRUE)
-                       n = length(Y)
-                       k_ = ifelse(length(k) == 0 || k <= 0. || k > n, getKnn(n), as.integer(k))
-                       weight = ridgeSolve(matricize(X[rankedHistory$ix[1:k_],]), Y[rankedHistory$ix[1:k_]], LAMBDA)
-
-                       return (sum(x * weight))
-               },
-               predict_noNA = function(XY, x)
-               {
-                       X = XY[,names(XY) != "Measure"]
-                       K = ncol(XY) - 1
-                       if (K == 1)
-                               X = as.matrix(X)
-                       else if (length(XY[["Measure"]]) == 1)
-                               X = t(as.matrix(X))
-                       Y = XY[,"Measure"]
-                       x = matricize(x)
-                       res = c()
-                       for (i in 1:nrow(x))
-                               res = c(res, predictOne(X, Y, x[i,]))
-                       return (res)
-               }
-       )
-)