def test_max_sigma_noise(self):
M = 51
N = 325
J = 25
TR = 1.
Thrf = 25.
dt = .5
data = self.data_simu
X = OrderedDict([])
Y = data.bold
onsets = data.get_joined_onsets()
durations = data.paradigm.stimDurations
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
TT, m_h = getCanoHRF(Thrf, dt)
sigma_epsilone = np.ones(J)
Gamma = np.identity(N)
m_A = np.zeros((J, M), dtype=np.float64)
Sigma_A = np.zeros((M, M, J), np.float64)
m_H = np.array(m_h).astype(np.float64)
D = len(m_H)
Sigma_H = np.ones((D, D), dtype=np.float64)
zerosMM = np.zeros((M, M), dtype=np.float64)
_, occurence_matrix, _ = vt.create_conditions(onsets, durations, M, N,
D, TR, dt)
sigma_eps = vt.maximization_noise_var(occurence_matrix, m_H, Sigma_H, m_A, Sigma_A,
Gamma, Y, N)
def test_expectH(self):
M = 51
J = 25
N = 325
D = 3
TR = 1.
Thrf = 25.
dt = .5
data = self.data_simu
Gamma = np.identity(N)
Q_barnCond = np.zeros((M, M, D, D), dtype=np.float64)
XGamma = np.zeros((M, D, N), dtype=np.float64)
XX = np.zeros((M, N, D), dtype=np.int32)
Y = data.bold
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - np.dot(P, L)
TT, m_h = getCanoHRF(Thrf, dt)
m_h = m_h[:D]
m_H = np.array(m_h)
sigma_epsilone = np.ones(J)
Sigma_H = np.ones((D, D), dtype=float)
m_A = np.zeros((J, M), dtype=np.float64)
Sigma_A = np.zeros((M, M, J), np.float64)
scale = 1
order = 2
D2 = vt.buildFiniteDiffMatrix(order, D)
R = np.dot(D2, D2) / pow(dt, 2 * order)
sigmaH = 0.1
UtilsC.expectation_H(XGamma, Q_barnCond, sigma_epsilone, Gamma,
R, Sigma_H, Y, y_tilde, m_A, m_H, Sigma_A,
XX.astype(np.int32), J, D, M, N, scale, sigmaH)
def test_expectA(self):
M = 51
K = 2
J = 25
N = 325
D = 3
TR = 1.
Thrf = 25.
dt = .5
data = self.data_simu
Y = data.bold
Onsets = data.get_joined_onsets()
Gamma = np.identity(N)
XX = np.zeros((M, N, D), dtype=np.int32)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - np.dot(P, L)
TT, m_h = getCanoHRF(Thrf, dt)
m_h = m_h[:D]
sigma_epsilone = np.ones(J)
m_H = np.array(m_h)
Sigma_H = np.ones((D, D), dtype=float)
m_A = np.zeros((J, M), dtype=np.float64)
Sigma_A = np.zeros((M, M, J), np.float64)
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01 * np.identity(M)
mu_M = np.zeros((M, K), dtype=np.float64)
sigma_M = np.ones((M, K), dtype=np.float64)
q_Z = np.zeros((M, K, J), dtype=np.float64)
UtilsC.expectation_A(q_Z, mu_M, sigma_M, PL, sigma_epsilone, Gamma,
Sigma_H, Y, y_tilde, m_A, m_H, Sigma_A,
XX.astype(np.int32), J, D, M, N, K)
def test_expectA(self):
M = 51
K = 2
J = 25
N = 325
D = 3
TR = 1.
Thrf = 25.
dt = .5
data = self.data_simu
Y = data.bold
Onsets = data.get_joined_onsets()
Gamma = np.identity(N)
XX = np.zeros((M, N, D), dtype=np.int32)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - np.dot(P, L)
TT, m_h = getCanoHRF(Thrf, dt)
m_h = m_h[:D]
sigma_epsilone = np.ones(J)
m_H = np.array(m_h)
Sigma_H = np.ones((D, D), dtype=float)
m_A = np.zeros((J, M), dtype=np.float64)
Sigma_A = np.zeros((M, M, J), np.float64)
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01 * np.identity(M)
mu_M = np.zeros((M, K), dtype=np.float64)
sigma_M = np.ones((M, K), dtype=np.float64)
q_Z = np.zeros((M, K, J), dtype=np.float64)
UtilsC.expectation_A(q_Z, mu_M, sigma_M, PL, sigma_epsilone, Gamma,
Sigma_H, Y, y_tilde, m_A, m_H, Sigma_A,
XX.astype(np.int32), J, D, M, N, K)
def test_expectH(self):
M = 51
J = 25
N = 325
D = 3
TR = 1.
Thrf = 25.
dt = .5
data = self.data_simu
Gamma = np.identity(N)
Q_barnCond = np.zeros((M, M, D, D), dtype=np.float64)
XGamma = np.zeros((M, D, N), dtype=np.float64)
XX = np.zeros((M, N, D), dtype=np.int32)
Y = data.bold
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - np.dot(P, L)
TT, m_h = getCanoHRF(Thrf, dt)
m_h = m_h[:D]
m_H = np.array(m_h)
sigma_epsilone = np.ones(J)
Sigma_H = np.ones((D, D), dtype=float)
m_A = np.zeros((J, M), dtype=np.float64)
Sigma_A = np.zeros((M, M, J), np.float64)
scale = 1
order = 2
D2 = vt.buildFiniteDiffMatrix(order, D)
R = np.dot(D2, D2) / pow(dt, 2 * order)
sigmaH = 0.1
UtilsC.expectation_H(XGamma, Q_barnCond, sigma_epsilone, Gamma, R,
Sigma_H, Y, y_tilde, m_A, m_H, Sigma_A,
XX.astype(np.int32), J, D, M, N, scale, sigmaH)
def test_polyFit(self):
N = 325
TR = 1.
data = self.data_simu
Y = data.bold
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
def test_free_energy(self):
""" Test of vem tool to compute free energy """
M = 51
D = 3
N = 325
J = 25
K = 2
TR = 1.
Thrf=25.
dt=.5
data = self.data_simu
Y = data.bold
graph = data.get_graph()
P = vt.PolyMat( N , 4 , TR)
L = vt.polyFit(Y, TR, 4,P)
y_tilde = Y - np.dot(P,L)
TT,m_h = getCanoHRF(Thrf,dt)
order = 2
D2 = vt.buildFiniteDiffMatrix(order,D)
R = np.dot(D2,D2) / pow(dt,2*order)
invR = np.linalg.inv(R)
Det_invR = np.linalg.det(invR)
q_Z = np.zeros((M,K,J),dtype=np.float64)
maxNeighbours = max([len(nl) for nl in graph])
neighboursIndexes = np.zeros((J, maxNeighbours), dtype=np.int32)
Beta = np.ones((M),dtype=np.float64)
sigma_epsilone = np.ones(J)
XX = np.zeros((M,N,D),dtype=np.int32)
Gamma = np.identity(N)
Det_Gamma = np.linalg.det(Gamma)
XGamma = np.zeros((M,D,N),dtype=np.float64)
m_A = np.zeros((J,M),dtype=np.float64)
Sigma_A = np.zeros((M,M,J),np.float64)
mu_M = np.zeros((M,K),dtype=np.float64)
sigma_M = np.ones((M,K),dtype=np.float64)
m_H = np.array(m_h).astype(np.float64)
Sigma_H = np.ones((D,D),dtype=np.float64)
p_Wtilde = np.zeros((M,K),dtype=np.float64)
p_Wtilde1 = np.zeros((M,K),dtype=np.float64)
p_Wtilde[:,1] = 1
FreeEnergy = vt.Compute_FreeEnergy(y_tilde,m_A,Sigma_A,mu_M,sigma_M,
m_H,Sigma_H,R,Det_invR,0.0,p_Wtilde,
0.0,0.0,q_Z,neighboursIndexes,
maxNeighbours,Beta,sigma_epsilone,
XX,Gamma,Det_Gamma,XGamma,
J,M,D,N,2,100,"CompMod")
def test_max_L(self):
M = 51
N = 325
J = 25
TR = 1.
Thrf=25.
dt=.5
TT,m_h = getCanoHRF(Thrf,dt)
m_A = np.zeros((J,M),dtype=np.float64)
m_H = np.array(m_h).astype(np.float64)
data = self.data_simu
Y = data.bold
X = OrderedDict([])
P = vt.PolyMat( N , 4 , TR)
L = vt.polyFit(Y, TR, 4,P)
zerosP = np.zeros((P.shape[0]),dtype=np.float64)
L = vt.maximization_L(Y,m_A,X,m_H,L,P,zerosP)
def test_free_energy(self):
""" Test of vem tool to compute free energy """
M = 51
D = 3
N = 325
J = 25
K = 2
TR = 1.
Thrf = 25.
dt = .5
gamma_h = 1000
data = self.data_simu
Y = data.bold
graph = data.get_graph()
onsets = data.paradigm.get_joined_onsets()
durations = data.paradigm.stimDurations
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
y_tilde = Y - np.dot(P, L)
TT, m_h = getCanoHRF(Thrf, dt)
order = 2
D2 = vt.buildFiniteDiffMatrix(order, D)
R = np.dot(D2, D2) / pow(dt, 2*order)
invR = np.linalg.inv(R)
Det_invR = np.linalg.det(invR)
q_Z = 0.5 * np.ones((M, K, J), dtype=np.float64)
neighbours_indexes = vt.create_neighbours(graph)
Beta = np.ones((M), dtype=np.float64)
sigma_epsilone = np.ones(J)
_, occurence_matrix, _ = vt.create_conditions(onsets, durations, M, N, D, TR, dt)
Gamma = np.identity(N)
Det_Gamma = np.linalg.det(Gamma)
XGamma = np.zeros((M, D, N), dtype=np.float64)
m_A = np.zeros((J, M), dtype=np.float64)
Sigma_A = np.zeros((M, M, J), np.float64)
mu_M = np.zeros((M, K), dtype=np.float64)
sigma_M = np.ones((M, K), dtype=np.float64)
m_H = np.array(m_h[:D]).astype(np.float64)
Sigma_H = np.ones((D, D), dtype=np.float64)
free_energy = vt.free_energy_computation(m_A, Sigma_A, m_H, Sigma_H, D,
q_Z, y_tilde, occurence_matrix,
sigma_epsilone, Gamma, M, J, N,
K, mu_M, sigma_M, neighbours_indexes,
Beta, Sigma_H, np.linalg.inv(R),
R, Det_Gamma, gamma_h)
def test_max_L(self):
M = 51
N = 325
J = 25
D = 3
TR = 1.
Thrf = 25.
dt = .5
TT, m_h = getCanoHRF(Thrf, dt)
m_A = np.zeros((J, M), dtype=np.float64)
m_H = np.array(m_h).astype(np.float64)
data = self.data_simu
Y = data.bold
XX = np.zeros((M, N, D), dtype=np.int32)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
Ndrift = L.shape[0]
zerosP = np.zeros((P.shape[0]), dtype=np.float64)
UtilsC.maximization_L(
Y, m_A, m_H, L, P, XX.astype(np.int32), J, D, M, Ndrift, N)
def test_max_L(self):
M = 51
N = 325
J = 25
D = 3
TR = 1.
Thrf = 25.
dt = .5
TT, m_h = getCanoHRF(Thrf, dt)
m_A = np.zeros((J, M), dtype=np.float64)
m_H = np.array(m_h).astype(np.float64)
data = self.data_simu
Y = data.bold
XX = np.zeros((M, N, D), dtype=np.int32)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
Ndrift = L.shape[0]
zerosP = np.zeros((P.shape[0]), dtype=np.float64)
UtilsC.maximization_L(Y, m_A, m_H, L, P, XX.astype(np.int32), J, D, M,
Ndrift, N)
def test_max_L(self):
M = 51
N = 325
J = 25
TR = 1.
Thrf = 25.
dt = .5
TT, m_h = getCanoHRF(Thrf, dt)
m_A = np.zeros((J, M), dtype=np.float64)
m_H = np.array(m_h).astype(np.float64)
data = self.data_simu
onsets = data.paradigm.get_joined_onsets()
durations = data.paradigm.get_joined_durations()
Y = data.bold
X = OrderedDict([])
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
zerosP = np.zeros((P.shape[0]), dtype=np.float64)
_, occurence_matrix, _ = vt.create_conditions(onsets, durations, M, N,
len(m_H), TR, dt)
L = vt.maximization_drift_coeffs(Y, m_A, occurence_matrix, m_H, np.identity(N), P)
def test_expectA(self):
M = 51
K = 2
J = 25
N = 325
D = 3
TR = 1.
Thrf = 25.
dt = .5
data = self.data_simu
Y = data.bold
Onsets = data.get_joined_onsets()
durations = data.paradigm.stimDurations
Gamma = np.identity(N)
X = OrderedDict([])
for condition, Ons in Onsets.iteritems():
X[condition] = vt.compute_mat_X_2(N, TR, D, dt, Ons)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - np.dot(P, L)
TT, m_h = getCanoHRF(Thrf, dt)
m_h = m_h[:D]
sigma_epsilone = np.ones(J)
m_H = np.array(m_h)
Sigma_H = np.ones((D, D), dtype=float)
m_A = np.zeros((J, M), dtype=np.float64)
Sigma_A = np.ones((M, M, J), np.float64)
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01*np.identity(M)
mu_M = np.zeros((M, K), dtype=np.float64)
sigma_M = np.ones((M, K), dtype=np.float64)
q_Z = 0.5 * np.ones((M, K, J), dtype=np.float64)
zerosJMD = np.zeros((J, M, D), dtype=np.float64)
_, occurence_matrix, _ = vt.create_conditions(Onsets, durations, M, N,
D, TR, dt)
m_A, Sigma_A = vt.nrls_expectation(m_H, m_A, occurence_matrix, Gamma,
q_Z, mu_M, sigma_M, M, y_tilde, Sigma_A,
Sigma_H, sigma_epsilone)
def test_expectH(self):
M = 51
K = 2
J = 25
N = 325
D = 3
TR = 1.
Thrf = 25.
dt = .5
data = self.data_simu
Gamma = np.identity(N)
X = OrderedDict([])
Y = data.bold
onsets = data.get_joined_onsets()
durations = data.paradigm.stimDurations
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - np.dot(P, L)
TT, m_h = getCanoHRF(Thrf, dt)
m_h = m_h[:D]
m_H = np.array(m_h)
sigma_epsilone = np.ones(J)
Sigma_H = np.ones((D, D), dtype=float)
m_A = np.zeros((J, M), dtype=np.float64)
Sigma_A = np.zeros((M, M, J), np.float64)
scale = 1
zerosDD = np.zeros((D, D), dtype=np.float64)
zerosD = np.zeros((D), dtype=np.float64)
zerosND = np.zeros((N, D), dtype=np.float64)
order = 2
D2 = vt.buildFiniteDiffMatrix(order, D)
R = np.dot(D2, D2) / pow(dt, 2*order)
sigmaH = 0.1
_, occurence_matrix, _ = vt.create_conditions(onsets, durations, M, N,
D, TR, dt)
m_H, Sigma_H = vt.hrf_expectation(Sigma_A, m_A, occurence_matrix, Gamma, R,
sigmaH, J, y_tilde, sigma_epsilone)
def test_max_sigma_noise(self):
M = 51
D = 3
N = 325
J = 25
TR = 1.
Thrf=25.
dt=.5
data = self.data_simu
X = OrderedDict([])
Y = data.bold
P = vt.PolyMat( N , 4 , TR)
L = vt.polyFit(Y, TR, 4,P)
PL = np.dot(P,L)
TT,m_h = getCanoHRF(Thrf,dt)
sigma_epsilone = np.ones(J)
m_A = np.zeros((J,M),dtype=np.float64)
Sigma_A = np.zeros((M,M,J),np.float64)
m_H = np.array(m_h).astype(np.float64)
Sigma_H = np.ones((D,D),dtype=np.float64)
zerosMM = np.zeros((M,M),dtype=np.float64)
sigma_eps = vt.maximization_sigma_noise(Y,X,m_A,m_H,Sigma_H,Sigma_A,
PL,sigma_epsilone,M,zerosMM)
def test_max_sigma_noise(self):
M = 51
D = 3
N = 325
J = 25
TR = 1.
Thrf = 25.
dt = .5
Gamma = np.identity(N)
data = self.data_simu
XX = np.zeros((M, N, D), dtype=np.int32)
Y = data.bold
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
TT, m_h = getCanoHRF(Thrf, dt)
sigma_epsilone = np.ones(J)
m_A = np.zeros((J, M), dtype=np.float64)
Sigma_A = np.zeros((M, M, J), np.float64)
m_H = np.array(m_h).astype(np.float64)
Sigma_H = np.ones((D, D), dtype=np.float64)
UtilsC.maximization_sigma_noise(Gamma, PL, sigma_epsilone,
Sigma_H, Y, m_A, m_H, Sigma_A,
XX.astype(np.int32), J, D, M, N)
def test_expectH(self):
M = 51
K = 2
J = 25
N = 325
D = 3
TR = 1.
Thrf=25.
dt=.5
data = self.data_simu
Gamma = np.identity(N)
X = OrderedDict([])
Y = data.bold
P = vt.PolyMat( N , 4 , TR)
L = vt.polyFit(Y, TR, 4,P)
PL = np.dot(P,L)
y_tilde = Y - np.dot(P,L)
TT,m_h = getCanoHRF(Thrf,dt)
m_h = m_h[:D]
m_H = np.array(m_h)
sigma_epsilone = np.ones(J)
Sigma_H = np.ones((D,D),dtype=float)
m_A = np.zeros((J,M),dtype=np.float64)
Sigma_A = np.zeros((M,M,J),np.float64)
scale=1
zerosDD = np.zeros((D,D),dtype=np.float64)
zerosD = np.zeros((D),dtype=np.float64)
zerosND = np.zeros((N,D),dtype=np.float64)
order = 2
D2 = vt.buildFiniteDiffMatrix(order,D)
R = np.dot(D2,D2) / pow(dt,2*order)
sigmaH = 0.1
Sigma_H, m_H = vt.expectation_H(Y,Sigma_A,m_A,X,Gamma,PL,D,R,
sigmaH,J,N,y_tilde,zerosND,
sigma_epsilone,scale,zerosDD,
zerosD)
def test_max_sigma_noise(self):
M = 51
D = 3
N = 325
J = 25
TR = 1.
