R Under development (unstable) (2024-08-17 r87027 ucrt) -- "Unsuffered Consequences"
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> library(VLMC)
> 
> set.seed(21001)
> c0 <- proc.time()
> 
> f1 <- c(1,0,0,0)
> f2 <- rep(1:0,2)
> (dt2 <- rep(c(f1,f1,f2,f1,f2,f2,f1),2))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0
[39] 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0
> dt3 <- c(unlist(sapply(1:5, function(k) c(rep(0,k),1))),
+          rep(1,6))# patterns NOI in dt2
> 
> (vlmc.dt2c15  <- vlmc(dt2, cutoff = 1.5))

'vlmc' a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 56.
Call: vlmc(dts = dt2, cutoff.prune = 1.5);  cutoff =1.5
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        6         8         4        46 
AIC =  36.65 
> draw(vlmc.dt2c15)
[x]-( 36 19| 55)-F
 +--[0]-( 16 19| 35) <2.84>-F
 |   +--[0]-( 8 7| 15) <0.17>-F
 |   |   +--[0]-( 0 7| 7) <5.33>-T
 |   |   '--[1]-( 8 0| 8) <5.03>-T
 |   '--[1]-( 8 12| 20) <0.13>
 |       '--[0]-( 7 12| 19) <0.04>
 |           '--[1]-( 4 8| 12) <0.03>
 |               '--[0]-( 4 8| 12) <0.00>
 |                   '--[0]-( 0 4| 4) <1.62>-T
 '--[1]-( 20 0| 20) <8.48>-T
> ## Predict "myself" (in sample)
> noquote(cbind(fit   = predict(vlmc.dt2c15, type = "response"),
+               probs = pr <- predict(vlmc.dt2c15),
+               lev   = predict(vlmc.dt2c15, type = "depth"),
+               ID    = (pid <- predict(vlmc.dt2c15, type = "id")),
+               flags = {ff <- attr(pr,"flags"); ff[ff == "0"] <- ""; ff},
+               context= id2ctxt(pid, m=2, alpha=TRUE)
+               ))
  fit  0                 1                 lev  ID   context
1 <NA> <NA>              <NA>              <NA> <NA> NA     
0 0    1                 0                 1    3    1      
0 0    1                 0                 2    5    01     
0 0    1                 0                 3    9    001    
1 1    0                 1                 3    8    000    
0 0    1                 0                 1    3    1      
0 1    0.428571428571429 0.571428571428571 3    10   010    
0 0    1                 0                 3    9    001    
1 1    0                 1                 3    8    000    
0 0    1                 0                 1    3    1      
1 1    0.428571428571429 0.571428571428571 3    10   010    
0 0    1                 0                 1    3    1      
1 1    0                 1                 6    84   010100 
0 0    1                 0                 1    3    1      
0 0    0.5               0.5               5    42   01010  
0 0    1                 0                 3    9    001    
1 1    0                 1                 3    8    000    
0 0    1                 0                 1    3    1      
1 1    0.428571428571429 0.571428571428571 3    10   010    
0 0    1                 0                 1    3    1      
1 1    0                 1                 6    84   010100 
0 0    1                 0                 1    3    1      
1 0    0.5               0.5               5    42   01010  
0 0    1                 0                 1    3    1      
1 0    0.5               0.5               5    42   01010  
0 0    1                 0                 1    3    1      
0 0    0.5               0.5               5    42   01010  
0 0    1                 0                 3    9    001    
1 1    0                 1                 3    8    000    
0 0    1                 0                 1    3    1      
0 1    0.428571428571429 0.571428571428571 3    10   010    
0 0    1                 0                 3    9    001    
1 1    0                 1                 3    8    000    
0 0    1                 0                 1    3    1      
0 1    0.428571428571429 0.571428571428571 3    10   010    
0 0    1                 0                 3    9    001    
1 1    0                 1                 3    8    000    
0 0    1                 0                 1    3    1      
1 1    0.428571428571429 0.571428571428571 3    10   010    
0 0    1                 0                 1    3    1      
1 1    0                 1                 6    84   010100 
0 0    1                 0                 1    3    1      
0 0    0.5               0.5               5    42   01010  
0 0    1                 0                 3    9    001    
1 1    0                 1                 3    8    000    
0 0    1                 0                 1    3    1      
1 1    0.428571428571429 0.571428571428571 3    10   010    
0 0    1                 0                 1    3    1      
1 1    0                 1                 6    84   010100 
0 0    1                 0                 1    3    1      
1 0    0.5               0.5               5    42   01010  
0 0    1                 0                 1    3    1      
1 0    0.5               0.5               5    42   01010  
0 0    1                 0                 1    3    1      
0 0    0.5               0.5               5    42   01010  
0 0    1                 0                 3    9    001    
> 
> (vlmc.dt2c.1 <- vlmc(dt2, cutoff = .1))

'vlmc' a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 56.
