Basic Idea: This is a Word Recommender .

Say you are typing : Messi plays this algorithm might recommend you tonight. However this totally depends upon the data in which the model is trained. As of now the collected data is mostly Nepali Roman Characters.

So , If you type in K cha you might get a recommendation of halkhabar

Data Structure Requirement:

I followed by am -> number of occurence
and -> number of occurence
have -> number of occurence

am followed by Ram -> number of occurence
a -> number of occurence

Initial thought

std::tuple(std::string, std::unordered_map<std::string, int>) 

big Noooo, we'd then need collection of those tuples.

why not have a struct then and then std::vector of those struct .

 struct Data{
   std::string parent;
   std::unordered_map<std::string, int> predecessors;
 }

and then

std::vector<Data> DataObject; -> sounds ok as of now. 

Or should It be tree. I need some more time to decide.

Implementation Detail

I decided to use something that sounds like a Lookup table. For every new word we encounter in our data set, we assign a std::unordered_map<std::string, int> that holds the frequencies of the next word appearing after that word which is stored in a std::vector. The index of the std::vector is stored in another std::unordered_map<std::string, int> where the key is the word we encounter and value is the index.

{
  "I": Index,
   .
   .
   .
}
LookUp Table:
{
  0 : {
        "word1": count,
	"word2": count
      }
  .
  .
  .
  .
  Index : {
           "word1": count,
	   "word2": count
          }
}

How is a word Recommended

we calculate all the possibilites

probability of occurence of am after I = Total occurence of am after I / Total words after I 

The one with the higher probability or above some threshold will be recommeded.