Thrf = 25.
dt = .5
Gamma = np.identity(N)
data = self.data_simu
XX = np.zeros((M, N, D), dtype=np.int32)
Y = data.bold
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
TT, m_h = getCanoHRF(Thrf, dt)
sigma_epsilone = np.ones(J)
m_A = np.zeros((J, M), dtype=np.float64)
Sigma_A = np.zeros((M, M, J), np.float64)
m_H = np.array(m_h).astype(np.float64)
Sigma_H = np.ones((D, D), dtype=np.float64)
UtilsC.maximization_sigma_noise(Gamma, PL, sigma_epsilone, Sigma_H,
Y, m_A, m_H, Sigma_A,
XX.astype(np.int32), J, D, M, N)
def Main_vbjde_Extension_constrained(graph, Y, Onsets, Thrf, K, TR, beta,
dt, scale=1, estimateSigmaH=True,
sigmaH=0.05, NitMax=-1,
NitMin=1, estimateBeta=True,
PLOT=False, contrasts=[],
computeContrast=False,
gamma_h=0, estimateHRF=True,
TrueHrfFlag=False,
HrfFilename='hrf.nii',
estimateLabels=True,
LabelsFilename='labels.nii',
MFapprox=False, InitVar=0.5,
InitMean=2.0, MiniVEMFlag=False,
NbItMiniVem=5):
# VBJDE Function for BOLD with contraints
logger.info("Fast EM with C extension started ...")
np.random.seed(6537546)
##########################################################################
# INITIALIZATIONS
# Initialize parameters
tau1 = 0.0
tau2 = 0.0
S = 100
Init_sigmaH = sigmaH
Nb2Norm = 1
NormFlag = False
if NitMax < 0:
NitMax = 100
gamma = 7.5
#gamma_h = 1000
gradientStep = 0.003
MaxItGrad = 200
Thresh = 1e-5
Thresh_FreeEnergy = 1e-5
estimateLabels = True # WARNING!! They should be estimated
# Initialize sizes vectors
D = int(np.ceil(Thrf / dt)) + 1 # D = int(np.ceil(Thrf/dt))
M = len(Onsets)
N = Y.shape[0]
J = Y.shape[1]
l = int(np.sqrt(J))
condition_names = []
# Neighbours
maxNeighbours = max([len(nl) for nl in graph])
neighboursIndexes = np.zeros((J, maxNeighbours), dtype=np.int32)
neighboursIndexes -= 1
for i in xrange(J):
neighboursIndexes[i, :len(graph[i])] = graph[i]
# Conditions
X = OrderedDict([])
for condition, Ons in Onsets.iteritems():
X[condition] = vt.compute_mat_X_2(N, TR, D, dt, Ons)
condition_names += [condition]
XX = np.zeros((M, N, D), dtype=np.int32)
nc = 0
for condition, Ons in Onsets.iteritems():
XX[nc, :, :] = X[condition]
nc += 1
# Covariance matrix
order = 2
D2 = vt.buildFiniteDiffMatrix(order, D)
R = np.dot(D2, D2) / pow(dt, 2 * order)
invR = np.linalg.inv(R)
Det_invR = np.linalg.det(invR)
Gamma = np.identity(N)
Det_Gamma = np.linalg.det(Gamma)
p_Wtilde = np.zeros((M, K), dtype=np.float64)
p_Wtilde1 = np.zeros((M, K), dtype=np.float64)
p_Wtilde[:, 1] = 1
Crit_H = 1
Crit_Z = 1
Crit_A = 1
Crit_AH = 1
AH = np.zeros((J, M, D), dtype=np.float64)
AH1 = np.zeros((J, M, D), dtype=np.float64)
Crit_FreeEnergy = 1
cA = []
cH = []
cZ = []
cAH = []
FreeEnergy_Iter = []
cTime = []
cFE = []
SUM_q_Z = [[] for m in xrange(M)]
mu1 = [[] for m in xrange(M)]
h_norm = []
h_norm2 = []
CONTRAST = np.zeros((J, len(contrasts)), dtype=np.float64)
CONTRASTVAR = np.zeros((J, len(contrasts)), dtype=np.float64)
Q_barnCond = np.zeros((M, M, D, D), dtype=np.float64)
XGamma = np.zeros((M, D, N), dtype=np.float64)
m1 = 0
for k1 in X: # Loop over the M conditions
m2 = 0
for k2 in X:
Q_barnCond[m1, m2, :, :] = np.dot(
np.dot(X[k1].transpose(), Gamma), X[k2])
m2 += 1
XGamma[m1, :, :] = np.dot(X[k1].transpose(), Gamma)
m1 += 1
if MiniVEMFlag:
logger.info("MiniVEM to choose the best initialisation...")
"""InitVar, InitMean, gamma_h = MiniVEM_CompMod(Thrf,TR,dt,beta,Y,K,
gamma,gradientStep,
MaxItGrad,D,M,N,J,S,
maxNeighbours,
neighboursIndexes,
XX,X,R,Det_invR,Gamma,
Det_Gamma,
scale,Q_barnCond,XGamma,
NbItMiniVem,
sigmaH,estimateHRF)"""
InitVar, InitMean, gamma_h = vt.MiniVEM_CompMod(Thrf, TR, dt, beta, Y, K, gamma, gradientStep, MaxItGrad, D, M, N, J, S, maxNeighbours,
neighboursIndexes, XX, X, R, Det_invR, Gamma, Det_Gamma, p_Wtilde, scale, Q_barnCond, XGamma, tau1, tau2, NbItMiniVem, sigmaH, estimateHRF)
sigmaH = Init_sigmaH
sigma_epsilone = np.ones(J)
logger.info(
"Labels are initialized by setting active probabilities to ones ...")
q_Z = np.zeros((M, K, J), dtype=np.float64)
q_Z[:, 1, :] = 1
q_Z1 = np.zeros((M, K, J), dtype=np.float64)
Z_tilde = q_Z.copy()
# TT,m_h = getCanoHRF(Thrf-dt,dt) #TODO: check
TT, m_h = getCanoHRF(Thrf, dt) # TODO: check
m_h = m_h[:D]
m_H = np.array(m_h).astype(np.float64)
m_H1 = np.array(m_h)
sigmaH1 = sigmaH
if estimateHRF:
Sigma_H = np.ones((D, D), dtype=np.float64)
else:
Sigma_H = np.zeros((D, D), dtype=np.float64)
Beta = beta * np.ones((M), dtype=np.float64)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - PL
Ndrift = L.shape[0]
sigma_M = np.ones((M, K), dtype=np.float64)
sigma_M[:, 0] = 0.5
sigma_M[:, 1] = 0.6
mu_M = np.zeros((M, K), dtype=np.float64)
for k in xrange(1, K):
mu_M[:, k] = InitMean
Sigma_A = np.zeros((M, M, J), np.float64)
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01 * np.identity(M)
m_A = np.zeros((J, M), dtype=np.float64)
m_A1 = np.zeros((J, M), dtype=np.float64)
for j in xrange(0, J):
for m in xrange(0, M):
for k in xrange(0, K):
m_A[j, m] += np.random.normal(mu_M[m, k],
np.sqrt(sigma_M[m, k])) * q_Z[m, k, j]
m_A1 = m_A
t1 = time.time()
##########################################################################
# VBJDE num. iter. minimum
ni = 0
while ((ni < NitMin) or (((Crit_FreeEnergy > Thresh_FreeEnergy) or (Crit_AH > Thresh)) and (ni < NitMax))):
logger.info("------------------------------ Iteration n° " +
str(ni + 1) + " ------------------------------")
#####################
# EXPECTATION
#####################
# A
logger.info("E A step ...")
UtilsC.expectation_A(q_Z, mu_M, sigma_M, PL, sigma_epsilone, Gamma,
Sigma_H, Y, y_tilde, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N, K)
val = np.reshape(m_A, (M * J))
val[np.where((val <= 1e-50) & (val > 0.0))] = 0.0
val[np.where((val >= -1e-50) & (val < 0.0))] = 0.0
# crit. A
DIFF = np.reshape(m_A - m_A1, (M * J))
# To avoid numerical problems
DIFF[np.where((DIFF < 1e-50) & (DIFF > 0.0))] = 0.0
# To avoid numerical problems
DIFF[np.where((DIFF > -1e-50) & (DIFF < 0.0))] = 0.0
Crit_A = (
np.linalg.norm(DIFF) / np.linalg.norm(np.reshape(m_A1, (M * J)))) ** 2
cA += [Crit_A]
m_A1[:, :] = m_A[:, :]
# HRF h
if estimateHRF:
################################
# HRF ESTIMATION
################################
UtilsC.expectation_H(XGamma, Q_barnCond, sigma_epsilone, Gamma, R, Sigma_H, Y,
y_tilde, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N, scale, sigmaH)
import cvxpy as cvx
m, n = Sigma_H.shape
Sigma_H_inv = np.linalg.inv(Sigma_H)
zeros_H = np.zeros_like(m_H[:, np.newaxis])
# Construct the problem. PRIMAL
h = cvx.Variable(n)
expression = cvx.quad_form(h - m_H[:, np.newaxis], Sigma_H_inv)
objective = cvx.Minimize(expression)
#constraints = [h[0] == 0, h[-1]==0, h >= zeros_H, cvx.square(cvx.norm(h,2))<=1]
constraints = [
h[0] == 0, h[-1] == 0, cvx.square(cvx.norm(h, 2)) <= 1]
prob = cvx.Problem(objective, constraints)
result = prob.solve(verbose=0, solver=cvx.CVXOPT)
# Now we update the mean of h
m_H_old = m_H
Sigma_H_old = Sigma_H
m_H = np.squeeze(np.array((h.value)))
Sigma_H = np.zeros_like(Sigma_H)
h_norm += [np.linalg.norm(m_H)]
# print 'h_norm = ', h_norm
# Plotting HRF
if PLOT and ni >= 0:
import matplotlib.pyplot as plt
plt.figure(M + 1)
plt.plot(m_H)
plt.hold(True)
else:
if TrueHrfFlag:
#TrueVal, head = read_volume(HrfFilename)
TrueVal, head = read_volume(HrfFilename)[:, 0, 0, 0]
print TrueVal
print TrueVal.shape
m_H = TrueVal
# crit. h
Crit_H = (np.linalg.norm(m_H - m_H1) / np.linalg.norm(m_H1)) ** 2
cH += [Crit_H]
m_H1[:] = m_H[:]
# crit. AH
for d in xrange(0, D):
AH[:, :, d] = m_A[:, :] * m_H[d]
DIFF = np.reshape(AH - AH1, (M * J * D))
# To avoid numerical problems
DIFF[np.where((DIFF < 1e-50) & (DIFF > 0.0))] = 0.0
# To avoid numerical problems
DIFF[np.where((DIFF > -1e-50) & (DIFF < 0.0))] = 0.0
if np.linalg.norm(np.reshape(AH1, (M * J * D))) == 0:
Crit_AH = 1000000000.
else:
Crit_AH = (
np.linalg.norm(DIFF) / np.linalg.norm(np.reshape(AH1, (M * J * D)))) ** 2
cAH += [Crit_AH]
AH1[:, :, :] = AH[:, :, :]
# Z labels
if estimateLabels:
logger.info("E Z step ...")
# WARNING!!! ParsiMod gives better results, but we need the other
# one.
if MFapprox:
UtilsC.expectation_Z(Sigma_A, m_A, sigma_M, Beta, Z_tilde, mu_M, q_Z, neighboursIndexes.astype(
np.int32), M, J, K, maxNeighbours)
if not MFapprox:
UtilsC.expectation_Z_ParsiMod_RVM_and_CompMod(
Sigma_A, m_A, sigma_M, Beta, mu_M, q_Z, neighboursIndexes.astype(np.int32), M, J, K, maxNeighbours)
else:
logger.info("Using True Z ...")
TrueZ = read_volume(LabelsFilename)
for m in xrange(M):
q_Z[m, 1, :] = np.reshape(TrueZ[0][:, :, :, m], J)
q_Z[m, 0, :] = 1 - q_Z[m, 1, :]
# crit. Z
val = np.reshape(q_Z, (M * K * J))
val[np.where((val <= 1e-50) & (val > 0.0))] = 0.0
DIFF = np.reshape(q_Z - q_Z1, (M * K * J))
# To avoid numerical problems
DIFF[np.where((DIFF < 1e-50) & (DIFF > 0.0))] = 0.0
# To avoid numerical problems
DIFF[np.where((DIFF > -1e-50) & (DIFF < 0.0))] = 0.0
if np.linalg.norm(np.reshape(q_Z1, (M * K * J))) == 0:
Crit_Z = 1000000000.
else:
Crit_Z = (
np.linalg.norm(DIFF) / np.linalg.norm(np.reshape(q_Z1, (M * K * J)))) ** 2
cZ += [Crit_Z]
q_Z1 = q_Z
#####################
# MAXIMIZATION
#####################
# HRF: Sigma_h
if estimateHRF:
if estimateSigmaH:
logger.info("M sigma_H step ...")
if gamma_h > 0:
sigmaH = vt.maximization_sigmaH_prior(
D, Sigma_H_old, R, m_H_old, gamma_h)
else:
sigmaH = vt.maximization_sigmaH(D, Sigma_H, R, m_H)
logger.info('sigmaH = %s', str(sigmaH))
# (mu,sigma)
logger.info("M (mu,sigma) step ...")
mu_M, sigma_M = vt.maximization_mu_sigma(
mu_M, sigma_M, q_Z, m_A, K, M, Sigma_A)
for m in xrange(M):
SUM_q_Z[m] += [sum(q_Z[m, 1, :])]
mu1[m] += [mu_M[m, 1]]
# Drift L
UtilsC.maximization_L(
Y, m_A, m_H, L, P, XX.astype(np.int32), J, D, M, Ndrift, N)
PL = np.dot(P, L)
y_tilde = Y - PL
# Beta
if estimateBeta:
logger.info("estimating beta")
for m in xrange(0, M):
if MFapprox:
Beta[m] = UtilsC.maximization_beta(beta, q_Z[m, :, :].astype(np.float64), Z_tilde[m, :, :].astype(
np.float64), J, K, neighboursIndexes.astype(np.int32), gamma, maxNeighbours, MaxItGrad, gradientStep)
if not MFapprox:
#Beta[m] = UtilsC.maximization_beta(beta,q_Z[m,:,:].astype(np.float64),q_Z[m,:,:].astype(np.float64),J,K,neighboursIndexes.astype(int32),gamma,maxNeighbours,MaxItGrad,gradientStep)
Beta[m] = UtilsC.maximization_beta_CB(beta, q_Z[m, :, :].astype(
np.float64), J, K, neighboursIndexes.astype(np.int32), gamma, maxNeighbours, MaxItGrad, gradientStep)
logger.info("End estimating beta")
logger.info(Beta)
# Sigma noise
logger.info("M sigma noise step ...")
UtilsC.maximization_sigma_noise(
Gamma, PL, sigma_epsilone, Sigma_H, Y, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N)
#### Computing Free Energy ####
if ni > 0:
FreeEnergy1 = FreeEnergy
"""FreeEnergy = vt.Compute_FreeEnergy(y_tilde,m_A,Sigma_A,mu_M,sigma_M,
m_H,Sigma_H,R,Det_invR,sigmaH,
p_Wtilde,q_Z,neighboursIndexes,
maxNeighbours,Beta,sigma_epsilone,
XX,Gamma,Det_Gamma,XGamma,J,D,M,
N,K,S,"CompMod")"""
FreeEnergy = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_M, sigma_M, m_H, Sigma_H, R, Det_invR, sigmaH, p_Wtilde, tau1,
tau2, q_Z, neighboursIndexes, maxNeighbours, Beta, sigma_epsilone, XX, Gamma, Det_Gamma, XGamma, J, D, M, N, K, S, "CompMod")
if ni > 0:
Crit_FreeEnergy = (FreeEnergy1 - FreeEnergy) / FreeEnergy1
FreeEnergy_Iter += [FreeEnergy]
cFE += [Crit_FreeEnergy]
# Update index
ni += 1
t02 = time.time()
cTime += [t02 - t1]
t2 = time.time()
##########################################################################
# PLOTS and SNR computation
FreeEnergyArray = np.zeros((ni), dtype=np.float64)
for i in xrange(ni):
FreeEnergyArray[i] = FreeEnergy_Iter[i]
SUM_q_Z_array = np.zeros((M, ni), dtype=np.float64)
mu1_array = np.zeros((M, ni), dtype=np.float64)
h_norm_array = np.zeros((ni), dtype=np.float64)
for m in xrange(M):
for i in xrange(ni):
SUM_q_Z_array[m, i] = SUM_q_Z[m][i]
mu1_array[m, i] = mu1[m][i]
h_norm_array[i] = h_norm[i]
if PLOT and 0:
import matplotlib.pyplot as plt
import matplotlib
font = {'size': 15}
matplotlib.rc('font', **font)
plt.savefig('./HRF_Iter_CompMod.png')
plt.hold(False)
plt.figure(2)
plt.plot(cAH[1:-1], 'lightblue')
plt.hold(True)
plt.plot(cFE[1:-1], 'm')
plt.hold(False)
#plt.legend( ('CA','CH', 'CZ', 'CAH', 'CFE') )
plt.legend(('CAH', 'CFE'))
plt.grid(True)
plt.savefig('./Crit_CompMod.png')
plt.figure(3)
plt.plot(FreeEnergyArray)
plt.grid(True)
plt.savefig('./FreeEnergy_CompMod.png')
plt.figure(4)
for m in xrange(M):
plt.plot(SUM_q_Z_array[m])
plt.hold(True)
plt.hold(False)
#plt.legend( ('m=0','m=1', 'm=2', 'm=3') )
#plt.legend( ('m=0','m=1') )
plt.savefig('./Sum_q_Z_Iter_CompMod.png')
plt.figure(5)
for m in xrange(M):
plt.plot(mu1_array[m])
plt.hold(True)
plt.hold(False)
plt.savefig('./mu1_Iter_CompMod.png')
plt.figure(6)
plt.plot(h_norm_array)
plt.savefig('./HRF_Norm_CompMod.png')
Data_save = xndarray(h_norm_array, ['Iteration'])
Data_save.save('./HRF_Norm_Comp.nii')
CompTime = t2 - t1
cTimeMean = CompTime / ni
sigma_M = np.sqrt(np.sqrt(sigma_M))
logger.info("Nb iterations to reach criterion: %d", ni)
logger.info("Computational time = %s min %s s", str(
int(CompTime // 60)), str(int(CompTime % 60)))
# print "Computational time = " + str(int( CompTime//60 ) ) + " min " + str(int(CompTime%60)) + " s"
# print "sigma_H = " + str(sigmaH)
logger.info('mu_M: %f', mu_M)
logger.info('sigma_M: %f', sigma_M)
logger.info("sigma_H = %s" + str(sigmaH))
logger.info("Beta = %s" + str(Beta))
StimulusInducedSignal = vt.computeFit(m_H, m_A, X, J, N)
SNR = 20 * \
np.log(
np.linalg.norm(Y) / np.linalg.norm(Y - StimulusInducedSignal - PL))
SNR /= np.log(10.)
logger.info("SNR = %d", SNR)
return ni, m_A, m_H, q_Z, sigma_epsilone, mu_M, sigma_M, Beta, L, PL, CONTRAST, CONTRASTVAR, cA[2:], cH[2:], cZ[2:], cAH[2:], cTime[2:], cTimeMean, Sigma_A, StimulusInducedSignal, FreeEnergyArray
def Main_vbjde_physio(graph, Y, Onsets, durations, Thrf, K, TR, beta, dt,
scale=1, estimateSigmaH=True, estimateSigmaG=True,
sigmaH=0.05, sigmaG=0.05, gamma_h=0, gamma_g=0,
NitMax=-1, NitMin=1, estimateBeta=True, PLOT=False,
contrasts=[], computeContrast=False,
idx_first_tag=0, simulation=None, sigmaMu=None,
estimateH=True, estimateG=True, estimateA=True,
estimateC=True, estimateZ=True, estimateNoise=True,
estimateMP=True, estimateLA=True, use_hyperprior=False,
positivity=False, constraint=False,
phy_params=PHY_PARAMS_KHALIDOV11, prior='omega', zc=False):
logger.info("EM for ASL!")
np.random.seed(6537540)
logger.info("data shape: ")
logger.info(Y.shape)
Thresh = 1e-5
D, M = np.int(np.ceil(Thrf / dt)) + 1, len(Onsets)
#D, M = np.int(np.ceil(Thrf / dt)), len(Onsets)
n_sess, N, J = Y.shape[0], Y.shape[1], Y.shape[2]
Crit_AH, Crit_CG, cTime, rerror, FE = 1, 1, [], [], []
EP, EPlh, Ent = [],[],[]
Crit_H, Crit_G, Crit_Z, Crit_A, Crit_C = 1, 1, 1, 1, 1
cAH, cCG, AH1, CG1 = [], [], [], []
cA, cC, cH, cG, cZ = [], [], [], [], []
h_norm, g_norm = [], []
SUM_q_Z = [[] for m in xrange(M)]
mua1 = [[] for m in xrange(M)]
muc1 = [[] for m in xrange(M)]
sigmaH = sigmaH * J / 100
print sigmaH
gamma_h = gamma_h * 100 / J
print gamma_h
# Beta data
MaxItGrad = 200
gradientStep = 0.005
gamma = 7.5
print 'gamma = ', gamma
print 'voxels = ', J
maxNeighbours, neighboursIndexes = vt.create_neighbours(graph, J)
print 'graph.shape = ', graph.shape
# Conditions
print 'Onsets: ', Onsets
print 'durations = ', durations
print 'creating conditions...'
