Finding the euclidean distance manually
Euclidean distance is the most popular distance metric in statistics. Its popularity mainly comes from the fact that it is intuitive to understand. It is the Pythagorean theorem applied in Cartesian coordinates.
Practice calculating it with NumPy manually, which is already loaded under its standard alias np.
Cet exercice fait partie du cours
Anomaly Detection in Python
Instructions
- Subtract
MfromN(or vice versa), square the results, and save them intosquared_diffs. - Calculate the sum of the differences into
sum_diffs. - Find the square root of the sum to find the final distance—
dist_MN.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
M = np.array([14, 17, 18, 20, 14, 12, 19, 13, 17, 20])
N = np.array([63, 74, 76, 72, 64, 75, 75, 61, 50, 53])
# Subtract M from N and square the result
squared_diffs = ____
# Calculate the sum
sum_diffs = ____
# Find the square root
dist_MN = ____
print(dist_MN)