Feature-Based Classification

Problem

Classify a series by nearest centroid in [mean, standard deviation, OLS slope] feature space. Compute the feature vector of value and of each training series, then return 0 if it is at least as close to train0 as to train1, otherwise 1.

Distances are squared Euclidean between the three-element feature vectors.

Hint

A tie in distance goes to class 0.

Test cases

case 1

in: [[1,1,1],[1,2,4],[1,1,1]]

expected: 0

case 2

in: [[1,1,1],[1,2,4],[1.1,2.1,4.1]]

expected: 1

case 3

in: [[0,0,0,0],[10,10,10,10],[3,3,3,3]]

expected: 0

case 4

in: [[0,0,0,0],[10,10,10,10],[6,6,6,6]]

expected: 1

Starter codePython

def classify_by_features(train0, train1, value):
    # Your code here
    pass

4 test cases · run your code against them in the editor