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gmm.py
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gmm.py
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#Expectation Maximisation Alrgorithm
import random as rnd
import math
from numpy import *
from matplotlib import pyplot as mp
#below is added for kmeans-------------------- ****************************************************************************
def distance(tupa, tupb):
''' Calculate the distance between two points '''
return math.sqrt(math.pow((tupa[0]-tupb[0]), 2) + math.pow((tupa[1]-tupb[1]), 2)) #euclidian distance
def assign(dp, means):
''' Take a data point(tuple), return the closest of the means '''
dists = [distance(dp, m) for m in means]
return dists.index(min(dists)) #return index centroid with smallest distance from dp
def updatemeans(dataset, assignments, k): #presumes 2 dimensions
'''calclate the Barycentre of the clusters in the assignments array'''
newcentroids = [[0,0]]*k #presumes two dimensions
for i in range(k):
nincluster = assignments.count(i)
if nincluster > 0: #Note: if no observations are assigned to centroid, centroid does not move
prior = 1 / nincluster ; sigma = [0,0] #presumes 2 dimensions
for x in range(len(assignments)):
if assignments[x] == i:
point = dataset[x]
sigma[0] += point[0] ; sigma[1] += point[1]
newcentroids[i] = [sigma[0]*prior, sigma[1]*prior] #arithmetic mean for 2 dimensions
return newcentroids
def squareerrorfunct(dataset, assignments, centroids, k):
sqe = 0
for i in range(k):
for x in range(len(assignments)):
if assignments[x] == i:
sqe += math.pow(distance(dataset[x], centroids[i]),2)
return sqe
def initialisekcentroids(k, dataset): #currently chooses k random centroids from data
'''Pick k initial centroids with Forgy method'''
centroids = [(0,0)] * k # initialise centroids as zero
for i in range(k): ###initialise means from random datapoints using the FOrgy method
centroids[i] = dataset[random.randint(0,len(dataset)-1)]
return centroids
#implement k-means:
def kMeansCluster(k, dataset):
'''Take a dataset (each entry[:-1] is an observation, entry[-1] is the class) and cluster into k clusters'''
centroids = initialisekcentroids(k, dataset) #this uses the Forgy method
oldassignments, newassignments = [], [1]
i = 0
while i <= 50 and newassignments != oldassignments: #this repeats until clusters dont change, could repeat until centroids dont move
oldassignments = newassignments
newassignments = []
for obs in dataset: #assign each datapoint to a cluster
newassignments.append(assign(obs , centroids))
#print(newassignments)
centroids = updatemeans(dataset,newassignments,k) #update the new centroid assignments based on the distance metrics over all cluster members
#print("Square error:", squareerrorfunct(dataset, newassignments, centroids, k))
i += 1
return centroids, newassignments, i
#---------------- change this part of GMM to make function GMM2
def GMM2(data):
'''GMM on dataset from ex2, return info for each class k=n'''
''' This functionaility is implemented in GMM itself with 3rd argument'''
theta = []
means, assignments, iterations = kMeansCluster(K, data)
for k in range(K):
theta.append([array(means[k]).reshape((2,1)) , matrix(cov([data[i] for i, x in enumerate(assignments) if x==k], rowvar=0)), assignments.count(k)/len(assignments)]) #means, covarmatrices, priors
print(theta)
#Iteration
pass # et cetera
#above was added for kmeans ---------- ***************************************************************************************
def plot_data(Gamma,theta):
#Calculating p(k|x)*p(x) = p(x|k)*p(k) and using that to compare the p(k|x) assuming p(x) is constant
klass_prob=[]
for point in range(len(Gamma)):
maxklassprob=0
maxklass=0
colors=[]
points_per_klass=[]
for i in range(len(theta)):
points_per_klass.append(0)
for klass in range(len(theta)):
p_of_k_by_x=Gamma[point][klass]*theta[klass][2]
#p_of_k_by_x=theta[klass][2]
if ( p_of_k_by_x >= maxklassprob ):
maxklass=klass
maxklassprob=p_of_k_by_x
points_per_klass[maxklass] += 1
klass_prob.append([data[point],maxklass])
#Define k no. of colors
for color in range(len(theta)):
colors.append(random.rand(3,1))
# Plotting
for point in klass_prob:
mp.scatter(point[0][0],point[0][1],c=colors[point[1]])
mp.show()
def GMM(data,K,km_init=False):
'''GMM on dataset from ex2, return info for each class k=n'''
theta = []
# Initialization of theta
if ( km_init ):
means, assignments, iterations = kMeansCluster(K, data)
for k in range(K):
theta.append([array(means[k]).reshape((2,1)) , matrix(cov([data[i] for i, x in enumerate(assignments) if x==k], rowvar=0)), assignments.count(k)/len(assignments)]) #means, covarmatrices, priors
else:
for k in range(K): #initialise for each cluster
prior = 1/K
theta.append(Initialise(data) + [prior] )
#Iterations
precision=0.01
maxiters=11
i=0
converging=False
old_mu_dists=[ 0 for every in range(K) ]
while( i<= maxiters and converging==False ):
Gamma=Expectation(data, theta)
newtheta=Maximisation(Gamma,theta)
# Check the distances between previous mu and new mu and decide on converging if
mu_distances=[]
#Calculate distances betweeen mu from old theta and mu from new theta
for klass in range(len(theta)):
mu_distances.append(linalg.norm(theta[klass][0] - newtheta[klass][0]))
# Criterion to stop the iterations - check the maximum movement of mus in consequtive theta's ,if the maximum movement is less
