There are plenty of Markov Chain examples for text simulations, however for a state change (for ex weather change based on probability over time) I couldn't find any examples. For ex, lets say
Sunny --> Sunny = probability is 0.8
Sunny --> Rainy = probability is 0.2
what I am looking is a way to write an algorithm which will display the current weather till n no of steps.
for e.g: f(3) => S,S,R
I guess what I am really finding it difficult is how to put the randomness to the algorithm.
This algorithm generate a sentence based on the probability of given words in a phrase, but I am unable to map it into my requirement.( I am not good in maths)
And pls let me know how can I extend the algorithm, for ex if the probability of a sunny day with high humidity is 0.3, the function should produce something like
f(4) -> [S,Low Hu],[S, Low Hu],[R,High Hu] etc..
Please let me know whether this approach is good for my requirement.
pseudo code would be enough.
You can use mockNeat.probabilities() method from the library with the same name, if you don't want to implement the same functionality by yourself. Or you can take a look on how it's implemented.