@vapurrmaid/markovchain
A lightweight TS library for computations with markov chains and probability matrices.
Installation
# yarn
yarn add @vapurrmaid/markovchain
# npm
npm install save @vapurrmaid/markovchain
Modules
Markov Chain
Represents a finite, discretetime Markov Chain.
The capabilities of this module are:
 Probabilistic state transition (see next)
 A state transition function can be used to dynamically update probabilities
 Reporting if the current state is terminal
(see isTerminal)
 A terminal state will always transition back to itself
MarkovChain Import
import { MarkovChain } from '@vapurrmaid/markovchain'
MarkovChain Constructor
 Must supply a N x N array of probabilities as
number[][]
 Must supply an array of values as
T[]
of size N  Optionally supply an initialState in
[0, N)
 If none is supplied, the default
initialState = 0
 If none is supplied, the default
Each row in the matrix corresponds to an index in the values array.
const values = ["a", "b", "c"]
const m = [
[0, 1, 0], // always selects row 1 = index 1 = "b"
[0, 0, 1], // always selects row 2 = index 2 = "c"
[1, 0, 0] // always selects row 0 = index 0 = "a"
]
const mc = new MarkovChain(values, m)
current
Property
 Returns the value associated to the current state (row) as
T
hasTransitionFn
Property
 Returns
true
if a transition function is defined, otherwisefalse
 see
setTransitionFn
isTerminal
Property
 Returns
true
if the current row is terminal  Returns
false
if the current row is not terminal
length
Property
 Returns the size of the matrix, N as
number
probabilityMatrix
Property
 Returns the probability matrix as:
number[][]
next()
Method
 Computes and returns the next value as
T
using the probability matrix  If a transition function is set, runs the transition function
setTransitionFn(prev, next)
Method
 Sets a transition function that is used to alter the probability matrix
Probability Matrix
Represents a probability matrix (aka transition matrix, Markov matrix or stochastic matrix). In typical mathematical representation, a probability matrix is formed as:
P = [p_{ij}].
Which represents the probability of transitioning to the i
th column from the
j
th column. However, column vectors are less intuitive in programming, as they
require methods that span multiple arrays.
Instead, in this implementation each row vector entry represents transitioning
from the i
th row to the j
th row. Therefore this representation is a
transpose of the mathematical definition: ProbabilityMatrix
=
P^{T}
Example
[ [0, 1, 0], // row 0 [0.5, 0, 0.5], // row 1 [1, 0, 0], // row 2 ];In the above example, row 1 (
[0.5, 0, 0.5]
) reads:
 P=0.5 to transition from row 1 to row 0
 P=0 to transition from row 1 to row 1
 P=0.5 to transition from row 1 to row 2
ProbabilityMatrix Import
import { ProbabilityMatrix } from '@vapurrmaid/markovchain'
ProbabilityMatrix Constructor
 Must be N x N
 Each row must add to
1.0
 Each value must be in the interval
[0, 1]
 Each value must be in the interval
const m = [
[0, 1, 0], // P = 1.0 to transition to row 1
[0, 0, 1], // P = 1.0 to transition to row 2
[1, 0, 0] // P = 1.0 to transition to row 0
]
const matrix = new ProbabilityMatrix(m)
Properties

value
 returns the supplied probabilities asnumber[][]

length
 returns the size of the matrix, N asnumber
getRowVector(aRow)
Method
 Returns the probability vector for the specified row as
number[]

aRow
must be a number in the interval[0, N)
selectFrom(aRow)
Method
 Using the probabilities defined in the given row, selects the next row as
number

aRow
must be a number in the interval[0, N)
From the matrix defined above:
let nextRow = matrix.selectFrom(0) // 1
nextRow = matrix.selectFrom(nextRow) // 2
nextRow = matrix.selectFrom(nextRow) // 0