Call: vlmc(dts = dt2, cutoff.prune = 0.1);  cutoff =0.1
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
       12        27        10       146 
AIC =  56.77 
> (vlmc.dt2c0  <- vlmc(dt2, cutoff = 0, thresh = 1))# need thresh=1 for "perfect fit"

'vlmc' a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 56.
Call: vlmc(dts = dt2, cutoff.prune = 0, threshold.gen = 1);  cutoff =0
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
       12        28        12       158 
AIC =  56 
> 
> draw(vlmc.dt2c.1)
[x]-( 36 19| 55)-F
 +--[0]-( 16 19| 35) <2.84>-F
 |   +--[0]-( 8 7| 15) <0.17>-F
 |   |   +--[0]-( 0 7| 7) <5.33>-T
 |   |   '--[1]-( 8 0| 8) <5.03>-T
 |   '--[1]-( 8 12| 20) <0.13>
 |       '--[0]-( 7 12| 19) <0.04>-F
 |           +--[0]-( 3 4| 7) <0.05>
 |           |   '--[0]-( 3 4| 7) <0.00>
 |           |       '--[1]-( 3 4| 7) <0.00>
 |           |           '--[0]-( 2 4| 6) <0.11>-F
 |           |               +--[0]-( 1 2| 3) <0.00>
 |           |               |   '--[0]-( 1 2| 3) <0.00>
 |           |               |       '--[1]-( 1 2| 3) <0.00>
 |           |               |           '--[0]-( 1 1| 2) <0.12>-T
 |           |               '--[1]-( 1 2| 3) <0.00>
 |           |                   '--[0]-( 1 2| 3) <0.00>
 |           |                       '--[1]-( 1 2| 3) <0.00>
 |           |                           '--[0]-( 1 2| 3) <0.00>
 |           |                               '--[0]-( 0 2| 2) <0.81>-T
 |           '--[1]-( 4 8| 12) <0.03>
 |               '--[0]-( 4 8| 12) <0.00>-F
 |                   +--[0]-( 0 4| 4) <1.62>-T
 |                   '--[1]-( 4 4| 8) <0.47>
 |                       '--[0]-( 4 4| 8) <0.00>-F
 |                           +--[0]-( 2 2| 4) <0.00>
 |                           |   '--[0]-( 2 2| 4) <0.00>
 |                           |       '--[1]-( 2 2| 4) <0.00>
 |                           |           '--[0]-( 2 2| 4) <0.00>-F
 |                           |               +--[0]-( 2 0| 2) <1.39>-T
 |                           |               '--[1]-( 0 2| 2) <1.39>-T
 |                           '--[1]-( 2 2| 4) <0.00>
 |                               '--[0]-( 2 2| 4) <0.00>-F
 |                                   +--[0]-( 0 2| 2) <1.39>-T
 |                                   '--[1]-( 2 0| 2) <1.39>-T
 '--[1]-( 20 0| 20) <8.48>-T
> 
> ## Predict "myself" (in sample)
> pp.1  <- predict(vlmc.dt2c.1)# pred.prob
> p.1   <- predict(vlmc.dt2c.1, type="response")
> pid.1 <- predict(vlmc.dt2c.1, type="id.node") ## now give "-" for initial ones!
