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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14 | source("sowas-superseded.r") # auxiliary CWT functions |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, |
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48 | # tw = 0, noctave = 13) |
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 | ||
55 | #17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube] | |
56 | ||
57 | #TODO: une fonction qui fait lignes 59 à 91 | |
58 | ||
59 | #cube: | |
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60 | # Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, |
61 | # scalevector = scalevector4, | |
62 | # lt = delta, smooth = FALSE, | |
63 | # nvoice = nvoice) # observations node with CWT | |
64 | # | |
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65 | # #matrix: |
66 | # ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) | |
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67 | # #Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) |
68 | # | |
572d139a | 69 | # #NOTE: delta et lscvect pourraient etre gardés à part (communs) |
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70 | # for(i in 1:n) |
71 | # Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) | |
72 | # | |
73 | # #rm(conso, Xcwt4); gc() | |
74 | # | |
75 | # ## _.b WER^2 distances ######## | |
76 | # Xwer_dist <- matrix(0.0, n, n) | |
77 | # for(i in 1:(n - 1)){ | |
78 | # mat1 <- vect2mat(Xcwt2[i,]) | |
79 | # for(j in (i + 1):n){ | |
80 | # mat2 <- vect2mat(Xcwt2[j,]) | |
81 | # num <- Mod(mat1 * Conj(mat2)) | |
82 | # WX <- Mod(mat1 * Conj(mat1)) | |
83 | # WY <- Mod(mat2 * Conj(mat2)) | |
84 | # smsmnum <- smCWT(num, scalevector = scalevector4) | |
85 | # smsmWX <- smCWT(WX, scalevector = scalevector4) | |
86 | # smsmWY <- smCWT(WY, scalevector = scalevector4) | |
87 | # wer2 <- sum(colSums(smsmnum)^2) / | |
88 | # sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) | |
89 | # Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2)) | |
90 | # Xwer_dist[j, i] <- Xwer_dist[i, j] | |
91 | # } | |
92 | # } | |
93 | # diag(Xwer_dist) <- numeric(n) | |
94 | # | |
95 | # save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata") | |
96 | # save(Xwer_dist, file = "../res/2009_synchros200-randomWER.Rdata") | |
97 | ||
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98 | |
99 | ||
100 | #lignes 59 à 91 "dépliées" : | |
101 | Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, | |
102 | scalevector = scalevector4, | |
103 | lt = delta, smooth = FALSE, | |
104 | nvoice = nvoice) # observations node with CWT | |
105 | ||
106 | #matrix: | |
107 | ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) | |
108 | Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) | |
109 | ||
110 | #NOTE: delta et lscvect pourraient etre gardés à part (communs) | |
111 | for(i in 1:n) | |
112 | Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) | |
113 | ||
114 | #rm(conso, Xcwt4); gc() | |
115 | ||
116 | ## _.b WER^2 distances ######## | |
117 | Xwer_dist <- matrix(0.0, n, n) | |
118 | for(i in 1:(n - 1)){ | |
119 | mat1 <- vect2mat(Xcwt2[i,]) | |
120 | ||
121 | #NOTE: vect2mat = as.matrix ?! (dans aux.R) | |
122 | vect2mat <- function(vect){ | |
123 | vect <- as.vector(vect) | |
124 | matrix(vect[-(1:2)], delta, lscvect) | |
125 | } | |
126 | ||
127 | for(j in (i + 1):n){ | |
128 | mat2 <- vect2mat(Xcwt2[j,]) | |
129 | num <- Mod(mat1 * Conj(mat2)) | |
