If I understand you correctly, I have to do the following: Suppose I have two 2x2 images. [2305.00402] Control Variate Sliced Wasserstein Estimators Does Python have a ternary conditional operator? June 14th, 2022 mazda 3 2021 bose sound system mazda 3 2021 bose sound system Say if you had two 3D arrays and you wanted to measure the similarity (or dissimilarity which is the distance), you may retrieve distributions using the above function and then use entropy, Kullback Liebler or Wasserstein Distance. : scipy.stats. What are the arguments for/against anonymous authorship of the Gospels. Calculate Earth Mover's Distance for two grayscale images, better sample complexity than the full Wasserstein, New blog post from our CEO Prashanth: Community is the future of AI, Improving the copy in the close modal and post notices - 2023 edition. What should I follow, if two altimeters show different altitudes? So if I understand you correctly, you're trying to transport the sampling distribution, i.e. The histograms will be a vector of size 256 in which the nth value indicates the percent of the pixels in the image with the given darkness level. Horizontal and vertical centering in xltabular. Asking for help, clarification, or responding to other answers. scipy.spatial.distance.jensenshannon SciPy v1.10.1 Manual The input distributions can be empirical, therefore coming from samples I reckon you want to measure the distance between two distributions anyway? Even if your data is multidimensional, you can derive distributions of each array by flattening your arrays flat_array1 = array1.flatten() and flat_array2 = array2.flatten(), measure the distributions of each (my code is for cumulative distribution but you can go Gaussian as well) - I am doing the flattening in my function here: and then measure the distances between the two distributions. \[l_1 (u, v) = \inf_{\pi \in \Gamma (u, v)} \int_{\mathbb{R} \times Doing this with POT, though, seems to require creating a matrix of the cost of moving any one pixel from image 1 to any pixel of image 2.
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