Easy

Update Centroids

~15 mincode completion

Implement update_centroids(X, labels, k) that returns the new centroid matrix of shape (k, d).

Examples

Two clusters: centroids at their means

Input
update_centroids([[0, 0], [2, 0], [10, 0], [12, 0]], [0, 0, 1, 1], 2)
Output
[[1, 0], [11, 0]]

All points in one cluster

Input
update_centroids([[1, 1], [3, 3], [5, 5]], [0, 0, 0], 1)
Output
[[3, 3]]

Hints

Hint 1

The reduction runs down the columns, so pass .

Hint 2

Watch for this: mean over wrong axis.

Requirements

  • X: Data matrix of shape (n, d)

  • labels: Cluster assignment array of shape (n,), values in [0, k)

  • k: Number of clusters

  • Return Centroid matrix of shape (k, d).

Constraints

  • Allowed library: NumPy only

  • Time limit: 200 ms, Memory: 64 MB

Try similar problems(1)
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Where this shows up

~15 min

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Python
import numpy as np

def update_centroids(X: np.ndarray, labels: np.ndarray, k: int) -> np.ndarray:
    """
    Compute new centroids as the mean of assigned points.

    Args:
        X:      Data matrix of shape (n, d)
        labels: Cluster assignment array of shape (n,), values in [0, k)
        k:      Number of clusters

    Returns:
        Centroid matrix of shape (k, d).
    """
    # YOUR CODE HERE
    pass
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