| 1 | lines(1:15, rep(0,15), col='red') |
| 2 | N = 300 |
| 3 | mugamma = c(1,2,3) |
| 4 | Gamma = matrix(c(5,-0.02,0.01,-0.02,1,0.1,0.01,0.1,1),3,3) |
| 5 | s2 = 0.01 |
| 6 | Si = getbasismatrix(seq(0,1,length=15),create.fourier.basis(nbasis = 3)) |
| 7 | gammai = t(sapply(1:N,FUN = function(i){ |
| 8 | rmvnorm(1,mugamma,Gamma) |
| 9 | })) |
| 10 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 11 | as.vector(Si%*%gammai[i,])+rnorm(15,0,s2) |
| 12 | })) |
| 13 | matplot(t(Xdata),type="l") |
| 14 | #beta = c(.1,.5,-.3) |
| 15 | beta = c(1,-3.5,4.5,-2.5) |
| 16 | pydata = sapply(1:N,FUN=function(i){ |
| 17 | a = sum(beta*c(1,gammai[i,])) |
| 18 | return(exp(a)/(1+exp(a))) |
| 19 | }) |
| 20 | U = runif(length(pydata)) |
| 21 | ydata = as.numeric(U<pydata) |
| 22 | plot(sort(pydata),col=ydata[order(pydata)]+2) |
| 23 | wi = Si %*% beta[-1] |
| 24 | plot(wi, type='l') |
| 25 | lines(1:15,rep(0,15)) |
| 26 | plot( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),typ="l",lty=1,col="green") |
| 27 | for (ite in 1:30){ |
| 28 | print(ite) |
| 29 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 30 | as.vector(Si%*%gammai[i,])+rnorm(15,0,s2) |
| 31 | })) |
| 32 | #beta = c(.1,.5,-.3) |
| 33 | beta = c(1,-3.5,4.5,-2.5) |
| 34 | pydata = sapply(1:N,FUN=function(i){ |
| 35 | a = sum(beta*c(1,gammai[i,])) |
| 36 | return(exp(a)/(1+exp(a))) |
| 37 | }) |
| 38 | U = runif(length(pydata)) |
| 39 | ydata = as.numeric(U<pydata) |
| 40 | lines( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),col="green") |
| 41 | } |
| 42 | lines(1:15, rep(0,15), col='red') |
| 43 | matplot(Xdata) |
| 44 | matplot(Xdata,type='l') |
| 45 | matplot(t(Xdata),type='l') |
| 46 | matplot(apply(Xdata, 2, mean),type='l') |
| 47 | mean(apply(Xdata, 2, mean)) |
| 48 | N = 3000 |
| 49 | mugamma = c(1,2,3) |
| 50 | Gamma = matrix(c(5,-0.02,0.01,-0.02,1,0.1,0.01,0.1,1),3,3) |
| 51 | s2 = 0.01 |
| 52 | Si = getbasismatrix(seq(0,1,length=15),create.fourier.basis(nbasis = 3)) |
| 53 | gammai = t(sapply(1:N,FUN = function(i){ |
| 54 | rmvnorm(1,mugamma,Gamma) |
| 55 | })) |
| 56 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 57 | as.vector(Si%*%gammai[i,])+rnorm(15,0,s2) |
| 58 | })) |
| 59 | matplot(t(Xdata),type="l") |
| 60 | #beta = c(.1,.5,-.3) |
| 61 | beta = c(1,-3.5,4.5,-2.5) |
| 62 | pydata = sapply(1:N,FUN=function(i){ |
| 63 | a = sum(beta*c(1,gammai[i,])) |
| 64 | return(exp(a)/(1+exp(a))) |
| 65 | }) |
| 66 | U = runif(length(pydata)) |
| 67 | ydata = as.numeric(U<pydata) |
| 68 | plot(sort(pydata),col=ydata[order(pydata)]+2) |
| 69 | wi = Si %*% beta[-1] |
