human
Solve the following python programming problem given the following code:
# First lets calculate the distance matrix on all of these dudes. :)
import scipy.spatial
j_dist_matrix = scipy.spatial.distance.pdist(j_avg_train, 'euclidean')

cache = {}
condensed_idx = lambda i,j,n: i*n + j - i*(i+1)/2 - i - 1
def getid(ii,jj):
    i = min(ii,jj)
    j = max(ii,jj)
    return condensed_idx(i,j,len(j_avg_train))

def get_k_classifier(k):
    print(k)
    def knn_score(user, joke):
        # get k closest neighboors
        if user not in cache:
            cache[user] = np.argsort([j_dist_matrix[getid(user,j)]*(j != user) for j in range(len(j_avg_train))])
        
        neighbors = cache[user][:k]
        return np.mean(np.array(list(map(lambda i: j_avg_train[i][joke], neighbors))))

    return knn_score
    

ks = [10, 100, 1000]
error = []
for k in ks:
    error.append(validate(get_k_classifier(k)))
plt.plot(ks, error)
Given the above code, generate some markdown instructions for it.
assistant
#Here is Python program to calculate the distance matrix on all of these dudes.

k-Nearest Neighboors.
We do a k-nearest neighboors approach and take the average of those nearest neighboors. Supervised power in the aggreegate that is the crowd.