926b22b5397d31396914ebe088ae4d2273c408c2
[aggexp.git] / pkg / R / m_KnearestNeighbors.R
1 #' @include b_Algorithm.R
2
3 #' @title K Nearest Neighbors Algorithm
4 #'
5 #' @description K Nearest Neighbors Algorithm.
6 #' Inherits \code{\link{Algorithm}}
7 #'
8 #' @field k Number of neighbors to consider. Default: \code{n^(2/3)}
9 #'
10 KnearestNeighbors = setRefClass(
11 Class = "KnearestNeighbors",
12
13 fields = c(
14 k = "numeric"
15 ),
16
17 contains = "Algorithm",
18
19 methods = list(
20 predictOne = function(X, Y, x)
21 {
22 "Find the neighbors of one row, and solve a constrained linear system to obtain weights"
23
24 distances = sqrt(apply(X, 1, function(z)(return (sum((z-x)^2)))))
25 rankedHistory = sort(distances, index.return=TRUE)
26 n = length(Y)
27 k_ = ifelse(length(k) == 0 || k <= 0. || k > n, getKnn(n), as.integer(k))
28 weight = ridgeSolve(matricize(X[rankedHistory$ix[1:k_],]), Y[rankedHistory$ix[1:k_]], LAMBDA)
29
30 return (sum(x * weight))
31 },
32 predict_noNA = function(XY, x)
33 {
34 X = XY[,names(XY) != "Measure"]
35 K = ncol(XY) - 1
36 if (K == 1)
37 X = as.matrix(X)
38 else if (length(XY[["Measure"]]) == 1)
39 X = t(as.matrix(X))
40 Y = XY[,"Measure"]
41 x = matricize(x)
42 res = c()
43 for (i in 1:nrow(x))
44 res = c(res, predictOne(X, Y, x[i,]))
45 return (res)
46 }
47 )
48 )