In the source code, transfer entropy is calculated as transfer_entropy(source, target, k, condition=None, local=False) but in the preprocessing the source is assigned to ys = np.ascontiguousarray(source, np.int32) (which should be assigned to xs) and the target to xs = np.ascontiguousarray(target, np.int32) (which should be ys).
As a result _local_transfer_entropy(ys, xs) seems to provide the reversed result of information flow from source to target distribution rather than a target to the source distribution.
Hi there,
Thanks for the great library! I discovered the following bug.
In the documentation Transfer Entropy considers X as Source input and Y as Target input. https://elife-asu.github.io/PyInform/timeseries.html#notation
In the source code, transfer entropy is calculated as
transfer_entropy(source, target, k, condition=None, local=False)but in the preprocessing the source is assigned toys = np.ascontiguousarray(source, np.int32)(which should be assigned to xs) and the target toxs = np.ascontiguousarray(target, np.int32)(which should be ys).As a result
_local_transfer_entropy(ys, xs)seems to provide the reversed result of information flow from source to target distribution rather than a target to the source distribution.