| 70 | plot(wi, type='l') |
| 71 | lines(1:15,rep(0,15)) |
| 72 | plot( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),typ="l",lty=1,col="green") |
| 73 | for (ite in 1:30){ |
| 74 | print(ite) |
| 75 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 76 | as.vector(Si%*%gammai[i,])+rnorm(15,0,s2) |
| 77 | })) |
| 78 | #beta = c(.1,.5,-.3) |
| 79 | beta = c(1,-3.5,4.5,-2.5) |
| 80 | pydata = sapply(1:N,FUN=function(i){ |
| 81 | a = sum(beta*c(1,gammai[i,])) |
| 82 | return(exp(a)/(1+exp(a))) |
| 83 | }) |
| 84 | U = runif(length(pydata)) |
| 85 | ydata = as.numeric(U<pydata) |
| 86 | lines( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),col="green") |
| 87 | } |
| 88 | lines(1:15, rep(0,15), col='red') |
| 89 | Gamma |
| 90 | Gamma = matrix(c(5,-0.02,0.01,-0.02,5,0.1,0.01,0.1,5),3,3) |
| 91 | s2 = 0.01 |
| 92 | Si = getbasismatrix(seq(0,1,length=15),create.fourier.basis(nbasis = 3)) |
| 93 | gammai = t(sapply(1:N,FUN = function(i){ |
| 94 | rmvnorm(1,mugamma,Gamma) |
| 95 | })) |
| 96 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 97 | as.vector(Si%*%gammai[i,])+rnorm(15,0,s2) |
| 98 | })) |
| 99 | matplot(t(Xdata),type="l") |
| 100 | #beta = c(.1,.5,-.3) |
| 101 | plot(sort(sapply(1:N,FUN=function(i) 1/(1+exp((-1)*(resEM$betah[1,ITfinal]+sum(resEM$betah[-1,ITfinal]*resEM$gammahat[i,]))))) )) |
| 102 | plot(wi, type='l') |
| 103 | wi = Si %*% beta[-1] |
| 104 | plot(wi, type='l') |
| 105 | lines(1:15,rep(0,15)) |
| 106 | plot( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),typ="l",lty=1,col="green") |
| 107 | for (ite in 1:30){ |
| 108 | print(ite) |
| 109 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 110 | as.vector(Si%*%gammai[i,])+rnorm(15,0,s2) |
| 111 | })) |
| 112 | #beta = c(.1,.5,-.3) |
| 113 | beta = c(1,-3.5,4.5,-2.5) |
| 114 | pydata = sapply(1:N,FUN=function(i){ |
| 115 | a = sum(beta*c(1,gammai[i,])) |
| 116 | return(exp(a)/(1+exp(a))) |
| 117 | }) |
| 118 | U = runif(length(pydata)) |
| 119 | ydata = as.numeric(U<pydata) |
| 120 | lines( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),col="green") |
| 121 | } |
| 122 | lines(1:15, rep(0,15), col='red') |
| 123 | N = 200 |
| 124 | M = 100 |
| 125 | mugamma = c(1,2,3,2,4,6) |
| 126 | LGamma = matrix(0,nrow=6,ncol=6) |
| 127 | LGamma[lower.tri(LGamma,diag=FALSE)] = rnorm(6*5/2,0,0.05) |
| 128 | LGamma = LGamma + diag(runif(6,0.2,1)) |
| 129 | Gamma = LGamma%*%t(LGamma) |
| 130 | s2 = 0.2 |
| 131 | freq = seq(0,1,length=M) |
| 132 | intervals = list(12:25,67:89) |
| 133 | Si = as.matrix(bdiag(getbasismatrix(freq[12:25],create.fourier.basis(nbasis = 3)), getbasismatrix(freq[67:89],create.fourier.basis(nbasis = 3)))) |
