tfp.experimental.distributions.marginal_fns.ps.argsort

Returns the indices of a tensor that give its sorted order along an axis.

tfp.experimental.distributions.marginal_fns.ps.argsort(
    values, axis=-1, direction='ASCENDING', stable=False, name=None
)

values = [1, 10, 26.9, 2.8, 166.32, 62.3]
sort_order = tf.argsort(values)
sort_order.numpy()
array([0, 3, 1, 2, 5, 4], dtype=int32)

For a 1D tensor:

sorted = tf.gather(values, sort_order)
assert tf.reduce_all(sorted == tf.sort(values))

For higher dimensions, the output has the same shape as values, but along the given axis, values represent the index of the sorted element in that slice of the tensor at the given position.

mat = [[30,20,10],
       [20,10,30],
       [10,30,20]]
indices = tf.argsort(mat)
indices.numpy()
array([[2, 1, 0],
       [1, 0, 2],
       [0, 2, 1]], dtype=int32)

If axis=-1 these indices can be used to apply a sort using tf.gather:

tf.gather(mat, indices, batch_dims=-1).numpy()
array([[10, 20, 30],
       [10, 20, 30],
       [10, 20, 30]], dtype=int32)

  • tf.sort: Sort along an axis.
  • tf.math.top_k: A partial sort that returns a fixed number of top values and corresponding indices.

values 1-D or higher numeric Tensor.
axis The axis along which to sort. The default is -1, which sorts the last axis.
direction The direction in which to sort the values ('ASCENDING' or 'DESCENDING').
stable If True, equal elements in the original tensor will not be re-ordered in the returned order. Unstable sort is not yet implemented, but will eventually be the default for performance reasons. If you require a stable order, pass stable=True for forwards compatibility.
name Optional name for the operation.

An int32 Tensor with the same shape as values. The indices that would sort each slice of the given values along the given axis.

ValueError If axis is not a constant scalar, or the direction is invalid.
tf.errors.InvalidArgumentError If the values.dtype is not a float or int type.