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1 | ## File : 05_cluster2stepWER.r |
2 | ## Description : | |
3 | ||
4 | rm(list = ls()) | |
5 | ||
6 | setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") | |
7 | ||
8 | library(Rwave) # CWT | |
9 | library(cluster) # pam | |
10 | #library(flexclust) # kcca | |
11 | source("aux.r") # auxiliary clustering functions | |
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12 | |
13 | #TODO: [plus tard] alternative à sowa (package disparu) : cwt.. | |
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14 | source("sowas-superseded.r") # auxiliary CWT functions |
15 | ||
16 | ## 1. Read auxiliar data files #### | |
17 | ||
18 | identifiants <- read.table("identifs.txt")[ ,1] | |
19 | dates0 <- read.table("datesall.txt")[, 1] | |
20 | dates <- as.character(dates0[grep("2009", dates0)]) | |
21 | rm(dates0) | |
22 | ||
23 | n <- length(identifiants) | |
24 | p <- delta <- length(dates) | |
25 | ||
26 | synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RC.txt"))) | |
27 | #synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200-random.txt"))) | |
28 | ||
29 | nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing | |
572d139a | 30 | synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) #valeurs après 1er janvier |
ad642dc6 | 31 | |
572d139a | 32 | #moyenne pondérée pour compléter deux demi-heures manquantes |
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33 | imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) |
34 | synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) | |
35 | ||
36 | conso <- synchros09[-201, ]; # series must be on rows | |
37 | n <- nrow(conso) | |
38 | delta <- ncol(conso) | |
39 | ||
40 | rm(synchros09, nas) | |
41 | ||
42 | ## 2. Compute WER distance matrix #### | |
43 | ||
44 | ## _.a CWT -- Filtering the lowest freqs (>6m) #### | |
47395e4f | 45 | nvoice <- 4 |
ad642dc6 | 46 | # # noctave4 = 2^13 = 8192 half hours ~ 180 days |
47395e4f | 47 | noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2, |
dc1aa85a | 48 | tw = 0, noctave = 13) |
ad642dc6 | 49 | # # 4 here represent 2^5 = 32 half-hours ~ 1 day |
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50 | scalevector4 <- 2^(4:(noctave4 * nvoice) / nvoice) * 2 |
51 | lscvect4 <- length(scalevector4) | |
52 | lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect | |
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53 | |
54 | ||
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55 | #(Benjamin) |
56 | #à partir de là, "conso" == courbes synchrones | |
57 | n <- nrow(conso) | |
58 | delta <- ncol(conso) | |
59 | ||
60 | ||
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61 | #17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube] |
62 | ||
63 | #TODO: une fonction qui fait lignes 59 à 91 | |
64 | ||
65 | #cube: | |
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66 | # Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, |
67 | # scalevector = scalevector4, | |
68 | # lt = delta, smooth = FALSE, | |
69 | # nvoice = nvoice) # observations node with CWT | |
70 | # | |
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71 | # #matrix: |
72 | # ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) | |
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73 | # #Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) |
74 | # | |
572d139a | 75 | # #NOTE: delta et lscvect pourraient etre gardés à part (communs) |
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76 | # for(i in 1:n) |
77 | # Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) | |
78 | # | |
79 | # #rm(conso, Xcwt4); gc() | |
80 | # | |
81 | # ## _.b WER^2 distances ######## | |
82 | # Xwer_dist <- matrix(0.0, n, n) | |
83 | # for(i in 1:(n - 1)){ | |
84 | # mat1 <- vect2mat(Xcwt2[i,]) | |
85 | # for(j in (i + 1):n){ | |
86 | # mat2 <- vect2mat(Xcwt2[j,]) | |
87 | # num <- Mod(mat1 * Conj(mat2)) | |
88 | # WX <- Mod(mat1 * Conj(mat1)) | |
89 | # WY <- Mod(mat2 * Conj(mat2)) | |
90 | # smsmnum <- smCWT(num, scalevector = scalevector4) | |
91 | # smsmWX <- smCWT(WX, scalevector = scalevector4) | |
92 | # smsmWY <- smCWT(WY, scalevector = scalevector4) | |
93 | # wer2 <- sum(colSums(smsmnum)^2) / | |
94 | # sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) | |
95 | # Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2)) | |
96 | # Xwer_dist[j, i] <- Xwer_dist[i, j] | |
97 | # } | |
98 | # } | |
99 | # diag(Xwer_dist) <- numeric(n) | |
100 | # | |
101 | # save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata") | |
102 | # save(Xwer_dist, file = "../res/2009_synchros200-randomWER.Rdata") | |
