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[aggexp.git] / pkg / R / z_util.R
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1#Maximum size of stored data to predict next PM10
2MAX_HISTORY = 10000
3
4#Default lambda value (when too few data)
5LAMBDA = 2.
6
7#Maximum error to keep a line in (incremental) data
8MAX_ERROR = 20.
9
10#Turn a "vector" into 1D matrix if needed (because R auto cast 1D matrices)
11matricize = 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
19mpPsInv = 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$u))
26}
27
28#Heuristic for k in knn algorithms
29getKnn = function(n)
30{
31 return ( max(1, min(50, ceiling(n^(2./3.)))) )
32}
33
34#Minimize lambda*||u||^2 + ||Xu - Y||^2
35ridgeSolve = function(X, Y, lambda)
36{
37 s = svd(X)
38 deltaDiag = s$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
46getNoNAindices = function(M, margin=1)
47{
48 return (apply(M, margin, function(z)(!any(is.na(z)))))
49}