[SOLVED] 程序代写 COMP20008 2021S2 workshop week 10 Answers¶

workshop-week10-2021S2

COMP20008 2021S2 workshop week 10 Answers¶

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Code version for Question 1¶

import numpy as np
from sklearn.cluster import KMeans

# Explicitly setting initial points to match those given in Q1 – normally you would not do this for KMeans.
# It’s generally better to let sklearn handle the initialisation and set a fixed random_state if you need reproducability.
points = np.array([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]])
initials=np.array([[1],[2]])

clusters = KMeans(n_clusters=2, init=initials).fit(points)
print(clusters.cluster_centers_)
print(clusters.labels_)

[0 0 0 0 1 1 1 1 1 1]

C:UserschrisAnaconda3libsite-packagessklearncluster_kmeans.py:984: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.
self._check_params(X)

Code version for Question 2¶

###Question 2
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.image as mpimg
%matplotlib inline

from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import pdist, squareform

inputs = np.array([[i] for i in range(1,11)])

d = pdist(inputs, ‘euclidean’)
print(squareform(d))
print(inputs)

[[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
[1. 0. 1. 2. 3. 4. 5. 6. 7. 8.]
[2. 1. 0. 1. 2. 3. 4. 5. 6. 7.]
[3. 2. 1. 0. 1. 2. 3. 4. 5. 6.]
[4. 3. 2. 1. 0. 1. 2. 3. 4. 5.]
[5. 4. 3. 2. 1. 0. 1. 2. 3. 4.]
[6. 5. 4. 3. 2. 1. 0. 1. 2. 3.]
[7. 6. 5. 4. 3. 2. 1. 0. 1. 2.]
[8. 7. 6. 5. 4. 3. 2. 1. 0. 1.]
[9. 8. 7. 6. 5. 4. 3. 2. 1. 0.]]

from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np

# print(squareform(d))
#At each iteration, the algorithm must update the distance matrix to reflect the distance of the newly formed cluster u with the remaining clusters in the forest.
hc1 = linkage(d, ‘single’) # min
dendrogram(hc1, labels=inputs)
plt.show()

C:UserschrisAnaconda3libsite-packagesmatplotlibtext.py:1165: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
if s != self._text:

hc2 = linkage(d, ‘average’)
dendrogram(hc2, labels=inputs)
plt.show()

hc3 = linkage(d, ‘complete’) # max
dendrogram(hc3, labels=inputs)
plt.show()

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