Use the two functions you implemented to calculate the accuracy for every cluster and the whole algorithm, defined as above

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Use the two functions you implemented to calculate the accuracy for every cluster and the whole algorithm, defined as above. Implement the following function in analysis.py:

def accuracy(data, labels, centroids):
    """
    Calculate the accuracy of the algorithm. You 
    should use update_assignment and majority_count 
    (that you previously implemented)

    Arguments:
        data: a list of lists representing all data points
        labels: a list of ints representing all data labels
        centroids: the centroid dictionary

    Returns: a float representing the accuracy of the algorithm
    """

 

 

I have already implemented:

 

def update_assignment(data, labels, centroids):

closest = {}

for centroid in centroids:

closest[centroid] = []

for label, point in zip(labels, data):

centroid = assign_data(point, centroids)

closest[centroid].append(label)

return {centroid: points for centroid, points in closest.items()

if len(points) > 0}

 

 

def majority_count(labels):

maj = {}

for label in labels:

if label in maj.keys():

maj[label] += 1

else:

maj[label] = 1

v = list(maj.values())

return max(v)