PINE LIBRARY

MarkovChain

已更新

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[])

發布通知

updated imported libraries to its most recent version.

發布通知

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