> (pl.1 <- predict(vlmc.dt2c.1, type="depth"))
 [1] NA  1  2  3  3  1  6  3  3  1 10  1  6  1 12  3  3  1 12  1  6  1 12  1 10
[26]  1 10  3  3  1 11  3  3  1 11  3  3  1 11  1  6  1 12  3  3  1 12  1  6  1
[51] 12  1 10  1 10  3
> 
> noquote(cbind(pp.1,fit=p.1, "="=c("<>","")[1+(dt2==p.1)], lev= pl.1,
+               id= pid.1, flags = attr(p.1,"flags"),
+               context = id2ctxt(pid.1, m=2, alpha=TRUE)
+ ))
  0    1    fit  =    lev  id   context     
1 <NA> <NA> <NA> <NA> <NA> <NA> NA          
0 1    0    0         1    3    1           
0 1    0    0         2    5    01          
0 1    0    0         3    9    001         
1 0    1    1         3    8    000         
0 1    0    0         1    3    1           
0 1    0    0         6    81   010001      
0 1    0    0         3    9    001         
1 0    1    1         3    8    000         
0 1    0    0         1    3    1           
1 0    1    1         10   1297 0100010001  
0 1    0    0         1    3    1           
1 0    1    1         6    84   010100      
0 1    0    0         1    3    1           
0 1    0    0         12   5444 010101000100
0 1    0    0         3    9    001         
1 0    1    1         3    8    000         
0 1    0    0         1    3    1           
1 0    1    1         12   5204 010001010100
0 1    0    0         1    3    1           
1 0    1    1         6    84   010100      
0 1    0    0         1    3    1           
1 0    1    1         12   5445 010101000101
0 1    0    0         1    3    1           
1 0    1    1         10   1364 0101010100  
0 1    0    0         1    3    1           
0 1    0    0         10   1365 0101010101  
0 1    0    0         3    9    001         
1 0    1    1         3    8    000         
0 1    0    0         1    3    1           
0 1    0    0         11   2602 01000101010 
0 1    0    0         3    9    001         
1 0    1    1         3    8    000         
0 1    0    0         1    3    1           
0 0.5  0.5  0         11   2594 01000100010 
0 1    0    0         3    9    001         
1 0    1    1         3    8    000         
0 1    0    0         1    3    1           
1 0.5  0.5  0    <>   11   2594 01000100010 
0 1    0    0         1    3    1           
1 0    1    1         6    84   010100      
0 1    0    0         1    3    1           
0 1    0    0         12   5444 010101000100
0 1    0    0         3    9    001         
1 0    1    1         3    8    000         
0 1    0    0         1    3    1           
1 0    1    1         12   5204 010001010100
0 1    0    0         1    3    1           
1 0    1    1         6    84   010100      
0 1    0    0         1    3    1           
1 0    1    1         12   5445 010101000101
0 1    0    0         1    3    1           
1 0    1    1         10   1364 0101010100  
0 1    0    0         1    3    1           
0 1    0    0         10   1365 0101010101  
0 1    0    0         3    9    001         
> ## Almost the same :
> predict(vlmc.dt2c.1, type = "ALL")
  fit  Pr[X= 0 ] Pr[X= 1 ] id   flags ctxt        
1 <NA> <NA>      <NA>      <NA> 0     NA          
0 0    1         0         3    55    1           
0 0    1         0         5    55    01          
0 0    1         0         9    55    001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
0 0    1         0         81   55    010001      
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
1 1    0         1         1297 55    0100010001  
0 0    1         0         3    0     1           
1 1    0         1         84   0     010100      
0 0    1         0         3    0     1           
0 0    1         0         5444 0     010101000100
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
1 1    0         1         5204 0     010001010100
0 0    1         0         3    0     1           
1 1    0         1         84   0     010100      
0 0    1         0         3    0     1           
1 1    0         1         5445 0     010101000101
0 0    1         0         3    0     1           
1 1    0         1         1364 0     0101010100  
0 0    1         0         3    0     1           
0 0    1         0         1365 0     0101010101  
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
0 0    1         0         2602 0     01000101010 
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
0 0    1/2       1/2       2594 0     01000100010 
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
1 0    1/2       1/2       2594 0     01000100010 
0 0    1         0         3    0     1           
1 1    0         1         84   0     010100      
0 0    1         0         3    0     1           
0 0    1         0         5444 0     010101000100
