reorganize folder

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

diff --git a/pkg/R/m_RidgeRegression.R b/pkg/R/m_RidgeRegression.R
new file mode 100644 (file)
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--- /dev/null
+++ b/pkg/R/m_RidgeRegression.R
@@ -0,0 +1,49 @@
+#' @include b_LinearAlgorithm.R
+
+#' @title Ridge Regression Algorithm
+#'
+#' @description Ridge Regression Algorithm.
+#' Inherits \code{\link{LinearAlgorithm}}
+#'
+#' @field lambda Value of lambda (let undefined for cross-validation). Default: undefined
+#' @field lambdas Vector of "optimal" lambda values over time. TODO: remove for production
+#'
+RidgeRegression = setRefClass(
+       Class = "RidgeRegression",
+
+       fields = c(
+               lambda = "numeric",
+               lambdas = "numeric"
+       ),
+
+       contains = "LinearAlgorithm",
+       
+       methods = list(
+               predict_noNA = function(XY, x)
+               {
+                       if (length(lambda) > 0 || nrow(XY) < 30) #TODO: magic number
+                       {
+                               #simple ridge regression with fixed lambda (not enough history for CV)
+                               X = matricize(XY[,names(XY) != "Measure"])
+                               Y = XY[,"Measure"]
+                               lambda_ = ifelse(length(lambda) > 0, lambda, LAMBDA)
+                               weight = ridgeSolve(X, Y, lambda_)
+                       }
+
+                       else
+                       {
+                               #enough data for cross-validations
+                               require(MASS, quietly=TRUE)
+                               gridLambda = seq(0.05,5.05,0.1)
+                               res_lmr = lm.ridge(Measure ~ . + 0, data=XY, lambda = gridLambda)
+                               lambda_ = res_lmr$lambda[which.min(res_lmr$GCV)]
+                               weight = as.matrix(coef(res_lmr))[which.min(res_lmr$GCV),]
+                       }
+
+                       lambdas <<- c(lambdas, lambda_)
+
+                       appendWeight(weight)
+                       return (matricize(x) %*% weight)
+               }
+       )
+)