52 lines
1.5 KiB
R
52 lines
1.5 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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# 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("01_intro_code.R")
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source("04_validation_croisee_code.R")
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set.seed(1123)
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n <- 100
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data = gendat(n,0.2)
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splitres <- splitdata(data,0.8)
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entr <- splitres$entr
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test <- splitres$test
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# K11 <- gausskernel(as.matrix(X[splidx,]), sigma2)
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#
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# K21 <- matrix(nrow = n-nspl, ncol = nspl)
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# for(i in 1:(n-nspl))
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# for(j in 1:nspl)
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# K21[i,j] <- sum(X[notsplidx[i],] - X[splidx[j],])^2
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# K21 <- exp(-1*K21/sigma2)
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source("19_nystroem_approximation_code.R")
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krm <- nakrr(entr$X, entr$Y, nspl=15)
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yh <- predict(krm,test$X)
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plt(test,f)
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points(test$X, yh, pch=4)
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# coef2 <- coef[order(strtoi(rownames(coef)))] |