def foward_error (n,m,ans,NN):
data=np.load('data.npy')
#from loadone1 import load_sample as load
w1=np.zeros((5,5,1,3,5))
w2=np.zeros((5,5,3,6,5))
wf1=np.zeros((7*7*2*3, 1024,5))
bf1=np.zeros((1024,5))
T=0
#m=np.array([0,3,6])
#M12,a12=load(data[:,:,m,12])
i=0
while i<5:
w,ww,Num=train_cov(data[:,:,m,n],200,3,8)
if Num==1:
w1[:,:,:,:,i]=w
w2[:,:,:,:,i]=ww
i+=1
for i in range (0,5):
wf1[:,:,i],bf1[:,i],Num=train_fcon(data[:,:,:,n],w1[:,:,:,:,i],w2[:,:,:,:,i],50,8)
if Num==1:
T=T+1
EE=np.zeros((8,5))
for j in range (0,5):
II=forward(data[:,:,:,n],w1[:,:,:,:,j],w2[:,:,:,:,j])
error01=II[1,:]-II[0,:]
error12=II[2,:]-II[1,:]
error34=II[4,:]-II[3,:]
error54=II[5,:]-II[4,:]
error67=II[7,:]-II[6,:]
error78=np.zeros((8,294))
for i in range (0,8):
error78[i,:]=II[8+i,:]-II[7,:]
O1=np.dot(error67.reshape(1,294),wf1[:,:,j])+bf1[:,j]
O2=np.zeros((8,1024))
for i in range (0,8):
O2[i,:]=np.dot(error78[i,:].reshape(1,294),wf1[:,:,j])+bf1[:,j]
E=np.zeros(8)
for i in range (0,8):
E[i]=abs(O2[i,:]-O1).sum()
EE[:,j]=E
N=np.zeros(5)
for i in range (0,5):
N[i]=np.argmin(EE[:,i])
print(np.argmin(np.average(EE,axis=1)))
print(np.argsort(np.average(EE,axis=1)))
print(np.average(EE,axis=1)[ans]/np.average(EE,axis=1).mean())
print(N)
print(T)
np.save('Data/'+str(NN)+'weight1_no_rotation_rerun_v.npy',w1)
np.save('Data/'+str(NN)+'weight2_no_rotation_rerun_v.npy',w2)
np.save('Data/'+str(NN)+'weightf_no_rotation_rerun_v.npy',wf1)
np.save('Data/'+str(NN)+'biasf_no_rotation_rerun_v.npy',bf1)
np.save('Data/'+str(NN)+'Error_no_rotation_rerun_v.npy',EE)
np.save('Data/'+str(NN)+'Termination_no_rotation_rerun_v.npy',T)
def foward_error (n,m,ans,N_str):
data=np.load('data_4.npy')
#from loadone1 import load_sample as load
w1=np.zeros((5,5,1,3,5))
w2=np.zeros((5,5,3,6,5))
wf1=np.zeros((180, 1024,5))
bf1=np.zeros((1024,5))
T=0
#m=np.array([0,3,6])
#M12,a12=load(data[:,:,m,12])
i=0
while i<5:
w,ww,Num=train_cov(data[:,:,m,n],200,3,8)
if Num==1:
w1[:,:,:,:,i]=w
w2[:,:,:,:,i]=ww
i+=1
for i in range (0,5):
wf1[:,:,i],bf1[:,i],Num=train_fcon(data[:,:,:,n],w1[:,:,:,:,i],w2[:,:,:,:,i],50,8)
if Num==1:
T=T+1
# EE=np.zeros((6,5))
# EE2=np.zeros((6,5))
# for j in range (0,5):
# II=forward(data[:,:,:,n],w1[:,:,:,:,j],w2[:,:,:,:,j])
# error01=II[1,:]-II[0,:]
# error02=II[2,:]-II[0,:]
# error23=np.zeros((6,180))
# error13=np.zeros((6,180))
# for i in range (1,7):
# error23[i-1,:]=II[2+i,:]-II[2,:]
# for i in range (1,7):
# error13[i-1,:]=II[2+i,:]-II[1,:]
# O1=np.dot(error01.reshape(1,180),wf1[:,:,j])+bf1[:,j]
# O2=np.zeros((6,1024))
# for i in range (0,6):
# O2[i,:]=np.dot(error23[i,:].reshape(1,180),wf1[:,:,j])+bf1[:,j]
# E=np.zeros(6)
# for i in range (0,6):
# E[i]=abs(O2[i,:]-O1).sum()
# EE[:,j]=E
# O3=np.dot(error02.reshape(1,180),wf1[:,:,j])+bf1[:,j]
# O4=np.zeros((6,1024))
# for i in range (0,6):
# O4[i,:]=np.dot(error13[i,:].reshape(1,180),wf1[:,:,j])+bf1[:,j]
# EEE=np.zeros(6)
# for i in range (0,6):
# EEE[i]=abs(O4[i,:]-O3).sum()
# EE2[:,j]=EEE
# N=np.zeros(5)
# for i in range (0,5):
# N[i]=np.argmin(EE[:,i])
# NN=np.zeros(5)
# for i in range (0,5):
# NN[i]=np.argmin(EE2[:,i])
# NNN=np.zeros(5)
# for i in range (0,5):
# NNN[i]=np.argmin(EE2[:,i]+EE[:,i])
# print('x direction error:',np.argsort(np.average(EE,axis=1)))
# print('y direction error:',np.argsort(np.average(EE2,axis=1)))
# print('normalized correct x direction error:',np.average(EE,axis=1)[ans]/np.average(EE,axis=1).mean())
# print('normalized correct y direction error:',np.average(EE2,axis=1)[ans]/np.average(EE2,axis=1).mean())
# print('sum error:',np.argsort(np.average(EE2+EE,axis=1)))
# print('normalized correct sum error:',np.average(EE2+EE,axis=1)[ans]/np.average(EE+EE2,axis=1).mean())
# print('number of correct trials, x:',N)
# print('number of correct trials, y:',NN)
# print(T)
np.save('Data/'+str(N_str)+'weight1_t.npy',w1)
np.save('Data/'+str(N_str)+'weight2_t.npy',w2)
np.save('Data/'+str(N_str)+'weightf_t.npy',wf1)
np.save('Data/'+str(N_str)+'biasf_t.npy',bf1)
# np.save('Data/'+str(N_str)+'Error.npy',EE)
# np.save('Data/'+str(N_str)+'Error.npy',EE2)
# np.save('Data/'+str(N_str)+'Termination.npy',T)