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The homework for this week consisted of implementing in Python an algorithm called k-means clustering, that is an automatized way of slicing a dataset into a pre-defined number of groups or clusters.

Here is the code in GitHub and I copied it below.

This was a good challenge, mainly because of the use of Python (getting used to the ways of looping), numpy (making sure I’m using the right dimensions) and matplotlib (figuring out how to use it). I tried to keep calm and to build one functional block at a time. At some point I had some problem with assignments between numpy arrays – apparently when copying them we have to use the copyto() function, otherwise a reference mess starts to appear (similar to the case when working with PVectors in Processing or p5.Vector in p5.js) .

I used randomly generated points, but now that I have this I want to try it with another dataset that makes more sense.

I was able to create a figure for each iteration of the process, and afterwards I created animated GIFs showing three different examples:

500 points and 8 clusters

1000 points and 5 clusters

100 points and 8 clusters

Result of the 500 points, 8 clusters case

Here’s my code:

import numpy as np
import matplotlib.pyplot as plt
N = 500 # Number of data points
D = 2 # Number of dimensions of data points
C = 8 # Number of clusters
pointSize = 3 # Data point size in the plot
X = np.random.random((N,D)) # Set of data points
V = np.random.random((C,D)) # Set of cluster centers
clusterColors = np.random.random( (C,3) ) # Colors for each cluster
assignments = np.random.randint(0,C, N)# List of correspondences between X and V
figCount = 0
# Return distance between two points
def distance(p1,p2):
    # calculate vector difference
    difference = p2-p1
    # calculate magnitude of difference (i.e. Euclidean distance)
    return np.linalg.norm(difference)
# Return index of closest cluster center
def closestClusterCenter(p):
    # Prepare variables of minimum distance
    minDistance = 10
    minIndex = 0
    for index, clusterCenter in enumerate(V):
        d = distance(p,clusterCenter)
        if d < minDistance:
            minDistance = d
            minIndex = index
    return minIndex
# Check distances to cluster centers and updates assignments
# Returns true if there was a change in assignments
def updateAssignments():
    # Store a copy of the assignments before the process
    originalAssignments = np.zeros(N)
    # For each point with index in X...
    for index,point in enumerate(X):
        # get the closest cluster center
        c = closestClusterCenter(point)
        # Assign it to the assignments list
        assignments[index] = c

    # Returns True if there was a change
    return not np.array_equal(originalAssignments, assignments)
# Make cluster centers the centroid of the points that belong to it
def updateClusterCenters():
    # Count how many points are there per cluster center
    counts = np.zeros(C)
    # Initialize a place where to sum the points
    sums = np.zeros( (C, D) )
    # For each point with index in X
    for index, point in enumerate(X):
        # Get currently assigned cluster center index
        ccIndex = assignments[index]
        # Increment counter for that cluster center
        counts[ccIndex] += 1
        # Vector sum of the points in that cluster
        sums[ccIndex] += point
    # We can know calculate the centroids
    for index, clusterCenter in enumerate(V):
        # Calculate centroid
        newClusterCenter = sums[index] / counts[index]
        # Copy new cluster center to cluster center
        np.copyto(clusterCenter, newClusterCenter)
def plot():
    # For each point with index in X...
    for index, point in enumerate(X):
        # Get the cluster it belongs to
        clusterNumber = int(assignments[index])
        # Get the corresponding color
        color = clusterColors[ clusterNumber ]
        # Add to the scatter plot with the specific color
        plt.scatter(point[0],point[1], c=color, s=pointSize)
    # For each cluster center...
    for index, clusterCenter in enumerate(V):
        # Get corresponding color
        color = clusterColors[index]
        # Add to the scatter plot with a star
        plt.scatter(clusterCenter[0], clusterCenter[1], c=color, marker='*')
    # Generate a name for the image
    figname = 'Figure%02d.png' % (figCount)
    # Save it as an image
# Repeat the process of updating assignments while there are changes
while updateAssignments():
    # After updating assignments, update the cluster centers
    # Make a plot
    figCount += 1
    print( "Iteration %d..." % figCount )
# Show the plot