Implementing our algorithm for a single variable

Let's implement the k-means algorithm for a single variable. You will start with one dimensional vector, which has 20 records, as shown here:

data = [1,2,3,2,1,3,9,8,11,12,10,11,14,25,26,24,30,22,24,27] 
 
trace1 = go.Scatter( 
    x=data, 
    y=[0 for x in data], 
    mode='markers', 
    name='Data', 
    marker=dict( 
        size=12 
    ) 
) 
 
layout = go.Layout( 

title='1D vector',
)

traces = [trace1]

fig = go.Figure(data=traces, layout=layout)

plot(fig)

This will output following plot, as shown in this diagram:

Our aim is to find 3 clusters which are visible in the data. In order to start implementing the k-means algorithm, you need to initialize cluster centers by choosing random indexes, as shown here:

n_clusters = 3

c_centers = np.random.choice(X, n_clusters)

print(c_centers)

# [ 1 22 26]

Next, you need to calculate distances between each point and the cluster centers, so use this code:

deltas = np.array([np.abs(point - c_centers...