# than precision value and iterations are atleast half of the max iterations , then confirm it as converging.
if ( max(subtract(array(mu_distances),array(old_mu_dists))) <= precision and i >= maxiters//2 ):
converging=True
#print ( "maximum mu distance - ", max(subtract(array(mu_distances),array(old_mu_dists))) )
old_mu_dists=mu_distances
theta=newtheta
print ( "K - ", K, ",finished iteration - ",i,", converging -",converging)
i += 1
print ( "Mu s for K = ",K,"---",[ newtheta[every][0] for every in range(K) ] )
plot_data(Gamma,newtheta)
return
def Initialise(data):
#for class
rans = rnd.sample(range(0,N),5)
dps = [data[x] for x in rans]
cov = matrix(identity(2))
mean = [sum(dps[x][0] for x in range(5))/len(rans), sum(dps[x][1] for x in range(5))/len(rans) ]
return [array(mean).reshape((2,1)), cov]
def Expectation(data, theta):
posterior=[]
for point in data:
arrpoint=array(point).reshape((2,1))
den = sum( norm_pdf_multivariate(arrpoint ,theta[i][0], theta[i][1]) * theta[i][2] for i in range(K) )
xposterior=[]
for k in range(K):
num = norm_pdf_multivariate(arrpoint ,theta[k][0], theta[k][1]) * theta[k][2]
xposterior.append( num/den )
posterior.append(xposterior)
return(posterior)
def norm_pdf_multivariate(x, mu, sigma):
size = len(x)
if size == len(mu) and (size, size) == sigma.shape:
det = linalg.det(sigma)
if det == 0:
raise NameError("The covariance matrix can't be singular")
norm_const = 1.0/ ( math.pow((2*pi),float(size)/2) * math.pow(det,1.0/2) )
x_mu = matrix(x - mu)
inv = sigma.I
result = math.pow(math.e, -0.5 * (x_mu.T * inv * x_mu))
return norm_const * result
else:
raise NameError("The dimensions of the input don't match")
return
def Maximisation(Gamma,theta):
newtheta=[]
for k in range(K):
normalizer=1/sum(Gamma,axis=0)[k]
x_newmu = normalizer * sum ( [ data[i][0]*Gamma[i][k] for i in range(N) ] )
y_newmu = normalizer * sum( [ data[i][1]*Gamma[i][k] for i in range(N) ] )
new_mu=matrix([[x_newmu],[y_newmu]])
sumofmatr=matrix([[0,0],[0,0]])
for i in range(N):
sumofmatr = sumofmatr + Gamma[i][k]* dot(matrix(array(data[i]).reshape((2,1)))-new_mu,transpose(matrix(array(data[i]).reshape((2,1)))-new_mu))
new_covar = normalizer * sumofmatr
denom_newprior=sum([ theta[k][2]*Gamma[i][k] for k in range(K) ] )
newprior= (1/N)*sum ( [ theta[k][2]*Gamma[i][k] / sum([ theta[l][2]*Gamma[i][l] for l in range(K) ] ) for i in range(N) ] )
newtheta.append([new_mu,new_covar,newprior])
return newtheta
data = [[float(line.split('\t')[0]),float(line.split('\t')[1])] for line in open("R15.txt", "r").readlines()]
mp.plot( [ data[i][0] for i in range(len(data)) ], [ data[i][1] for i in range(len(data)) ] ,'bo')
mp.show()
N=len(data)
# For k=4 and k=15 , normal GMM with random theta initialization
K=4
GMM(data,K)
K=15
GMM(data,K)
## BONUS ###
K=4
GMM(data,K,True) # To call with km-initialisation
K=15
GMM(data,K,True) # To call with km-initialisation