Q.1 Lift the measures to distance between K objects. The input is a numpy array that...

Transcribed Image Text:

Q.1 Lift the measures to distance between K objects. The input is a numpy array that contains K rows and N columns. Compute pairwise distances (or similarities) and place them into a single distance matrix. You can assume that all values of the input matrix have the same type (that you must check before applying the measures). Q.2 Provide testing procedures for your solution in Question Select numpy vectors of at least length 10 of different types and show distance computations (the distance matrices you computed in Question ). Make sure that in your tests you cover all types of attributes (nominal, binary, ordinal, and numeric). Make sure to test the Minkowski distance for different values of h. Implement without assuming that all attributes of an object have the same type. Q.1 Lift the measures to distance between K objects. The input is a numpy array that contains K rows and N columns. Compute pairwise distances (or similarities) and place them into a single distance matrix. You can assume that all values of the input matrix have the same type (that you must check before applying the measures). Q.2 Provide testing procedures for your solution in Question Select numpy vectors of at least length 10 of different types and show distance computations (the distance matrices you computed in Question ). Make sure that in your tests you cover all types of attributes (nominal, binary, ordinal, and numeric). Make sure to test the Minkowski distance for different values of h. Implement without assuming that all attributes of an object have the same type.

Answer rating: 100% (QA)

Answer import numpy as np from scipyspatialdistance import cdist Function to compute pairwise distan... View the full answer