# options(width = 500) # options(fig_width="120pt")
knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(fig.width=12) knitr::opts_chunk$set(fig.height=9) library(magrittr) library(ggplot2) library(dplyr)
We want to compare our SGGP
to other methods.
SGGP
laGP: Using all points, just a GP
GPflow: Python, based on GPy, can scale to many points
MRFA
(5) functions as usual
(4) n = 100, 250, 500, 750, 1000
e1 <- readRDS("./ExComp1_completed.rds") e1df <- e1$outcleandf e1$completed_runs %>% table
plyr::dlply(e1df %>% select(-start_time, -end_time, -run_number), "d")
ggplot(data=e1df, mapping=aes(n, RMSE)) + geom_point() + facet_grid(f ~ package, scales="free_y") + scale_y_log10()
ggplot(data=e1df, mapping=aes(n, score)) + geom_point() + facet_grid(f ~ package, scales="free_y")
ggplot(data=e1df, mapping=aes(n, CRPscore)) + geom_point() + facet_grid(f ~ package, scales="free_y") + scale_y_log10()
ggplot(data=e1df, mapping=aes(n, runtime)) + geom_point() + facet_grid(f ~ package, scales="free_y") + scale_y_log10()
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