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Function for writing actions of a HMDP model to binary files. The function defines sub-functions which can be used to define actions saved in a set of binary files. It is assumed that the states have been defined using binaryMDPWriter and that the id of the states is known (can be retrieved using e.g. stateIdxDf).

Source: R/binary.R
binaryActionWriter.Rd

Binary files are efficient for storing large models. Compared to the HMP (XML) format the binary files use less storage space and loading the model is faster.

Usage

binaryActionWriter(
  prefix = "",
  binNames = c("actionIdx.bin", "actionIdxLbl.bin", "actionWeight.bin",
    "actionWeightLbl.bin", "transProb.bin"),
  append = TRUE
)

Arguments

prefix

A character string with the prefix added to binNames.

binNames

A character vector of length 5 giving the names of the binary files storing the model.

append

Logical indicating whether should keep the currents actions (default - TRUE) defined or delete them and start over (FALSE).

Value

A list of functions.

Details

The functions which can be used are:

  • setWeights(labels, ...): Set the labels of the weights used in the actions. labels is a vector of label names, ... are not used. The function must be called before starting building the model.

  • addAction(label=NULL, sIdx, weights, prob, ...): Add an action. Parameter sIdx is the id of the state defining the action, weights must be a vector of action weights, prob is a matrix (sIdx,pr) where the first column contain the id of the transition state (see the description of actionIdx.bin below - scope is assumed to the 3), ... is currently not used.

  • endAction(): Ends an action.

  • closeWriter(): Close the writer. Must be called when the model description has finished.

Five binary files are created using the following format:

  • actionIdx.bin: File of integers containing the indexes defining all actions in the format sIdx scope idx scope idx scope idx -1 sIdx scope idx scope idx -1 sIdx scope -1 .... sIdx corresponds to the index/line number in stateIdx.bin (index starts from 0). Next pairs (scope idx) will follow indicating the possible transitions. Scope can be 4 values: 2 - A transition to a child process (stage zero in the child process), 1 - A transition to next stage in the current process, 0 - A transition to the next stage in the father process. Here idx in the pair denote the index of the state at the stage considered, e.g. if scope=1 and idx=2 we consider state number 3 at next stage in the current process. Finally, if scope = 3 then a transition to a state specified by it's state sIdx is given. That is, if scope=3 and idx=5 then we have a transition to the state specified at line 6 in stateIdxLbl.bin. This is useful when considering shared child processes.

  • actionIdxLbl.bin: File of characters in the format aIdx label aIdx label ... Here aIdx corresponds to the index/line number in actionIdx.bin (index starts from 0). Note no delimiter is used.

  • actionWeight.bin: File of doubles containing the weights of the actions in the format "c1 c2 c3 c1 c2 c3 ..." assuming three weights for each action.

  • actionWeightLbl.bin: File of characters containing the labels of the weights in the format label1 label2 label3 assuming three weights for each action.

  • transProb.bin: File of doubles containing the probabilities of the transitions defined in actions in actionIdx.bin. The format is "p1 p2 p3 -1 p1 -1 p1 p2 -1 ...". Here -1 is used to indicate that a new action is considered (new line).

Note

Note all indexes are starting from zero (C/C++ style).

