def rae_encoding(vect, w, w_size, h_vect, wp):
vect21={}
vect22={}
o21={}
w1=w['w1']
w2=w['w2']
for i in range(len(h_vect)):
temp1=0.0
# print w_size + i
for j in range(len(h_vect[i])):
if h_vect[i][j] < w_size:
# print '\tv->h1\t',str(h_vect[i][j])+'->'+str(w_size+i), wp[i][j]
temp1 += np.dot(w1[wp[i][j]],vect[h_vect[i][j]])
else:
# print '\th2->h1\t',str(h_vect[i][j]),'w2`'
temp11 = np.dot(w2.transpose(),vect[h_vect[i][j]])
o21[h_vect[i][j]] = temp11
temp12 = sigmoid(temp11)
vect22[h_vect[i][j]] = temp12
# print '\th1->h1\t', str(h_vect[i][j]) + '->' + str(w_size + i), wp[i][j]
temp1 += np.dot(w1[wp[i][j]],temp12)
temp12 = sigmoid(temp1)
vect21[w_size+i] = temp12
temp2 = np.dot(w2,temp12)
# print '\th1->h2\t','w2'
vect[w_size+i] = sigmoid(temp2)
# print
return vect21, vect22, o21
def rae_decoding(vect1, w1, w2, w_size, h_vect, wp):
vect1_ = {}
vect21_ = {}
vect22_ = {}
o_ = {2 * w_size - 1:np.nan}
o21_ = {}
vect1_[2 * w_size - 1] = vect1[2 * w_size - 1]
for i in reversed(range(len(h_vect))):
to2 = np.dot(w2.transpose(),vect1_[w_size + i])
# print w_size + i
o21_[w_size+i] = to2
# print '\th2->h1\t','w2`'
t2 = sigmoid(to2)
vect22_[w_size+i] = t2
for j in range(len(h_vect[i])):
if h_vect[i][j] < w_size:
to1 = np.dot(w1[wp[i][j]].transpose(),t2)
o_[h_vect[i][j]]=to1
# print '\th1->v\t',str(w_size+i)+'->'+str(h_vect[i][j]),wp[i][j]
vect1_[h_vect[i][j]] = sigmoid(to1)
else:
to1=np.dot(w1[wp[i][j]].transpose(), t2)
o_[h_vect[i][j]] = to1
# print '\th1->h1\t',str(w_size+i)+'->'+str( h_vect[i][j]),wp[i][j]
t1 = sigmoid(to1)
vect21_[h_vect[i][j]] = t1
to2 = np.dot(w2, t1)
# print '\th1->h2\t', h_vect[i][j],'w2'
vect1_[h_vect[i][j]] = sigmoid(to2)
return vect1_, o_, vect21_, vect22_, o21_
def rae_encoding(vect, w, w_size, h_vect, wp):
for i in range(len(h_vect)):
temp1 = 0.0
# print w_size+i
for j in range(len(h_vect[i])):
# print '\t',h_vect[i][j],
temp1 += np.dot(w[wp[i][j]], vect[h_vect[i][j]])
# print
vect[w_size + i] = sigmoid(temp1)
return
def rae_decoding(vect, o, w, w_size, h_vect, wp):
vect_ = {}
vect_[2 * w_size - 1] = vect[2 * w_size - 1]
o[2 * w_size - 1] = np.nan
for i in reversed(range(len(h_vect))):
# print w_size+i
for j in range(len(h_vect[i])):
# print '\t',h_vect[i][j],
o[h_vect[i][j]] = np.dot(w[wp[i][j]].transpose(),
vect_[w_size + i])
vect_[h_vect[i][j]] = sigmoid(o[h_vect[i][j]])
# print
return vect_
def gradiant2(vect21, vect22, o21, vect21_, vect22_, o21_, w_size, vect1_):
grad2 = []
cost2 = []
v=[]
vect21_[w_size*2-1]=vect21[w_size*2-1]
vect22[w_size * 2 - 1] = vect22_[w_size * 2 - 1]
o21[w_size*2-1] = o21_[w_size*2-1]
# print "encoding grad"
for i in vect22:
# print '\t',i
tcost = np.power(vect21[i] - vect22[i],2)
cost2.append(tcost)
grad2.append(tcost*sigmoid(o21[i]))
v.append(vect1_[i])
# print "decoding grad"
for i in vect22_:
# print '\t',i
tcost = np.power(vect21_[i] - vect22_[i],2)
cost2.append(tcost)
grad2.append(tcost * sigmoid(o21_[i]))
v.append(vect1_[i])
grad2=grad2[:-1]
cost2=cost2[:-1]
return grad2, cost2, v
