Paper, Order, or Assignment Requirements

I really need help with this computer science homework. The language is python, more specifically numpy. This problem I solved already, but you need to understand it to do the next one which is the one I need help on.

def update_assignments(num_clusters, X, centers):
“””
Returns the cluster assignment (number) for each data point
in X, computed as the closest cluster center.

Parameters
———-
num_clusters : int
The number of disjoint clusters (i.e., k) in
the X

X : numpy array of shape (m, 2)
An array of m data points in R^2.

centers : numpy array of shape (num_clusters, 2)
The coordinates for the centers of each cluster

Returns
——-
cluster_assignments : numpy array of shape (m,)
An array containing the cluster label assignments
for each data point in X. Each cluster label is an integer
between 0 and (num_clusters – 1).
“””

closest = []
for i in range(len(X)):
dist = []
for j in range(len(centers)):
dist.append(distance(X[i], centers[j]))
closest.append(dist.index(min(dist)))
cluster_assignments = np.array(closest)
return cluster_assignments

After you understand that, I need help on this one:

Now, we need to do the next step of the clustering algorithm: recompute the cluster centers based on which points are assigned to that cluster. Recall that the new centers are simply the two-dimensional means of each group of data points. A two-dimensional mean is calculated by simply finding the mean of the x coordinates and the mean of the y coordinates. Complete the update_parameters function to do this.

def update_parameters(num_clusters, X, cluster_assignment):
“””
Recalculates cluster centers running update_assignments.

Parameters
———-
num_clusters : int
The number of disjoint clusters (i.e., k) in
the X

X : numpy array of shape (m, 2)
An array of m data points in R^2

cluster_assignment : numpy array of shape (m,)
The array of cluster labels assigned to each data
point as returned from update_assignments

Returns
——-
updated_centers : numpy array of shape (num_clusters, 2)
An array containing the new positions for each of
the cluster centers
“””