-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathscript.py
133 lines (109 loc) · 4.2 KB
/
script.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 14 16:58:59 2018
@author: Keidi Kapllani, Antonio Enas
"""
from facerec import *
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as ldah
import numpy as np
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
x_train, y_train, x_test, y_test = load_data()
[d,n] = x_train.shape
##Fisherface
#'''
#Vary M_pca and M_lda for fisherface and store predication accuracies
#'''
#cntr = 0
#m_pca = [i for i in range(52,417,30)]
#m_lda = [i for i in range(11,53,2)]
#accuracy = np.zeros((len(m_pca)*len(m_lda),3))
#y_knn = np.zeros((104,(len(m_pca)*len(m_lda))))
#
#for i in m_pca:
#
# for j in m_lda:
#
# [W, mu] = fisherfaces(x_train, y_train, i,j)
# x_final = np.dot((x_train-mu_pca).T,W)
# x_tst_proj = np.dot((x_test-mu).T,W)
# knn = KNeighborsClassifier(n_neighbors = 1)
# knn.fit(x_final, y_train.T)
# y_knn[:,cntr]= knn.predict(x_tst_proj)
# accuracy[cntr,0] = 100*accuracy_score(y_test.T, y_knn[:,cntr])
# accuracy[cntr,1] = i
# accuracy[cntr,2] = j
# cntr += 1
#PCA-LDA Ensemble
'''
Apply feature resampling and bootstrapping and create a commitee machine with majority voting
'''
k_range = range(5,50)
accuracy_av = np.zeros((60,3))
accuracy_ens = np.zeros((60,3))
for j in range(0,3):
cntr = 0
for k in range(2,100,5):
#Resampling
accuracy = np.zeros((k*2,))
y_knn = np.zeros((104,k*2))
[W, mu_pca] = pca(x_train, y_train, None)
subspaces, rn = resample_w_pca(W,10,k)
for i in range(0,k):
Wi= subspaces[i,:,:rn[i]]
x_pca = project(Wi,x_train,mu_pca).T
#w_lda = lda(x_pca,y_train,0)
kd = ldah(n_components=0, priors=None, shrinkage=None, solver='svd', store_covariance=True)
kd.fit(x_pca.T,y_train.T)
w_lda = kd.scalings_
eigen = np.dot(Wi,w_lda)
x_final = np.dot((x_train-mu_pca).T,eigen)
x_tst_proj = np.dot((x_test-mu_pca).T,eigen)
knn = KNeighborsClassifier(n_neighbors = 1)
knn.fit(x_final, y_train.T)
y_knn[:,i] = knn.predict(x_tst_proj)
accuracy[i] = 100*accuracy_score(y_test.T, y_knn[:,i])
#rec = np.dot(np.real(w_lda),x_pca)
#plt.imshow(np.reshape(np.real(eigen[:,2]),(46,56)).T,cmap = 'gist_gray')
##Bootstrapping
#y_knn = np.zeros((104,k_boot))
x_train_pca = np.dot((x_train-mu_pca).T,W).T
x_tst_pca = np.dot((x_test-mu_pca).T,W)
subfaces, y_sub, rn = resample_faces(x_train_pca,y_train,k)
for i in range(0,k):
subface = subfaces[i,:,:rn[i]]
kd = ldah(n_components=0, priors=None, shrinkage=None, solver='svd', store_covariance=True)
kd.fit(subface.T,y_sub[i,:rn[i]].T)
w_lda = kd.scalings_
x_final = np.dot(subface.T,w_lda)
x_tst_proj = np.dot(x_tst_pca,w_lda)
knn = KNeighborsClassifier(n_neighbors = 1)
knn.fit(x_final, y_sub[i,:rn[i]].T)
y_knn[:,k+i] = knn.predict(x_tst_proj)
accuracy[i+k] = 100*accuracy_score(y_test.T, y_knn[:,i])
y_ensemble,accuracy_ens[cntr,j] = maj_voting(y_knn,y_train,y_test)
accuracy_av[cntr,j]= accuracy.mean(axis=0)
cntr+=1
"""
Maj_voting doesnt currently work.
"""
y_ensemble,accuracy_ens = maj_voting(y_knn,y_train,y_test)
## Plot 52 faces ______________________________________________________________
#c = 1
#for i in range(0,416,8):
# plt.figure()
# plt.imshow(np.reshape(x_train[:,i],(46,56)).T,cmap = 'gist_gray')
# plt.title(f'Class {c}')
# c += 1
#
## Plot confusion matrix ______________________________________________________
## Compute confusion matrix
#cnf_matrix = confusion_matrix(y_test.T, y_test.T)
#np.set_printoptions(precision=2)
#
## Plot normalized confusion matrix
#plt.figure()
#plot_confusion_matrix(cnf_matrix, classes=[i for i in range(1,53)], normalize=True,
# title='Normalized confusion matrix')