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import numpy as np
from dtuimldmtools import similarity
# Generate two data objects with M random attributes
M = 5
x = np.random.rand(1, M)
y = np.random.rand(1, M)
# Two constants
a = 1.5
b = 1.5
# Check the statements in the exercise
print(
"Cosine scaling: %.4f "
% (similarity(x, y, "cos") - similarity(a * x, y, "cos"))[0, 0]
)
print(
"ExtendedJaccard scaling: %.4f "
% (similarity(x, y, "ext") - similarity(a * x, y, "ext"))[0, 0]
)
print(
"Correlation scaling: %.4f "
% (similarity(x, y, "cor") - similarity(a * x, y, "cor"))[0, 0]
)
print(
"Cosine translation: %.4f "
% (similarity(x, y, "cos") - similarity(b + x, y, "cos"))[0, 0]
)
print(
"ExtendedJaccard translation: %.4f "
% (similarity(x, y, "ext") - similarity(b + x, y, "ext"))[0, 0]
)
print(
"Correlation translation: %.4f "
% (similarity(x, y, "cor") - similarity(b + x, y, "cor"))[0, 0]
)
print("Ran Exercise 2.2.2")