Note that computing Markov using incoming/outgoing paths of length 0 is just a distribution of letters, tagged with pos/neg/fail. It is not based on earlier knowledge hence could be built either forwards or sideways in the same way. @param vert state to predict for @param alphabet alphabet of the graph of interest @param chunkLength how many steps to make a prediction for.
Note that computing Markov using incoming/outgoing paths of length 0 is just a distribution of letters, tagged with pos/neg/fail. It is not based on earlier knowledge hence could be built either forwards or sideways in the same way. @param graph the graph to predict transitions for @param markovMatrix the matrix used for predictions @param Inverse_Graph graph used to make predictions @param predictForwardOrSideways whether to predict forward (true) or sideways (false). @param vert state to predict for @param alphabet alphabet of the graph of interest @param chunkLength how many steps to make a prediction for.
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