# exercise 2.2.2 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")