def SNF_ORIGINAL(Ws, param):
""" main function in SNFtools
input : param -- K : k nearest neighbour
+- T : iterations
return unified similariy matrix
"""
T = param.iterate
# Ps, and Ss used for store P^{v} and S^{v} -- initialized
Ps = []
Ss = []
for W in Ws:
Ss.append(mO.kernelMatrix(W, param))
Ps.append(mO.statusMatrix(W))
Ps = np.array(Ps)
Ss = np.array(Ss)
for iterate in range(T):
for idx in range(Ps.__len__()):
Ps_diag = np.diag(np.diag(Ps[idx]))
Ps[idx] = np.matrix(Ss[idx]) * np.matrix(np.add.reduce(Ps) - Ps[idx]) / (Ps.__len__() - 1) * np.matrix(Ss[idx]).transpose()
Ps[idx] = normalized(Ps[idx])
Ps[idx] = Ps[idx] - np.diag(np.diag(Ps[idx])) + Ps_diag
P = np.matrix(np.add.reduce(Ps)) / Ps.__len__()
return (P + P.transpose()) / 2 # ensure affinity matrix is symmetrical
def SNF_GPU(Ws, param):
""" main function in SNFtools
input : param -- K : k nearest neighbour
+- T : iterations
return unified similariy matrix
"""
T = param.iterate
# Ps, and Ss used for store P^{v} and S^{v} -- initialized
Ps = []
Ss = []
for W in Ws:
Ss.append(mO.kernelMatrix(W, param))
Ps.append(mO.statusMatrix(W))
Ps = np.array(Ps)
Ss = np.array(Ss)
s_Ps = theano.shared(Ps, borrow=True)
s_Ss = theano.shared(Ss, borrow=True)
Fs = []
for idx in range(len(Ws)):
Fs.append(theano.function([], (s_Ss[idx] * (theano.tensor.sum(s_Ps) - s_Ps[idx]) / (len(Ws) - 1) * s_Ss[idx].transpose())))
print "compile done"
for iterate in range(T):
for idx in range(len(Ws)):
Ps_diag = np.diag(np.diag(Ps[idx]))
# Ps[idx] = Ss[idx] * (np.add.reduce(Ps) - Ps[idx]) / (len(Ws) - 1) * Ss[idx].transpose()
# Ps[idx] = Ss[idx] * (np.sum(Ps) - Ps[idx]) / (len(Ws) - 1) * Ss[idx].transpose()
#Ps[idx] = (s_Ss[idx] * (theano.tensor.sum(s_Ps) - s_Ps[idx]) / (len(Ws) - 1) * s_Ss[idx].transpose() ).eval()
Ps[idx] = Fs[idx]()
Ps[idx] = normalized(Ps[idx])
Ps[idx] = Ps[idx] - np.diag(np.diag(Ps[idx])) + Ps_diag
P = np.matrix(np.mean(Ps, axis=0))
return (P + P.transpose()) / 2 # ensure affinity matrix is symmetrical
def SNF_SPARSE(Ws, param):
""" main function in SNFtools
input : param -- K : k nearest neighbour
+- T : iterations
return unified similariy matrix
"""
T = param.iterate
# Ps, and Ss used for store P^{v} and S^{v} -- initialized
sPs = []
sSs = []
for W in Ws:
k = np.int(.9 * W.shape[1])
S = mO.kernelMatrix(W, param)
P = mO.statusMatrix(W)
S[S < np.partition(S, k, axis=1)[:,k]] = 0
P[P < np.partition(P, k, axis=1)[:,k]] = 0
sS = scipy.sparse.dia_matrix(S)
sP = scipy.sparse.dia_matrix(P)
sSs.append(sS)
sPs.append(sP)
Ps = np.array(sPs)
Ss = np.array(sSs)
for iterate in range(T):
for idx in range(Ps.__len__()):
#Ps_diag = Ps[idx].multiply(scipy.sparse.eye(Ps[idx].shape[0], Ps[idx].shape[1]))
Ps_diag = scipy.sparse.diags([Ps[idx].data[Ps[idx].offsets[0]:(Ps[idx].shape[0]+Ps[idx].offsets[0])]], [0])
#print Ps_diag
Ps[idx] = Ss[idx] * (np.add.reduce(Ps) - Ps[idx]) / (Ps.__len__() - 1) *Ss[idx].transpose()
#Ps[idx] = normalized(Ps[idx])
Ps[idx] = Ps[idx].multiply(scipy.sparse.dia_matrix(( 1 / Ps[idx].sum(axis=1))))
Ps[idx] = Ps[idx] - scipy.sparse.diags([Ps[idx].data[Ps[idx].offsets[0]:(Ps[idx].shape[0]+Ps[idx].offsets[0])]], [0]) + Ps_diag
sP = (np.add.reduce(Ps)) / Ps.__len__()
return (sP + sP.transpose()) / 2 # ensure affinity matrix is symmetrical
def SNF_GPU_NOSHARE(Ws, param):
""" main function in SNFtools
input : param -- K : k nearest neighbour
+- T : iterations
return unified similariy matrix
"""
T = param.iterate
# Ps, and Ss used for store P^{v} and S^{v} -- initialized
Ps = []
Ss = []
for W in Ws:
