projects / ppam-mpi.git / blame
| Commit | Line | Data |
|---|---|---|
| bf3db2b6 BA |
1 | # Original R script (not required by the C program) |
| 2 | ||
| 3 | ||
| 81923e5c BA |
4 | ################################################################## |
| 5 | ## File: script_clustering_by_pam.r | |
| 6 | ## | |
| 7 | ## Description: Using PAM to clustering conso using pam | |
| 8 | ## Last modified: jan 2012 by JC | |
| 9 | ## | |
| 10 | ################################################################## | |
| 11 | ||
| 12 | ## Function: Converts a matrix of curves to the discret wavelet domain | |
| 13 | toDWT <- function(x, filter.number = 6, family = "DaubLeAsymm"){ | |
| 14 | x2 <- spline(x, n = 2^ceiling( log(length(x), 2) ), | |
| 15 | method = 'natural')$y | |
| 16 | Dx2 <- wd(x2, family = family, filter.number = filter.number)$D | |
| 17 | return(Dx2) | |
| 18 | } | |
| 19 | ||
| 20 | ## Function: Computes the absolute contribution of the wavelet's scale | |
| 21 | ## to the total total of the curve. | |
| 22 | contrib <- function(x) { | |
| 23 | J <- log( length(x)+1, 2) | |
| 24 | nrj <- numeric(J) | |
| 25 | t0 <- 1 | |
| 26 | t1 <- 0 | |
| 27 | for( j in 1:J ) { | |
| 28 | t1 <- t1 + 2^(J-j) | |
| 29 | nrj[j] <- sqrt( sum( x[t0:t1]^2 ) ) | |
| 30 | t0 <- t1 + 1 | |
| 31 | } | |
| 32 | return(nrj) | |
| 33 | } | |
| 34 | ||
| 35 | ## 1. Load libraries & data #### | |
| 36 | library(wavethresh) | |
| 37 | library(cluster) | |
| 38 | ||
| 39 | ||
| 40 | # powerload is a matrix that contains on each line one observation | |
| 41 | # of length delta | |
| 42 | ||
| 43 | ## 2. DWT ################## | |
| 44 | delta <- ncol(powerload) | |
| 45 | n <- nrow(powerload) | |
| 46 | Xdwt <- t(apply(powerload, 1, toDWT)) # DWT over the lines of powerload. | |
| 47 | Xnrj <- t(apply(Xdwt, 1, contrib)) # Absolute contribution to the energy. | |
| 48 | ||
| 49 | ## 3. Cluster ############## | |
| 50 | K <- 8 # Number of clusters | |
| 51 | Xnrj_dist <- dist(Xnrj) | |
| 52 | Xnrj_pam <- pam(as.dist(Xnrj_dist), K) |