X, XX, condition_names = vt.create_conditions_block_ms(Onsets, durations,
M, N, D, n_sess, TR, dt)
# Covariance matrix
#R = vt.covariance_matrix(2, D, dt)
_, R_inv = genGaussianSmoothHRF(zc, D, dt, 1., 2)
R = np.linalg.inv(R_inv)
if zc:
XX = XX[:, :, :, 1:-1] # XX shape (S, M, N, D)
D = D - 2
AH1, CG1 = np.zeros((J, M, D)), np.zeros((J, M, D))
print 'HRF length = ', D
print 'Condition number = ', M
print 'Number of scans = ', N
print 'Number of voxels = ', J
print 'Number of sessions = ', n_sess
print 'XX.shape = ', XX.shape
# Noise matrix
Gamma = np.identity(N)
# Noise initialization
sigma_eps = np.ones((n_sess, J))
# Labels
logger.info("Labels are initialized by setting active probabilities "
"to ones ...")
q_Z = np.ones((M, K, J), dtype=np.float64) / 2.
#q_Z = np.zeros((M, K, J), dtype=np.float64)
#q_Z[:, 1, :] = 1
q_Z1 = copy.deepcopy(q_Z)
Z_tilde = copy.deepcopy(q_Z)
# H and G
TT, m_h = getCanoHRF(Thrf, dt)
H = np.array(m_h[:D]).astype(np.float64)
H /= np.linalg.norm(H)
Hb = create_physio_brf(phy_params, response_dt=dt, response_duration=Thrf)
Hb /= np.linalg.norm(Hb)
if prior=='balloon':
H = Hb.copy()
H1 = copy.deepcopy(H)
Sigma_H = np.zeros((D, D), dtype=np.float64)
# Initialize model parameters
Beta = beta * np.ones((M), dtype=np.float64)
n_drift = 4
P = np.zeros((n_sess, N, n_drift+1), dtype=np.float64)
L = np.zeros((n_drift+1, J, n_sess), dtype=np.float64)
for s in xrange(0, n_sess):
P[s, :, :] = vt.PolyMat(N, n_drift, TR)
L[:, :, s] = vt.polyFit(Y[s, :, :], TR, n_drift, P[s, :, :])
print 'P shape = ', P.shape
print 'L shape = ', L.shape
WP = P.copy()
AL = L.copy()
PL = np.einsum('ijk,kli->ijl', P, L)
y_tilde = Y - PL
# Parameters Gaussian mixtures
mu_Ma = np.append(np.zeros((M, 1)), np.ones((M, 1)), axis=1).astype(np.float64)
sigma_Ma = np.ones((M, K), dtype=np.float64) * 0.3
# Params RLs
m_A = np.zeros((n_sess, J, M), dtype=np.float64)
for s in xrange(0, n_sess):
for j in xrange(0, J):
m_A[s, j, :] = (np.random.normal(mu_Ma, np.sqrt(sigma_Ma)) * q_Z[:, :, j]).sum(axis=1).T
m_A1 = m_A.copy()
Sigma_A = np.ones((M, M, J, n_sess)) * np.identity(M)[:, :, np.newaxis, np.newaxis]
G = np.zeros_like(H)
m_C = np.zeros_like(m_A)
Sigma_G = np.zeros_like(Sigma_H)
Sigma_C = np.zeros_like(Sigma_A)
mu_Mc = np.zeros_like(mu_Ma)
sigma_Mc = np.ones_like(sigma_Ma)
W = np.zeros_like(Gamma) # (N, N)
# Precomputations
print 'W shape is ', W.shape
WX = W.dot(XX).transpose(1, 2, 0, 3) # shape (S, M, N, D)
Gamma_X = np.zeros((N, n_sess, M, D), dtype=np.float64) # shape (N, S, M, D)
X_Gamma_X = np.zeros((D, M, n_sess, M, D), dtype=np.float64) # shape (D, M, S, M, D)
Gamma_WX = np.zeros((N, n_sess, M, D), dtype=np.float64) # shape (N, S, M, D)
XW_Gamma_WX = np.zeros((D, M, n_sess, M, D), dtype=np.float64) # shape (D, M, S, M, D)
Gamma_WP = np.zeros((N, n_sess, n_drift+1), dtype=np.float64) # shape (N, S, M, D)
WP_Gamma_WP = np.zeros((n_sess, n_drift+1, n_drift+1), dtype=np.float64) # shape (D, M, S, M, D)
for s in xrange(0, n_sess):
Gamma_X[:, s, :, :] = np.tensordot(Gamma, XX[s, :, :, :], axes=(1, 1))
X_Gamma_X[:, :, s, :, :] = np.tensordot(XX[s, :, :, :].T, Gamma_X[:, s, :, :], axes=(1, 0))
Gamma_WX[:, s, :, :] = np.tensordot(Gamma, WX[s, :, :, :], axes=(1, 1))
XW_Gamma_WX[:, :, s, :, :] = np.tensordot(WX[s, :, :, :].T, Gamma_WX[:, s, :, :], axes=(1, 0))
Gamma_WP[:, s, :] = Gamma.dot(WP[s, :, :]) # (N, n_drift)
WP_Gamma_WP[s, :, :] = WP[s, :, :].T.dot(Gamma_WP[:, s, :]) # (n_drift, n_drift)
sigma_eps_m = np.maximum(sigma_eps, eps) # (n_sess, J)
cov_noise = sigma_eps_m[:, :, np.newaxis, np.newaxis] # (n_sess, J, 1, 1)
###########################################################################
############################################# VBJDE
t1 = time.time()
ni = 0
#while ((ni < NitMin + 1) or (((Crit_AH > Thresh) or (Crit_CG > Thresh)) \
# and (ni < NitMax))):
#while ((ni < NitMin + 1) or (((Crit_AH > Thresh)) \
# and (ni < NitMax))):
while ((ni < NitMin + 1) or (((Crit_FE > Thresh * np.ones_like(Crit_FE)).any()) \
and (ni < NitMax))):
logger.info("-------- Iteration n° " + str(ni + 1) + " --------")
if PLOT and ni >= 0: # Plotting HRF and PRF
logger.info("Plotting HRF and PRF for current iteration")
vt.plot_response_functions_it(ni, NitMin, M, H, G)
# Managing types of prior
priorH_cov_term = np.zeros_like(R_inv)
matrix_covH = R_inv.copy()
if prior=='balloon':
logger.info(" prior balloon")
#matrix_covH = np.eye(R_inv.shape[0], R_inv.shape[1])
priorH_mean_term = np.dot(matrix_covH / sigmaH, Hb)
else:
logger.info(" NO prior")
priorH_mean_term = np.zeros_like(H)
priorG_mean_term = np.zeros_like(G)
#####################
# EXPECTATION
#####################
# HRF H
if estimateH:
logger.info("E H step ...")
Ht, Sigma_H = vt.expectation_H_ms(Sigma_A, m_A, m_C, G, XX, W, Gamma,
Gamma_X, X_Gamma_X, J, y_tilde,
cov_noise, matrix_covH, sigmaH,
priorH_mean_term, priorH_cov_term, N, M, D, n_sess)
if constraint:
if not np.linalg.norm(Ht)==1:
logger.info(" constraint l2-norm = 1")
H = vt.constraint_norm1_b(Ht, Sigma_H)
#H = Ht / np.linalg.norm(Ht)
else:
logger.info(" l2-norm already 1!!!!!")
H = Ht.copy()
Sigma_H = np.zeros_like(Sigma_H)
else:
H = Ht.copy()
h_norm = np.append(h_norm, np.linalg.norm(H))
print 'h_norm = ', h_norm
Crit_H = (np.linalg.norm(H - H1) / np.linalg.norm(H1)) ** 2
cH += [Crit_H]
H1[:] = H[:]
# A
if estimateA:
logger.info("E A step ...")
m_A, Sigma_A = vt.expectation_A_ms(m_A, Sigma_A, H, G, m_C, W, XX,
Gamma, Gamma_X, q_Z,
mu_Ma, sigma_Ma, J, y_tilde,
Sigma_H, sigma_eps_m, N, M, D, n_sess)
cA += [(np.linalg.norm(m_A - m_A1) / np.linalg.norm(m_A1)) ** 2]
m_A1[:, :, :] = m_A[:, :, :]
# Q labels
if estimateZ:
logger.info("E Q step ...")
q_Z, Z_tilde = vt.expectation_Q_ms(Sigma_A, m_A, Sigma_C, m_C,
sigma_Ma, mu_Ma, sigma_Mc, mu_Mc,
Beta, Z_tilde, q_Z, neighboursIndexes, graph, M, J, K, n_sess)
if 0:
import matplotlib.pyplot as plt
plt.close('all')
fig = plt.figure(1)
for m in xrange(M):
ax = fig.add_subplot(2, M, m + 1)
im = ax.matshow(m_A[:, :, m].mean(0).reshape(20, 20))
plt.colorbar(im, ax=ax)
ax = fig.add_subplot(2, M, m + 3)
im = ax.matshow(q_Z[m, 1, :].reshape(20, 20))
plt.colorbar(im, ax=ax)
fig = plt.figure(2)
for m in xrange(M):
for s in xrange(n_sess):
ax = fig.add_subplot(M, n_sess, n_sess * m + s + 1)
im = ax.matshow(m_A[s, :, m].reshape(20, 20))
plt.colorbar(im, ax=ax)
plt.show()
cZ += [(np.linalg.norm(q_Z - q_Z1) / (np.linalg.norm(q_Z1) + eps)) ** 2]
q_Z1 = q_Z
if ni > 0:
free_energyE = 0
for s in xrange(n_sess):
free_energyE += vt.Compute_FreeEnergy(y_tilde[s, :, :], m_A[s, :, :], Sigma_A[:, :, :, s],
mu_Ma, sigma_Ma, H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C[s, :, :], Sigma_C[:, :, :, s], mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps[s, :], XX[s, :, :, :],
W, J, D, M, N, K, use_hyperprior, Gamma_X[:, s, :, :],
Gamma_WX[:, s, :, :], bold=True, S=n_sess)
if free_energyE < free_energy:
logger.info("free energy has decreased after E step from %f to %f", free_energy, free_energyE)
# crit. AH and CG
logger.info("crit. AH and CG")
AH = m_A[:, :, :, np.newaxis] * H[np.newaxis, np.newaxis, :]
Crit_AH = (np.linalg.norm(AH - AH1) / (np.linalg.norm(AH1) + eps)) ** 2
cAH += [Crit_AH]
AH1 = AH.copy()
logger.info("Crit_AH = " + str(Crit_AH))
#####################
# MAXIMIZATION
#####################
if prior=='balloon':
logger.info(" prior balloon")
AuxH = H - Hb
AuxG = G - Gb
else:
logger.info(" NO prior")
AuxH = H.copy()
AuxG = G.copy()
# Variance HRF: sigmaH
if estimateSigmaH:
logger.info("M sigma_H step ...")
sigmaH = vt.maximization_sigma_asl(D, Sigma_H, matrix_covH, AuxH, use_hyperprior, gamma_h)
logger.info('sigmaH = ' + str(sigmaH))
if ni > 0:
free_energyVh = 0
for s in xrange(n_sess):
free_energyVh += vt.Compute_FreeEnergy(y_tilde[s, :, :], m_A[s, :, :], Sigma_A[:, :, :, s], mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C[s, :, :], Sigma_C[:, :, :, s], mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps[s, :], XX[s, :, :, :], W,
J, D, M, N, K, use_hyperprior, Gamma_X[:, s, :, :], Gamma_WX[:, s, :, :], bold=True, S=n_sess)
if free_energyVh < free_energyE:
logger.info("free energy has decreased after v_h computation from %f to %f", free_energyE, free_energyVh)
# (mu,sigma)
if estimateMP:
logger.info("M (mu,sigma) a and c step ...")
#print 'q_Z = ', q_Z
#print q_Z.shape
mu_Ma, sigma_Ma = vt.maximization_mu_sigma_ms(q_Z, m_A, Sigma_A, M, J, n_sess, K)
print 'mu_Ma = ', mu_Ma
print 'sigma_Ma = ', sigma_Ma
if ni > 0:
free_energyMP = 0
for s in xrange(n_sess):
free_energyMP += vt.Compute_FreeEnergy(y_tilde[s, :, :], m_A[s, :, :], Sigma_A[:, :, :, s], mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C[s, :, :], Sigma_C[:, :, :, s], mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps[s, :], XX[s, :, :, :], W,
J, D, M, N, K, use_hyperprior, Gamma_X[:, s, :, :], Gamma_WX[:, s, :, :], bold=True, S=n_sess)
if free_energyMP < free_energyVh:
logger.info("free energy has decreased after GMM parameters computation from %f to %f", free_energyVh, free_energyMP)
# Drift L, alpha
if estimateLA:
logger.info("M L, alpha step ...")
for s in xrange(n_sess):
AL[:, :, s] = vt.maximization_LA_asl(Y[s, :, :], m_A[s, :, :], m_C[s, :, :], XX[s, :, :, :],
WP[s, :, :], W, WP_Gamma_WP[s, :, :], H, G, Gamma)
PL = np.einsum('ijk,kli->ijl', WP, AL)
y_tilde = Y - PL
if ni > 0:
free_energyLA = 0
for s in xrange(n_sess):
free_energyLA += vt.Compute_FreeEnergy(y_tilde[s, :, :], m_A[s, :, :], Sigma_A[:, :, :, s], mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C[s, :, :], Sigma_C[:, :, :, s], mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps[s, :], XX[s, :, :, :], W,
J, D, M, N, K, use_hyperprior, Gamma_X[:, s, :, :], Gamma_WX[:, s, :, :], bold=True, S=n_sess)
if free_energyLA < free_energyMP:
logger.info("free energy has decreased after drifts computation from %f to %f", free_energyMP, free_energyLA)
# Beta
if estimateBeta:
logger.info("M beta step ...")
"""Qtilde = np.concatenate((Z_tilde, np.zeros((M, K, 1), dtype=Z_tilde.dtype)), axis=2)
Qtilde_sumneighbour = Qtilde[:, :, neighboursIndexes].sum(axis=3)
Beta = vt.maximization_beta_m2(Beta.copy(), q_Z, Qtilde_sumneighbour,
Qtilde, neighboursIndexes, maxNeighbours,
gamma, MaxItGrad, gradientStep)
logger.info(Beta)
"""
logger.info("M beta step ...")
Qtilde = np.concatenate((Z_tilde, np.zeros((M, K, 1), dtype=Z_tilde.dtype)), axis=2)
Qtilde_sumneighbour = Qtilde[:, :, neighboursIndexes].sum(axis=3)
for m in xrange(0, M):
Beta[m] = vt.maximization_beta_m2_scipy_asl(Beta[m].copy(), q_Z[m, :, :], Qtilde_sumneighbour[m, :, :],
Qtilde[m, :, :], neighboursIndexes, maxNeighbours,
gamma, MaxItGrad, gradientStep)
logger.info(Beta)
if ni > 0:
free_energyB = 0
for s in xrange(n_sess):
free_energyB += vt.Compute_FreeEnergy(y_tilde[s, :, :], m_A[s, :, :], Sigma_A[:, :, :, s], mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C[s, :, :], Sigma_C[:, :, :, s], mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps[s, :], XX[s, :, :, :], W,
J, D, M, N, K, use_hyperprior, Gamma_X[:, s, :, :], Gamma_WX[:, s, :, :], bold=True, S=n_sess)
if free_energyB < free_energyLA:
logger.info("free energy has decreased after Beta computation from %f to %f", \
free_energyLA, free_energyB)
if 0 and ni < 5:
plt.close('all')
for m in xrange(0, M):
range_b = np.arange(-10., 20., 0.1)
beta_plotting = np.zeros_like(range_b)
grad_plotting = np.zeros_like(range_b)
for ib, b in enumerate(range_b):
beta_plotting[ib] = vt.fun(b, q_Z[m, :, :], Qtilde_sumneighbour[m, :, :],
neighboursIndexes, gamma)
grad_plotting[ib] = vt.grad_fun(b, q_Z[m, :, :], Qtilde_sumneighbour[m, :, :],
neighboursIndexes, gamma)
#print beta_plotting
plt.figure(1)
plt.hold('on')
plt.plot(range_b, beta_plotting)
plt.figure(2)
plt.hold('on')
plt.plot(range_b, grad_plotting)
plt.show()
# Sigma noise
if estimateNoise:
logger.info("M sigma noise step ...")
for s in xrange(n_sess):
sigma_eps[s, :] = vt.maximization_sigma_noise_asl(XX[s, :, :, :], m_A[s, :, :], Sigma_A[:, :, :, s], H, m_C[s, :, :], Sigma_C[:, :, :, s], \
G, Sigma_H, Sigma_G, W, y_tilde[s, :, :], Gamma, \
Gamma_X[:, s, :, :], Gamma_WX[:, s, :, :], N)
if PLOT:
for m in xrange(M):
SUM_q_Z[m] += [q_Z[m, 1, :].sum()]
mua1[m] += [mu_Ma[m, 1]]
free_energy = 0
for s in xrange(n_sess):
if s==n_sess-1:
plotFE = True
else:
plotFE = False
free_energy += vt.Compute_FreeEnergy(y_tilde[s, :, :], m_A[s, :, :], Sigma_A[:, :, :, s], mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C[s, :, :], Sigma_C[:, :, :, s], mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps[s, :], XX[s, :, :, :], W,
J, D, M, N, K, use_hyperprior, Gamma_X[:, s, :, :], Gamma_WX[:, s, :, :],
plot=plotFE, bold=True, S=n_sess)
if ni > 0:
if free_energy < free_energyB:
logger.info("free energy has decreased after Noise computation from %f to %f", free_energyB, free_energy)
if ni > 0:
if free_energy < FE[-1]:
logger.info("WARNING! free energy has decreased in this iteration from %f to %f", FE[-1], free_energy)
FE += [free_energy]
if ni > 5:
#Crit_FE = np.abs((FE[-1] - FE[-2]) / FE[-2])
FE0 = np.array(FE)
Crit_FE = np.abs((FE0[-5:] - FE0[-6:-1]) / FE0[-6:-1])
print Crit_FE
print (Crit_FE > Thresh * np.ones_like(Crit_FE)).any()
else:
Crit_FE = 100
ni += 1
cTime += [time.time() - t1]
logger.info("Computing reconstruction error")
StimulusInducedSignal = vt.computeFit_asl(H, m_A[s, :, :], G, m_C[s, :, :], W, XX[s, :, :, :])
rerror = np.append(rerror, \
np.mean(((Y[s, :, :] - StimulusInducedSignal) ** 2).sum(axis=0)) \
/ np.mean((Y[s, :, :] ** 2).sum(axis=0)))
CompTime = time.time() - t1
# Normalize if not done already
if not constraint: # or not normg:
logger.info("l2-norm of H and G to 1 if not constraint")
Hnorm = np.linalg.norm(H)
H /= Hnorm
Sigma_H /= Hnorm**2
m_A *= Hnorm
if zc:
H = np.concatenate(([0], H, [0]))
## Compute contrast maps and variance
if computeContrast and len(contrasts) > 0:
logger.info("Computing contrasts ... ")
CONTRAST_A, CONTRASTVAR_A, \
CONTRAST_C, CONTRASTVAR_C = vt.compute_contrasts(condition_names,
contrasts, m_A[s, :, :], m_C[s, :, :],
Sigma_A[:, :, :, s], Sigma_C[:, :, :, s], M, J)
else:
CONTRAST_A, CONTRASTVAR_A, CONTRAST_C, CONTRASTVAR_C = 0, 0, 0, 0
###########################################################################
########################################## PLOTS and SNR computation
logger.info("Nb iterations to reach criterion: %d", ni)
logger.info("Computational time = %s min %s s",
str(np.int(CompTime // 60)), str(np.int(CompTime % 60)))
logger.info("Iteration time = %s min %s s",
str(np.int((CompTime // ni) // 60)), str(np.int((CompTime / ni) % 60)))
logger.info("perfusion baseline mean = %f", np.mean(AL[0, :, s]))
logger.info("perfusion baseline var = %f", np.var(AL[0, :, s]))
logger.info("drifts mean = %f", np.mean(AL[1:, :, s]))
logger.info("drifts var = %f", np.var(AL[1:, :, s]))
logger.info("noise mean = %f", np.mean(sigma_eps[s, :]))
logger.info("noise var = %f", np.var(sigma_eps[s, :]))
SNR10 = 20 * (np.log10(np.linalg.norm(Y[s, :, :]) / \
np.linalg.norm(Y[s, :, :] - StimulusInducedSignal - PL[s, :, :])))
logger.info("SNR = %d", SNR10)
return ni, m_A.mean(0), H, m_C.mean(0), G, Z_tilde, sigma_eps[s, :], \
mu_Ma, sigma_Ma, mu_Mc, sigma_Mc, Beta, AL[:, :, s], PL[s, :, :], \
np.zeros_like(AL[0, :, s]), Sigma_A[:, :, :, s], Sigma_C[:, :, :, s], Sigma_H, Sigma_G, rerror, \
CONTRAST_A, CONTRASTVAR_A, CONTRAST_C, CONTRASTVAR_C, \
cA[:], cH[2:], cC[2:], cG[2:], cZ[2:], cAH[2:], cCG[2:], \
cTime, FE
def Main_vbjde_Extension_constrained_stable(graph, Y, Onsets, Thrf, K, TR, beta,
dt, scale=1, estimateSigmaH=True,
sigmaH=0.05, NitMax=-1,
NitMin=1, estimateBeta=True,
PLOT=False, contrasts=[],
computeContrast=False,
gamma_h=0):
""" Version modified by Lofti from Christine's version """
logger.info(
"Fast EM with C extension started ... Here is the stable version !")
