Calculating Pairwise Euclidean distances in a 3x3 array using NumPy
Write a NumPy program to create a 3x3 array with random values and calculate the pairwise Euclidean distance between each pair of rows.
Sample Solution:
Python Code:
import numpy as np
from scipy.spatial import distance
# Create a 3x3 array with random values
array = np.random.random((3, 3))
# Print the original array
print("Original Array:\n", array)
# Calculate pairwise Euclidean distances between each pair of rows
pairwise_distances = distance.cdist(array, array, 'euclidean')
# Print the pairwise distances
print("Pairwise Euclidean distances between each pair of rows:\n", pairwise_distances)
Output:
Original Array: [[0.47864642 0.98844776 0.46615728] [0.69647683 0.8741336 0.80914819] [0.17481078 0.88159549 0.2119323 ]] Pairwise Euclidean distances between each pair of rows: [[0. 0.42209072 0.41032164] [0.42209072 0. 0.79300566] [0.41032164 0.79300566 0. ]]
Explanation:
- Import NumPy and SciPy: Import the NumPy library for array operations and the distance module from SciPy for distance calculations.
- Create a Random 3x3 Array: Generate a 3x3 array filled with random values using np.random.random.
- Print the Original Array: Print the original 3x3 array for reference.
- Calculate Pairwise Euclidean Distances: Use distance.cdist with the metric 'euclidean' to compute the pairwise Euclidean distances between each pair of rows.
- Print the Pairwise Distances: Print the resulting pairwise Euclidean distances.
Python-Numpy Code Editor:
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