**Order Instructions/Description**

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):

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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).

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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

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