130 | WX <- Mod(mat1 * Conj(mat1)) | |
131 | WY <- Mod(mat2 * Conj(mat2)) | |
132 | smsmnum <- smCWT(num, scalevector = scalevector4) | |
133 | smsmWX <- smCWT(WX, scalevector = scalevector4) | |
134 | smsmWY <- smCWT(WY, scalevector = scalevector4) | |
135 | wer2 <- sum(colSums(smsmnum)^2) / | |
136 | sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) | |
137 | Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2)) | |
138 | Xwer_dist[j, i] <- Xwer_dist[i, j] | |
139 | } | |
140 | } | |
141 | diag(Xwer_dist) <- numeric(n) | |
142 | ||
143 | #fonction smCWT (dans aux.R) | |
144 | smCWT <- function(CWT, sw= 0, tw= 0, swabs= 0, | |
145 | nvoice= 12, noctave= 2, s0= 2, w0= 2*pi, | |
146 | lt= 24, dt= 0.5, scalevector ) | |
147 | { | |
148 | # noctave <- adjust.noctave(lt, dt, s0, tw, noctave) | |
149 | # scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0 | |
150 | wsp <- Mod(CWT) | |
151 | smwsp <- smooth.matrix(wsp, swabs) | |
152 | smsmwsp <- smooth.time(smwsp, tw, dt, scalevector) | |
153 | smsmwsp | |
154 | } | |
155 | ||
156 | #dans sowas.R | |
157 | smooth.matrix <- function(wt,swabs){ | |
158 | ||
159 | if (swabs != 0) | |
160 | smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1))) | |
161 | else | |
162 | smwt <- wt | |
163 | ||
164 | smwt | |
165 | ||
166 | } | |
167 | smooth.time <- function(wt,tw,dt,scalevector){ | |
168 | ||
169 | smwt <- wt | |
170 | ||
171 | if (tw != 0){ | |
172 | for (i in 1:length(scalevector)){ | |
173 | ||
174 | twi <- as.integer(scalevector[i]*tw/dt) | |
175 | smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1)) | |
176 | ||
177 | } | |
178 | } | |
179 | smwt | |
180 | } | |
181 | ||
182 | #et filter() est dans stats:: | |
183 | ||
184 | #cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c | |
185 | ||
186 | ||
187 | ||
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188 | load("../res/2009_synchros200WER.Rdata") |
189 | #load("../res/2009_synchros200-randomWER.Rdata") | |
190 | ||
191 | ## 3. Cluster using WER distance matrix #### | |
192 | ||
193 | #hc <- hclust(as.dist(Xwer_dist), method = "ward.D") | |
194 | #plot(hc) | |
195 | # | |
196 | # #clust <- cutree(hc, 2) | |
197 | # | |
198 | for(K in 2:30){ | |
199 | #K <- 3 | |
200 | #pamfit <- pam(tdata[-201, ci$selectv], k = K) | |
201 | pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE) | |
202 | ||
203 | #table(pamfit$clustering) | |
204 | ||
205 | SC <- matrix(0, ncol = p, nrow = K) | |
206 | ||
207 | clustfactor <- pamfit$clustering | |
208 | # for(k in 1:K){ | |
209 | # clustk <- which(clustfactor == k) | |
210 | # if(length(clustk) > 0) { | |
211 | # if(length(clustk) > 1) { | |
212 | # SCk <- colSums(synchros09[which(clustfactor == k), ]) | |
213 | # } else { | |
214 | # SCk <- synchros09[which(clustfactor == k), ] | |
215 | # } | |
216 | # SC[k, ] <- SC[k, ] + SCk | |
217 | # rm(SCk) | |
218 | # } | |
219 | #} | |
220 | ||
221 | #write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt")) | |
222 | #write.table(clustfactor, file = "~/tmp/clustfactor3.txt") | |
223 | #write.table(clustfactor, file = paste0("~/tmp/clustfactorWER", K, ".txt")) | |
224 | write.table(clustfactor, file = paste0("~/tmp/clustfactor-randomWER", K, ".txt")) | |
225 | } | |
226 | # | |
227 | # # Plots | |
228 | # layout(1) | |
229 | # matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1) | |
230 | # matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1) | |
231 | # | |
232 | # | |
233 | # |