When a sequence of events is processed through the History class, the Gaussians of the Player objects are not themselves updated. Rather, it appears that one has to query the History class learning_curves() method to get the updated Gaussians.
If we are running games at scale (tens of millions of games), we will eventually run out of memory using the History Class. There should be a function that updates the original Player objects with their new Gaussians, so that after running some number of games, the updated Gaussians (and any other info, such as time) can be stored in new Player objects, and a new History class can be greated for upcoming games.
In other words, I would like a serialized object where we can recompute player rankings from a loaded state with each player's mu, sigma, and other parameters saved. This object's size should not grow as a function of games played -- it should be the same size whether ten games have been played or ten million games have been played.
When a sequence of events is processed through the History class, the Gaussians of the Player objects are not themselves updated. Rather, it appears that one has to query the History class learning_curves() method to get the updated Gaussians.
If we are running games at scale (tens of millions of games), we will eventually run out of memory using the History Class. There should be a function that updates the original Player objects with their new Gaussians, so that after running some number of games, the updated Gaussians (and any other info, such as time) can be stored in new Player objects, and a new History class can be greated for upcoming games.
In other words, I would like a serialized object where we can recompute player rankings from a loaded state with each player's mu, sigma, and other parameters saved. This object's size should not grow as a function of games played -- it should be the same size whether ten games have been played or ten million games have been played.