Since the pseudocode in the text doesn’t belabor itself with describing how this is implemented, I opted to do the same. Which of these approaches to implement depends heavily on how line 5 of PERMUTE-BY-SORTING is implemented. def checkpermfnwithmask(self, inp: Tensor, mask: Tensor) -> None: perminp permutefeature(inp, mask) self.checkfeaturesarepermuted(inp, perminp, mask. This approach is similar to the one previously discussed in that a collision results in generating another \(P\) array, but with the near-guarantee that the value of \(n\) will be smaller (all cases except where there are \(n\) collisions). permute prediction input.view(bs, self.numanchors, self.bboxattrs, inh, inw).permute(0, 1, 3, 4, 2).contiguous() import torch x torch.linspace(1, 9, steps9).view(3, 3) bx.permute(1,0) print(b) print(b. These values will reside sequentially in the output and this secondary call to PERMUTE-BY-SORTING would simply determine the order of their sequence. Exercise 5.3-5 tells us the probability of \(P\) containing all unique elements is at least \(1 - \frac\) so it is likely that we will not need to generate \(P\) more than twice.Īlternatively, we could bundle together duplicate sort keys and call PERMUTE-BY-SORTING again on the subset of values that contain duplicate keys. The simplest approach would be to verify if \(P\) is unique before using it to sort \(A\). That is, your algorithm should produce a uniform random permutation, even if two or more priorities are identical. Explain how to implement the algorithm PERMUTE-BY-SORTING to handle the case in which two or more priorities are identical.
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