def dargsort(original, sortd, sigma, transpose=False):
"""Take an input vector `original` and a sorted vector `sortd`
along with an RBF kernel width `sigma`, return an approximate ranking.
If transpose is True, returns approximate argsort (but note that ties have identical values)
If transpose is False (default), returns ranking"""
order = order_matrix(original, sortd, sigma=sigma)
if transpose:
order = order.T
return order @ np.arange(len(original))
def diff_argsort(matrices, x, sigma=0.1, softmax=softmax):
"""Return the smoothed, differentiable ranking of each element of x. Sigma
specifies the smoothing of the ranking. """
sortd = diff_sort(matrices, x, softmax)
return order_matrix(x, sortd, sigma=sigma) @ np.arange(len(x))
def dargsort(original, sortd, sigma, transpose=False):
order = order_matrix(original, sortd, sigma=sigma)
if transpose:
order = order.T
return order @ np.arange(len(original))