Examples

## Use temp dir
wd <- setwd(tempdir())

# Create a small HMDP with two levels
w<-binaryMDPWriter()
w$setWeights(c("Duration","Net reward","Items"))
w$process()
   w$stage()
      w$state(label="M0")
         w$action(label="A0",weights=c(0,0,0),prob=c(2,0,1))
            w$process()
               w$stage()
                  w$state(label="D")
                     w$action(label="A0",weights=c(0,0,1),prob=c(1,0,0.5,1,1,0.5))
                     w$endAction()
                  w$endState()
               w$endStage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$endAction()
                     w$action(label="A1",weights=c(1,2,1),prob=c(1,0,0.5,1,1,0.5))
                     w$endAction()
                  w$endState()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$endAction()
                     w$action(label="A1",weights=c(1,2,1),prob=c(1,0,0.5,1,1,0.5))
                     w$endAction()
                  w$endState()
               w$endStage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$endAction()
                  w$endState()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$endAction()
                  w$endState()
               w$endStage()
            w$endProcess()
         w$endAction()
         w$action(label="A1",weights=c(0,0,0),prob=c(2,0,1))
            w$process()
               w$stage()
                  w$state(label="D")
                     w$action(label="A0",weights=c(0,0,1),prob=c(1,0,1))
                     w$endAction()
                  w$endState()
               w$endStage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$endAction()
                     w$action(label="A1",weights=c(1,2,1),prob=c(1,0,0.5,1,1,0.5))
                     w$endAction()
                  w$endState()
               w$endStage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$endAction()
                  w$endState()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$endAction()
                     w$action(label="A1",weights=c(0,10,5),prob=c(0,0,0.5,0,1,0.5))
                     w$endAction()
                  w$endState()
               w$endStage()
            w$endProcess()
         w$endAction()
      w$endState()
      w$state(label="M1")
         w$action(label="A0",weights=c(0,0,0),prob=c(2,0,1))
            w$process()
               w$stage()
                  w$state(label="D")
                     w$action(label="A0",weights=c(0,0,1),prob=c(1,0,0.5,1,1,0.5))
                     w$endAction()
                  w$endState()
               w$endStage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$endAction()
                  w$endState()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$endAction()
                  w$endState()
               w$endStage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$endAction()
                  w$endState()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$endAction()
                  w$endState()
               w$endStage()
            w$endProcess()
         w$endAction()
      w$endState()
   w$endStage()
w$endProcess()
w$closeWriter()
#> 
#>   Statistics:
#>     states : 16 
#>     actions: 21 
#>     weights: 3 
#> 
#>   Closing binary MDP writer.
#> 

## Info about the binary files (don't have to load the model first)
getBinInfoStates()
#> # A tibble: 16 × 3
#>      sId stageStr  label
#>    <dbl> <chr>     <chr>
#>  1     0 0,0       M0   
#>  2     1 0,0,0,0,0 D    
#>  3     2 0,0,0,1,0 C0   
#>  4     3 0,0,0,1,1 C1   
#>  5     4 0,0,0,2,0 C0   
#>  6     5 0,0,0,2,1 C1   
#>  7     6 0,0,1,0,0 D    
#>  8     7 0,0,1,1,0 C0   
#>  9     8 0,0,1,2,0 C0   
#> 10     9 0,0,1,2,1 C1   
#> 11    10 0,1       M1   
#> 12    11 0,1,0,0,0 D    
#> 13    12 0,1,0,1,0 C0   
#> 14    13 0,1,0,1,1 C1   
#> 15    14 0,1,0,2,0 C0   
#> 16    15 0,1,0,2,1 C1   
getBinInfoActions()
#> # A tibble: 21 × 9
#>      aId   sId scope index pr      Duration `Net reward` Items label
#>    <dbl> <int> <chr> <chr> <chr>      <dbl>        <dbl> <dbl> <chr>
#>  1     0     0 2     0     1              0            0     0 A0   
#>  2     1     1 1,1   0,1   0.5,0.5        0            0     1 A0   
#>  3     2     2 1     0     1              0            0     0 A0   
#>  4     3     2 1,1   0,1   0.5,0.5        1            2     1 A1   
#>  5     4     3 1     0     1              0            0     0 A0   
#>  6     5     3 1,1   0,1   0.5,0.5        1            2     1 A1   
#>  7     6     4 0     0     1              1            4     0 A0   
#>  8     7     5 0     0     1              1            4     0 A0   
#>  9     8     0 2     0     1              0            0     0 A1   
#> 10     9     6 1     0     1              0            0     1 A0   
#> # ℹ 11 more rows

## reset working dir
setwd(wd)