rename pkg --> aggexp

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

diff --git a/pkg/R/z_util.R b/pkg/R/z_util.R
deleted file mode 100644 (file)
index 996a5f8..0000000
--- a/pkg/R/z_util.R
+++ /dev/null
@@ -1,49 +0,0 @@
-#Maximum size of stored data to predict next PM10
-MAX_HISTORY = 10000
-
-#Default lambda value (when too few data)
-LAMBDA = 2.
-
-#Maximum error to keep a line in (incremental) data
-MAX_ERROR = 20.
-
-#Turn a "vector" into 1D matrix if needed (because R auto cast 1D matrices)
-matricize = function(x)
-{
-       if (!is.null(dim(x)))
-               return (as.matrix(x))
-       return (t(as.matrix(x)))
-}
-
-#Moore-Penrose pseudo inverse
-mpPsInv = function(M)
-{
-       epsilon = 1e-10
-    s = svd(M)
-    sd = s$d ; sd[sd < epsilon] = Inf
-    sd = diag(1.0 / sd, min(nrow(M),ncol(M)))
-    return (s$v %*% sd %*% t(s$u))
-}
-
-#Heuristic for k in knn algorithms
-getKnn = function(n)
-{
-       return ( max(1, min(50, ceiling(n^(2./3.)))) )
-}
-
-#Minimize lambda*||u||^2 + ||Xu - Y||^2
-ridgeSolve = function(X, Y, lambda)
-{
-       s = svd(X)
-       deltaDiag = s$d / (s$d^2 + lambda)
-       deltaDiag[!is.finite(deltaDiag)] = 0.0
-       if (length(deltaDiag) > 1)
-               deltaDiag = diag(deltaDiag)
-       return (s$v %*% deltaDiag %*% t(s$u) %*% Y)
-}
-
-#Return the indices (of rows, by default) without any NA
-getNoNAindices = function(M, margin=1)
-{
-       return (apply(M, margin, function(z)(!any(is.na(z)))))
-}