np.random.seed(6537546)
# Initialize parameters
S = 100
if NitMax < 0:
NitMax = 100
gamma = 7.5 # 7.5
gradientStep = 0.003
MaxItGrad = 200
Thresh = 1e-5
# Initialize sizes vectors
D = np.int(np.ceil(Thrf / dt)) + 1
M = len(Onsets)
N = Y.shape[0]
J = Y.shape[1]
l = np.int(np.sqrt(J))
condition_names = []
# Neighbours
maxNeighbours = max([len(nl) for nl in graph])
neighboursIndexes = np.zeros((J, maxNeighbours), dtype=np.int32)
neighboursIndexes -= 1
for i in xrange(J):
neighboursIndexes[i, :len(graph[i])] = graph[i]
# Conditions
X = OrderedDict([])
for condition, Ons in Onsets.iteritems():
X[condition] = vt.compute_mat_X_2(N, TR, D, dt, Ons)
condition_names += [condition]
XX = np.zeros((M, N, D), dtype=np.int32)
nc = 0
for condition, Ons in Onsets.iteritems():
XX[nc, :, :] = X[condition]
nc += 1
# Covariance matrix
order = 2
D2 = vt.buildFiniteDiffMatrix(order, D)
R = np.dot(D2, D2) / pow(dt, 2 * order)
invR = np.linalg.inv(R)
Det_invR = np.linalg.det(invR)
Gamma = np.identity(N)
Det_Gamma = np.linalg.det(Gamma)
Crit_H = 1
Crit_Z = 1
Crit_A = 1
Crit_AH = 1
AH = np.zeros((J, M, D), dtype=np.float64)
AH1 = np.zeros((J, M, D), dtype=np.float64)
Crit_FreeEnergy = 1
cTime = []
cA = []
cH = []
cZ = []
cAH = []
CONTRAST = np.zeros((J, len(contrasts)), dtype=np.float64)
CONTRASTVAR = np.zeros((J, len(contrasts)), dtype=np.float64)
Q_barnCond = np.zeros((M, M, D, D), dtype=np.float64)
XGamma = np.zeros((M, D, N), dtype=np.float64)
m1 = 0
for k1 in X: # Loop over the M conditions
m2 = 0
for k2 in X:
Q_barnCond[m1, m2, :, :] = np.dot(
np.dot(X[k1].transpose(), Gamma), X[k2])
m2 += 1
XGamma[m1, :, :] = np.dot(X[k1].transpose(), Gamma)
m1 += 1
sigma_epsilone = np.ones(J)
logger.info(
"Labels are initialized by setting active probabilities to ones ...")
q_Z = np.zeros((M, K, J), dtype=np.float64)
q_Z[:, 1, :] = 1
q_Z1 = np.zeros((M, K, J), dtype=np.float64)
Z_tilde = q_Z.copy()
TT, m_h = getCanoHRF(Thrf, dt) # TODO: check
m_h = m_h[:D]
m_H = np.array(m_h).astype(np.float64)
m_H1 = np.array(m_h)
sigmaH1 = sigmaH
Sigma_H = np.ones((D, D), dtype=np.float64)
Beta = beta * np.ones((M), dtype=np.float64)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - PL
Ndrift = L.shape[0]
sigma_M = np.ones((M, K), dtype=np.float64)
sigma_M[:, 0] = 0.5
sigma_M[:, 1] = 0.6
mu_M = np.zeros((M, K), dtype=np.float64)
for k in xrange(1, K):
mu_M[:, k] = 1 # InitMean
Sigma_A = np.zeros((M, M, J), np.float64)
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01 * np.identity(M)
m_A = np.zeros((J, M), dtype=np.float64)
m_A1 = np.zeros((J, M), dtype=np.float64)
for j in xrange(0, J):
for m in xrange(0, M):
for k in xrange(0, K):
m_A[j, m] += np.random.normal(mu_M[m, k],
np.sqrt(sigma_M[m, k])) * q_Z[m, k, j]
m_A1 = m_A
t1 = time.time()
##########################################################################
# VBJDE num. iter. minimum
ni = 0
while ((ni < NitMin + 1) or ((Crit_AH > Thresh) and (ni < NitMax))):
logger.info("------------------------------ Iteration n° " +
str(ni + 1) + " ------------------------------")
#####################
# EXPECTATION
#####################
# A
logger.info("E A step ...")
UtilsC.expectation_A(q_Z, mu_M, sigma_M, PL, sigma_epsilone, Gamma,
Sigma_H, Y, y_tilde, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N, K)
# crit. A
DIFF = np.reshape(m_A - m_A1, (M * J))
Crit_A = (
np.linalg.norm(DIFF) / np.linalg.norm(np.reshape(m_A1, (M * J)))) ** 2
cA += [Crit_A]
m_A1[:, :] = m_A[:, :]
# HRF h
UtilsC.expectation_H(XGamma, Q_barnCond, sigma_epsilone, Gamma, R, Sigma_H, Y,
y_tilde, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N, scale, sigmaH)
#m_H[0] = 0
#m_H[-1] = 0
# Constrain with optimization strategy
import cvxpy as cvx
m, n = Sigma_H.shape
Sigma_H_inv = np.linalg.inv(Sigma_H)
zeros_H = np.zeros_like(m_H[:, np.newaxis])
# Construct the problem. PRIMAL
h = cvx.Variable(n)
expression = cvx.quad_form(h - m_H[:, np.newaxis], Sigma_H_inv)
objective = cvx.Minimize(expression)
#constraints = [h[0] == 0, h[-1]==0, h >= zeros_H, cvx.square(cvx.norm(h,2))<=1]
constraints = [h[0] == 0, h[-1] == 0, cvx.square(cvx.norm(h, 2)) <= 1]
prob = cvx.Problem(objective, constraints)
result = prob.solve(verbose=0, solver=cvx.CVXOPT)
# Now we update the mean of h
m_H_old = m_H
Sigma_H_old = Sigma_H
m_H = np.squeeze(np.array((h.value)))
Sigma_H = np.zeros_like(Sigma_H)
# and the norm
h_norm += [np.linalg.norm(m_H)]
# crit. h
Crit_H = (np.linalg.norm(m_H - m_H1) / np.linalg.norm(m_H1)) ** 2
cH += [Crit_H]
m_H1[:] = m_H[:]
# crit. AH
for d in xrange(0, D):
AH[:, :, d] = m_A[:, :] * m_H[d]
DIFF = np.reshape(AH - AH1, (M * J * D))
Crit_AH = (np.linalg.norm(
DIFF) / (np.linalg.norm(np.reshape(AH1, (M * J * D))) + eps)) ** 2
cAH += [Crit_AH]
AH1[:, :, :] = AH[:, :, :]
# Z labels
logger.info("E Z step ...")
UtilsC.expectation_Z(Sigma_A, m_A, sigma_M, Beta, Z_tilde, mu_M,
q_Z, neighboursIndexes.astype(np.int32), M, J, K, maxNeighbours)
# crit. Z
DIFF = np.reshape(q_Z - q_Z1, (M * K * J))
Crit_Z = (np.linalg.norm(DIFF) /
(np.linalg.norm(np.reshape(q_Z1, (M * K * J))) + eps)) ** 2
cZ += [Crit_Z]
q_Z1[:, :, :] = q_Z[:, :, :]
#####################
# MAXIMIZATION
#####################
# HRF: Sigma_h
if estimateSigmaH:
logger.info("M sigma_H step ...")
if gamma_h > 0:
sigmaH = vt.maximization_sigmaH_prior(
D, Sigma_H, R, m_H, gamma_h)
else:
sigmaH = vt.maximization_sigmaH(D, Sigma_H, R, m_H)
logger.info('sigmaH = %s', str(sigmaH))
# (mu,sigma)
logger.info("M (mu,sigma) step ...")
mu_M, sigma_M = vt.maximization_mu_sigma(
mu_M, sigma_M, q_Z, m_A, K, M, Sigma_A)
# Drift L
UtilsC.maximization_L(
Y, m_A, m_H, L, P, XX.astype(np.int32), J, D, M, Ndrift, N)
PL = np.dot(P, L)
y_tilde = Y - PL
# Beta
if estimateBeta:
logger.info("estimating beta")
for m in xrange(0, M):
Beta[m] = UtilsC.maximization_beta(beta, q_Z[m, :, :].astype(np.float64), Z_tilde[m, :, :].astype(
np.float64), J, K, neighboursIndexes.astype(np.int32), gamma, maxNeighbours, MaxItGrad, gradientStep)
logger.info("End estimating beta")
logger.info(Beta)
# Sigma noise
logger.info("M sigma noise step ...")
UtilsC.maximization_sigma_noise(
Gamma, PL, sigma_epsilone, Sigma_H, Y, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N)
t02 = time.time()
cTime += [t02 - t1]
t2 = time.time()
##########################################################################
# PLOTS and SNR computation
if PLOT and 0:
font = {'size': 15}
matplotlib.rc('font', **font)
savefig('./HRF_Iter_CompMod.png')
hold(False)
figure(2)
plot(cAH[1:-1], 'lightblue')
hold(True)
plot(cFE[1:-1], 'm')
hold(False)
legend(('CAH', 'CFE'))
grid(True)
savefig('./Crit_CompMod.png')
figure(3)
plot(FreeEnergyArray)
grid(True)
savefig('./FreeEnergy_CompMod.png')
figure(4)
for m in xrange(M):
plot(SUM_q_Z_array[m])
hold(True)
hold(False)
savefig('./Sum_q_Z_Iter_CompMod.png')
figure(5)
for m in xrange(M):
plot(mu1_array[m])
hold(True)
hold(False)
savefig('./mu1_Iter_CompMod.png')
figure(6)
plot(h_norm_array)
savefig('./HRF_Norm_CompMod.png')
Data_save = xndarray(h_norm_array, ['Iteration'])
Data_save.save('./HRF_Norm_Comp.nii')
CompTime = t2 - t1
cTimeMean = CompTime / ni
"""
Norm = np.linalg.norm(m_H)
m_H /= Norm
Sigma_H /= Norm**2
sigmaH /= Norm**2
m_A *= Norm
Sigma_A *= Norm**2
mu_M *= Norm
sigma_M *= Norm**2
sigma_M = np.sqrt(np.sqrt(sigma_M))
"""
logger.info("Nb iterations to reach criterion: %d", ni)
logger.info("Computational time = %s min %s s", str(
np.int(CompTime // 60)), str(np.int(CompTime % 60)))
logger.info('mu_M: %f', mu_M)
logger.info('sigma_M: %f', sigma_M)
logger.info("sigma_H = %s", str(sigmaH))
logger.info("Beta = %s", str(Beta))
StimulusInducedSignal = vt.computeFit(m_H, m_A, X, J, N)
SNR = 20 * \
np.log(
np.linalg.norm(Y) / np.linalg.norm(Y - StimulusInducedSignal - PL))
SNR /= np.log(10.)
logger.info('SNR comp = %f', SNR)
return ni, m_A, m_H, q_Z, sigma_epsilone, mu_M, sigma_M, Beta, L, PL, CONTRAST, CONTRASTVAR, cA[2:], cH[2:], cZ[2:], cAH[2:], cTime[2:], cTimeMean, Sigma_A, StimulusInducedSignal
def Main_vbjde_constrained(graph, Y, Onsets, Thrf, K, TR, beta, dt, scale=1,
estimateSigmaH=True, estimateSigmaG=True,
sigmaH=0.05, sigmaG=0.05, gamma_h=0, gamma_g=0,
NitMax=-1, NitMin=1, estimateBeta=True, PLOT=False,
idx_first_tag=0, simulation=None,
estimateH=True, estimateG=True, estimateA=True,
estimateC=True, estimateZ=True, estimateNoise=True,
estimateMP=True, estimateLA=True):
""" Version modified by Lofti from Christine's version """
logger.info("EM for ASL!")
np.random.seed(6537546)
# Initialization
gamma_h = 1000000000 #7.5
gamma_g = 1000000000 #7.5
gamma = 0.0000000001
beta = 100
Thresh = 1e-5
D, M = np.int(np.ceil(Thrf / dt)) + 1, len(Onsets)
N, J = Y.shape[0], Y.shape[1]
Crit_AH, Crit_CG = 1, 1
AH = np.zeros((J, M, D), dtype=np.float64)
AH1 = np.zeros((J, M, D), dtype=np.float64)
CG = np.zeros((J, M, D), dtype=np.float64)
CG1 = np.zeros((J, M, D), dtype=np.float64)
cTime = []
cZ = []
cAH = []
cCG = []
h_norm = []
g_norm = []
SUM_q_Z = [[] for m in xrange(M)]
mua1 = [[] for m in xrange(M)]
muc1 = [[] for m in xrange(M)]
# Neighbours
maxNeighbours, neighboursIndexes = EM.create_neighbours(graph, J)
# Conditions
X, XX = EM.create_conditions(Onsets, M, N, D, TR, dt)
# Covariance matrix
R = EM.covariance_matrix(2, D, dt)
# Noise matrix
Gamma = np.identity(N)
# Noise initialization
sigma_eps = np.ones(J)
#Labels
logger.info("Labels are initialized by setting active probabilities "
"to ones ...")
q_Z = np.zeros((M, K, J), dtype=np.float64)
q_Z[:, 1, :] = 1
q_Z1 = copy.deepcopy(q_Z)
Z_tilde = copy.deepcopy(q_Z)
# H and G
TT, m_h = getCanoHRF(Thrf, dt)
m_h = m_h[:D]
H = np.array(m_h).astype(np.float64)
Ht = copy.deepcopy(H)
Sigma_H = np.zeros((D, D), dtype=np.float64)
G = copy.deepcopy(H)
Gt = copy.deepcopy(H)
Sigma_G = copy.deepcopy(Sigma_H)
# others
Beta = beta * np.ones((M), dtype=np.float64)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
alpha = np.zeros((J), dtype=np.float64)
w = np.ones((N))
w[idx_first_tag::2] = -1
w = - w
W = np.diag(w)
wa = np.dot(w[:, np.newaxis], alpha[np.newaxis, :])
y_tilde = Y - PL - wa
# Parameters Gaussian mixtures
sigma_Ma = np.ones((M, K), dtype=np.float64)
sigma_Ma[:, 0] = 0.5
sigma_Ma[:, 1] = 0.6
mu_Ma = np.zeros((M, K), dtype=np.float64)
for k in xrange(1, K):
mu_Ma[:, k] = 1
sigma_Mc = copy.deepcopy(sigma_Ma)
mu_Mc = copy.deepcopy(mu_Ma)
# Params RLs
Sigma_A = np.zeros((M, M, J), np.float64)
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01 * np.identity(M)
m_A = np.zeros((J, M), dtype=np.float64)
for j in xrange(0, J):
for m in xrange(0, M):
for k in xrange(0, K):
m_A[j, m] += np.random.normal(mu_Ma[m, k], \
np.sqrt(sigma_Ma[m, k])) * q_Z[m, k, j]
Sigma_C = copy.deepcopy(Sigma_A)
m_C = copy.deepcopy(m_A)
if simulation is not None:
#print simulation
# simulated values
if not estimateH:
H = Ht = simulation['brf'][:, 0]
sigmaH = 20.
if not estimateG:
G = Gt = simulation['prf'][:, 0]
sigmaG = 40.
A = simulation['brls'].T
if not estimateA:
m_A = A
C = simulation['prls'].T
if not estimateC:
m_C = C
Z = simulation['labels']
Z = np.append(Z[:, np.newaxis, :], Z[:, np.newaxis, :], axis=1)
#Z[:, 1, :] = 1
if not estimateZ:
q_Z = copy.deepcopy(Z)
Z_tilde = copy.deepcopy(Z)
if not estimateLA:
alpha = np.mean(simulation['perf_baseline'], 0)
L = simulation['drift_coeffs']
PL = np.dot(P, L)
wa = np.dot(w[:, np.newaxis], alpha[np.newaxis, :])
y_tilde = Y - PL - wa
if not estimateNoise:
sigma_eps = np.var(simulation['noise'], 0)
if not estimateMP:
#print simulation['condition_defs'][0]
mu_Ma = np.array([[0, 2.2], [0, 2.2]])
sigma_Ma = np.array([[.3, .3], [.3, .3]])
mu_Mc = np.array([[0, 1.6], [0, 1.6]])
sigma_Mc = np.array([[.3, .3], [.3, .3]])
#print simulation['condition_defs'][0]
#print simulation['condition_defs'][0]
#sigmaH = 0.0001
#sigmaG = 0.0001
###########################################################################
############################################# VBJDE
t1 = time.time()
ni = 0
while ((ni < NitMin + 1) or ((Crit_AH > Thresh) and (Crit_CG > Thresh) \
and (ni < NitMax))):
logger.info("-------- Iteration n° " + str(ni + 1) + " ---------")
#####################
# EXPECTATION
#####################
# HRF H
logger.info("E H step ...")
if estimateH:
logger.info("estimation")
#sigmaH = 0.0001
print sigmaH
Ht, Sigma_H = EM.expectation_H(Sigma_A, m_A, m_C, G, X, W, Gamma,
D, J, N, y_tilde, sigma_eps, scale,
R, sigmaH)
H = EM.constraint_norm1_b(Ht, Sigma_H)
#H = Ht / np.linalg.norm(Ht)
print 'BRF ERROR = ', EM.error(H, simulation['brf'][:, 0])
h_norm = np.append(h_norm, np.linalg.norm(H))
print 'h_norm = ', h_norm
# PRF G
logger.info("E G step ...")
if estimateG:
logger.info("estimation")
Gt, Sigma_G = EM.expectation_G(Sigma_C, m_C, m_A, H, X, W, Gamma,
D, J, N, y_tilde, sigma_eps, scale,
R, sigmaG)
G = EM.constraint_norm1_b(Gt, Sigma_G, positivity=True,
perfusion=alpha)
#G = Gt / np.linalg.norm(Gt)
print 'PRF ERROR = ', EM.error(G, simulation['prf'][:, 0])
g_norm = np.append(g_norm, np.linalg.norm(G))
print 'g_norm = ', g_norm
# A
logger.info("E A step ...")
if estimateA:
logger.info("estimation")
m_A, Sigma_A = EM.expectation_A(H, m_A, G, m_C, W, X, Gamma, q_Z,
mu_Ma, sigma_Ma, D, J, M, K,
y_tilde, Sigma_A, sigma_eps)
print 'BRLS ERROR = ', EM.error(m_A, A)
# C
logger.info("E C step ...")
if estimateC:
logger.info("estimation")
m_C, Sigma_C = EM.expectation_C(G, m_C, H, m_A, W, X, Gamma, q_Z,
mu_Mc, sigma_Mc, D, J, M, K,
y_tilde, Sigma_C, sigma_eps)
#print 'true values: ', C
#print 'estimated values: ', m_C
print 'PRLS ERROR = ', EM.error(m_C, C)
# Q labels
logger.info("E Z step ...")
if estimateZ:
logger.info("estimation")
q_Z, Z_tilde = EM.expectation_Z(Sigma_A, m_A, Sigma_C, m_C,
sigma_Ma, mu_Ma, sigma_Mc, mu_Mc,
Beta, Z_tilde, q_Z, graph, M, J, K)
#print 'LABELS ERROR = ', EM.error(q_Z, Z)
# crit. Z
Crit_Z = (np.linalg.norm((q_Z - q_Z1).flatten()) / \
(np.linalg.norm(q_Z1).flatten() + eps)) ** 2
cZ += [Crit_Z]
q_Z1 = q_Z
# crit. AH and CG
for d in xrange(0, D):
AH[:, :, d] = m_A[:, :] * H[d]
CG[:, :, d] = (m_C[:, :] * G[d])
Crit_AH = (np.linalg.norm(np.reshape(AH - AH1, (M * J * D))) / \
(np.linalg.norm(np.reshape(AH1, (M * J * D))) + eps)) ** 2
cAH += [Crit_AH]
AH1 = AH
Crit_CG = (np.linalg.norm(np.reshape(CG - CG1, (M * J * D))) / \
(np.linalg.norm(np.reshape(CG1, (M * J * D))) + eps)) ** 2
cCG += [Crit_CG]
CG1 = CG
if PLOT and ni >= 0: # Plotting HRF and PRF
import matplotlib.pyplot as plt
plt.figure(M + 1)
plt.plot(H)
plt.hold(True)
plt.figure(M + 2)
plt.plot(G)
plt.hold(True)
#####################
# MAXIMIZATION
#####################
# HRF: Sigma_h
if estimateSigmaH:
logger.info("M sigma_H step ...")
print gamma_h
sigmaH = EM.maximization_sigma_prior(D, R, H, gamma_h)
logger.info('sigmaH = ' + str(sigmaH))
# PRF: Sigma_g
if estimateSigmaG:
logger.info("M sigma_G step ...")
sigmaG = EM.maximization_sigma_prior(D, R, G, gamma_g)
logger.info('sigmaG = ' + str(sigmaG))
# (mu,sigma)
if estimateMP:
logger.info("M (mu,sigma) a and c step ...")