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
1 1    0         1         5204 0     010001010100
0 0    1         0         3    0     1           
1 1    0         1         84   0     010100      
0 0    1         0         3    0     1           
1 1    0         1         5445 0     010101000101
0 0    1         0         3    0     1           
1 1    0         1         1364 0     0101010100  
0 0    1         0         3    0     1           
0 0    1         0         1365 0     0101010101  
0 0    1         0         9    0     001         
> 
> 
> draw(vlmc.dt2c0)
[x]-( 36 19| 55)-F
 +--[0]-( 16 19| 35) <2.84>-F
 |   +--[0]-( 8 7| 15) <0.17>-F
 |   |   +--[0]-( 0 7| 7) <5.33>-T
 |   |   '--[1]-( 8 0| 8) <5.03>-T
 |   '--[1]-( 8 12| 20) <0.13>
 |       '--[0]-( 7 12| 19) <0.04>-F
 |           +--[0]-( 3 4| 7) <0.05>
 |           |   '--[0]-( 3 4| 7) <0.00>
 |           |       '--[1]-( 3 4| 7) <0.00>
 |           |           '--[0]-( 2 4| 6) <0.11>-F
 |           |               +--[0]-( 1 2| 3) <0.00>
 |           |               |   '--[0]-( 1 2| 3) <0.00>
 |           |               |       '--[1]-( 1 2| 3) <0.00>
 |           |               |           '--[0]-( 1 1| 2) <0.12>-F
 |           |               |               +--[0]-( 0 1| 1) <0.69>-T
 |           |               |               '--[1]-( 1 0| 1) <0.69>-T
 |           |               '--[1]-( 1 2| 3) <0.00>
 |           |                   '--[0]-( 1 2| 3) <0.00>
 |           |                       '--[1]-( 1 2| 3) <0.00>
 |           |                           '--[0]-( 1 2| 3) <0.00>-F
 |           |                               +--[0]-( 0 2| 2) <0.81>-T
 |           |                               '--[1]-( 1 0| 1) <1.10>-T
 |           '--[1]-( 4 8| 12) <0.03>
 |               '--[0]-( 4 8| 12) <0.00>-F
 |                   +--[0]-( 0 4| 4) <1.62>-T
 |                   '--[1]-( 4 4| 8) <0.47>
 |                       '--[0]-( 4 4| 8) <0.00>-F
 |                           +--[0]-( 2 2| 4) <0.00>
 |                           |   '--[0]-( 2 2| 4) <0.00>
 |                           |       '--[1]-( 2 2| 4) <0.00>
 |                           |           '--[0]-( 2 2| 4) <0.00>-F
 |                           |               +--[0]-( 2 0| 2) <1.39>-T
 |                           |               '--[1]-( 0 2| 2) <1.39>-T
 |                           '--[1]-( 2 2| 4) <0.00>
 |                               '--[0]-( 2 2| 4) <0.00>-F
 |                                   +--[0]-( 0 2| 2) <1.39>-T
 |                                   '--[1]-( 2 0| 2) <1.39>-T
 '--[1]-( 20 0| 20) <8.48>-T
> draw(vlmc.dt2c0, cumulative = FALSE)
[x]-( 0 0| 0)-F
 +--[0]-( 0 0| 0)-F
 |   +--[0]-( 0 0| 0)-F
 |   |   +--[0]-( 0 7| 7)-T
 |   |   '--[1]-( 8 0| 8)-T
 |   '--[1]-( 1 0| 1)
 |       '--[0]-( 0 0| 0)-F
 |           +--[0]-( 0 0| 0)
 |           |   '--[0]-( 0 0| 0)
 |           |       '--[1]-( 1 0| 1)
 |           |           '--[0]-( 0 0| 0)-F
 |           |               +--[0]-( 0 0| 0)
 |           |               |   '--[0]-( 0 0| 0)
 |           |               |       '--[1]-( 0 1| 1)
 |           |               |           '--[0]-( 0 0| 0)-F
 |           |               |               +--[0]-( 0 1| 1)-T
 |           |               |               '--[1]-( 1 0| 1)-T
 |           |               '--[1]-( 0 0| 0)
 |           |                   '--[0]-( 0 0| 0)
 |           |                       '--[1]-( 0 0| 0)
 |           |                           '--[0]-( 0 0| 0)-F
 |           |                               +--[0]-( 0 2| 2)-T
 |           |                               '--[1]-( 1 0| 1)-T
 |           '--[1]-( 0 0| 0)
 |               '--[0]-( 0 0| 0)-F
 |                   +--[0]-( 0 4| 4)-T
 |                   '--[1]-( 0 0| 0)
 |                       '--[0]-( 0 0| 0)-F
 |                           +--[0]-( 0 0| 0)
 |                           |   '--[0]-( 0 0| 0)
 |                           |       '--[1]-( 0 0| 0)
 |                           |           '--[0]-( 0 0| 0)-F
 |                           |               +--[0]-( 2 0| 2)-T
 |                           |               '--[1]-( 0 2| 2)-T
 |                           '--[1]-( 0 0| 0)
 |                               '--[0]-( 0 0| 0)-F
 |                                   +--[0]-( 0 2| 2)-T
 |                                   '--[1]-( 2 0| 2)-T
 '--[1]-( 20 0| 20)-T
> 
> ## Predict "myself" (in sample)
> (p0 <- predict(vlmc.dt2c0, type="response"))
 [1] NA  0  0  0  1  0  0  0  1  0  1  0  1  0  0  0  1  0  1  0  1  0  1  0  1
[26]  0  0  0  1  0  0  0  1  0  0  0  1  0  1  0  1  0  0  0  1  0  1  0  1  0
[51]  1  0  1  0  0  0
> all(dt2[-1] == abs(p0[-1])) # TRUE !
