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Unsupervised Learning
Figure 3: Randomly generated data, from three randomly selected centroids, plotted
We define the function closest_centroids() to assign each point to the centroid it
is closest to:
def closest_centroids(points, centroids):
distances = tf.reduce_sum(tf.square(tf.subtract(points,
centroids[:,None])), 2)
assignments = tf.argmin(distances, 0)
return assignments
We create another function move_centroids(). It recalculates the centroids such
that the sum of squared distances decreases:
def move_centroids(points, closest, centroids):
return np.array([points[closest==k].mean(axis=0) for k in
range(centroids.shape[0])])
Now we call these two functions iteratively for 100 iterations. We have chosen the
number of iterations arbitrarily; you can increase and decrease it to see the effect:
for step in range(iteration_n):
closest = closest_centroids(points, centroids)
centroids = move_centroids(points, closest, centroids)
In the following graph, you can see the final centroids after 100 iterations. We have
also colored the points based on which centroid they are closest to. The yellow points
correspond to one cluster (nearest the cross in its center), and the same is true for the
purple and green cluster points:
plt.scatter(points[:, 0], points[:, 1], c=closest, s=50, alpha=0.5)
plt.plot(centroids[:, 0], centroids[:, 1], 'kx', markersize=15)
plt.show()
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