Markov chains

Screenshot

Download Model

Download the .scn, .sel and .lse files by clicking on the following link: File:MarkovChain.zip

Model Code Exploration

In the following sections we will examine all of the model files for this model. Note that instead of downloading the zip file above, you could just copy the text in the boxes below into a text editor and save it with the appropriate name (Section title). Opening the resulting .scn file in the SELES simulator would run this model.

MarkovChain.scn

SELES Scenario
MarkovChain.sel
SimPriority Low Priority

MarkovChain.sel

Seles Model
Model Size: 200, 200
Time Units: na Step 1 1000
Landscape Events:
MarkovChain.lse
Spatial Variables:
CellState[4] <= 0
PrevCellState[4] <= 0
Global Variables:
pInitial = 0, 1, 0, 0, 0
pChange = 0.01
UseNeighbs = FALSE
Output Frequency: 1

MarkovChain.lse

// Simple Markov Chain
LSEVENT: MarkovChain
 DEFINITIONS
LAYER: CellState, PrevCellState
GLOBAL VARIABLE: pInitial[], pChange, UseNeighbs 
ENDDEF
 INITIALSTATE
INITIALSTATE = 1
CellState = CLASSIFIED_DIST[pInitial]
ENDIS
 RETURNTIME
RETURNTIME = 1
PrevCellState = CellState
ENDRT
 TRANSITIONS
TRANSITIONS = TRUE
IF UseNeighbs 
     n = 0
     t = 0
     x = PrevCellState
     OVER REGION CENTRED(0, 1.5)
        n = n + 1
        t = t + PrevCellState
        x = IF (PrevCellState EQ 0) OR (x EQ 0) THEN 0 ELSE MAX(x, PrevCellState)
     ENDFN
     currState = CLAMP(ROUND(t/n), 0, 4)
     currState = x
  ELSE
     currState = PrevCellState
  ENDFN
CellState = CLASSIFY(currState)
               0: 0
               1: CLASSIFIED_DIST
                    1:1 - pChange
                    2:pChange
                  ENDFN
               2: CLASSIFIED_DIST
                    2:1 - pChange 
                    3:pChange
                  ENDFN
               3: CLASSIFIED_DIST
                    3:1 - pChange
                    4:pChange
                  ENDFN
               4: CLASSIFIED_DIST
                    0: pChange
                    4:1 - pChange
                  ENDFN
             ENDFN
 ENDTR

Suggested Experiments

To explore this cellular automata model further, try the following: