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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 binary_mdp_writer and that the id of the states is known (can be retrieved using e.g. state_idx_df).

Source: R/writer.R
binary_action_writer.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

binary_action_writer(
  prefix = "",
  bin_names = c("actionIdx.bin", "actionIdxLbl.bin", "actionWeight.bin",
    "actionWeightLbl.bin", "transProb.bin", "transWeight.bin", "transWeightLbl.bin"),
  append = TRUE
)

Arguments

prefix

A character string with the prefix added to bin_names.

bin_names

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 returned writer exposes these functions:

  • set_weights(labels, ...): sets the labels of the weights used in the actions. labels is a vector of label names. ... is currently ignored. Call this before building the model.

  • add_action(label = NULL, s_idx, weights, prob, ...): adds an action. s_idx is the id of the state defining the action. weights must be a vector of action weights. prob is a matrix (s_idx, pr) where the first column contains the id of the transition state; see the description of actionIdx.bin below, where scope is assumed to be 3. ... is currently ignored.

  • end_action(): ends an action.

  • close_writer(): closes the writer. Call this when the model description is finished.

Five binary files are created:

  • actionIdx.bin: integers defining all actions in the format s_idx scope idx scope idx scope idx -1 s_idx scope idx scope idx -1 s_idx scope -1 .... s_idx corresponds to the index or line number in stateIdx.bin, starting from 0. The following (scope, idx) pairs indicate possible transitions. Scope can take four values:

    • 2: a transition to a child process, at stage zero in the child process.

    • 1: a transition to the next stage in the current process.

    • 0: a transition to the next stage in the father process.

    • 3: a transition to a state specified by its state s_idx.

    For example, if scope = 1 and idx = 2, the transition is to state number 3 at the next stage in the current process. If scope = 3 and idx = 5, the transition is to the state specified at line 6 in stateIdxLbl.bin. This is useful when considering shared child processes.

  • actionIdxLbl.bin: character data in the format a_idx label a_idx label .... Here a_idx corresponds to the index or line number in actionIdx.bin, starting from 0. No delimiter is used.

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

  • actionWeightLbl.bin: character data containing the weight labels in the format label1 label2 label3, assuming three weights for each action.

  • transProb.bin: doubles containing the transition probabilities defined in actionIdx.bin. The format is "p1 p2 p3 -1 p1 -1 p1 p2 -1 ...". Here -1 indicates that a new action is considered.

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<-binary_mdp_writer()
w$set_weights(c("Duration","Net reward","Items"))
w$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$action(label="A0",weights=c(0,0,1),prob=c(1,0,0.5,1,1,0.5))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$end_action()
                     w$action(label="A1",weights=c(1,2,1),prob=c(1,0,0.5,1,1,0.5))
                     w$end_action()
                  w$end_state()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$end_action()
                     w$action(label="A1",weights=c(1,2,1),prob=c(1,0,0.5,1,1,0.5))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                  w$end_state()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                  w$end_state()
               w$end_stage()
            w$end_process()
         w$end_action()
         w$action(label="A1",weights=c(0,0,0),prob=c(2,0,1))
            w$process()
               w$stage()
                  w$state(label="D")
                     w$action(label="A0",weights=c(0,0,1),prob=c(1,0,1))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$end_action()
                     w$action(label="A1",weights=c(1,2,1),prob=c(1,0,0.5,1,1,0.5))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                  w$end_state()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                     w$action(label="A1",weights=c(0,10,5),prob=c(0,0,0.5,0,1,0.5))
                     w$end_action()
                  w$end_state()
               w$end_stage()
            w$end_process()
         w$end_action()
      w$end_state()
      w$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$action(label="A0",weights=c(0,0,1),prob=c(1,0,0.5,1,1,0.5))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$end_action()
                  w$end_state()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                  w$end_state()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                  w$end_state()
               w$end_stage()
            w$end_process()
         w$end_action()
      w$end_state()
   w$end_stage()
w$end_process()
w$close_writer()
#> 
#>   Statistics:
#>     states : 16 
#>     actions: 21 
#>     weights: 3 
#> 
#>   Closing binary MDP writer.
#> 

## Info about the binary files (don't have to load the model first)
get_bin_info_states()
#> # A tibble: 16 × 3
#>     s_id stage_str 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   
get_bin_info_actions()
#> # A tibble: 21 × 9
#>      aId  s_id 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)