'update'

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

diff --git a/pkg/R/m_KnearestNeighbors.R b/pkg/R/m_KnearestNeighbors.R
new file mode 100644 (file)
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+++ b/pkg/R/m_KnearestNeighbors.R
@@ -0,0 +1,48 @@
+#' @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)
+               }
+       )
+)