Mu_Ma, sigma_Ma = EM.maximization_mu_sigma(mu_Ma, sigma_Ma,
q_Z, m_A, K, M, Sigma_A)
mu_Mc, sigma_Mc = EM.maximization_mu_sigma(mu_Mc, sigma_Mc,
q_Z, m_C, K, M, Sigma_C)
# Drift L, alpha
if estimateLA:
L, alpha = EM.maximization_L_alpha(Y, m_A, m_C, X, W, w, H, \
G, L, P, alpha)
print 'ALPHA ERROR = ', EM.error(alpha, np.mean(\
simulation['perf_baseline'], 0))
print 'DRIFT ERROR = ', EM.error(L, simulation['drift_coeffs'])
#alpha = np.zeros_like(np.mean(simulation['perf_baseline'], 0))
PL = np.dot(P, L)
wa = np.dot(w[:, np.newaxis], alpha[np.newaxis, :])
y_tilde = Y - PL - wa
# Beta
if estimateBeta:
logger.info("estimating beta")
for m in xrange(0, M):
Beta[m] = EM.maximization_beta(Beta[m], q_Z, Z_tilde,
J, K, m, graph, gamma,
neighboursIndexes, maxNeighbours)
print Beta
logger.info("End estimating beta")
logger.info(Beta)
# Sigma noise
if estimateNoise:
logger.info("M sigma noise step ...")
sigma_eps = EM.maximization_sigma_noise(Y, X, m_A, Sigma_A, H,
m_C, Sigma_C, G, W, M, N, J, y_tilde, sigma_eps)
print 'NOISE ERROR = ', EM.error(sigma_eps,
np.var(simulation['noise'], 0))
#print ' - est var noise: ', sigma_eps
#print ' - sim var noise: ', np.var(simulation['noise'], 0)
for m in xrange(M):
SUM_q_Z[m] += [sum(q_Z[m, 1, :])]
mua1[m] += [mu_Ma[m, 1]]
muc1[m] += [mu_Mc[m, 1]]
ni += 1
t02 = time.time()
cTime += [t02 - t1]
t2 = time.time()
CompTime = t2 - t1
cTimeMean = CompTime / ni
###########################################################################
########################################## PLOTS and SNR computation
SUM_q_Z_array = np.zeros((M, ni), dtype=np.float64)
mua1_array = np.zeros((M, ni), dtype=np.float64)
muc1_array = np.zeros((M, ni), dtype=np.float64)
#h_norm_array = np.zeros((ni), dtype=np.float64)
for m in xrange(M):
for i in xrange(ni):
SUM_q_Z_array[m, i] = SUM_q_Z[m][i]
mua1_array[m, i] = mua1[m][i]
muc1_array[m, i] = muc1[m][i]
#h_norm_array[i] = h_norm[i]
if PLOT:
font = {'size': 15}
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rc('font', **font)
plt.figure(M + 1)
plt.savefig('./BRF_Iter_ASL.png')
plt.figure(M + 2)
plt.savefig('./PRF_Iter_ASL.png')
plt.hold(False)
plt.figure(2)
plt.plot(cAH[1:-1], 'lightblue')
plt.hold(True)
plt.plot(cCG[1:-1], 'm')
plt.hold(False)
plt.legend(('CAH', 'CCG'))
plt.grid(True)
plt.savefig('./Crit_ASL.png')
plt.figure(4)
for m in xrange(M):
plt.plot(SUM_q_Z_array[m])
plt.hold(True)
plt.hold(False)
plt.savefig('./Sum_q_Z_Iter_ASL.png')
"""plt.figure(5)
for m in xrange(M):
plt.plot(mua1_array[m])
plt.hold(True)
plt.plot(muc1_array[m])
plt.hold(False)
plt.legend(('mu_a', 'mu_c'))
plt.savefig('./mu1_Iter_ASL.png')
plt.figure(6)
plt.plot(h_norm_array)
plt.savefig('./HRF_Norm_ASL.png')"""
logger.info("Nb iterations to reach criterion: %d", ni)
logger.info("Computational time = %s min %s s",
str(np.int(CompTime // 60)), str(np.int(CompTime % 60)))
StimulusInducedSignal = EM.computeFit(H, m_A, G, m_C, W, X, J, N)
SNR = 20 * (np.log(np.linalg.norm(Y) / \
np.linalg.norm(Y - StimulusInducedSignal - PL))) / np.log(10.)
logger.info('mu_Ma: %f', mu_Ma)
logger.info('sigma_Ma: %f', sigma_Ma)
logger.info("sigma_H = %s" + str(sigmaH))
logger.info('mu_Mc: %f', mu_Mc)
logger.info('sigma_Mc: %f', sigma_Mc)
logger.info("sigma_G = %s" + str(sigmaG))
logger.info("Beta = %s" + str(Beta))
logger.info('SNR comp = %f', SNR)
return ni, m_A, H, m_C, G, q_Z, sigma_eps, cZ[2:],\
cTime, cTimeMean, mu_Ma, sigma_Ma, mu_Mc, sigma_Mc, Beta, L, PL, \
Sigma_A, Sigma_C, StimulusInducedSignal
def Main_vbjde_Python_constrained(graph, Y, Onsets, Thrf, K, TR, beta, dt, scale=1, estimateSigmaH=True, sigmaH=0.1, NitMax=-1, NitMin=1, estimateBeta=False, PLOT=False):
logger.info("EM started ...")
np.random.seed(6537546)
##########################################################################
# INITIALIZATIONS
# Initialize parameters
if NitMax < 0:
NitMax = 100
gamma = 7.5
gradientStep = 0.005
MaxItGrad = 120
#Thresh = 1e-5
Thresh_FreeEnergy = 1e-5
# Initialize sizes vectors
D = int(np.ceil(Thrf / dt))
M = len(Onsets)
N = Y.shape[0]
J = Y.shape[1]
l = int(np.sqrt(J))
sigma_epsilone = np.ones(J)
# Neighbours
maxNeighbours = max([len(nl) for nl in graph])
neighboursIndexes = np.zeros((J, maxNeighbours), dtype=np.int32)
neighboursIndexes -= 1
for i in xrange(J):
neighboursIndexes[i, :len(graph[i])] = graph[i]
# Conditions
X = OrderedDict([])
for condition, Ons in Onsets.iteritems():
X[condition] = vt.compute_mat_X_2(N, TR, D, dt, Ons)
XX = np.zeros((M, N, D), dtype=np.int32)
nc = 0
for condition, Ons in Onsets.iteritems():
XX[nc, :, :] = X[condition]
nc += 1
# Sigma and mu
mu_M = np.zeros((M, K), dtype=np.float64)
sigma_M = 0.5 * np.ones((M, K), dtype=np.float64)
sigma_M0 = 0.5 * np.ones((M, K), dtype=np.float64)
for k in xrange(1, K):
mu_M[:, k] = 2.0
# Covariance matrix
order = 2
D2 = vt.buildFiniteDiffMatrix(order, D)
R = np.dot(D2, D2) / pow(dt, 2 * order)
Gamma = np.identity(N)
q_Z = np.zeros((M, K, J), dtype=np.float64)
# for k in xrange(0,K):
q_Z[:, 1, :] = 1
q_Z1 = np.zeros((M, K, J), dtype=np.float64)
Z_tilde = q_Z.copy()
Sigma_A = np.zeros((M, M, J), np.float64)
m_A = np.zeros((J, M), dtype=np.float64)
TT, m_h = getCanoHRF(Thrf - dt, dt) # TODO: check
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01 * np.identity(M)
for m in xrange(0, M):
for k in xrange(0, K):
m_A[j, m] += np.random.normal(mu_M[m, k],
np.sqrt(sigma_M[m, k])) * Z_tilde[m, k, j]
m_H = np.array(m_h).astype(np.float64)
m_H1 = np.array(m_h)
Sigma_H = np.ones((D, D), dtype=np.float64)
Beta = beta * np.ones((M), dtype=np.float64)
zerosDD = np.zeros((D, D), dtype=np.float64)
zerosD = np.zeros((D), dtype=np.float64)
zerosND = np.zeros((N, D), dtype=np.float64)
zerosMM = np.zeros((M, M), dtype=np.float64)
zerosJMD = np.zeros((J, M, D), dtype=np.float64)
zerosK = np.zeros(K)
P = vt.PolyMat(N, 4, TR)
zerosP = np.zeros((P.shape[0]), dtype=np.float64)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - PL
sigmaH1 = sigmaH
Crit_H = 1
Crit_Z = 1
Crit_A = 1
cA = []
cH = []
cZ = []
Ndrift = L.shape[0]
Crit_FreeEnergy = 1
t1 = time.time()
##########################################################################
# VBJDE num. iter. minimum
ni = 0
while ((ni < NitMin) or (((Crit_FreeEnergy > Thresh_FreeEnergy) or ((Crit_H > Thresh) and (Crit_Z > Thresh) and (Crit_A > Thresh))) and (ni < NitMax))):
logger.info("------------------------------ Iteration n° " +
str(ni + 1) + " ------------------------------")
#####################
# EXPECTATION
#####################
# A
logger.info("E A step ...")
Sigma_A, m_A = vt.expectation_A(
Y, Sigma_H, m_H, m_A, X, Gamma, PL, sigma_M, q_Z, mu_M, D, N, J, M, K, y_tilde, Sigma_A, sigma_epsilone, zerosJMD)
m_A1 = m_A
# crit A
DIFF = np.abs(np.reshape(m_A, (M * J)) - np.reshape(m_A1, (M * J)))
Crit_A = sum(DIFF) / len(np.where(DIFF != 0))
cA += [Crit_A]
# H
logger.info("E H step ...")
Sigma_H, m_H = vt.expectation_H(
Y, Sigma_A, m_A, X, Gamma, PL, D, R, sigmaH, J, N, y_tilde, zerosND, sigma_epsilone, scale, zerosDD, zerosD)
m_H[0] = 0
m_H[-1] = 0
m_H1 = m_H
# crit H
Crit_H = np.abs(np.mean(m_H - m_H1) / np.mean(m_H))
cH += [Crit_H]
# Z
logger.info("E Z step ...")
q_Z, Z_tilde = vt.expectation_Z(
Sigma_A, m_A, sigma_M, Beta, Z_tilde, mu_M, q_Z, graph, M, J, K, zerosK)
# crit Z
DIFF = np.abs(
np.reshape(q_Z, (M * K * J)) - np.reshape(q_Z1, (M * K * J)))
Crit_Z = sum(DIFF) / len(np.where(DIFF != 0))
cZ += [Crit_Z]
q_Z1 = q_Z
# Plotting HRF
if PLOT and ni >= 0:
import matplotlib.pyplot as plt
plt.figure(M + 1)
plt.plot(m_H)
plt.hold(True)
####################
# MAXIMIZATION
#####################
# HRF: Sigma_h
if estimateSigmaH:
logger.info("M sigma_H step ...")
sigmaH = (np.dot(vt.mult(m_H, m_H) + Sigma_H, R)).trace()
sigmaH /= D
# (mu,sigma)
logger.info("M (mu,sigma) step ...")
mu_M, sigma_M = vt.maximization_mu_sigma(
mu_M, sigma_M, q_Z, m_A, K, M, Sigma_A)
# Drift L
L = vt.maximization_L(Y, m_A, X, m_H, L, P, zerosP)
PL = np.dot(P, L)
y_tilde = Y - PL
# Beta
if estimateBeta:
logger.info("estimating beta")
for m in xrange(0, M):
Beta[m] = vt.maximization_beta(
Beta[m], q_Z, Z_tilde, J, K, m, graph, gamma, neighboursIndexes, maxNeighbours)
logger.info("End estimating beta")
# logger.info(Beta)
# Sigma noise
logger.info("M sigma noise step ...")
sigma_epsilone = vt.maximization_sigma_noise(
Y, X, m_A, m_H, Sigma_H, Sigma_A, PL, sigma_epsilone, M, zerosMM)
#### Computing Free Energy ####
"""
if ni > 0:
FreeEnergy1 = FreeEnergy
FreeEnergy = Compute_FreeEnergy(y_tilde,m_A,Sigma_A,mu_M,sigma_M,m_H,Sigma_H,R,Det_invR,sigmaH,p_Wtilde,tau1,tau2,q_Z,neighboursIndexes,maxNeighbours,Beta,sigma_epsilone,XX,Gamma,Det_Gamma,XGamma,J,D,M,N,K,S,"CompMod")
if ni > 0:
Crit_FreeEnergy = (FreeEnergy1 - FreeEnergy) / FreeEnergy1
FreeEnergy_Iter += [FreeEnergy]
cFE += [Crit_FreeEnergy]
"""
# Update index
ni += 1
t2 = time.time()
##########################################################################
# PLOTS and SNR computation
"""FreeEnergyArray = np.zeros((ni),dtype=np.float64)
for i in xrange(ni):
FreeEnergyArray[i] = FreeEnergy_Iter[i]
SUM_q_Z_array = np.zeros((M,ni),dtype=np.float64)
mu1_array = np.zeros((M,ni),dtype=np.float64)
h_norm_array = np.zeros((ni),dtype=np.float64)
for m in xrange(M):
for i in xrange(ni):
SUM_q_Z_array[m,i] = SUM_q_Z[m][i]
mu1_array[m,i] = mu1[m][i]
h_norm_array[i] = h_norm[i]
sigma_M = np.sqrt(np.sqrt(sigma_M))
"""
Norm = np.linalg.norm(m_H)
m_H /= Norm
m_A *= Norm
mu_M *= Norm
sigma_M *= Norm
sigma_M = np.sqrt(sigma_M)
if PLOT and 0:
import matplotlib.pyplot as plt
import matplotlib
font = {'size': 15}
matplotlib.rc('font', **font)
plt.savefig('./HRF_Iter_CompMod.png')
plt.hold(False)
plt.figure(2)
plt.plot(cAH[1:-1], 'lightblue')
plt.hold(True)
plt.plot(cFE[1:-1], 'm')
plt.hold(False)
#plt.legend( ('CA','CH', 'CZ', 'CAH', 'CFE') )
plt.legend(('CAH', 'CFE'))
plt.grid(True)
plt.savefig('./Crit_CompMod.png')
plt.figure(3)
plt.plot(FreeEnergyArray)
plt.grid(True)
plt.savefig('./FreeEnergy_CompMod.png')
plt.figure(4)
for m in xrange(M):
plt.plot(SUM_q_Z_array[m])
plt.hold(True)
plt.hold(False)
plt.savefig('./Sum_q_Z_Iter_CompMod.png')
plt.figure(5)
for m in xrange(M):
plt.plot(mu1_array[m])
plt.hold(True)
plt.hold(False)
plt.savefig('./mu1_Iter_CompMod.png')
plt.figure(6)
plt.plot(h_norm_array)
plt.savefig('./HRF_Norm_CompMod.png')
Data_save = xndarray(h_norm_array, ['Iteration'])
Data_save.save('./HRF_Norm_Comp.nii')
CompTime = t2 - t1
cTimeMean = CompTime / ni
logger.info("Nb iterations to reach criterion: %d", ni)
logger.info("Computational time = %s min %s s", str(
int(CompTime // 60)), str(int(CompTime % 60)))
# print "Computational time = " + str(int( CompTime//60 ) ) + " min " +
# str(int(CompTime%60)) + " s"
logger.info('mu_M: %f', mu_M)
logger.info('sigma_M: %f', sigma_M)
logger.info("sigma_H = %s" + str(sigmaH))
logger.info("Beta = %s" + str(Beta))
StimulusInducedSignal = vt.computeFit(m_H, m_A, X, J, N)
SNR = 20 * \
np.log(
np.linalg.norm(Y) / np.linalg.norm(Y - StimulusInducedSignal - PL))
SNR /= np.log(10.)
logger.info('SNR comp = %f', SNR)
return m_A, m_H, q_Z, sigma_epsilone, mu_M, sigma_M, Beta, L, PL
def Main_vbjde_c_constrained(graph, Y, Onsets, Thrf, K, TR, beta, dt, scale=1,
estimateSigmaH=True, estimateSigmaG=True,
sigmaH=0.05, sigmaG=0.05, gamma_h=0, gamma_g=0,
NitMax=-1, NitMin=1, estimateBeta=True,
PLOT=False, idx_first_tag=0, simulation=None,
estimateH=True, estimateG=True, estimateA=True,
estimateC=True, estimateZ=True, M_step=True,
estimateNoise=True, estimateMP=True,
estimateLA=True):
""" Version modified by Lofti from Christine's version """
logger.info("Fast EM with C extension started ... "
"Here is the stable version !")
np.random.seed(6537546)
#Initialize parameters
#gamma_h = 7.5
#gamma_g = 7.5
D, M = np.int(np.ceil(Thrf / dt)) + 1, len(Onsets)
N, J = Y.shape[0], Y.shape[1]
if NitMax < 0:
NitMax = 100
gamma = 7.5
gradientStep = 0.003
MaxItGrad = 200
Thresh = 1e-5
# Neighbours
maxNeighbours, neighboursIndexes = EM.create_neighbours(graph, J)
# Conditions
X, XX = EM.create_conditions(Onsets, M, N, D, TR, dt)
# Covariance matrix
R = EM.covariance_matrix(2, D, dt)
# Noise matrix
Gamma = np.identity(N)
# Noise initialization
sigma_eps = np.ones(J)
Crit_AH, Crit_CG = 1, 1
Crit_H, Crit_G, Crit_Z, Crit_A, Crit_C = 1, 1, 1, 1, 1
AH = np.zeros((J, M, D), dtype=np.float64)
AH1 = np.zeros((J, M, D), dtype=np.float64)
CG = np.zeros((J, M, D), dtype=np.float64)
CG1 = np.zeros((J, M, D), dtype=np.float64)
cTime = []
cAH, cCG = [], []
cA, cC, cH, cG, cZ = [], [], [], [], []
h_norm, g_norm = [], []
#Labels
logger.info("Labels are initialized by setting active probabilities "
"to ones ...")
