I used the notebook linked in the original post [1]. It evaluates using 100 samples from the test set, (I'm guessing because the gzip method is slow - it would

I used the notebook linked in the original post [1]. It evaluates using 100 samples from the test set, (I'm guessing because the gzip method is slow - it would take ~7 hours on the full test set, on my machine). I plugged in the distance measures, for the `compute_ncd` function. (Jaccard/Dice have been negated and the -1 removed.) def euclidean(x1, x2): return np.sum(np.square(x1 - x2)) def jaccard(x1, x2): x1_binary = x1 > 0.5 x2_binary = x2 > 0.5 return np.logical_or(x1_binary, x2_binary).sum() / np.logical_and(x1_binary, x2_binary).sum() def dice(x1, x2): x1_binary = x1 > 0.5 x2_binary = x2 > 0.5 return 2 * np.logical_or(x1_binary, x2_binary).sum() / (x1_binary.sum() + x2_binary.sum()) [1] https://github.com/Jakob-98/mono/blob/73168bc0ea904e7586581573da5907020d88736f/python/gzip_mnist/mnist_gzip.ipynb https://github.com/Jakob-98/mono/blob/73168bc0ea904e75865815...