supraHex 1.13.3

How to control the device output generated by visCompReorder?

Notes:
  • All results are based on supraHex (version 1.13.3).
  • R scripts (i.e. R expressions) plus necessary comments are highlighted in light-cyan background, and the rest are outputs in the screen.
  • Images displayed below may be distorted, but should be normal in your screen.
  • Functions contained in supraHex 1.13.3 are hyperlinked in-place and also listed on the right side.
  • Key texts are underlined, in bold and in pumpkin-orange color. 
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    # The output generated by visCompReorder is only shown in the screen. The reason for that is to make sure the supra-hexagon is seamlessly formed and visually friendly.

# Internally, the function visVp opens a new page each time.

# In order to save the output as a file, below is a universal solution of how to save an image shown in the screen into a file (eg a png file).

## First, install and load the package 'evaluate'
# install.packages("evaluate",repos="http://cran.r-project.org",type="source")
library(evaluate)

## Then, produce an image in the screen
data <- matrix( rnorm(100*10,mean=0,sd=1), nrow=100, ncol=10)
colnames(data) <- paste('S', seq(1:10), sep="")
sMap <- sPipeline(data=data)

    
Start at 2017-03-27 18:59:37

First, define topology of a map grid (2017-03-27 18:59:37)...
Second, initialise the codebook matrix (61 X 10) using 'linear' initialisation, given a topology and input data (2017-03-27 18:59:37)...
Third, get training at the rough stage (2017-03-27 18:59:37)...
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Fourth, get training at the finetune stage (2017-03-27 18:59:37)...
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Next, identify the best-matching hexagon/rectangle for the input data (2017-03-27 18:59:37)...
Finally, append the response data (hits and mqe) into the sMap object (2017-03-27 18:59:37)...

Below are the summaries of the training results:
   dimension of input data: 100x10
   xy-dimension of map grid: xdim=9, ydim=9, r=5
   grid lattice: hexa
   grid shape: suprahex
   dimension of grid coord: 61x2
   initialisation method: linear
   dimension of codebook matrix: 61x10
   mean quantization error: 4.74459667018025

Below are the details of trainology:
   training algorithm: batch
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 7 at rough stage; 25 at finetune stage
   radius (at rough stage): from 3 to 1
   radius (at finetune stage): from 1 to 1

End at 2017-03-27 18:59:37
Runtime in total is: 0 secs


    sReorder <- sCompReorder(sMap=sMap)

    
Start at 2017-03-27 18:59:37

First, define topology of a map grid (2017-03-27 18:59:37)...
Second, initialise the codebook matrix (30 X 61) using 'linear' initialisation, given a topology and input data (2017-03-27 18:59:37)...
Third, get training at the rough stage (2017-03-27 18:59:37)...
	1 out of 300 (2017-03-27 18:59:37)
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Fourth, get training at the finetune stage (2017-03-27 18:59:37)...
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	1200 out of 1200 (2017-03-27 18:59:37)
Next, identify the best-matching hexagon/rectangle for the input data (2017-03-27 18:59:37)...
Finally, append the response data (hits and mqe) into the sMap object (2017-03-27 18:59:37)...

Below are the summaries of the training results:
   dimension of input data: 10x61
   xy-dimension of map grid: xdim=6, ydim=5, r=3
   grid lattice: rect
   grid shape: sheet
   dimension of grid coord: 30x2
   initialisation method: linear
   dimension of codebook matrix: 30x61
   mean quantization error: 4.99079289882425

Below are the details of trainology:
   training algorithm: sequential
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 30 at rough stage; 120 at finetune stage
   radius (at rough stage): from 1 to 1
   radius (at finetune stage): from 1 to 1

End at 2017-03-27 18:59:37
Runtime in total is: 0 secs


    visCompReorder(sMap=sMap, sReorder=sReorder)

## Third, treat the last R expression as an input to the function evaluate::evaluate
r_exp <- 'visCompReorder(sMap=sMap, sReorder=sReorder)'

    


    
## Last, save it into the file called 'test.png' (with width=1200 and resolution=72)
x <- evaluate::evaluate(r_exp)
wh <- dev.size(units="px")

    


    wth <- 1200
hgt <- wth*wh[2]/wh[1]
res <- min(c(wth,hgt))*72/480
png("test.png", width=wth, height=hgt, res=res)
on.exit(dev.off())
print(x)

    

    [[1]]
$src
[1] "visCompReorder(sMap=sMap, sReorder=sReorder)"

attr(,"class")
[1] "source"


    

    graphics.off()

    Source faq

    FAQ3.r

    Functions used in this FAQ