56 lines
2.0 KiB
R
56 lines
2.0 KiB
R
# Font /mnt/font/InputMonoNarrow-Regular/20a/font
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# rm(list=ls())
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# bookdown::render_book()
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# :/^\#
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# save.image(file="~/tmp/202301231942.Rdata")
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# load(file = "~/tmp/202301231942.Rdata")
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# bash make_chapter 19_nystroem_approximation.Rmd
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# knitr::purl("05_c_svd_ca.Rmd")
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# Extrait de 05_c_svd_ca.Rmd
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#
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# Affichons encore une carte avec les coordonnées principales sur les dimensions n°1 et n°2, mais uniquement pour les profils lignes et les profils colonnes considérés importants.
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#
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# ```{r}
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# selI <- CTRI > (1/I)
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# selI12 <- selI[,1] | selI[,2]
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# selJ <- CTRJ > (1/J)
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# selJ12 <- selJ[,1] | selJ[,2]
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# par(pty="s") # square plotting region
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# plot(c(F[selI12,1], G[selJ12,1]), c(F[selI12,2], G[selJ12,2]),
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# main = "x: d1, y: d2", type = "n",
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# xlab="", ylab="", asp = 1, xaxt = "n", yaxt = "n")
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# text(c(F[selI12,1], G[selJ12,1]), c(F[selI12,2], G[selJ12,2]),
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# c(rownames(P)[selI12], colnames(P)[selJ12]),
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# adj = 0, cex = 0.6)
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# points(0, 0, pch = 3)
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# ```
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# source("19_b_nystroem_approximation_housing_experiment_code.R")
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# rdat <- hous.dat.nakr$dat[sample(nrow(hous.dat.nakr$dat), size=2000, replace=FALSE),]
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# X <- rdat[,!(colnames(rdat) %in% c('median_house_value'))]
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# Y <- rdat[,c('median_house_value')]
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# names(Y) <- rownames(X)
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# rsplt <- splitdata(list(X = X, Y = Y), 0.8)
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# X.entr <- rsplt$entr$X
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# Y.entr <- rsplt$entr$Y
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# X.test <- rsplt$test$X
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# Y.test <- rsplt$test$Y
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# source("18_kernel_ridge_regression_code.R")
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# krm <- krr(X.entr, Y.entr)
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# krm.yh <- predict(krm, X.test)
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# krm.mae <- mean(abs(krm.yh - Y.test)) # 35445.1
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# nakrm <- nakr(X.entr, Y.entr, nb.landmarks=1600)
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# nakrm.yh <- predict(nakrm, X.test)
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# nakrm.mae <- mean(abs(nakrm.yh - Y.test)) # 65454.18
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# source("15_loocv_code.R")
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# rm <- ridge(X.entr, Y.entr)
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# rm.yh <- predict(rm, X.test)
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# rm.mae <- mean(abs(rm.yh - Y.test)) # 45786.62
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# library(randomForest)
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# rfm <- randomForest(X.entr, Y.entr)
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# rfm.yh <- predict(rfm, X.test)
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# rfm.mae <- mean(abs(rfm.yh - Y.test)) # 34229.02
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