103 | ||
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104 | |
105 | ||
106 | #lignes 59 à 91 "dépliées" : | |
107 | Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, | |
108 | scalevector = scalevector4, | |
109 | lt = delta, smooth = FALSE, | |
110 | nvoice = nvoice) # observations node with CWT | |
111 | ||
112 | #matrix: | |
113 | ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) | |
114 | Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) | |
115 | ||
116 | #NOTE: delta et lscvect pourraient etre gardés à part (communs) | |
117 | for(i in 1:n) | |
118 | Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) | |
119 | ||
120 | #rm(conso, Xcwt4); gc() | |
121 | ||
122 | ## _.b WER^2 distances ######## | |
123 | Xwer_dist <- matrix(0.0, n, n) | |
124 | for(i in 1:(n - 1)){ | |
125 | mat1 <- vect2mat(Xcwt2[i,]) | |
126 | ||
127 | #NOTE: vect2mat = as.matrix ?! (dans aux.R) | |
128 | vect2mat <- function(vect){ | |
129 | vect <- as.vector(vect) | |
130 | matrix(vect[-(1:2)], delta, lscvect) | |
131 | } | |
132 | ||
133 | for(j in (i + 1):n){ | |
134 | mat2 <- vect2mat(Xcwt2[j,]) | |
135 | num <- Mod(mat1 * Conj(mat2)) | |
136 | WX <- Mod(mat1 * Conj(mat1)) | |
137 | WY <- Mod(mat2 * Conj(mat2)) | |
138 | smsmnum <- smCWT(num, scalevector = scalevector4) | |
139 | smsmWX <- smCWT(WX, scalevector = scalevector4) | |
140 | smsmWY <- smCWT(WY, scalevector = scalevector4) | |
141 | wer2 <- sum(colSums(smsmnum)^2) / | |
142 | sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) | |
143 | Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2)) | |
144 | Xwer_dist[j, i] <- Xwer_dist[i, j] | |
145 | } | |
146 | } | |
147 | diag(Xwer_dist) <- numeric(n) | |
148 | ||
149 | #fonction smCWT (dans aux.R) | |
150 | smCWT <- function(CWT, sw= 0, tw= 0, swabs= 0, | |
151 | nvoice= 12, noctave= 2, s0= 2, w0= 2*pi, | |
152 | lt= 24, dt= 0.5, scalevector ) | |
153 | { | |
154 | # noctave <- adjust.noctave(lt, dt, s0, tw, noctave) | |
155 | # scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0 | |
156 | wsp <- Mod(CWT) | |
157 | smwsp <- smooth.matrix(wsp, swabs) | |
158 | smsmwsp <- smooth.time(smwsp, tw, dt, scalevector) | |
159 | smsmwsp | |
160 | } | |
161 | ||
162 | #dans sowas.R | |
163 | smooth.matrix <- function(wt,swabs){ | |
164 | ||
165 | if (swabs != 0) | |
166 | smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1))) | |
167 | else | |
168 | smwt <- wt | |
169 | ||
170 | smwt | |
171 | ||
172 | } | |
173 | smooth.time <- function(wt,tw,dt,scalevector){ | |
174 | ||
175 | smwt <- wt | |
176 | ||
177 | if (tw != 0){ | |
178 | for (i in 1:length(scalevector)){ | |
179 | ||
180 | twi <- as.integer(scalevector[i]*tw/dt) | |
181 | smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1)) | |
182 | ||
183 | } | |
184 | } | |
185 | smwt | |
186 | } | |
187 | ||
188 | #et filter() est dans stats:: | |
189 | ||
190 | #cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c | |
191 | ||
192 | ||
193 | ||
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194 | load("../res/2009_synchros200WER.Rdata") |
195 | #load("../res/2009_synchros200-randomWER.Rdata") | |
196 | ||
197 | ## 3. Cluster using WER distance matrix #### | |
198 | ||
199 | #hc <- hclust(as.dist(Xwer_dist), method = "ward.D") | |
200 | #plot(hc) | |
201 | # | |
202 | # #clust <- cutree(hc, 2) | |
203 | # | |
204 | for(K in 2:30){ | |
205 | #K <- 3 | |
206 | #pamfit <- pam(tdata[-201, ci$selectv], k = K) | |
207 | pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE) | |
208 | ||
209 | #table(pamfit$clustering) | |
210 | ||
211 | SC <- matrix(0, ncol = p, nrow = K) | |
212 | ||
213 | clustfactor <- pamfit$clustering | |
214 | # for(k in 1:K){ | |
215 | # clustk <- which(clustfactor == k) | |
216 | # if(length(clustk) > 0) { | |
217 | # if(length(clustk) > 1) { | |
218 | # SCk <- colSums(synchros09[which(clustfactor == k), ]) | |
219 | # } else { | |
220 | # SCk <- synchros09[which(clustfactor == k), ] | |
221 | # } | |
222 | # SC[k, ] <- SC[k, ] + SCk | |
223 | # rm(SCk) | |
224 | # } | |
225 | #} | |
226 | ||
227 | #write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt")) | |
228 | #write.table(clustfactor, file = "~/tmp/clustfactor3.txt") | |
229 | #write.table(clustfactor, file = paste0("~/tmp/clustfactorWER", K, ".txt")) | |
230 | write.table(clustfactor, file = paste0("~/tmp/clustfactor-randomWER", K, ".txt")) | |
231 | } | |
232 | # | |
233 | # # Plots | |
234 | # layout(1) | |
235 | # matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1) | |
236 | # matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1) | |
237 | # | |
238 | # | |
239 | # |