| 134 | gammai = t(sapply(1:N,FUN = function(i){ |
| 135 | rmvnorm(1,mugamma,Gamma) |
| 136 | })) |
| 137 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 138 | as.vector(Si%*%gammai[i,])+rnorm(nrow(Si),0,s2) |
| 139 | })) |
| 140 | xdata = matrix(0,nrow=N,ncol=M) |
| 141 | xdata[,intervals[[1]] ] = Xdata[,1:14] |
| 142 | xdata[,intervals[[2]] ] = Xdata[,15:37] |
| 143 | beta = c(2,0.5,-2,1,0.2,1,-1) |
| 144 | pydata = sapply(1:N,FUN=function(i){ |
| 145 | a = sum(beta*c(1,gammai[i,])) |
| 146 | return(exp(a)/(1+exp(a))) |
| 147 | }) |
| 148 | U = runif(length(pydata)) |
| 149 | ydata = as.numeric(U<pydata) |
| 150 | plot(sort(pydata),col=ydata[order(pydata)]+1) |
| 151 | nBasis = c(3,3) |
| 152 | L = 20 |
| 153 | eer = rep(0,L) |
| 154 | for (ell in 1:L){ |
| 155 | kapp = sample(1:200,0.8*200,replace=FALSE) |
| 156 | xdata.app = xdata[kapp,] |
| 157 | ydata.app = ydata[kapp] |
| 158 | ktest = setdiff(1:200,kapp) |
| 159 | xdata.test = xdata[ktest,] |
| 160 | ydata.test = ydata[ktest] |
| 161 | resEM = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=1000,25,eps=10^-8,keep=TRUE) |
| 162 | Yres = Y.predB(xdata.test,freq,intervals,resEM$muh,resEM$Gammah,resEM$s2h,resEM$betah,nBasis=c(3,3)) |
| 163 | eer[ell] = mean(Yres$Ypred==ydata.test) |
| 164 | cat("\n", "ell =", ell) |
| 165 | } |
| 166 | source('~/Dropbox/projet spectres/algoEM/EMalgorithmBandes.R') |
| 167 | for (ell in 1:L){ |
| 168 | kapp = sample(1:200,0.8*200,replace=FALSE) |
| 169 | xdata.app = xdata[kapp,] |
| 170 | ydata.app = ydata[kapp] |
| 171 | ktest = setdiff(1:200,kapp) |
| 172 | xdata.test = xdata[ktest,] |
| 173 | ydata.test = ydata[ktest] |
| 174 | resEM = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=1000,25,eps=10^-8,keep=TRUE) |
| 175 | Yres = Y.predB(xdata.test,freq,intervals,resEM$muh,resEM$Gammah,resEM$s2h,resEM$betah,nBasis=c(3,3)) |
| 176 | eer[ell] = mean(Yres$Ypred==ydata.test) |
| 177 | cat("\n", "ell =", ell) |
| 178 | } |
| 179 | N = 3000 |
| 180 | mugamma = c(1,2,3) |
| 181 | Gamma = matrix(c(5,-0.02,0.01,-0.02,5,0.1,0.01,0.1,5),3,3) |
| 182 | s2 = 0.01 |
| 183 | Si = getbasismatrix(seq(0,1,length=15),create.fourier.basis(nbasis = 3)) |
| 184 | gammai = t(sapply(1:N,FUN = function(i){ |
| 185 | rmvnorm(1,mugamma,Gamma) |
| 186 | })) |
| 187 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 188 | as.vector(Si%*%gammai[i,])+rnorm(15,0,s2) |
| 189 | })) |
| 190 | matplot(t(Xdata),type="l") |
| 191 | #beta = c(.1,.5,-.3) |
| 192 | beta = c(1,-3.5,4.5,-2.5) |