[1] TRUE
> pp     <- predict(vlmc.dt2c0)# pred.prob
> !any(is.na(match(pp[-1,],0:1)))#> TRUE
[1] TRUE
> ## since all pred.probabilties are 0 or 1 !
> 
> p.id   <- predict(vlmc.dt2c0, type="id.node")
> p.lev <- predict(vlmc.dt2c0, type="depth")
> noquote(cbind(pp, flags = attr(p.id,"flags"),
+               ID= p.id, lev = p.lev, ctxt = id2ctxt(p.id,m=2, a=TRUE)))
  0    1    ID   lev  ctxt        
1 <NA> <NA> <NA> <NA> NA          
0 1    0    3    1    1           
0 1    0    5    2    01          
0 1    0    9    3    001         
1 0    1    8    3    000         
0 1    0    3    1    1           
0 1    0    81   6    010001      
0 1    0    9    3    001         
1 0    1    8    3    000         
0 1    0    3    1    1           
1 0    1    1297 10   0100010001  
0 1    0    3    1    1           
1 0    1    84   6    010100      
0 1    0    3    1    1           
0 1    0    5444 12   010101000100
0 1    0    9    3    001         
1 0    1    8    3    000         
0 1    0    3    1    1           
1 0    1    5204 12   010001010100
0 1    0    3    1    1           
1 0    1    84   6    010100      
0 1    0    3    1    1           
1 0    1    5445 12   010101000101
0 1    0    3    1    1           
1 0    1    1364 10   0101010100  
0 1    0    3    1    1           
0 1    0    1365 10   0101010101  
0 1    0    9    3    001         
1 0    1    8    3    000         
0 1    0    3    1    1           
0 1    0    5205 12   010001010101
0 1    0    9    3    001         
1 0    1    8    3    000         
0 1    0    3    1    1           
0 1    0    5189 12   010001000101
0 1    0    9    3    001         
1 0    1    8    3    000         
0 1    0    3    1    1           
1 0    1    5188 12   010001000100
0 1    0    3    1    1           
1 0    1    84   6    010100      
0 1    0    3    1    1           
0 1    0    5444 12   010101000100
0 1    0    9    3    001         
1 0    1    8    3    000         
0 1    0    3    1    1           
1 0    1    5204 12   010001010100
0 1    0    3    1    1           
1 0    1    84   6    010100      
0 1    0    3    1    1           
1 0    1    5445 12   010101000101
0 1    0    3    1    1           
1 0    1    1364 10   0101010100  
0 1    0    3    1    1           
0 1    0    1365 10   0101010101  
0 1    0    9    3    001         
> ## or almost the same:
> predict(vlmc.dt2c0, type="ALL")
  fit  Pr[X= 0 ] Pr[X= 1 ] id   flags ctxt        
1 <NA> <NA>      <NA>      <NA> 0     NA          
0 0    1         0         3    55    1           
0 0    1         0         5    55    01          
0 0    1         0         9    55    001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
0 0    1         0         81   55    010001      
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
1 1    0         1         1297 55    0100010001  
0 0    1         0         3    0     1           
1 1    0         1         84   0     010100      
0 0    1         0         3    0     1           
0 0    1         0         5444 0     010101000100
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
1 1    0         1         5204 0     010001010100
0 0    1         0         3    0     1           
1 1    0         1         84   0     010100      
0 0    1         0         3    0     1           
1 1    0         1         5445 0     010101000101
0 0    1         0         3    0     1           
1 1    0         1         1364 0     0101010100  
0 0    1         0         3    0     1           
0 0    1         0         1365 0     0101010101  
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
0 0    1         0         5205 0     010001010101
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
0 0    1         0         5189 0     010001000101
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
1 1    0         1         5188 0     010001000100
0 0    1         0         3    0     1           
1 1    0         1         84   0     010100      
0 0    1         0         3    0     1           
0 0    1         0         5444 0     010101000100
0 0    1         0         9    0     001         
1 1    0         1         8    0     000         
0 0    1         0         3    0     1           
1 1    0         1         5204 0     010001010100
0 0    1         0         3    0     1           
1 1    0         1         84   0     010100      
0 0    1         0         3    0     1           
1 1    0         1         5445 0     010101000101
0 0    1         0         3    0     1           
1 1    0         1         1364 0     0101010100  
0 0    1         0         3    0     1           
0 0    1         0         1365 0     0101010101  
0 0    1         0         9    0     001         
> 
> ## This once gave a seg.fault!
