Not so simple as I try to avoid randomness, U know, if u extract 4000 articles from spintax, u can end up with 100 exactly similar articles and also u did not full utilize the potential of your spintax. it is a matter of probability.OK so just a simple spin then?
Not so simple as I try to avoid randomness, U know, if u extract 4000 articles from spintax, u can end up with 100 exactly similar articles and also u did not full utilize the potential of your spintax. it is a matter of probability.
I want to break down the probability factor and make it based on a calculation which should be there before processing the generation
Not so simple as I try to avoid randomness, U know, if u extract 4000 articles from spintax, u can end up with 100 exactly similar articles and also u did not full utilize the potential of your spintax. it is a matter of probability.
I want to break down the probability factor and make it based on a calculation which should be there before processing the generation
With the complexity of levels deep we may have in the spintax, I prefer to avoid to do any calculation or comparison during this step.You could iterate through, and save the number of possible variations of each nest and layer similar to deki33's suggestion into an array (of arrays), to create a navigable tree of values. You can then create a set of predefined navigation routes, to extract the spin based on non-reoccurring patterns. You can compare previously created routes to try not to re-use previously used synonyms. You will then have an array of 'routes' which you can plug the spintax through, giving unique, methodically spun content.