R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> # This is the simplest code for applying the DFT model using the DFT.R
> # library and the DFT.so and DFT_parallel.so C libraries to the similarity,
> # attraction, and compromise effects described in Roe, Busemeyer, and
> # Townsend (2001). Results should match the matlab code at
> # http://mypage.iu.edu/~jbusemey/Dec_lect/Dec_prg/sim_mdf.txt (in
> # particular, the first, fourth, and sixth problems). The single-thread
> # version of the code is about 300 times faster than the matlab version. The
> # multi-threaded version is, obviously, faster still.
> 
> # R CMD BATCH big_three_example.R should produce example output
> 
> rm(list=ls())
> 
> # Load the DFT library
> # Note, you will need the DFT.so and DFT_parallel.so libraries in the same directory
> source("DFT.R")
> 
> 
> iterations <- 100000
> (C <- matrix(c(1.0, -0.5, -0.5, 
+               -0.5,  1.0, -0.5, 
+               -0.5, -0.5,  1.0), nrow=3, byrow=TRUE))
     [,1] [,2] [,3]
[1,]  1.0 -0.5 -0.5
[2,] -0.5  1.0 -0.5
[3,] -0.5 -0.5  1.0
> (W <- c(.5, .5))
[1] 0.5 0.5
> (T <- rep(17.5, 3))
[1] 17.5 17.5 17.5
> (P <- rep(0.0, 3))
[1] 0 0 0
> (sigma <- 1.0)
[1] 1
> 
> # Similarity effect
> (M <- matrix(c(1.0, 3.0, 
+                3.0, 1.0, 
+                0.9, 3.1), nrow=3, byrow=T))
     [,1] [,2]
[1,]  1.0  3.0
[2,]  3.0  1.0
[3,]  0.9  3.1
> (S <- matrix(c(0.9500000, -0.0122316, -0.04999996, 
+               -0.0122316,  0.9500000, -0.0090303, 
+               -0.0499996, -0.0090303, 0.9500000),
+     nrow=3, byrow=TRUE))
           [,1]       [,2]        [,3]
[1,]  0.9500000 -0.0122316 -0.04999996
[2,] -0.0122316  0.9500000 -0.00903030
[3,] -0.0499996 -0.0090303  0.95000000
> 
> result <- DFT(C, M, W, S, T, P, sigma, iterations=iterations, seed=1, parallel=TRUE)
> result/iterations
[1] 0.30468 0.39536 0.29996
> 
> 
> 
> # Compromise effect
> (M <- matrix(c(1.0, 3.0, 
+                3.0, 1.0, 
+                2.0, 2.0), nrow=3, byrow=T))
     [,1] [,2]
[1,]    1    3
[2,]    3    1
[3,]    2    2
> (S <- matrix(c(0.950000, -0.012232, -0.045788, 
+               -0.012232,  0.950000, -0.045788, 
+               -0.045788, -0.045788,  0.950000),
+     nrow=3, byrow=TRUE))
          [,1]      [,2]      [,3]
[1,]  0.950000 -0.012232 -0.045788
[2,] -0.012232  0.950000 -0.045788
[3,] -0.045788 -0.045788  0.950000
> 
> result <- DFT(C, M, W, S, T, P, sigma, iterations=iterations, seed=1, parallel=TRUE)
> result/iterations
[1] 0.28229 0.28686 0.43085
> 
> 
> 
> # Attraction effect
> (M <- matrix(c(1.0, 3.0, 
+                3.0, 1.0, 
+                0.5, 2.5), nrow=3, byrow=T))
     [,1] [,2]
[1,]  1.0  3.0
[2,]  3.0  1.0
[3,]  0.5  2.5
> (S <- matrix(c(0.95, -1.2232e-02, -2.2647e-02, 
+               -1.2232e-02, 0.95, -6.7034e-04,
+               -2.2647e-02, -6.7034e-04, 0.95),
+     nrow=3, byrow=TRUE))
          [,1]        [,2]        [,3]
[1,]  0.950000 -0.01223200 -0.02264700
[2,] -0.012232  0.95000000 -0.00067034
[3,] -0.022647 -0.00067034  0.95000000
> 
> result <- DFT(C, M, W, S, T, P, sigma, iterations=iterations, seed=1, parallel=TRUE)
> result/iterations
[1] 0.55764 0.44236 0.00000
> 
> 
> proc.time()
   user  system elapsed 
 11.468   0.068   6.074