67 lines
2.2 KiB
R
67 lines
2.2 KiB
R
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# Source Code. # Loaded functions.
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source('../tensor_predictors/poi.R') # POI
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# Load C implentation of 'FastPOI-C' subroutine.
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# Required for using 'use.C = TRUE' in the POI method.
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# Compiled via.
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# $ cd ../tensor_predictors/
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# $ R CMD SHLIB poi.c
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dyn.load('../tensor_predictors/poi.so')
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# dyn.load('../tensor_predictors/poi.dll') # On Windows
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# In this case 'use.C = TRUE' is required cause the R implementation is not
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# sufficient due to memory exhaustion (and runtime).
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# Load Dataset.
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# > dataset <- read.table(file = 'egg.extracted.means.txt', header = TRUE,
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# > stringsAsFactors = FALSE, check.names = FALSE)
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# Save as Rdata file for faster loading.
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# > saveRDS(dataset, file = 'eeg_data.rds')
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dataset <- readRDS('../data_analysis/eeg_data.rds')
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# Positive and negative case index.
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set.seed(42)
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zero <- sample(which(dataset$Case_Control == 0))
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one <- sample(which(dataset$Case_Control == 1))
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# 10-fold test groups.
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zero <- list(zero[ 1: 4], zero[ 5: 8], zero[ 9:12], zero[13:16],
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zero[17:20], zero[21:25], zero[26:30],
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zero[31:35], zero[36:40], zero[41:45])
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one <- list(one[ 1: 8], one[ 9:16], one[17:24], one[25:32],
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one[33:40], one[41:48], one[49:56],
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one[57:63], one[64:70], one[71:77])
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# Iterate data folds.
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folds <- vector('list', 10)
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for (i in seq_along(folds)) {
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cat('\r%d/%d ', i, length(folds))
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# Call garbage collector.
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gc()
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# Formulate PFC-GEP for EEG data.
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index <- c(zero[[i]], one[[i]])
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X <- scale(dataset[-index, -(1:2)], scale = FALSE, center = TRUE)
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Fy <- scale(dataset$Case_Control[-index], scale = FALSE, center = TRUE)
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B <- crossprod(X) / nrow(X) # Sigma
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P_Fy <- Fy %*% solve(crossprod(Fy), t(Fy))
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A <- crossprod(X, P_Fy %*% X) / nrow(X) # Sigma_fit
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# Before Starting POI on (very big GEP) call the garbage collector.
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gc()
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poi <- POI(A, B, 1L, lambda = lambda, use.C = TRUE)
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rm(A, B)
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gc()
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# Set fold index.
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poi$index = index
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folds[[i]] <- poi
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}
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cat('\n')
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# Save complete 10 fold results.
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file <- sprintf('eeg_analysis_poi.rds')
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saveRDS(folds, file = file)
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