R/z_util.R

   1 #Maximum size of stored data to predict next PM10
   2 MAX_HISTORY = 10000
   3
   4 #Default lambda value (when too few data)
   5 LAMBDA = 2.
   6
   7 #Maximum error to keep a line in (incremental) data
   8 MAX_ERROR = 20.
   9
  10 #Turn a "vector" into 1D matrix if needed (because R auto cast 1D matrices)
  11 matricize = function(x)
  12 {
  13     if (!is.null(dim(x)))
  14         return (as.matrix(x))
  15     return (t(as.matrix(x)))
  16 }
  17
  18 #Moore-Penrose pseudo inverse
  19 mpPsInv = function(M)
  20 {
  21     epsilon = 1e-10
  22     s = svd(M)
  23     sd = s$d ; sd[sd < epsilon] = Inf
  24     sd = diag(1.0 / sd, min(nrow(M),ncol(M)))
  25     return (s$v %*% sd %*% t(s$u))
  26 }
  27
  28 #Heuristic for k in knn algorithms
  29 getKnn = function(n)
  30 {
  31     return ( max(1, min(50, ceiling(n^(2./3.)))) )
  32 }
  33
  34 #Minimize lambda*||u||^2 + ||Xu - Y||^2
  35 ridgeSolve = function(X, Y, lambda)
  36 {
  37     s = svd(X)
  38     deltaDiag = s$d / (s$d^2 + lambda)
  39     deltaDiag[!is.finite(deltaDiag)] = 0.0
  40     if (length(deltaDiag) > 1)
  41         deltaDiag = diag(deltaDiag)
  42     return (s$v %*% deltaDiag %*% t(s$u) %*% Y)
  43 }
  44
  45 #Return the indices (of rows, by default) without any NA
  46 getNoNAindices = function(M, margin=1)
  47 {
  48     return (apply(M, margin, function(z)(!any(is.na(z)))))
  49 }