To compute the pairwise Euclidean distance between elements of a NumPy array, excluding the distance with self, you can use the scipy.spatial.distance.pdist function. The pdist function calculates the pairwise distances between the rows of a given array. You can then reshape the output into a square distance matrix without the distances to self. Here's how you can do it:

python
import numpy as np from scipy.spatial.distance import pdist, squareform # Sample data as a 2D NumPy array data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]) # Compute pairwise Euclidean distance using pdist pairwise_distances = pdist(data) # Convert the output to a square distance matrix distance_matrix = squareform(pairwise_distances) # Set the diagonal values to a large value (e.g., infinity) to exclude distances to self np.fill_diagonal(distance_matrix, np.inf) print(distance_matrix)

In the example above, data is a 2D NumPy array where each row represents a data point. The pdist function computes the pairwise Euclidean distances between all rows of the array, excluding distances to self. The squareform function then reshapes the output to a square distance matrix.

The resulting distance_matrix will be a 2D NumPy array representing the pairwise Euclidean distances between the rows of data, excluding the distances to self (the diagonal values will be set to infinity).

Make sure to replace the data array with your actual data. The above code snippet assumes that you have already imported the necessary libraries (numpy and scipy.spatial.distance).

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