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GMMimages.py
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GMMimages.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import os
import numpy as np
from sklearn.metrics import DetCurveDisplay, ConfusionMatrixDisplay, confusion_matrix
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt
import random
def extract_data(directory):
Xt_class = []
for filename in os.listdir(directory):
with open(os.path.join(directory, filename), 'r') as f:
data = f.readlines()
f.close()
for d in data:
Xt_class.append(np.array(list(map(float,d[:].split( )))))
return np.array(Xt_class)
# In[45]:
Xt = [0]*5
Xt[0] = extract_data(r"C:\Users\HEMA\OneDrive\Documents\PRML\Assignment_3\Features\coast\train")
Xt[1] = extract_data(r"C:\Users\HEMA\OneDrive\Documents\PRML\Assignment_3\Features\forest\train")
Xt[2] = extract_data(r"C:\Users\HEMA\OneDrive\Documents\PRML\Assignment_3\Features\highway\train")
Xt[3] = extract_data(r"C:\Users\HEMA\OneDrive\Documents\PRML\Assignment_3\Features\mountain\train")
Xt[4] = extract_data(r"C:\Users\HEMA\OneDrive\Documents\PRML\Assignment_3\Features\opencountry\train")
# In[24]:
import os
import numpy as np
def extract_data_dev(directory):
Xd_class = []
for filename in os.listdir(directory):
with open(os.path.join(directory, filename), 'r') as f:
temp = []
data = f.readlines()
f.close()
for d in data:
temp.append(np.array(list(map(float,d[:].split( )))))
Xd_class.append(np.array(temp))
return (Xd_class)
# In[46]:
Xd = []; numd= []
class_actual = []
Xd_class = extract_data_dev(r"C:\Users\HEMA\OneDrive\Documents\PRML\Assignment_3\Features\coast\dev")
Xd.extend(Xd_class)
class_actual.extend([1]*len(Xd_class))
numd.append(len(Xd_class))
Xd_class = extract_data_dev(r"C:\Users\HEMA\OneDrive\Documents\PRML\Assignment_3\Features\forest\dev")
Xd.extend(Xd_class)
class_actual.extend([2]*len(Xd_class))
numd.append(len(Xd_class))
Xd_class = extract_data_dev(r"C:\Users\HEMA\OneDrive\Documents\PRML\Assignment_3\Features\highway\dev")
Xd.extend(Xd_class)
class_actual.extend([3]*len(Xd_class))
numd.append(len(Xd_class))
Xd_class = extract_data_dev(r"C:\Users\HEMA\OneDrive\Documents\PRML\Assignment_3\Features\mountain\dev")
Xd.extend(Xd_class)
class_actual.extend([4]*len(Xd_class))
numd.append(len(Xd_class))
Xd_class = extract_data_dev(r"C:\Users\HEMA\OneDrive\Documents\PRML\Assignment_3\Features\opencountry\dev")
Xd.extend(Xd_class)
class_actual.extend([5]*len(Xd_class))
numd.append(len(Xd_class))
Xd = np.array(Xd)
# In[40]:
d = 23
for c in range(5):
for j in range(d):
mini = min(Xt[c][:,j])
maxi = max(Xt[c][:,j])
diff = maxi - mini
Xt[c][:,j] = (Xt[c][:,j]-mini)*100/diff
Xd[:numd[c],:,j] = (Xd[:numd[c],:,j]-mini)*100/diff
# In[11]:
def Kmeans_update(data,K,Niter = 10):
indices = np.random.choice(data.shape[0],K)
means = data[indices,:]
distances = cdist(data,means)
points = np.array([np.argmin(d) for d in distances])
for _ in range(Niter):
means = [sum([data[i] for i in np.where(points==j)[0]])/len(np.where(points==j)[0]) for j in range(K)]
distances = cdist(data,means)
points = np.array([np.argmin(d) for d in distances])
cov = []
pik = []
for j in range(K):
nk = len(np.where(points==j)[0])
cov.append(sum([(data[i].reshape(d,1)-means[j].reshape(d,1))@(data[i].reshape(d,1)-means[j].reshape(d,1)).T for i in np.where(points==j)[0]])/nk)
pik.append(nk/n)
return np.array(means),np.array(cov),pik
# In[12]:
def clusters_update(pik,means,cov):
gamma = np.zeros((n,K))
for i in range(n):
den = 0
for j in range(K):
t1 = (-0.5*(Xt[c][i].reshape(1,d)-means[j].reshape(1,d))@np.linalg.inv(cov[j])@(Xt[c][i].reshape(d,1)-means[j].reshape(d,1)))
den += pik[j]*np.exp(t1)/(np.linalg.det(cov[j]))**0.5
for j in range(K):
num = pik[j]*np.exp(-0.5*(Xt[c][i].reshape(1,d)-means[j].reshape(1,d))@np.linalg.inv(cov[j])@(Xt[c][i].reshape(d,1)-means[j].reshape(d,1)))/(np.linalg.det(cov[j]))**0.5
gamma[i,j] = num/den
nk = [sum([gamma[i,j] for i in range(n)]) for j in range(K)]
temp_means = [sum([gamma[i,j]*Xt[c][i] for i in range(n)])/nk[j] for j in range(K)]
temp_cov = [sum([gamma[i,j]*(Xt[c][i].reshape(d,1)-means[j].reshape(d,1))@(Xt[c][i].reshape(d,1)-means[j].reshape(d,1)).T for i in range(n)])/nk[j] for j in range(K)]
temp_pik = [nk[j]/n for j in range(K)]
print(sum(temp_pik))
return np.array(temp_pik), np.array(temp_means), np.array(temp_cov)
# In[59]:
k = 20; d=23 ; niter =7
means_all = []; cov_all = []; pik_all = []
for c in [0,1,2,3,4]:
n = len(Xt[c])
clusters_means,clusters_cov,clusters_pik = Kmeans_update(Xt[c],K)
for i in range(niter):
clusters_pik,clusters_means,clusters_cov = clusters_update(clusters_pik,clusters_means,clusters_cov)
means_all.append(clusters_means)
cov_all.append(clusters_cov)
pik_all.append(clusters_pik)
# In[65]:
def classification(X,pik,means,cov):
Class = []
b = 36
ans = -1
temp1 = 0
for i in range(len(X)):
for c in range(5):
temp2 = 0
for a in range(b):
temp2 += np.log(sum([pik[c][j]*np.exp(-0.5*(X[i][a]-means[c][j])[email protected](cov[c][j])@(X[i][a]-means[c][j]))/(np.linalg.det(cov[c][j]))**0.5 for j in range(K)]))
#print(temp2)
if temp2>temp1:
temp1 = temp2
ans = c
Class.append(ans+1)
temp1 = 0
return np.array(Class)
# In[69]:
Cdev = confusion_matrix(class_actual, class_pre)
ConfusionMatrixDisplay(confusion_matrix=Cdev,display_labels=['Coast','forest','highway','mountain','open country']).plot()
plt.title('Confusion Matrix:(DEV)')
plt.show()