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.
Este exercício faz parte do curso
Anomaly Detection in Python
Instruções do exercício
- 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.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
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)