S = np.matrix(mO.kernelMatrix(W, param))
Ss.append(S)
P = mO.statusMatrix(W)
Ps.append(P)
Ps = np.array(Ps)
s_P = theano.tensor.fmatrix('s_P')
s_S = theano.tensor.fmatrix('s_S')
F = theano.function([s_P, s_S], theano.tensor.dot(theano.tensor.dot(s_S, (theano.tensor.sum(Ps, axis=0) - s_P) / (len(Ws) - 1)), s_S.T))
print "compile done"
for iterate in range(T):
for idx in range(len(Ws)):
Ps_diag = np.diag(np.diag(Ps[idx]))
Ps[idx] = F(Ps[idx], Ss[idx])
Ps[idx] = normalized(Ps[idx])
Ps[idx] = Ps[idx] - np.diag(np.diag(Ps[idx])) + Ps_diag
P = np.matrix(np.mean(Ps, axis=0))
return (P + P.transpose()) / 2 # ensure affinity matrix is symmetrical
def SNF_GPU2(Ws, param):
""" main function in SNFtools
input : param -- K : k nearest neighbour
+- T : iterations
return unified similariy matrix
"""
T = param.iterate
# Ps, and Ss used for store P^{v} and S^{v} -- initialized
Ps = []
Ss = []
shared_Ps = []
shared_Ss = []
for W in Ws:
S = mO.kernelMatrix(W, param)
Ss.append(S)
shared_Ss.append(theano.shared(S, borrow=True))
P = mO.statusMatrix(W)
Ps.append(P)
shared_Ps.append(theano.shared(P, borrow=True))
Ps = np.array(Ps)
Ss = np.array(Ss)
s_P = theano.tensor.fmatrix('s_P')
s_S = theano.tensor.fmatrix('s_S')
GetPsDiag = []
UpdatePs = []
NormalizePs = []
AdjustPs = []
prev, now = theano.tensor.fmatrices('prev','now')
for idx in range(len(Ws)):
GetPsDiag.append(
theano.function([],
theano.tensor.diag(theano.tensor.diag(shared_P)),
)
)
UpdatePs.append(
theano.function([],
(),
updates=[(shared_Ps[idx], s_S*(theano.tensor.sum(shared_Ps) - s_P)/(len(Ws) - 1) * s_S.transpose())],
givens=[(s_P, shared_Ps[idx]), (s_S, shared_Ss[idx])]
)
)
W = W / np.add.reduce(W, axis=1).transpose()
NormalizePs.append(
theano.function([],
(),
updates=[(shared_Ps[idx], s_P / theano.tensor.sum(s_P, axis=1).transpose())],
givens=[(s_P, shared_Ps[idx])]
)
)
AdjustPs.append(
theano.function([prev, now],
(),
updates=[(shared_Ps[idx], s_P - now + prev)],
givens=[(s_P, shared_Ps[idx])]
)
)
GetPsDiag.append(
theano.function([],
theano.tensor.diag(theano.tensor.diag(s_P)),
givens=[(s_P, shared_Ps[idx])]
)
)
UpdatePs.append(
theano.function([],
(),
updates=[(shared_Ps[idx], s_S*(theano.tensor.sum(shared_Ps) - s_P)/(len(Ws) - 1) * s_S.transpose())],
givens=[(s_P, shared_Ps[idx]), (s_S, shared_Ss[idx])]
)
)
W = W / np.add.reduce(W, axis=1).transpose()
NormalizePs.append(
theano.function([],
(),
updates=[(shared_Ps[idx], s_P / theano.tensor.sum(s_P, axis=1).transpose())],
givens=[(s_P, shared_Ps[idx])]
)
)
AdjustPs.append(
theano.function([prev, now],
(),
updates=[(shared_Ps[idx], s_P - now + prev)],
givens=[(s_P, shared_Ps[idx])]
)
)
print "compile done gpu2"
"""
for iterate in range(T):
for idx in range(len(Ws)):
Ps_diag = np.diag(np.diag(Ps[idx]))
# Ps[idx] = Ss[idx] * (np.add.reduce(Ps) - Ps[idx]) / (len(Ws) - 1) * Ss[idx].transpose()
# Ps[idx] = Ss[idx] * (np.sum(Ps) - Ps[idx]) / (len(Ws) - 1) * Ss[idx].transpose()
#Ps[idx] = (s_Ss[idx] * (theano.tensor.sum(s_Ps) - s_Ps[idx]) / (len(Ws) - 1) * s_Ss[idx].transpose() ).eval()
Ps[idx] = Fs[idx](Ps[idx], Ss[idx])
#normalize
Ps[idx] = normalized(Ps[idx])
Ps[idx] = Ps[idx] - np.diag(np.diag(Ps[idx])) + Ps_diag
"""
for iterate in range(T):
#print shared_Ps[0].get_value()
for idx in range(len(Ws)):
prev_P_diag = GetPsDiag[idx]()
UpdatePs[idx]()
NormalizePs[idx]()
now_P_diag = GetPsDiag[idx]()
AdjustPs[idx](prev_P_diag, now_P_diag)
P = np.matrix(np.mean([p.get_value() for p in shared_Ps], axis=0))
return (P + P.transpose()) / 2 # ensure affinity matrix is symmetrical