> p.dt2c0dt3 <- predict(vlmc.dt2c0, dt3) ## quite many NA (but not at end!)
> ## which(is.na(p.dt2c0dt3[,1]))
> which(is.na(unname(p.dt2c0dt3[-1,1])))
integer(0)
> ##  1  3  6 10 15 ---- SAME as ../VLMC/dt2_c0.dt3.pred !!
> ## NONE!
> 
> predict(vlmc.dt2c0, dt3, type = "ALL")
  fit  Pr[X= 0 ] Pr[X= 1 ] id   flags ctxt    
0 <NA> <NA>      <NA>      <NA> 0     NA      
1 1    16/35     19/35     2    65    0       
0 0    1         0         3    0     1       
0 1    7/19      12/19     10   65    010     
1 0    1         0         9    0     001     
0 0    1         0         3    0     1       
0 1    3/7       4/7       20   10    0100    
0 0    1         0         9    0     001     
1 1    0         1         8    0     000     
0 0    1         0         3    0     1       
0 1    1/3       2/3       324  10    01000100
0 0    1         0         9    0     001     
0 1    0         1         8    0     000     
1 1    0         1         8    0     000     
0 0    1         0         3    0     1       
0 1    3/7       4/7       40   10    01000   
0 0    1         0         9    0     001     
0 1    0         1         8    0     000     
0 1    0         1         8    0     000     
1 1    0         1         8    0     000     
1 0    1         0         3    0     1       
1 0    1         0         3    0     1       
1 0    1         0         3    0     1       
1 0    1         0         3    0     1       
1 0    1         0         3    0     1       
1 0    1         0         3    0     1       
> 
> ## NA NA's :
> prm <- cbind(fit   = predict(vlmc.dt2c0, dt3, type = "response"),
+               probs = (pr  <- predict(vlmc.dt2c0, dt3)),
+               lev   =         predict(vlmc.dt2c0, dt3, type = "depth"),
+               ID    = (pid <- predict(vlmc.dt2c0, dt3, type = "id")),
+               flags = attr(pr,"flags"))
> noquote(cbind(prm, context = id2ctxt(pid, m=2, alpha=TRUE)))
  fit  0                 1                 lev  ID   context 
0 <NA> <NA>              <NA>              <NA> <NA> NA      
1 1    0.457142857142857 0.542857142857143 1    2    0       
0 0    1                 0                 1    3    1       
0 1    0.368421052631579 0.631578947368421 3    10   010     
1 0    1                 0                 3    9    001     
0 0    1                 0                 1    3    1       
0 1    0.428571428571429 0.571428571428571 4    20   0100    
0 0    1                 0                 3    9    001     
1 1    0                 1                 3    8    000     
0 0    1                 0                 1    3    1       
0 1    0.333333333333333 0.666666666666667 8    324  01000100
0 0    1                 0                 3    9    001     
0 1    0                 1                 3    8    000     
1 1    0                 1                 3    8    000     
0 0    1                 0                 1    3    1       
0 1    0.428571428571429 0.571428571428571 5    40   01000   
0 0    1                 0                 3    9    001     
0 1    0                 1                 3    8    000     
0 1    0                 1                 3    8    000     
1 1    0                 1                 3    8    000     
1 0    1                 0                 1    3    1       
1 0    1                 0                 1    3    1       
1 0    1                 0                 1    3    1       
1 0    1                 0                 1    3    1       
1 0    1                 0                 1    3    1       
1 0    1                 0                 1    3    1       
> 
> ## Constant data prediction:  |alphabet| = 1 (!!)
> mod <- vlmc(rep(1, 99))
> pr <- predict(mod, c(1, 0, 1))  # crash did occur here (in C code)
> stopifnot(pr[-1] == 1)
> 
> 
> cat("Time elapsed:", format(proc.time() - c0),"\n")
Time elapsed: 0.08 0.00 0.08   NA   NA 
> 
> proc.time()
   user  system elapsed 
   0.26    0.10    0.31