FAQ: K-Means Clustering - Implementing K-Means: Step 2

This community-built FAQ covers the “Implementing K-Means: Step 2” exercise from the lesson “K-Means Clustering”.

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This exercise can be found in the following Codecademy content:

Machine Learning Fundamentals

FAQs on the exercise Implementing K-Means: Step 2

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I found it easier to use list comprehensions than the method described in the hints:

Distance formula

def distance(a, b):
    p1 = a[0] - b[0]
    p2 = a[1] - b[1]
    return np.sqrt(p1**2 + p2**2)

Cluster labels for each point (either 0, 1, or 2)

labels = np.zeros(len(samples)) # unnecessary in this method but required to check the box

A function that assigns the nearest centroid to a sample

def assign_to_centroid(sample, centroid):
      distances = [distance(sample, cent) for cent in centroid]
      return np.argmin(distances)  # np.argmin returns index of array which holds minimum value

Assign the nearest centroid to each sample

labels = np.array([assign_to_centroid(sample, centroids) for sample in samples])

Print labels