Graph Based Naive Bayes

A subset of Naive Bayes that gets its probability from matrixe / graphs rather than through flat array spaces, in order to place constraints finding a comrpomise between random and iterative. Such a graph can be simple digraphs:

matrix = [
  [[a, a], [a, b], [a, c]],
  [[b, a], [b, b], [b, c]],
  [[c, a], [c, b], [c, c]],
]

Or more complex grid patterns:

  matrix = [
    [[a, a, a, a, a, a],
     [a, a, a, a, a, b],
     [a, a, a, a, a, c],
     [a, a, a, a, a, d],
     [a, a, a, a, a, e],
     [a, a, a, a, a, f]],
    
    [[b, b, b, b, b, a],
     [b, b, b, b, b, b],
     [b, b, b, b, b, c],
     [b, b, b, b, b, d],
     [b, b, b, b, b, e],
     [b, b, b, b, b, f]],
    
    [[c, c, c, c, c, a],
     [c, c, c, c, c, b],
     [c, c, c, c, c, c],
     [c, c, c, c, c, d],
     [c, c, c, c, c, e],
     [c, c, c, c, c, f]],
    
    [[d, d, d, d, d, a],
     [d, d, d, d, d, b],
     [d, d, d, d, d, c],
     [d, d, d, d, d, d],
     [d, d, d, d, d, e],
     [d, d, d, d, d, f]],
    
    [[e, e, e, e, e, a],
     [e, e, e, e, e, b],
     [e, e, e, e, e, c],
     [e, e, e, e, e, d],
     [e, e, e, e, e, e],
     [e, e, e, e, e, f]],
    
    [[f, f, f, f, f, a],
     [f, f, f, f, f, b],
     [f, f, f, f, f, c],
     [f, f, f, f, f, d],
     [f, f, f, f, f, e],
     [f, f, f, f, f, f]],
  ]