1. Clone the project

git clone https://github.com/wwszola/interactive_story
cd interactive_story

2. Create, activate and prepare a virtual environment

python -m venv venv
./venv/Scripts/activate

pip install -r requirements.txt
python setup.py install

3. Run some examples

python examples/example2.py

4. In order to run tests:

pip install pytest
pytest tests/test1.py

4. Deactivate the virtual environment after use

deactivate

from MarkovTool import Markov, Endless
process = Markov(3).fill_random(seed_ = 0)

print(Endless(process).take(10))

[0, 0, 2, 2, 0, 1, 1, 1, 1, 2]

We set dimension of the state space by calling constructor Markov. Method fill_random generates values for probability matrix and initial probability vector. Identical description is generated every time by passing seed_ argument. Constructor Endless creates a new running instance. We generate first 10 states and print them to the terminal.

for _ in range(3): 
    print(Endless(process).take(10))

[2, 2, 2, 0, 1, 0, 1, 0, 2, 0]
[2, 0, 1, 0, 2, 0, 2, 0, 1, 0]
[0, 1, 2, 0, 2, 2, 0, 2, 0, 2]

The same description generates different processes. That happens because my_seed property of Markov defaults to None, generating unique RNGs. Set this property to make sure the process behaves in repeatable manner.

process.my_seed = 0
for _ in range(3): 
    print(Endless(process).take(10))

[2, 2, 0, 0, 0, 2, 2, 2, 2, 2]
[0, 2, 0, 0, 0, 2, 2, 2, 2, 2]
[1, 2, 0, 0, 0, 2, 2, 2, 2, 2]

To ensure the first state is always the same set initial_state to an int value.

process.initial_state = 1
for _ in range(3): 
    print(Endless(d).take(10))

[1, 2, 0, 0, 0, 2, 2, 2, 2, 2]
[1, 2, 0, 0, 0, 2, 2, 2, 2, 2]
[1, 2, 0, 0, 0, 2, 2, 2, 2, 2]

See also Markov.random, Endless.skip

process = Markov(2).fill_random()
process.initial_state = 0
p_mat = process.matrix
# removing transition from state 1 to state 0 entirely 
p_mat[1, 0] = 0.0
variation = process.variant(matrix = p_mat)
print(Endless(process).take(10))
print(Endless(variation).take(10))

[0, 0, 1, 1, 0, 1, 1, 0, 0, 1]
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]

Call method variant to create a copy of the description. Pass properties you wish to change as keyword arguments.

You may run instances with different properties while keeping others to create complex behaviour. First, generate 5 steps from Endless instance.

process = Markov(7, my_seed = 17).fill_random()
instance = Endless(process)
print(instance.take(5))

[4, 6, 1, 5, 3]

Next, run second unique process, which has _state set to 4, by calling branch. Both of them eventually converge to the same output. Instances produces the same states at the same steps, but only given the same previous state.

print(instance.branch(state=4).take(10))
print(instance.take(10))

[4, 1, 4, 5, 5, 0, 2, 4, 0, 6]
[1, 3, 2, 4, 5, 0, 2, 4, 0, 6]

Collector allows to gather all instances running in a manner that takes into a consideration parallel branches, but no duplicates will be present. Present in stat module

• parallel is itertools.zip_longest? : kinda, firing order if they depend on themselves
• just use itertools to get the result you want

• fill_random argument if initial_state too
• better collector:
• • diff
• • pretty summary
• • counting occurences, pairs, triples
• • playback: maybe cashe full chunks??
• model: parallel, correct firing order for dependence
• package distribution using setuptools
• load from file: we want json maybe?

• benchmark tests, how slow all of it is?
• tests: description -> instance -> stat