| 193 | pydata = sapply(1:N,FUN=function(i){ |
| 194 | a = sum(beta*c(1,gammai[i,])) |
| 195 | return(exp(a)/(1+exp(a))) |
| 196 | }) |
| 197 | U = runif(length(pydata)) |
| 198 | ydata = as.numeric(U<pydata) |
| 199 | plot(sort(pydata),col=ydata[order(pydata)]+2) |
| 200 | wi = Si %*% beta[-1] |
| 201 | plot(wi, type='l') |
| 202 | lines(1:15,rep(0,15)) |
| 203 | plot( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),typ="l",lty=1,col="green") |
| 204 | for (ite in 1:30){ |
| 205 | print(ite) |
| 206 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 207 | as.vector(Si%*%gammai[i,])+rnorm(15,0,s2) |
| 208 | })) |
| 209 | #beta = c(.1,.5,-.3) |
| 210 | beta = c(1,-3.5,4.5,-2.5) |
| 211 | pydata = sapply(1:N,FUN=function(i){ |
| 212 | a = sum(beta*c(1,gammai[i,])) |
| 213 | return(exp(a)/(1+exp(a))) |
| 214 | }) |
| 215 | U = runif(length(pydata)) |
| 216 | ydata = as.numeric(U<pydata) |
| 217 | lines( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),col="green") |
| 218 | } |
| 219 | lines(1:15, rep(0,15), col='red') |
| 220 | N = 3000 |
| 221 | mugamma = c(1,2,3) |
| 222 | Gamma = matrix(c(5,-0.02,0.01,-0.02,1,0.1,0.01,0.1,5),3,3) |
| 223 | s2 = 0.01 |
| 224 | Si = getbasismatrix(seq(0,1,length=15),create.fourier.basis(nbasis = 3)) |
| 225 | gammai = t(sapply(1:N,FUN = function(i){ |
| 226 | rmvnorm(1,mugamma,Gamma) |
| 227 | })) |
| 228 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 229 | as.vector(Si%*%gammai[i,])+rnorm(15,0,s2) |
| 230 | })) |
| 231 | matplot(t(Xdata),type="l") |
| 232 | #beta = c(.1,.5,-.3) |
| 233 | beta = c(1,-3.5,4.5,-2.5) |
| 234 | pydata = sapply(1:N,FUN=function(i){ |
| 235 | a = sum(beta*c(1,gammai[i,])) |
| 236 | return(exp(a)/(1+exp(a))) |
| 237 | }) |
| 238 | U = runif(length(pydata)) |
| 239 | ydata = as.numeric(U<pydata) |
| 240 | plot(sort(pydata),col=ydata[order(pydata)]+2) |
| 241 | # ydata = ydata.noise |
| 242 | wi = Si %*% beta[-1] |
| 243 | plot(wi, type='l') |
| 244 | lines(1:15,rep(0,15)) |
| 245 | plot( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),typ="l",lty=1,col="green") |
| 246 | for (ite in 1:30){ |
| 247 | print(ite) |
| 248 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 249 | as.vector(Si%*%gammai[i,])+rnorm(15,0,s2) |
| 250 | })) |
| 251 | #beta = c(.1,.5,-.3) |
| 252 | beta = c(1,-3.5,4.5,-2.5) |
| 253 | pydata = sapply(1:N,FUN=function(i){ |
| 254 | a = sum(beta*c(1,gammai[i,])) |
| 255 | return(exp(a)/(1+exp(a))) |
| 256 | }) |
| 257 | U = runif(length(pydata)) |
| 258 | ydata = as.numeric(U<pydata) |
| 259 | lines( apply(t(Xdata[ydata==1,]),1,mean)-apply(t(Xdata[ydata==0,]),1,mean),col="green") |
