5_kmeans_clustering.py

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from IPython import get_ipython

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#<h1> SIT 720 - Python Intro </h1>
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# #### Kmeans clustering in Python
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# Load required mdoules

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import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

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# Create sample dataset

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x = [1,5,1.5,8,1,9]
y=[2,8,1.8,8,0.6,11]

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# Plot data

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plt.scatter(x,y)
plt.show()

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# Now lets create a matrix X with x,y coordinates

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X = np.array([
    [1,2],
    [5,8],
    [1.5, 1.8],
    [8,8],
    [1,0.6],
    [9,11]
])

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# Initiate K-Means algorithm with 2 clusters

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kmeans_1 = KMeans(n_clusters=2)
kmeans_1.fit(X)

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# Now, we have fit the KMeans model to our data, X. The model will have identified 2 clusters, with 2 cluster centres (centroids). we can get this data as:

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centroids = kmeans_1.cluster_centers_
labels = kmeans_1.labels_

print(centroids)
print(labels)

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# Lets try to visualise the clusters by plotting them. The centroids will be marked as “X”

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colors = ['g.', 'r.', 'c.', 'y.']

for i in range(len(X)):
    print("coordinates: ", X[i], "label: ", labels[i])
    plt.plot(X[i][0], X[i][1], colors[labels[i]], markersize = 10)
    
#Visualise the centroids
plt.scatter(centroids[:, 0], centroids[:, 1], marker = "X", s = 150, linewidths = 1, zorder = 10 )
plt.show()


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import numpy as np


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x=np.array([[1,0],[0,-1]])
y=np.array([[-3,-2], [-4,1], [0,4], [4,1], [2,-3]])


z= np.dot(y,x)
print(z)