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

#' @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)
		}
	)
)
Commit	Line	Data
a961f8a1 BA	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	)