| 260 | } |
| 261 | lines(1:15, rep(0,15), col='red') |
| 262 | matplot(t(Xdata[ydata==1,]),typ="l",lty=1,col="red") |
| 263 | matplot(t(Xdata[ydata==0,]),typ="l",lty=1,col="black",add=TRUE) |
| 264 | N = 200 |
| 265 | M = 100 |
| 266 | mugamma = c(1,2,3,2,4,6) |
| 267 | LGamma = matrix(0,nrow=6,ncol=6) |
| 268 | LGamma[lower.tri(LGamma,diag=FALSE)] = rnorm(6*5/2,0,0.05) |
| 269 | LGamma = LGamma + diag(runif(6,0.2,1)) |
| 270 | Gamma = LGamma%*%t(LGamma) |
| 271 | s2 = 0.2 |
| 272 | freq = seq(0,1,length=M) |
| 273 | intervals = list(12:25,67:89) |
| 274 | Si = as.matrix(bdiag(getbasismatrix(freq[12:25],create.fourier.basis(nbasis = 3)), getbasismatrix(freq[67:89],create.fourier.basis(nbasis = 3)))) |
| 275 | gammai = t(sapply(1:N,FUN = function(i){ |
| 276 | rmvnorm(1,mugamma,Gamma) |
| 277 | })) |
| 278 | Xdata = t(sapply(1:N,FUN = function(i){ |
| 279 | as.vector(Si%*%gammai[i,])+rnorm(nrow(Si),0,s2) |
| 280 | })) |
| 281 | xdata = matrix(0,nrow=N,ncol=M) |
| 282 | xdata[,intervals[[1]] ] = Xdata[,1:14] |
| 283 | xdata[,intervals[[2]] ] = Xdata[,15:37] |
| 284 | beta = c(2,0.5,-2,1,0.2,1,-1) |
| 285 | pydata = sapply(1:N,FUN=function(i){ |
| 286 | a = sum(beta*c(1,gammai[i,])) |
| 287 | return(exp(a)/(1+exp(a))) |
| 288 | }) |
| 289 | U = runif(length(pydata)) |
| 290 | ydata = as.numeric(U<pydata) |
| 291 | plot(sort(pydata),col=ydata[order(pydata)]+1) |
| 292 | nBasis = c(3,3) |
| 293 | resEM = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=500,100,eps=10^-8,keep=TRUE) |
| 294 | source('~/Dropbox/projet spectres/algoEM/EMalgorithmBandes.R') |
| 295 | resEM = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=500,100,eps=10^-8,keep=TRUE) |
| 296 | L = 20 |
| 297 | eer = rep(0,L) |
| 298 | for (ell in 1:L){ |
| 299 | kapp = sample(1:200,0.8*200,replace=FALSE) |
| 300 | xdata.app = xdata[kapp,] |
| 301 | ydata.app = ydata[kapp] |
| 302 | ktest = setdiff(1:200,kapp) |
| 303 | xdata.test = xdata[ktest,] |
| 304 | ydata.test = ydata[ktest] |
| 305 | resEM = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=1000,25,eps=10^-8,keep=FALSE) |
| 306 | Yres = Y.predB(xdata.test,freq,intervals,resEM$muh,resEM$Gammah,resEM$s2h,resEM$betah,nBasis=c(3,3)) |
| 307 | eer[ell] = mean(Yres$Ypred==ydata.test) |
| 308 | cat("\n", "ell =", ell) |
| 309 | } |
| 310 | source('~/Dropbox/projet spectres/algoEM/EMalgorithmBandes.R') |
| 311 | for (ell in 1:L){ |
| 312 | kapp = sample(1:200,0.8*200,replace=FALSE) |
| 313 | xdata.app = xdata[kapp,] |
| 314 | ydata.app = ydata[kapp] |
| 315 | ktest = setdiff(1:200,kapp) |
| 316 | xdata.test = xdata[ktest,] |
