As described in the issue, I think that the description of the algorithm:
For each iteration, a random instance z is selected from the data and a random order of the features is generated. Two new instances are created by combining values from the instance of interest x and the sample z. The instance x+j is the instance of interest, but all values in the order before feature j are replaced by feature values from the sample z
was not represented correctly by the pseudocode. there was not a proper distinction between the feature vector ordered with the permutation o and the true original ordering.
Addresses this issue
As described in the issue, I think that the description of the algorithm:
was not represented correctly by the pseudocode. there was not a proper distinction between the feature vector ordered with the permutation
oand the true original ordering.