throw away old code, prepare tests

[epclust.git] / old_C_code / stage2_UNFINISHED / src / unused / aux.r

diff --git a/old_C_code/stage2_UNFINISHED/src/unused/aux.r b/old_C_code/stage2_UNFINISHED/src/unused/aux.r
deleted file mode 100644 (file)
index 15021c0..0000000
--- a/old_C_code/stage2_UNFINISHED/src/unused/aux.r
+++ /dev/null
@@ -1,127 +0,0 @@
-####################################################################
-##
-## File:        aux.r
-##
-## Description: Miscelaneous functions for clustering with kcca
-##
-## Modified:    june 2010
-##
-####################################################################
-
-
-  #######################################################
-
-  # Transforms a matrix of data (one observation by row)
-  #     into an array where position[ , , i] gives
-  #     the smoothed modulus of the i-th cwt observation 
-
-  ########################################################
-
-
-##NOTE: renvoie une matrice 3D
-  toCWT  <- function(X, sw=  0,  tw=  0, swabs= 0,
-                       nvoice= 12, noctave= 5, 
-                       s0= 2, w0= 2*pi, lt= 24, dt= 0.5,
-                       spectra = FALSE, smooth = TRUE,
-                       scaled  = FALSE,
-                     scalevector)
-     { noctave  <- adjust.noctave(lt, dt, s0, tw, noctave)
-       if(missing(scalevector)) 
-          scalevector  <- 2^(0:(noctave * nvoice) / nvoice) * s0
-       res <- lapply(1:nrow(X), function(n)
-           { tsX         <- ts( X[n,] )
-             tsCent      <- tsX - mean(tsX)
-             if(scaled)  tsCent <- ts(scale(tsCent))           
-             tsCent.cwt  <- cwt.ts(tsCent, s0, noctave, nvoice, w0)
-             tsCent.cwt
-           } )
-          if( spectra ) res <- lapply(res, function(l) Mod(l)^2 )
-          if( smooth  ) res <- lapply(res, smCWT, swabs = swabs,
-                                      tw = tw, dt = dt, 
-                                      scalevector = scalevector)
-       resArray <- array(NA, c(nrow(res[[1]]), ncol(res[[1]]),
-                               length(res)))
-       for( l in 1:length(res) ) resArray[ , , l] <- res[[l]]
-       resArray
-     }
-
-
-  # ===============================================================
-
-  smCWT <- function(CWT, sw=  0,  tw=  0, swabs= 0,
-                       nvoice= 12, noctave= 2, s0= 2, w0= 2*pi, 
-                                          lt= 24, dt= 0.5, scalevector )
-                {
-#         noctave  <- adjust.noctave(lt, dt, s0, tw, noctave)
-#         scalevector  <- 2^(0:(noctave * nvoice) / nvoice) * s0
-         wsp     <- Mod(CWT)  
-         smwsp   <- smooth.matrix(wsp, swabs)
-         smsmwsp <- smooth.time(smwsp, tw, dt, scalevector)
-         smsmwsp
-       }
-
-
-  # ===============================================================
-
-  toDWT <- function(x, filter.number = 6, family = "DaubLeAsymm")
-{ x2   <- spline(x, n = 2^ceiling( log(length(x), 2) ),
-            method = 'natural')$y
-  Dx2 <- wd(x2, family = family, filter.number = filter.number)$D
-               Dx2
-}
-
-  # ===============================================================
-
-  contrib <- function(x) 
-      { J   <- log( length(x)+1, 2)
-        nrj <- numeric(J)
-        t0  <- 1
-        t1  <- 0
-        for( j in 1:J ) {
-          t1     <- t1 + 2^(J-j)
-          nrj[j] <- sqrt( sum( x[t0:t1]^2 ) )
-          t0     <- t1 + 1
-        }
-        return(nrj)  
-         }
-
-
-    # ========================================= distance for coh ===
-
-  coherence <- function( x, y)
-       { J <- log(length(x) + 1, 2)
-            t0 <- 1
-                sg2_x <- 0
-                sg2_y <- 0
-                sg_xy <- 0
-            for(j in 0:(J - 1))
-                {  t1 <- t0 + 2^(J - j)/2  - 1
-                   tt <- t0:t1
-                   sg2_x <- sg2_x + mean(x[t0:t1]^2)
-                       sg2_y <- sg2_y + mean(y[t0:t1]^2)
-                       sg_xy <- sg_xy + mean(x[t0:t1] * y[t0:t1])
-            t0 <- t1 + 1
-                }
-               res <- sg_xy^2 / sg2_x / sg2_y
-               res
-          }
-
-
-  vect2mat <- function(vect){
-                 vect <- as.vector(vect)
-                 matrix(vect[-(1:2)], delta, lscvect)
-               }
-
-
-  # =========================================  # myimg for graphics 
-  jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", 
-                                "cyan", "#7FFF7F", "yellow", 
-                                "#FF7F00", "red", "#7F0000"))
-
-  myimg <- function(MAT, x = 1:nrow(MAT), y = 1:col(MAT), ... )
-              filled.contour(  x = x, y = y, z = MAT, 
-                              xlab= 'Time', ylab= 'scale',
-                              color.palette = jet.colors,
-                              ... )
-
-