https://github.com/brendan-rius/markov-model
Markov model playground
https://github.com/brendan-rius/markov-model
Last synced: 3 months ago
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Markov model playground
- Host: GitHub
- URL: https://github.com/brendan-rius/markov-model
- Owner: brendan-rius
- Created: 2015-12-13T23:39:13.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2015-12-28T00:15:10.000Z (over 9 years ago)
- Last Synced: 2025-01-27T10:12:05.616Z (5 months ago)
- Language: Python
- Homepage:
- Size: 320 KB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# A stupid Markov Model :)
Markov model playground.
Currently only handles discrete-time, discrete-space, first order Markov chains.
## Requirements
+ Python 3
## General markov model
The class `MarkovModel` represents a general first-order, space-discrete and time-discrete markov model.
It has to be initialized with its space (a set of all possible states) like:
```python
model = MarkovModel({'A', 'B', 'C'}) // we create a model with 3 different states
```
It then has to be trained with a markov chain of those states:
```python
model.train(['A', 'A', 'A', 'B', 'A', 'C', 'C', 'A', 'A', 'A', 'A', 'A']) // we train it against this ordered sequence of states
```
And it can then predict a the next state of a state:
```python
model.next_state('A') // will likely return A, since most of the times, A are followed by A during the training phase
```
Or predict the probability of a sequence:
```python
model.probability_of_chain(['A', 'C', 'C']) // the probability for this sequence to be generated
```
It can even create a sequence from a starting state:
```python
model.generate_chain('C', 5) // generate a 5-states sequence beginning with 'C', using the probability of the dataset it has be trained with
```
## Available Markov Models implementations
### Text Markov Model
The time is the order of appearance of tokens (words) in a text, the space is made of the set of words.
Can generate a sentence after having been trained with a text. Code example:
The sentences will look like english sentences, but will have no meaning.
```python
from text import TextHMM
with open('text.txt', 'r') as file:
text = file.read()
hmm = TextHMM(text) # We create the HMM from the text
hmm.train() # We train it
print(' '.join(hmm.generate_chain("the", 7))) # We generate 7 words, starting with "the"
```
```
the very good sort of injustice towards Highbury
```
The example test `text.txt` comes from Shakespeare.
## Improvements to be made
+ Handle not only first order Markov Model but n-th order chains
+ Live training (being able to use the model as we train it, that is being to update the probability matrix after having
created it)