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182 lines (170 loc) · 8.69 KB
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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 os
def grad(alpha,K,Y,L,J,l,u,gamma_I,gamma_A):
A=J*K*alpha
B=gamma_A*l*alpha
C=(gamma_I/(l+u)**2)*L*K*alpha
d=A+B+C-Y
return(d)
mat1 = sio.loadmat('C:\Users\sandippk\Desktop\Machine Learning\Fourth Feature\X_train.mat')
mat2 = sio.loadmat('C:\Users\sandippk\Desktop\Machine Learning\Fourth Feature\Y_train.mat')
mat3 = sio.loadmat('C:\Users\sandippk\Desktop\Machine Learning\Fourth Feature\X_test.mat')
mat4 = sio.loadmat('C:\Users\sandippk\Desktop\Machine Learning\Fourth Feature\Y_test.mat')
X1m=np.asmatrix(mat1['X_final'])
Y1m_trncomplte=np.asmatrix(mat2['Y_final'])
X1m_test=np.asmatrix(mat3['X_test'])
Y1m_tstcomplte=np.asmatrix(mat4['Y_test'])
mat1 = sio.loadmat('C:\Users\sandippk\Desktop\Machine Learning\Fifth Feature\X_train.mat')
mat2 = sio.loadmat('C:\Users\sandippk\Desktop\Machine Learning\Fifth Feature\Y_train.mat')
mat3 = sio.loadmat('C:\Users\sandippk\Desktop\Machine Learning\Fifth Feature\X_test.mat')
mat4 = sio.loadmat('C:\Users\sandippk\Desktop\Machine Learning\Fifth Feature\Y_test.mat')
X2m=np.asmatrix(mat1['X_final'])
Y2m_trncomplte=np.asmatrix(mat2['Y_final'])
X2m_test=np.asmatrix(mat3['X_test'])
Y2m_tstcomplte=np.asmatrix(mat4['Y_test'])
no_label=5
S1 = [1000000, 100000, 25000, 1e8, 500000 ]
S2 = [0.01]
G = [0.5]
Per = np.zeros((len(S1),len(S2),len(G)))
for s1 in range(0,len(S1)):
for s2 in range(0,len(S2)):
for g in range(0,len(G)):
f_final1=np.zeros((len(X1m_test),no_label-1,no_label-1))
f_final2=np.zeros((len(X1m_test),no_label-1,no_label-1))
## Selecting two classes loop
alpha_final1 = np.zeros((1200,no_label,no_label))
alpha_final2 = np.zeros((1200,no_label,no_label))
f_final=np.zeros((len(X1m_test),no_label-1,no_label-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 Y1m_trncomplte[i]==label:
Y.append(1)
X1.append(X1m[i])
X2.append(X2m[i])
elif Y1m_trncomplte[i]==label_2:
Y.append(-1)
X1.append(X1m[i])
X2.append(X2m[i])
Y=np.transpose(np.asmatrix(Y))
Y[1500:3000]=0
## Unlabeling
#Y[500:6000]=0
l=np.count_nonzero(Y)
u=len(X1)-l
sigma1 = S1[s1]
sigma2 = S2[s2]
#sigma1=float(1000000)
#sigma2=float(1)
L=np.matlib.zeros((l+u,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))
K1=np.matlib.zeros((l+u,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))
K2=np.matlib.zeros((l+u,l+u))
f1=np.matlib.zeros((len(Y),1))
f2=np.matlib.zeros((len(Y),1))
Y_predcted=np.matlib.zeros((len(X1m_test),1))
for i in range(0,l,1):
xi=X1[i]
for j in range(0,l,1):
xj=X1[j]
K1[i,j]=np.exp(-((xi-xj)*np.transpose(xi-xj))/sigma1)
W1[i,j]=np.exp(-(LA.norm(xi-xj))**2)
d1=np.sum(W1,axis=1)
for i in range(0,l+u,1):
D1[i,i]=d1[i]
L1=D1-W1
for i in range(0,l,1):
xi=X2[i]
for j in range(0,l,1):
xj=X2[j]
K2[i,j]=np.exp(-((xi-xj)*np.transpose(xi-xj))/sigma2)
W2[i,j]=np.exp(-(LA.norm(xi-xj))**2)
d2=np.sum(W2,axis=1)
for i in range(0,l+u,1):
D2[i,i]=d2[i]
L2=D2-W2
alpha_final=np.matlib.zeros((l+u,2))
alpha_multi=.5
L=(1-alpha_multi)*L1+(alpha_multi)*L2
## Load Data for view 1
for q in range (0,2):
if q==0:
X=X1
K=K1
else:
X=X2
K=K2
## Intialize Variables
n_crrct=0
gamma_I = G[g]
#gamma_I=float(.5)
gamma_A=float(0.005)
itrn=40000
error=1e-8
alpha=np.matlib.zeros((l+u,itrn))
alpha[:,0]=np.asmatrix(np.random.randn(l+u,1))
a=np.matlib.zeros((l+u,itrn))
J=np.matlib.zeros((l+u,l+u))
I=np.asmatrix(np.identity(l+u))
for i in range(0,l,1):
J[i,i]=1
## Accelerated Gradient Decent
for i in range(0,itrn-1):
a[:,i]=grad(alpha[:,i],K,Y,L,J,l,u,gamma_I,gamma_A)
alpha[:,i+1]=alpha[:,i]-(0.001)*a[:,i]
z1=alpha[:,i+1]-alpha[:,i]
z2=alpha[:,i]-alpha[:,i-1]
z1n=LA.norm(z1)
z2n=LA.norm(z2)
e=np.abs(z1n-z2n)
#print(i)
print(e)
#print(error)
if e<=error:
break
fnl=i+1
if q==0:
alpha_final1[:,label-1,label_2-1] = np.reshape(alpha[:,fnl],1200,0)
for i in range(0,len(X1m_test)):
f1=0
for j in range(0,l+u):
Kx1=np.exp(-((X1m_test[i]-X[j])*np.transpose(X1m_test[i]-X[j]))/sigma1)
f1=f1 + (alpha_final1[j,label-1,label_2-1]*Kx1)
f_final1[i,label-1,label_2-1]= f1
else:
alpha_final2[:,label-1,label_2-1] = np.reshape(alpha[:,fnl],1200,0)
for i in range(0,len(X1m_test)):
f2=0
for j in range(0,l+u):
Kx2=np.exp(-((X2m_test[i]-X[j])*np.transpose(X2m_test[i]-X[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(X1m_test)):
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]==Y1m_tstcomplte[i]:
n_crrct=n_crrct+1
Percnt_crrct=100*n_crrct/float(len(X1m_test))
print(Percnt_crrct)
Per[s1,s2,g]= Percnt_crrct