+++ /dev/null
-#' @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)
- }
- )
-)