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import math
import numpy as np
import numpy.matlib
import scipy.io as sio
from numpy import linalg as LA
import cvxopt
from cvxopt import solvers
from cvxopt import matrix
## Load Data feature set 2
mat1 = sio.loadmat('X_train_2.mat')
mat2 = sio.loadmat('Y_train_2.mat')
mat3 = sio.loadmat('X_test_2.mat')
mat4 = sio.loadmat('Y_test_2.mat')
X2m=np.asmatrix(mat1['X_final'])
Y_train_2=np.asmatrix(mat2['Y_final'])
X_test_2=np.asmatrix(mat3['X_test'])
Y_test_2=np.asmatrix(mat4['Y_test'])
Y_train_2=np.int32(Y_train_2)
Y_test_2=np.int32(Y_test_2)
#Load Data feature set 1
mat1 = sio.loadmat('X_train.mat')
mat2 = sio.loadmat('Y_train.mat')
mat3 = sio.loadmat('X_test.mat')
mat4 = sio.loadmat('Y_test.mat')
X1m=np.asmatrix(mat1['X_train'])
Y_train_1=np.asmatrix(mat2['Y_train'])
X_test_1=np.asmatrix(mat3['X_test'])
Y_test_1=np.asmatrix(mat4['Y_test'])
no_label=6
n_crrct=0
f_final1=np.zeros((len(X_test_1),no_label-1,no_label-1))
f_final2=np.zeros((len(X_test_2),no_label-1,no_label-1))
alpha_final1 = np.zeros((160,no_label-1,no_label-1))
alpha_final2 = np.zeros((160,no_label-1,no_label-1))
Y_predcted=np.matlib.zeros((len(X_test_1),1))
for label in range(1,no_label):
for label_2 in range(1,no_label):
if label != label_2:
print(label,label_2)
Y= []
X1 = []
X2 = []
for i in range(0,len(X1m)):
if Y_train_1[i]==label:
Y.append(1)
X1.append(X1m[i])
X2.append(X2m[i])
elif Y_train_1[i]==label_2:
Y.append(-1)
X1.append(X1m[i])
X2.append(X2m[i])
Y=np.transpose(np.asmatrix(Y))
l=np.count_nonzero(Y)
u=len(Y)-l
n=l+u
alpha2=np.matlib.zeros((l+u,1))
beta2=np.matlib.zeros((l,1))
K2=np.matlib.zeros((l+u,l+u))
Kx2=np.matlib.zeros((len(X_test_2),l+u))
J2=np.matlib.zeros((l,l+u))
L2=np.matlib.zeros((l+u,l+u))
W2=np.matlib.zeros((l+u,l+u))
D2=np.matlib.zeros((l+u,l+u))
Y_predcted2=np.matlib.zeros((len(X_test_2),1))
Q2=np.matlib.zeros((l,l))
Yd2=np.matlib.zeros((l,l))
f2=np.matlib.zeros((len(X_test_2),1))
sigma2=200
gamma_A2=100
gamma_I2=10 #gamma_I2=0 for supervised SVM
sigma1=0.05
gamma_A1=100
gamma_I1=10 #gamma_I1=0 for supervised SVM
alpha1=np.matlib.zeros((l+u,1))
beta1=np.matlib.zeros((l,1))
K1=np.matlib.zeros((l+u,l+u))
Kx1=np.matlib.zeros((len(X_test_1),l+u))
J1=np.matlib.zeros((l,l+u))
L1=np.matlib.zeros((l+u,l+u))
W1=np.matlib.zeros((l+u,l+u))
D1=np.matlib.zeros((l+u,l+u))
Y_predcted1=np.matlib.zeros((len(X_test_1),1))
Q1=np.matlib.zeros((l,l))
Yd1=np.matlib.zeros((l,l))
