Aim: Implement any one machine learning algorithm for classification / clustering task in BIG data Analytics - KMEAN

Steps for Execution:
`sudo python kmean.py`

Pyhton Code:
``````
import sys
import math
import random
import subprocess
def main():
# How many points are in our dataset?
num_points = 10
# For each of those points how many dimensions do they have?
dimensions = 2
# Bounds for the values of those points in each dimension
lower = 0
upper = 200
# The K in k-means. How many clusters do we assume exist?
num_clusters = 3
# When do we say the optimization has 'converged' and stop updating clusters
opt_cutoff = 0.5
# Generate some points
points = [makeRandomPoint(dimensions, lower, upper) for i in xrange(num_points)]
# Cluster those data!
clusters = kmeans(points, num_clusters, opt_cutoff)
# Print our clusters
for i,c in enumerate(clusters):
for p in c.points:
print " Cluster: ", i, "\t Point :", p
class Point:
'''
An point in n dimensional space
'''
def __init__(self, coords):
'''
coords - A list of values, one per dimension
'''
self.coords = coords
self.n = len(coords)
def __repr__(self):
return str(self.coords)
class Cluster:
'''
A set of points and their centroid
'''
def __init__(self, points):
'''
points - A list of point objects
'''
if len(points) == 0: raise Exception("ILLEGAL: empty cluster")
# The points that belong to this cluster
self.points = points
# The dimensionality of the points in this cluster
self.n = points.n
# Assert that all points are of the same dimensionality
for p in points:
if p.n != self.n: raise Exception("ILLEGAL: wrong dimensions")
# Set up the initial centroid (this is usually based off one point)
self.centroid = self.calculateCentroid()
def __repr__(self):
'''
String representation of this object
'''
return str(self.points)
def update(self, points):
'''
Returns the distance between the previous centroid and the new after
recalculating and storing the new centroid.
'''
old_centroid = self.centroid
self.points = points
self.centroid = self.calculateCentroid()
shift = getDistance(old_centroid, self.centroid)
return shift
def calculateCentroid(self):
'''
Finds a virtual center point for a group of n-dimensional points
'''
numPoints = len(self.points)
# Get a list of all coordinates in this cluster
coords = [p.coords for p in self.points]
# Reformat that so all x's are together, all y'z etc.
unzipped = zip(*coords)
# Calculate the mean for each dimension
centroid_coords = [math.fsum(dList)/numPoints for dList in unzipped]
return Point(centroid_coords)
def kmeans(points, k, cutoff):
# Pick out k random points to use as our initial centroids
initial = random.sample(points, k)
# Create k clusters using those centroids
clusters = [Cluster([p]) for p in initial]
# Loop through the dataset until the clusters stabilize
loopCounter = 0
while True:
# Create a list of lists to hold the points in each cluster
lists = [ [] for c in clusters]
clusterCount = len(clusters)
# Start counting loops
loopCounter += 1
# For every point in the dataset ...
for p in points:
# Get the distance between that point and the centroid of the first
# cluster.
smallest_distance = getDistance(p, clusters.centroid)
# Set the cluster this point belongs to
clusterIndex = 0
# For the remainder of the clusters ...
for i in range(clusterCount - 1):
# calculate the distance of that point to each other cluster's
# centroid.
distance = getDistance(p, clusters[i+1].centroid)
# If it's closer to that cluster's centroid update what we
# think the smallest distance is, and set the point to belong
# to that cluster
if distance < smallest_distance:
smallest_distance = distance
clusterIndex = i+1
lists[clusterIndex].append(p)
# Set our biggest_shift to zero for this iteration
biggest_shift = 0.0
# As many times as there are clusters ...
for i in range(clusterCount):
# Calculate how far the centroid moved in this iteration
shift = clusters[i].update(lists[i])
# Keep track of the largest move from all cluster centroid updates
biggest_shift = max(biggest_shift, shift)
# If the centroids have stopped moving much, say we're done!
if biggest_shift < cutoff:
print "Converged after %s iterations" % loopCounter
break
return clusters
def getDistance(a, b):
''' Euclidean distance between two n-dimensional points.
Note: This can be very slow and does not scale well
'''
if a.n != b.n:
raise Exception("ILLEGAL: non comparable points")
ret = reduce(lambda x,y: x + pow((a.coords[y]-b.coords[y]), 2),range(a.n),0.0)
return math.sqrt(ret)
def makeRandomPoint(n, lower, upper):
'''Returns a Point object with n dimensions and values between lower and upper in each of those dimensions'''
p = Point([random.uniform(lower, upper) for i in range(n)])
return p
if __name__ == "__main__":
main()

``````