| 317 | ydata.test = ydata[ktest] |
| 318 | resEM = EM.Bands.function(ydata,xdata,freq,intervals,nBasis,MCit=1000,25,eps=10^-8,keep=FALSE) |
| 319 | Yres = Y.predB(xdata.test,freq,intervals,resEM$muh,resEM$Gammah,resEM$s2h,resEM$betah,nBasis=c(3,3)) |
| 320 | eer[ell] = mean(Yres$Ypred==ydata.test) |
| 321 | cat("\n", "ell =", ell) |
| 322 | } |
| 323 | eer |
| 324 | mean(eer) |
| 325 | source('~/Dropbox/projet spectres/cluster/EMLiqArtBandeHugues.R') |
| 326 | library(fda) |
| 327 | library(Matrix) |
| 328 | library(mvtnorm) |
| 329 | source('EMalgorithm.R') |
| 330 | source('EMLiqArtBandeHugues.R') |
| 331 | for (nVC in 1:100){ |
| 332 | EMLiqArtBandeHugues(nVC) |
| 333 | } |
| 334 | setwd("~/Dropbox/projet spectres/cluster") |
| 335 | library(fda) |
| 336 | library(Matrix) |
| 337 | library(mvtnorm) |
| 338 | source('EMalgorithm.R') |
| 339 | source('EMLiqArtBandeHugues.R') |
| 340 | for (nVC in 1:100){ |
| 341 | EMLiqArtBandeHugues(nVC) |
| 342 | } |
| 343 | sims= c(63:67,69,71:74,76:85,88:90,93:100) |
| 344 | #simulation_settings=expand.grid(c(1,2,3,4,5),c(1,2),c(600),c(300)) |
| 345 | #for (i in 1:nrow(simulation_settings)){ |
| 346 | aa=cbind(sims,rep(9, each =length(sims))) |
| 347 | colnames(aa)=c("isim","model") |
| 348 | write.table(aa,file=paste("SimulationSettings9.txt",sep=""),row.names=FALSE,sep=",") |
| 349 | setwd("~/Dropbox/WarpMixedModel/warpingMixedModel/SuperComputer") |
| 350 | write.table(aa,file=paste("SimulationSettings9.txt",sep=""),row.names=FALSE,sep=",") |
| 351 | sims= 1:100 |
| 352 | colnames(sims) = "isim" |
| 353 | sims= 1:100 |
| 354 | #simulation_settings=expand.grid(c(1,2,3,4,5),c(1,2),c(600),c(300)) |
| 355 | #for (i in 1:nrow(simulation_settings)){ |
| 356 | aa=cbind(sims,rep(10, each =length(sims))) |
| 357 | colnames(aa)=c("isim","model") |
| 358 | write.table(aa,file=paste("SimulationSettings10.txt",sep=""),row.names=FALSE,sep=",") |
| 359 | load("~/valse/data/data.RData") |
| 360 | load("~/valse/data/data.RData") |
| 361 | X |
| 362 | Y |
| 363 | library("devtools", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3") |
| 364 | library(roxygen2) |
| 365 | setwd("~/valse") |
| 366 | document() |
| 367 | document() |
| 368 | setwd("~/") |
| 369 | install("valse") |
| 370 | c(9.75, |
| 371 | 20, |
| 372 | 11.75, |
| 373 | 11, |
| 374 | 8.25, |
| 375 | 12.5, |
| 376 | 11, |
| 377 | 18, |
| 378 | 7, |
| 379 | 12.75, |
| 380 | 13, |
| 381 | 14.75, |
| 382 | 4.75, |
| 383 | 11, |
| 384 | 20, |
| 385 | 13.5, |
| 386 | 8, |
| 387 | 13.25, |
| 388 | 6, |
| 389 | 17.5, |
| 390 | 13.25, |
| 391 | 8.5, |
