test

# code: imports and data ->
import scipy.cluster.hierarchy as hier from matplotlib import pyplot as plt from scipy.spatial.distance import pdist import numpy as np
import distance # <- you most likely need pip install distance here data = ?
labels = ?
# The levenshtein distance matrix ->
mat = np.zeros((len(data), len(data)), dtype=int) for i in range(0, len(data)):
for j in range(0, len(data)):
mat[i][j] = distance.levenshtein(data[i], data[j]) print(mat[i][j], end="")
print("\n")
mat = pdist(mat) # make an upper triangle matrix
# The hierarchy clustering ->
# here run scipy.cluster.hierarchy.linkage() on triangle matrix
z =?
fig = plt.figure(figsize=(25, 10))
# here run scipy.cluster.hierarchy.dendrogram() with the linkage z and labels = labels dn =?
plt.show()