q_Z = np.zeros((M, K, J), dtype=np.float64)
q_Z[:, 1, :] = 1
q_Z1 = np.zeros((M, K, J), dtype=np.float64)
Z_tilde = copy.deepcopy(q_Z)
# H and G
TT, m_h = getCanoHRF(Thrf, dt)
m_h = m_h[:D]
H = np.array(m_h).astype(np.float64)
H1 = copy.deepcopy(H)
Sigma_H = np.zeros((D, D), dtype=np.float64)
G = copy.deepcopy(H)
G1 = copy.deepcopy(H)
Sigma_G = copy.deepcopy(Sigma_H)
#m_H = np.array(m_h).astype(np.float64)
#m_H1 = np.array(m_h)
# others
Beta = beta * np.ones((M), dtype=np.float64)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
Ndrift = L.shape[0]
PL = np.dot(P, L)
alpha = np.zeros((J), dtype=np.float64)
w = np.ones((N))
w[idx_first_tag::2] = -1
w = - w
W = np.diag(w)
wa = np.dot(w[:, np.newaxis], alpha[np.newaxis, :])
y_tilde = Y - PL - wa
# Parameters Gaussian mixtures
sigma_Ma = np.ones((M, K), dtype=np.float64)
sigma_Ma[:, 0] = 0.5
sigma_Ma[:, 1] = 0.6
mu_Ma = np.zeros((M, K), dtype=np.float64)
for k in xrange(1, K):
mu_Ma[:, k] = 1
sigma_Mc = copy.deepcopy(sigma_Ma)
mu_Mc = copy.deepcopy(mu_Ma)
# Params RLs
Sigma_A = np.zeros((M, M, J), np.float64)
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01 * np.identity(M)
m_A = np.zeros((J, M), dtype=np.float64)
m_A1 = np.zeros((J, M), dtype=np.float64)
for j in xrange(0, J):
for m in xrange(0, M):
for k in xrange(0, K):
m_A[j, m] += np.random.normal(mu_Ma[m, k], \
np.sqrt(sigma_Ma[m, k])) * q_Z[m, k, j]
m_A1 = m_A
Sigma_C = copy.deepcopy(Sigma_A)
m_C1 = m_C = copy.deepcopy(m_A)
Q_barnCond = np.zeros((M, M, D, D), dtype=np.float64)
XGamma = np.zeros((M, D, N), dtype=np.float64)
XWGamma = np.zeros((M, D, N), dtype=np.float64)
for m1, k1 in enumerate(X): # Loop over the M conditions
for m2, k2 in enumerate(X):
Q_barnCond[m1, m2, :, :] = np.dot(np.dot(X[k1].T, \
Gamma), X[k2])
XGamma[m1, :, :] = np.dot(X[k1].T, Gamma)
XWGamma[m1, :, :] = np.dot(np.dot(X[k1].T, W), Gamma)
sigma_eps = np.ones(J)
# simulated values
if not estimateH:
H = H1 = simulation['primary_brf']
sigmaH = 20.
if not estimateG:
G = G1 = simulation['primary_prf']
sigmaG = 40.
if not estimateA:
A = simulation['brls'].T
print 'shape BRLs: ', A.shape
m_A = A
if not estimateC:
C = simulation['prls'].T
print 'shape PRLs: ', C.shape
m_C = C
if not estimateZ:
Z = np.reshape(simulation['labels_vol'], [2, 1, 400])
Z = np.append(Z, np.ones_like(Z), axis=1)
print np.reshape(Z[0, 0, :], [20, 20])
if not estimateLA:
alpha = np.mean(simulation['perf_baseline'], 0)
L = simulation['drift_coeffs']
PL = np.dot(P, L)
wa = np.dot(w[:, np.newaxis], alpha[np.newaxis, :])
y_tilde = Y - PL - wa
if not estimateNoise:
sigma_eps = np.mean(simulation['noise'], 0)
if not estimateMP:
mu_Ma = np.array([[0, 2.2], [0, 2.2]])
sigma_Ma = np.array([[.3, .3], [.3, .3]])
mu_Mc = np.array([[0, 1.6], [0, 1.6]])
sigma_Mc = np.array([[.3, .3], [.3, .3]])
t1 = time.time()
##########################################################################
############################################# VBJDE num. iter. minimum
ni = 0
while ((ni < NitMin + 1) or ((Crit_AH > Thresh) and (ni < NitMax))):
logger.info("------------------------------ Iteration n° " + \
str(ni + 1) + " ------------------------------")
#####################
# EXPECTATION
#####################
# A
if estimateA:
logger.info("E A step ...")
UtilsC.expectation_A(q_Z, mu_Mc, sigma_Mc, PL, sigma_eps,
Gamma, Sigma_H, Y, y_tilde, m_A, H, Sigma_A,
XX.astype(np.int32), J, D, M, N, K)
# crit. A
DIFF = np.reshape(m_A - m_A1, (M * J))
Crit_A = (np.linalg.norm(DIFF) / \
np.linalg.norm(np.reshape(m_A1, (M * J)))) ** 2
cA += [Crit_A]
m_A1[:, :] = m_A[:, :]
# C
if estimateC:
logger.info("E C step ...")
UtilsC.expectation_C(q_Z, mu_Mc, sigma_Mc, PL, sigma_eps,
Gamma, Sigma_H, Y, y_tilde, m_A, H, Sigma_A,
XX.astype(np.int32), J, D, M, N, K)
# crit. C
DIFF = np.reshape(m_C - m_C1, (M * J))
Crit_C = (np.linalg.norm(DIFF) / \
np.linalg.norm(np.reshape(m_C1, (M * J)))) ** 2
cC += [Crit_C]
m_C1[:, :] = m_C[:, :]
# HRF h
if estimateH:
logger.info("E H step ...")
UtilsC.expectation_H(XGamma, Q_barnCond, sigma_eps, Gamma, R,
Sigma_H, Y, y_tilde, m_A, H, Sigma_A,
XX.astype(np.int32), J, D, M, N, scale,
sigmaH)
#H = EM.constraint_norm1(m_H, Sigma_H)
H = H / np.linalg.norm(H)
print 'BRF ERROR = ', EM.error(H, simulation['primary_brf'])
h_norm = np.append(h_norm, np.linalg.norm(H))
print 'h_norm = ', h_norm
# crit. h
Crit_H = (np.linalg.norm(H - H1) / np.linalg.norm(H1)) ** 2
cH += [Crit_H]
H1[:] = H[:]
# PRF g
if estimateG:
logger.info("E G step ...")
UtilsC.expectation_G(XGamma, Q_barnCond, sigma_eps, Gamma, R,
Sigma_H, Y, y_tilde, m_A, H, Sigma_A,
XX.astype(np.int32), J, D, M, N, scale,
sigmaH)
G = EM.constraint_norm1(G, Sigma_G)
print 'PRF ERROR = ', EM.error(G, simulation['primary_prf'])
g_norm = np.append(g_norm, np.linalg.norm(G))
print 'g_norm = ', g_norm
# crit. g
Crit_G = (np.linalg.norm(G - G1) / np.linalg.norm(G1)) ** 2
cG += [Crit_G]
G1[:] = G[:]
# crit. AH
for d in xrange(0, D):
AH[:, :, d] = m_A[:, :] * H[d]
DIFF = np.reshape(AH - AH1, (M * J * D))
Crit_AH = (np.linalg.norm(DIFF) / \
(np.linalg.norm(np.reshape(AH1, (M * J * D))) + eps)) ** 2
cAH += [Crit_AH]
AH1[:, :, :] = AH[:, :, :]
# crit. CG
for d in xrange(0, D):
CG[:, :, d] = m_C[:, :] * G[d]
DIFF = np.reshape(CG - CG1, (M * J * D))
Crit_CG = (np.linalg.norm(DIFF) / \
(np.linalg.norm(np.reshape(CG1, (M * J * D))) + eps)) ** 2
cCG += [Crit_CG]
CG1[:, :, :] = CG[:, :, :]
# Z labels
if estimateZ:
logger.info("E Z step ...")
UtilsC.expectation_Z(Sigma_A, m_A, sigma_M, Beta, Z_tilde, mu_M,
q_Z, neighboursIndexes.astype(np.int32), M,
J, K, maxNeighbours)
# crit. Z
DIFF = np.reshape(q_Z - q_Z1, (M * K * J))
Crit_Z = (np.linalg.norm(DIFF) / \
(np.linalg.norm(np.reshape(q_Z1, (M * K * J))) + eps)) ** 2
cZ += [Crit_Z]
q_Z1[:, :, :] = q_Z[:, :, :]
#####################
# MAXIMIZATION
#####################
# HRF: Sigma_h
if estimateSigmaH:
logger.info("M sigma_H step ...")
if gamma_h > 0:
sigmaH = EM.maximization_sigma_prior(D, Sigma_H, R, H,
gamma_h)
else:
sigmaH = EM.maximization_sigma(D, Sigma_H, R, H)
logger.info('sigmaH = %s', str(sigmaH))
# PRF: Sigma_g
if estimateSigmaG:
logger.info("M sigma_G step ...")
if gamma_g > 0:
sigmaG = vt.maximization_sigma_prior(D, Sigma_G, R, G,
gamma_g)
else:
sigmaG = vt.maximization_sigma(D, Sigma_G, R, G)
logger.info('sigmaG = %s', str(sigmaG))
# (mu_a,sigma_a)
if estimateMP:
logger.info("M (mu_a,sigma_a) step ...")
mu_Ma, sigma_Ma = vt.maximization_mu_sigma(mu_Ma, sigma_Ma, q_Z,
m_A, K, M, Sigma_A)
# (mu_c,sigma_c)
logger.info("M (mu_c,sigma_c) step ...")
mu_Mc, sigma_Mc = vt.maximization_mu_sigma(mu_Mc, sigma_Mc, q_Z,
m_A, K, M, Sigma_A)
# Drift L
if estimateLA:
UtilsC.maximization_L(Y, m_A, H, L, P, XX.astype(np.int32), J, D,
M, Ndrift, N)
PL = np.dot(P, L)
y_tilde = Y - PL
# Beta
if estimateBeta:
logger.info("estimating beta")
for m in xrange(0, M):
Beta[m] = UtilsC.maximization_beta(beta, \
q_Z[m, :, :].astype(np.float64),
Z_tilde[m, :, :].astype(np.float64), J, K,
neighboursIndexes.astype(np.int32), gamma,
maxNeighbours, MaxItGrad, gradientStep)
logger.info("End estimating beta")
logger.info(Beta)
# Sigma noise
if estimateNoise:
logger.info("M sigma noise step ...")
UtilsC.maximization_sigma_noise(Gamma, PL, sigma_eps, Sigma_H,
Y, m_A, H, Sigma_A,
XX.astype(np.int32), J, D, M, N)
t02 = time.time()
cTime += [t02 - t1]
t2 = time.time()
###########################################################################
########################################### PLOTS and SNR computation
CompTime = t2 - t1
cTimeMean = CompTime / ni
if 0:
Norm = np.linalg.norm(H)
H /= Norm
Sigma_H /= Norm ** 2
sigmaH /= Norm ** 2
m_A *= Norm
Sigma_A *= Norm ** 2
mu_Ma *= Norm
sigma_Ma *= Norm ** 2
sigma_Ma = np.sqrt(np.sqrt(sigma_Ma))
Norm = np.linalg.norm(G)
G /= Norm
Sigma_G /= Norm ** 2
sigmaG /= Norm ** 2
m_C *= Norm
Sigma_C *= Norm ** 2
mu_Mc *= Norm
sigma_Mc *= Norm ** 2
sigma_Mc = np.sqrt(np.sqrt(sigma_Mc))
logger.info("Nb iterations to reach criterion: %d", ni)
logger.info("Computational time = %s min %s s", str(np.int(CompTime // 60)), str(np.int(CompTime % 60)))
StimulusInducedSignal = vt.computeFit(H, m_A, X, J, N)
SNR = 20 * np.log(np.linalg.norm(Y) / \
np.linalg.norm(Y - StimulusInducedSignal - PL))
SNR /= np.log(10.)
logger.info('mu_Ma: %f', mu_Ma)
logger.info('sigma_Ma: %f', sigma_Ma)
logger.info("sigma_H = %s" + str(sigmaH))
logger.info("Beta = %s" + str(Beta))
logger.info('SNR comp = %f', SNR)
return ni, m_A, H, q_Z, sigma_eps, mu_Ma, sigma_Ma, Beta, L, PL, \
cA[2:], cH[2:], cZ[2:], cAH[2:], cTime[2:], \
cTimeMean, Sigma_A, StimulusInducedSignal
def Main_vbjde_physio(graph, Y, Onsets, durations, Thrf, K, TR, beta, dt,
scale=1, estimateSigmaH=True, estimateSigmaG=True,
sigmaH=0.05, sigmaG=0.05, gamma_h=0, gamma_g=0,
NitMax=-1, NitMin=1, estimateBeta=True, PLOT=False,
contrasts=[], computeContrast=False,
idx_first_tag=0, simulation=None, sigmaMu=None,
estimateH=True, estimateG=True, estimateA=True,
estimateC=True, estimateZ=True, estimateNoise=True,
estimateMP=True, estimateLA=True, use_hyperprior=False,
positivity=False, constraint=False,
phy_params=PHY_PARAMS_KHALIDOV11, prior='omega', zc=False):
logger.info("EM for ASL!")
np.random.seed(6537540)
logger.info("data shape: ")
logger.info(Y.shape)
Thresh = 1e-5
D, M = np.int(np.ceil(Thrf / dt)) + 1, len(Onsets)
#D, M = np.int(np.ceil(Thrf / dt)), len(Onsets)
N, J = Y.shape[0], Y.shape[1]
Crit_AH, Crit_CG, cTime, rerror, FE = 1, 1, [], [], []
EP, EPlh, Ent = [],[],[]
Crit_H, Crit_G, Crit_Z, Crit_A, Crit_C = 1, 1, 1, 1, 1
cAH, cCG, AH1, CG1 = [], [], [], []
cA, cC, cH, cG, cZ = [], [], [], [], []
h_norm, g_norm = [], []
SUM_q_Z = [[] for m in xrange(M)]
mua1 = [[] for m in xrange(M)]
muc1 = [[] for m in xrange(M)]
# Beta data
MaxItGrad = 200
gradientStep = 0.005
gamma = 7.5
maxNeighbours, neighboursIndexes = vt.create_neighbours(graph, J)
# Control-tag
w = np.ones((N))
w[idx_first_tag + 1::2] = -1
W = np.diag(w)
# Conditions
X, XX, condition_names = vt.create_conditions_block(Onsets, durations, M, N, D, TR, dt)
#X, XX, condition_names = vt.create_conditions(Onsets, M, N, D, TR, dt)
if zc:
XX = XX[:, :, 1:-1] # XX shape (S, M, N, D)
D = D - 2
AH1, CG1 = np.zeros((J, M, D)), np.zeros((J, M, D))
# Covariance matrix
#R = vt.covariance_matrix(2, D, dt)
_, R_inv = genGaussianSmoothHRF(False, D, dt, 1., 2)
R = np.linalg.inv(R_inv)
# Noise matrix
Gamma = np.identity(N)
# Noise initialization
sigma_eps = np.ones(J)
# Labels
logger.info("Labels are initialized by setting active probabilities "
"to ones ...")
q_Z = np.ones((M, K, J), dtype=np.float64) / 2.
#q_Z = np.zeros((M, K, J), dtype=np.float64)
#q_Z[:, 1, :] = 1
q_Z1 = copy.deepcopy(q_Z)
Z_tilde = copy.deepcopy(q_Z)
# H and G
TT, m_h = getCanoHRF(Thrf, dt)
H = np.array(m_h[1:D+1]).astype(np.float64)
H /= np.linalg.norm(H)
G = copy.deepcopy(H)
Hb = create_physio_brf(phy_params, response_dt=dt, response_duration=Thrf)
Hb /= np.linalg.norm(Hb)
Gb = create_physio_prf(phy_params, response_dt=dt, response_duration=Thrf)
Gb /= np.linalg.norm(Gb)
if prior=='balloon':
H = Hb.copy()
G = Gb.copy()
Mu = Hb.copy()
H1 = copy.deepcopy(H)
Sigma_H = np.zeros((D, D), dtype=np.float64)
G1 = copy.deepcopy(G)
Sigma_G = copy.deepcopy(Sigma_H)
normOh = False
normg = False
if prior=='hierarchical' or prior=='omega':
Omega = linear_rf_operator(len(H), phy_params, dt, calculating_brf=False)
if prior=='omega':
Omega0 = Omega.copy()
OmegaH = np.dot(Omega, H)
G = np.dot(Omega, H)
if normOh or normg:
Omega /= np.linalg.norm(OmegaH)
OmegaH /=np.linalg.norm(OmegaH)
G /= np.linalg.norm(G)
# Initialize model parameters
Beta = beta * np.ones((M), dtype=np.float64)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
alpha = np.zeros((J), dtype=np.float64)
WP = np.append(w[:, np.newaxis], P, axis=1)
AL = np.append(alpha[np.newaxis, :], L, axis=0)
y_tilde = Y - WP.dot(AL)
# Parameters Gaussian mixtures
mu_Ma = np.append(np.zeros((M, 1)), np.ones((M, 1)), axis=1).astype(np.float64)
mu_Mc = mu_Ma.copy()
sigma_Ma = np.ones((M, K), dtype=np.float64) * 0.3
sigma_Mc = sigma_Ma.copy()
# Params RLs
m_A = np.zeros((J, M), dtype=np.float64)
for j in xrange(0, J):
m_A[j, :] = (np.random.normal(mu_Ma, np.sqrt(sigma_Ma)) * q_Z[:, :, j]).sum(axis=1).T
m_A1 = m_A.copy()
Sigma_A = np.ones((M, M, J)) * np.identity(M)[:, :, np.newaxis]
m_C = m_A.copy()
m_C1 = m_C.copy()
Sigma_C = Sigma_A.copy()
# Precomputations
WX = W.dot(XX).transpose(1, 0, 2)
Gamma_X = np.tensordot(Gamma, XX, axes=(1, 1))
X_Gamma_X = np.tensordot(XX.T, Gamma_X, axes=(1, 0)) # shape (D, M, M, D)
Gamma_WX = np.tensordot(Gamma, WX, axes=(1, 1))
XW_Gamma_WX = np.tensordot(WX.T, Gamma_WX, axes=(1, 0)) # shape (D, M, M, D)
Gamma_WP = Gamma.dot(WP)
WP_Gamma_WP = WP.T.dot(Gamma_WP)
sigma_eps_m = np.maximum(sigma_eps, eps)
cov_noise = sigma_eps_m[:, np.newaxis, np.newaxis]
###########################################################################
############################################# VBJDE
t1 = time.time()
ni = 0
#while ((ni < NitMin + 1) or (((Crit_AH > Thresh) or (Crit_CG > Thresh)) \
# and (ni < NitMax))):
#while ((ni < NitMin + 1) or (((Crit_AH > Thresh)) \
# and (ni < NitMax))):
while ((ni < NitMin + 1) or (((Crit_FE > Thresh * np.ones_like(Crit_FE)).any()) \
and (ni < NitMax))):
logger.info("-------- Iteration n° " + str(ni + 1) + " --------")
if PLOT and ni >= 0: # Plotting HRF and PRF
logger.info("Plotting HRF and PRF for current iteration")
vt.plot_response_functions_it(ni, NitMin, M, H, G, Mu, prior)
# Managing types of prior
priorH_cov_term = np.zeros_like(R_inv)
priorG_cov_term = np.zeros_like(R_inv)
matrix_covH = R_inv.copy()
matrix_covG = R_inv.copy()
if prior=='balloon':
logger.info(" prior balloon")
#matrix_covH = np.eye(R_inv.shape[0], R_inv.shape[1])
#matrix_covG = np.eye(R_inv.shape[0], R_inv.shape[1])
priorH_mean_term = np.dot(matrix_covH / sigmaH, Hb)
priorG_mean_term = np.dot(matrix_covG / sigmaG, Gb)
elif prior=='omega':
logger.info(" prior omega")
#matrix_covG = np.eye(R_inv.shape[0], R_inv.shape[1])
priorH_mean_term = np.dot(np.dot(Omega.T, matrix_covG / sigmaG), G)
priorH_cov_term = np.dot(np.dot(Omega.T, matrix_covG / sigmaG), Omega)
priorG_mean_term = np.dot(matrix_covG / sigmaG, OmegaH)
elif prior=='hierarchical':
logger.info(" prior hierarchical")
matrix_covH = np.eye(R_inv.shape[0], R_inv.shape[1])
matrix_covG = np.eye(R_inv.shape[0], R_inv.shape[1])
priorH_mean_term = Mu / sigmaH
priorG_mean_term = np.dot(Omega, Mu / sigmaG)
else:
logger.info(" NO prior")
priorH_mean_term = np.zeros_like(H)
priorG_mean_term = np.zeros_like(G)
#####################
# EXPECTATION
#####################
# HRF H
if estimateH:
logger.info("E H step ...")
