Find the index of the min value in a pdist condensed distance matrix
data-structures, matrix, numpy, python, scipy
Solution
We'll operate on the square form of the results. First, to exclude "New York is closest to New York" answers,
numpy.fill_diagonal(distances, numpy.inf)
Then, it's a simple `argmin` along an axis:
closest_points = distances.argmin(axis=0)
Problem
I have used `scipy.spatial.distance.pdist(X)` to calculate the euclidian distance metric between each pair of elements of the below list X: ``` X = [[0, 3, 4, 2], [23, 5, 32, 1], [3, 4, 2, 1], [33, 54, 5, 12]] ``` This returns a condensed distance matrix: ``` array([ 36.30426972, 3.87298335, 61.57109712, 36.06937759, 57.88782255, 59.41380311]) ``` For each element X, I need to find the index of the closest other element. Converting the condensed distance matrix to square form help visualize the results, but I can't figure out how to programmatically identify the index of the closest element X for each element in X. ``` array([[ 0. , 36.30426972, 3.87298335, 61.57109712], [ 36.30426972, 0. , 36.06937759, 57.88782255], [ 3.87298335, 36.06937759, 0. , 59.41380311], [ 61.57109712, 57.88782255, 59.41380311, 0. ]]) ``` I believe `argmin()` is the function to use, but I'm lost from here. Thanks for any help in advance.