f1=np.matlib.zeros((len(X_test_1),1))
for i in range(0,l,1):
J1[i,i]=1
Yd1[i,i]=Y[i]
for i in range(0,n,1):
xi=X1[i]
for j in range(0,n,1):
xj=X1[j]
K1[i,j]=np.exp(-((xi-xj)*np.transpose(xi-xj))/(2*(sigma1**2)))
W1[i,j]=np.exp(-(LA.norm(xi-xj)))
d=np.sum(W1,axis=1)
for i in range(0,l+u,1):
D1[i,i]=d[i]
L1=D1-W1
for i in range(0,l,1):
J2[i,i]=1
Yd2[i,i]=Y[i]
for i in range(0,n,1):
xi=X2[i]
for j in range(0,n,1):
xj=X2[j]
K2[i,j]=np.exp(-((xi-xj)*np.transpose(xi-xj))/(2*(sigma2**2)))
W2[i,j]=np.exp(-(LA.norm(xi-xj)))
d=np.sum(W2,axis=1)
for i in range(0,l+u,1):
D2[i,i]=d[i]
L2=D2-W2
alpha_multi=0.5
L=(1-alpha_multi)*L1+(alpha_multi)*L2
Q2=Yd2*J2*K2*(LA.inv((2*gamma_A2*np.eye(n))+((2*gamma_I2/(n^2))*L*K2)))*np.transpose(J2)*Yd2
Q1=Yd1*J1*K1*(LA.inv((2*gamma_A1*np.eye(n))+((2*gamma_I1/(n^2))*L*K1)))*np.transpose(J1)*Yd1
y=(Y)
y=y.astype(np.double)
P = matrix(Q2)
q = -np.ones((l, 1))
q = q.astype(np.double)
cvx_q = matrix(q)
G = matrix(-np.eye(l))
h = matrix(np.zeros(l))
A = matrix(y.reshape(1, -1))
b = matrix(np.zeros(1))
solvers.options['show_progress'] = False
sol = solvers.qp(P, cvx_q, G, h, A, b)
beta2 = np.array(sol['x'])
alpha2=(LA.inv((2*gamma_A2*np.eye(n))+((2*gamma_I2/(n^2))*L*K2)))*np.transpose(J2)*Yd2*beta2
P = matrix(Q1)
solvers.options['show_progress'] = False
sol = solvers.qp(P, cvx_q, G, h, A, b)
beta1 = np.array(sol['x'])
alpha1=(LA.inv((2*gamma_A1*np.eye(n))+((2*gamma_I1/(n^2))*L*K1)))*np.transpose(J1)*Yd1*beta1
alpha_final1[:,label-1,label_2-1] = np.transpose(alpha1)
alpha_final2[:,label-1,label_2-1] = np.transpose(alpha2)
for i in range(0,len(X_test_1)):
f1=0
for j in range(0,l):
Kx1=np.exp(-((X_test_1[i]-X1[j])*np.transpose(X_test_1[i]-X1[j]))/sigma1)
f1=f1 + (alpha_final1[j,label-1,label_2-1]*Kx1)
f_final1[i,label-1,label_2-1]= f1
for i in range(0,len(X_test_2)):
f2=0
for j in range(0,l):
Kx2=np.exp(-((X_test_2[i]-X2[j])*np.transpose(X_test_2[i]-X2[j]))/sigma2)
f2=f2 + (alpha_final2[j,label-1,label_2-1]*Kx2)
f_final2[i,label-1,label_2-1]= f2
for i in range(0,len(X_test_1)):
f1_values = np.matrix.sum(np.asmatrix(f_final1[i,:,:]),1)
f2_values = np.matrix.sum(np.asmatrix(f_final2[i,:,:]),1)
lar1 = max(f1_values)
lar2 = max(f2_values)
if lar1>lar2:
Y_predcted[i]=[p+1 for p, z in enumerate(f1_values) if z == lar1][0]
else:
Y_predcted[i]=[p+1 for p, z in enumerate(f2_values) if z == lar2][0]
if Y_predcted[i]==Y_test_1[i]:
n_crrct=n_crrct+1
Percnt_crrct=100*n_crrct/float(len(X_test_1))
print(Percnt_crrct)