Numpy Conversion

For deep learning applications, it is often desirable to represent a board as a numpy array so that it can be passed to a deep learning library such as TensorFlow or PyTorch. The sente.game object provides the numpy() method to create a numpy representation of the board.

>>> import sente
>>> game = sente.Game()
>>> numpy_array = game.numpy()
>>> print(numpy_array)
[[[0 0 1 0]
  [0 0 1 0]
  [0 0 1 0]
  ...
  [0 0 1 0]
  [0 0 1 0]
  [0 0 1 0]]

 ...

 [[0 0 1 0]
  [0 0 1 0]
  [0 0 1 0]
  ...
  [0 0 1 0]
  [0 0 1 0]
  [0 0 1 0]]]

Sente converts a game to a numpy array by creating several 19x19 (or 9x9 or 13x13 respectively) numpy array that contains some feature associated with each point on the board. These features are 1-hot encoded facts about the board such as the presence of a black or white stone or whether or not the point in question is a Ko point.

By default, the numpy() method uses 4 such features, namely the presence of white stones, black stones, empty points and ko points. However, if a different set of features is desired, it is possible to pass the numpy() method a list of strings specifying the desired features as such.

>>> import sente
>>> game = sente.Game()
>>> array = game.numpy(["black_stones", "white_stones"])
>>> array.shape
(19, 19, 2)

Thus, the Game.numpy() method returns a NxNxF numpy array where N denotes the size of the board (ie 19) and F denotes the number of features per point on the board.