38 lines
1.4 KiB
R
38 lines
1.4 KiB
R
# implementation contains fallback if the package is not available but for this
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# case required!
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library(RSpectra)
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# Load POI function and compiled C subroutine.
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source('../tensor_predictors/poi.R')
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dyn.load('../tensor_predictors/poi.so') # "Shared Object" of POI-Subrountine
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# Load data from sent data file (last Email)
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dataset <- readRDS('../eeg_analysis/eeg_data.rds')
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maxit <- 400L # Upper bound for number of optimization iterations.
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for (i in 1:nrow(dataset)) {
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gc() # To be on the save side, call the garbage collector (free memory)
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# Formulate PFC-GEP (Principal Fitted Components - Generalized Eigenvalue
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# Problem) for EEG data.
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X <- scale(dataset[-i, -(1:2)], scale = FALSE, center = TRUE)
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Fy <- scale(dataset$Case_Control[-i], scale = FALSE, center = TRUE)
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B <- crossprod(X) / nrow(X) # Sigma
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P_Fy <- Fy %*% solve(crossprod(Fy), t(Fy))
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A <- crossprod(X, P_Fy %*% X) / nrow(X) # Sigma_fit
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# Call POI using C subroutine (requires "dyn.load" of subroutine)
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poi_res <- POI(A, B, 1L, maxit = maxit, use.C = TRUE)
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# Again, be nice to memory and delete with an explicit fall to gc.
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rm(A, B)
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gc()
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# Store results, do analysis, ... (addapt to needs) .
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poi_res$maxit = maxit
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poi_res$loo_index = i # Keep track of LOO position.
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# Save i-th LOO result to file for analysis/validation/visualization/...
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saveRDS(poi_res, file = sprintf('eeg_poi_loo_%d.rds', i))
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}
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