| 392 | 9.5, |
| 393 | 16, |
| 394 | 8, |
| 395 | 9.5, |
| 396 | 10.25, |
| 397 | 13.75, |
| 398 | 9, |
| 399 | 14.75, |
| 400 | 12.5, |
| 401 | 19.5, |
| 402 | 17.5, |
| 403 | 7, |
| 404 | 11.5, |
| 405 | 4, |
| 406 | 7.5, |
| 407 | 13.25, |
| 408 | 10.5 |
| 409 | ) |
| 410 | notes = c(9.75, |
| 411 | 20, |
| 412 | 11.75, |
| 413 | 11, |
| 414 | 8.25, |
| 415 | 12.5, |
| 416 | 11, |
| 417 | 18, |
| 418 | 7, |
| 419 | 12.75, |
| 420 | 13, |
| 421 | 14.75, |
| 422 | 4.75, |
| 423 | 11, |
| 424 | 20, |
| 425 | 13.5, |
| 426 | 8, |
| 427 | 13.25, |
| 428 | 6, |
| 429 | 17.5, |
| 430 | 13.25, |
| 431 | 8.5, |
| 432 | 9.5, |
| 433 | 16, |
| 434 | 8, |
| 435 | 9.5, |
| 436 | 10.25, |
| 437 | 13.75, |
| 438 | 9, |
| 439 | 14.75, |
| 440 | 12.5, |
| 441 | 19.5, |
| 442 | 17.5, |
| 443 | 7, |
| 444 | 11.5, |
| 445 | 4, |
| 446 | 7.5, |
| 447 | 13.25, |
| 448 | 10.5 |
| 449 | ) |
| 450 | hist(notes) |
| 451 | hist(notes, nclass = 20) |
| 452 | notesBis = c(7.375,19.375,10.125,9,6,12.25,11,18.75,10.5,11.5,15.5,13,2.375,12.75,17.75,15.375,8.125,16,8.5,14.5,11.625,11.25,9.625,14, |
| 453 | 6.5,11.375,11.875,16.25,10.125,12,6.25,9.75,8.75,3.5,0,0,0,5.75,2,3.75,6.625,5.25) |
| 454 | hist(notesBis) |
| 455 | notes = c(7, |
| 456 | 19, |
| 457 | 10, |
| 458 | 9, |
| 459 | 6, |
| 460 | 12, |
| 461 | 11, |
| 462 | 19, |
| 463 | 11, |
| 464 | 12, |
| 465 | 16, |
| 466 | 13, |
| 467 | 2, |
| 468 | 13, |
| 469 | 18, |
| 470 | 15, |
| 471 | 8, |
| 472 | 16, |
| 473 | 9, |
| 474 | 15, |
| 475 | 12, |
| 476 | 11, |
| 477 | 10, |
| 478 | 14, |
| 479 | 7, |
| 480 | 11, |
| 481 | 12, |
| 482 | 16, |
| 483 | 13, |
| 484 | 15, |
| 485 | 14, |
| 486 | 19, |
| 487 | 14, |
| 488 | 4, 9, |
| 489 | 8, |
| 490 | 7, |
| 491 | 8, |
| 492 | 8 |
| 493 | ) |
| 494 | hist(notes) |
| 495 | shiny::runApp('Dropbox/cranview-master') |
| 496 | packages = 'shock' |
| 497 | min_date = Sys.Date() - 1 |
| 498 | for (pkg in packages) |
| 499 | { |
| 500 | # api data for package. we want the initial release - the first element of the "timeline" |
| 501 | pkg_data = httr::GET(paste0("http://crandb.r-pkg.org/", pkg, "/all")) |
| 502 | pkg_data = httr::content(pkg_data) |
| 503 | initial_release = pkg_data$timeline[[1]] |
| 504 | min_date = min(min_date, as.Date(initial_release)) |
| 505 | } |
| 506 | min_date |
| 507 | load("~/valse/data/data.RData") |
| 508 | k = 2 |
| 509 | init = initSmallEM(k, X, Y) |
| 510 | setwd("~/valse") |
| 511 | library("devtools", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3") |
| 512 | library("roxygen2", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3") |