Ht, Sigma_H = vt.expectation_H_asl(Sigma_A, m_A, m_C, G, XX, W, Gamma,
Gamma_X, X_Gamma_X, J, y_tilde,
cov_noise, matrix_covH, sigmaH,
priorH_mean_term, priorH_cov_term)
if constraint:
if not np.linalg.norm(Ht)==1:
logger.info(" constraint l2-norm = 1")
H = vt.constraint_norm1_b(Ht, Sigma_H)
#H = Ht / np.linalg.norm(Ht)
else:
logger.info(" l2-norm already 1!!!!!")
H = Ht.copy()
Sigma_H = np.zeros_like(Sigma_H)
else:
H = Ht.copy()
h_norm = np.append(h_norm, np.linalg.norm(H))
print 'h_norm = ', h_norm
Crit_H = (np.linalg.norm(H - H1) / np.linalg.norm(H1)) ** 2
cH += [Crit_H]
H1[:] = H[:]
if prior=='omega':
OmegaH = np.dot(Omega0, H)
Omega = Omega0
if normOh:
Omega /= np.linalg.norm(OmegaH)
OmegaH /= np.linalg.norm(OmegaH)
if ni > 0:
free_energyH = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyH < free_energy:
logger.info("free energy has decreased after E-H step from %f to %f", free_energy, free_energyH)
# A
if estimateA:
logger.info("E A step ...")
m_A, Sigma_A = vt.expectation_A_asl(H, G, m_C, W, XX, Gamma, Gamma_X, q_Z,
mu_Ma, sigma_Ma, J, y_tilde,
Sigma_H, sigma_eps_m)
cA += [(np.linalg.norm(m_A - m_A1) / np.linalg.norm(m_A1)) ** 2]
m_A1[:, :] = m_A[:, :]
if ni > 0:
free_energyA = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyA < free_energyH:
logger.info("free energy has decreased after E-A step from %f to %f", free_energyH, free_energyA)
# PRF G
if estimateG:
logger.info("E G step ...")
Gt, Sigma_G = vt.expectation_G_asl(Sigma_C, m_C, m_A, H, XX, W, WX, Gamma,
Gamma_WX, XW_Gamma_WX, J, y_tilde,
cov_noise, matrix_covG, sigmaG,
priorG_mean_term, priorG_cov_term)
if constraint and normg:
if not np.linalg.norm(Gt)==1:
logger.info(" constraint l2-norm = 1")
G = vt.constraint_norm1_b(Gt, Sigma_G, positivity=positivity)
#G = Gt / np.linalg.norm(Gt)
else:
logger.info(" l2-norm already 1!!!!!")
G = Gt.copy()
Sigma_G = np.zeros_like(Sigma_G)
else:
G = Gt.copy()
g_norm = np.append(g_norm, np.linalg.norm(G))
print 'g_norm = ', g_norm
cG += [(np.linalg.norm(G - G1) / np.linalg.norm(G1)) ** 2]
G1[:] = G[:]
if ni > 0:
free_energyG = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyG < free_energyA:
logger.info("free energy has decreased after E-G step from %f to %f", free_energyA, free_energyG)
# C
if estimateC:
logger.info("E C step ...")
m_C, Sigma_C = vt.expectation_C_asl(G, H, m_A, W, XX, Gamma, Gamma_X, q_Z,
mu_Mc, sigma_Mc, J, y_tilde,
Sigma_G, sigma_eps_m)
cC += [(np.linalg.norm(m_C - m_C1) / np.linalg.norm(m_C1)) ** 2]
m_C1[:, :] = m_C[:, :]
if ni > 0:
free_energyC = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyC < free_energyG:
logger.info("free energy has decreased after E-C step from %f to %f", free_energyG, free_energyC)
# Q labels
if estimateZ:
logger.info("E Q step ...")
q_Z, Z_tilde = vt.expectation_Q_asl(Sigma_A, m_A, Sigma_C, m_C,
sigma_Ma, mu_Ma, sigma_Mc, mu_Mc,
Beta, Z_tilde, q_Z, neighboursIndexes, graph, M, J, K)
#q_Z0, Z_tilde0 = vt.expectation_Q_async(Sigma_A, m_A, Sigma_C, m_C,
# sigma_Ma, mu_Ma, sigma_Mc, mu_Mc,
# Beta, Z_tilde, q_Z, neighboursIndexes, graph, M, J, K)
#print 'synchronous vs asynchronous: ', np.abs(q_Z - q_Z0).sum()
cZ += [(np.linalg.norm(q_Z - q_Z1) / (np.linalg.norm(q_Z1) + eps)) ** 2]
q_Z1 = q_Z
if ni > 0:
free_energyQ = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyQ < free_energyC:
logger.info("free energy has decreased after E-Q step from %f to %f", free_energyC, free_energyQ)
# crit. AH and CG
logger.info("crit. AH and CG")
AH = m_A[:, :, np.newaxis] * H[np.newaxis, np.newaxis, :]
CG = m_C[:, :, np.newaxis] * G[np.newaxis, np.newaxis, :]
Crit_AH = (np.linalg.norm(AH - AH1) / (np.linalg.norm(AH1) + eps)) ** 2
cAH += [Crit_AH]
AH1 = AH.copy()
Crit_CG = (np.linalg.norm(CG - CG1) / (np.linalg.norm(CG1) + eps)) ** 2
cCG += [Crit_CG]
CG1 = CG.copy()
logger.info("Crit_AH = " + str(Crit_AH))
logger.info("Crit_CG = " + str(Crit_CG))
#####################
# MAXIMIZATION
#####################
if prior=='balloon':
logger.info(" prior balloon")
AuxH = H - Hb
AuxG = G - Gb
elif prior=='omega':
logger.info(" prior omega")
AuxH = H.copy()
AuxG = G - np.dot(Omega, H) #/np.linalg.norm(np.dot(Omega, H))
elif prior=='hierarchical':
logger.info(" prior hierarchical")
AuxH = H - Mu
AuxG = G - np.dot(Omega, Mu)
else:
logger.info(" NO prior")
AuxH = H.copy()
AuxG = G.copy()
# Variance HRF: sigmaH
if estimateSigmaH:
logger.info("M sigma_H step ...")
sigmaH = vt.maximization_sigma_asl(D, Sigma_H, matrix_covH, AuxH, use_hyperprior, gamma_h)
logger.info('sigmaH = ' + str(sigmaH))
if ni > 0:
free_energyVh = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyVh < free_energyQ:
logger.info("free energy has decreased after v_h computation from %f to %f", free_energyQ, free_energyVh)
# Variance PRF: sigmaG
if estimateSigmaG:
logger.info("M sigma_G step ...")
sigmaG = vt.maximization_sigma_asl(D, Sigma_G, matrix_covG, AuxG, use_hyperprior, gamma_g)
logger.info('sigmaG = ' + str(sigmaG))
if ni > 0:
free_energyVg = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyVg < free_energyVh:
logger.info("free energy has decreased after v_g computation from %f to %f", free_energyVh, free_energyVg)
# Mu: True HRF in the hierarchical prior case
if prior=='hierarchical':
logger.info("M sigma_G step ...")
Mu = vt.maximization_Mu_asl(H, G, matrix_covH, matrix_covG,
sigmaH, sigmaG, sigmaMu, Omega, R_inv)
logger.info('sigmaG = ' + str(sigmaG))
if ni > 0:
free_energyMu = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyMu < free_energyVg:
logger.info("free energy has decreased after v_g computation from %f to %f", free_energyVg, free_energyMu)
# (mu,sigma)
if estimateMP:
logger.info("M (mu,sigma) a and c step ...")
mu_Ma, sigma_Ma = vt.maximization_mu_sigma_asl(q_Z, m_A, Sigma_A)
mu_Mc, sigma_Mc = vt.maximization_mu_sigma_asl(q_Z, m_C, Sigma_C)
if ni > 0:
free_energyMP = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyMP < free_energyVg:
logger.info("free energy has decreased after GMM parameters computation from %f to %f", free_energyVg, free_energyMP)
# Drift L, alpha
if estimateLA:
logger.info("M L, alpha step ...")
AL = vt.maximization_LA_asl(Y, m_A, m_C, XX, WP, W, WP_Gamma_WP, H, G, Gamma)
y_tilde = Y - WP.dot(AL)
if ni > 0:
free_energyLA = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyLA < free_energyMP:
logger.info("free energy has decreased after drifts computation from %f to %f", free_energyMP, free_energyLA)
# Beta
if estimateBeta:
logger.info("M beta step ...")
"""
Qtilde = np.concatenate((Z_tilde, np.zeros((M, K, 1), dtype=Z_tilde.dtype)), axis=2)
Qtilde_sumneighbour = Qtilde[:, :, neighboursIndexes].sum(axis=3)
Beta = vt.maximization_beta_m2(Beta.copy(), q_Z, Qtilde_sumneighbour,
Qtilde, neighboursIndexes, maxNeighbours,
gamma, MaxItGrad, gradientStep)
"""
Qtilde = np.concatenate((Z_tilde, np.zeros((M, K, 1), dtype=Z_tilde.dtype)), axis=2)
Qtilde_sumneighbour = Qtilde[:, :, neighboursIndexes].sum(axis=3)
for m in xrange(0, M):
Beta[m] = vt.maximization_beta_m2_scipy_asl(Beta[m].copy(), q_Z[m, :, :],
Qtilde_sumneighbour[m, :, :],
Qtilde[m, :, :], neighboursIndexes,
maxNeighbours, gamma, MaxItGrad, gradientStep)
logger.info(Beta)
if ni > 0:
free_energyB = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX)
if free_energyB < free_energyLA:
logger.info("free energy has decreased after Beta computation from %f to %f", \
free_energyLA, free_energyB)
if 0:
plt.close('all')
for m in xrange(0, M):
range_b = np.arange(-10., 20., 0.1)
beta_plotting = np.zeros_like(range_b)
for ib, b in enumerate(range_b):
beta_plotting[ib] = vt.beta_function(b, q_Z[m, :, :], Qtilde_sumneighbour[m, :, :],
neighboursIndexes, gamma)
#print beta_plotting
plt.figure(1)
plt.hold('on')
plt.plot(range_b, beta_plotting)
plt.show()
# Sigma noise
if estimateNoise:
logger.info("M sigma noise step ...")
sigma_eps = vt.maximization_sigma_noise_asl(XX, m_A, Sigma_A, H, m_C, Sigma_C, \
G, Sigma_H, Sigma_G, W, y_tilde, Gamma, \
Gamma_X, Gamma_WX, N)
if PLOT:
for m in xrange(M):
SUM_q_Z[m] += [q_Z[m, 1, :].sum()]
mua1[m] += [mu_Ma[m, 1]]
muc1[m] += [mu_Mc[m, 1]]
free_energy = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_Ma, sigma_Ma,
H, Sigma_H, AuxH, R, R_inv, sigmaH, sigmaG,
m_C, Sigma_C, mu_Mc, sigma_Mc, G, Sigma_G,
AuxG, q_Z, neighboursIndexes, Beta, Gamma,
gamma, gamma_h, gamma_g, sigma_eps, XX, W,
J, D, M, N, K, use_hyperprior, Gamma_X, Gamma_WX, plot=True)
if ni > 0:
if free_energy < free_energyB:
logger.info("free energy has decreased after Noise computation from %f to %f", free_energyB, free_energy)
if ni > 0:
if free_energy < FE[-1]:
logger.info("WARNING! free energy has decreased in this iteration from %f to %f", FE[-1], free_energy)
FE += [free_energy]
#EP += [EPt]
#EPlh += [EPt_lh]
#Ent += [Entropy]
if ni > NitMin:
#Crit_FE = np.abs((FE[-1] - FE[-2]) / FE[-2])
FE0 = np.array(FE)
Crit_FE = np.abs((FE0[-5:] - FE0[-6:-1]) / FE0[-6:-1])
print Crit_FE
print (Crit_FE > Thresh * np.ones_like(Crit_FE)).any()
else:
Crit_FE = 100
ni += 1
cTime += [time.time() - t1]
logger.info("Computing reconstruction error")
StimulusInducedSignal = vt.computeFit_asl(H, m_A, G, m_C, W, XX)
rerror = np.append(rerror, \
np.mean(((Y - StimulusInducedSignal) ** 2).sum(axis=0)) \
/ np.mean((Y ** 2).sum(axis=0)))
CompTime = time.time() - t1
# Normalize if not done already
if not constraint or not normg:
logger.info("l2-norm of H and G to 1 if not constraint")
Hnorm = np.linalg.norm(H)
H /= Hnorm
Sigma_H /= Hnorm**2
m_A *= Hnorm
Gnorm = np.linalg.norm(G)
G /= Gnorm
Sigma_G /= Gnorm**2
m_C *= Gnorm
if zc:
H = np.concatenate(([0], H, [0]))
G = np.concatenate(([0], G, [0]))
## Compute contrast maps and variance
if computeContrast and len(contrasts) > 0:
logger.info("Computing contrasts ... ")
CONTRAST_A, CONTRASTVAR_A, \
CONTRAST_C, CONTRASTVAR_C = vt.compute_contrasts(condition_names,
contrasts, m_A, m_C,
Sigma_A, Sigma_C, M, J)
else:
CONTRAST_A, CONTRASTVAR_A, CONTRAST_C, CONTRASTVAR_C = 0, 0, 0, 0
###########################################################################
########################################## PLOTS and SNR computation
if PLOT:
logger.info("plotting...")
print 'FE = ', FE
vt.plot_convergence(ni, M, cA, cC, cH, cG, cAH, cCG, SUM_q_Z, mua1, muc1, FE)
logger.info("Nb iterations to reach criterion: %d", ni)
logger.info("Computational time = %s min %s s",
str(np.int(CompTime // 60)), str(np.int(CompTime % 60)))
logger.info("Iteration time = %s min %s s",
str(np.int((CompTime // ni) // 60)), str(np.int((CompTime / ni) % 60)))
logger.info("perfusion baseline mean = %f", np.mean(AL[0, :]))
logger.info("perfusion baseline var = %f", np.var(AL[0, :]))
logger.info("drifts mean = %f", np.mean(AL[1:, :]))
logger.info("drifts var = %f", np.var(AL[1:, :]))
logger.info("noise mean = %f", np.mean(sigma_eps))
logger.info("noise var = %f", np.var(sigma_eps))
SNR10 = 20 * (np.log10(np.linalg.norm(Y) / \
np.linalg.norm(Y - StimulusInducedSignal - WP.dot(AL))))
logger.info("SNR = %d", SNR10)
return ni, m_A, H, m_C, G, Z_tilde, sigma_eps, \
mu_Ma, sigma_Ma, mu_Mc, sigma_Mc, Beta, AL[1:, :], np.dot(P, AL[1:, :]), \
AL[0, :], Sigma_A, Sigma_C, Sigma_H, Sigma_G, rerror, \
CONTRAST_A, CONTRASTVAR_A, CONTRAST_C, CONTRASTVAR_C, \
cA[:], cH[2:], cC[2:], cG[2:], cZ[2:], cAH[2:], cCG[2:], \
cTime, FE #, EP, EPlh, Ent
def Main_vbjde_Extension_constrained_stable(graph, Y, Onsets, Thrf, K, TR, beta,
dt, scale=1, estimateSigmaH=True,
sigmaH=0.05, NitMax=-1,
NitMin=1, estimateBeta=True,
PLOT=False, contrasts=[],
computeContrast=False,
gamma_h=0):
""" Version modified by Lofti from Christine's version """
logger.info(
"Fast EM with C extension started ... Here is the stable version !")
np.random.seed(6537546)
# Initialize parameters
S = 100
if NitMax < 0:
NitMax = 100
gamma = 7.5 # 7.5
gradientStep = 0.003
MaxItGrad = 200
Thresh = 1e-5
# Initialize sizes vectors
D = np.int(np.ceil(Thrf / dt)) + 1
M = len(Onsets)
N = Y.shape[0]
J = Y.shape[1]
l = np.int(np.sqrt(J))
condition_names = []
# Neighbours
maxNeighbours = max([len(nl) for nl in graph])
neighboursIndexes = np.zeros((J, maxNeighbours), dtype=np.int32)
neighboursIndexes -= 1
for i in xrange(J):
neighboursIndexes[i, :len(graph[i])] = graph[i]
# Conditions
X = OrderedDict([])
for condition, Ons in Onsets.iteritems():
X[condition] = vt.compute_mat_X_2(N, TR, D, dt, Ons)
condition_names += [condition]
XX = np.zeros((M, N, D), dtype=np.int32)
nc = 0
for condition, Ons in Onsets.iteritems():
XX[nc, :, :] = X[condition]
nc += 1
# Covariance matrix
order = 2
D2 = vt.buildFiniteDiffMatrix(order, D)
R = np.dot(D2, D2) / pow(dt, 2 * order)
invR = np.linalg.inv(R)
Det_invR = np.linalg.det(invR)
Gamma = np.identity(N)
Det_Gamma = np.linalg.det(Gamma)
Crit_H = 1
Crit_Z = 1
Crit_A = 1
Crit_AH = 1
AH = np.zeros((J, M, D), dtype=np.float64)
AH1 = np.zeros((J, M, D), dtype=np.float64)
Crit_FreeEnergy = 1
cTime = []
cA = []
cH = []
cZ = []
cAH = []
CONTRAST = np.zeros((J, len(contrasts)), dtype=np.float64)
CONTRASTVAR = np.zeros((J, len(contrasts)), dtype=np.float64)
Q_barnCond = np.zeros((M, M, D, D), dtype=np.float64)
XGamma = np.zeros((M, D, N), dtype=np.float64)
m1 = 0
for k1 in X: # Loop over the M conditions
m2 = 0
for k2 in X:
Q_barnCond[m1, m2, :, :] = np.dot(
np.dot(X[k1].transpose(), Gamma), X[k2])
m2 += 1
XGamma[m1, :, :] = np.dot(X[k1].transpose(), Gamma)
m1 += 1
sigma_epsilone = np.ones(J)
logger.info(
"Labels are initialized by setting active probabilities to ones ...")
q_Z = np.zeros((M, K, J), dtype=np.float64)
q_Z[:, 1, :] = 1
q_Z1 = np.zeros((M, K, J), dtype=np.float64)
Z_tilde = q_Z.copy()
TT, m_h = getCanoHRF(Thrf, dt) # TODO: check
m_h = m_h[:D]
m_H = np.array(m_h).astype(np.float64)
m_H1 = np.array(m_h)
sigmaH1 = sigmaH
Sigma_H = np.ones((D, D), dtype=np.float64)
Beta = beta * np.ones((M), dtype=np.float64)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - PL
Ndrift = L.shape[0]
sigma_M = np.ones((M, K), dtype=np.float64)
sigma_M[:, 0] = 0.5
sigma_M[:, 1] = 0.6
mu_M = np.zeros((M, K), dtype=np.float64)
for k in xrange(1, K):
mu_M[:, k] = 1 # InitMean
Sigma_A = np.zeros((M, M, J), np.float64)
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01 * np.identity(M)
m_A = np.zeros((J, M), dtype=np.float64)
m_A1 = np.zeros((J, M), dtype=np.float64)
for j in xrange(0, J):
for m in xrange(0, M):
for k in xrange(0, K):
m_A[j, m] += np.random.normal(mu_M[m, k],
np.sqrt(sigma_M[m, k])) * q_Z[m, k, j]
m_A1 = m_A
t1 = time.time()
##########################################################################
# VBJDE num. iter. minimum
ni = 0
while ((ni < NitMin + 1) or ((Crit_AH > Thresh) and (ni < NitMax))):
logger.info("------------------------------ Iteration n° " +
str(ni + 1) + " ------------------------------")
#####################
# EXPECTATION
#####################
# A
logger.info("E A step ...")
