projects / aggexp.git / blame
| Commit | Line | Data |
|---|---|---|
| b76a24cd BA |
1 | #' @include b_LinearAlgorithm.R |
| 2 | ||
| 3 | #' @title Exponential Weights Algorithm | |
| 4 | #' | |
| 5 | #' @description Exponential Weights Algorithm. | |
| 6 | #' Inherits \code{\link{LinearAlgorithm}} | |
| 7 | #' | |
| 8 | #' @field alpha Importance of weights redistribution, in [0,1]. Default: 0 | |
| 9 | #' @field grad Whether to use or not the (sub)gradient trick. Default: FALSE | |
| 10 | #' | |
| 11 | ExponentialWeights = setRefClass( | |
| 12 | Class = "ExponentialWeights", | |
| 13 | ||
| 14 | fields = c( | |
| 15 | alpha = "numeric", | |
| 16 | grad = "logical" | |
| 17 | ), | |
| 18 | ||
| 19 | contains = "LinearAlgorithm", | |
| 20 | ||
| 21 | methods = list( | |
| 22 | initialize = function(...) | |
| 23 | { | |
| 24 | callSuper(...) | |
| 25 | if (length(alpha) == 0 || alpha < 0. || alpha > 1.) | |
| 26 | alpha <<- 0. #no redistribution | |
| 27 | if (length(grad) == 0) | |
| 28 | grad <<- FALSE | |
| 29 | }, | |
| 30 | predict_noNA = function(XY, x) | |
| 31 | { | |
| 32 | K = ncol(XY) - 1 | |
| 33 | if (K == 1) | |
| 34 | { | |
| 35 | #shortcut: nothing to combine | |
| 36 | finalWeight = 1. | |
| 37 | } | |
| 38 | ||
| 39 | else | |
| 40 | { | |
| 41 | X = XY[,names(XY) != "Measure"] | |
| 42 | Y = XY[,"Measure"] | |
| 43 | finalWeight = .C("ew_predict_noNA", X = as.double(t(X)), Y = as.double(Y), n = as.integer(nrow(XY)), | |
| 44 | K = as.integer(K), alpha=as.double(alpha), grad = as.integer(grad), weight=double(K))$weight | |
| 45 | } | |
| 46 | ||
| 47 | appendWeight(finalWeight) | |
| 48 | return (matricize(x) %*% finalWeight) | |
| 49 | } | |
| 50 | ) | |
| 51 | ) |