MarkovChain

29 mar 2023

Library "MarkovChain"
Generic Markov Chain type functions.
---
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the
probability of each event depends only on the state attained in the previous event.
---
reference:
Understanding Markov Chains, Examples and Applications. Second Edition. Book by Nicolas Privault.
en.wikipedia.org/wiki/Markov_chain
geeksforgeeks.org/finding-the-probability-of-a-state-at-a-given-time-in-a-markov-chain-set-2/
towardsdatascience.com/brief-introduction-to-markov-chains-2c8cab9c98ab
github.com/mxgmn/MarkovJunior
stats.stackexchange.com/questions/36099/estimating-markov-transition-probabilities-from-sequence-data
timeseriesreasoning.com/contents/hidden-markov-models/
ris-ai.com/markov-chain
github.com/coin-or/jMarkov/blob/master/src/jmarkov/MarkovProcess.java
gist.github.com/mschauer/4c81a0529220b21fdf819e097f570f06
github.com/rasmusab/bayes.js/blob/master/mcmc.js
gist.github.com/sathomas/cf526d6495811a8ca779946ef5558702
writings.stephenwolfram.com/2022/06/games-and-puzzles-as-multicomputational-systems/
kevingal.com/blog/boardgame.html
towardsdatascience.com/brief-introduction-to-markov-chains-2c8cab9c98ab
spedygiorgio.github.io/markovchain/reference/index.html
github.com/alexsosn/MarslandMLAlgo/blob/4277b24db88c4cb70d6b249921c5d21bc8f86eb4/Ch16/HMM.py
projectrhea.org/rhea/index.php/Introduction_to_Hidden_Markov_Chains

method to_string(this)
  Translate a Markov Chain object to a string format.
  Namespace types: MC
  Parameters:
    this (MC): `MC` . Markov Chain object.
  Returns: string

method to_table(this, position, text_color, text_size)
  Namespace types: MC
  Parameters:
    this (MC)
    position (string)
    text_color (color)
    text_size (string)

method create_transition_matrix(this)
  Namespace types: MC
  Parameters:
    this (MC)

method generate_transition_matrix(this)
  Namespace types: MC
  Parameters:
    this (MC)

new_chain(states, name)
  Parameters:
    states (state[])
    name (string)

from_data(data, name)
  Parameters:
    data (string[])
    name (string)

method probability_at_step(this, target_step)
  Namespace types: MC
  Parameters:
    this (MC)
    target_step (int)

method state_at_step(this, start_state, target_state, target_step)
  Namespace types: MC
  Parameters:
    this (MC)
    start_state (int)
    target_state (int)
    target_step (int)

method forward(this, obs)
  Namespace types: HMC
  Parameters:
    this (HMC)
    obs (int[])

method backward(this, obs)
  Namespace types: HMC
  Parameters:
    this (HMC)
    obs (int[])

method viterbi(this, observations)
  Namespace types: HMC
  Parameters:
    this (HMC)
    observations (int[])

method baumwelch(this, observations)
  Namespace types: HMC
  Parameters:
    this (HMC)
    observations (int[])

Node
  Target node.
  Fields:
    index (series int): . Key index of the node.
    probability (series float): . Probability rate of activation.

state
  State reference.
  Fields:
    name (series string): . Name of the state.
    index (series int): . Key index of the state.
    target_nodes (Node[]): . List of index references and probabilities to target states.

MC
  Markov Chain reference object.
  Fields:
    name (series string): . Name of the chain.
    states (state[]): . List of state nodes and its name, index, targets and transition probabilities.
    size (series int): . Number of unique states
    transitions (matrix<float>): . Transition matrix

HMC
  Hidden Markov Chain reference object.
  Fields:
    name (series string): . Name of thehidden chain.
    states_hidden (state[]): . List of state nodes and its name, index, targets and transition probabilities.
    states_obs (state[]): . List of state nodes and its name, index, targets and transition probabilities.
    transitions (matrix<float>): . Transition matrix
    emissions (matrix<float>): . Emission matrix
    initial_distribution (float[])

20 dic 2023

Note di rilascio

updated imported libraries to its most recent version.

16 set 2024

Note di rilascio

v3 it now uses the builtin matrix.pow() function.