euclidean-distance

问题 I have set of line segments (not lines) , (A1, B1) , (A2, B2) , (A3, B3) , where A , B are ending points of the line segment. Each A and B has (x,y) coordinates. QUESTION: I need to know the shortest distance between point O and line segments as shown in the shown figure implemented in line of codes. The code I can really understand is either pseudo-code or Python. CODE: I tried to solve the problem with this code, unfortunately, it does not work properly. def dist(A, B, O): A_ = complex(*A)

问题 I have set of line segments (not lines) , (A1, B1) , (A2, B2) , (A3, B3) , where A , B are ending points of the line segment. Each A and B has (x,y) coordinates. QUESTION: I need to know the shortest distance between point O and line segments as shown in the shown figure implemented in line of codes. The code I can really understand is either pseudo-code or Python. CODE: I tried to solve the problem with this code, unfortunately, it does not work properly. def dist(A, B, O): A_ = complex(*A)

问题 I'm working on a kmeans functionality for a uni assignment. We need to run euclidean clustering on one set of data and then Jaccard on the other. We need to explore a few different models to evaluate the number of clusters and for the Euclidean it was quite straight forward using sklearn.metrics.silhouette_score , but this does not give the option to use Jaccard distance. As such I was wondering if anyone has an idea of how to calculate it for Jaccard distance? I have managed to create a

来源： https://stackoverflow.com/questions/58768373/fastest-way-to-calculate-the-shortest-euclidean-distance-between-points-in-pa

来源： https://stackoverflow.com/questions/58768373/fastest-way-to-calculate-the-shortest-euclidean-distance-between-points-in-pa

来源： https://stackoverflow.com/questions/61674231/how-to-calculate-distance-between-two-person-using-python-opencv

来源： https://stackoverflow.com/questions/61674231/how-to-calculate-distance-between-two-person-using-python-opencv

来源： https://stackoverflow.com/questions/61674231/how-to-calculate-distance-between-two-person-using-python-opencv

问题 I've seen another StackOverflow thread talking about the various implementations for calculating the Euclidian norm and I'm having trouble seeing why/how a particular implementation works. The code is found in an implementation of the MMD metric: https://github.com/josipd/torch-two-sample/blob/master/torch_two_sample/statistics_diff.py Here is some beginning boilerplate: import torch sample_1, sample_2 = torch.ones((10,2)), torch.zeros((10,2)) Then the next part is where we pick up from the

问题 I wanted to create a distance proximity matrix for 10060 records/ points, where each record/point has 23 attributes using euclidean distance as metric. I wrote code using nested for loops to calculate distance between each point(leading to (n(n-1))/2) computations). It took a long time(about 8 minutes). When I used cdist it took so much lesser time( just 3 seconds !!! ). When I looked at the source code, the cdist also uses nested for loops and moreover it makes n^2 computations(which is