UtilsC.expectation_A(q_Z, mu_M, sigma_M, PL, sigma_epsilone, Gamma,
Sigma_H, Y, y_tilde, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N, K)
# crit. A
DIFF = np.reshape(m_A - m_A1, (M * J))
Crit_A = (
np.linalg.norm(DIFF) / np.linalg.norm(np.reshape(m_A1, (M * J)))) ** 2
cA += [Crit_A]
m_A1[:, :] = m_A[:, :]
# HRF h
UtilsC.expectation_H(XGamma, Q_barnCond, sigma_epsilone, Gamma, R, Sigma_H, Y,
y_tilde, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N, scale, sigmaH)
#m_H[0] = 0
#m_H[-1] = 0
# Constrain with optimization strategy
import cvxpy as cvx
m, n = Sigma_H.shape
Sigma_H_inv = np.linalg.inv(Sigma_H)
zeros_H = np.zeros_like(m_H[:, np.newaxis])
# Construct the problem. PRIMAL
h = cvx.Variable(n)
expression = cvx.quad_form(h - m_H[:, np.newaxis], Sigma_H_inv)
objective = cvx.Minimize(expression)
#constraints = [h[0] == 0, h[-1]==0, h >= zeros_H, cvx.square(cvx.norm(h,2))<=1]
constraints = [h[0] == 0, h[-1] == 0, cvx.square(cvx.norm(h, 2)) <= 1]
prob = cvx.Problem(objective, constraints)
result = prob.solve(verbose=0, solver=cvx.CVXOPT)
# Now we update the mean of h
m_H_old = m_H
Sigma_H_old = Sigma_H
m_H = np.squeeze(np.array((h.value)))
Sigma_H = np.zeros_like(Sigma_H)
# and the norm
h_norm += [np.linalg.norm(m_H)]
# crit. h
Crit_H = (np.linalg.norm(m_H - m_H1) / np.linalg.norm(m_H1)) ** 2
cH += [Crit_H]
m_H1[:] = m_H[:]
# crit. AH
for d in xrange(0, D):
AH[:, :, d] = m_A[:, :] * m_H[d]
DIFF = np.reshape(AH - AH1, (M * J * D))
Crit_AH = (np.linalg.norm(
DIFF) / (np.linalg.norm(np.reshape(AH1, (M * J * D))) + eps)) ** 2
cAH += [Crit_AH]
AH1[:, :, :] = AH[:, :, :]
# Z labels
logger.info("E Z step ...")
UtilsC.expectation_Z(Sigma_A, m_A, sigma_M, Beta, Z_tilde, mu_M,
q_Z, neighboursIndexes.astype(np.int32), M, J, K, maxNeighbours)
# crit. Z
DIFF = np.reshape(q_Z - q_Z1, (M * K * J))
Crit_Z = (np.linalg.norm(DIFF) /
(np.linalg.norm(np.reshape(q_Z1, (M * K * J))) + eps)) ** 2
cZ += [Crit_Z]
q_Z1[:, :, :] = q_Z[:, :, :]
#####################
# MAXIMIZATION
#####################
# HRF: Sigma_h
if estimateSigmaH:
logger.info("M sigma_H step ...")
if gamma_h > 0:
sigmaH = vt.maximization_sigmaH_prior(
D, Sigma_H, R, m_H, gamma_h)
else:
sigmaH = vt.maximization_sigmaH(D, Sigma_H, R, m_H)
logger.info('sigmaH = %s', str(sigmaH))
# (mu,sigma)
logger.info("M (mu,sigma) step ...")
mu_M, sigma_M = vt.maximization_mu_sigma(
mu_M, sigma_M, q_Z, m_A, K, M, Sigma_A)
# Drift L
UtilsC.maximization_L(
Y, m_A, m_H, L, P, XX.astype(np.int32), J, D, M, Ndrift, N)
PL = np.dot(P, L)
y_tilde = Y - PL
# Beta
if estimateBeta:
logger.info("estimating beta")
for m in xrange(0, M):
Beta[m] = UtilsC.maximization_beta(beta, q_Z[m, :, :].astype(np.float64), Z_tilde[m, :, :].astype(
np.float64), J, K, neighboursIndexes.astype(np.int32), gamma, maxNeighbours, MaxItGrad, gradientStep)
logger.info("End estimating beta")
logger.info(Beta)
# Sigma noise
logger.info("M sigma noise step ...")
UtilsC.maximization_sigma_noise(
Gamma, PL, sigma_epsilone, Sigma_H, Y, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N)
t02 = time.time()
cTime += [t02 - t1]
t2 = time.time()
##########################################################################
# PLOTS and SNR computation
if PLOT and 0:
font = {'size': 15}
matplotlib.rc('font', **font)
savefig('./HRF_Iter_CompMod.png')
hold(False)
figure(2)
plot(cAH[1:-1], 'lightblue')
hold(True)
plot(cFE[1:-1], 'm')
hold(False)
legend(('CAH', 'CFE'))
grid(True)
savefig('./Crit_CompMod.png')
figure(3)
plot(FreeEnergyArray)
grid(True)
savefig('./FreeEnergy_CompMod.png')
figure(4)
for m in xrange(M):
plot(SUM_q_Z_array[m])
hold(True)
hold(False)
savefig('./Sum_q_Z_Iter_CompMod.png')
figure(5)
for m in xrange(M):
plot(mu1_array[m])
hold(True)
hold(False)
savefig('./mu1_Iter_CompMod.png')
figure(6)
plot(h_norm_array)
savefig('./HRF_Norm_CompMod.png')
Data_save = xndarray(h_norm_array, ['Iteration'])
Data_save.save('./HRF_Norm_Comp.nii')
CompTime = t2 - t1
cTimeMean = CompTime / ni
"""
Norm = np.linalg.norm(m_H)
m_H /= Norm
Sigma_H /= Norm**2
sigmaH /= Norm**2
m_A *= Norm
Sigma_A *= Norm**2
mu_M *= Norm
sigma_M *= Norm**2
sigma_M = np.sqrt(np.sqrt(sigma_M))
"""
logger.info("Nb iterations to reach criterion: %d", ni)
logger.info("Computational time = %s min %s s", str(
np.int(CompTime // 60)), str(np.int(CompTime % 60)))
logger.info('mu_M: %f', mu_M)
logger.info('sigma_M: %f', sigma_M)
logger.info("sigma_H = %s", str(sigmaH))
logger.info("Beta = %s", str(Beta))
StimulusInducedSignal = vt.computeFit(m_H, m_A, X, J, N)
SNR = 20 * \
np.log(
np.linalg.norm(Y) / np.linalg.norm(Y - StimulusInducedSignal - PL))
SNR /= np.log(10.)
print 'SNR comp =', SNR
return ni, m_A, m_H, q_Z, sigma_epsilone, mu_M, sigma_M, Beta, L, PL, CONTRAST, CONTRASTVAR, cA[2:], cH[2:], cZ[2:], cAH[2:], cTime[2:], cTimeMean, Sigma_A, StimulusInducedSignal
def Main_vbjde_Python_constrained(graph, Y, Onsets, Thrf, K, TR, beta, dt, scale=1, estimateSigmaH=True, sigmaH=0.1, NitMax=-1, NitMin=1, estimateBeta=False, PLOT=False):
logger.info("EM started ...")
np.random.seed(6537546)
##########################################################################
# INITIALIZATIONS
# Initialize parameters
if NitMax < 0:
NitMax = 100
gamma = 7.5
gradientStep = 0.005
MaxItGrad = 120
#Thresh = 1e-5
Thresh_FreeEnergy = 1e-5
# Initialize sizes vectors
D = int(np.ceil(Thrf / dt))
M = len(Onsets)
N = Y.shape[0]
J = Y.shape[1]
l = int(np.sqrt(J))
sigma_epsilone = np.ones(J)
# Neighbours
maxNeighbours = max([len(nl) for nl in graph])
neighboursIndexes = np.zeros((J, maxNeighbours), dtype=np.int32)
neighboursIndexes -= 1
for i in xrange(J):
neighboursIndexes[i, :len(graph[i])] = graph[i]
# Conditions
X = OrderedDict([])
for condition, Ons in Onsets.iteritems():
X[condition] = vt.compute_mat_X_2(N, TR, D, dt, Ons)
XX = np.zeros((M, N, D), dtype=np.int32)
nc = 0
for condition, Ons in Onsets.iteritems():
XX[nc, :, :] = X[condition]
nc += 1
# Sigma and mu
mu_M = np.zeros((M, K), dtype=np.float64)
sigma_M = 0.5 * np.ones((M, K), dtype=np.float64)
sigma_M0 = 0.5 * np.ones((M, K), dtype=np.float64)
for k in xrange(1, K):
mu_M[:, k] = 2.0
# Covariance matrix
order = 2
D2 = vt.buildFiniteDiffMatrix(order, D)
R = np.dot(D2, D2) / pow(dt, 2 * order)
Gamma = np.identity(N)
q_Z = np.zeros((M, K, J), dtype=np.float64)
# for k in xrange(0,K):
q_Z[:, 1, :] = 1
q_Z1 = np.zeros((M, K, J), dtype=np.float64)
Z_tilde = q_Z.copy()
Sigma_A = np.zeros((M, M, J), np.float64)
m_A = np.zeros((J, M), dtype=np.float64)
TT, m_h = getCanoHRF(Thrf - dt, dt) # TODO: check
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01 * np.identity(M)
for m in xrange(0, M):
for k in xrange(0, K):
m_A[j, m] += np.random.normal(mu_M[m, k],
np.sqrt(sigma_M[m, k])) * Z_tilde[m, k, j]
m_H = np.array(m_h).astype(np.float64)
m_H1 = np.array(m_h)
Sigma_H = np.ones((D, D), dtype=np.float64)
Beta = beta * np.ones((M), dtype=np.float64)
zerosDD = np.zeros((D, D), dtype=np.float64)
zerosD = np.zeros((D), dtype=np.float64)
zerosND = np.zeros((N, D), dtype=np.float64)
zerosMM = np.zeros((M, M), dtype=np.float64)
zerosJMD = np.zeros((J, M, D), dtype=np.float64)
zerosK = np.zeros(K)
P = vt.PolyMat(N, 4, TR)
zerosP = np.zeros((P.shape[0]), dtype=np.float64)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - PL
sigmaH1 = sigmaH
Crit_H = 1
Crit_Z = 1
Crit_A = 1
cA = []
cH = []
cZ = []
Ndrift = L.shape[0]
Crit_FreeEnergy = 1
t1 = time.time()
##########################################################################
# VBJDE num. iter. minimum
ni = 0
while ((ni < NitMin) or (((Crit_FreeEnergy > Thresh_FreeEnergy) or ((Crit_H > Thresh) and (Crit_Z > Thresh) and (Crit_A > Thresh))) and (ni < NitMax))):
logger.info("------------------------------ Iteration n° " +
str(ni + 1) + " ------------------------------")
#####################
# EXPECTATION
#####################
# A
logger.info("E A step ...")
Sigma_A, m_A = vt.expectation_A(
Y, Sigma_H, m_H, m_A, X, Gamma, PL, sigma_M, q_Z, mu_M, D, N, J, M, K, y_tilde, Sigma_A, sigma_epsilone, zerosJMD)
m_A1 = m_A
# crit A
DIFF = np.abs(np.reshape(m_A, (M * J)) - np.reshape(m_A1, (M * J)))
Crit_A = sum(DIFF) / len(np.where(DIFF != 0))
cA += [Crit_A]
# H
logger.info("E H step ...")
Sigma_H, m_H = vt.expectation_H(
Y, Sigma_A, m_A, X, Gamma, PL, D, R, sigmaH, J, N, y_tilde, zerosND, sigma_epsilone, scale, zerosDD, zerosD)
m_H[0] = 0
m_H[-1] = 0
m_H1 = m_H
# crit H
Crit_H = np.abs(np.mean(m_H - m_H1) / np.mean(m_H))
cH += [Crit_H]
# Z
logger.info("E Z step ...")
q_Z, Z_tilde = vt.expectation_Z(
Sigma_A, m_A, sigma_M, Beta, Z_tilde, mu_M, q_Z, graph, M, J, K, zerosK)
# crit Z
DIFF = np.abs(
np.reshape(q_Z, (M * K * J)) - np.reshape(q_Z1, (M * K * J)))
Crit_Z = sum(DIFF) / len(np.where(DIFF != 0))
cZ += [Crit_Z]
q_Z1 = q_Z
# Plotting HRF
if PLOT and ni >= 0:
import matplotlib.pyplot as plt
plt.figure(M + 1)
plt.plot(m_H)
plt.hold(True)
####################
# MAXIMIZATION
#####################
# HRF: Sigma_h
if estimateSigmaH:
logger.info("M sigma_H step ...")
sigmaH = (np.dot(vt.mult(m_H, m_H) + Sigma_H, R)).trace()
sigmaH /= D
# (mu,sigma)
logger.info("M (mu,sigma) step ...")
mu_M, sigma_M = vt.maximization_mu_sigma(
mu_M, sigma_M, q_Z, m_A, K, M, Sigma_A)
# Drift L
L = vt.maximization_L(Y, m_A, X, m_H, L, P, zerosP)
PL = np.dot(P, L)
y_tilde = Y - PL
# Beta
if estimateBeta:
logger.info("estimating beta")
for m in xrange(0, M):
Beta[m] = vt.maximization_beta(
Beta[m], q_Z, Z_tilde, J, K, m, graph, gamma, neighboursIndexes, maxNeighbours)
logger.info("End estimating beta")
# logger.info(Beta)
# Sigma noise
logger.info("M sigma noise step ...")
sigma_epsilone = vt.maximization_sigma_noise(
Y, X, m_A, m_H, Sigma_H, Sigma_A, PL, sigma_epsilone, M, zerosMM)
#### Computing Free Energy ####
"""
if ni > 0:
FreeEnergy1 = FreeEnergy
FreeEnergy = Compute_FreeEnergy(y_tilde,m_A,Sigma_A,mu_M,sigma_M,m_H,Sigma_H,R,Det_invR,sigmaH,p_Wtilde,tau1,tau2,q_Z,neighboursIndexes,maxNeighbours,Beta,sigma_epsilone,XX,Gamma,Det_Gamma,XGamma,J,D,M,N,K,S,"CompMod")
if ni > 0:
Crit_FreeEnergy = (FreeEnergy1 - FreeEnergy) / FreeEnergy1
FreeEnergy_Iter += [FreeEnergy]
cFE += [Crit_FreeEnergy]
"""
# Update index
ni += 1
t2 = time.time()
##########################################################################
# PLOTS and SNR computation
"""FreeEnergyArray = np.zeros((ni),dtype=np.float64)
for i in xrange(ni):
FreeEnergyArray[i] = FreeEnergy_Iter[i]
SUM_q_Z_array = np.zeros((M,ni),dtype=np.float64)
mu1_array = np.zeros((M,ni),dtype=np.float64)
h_norm_array = np.zeros((ni),dtype=np.float64)
for m in xrange(M):
for i in xrange(ni):
SUM_q_Z_array[m,i] = SUM_q_Z[m][i]
mu1_array[m,i] = mu1[m][i]
h_norm_array[i] = h_norm[i]
sigma_M = np.sqrt(np.sqrt(sigma_M))
"""
Norm = np.linalg.norm(m_H)
m_H /= Norm
m_A *= Norm
mu_M *= Norm
sigma_M *= Norm
sigma_M = np.sqrt(sigma_M)
if PLOT and 0:
import matplotlib.pyplot as plt
import matplotlib
font = {'size': 15}
matplotlib.rc('font', **font)
plt.savefig('./HRF_Iter_CompMod.png')
plt.hold(False)
plt.figure(2)
plt.plot(cAH[1:-1], 'lightblue')
plt.hold(True)
plt.plot(cFE[1:-1], 'm')
plt.hold(False)
#plt.legend( ('CA','CH', 'CZ', 'CAH', 'CFE') )
plt.legend(('CAH', 'CFE'))
plt.grid(True)
plt.savefig('./Crit_CompMod.png')
plt.figure(3)
plt.plot(FreeEnergyArray)
plt.grid(True)
plt.savefig('./FreeEnergy_CompMod.png')
plt.figure(4)
for m in xrange(M):
plt.plot(SUM_q_Z_array[m])
plt.hold(True)
plt.hold(False)
plt.savefig('./Sum_q_Z_Iter_CompMod.png')
plt.figure(5)
for m in xrange(M):
plt.plot(mu1_array[m])
plt.hold(True)
plt.hold(False)
plt.savefig('./mu1_Iter_CompMod.png')
plt.figure(6)
plt.plot(h_norm_array)
plt.savefig('./HRF_Norm_CompMod.png')
Data_save = xndarray(h_norm_array, ['Iteration'])
Data_save.save('./HRF_Norm_Comp.nii')
CompTime = t2 - t1
cTimeMean = CompTime / ni
logger.info("Nb iterations to reach criterion: %d", ni)
logger.info("Computational time = %s min %s s", str(
int(CompTime // 60)), str(int(CompTime % 60)))
# print "Computational time = " + str(int( CompTime//60 ) ) + " min " +
# str(int(CompTime%60)) + " s"
logger.info('mu_M: %f', mu_M)
logger.info('sigma_M: %f', sigma_M)
logger.info("sigma_H = %s" + str(sigmaH))
logger.info("Beta = %s" + str(Beta))
StimulusInducedSignal = vt.computeFit(m_H, m_A, X, J, N)
SNR = 20 * \
np.log(
np.linalg.norm(Y) / np.linalg.norm(Y - StimulusInducedSignal - PL))
SNR /= np.log(10.)
print 'SNR comp =', SNR
return m_A, m_H, q_Z, sigma_epsilone, mu_M, sigma_M, Beta, L, PL
def Main_vbjde_Extension_constrained(graph, Y, Onsets, Thrf, K, TR, beta,
dt, scale=1, estimateSigmaH=True,
sigmaH=0.05, NitMax=-1,
NitMin=1, estimateBeta=True,
PLOT=False, contrasts=[],
computeContrast=False,
gamma_h=0, estimateHRF=True,
TrueHrfFlag=False,
HrfFilename='hrf.nii',
estimateLabels=True,
LabelsFilename='labels.nii',
MFapprox=False, InitVar=0.5,
InitMean=2.0, MiniVEMFlag=False,
NbItMiniVem=5):
# VBJDE Function for BOLD with contraints
logger.info("Fast EM with C extension started ...")
np.random.seed(6537546)
##########################################################################
# INITIALIZATIONS
# Initialize parameters
tau1 = 0.0
tau2 = 0.0
S = 100
Init_sigmaH = sigmaH
Nb2Norm = 1
NormFlag = False
if NitMax < 0:
NitMax = 100
gamma = 7.5
#gamma_h = 1000
gradientStep = 0.003
MaxItGrad = 200
Thresh = 1e-5
Thresh_FreeEnergy = 1e-5
estimateLabels = True # WARNING!! They should be estimated
# Initialize sizes vectors
D = int(np.ceil(Thrf / dt)) + 1 # D = int(np.ceil(Thrf/dt))
M = len(Onsets)
N = Y.shape[0]
J = Y.shape[1]
l = int(np.sqrt(J))
condition_names = []
# Neighbours
maxNeighbours = max([len(nl) for nl in graph])
neighboursIndexes = np.zeros((J, maxNeighbours), dtype=np.int32)
neighboursIndexes -= 1
for i in xrange(J):
neighboursIndexes[i, :len(graph[i])] = graph[i]
# Conditions
X = OrderedDict([])
for condition, Ons in Onsets.iteritems():
X[condition] = vt.compute_mat_X_2(N, TR, D, dt, Ons)
condition_names += [condition]
XX = np.zeros((M, N, D), dtype=np.int32)
nc = 0
for condition, Ons in Onsets.iteritems():
XX[nc, :, :] = X[condition]
nc += 1
# Covariance matrix
order = 2
D2 = vt.buildFiniteDiffMatrix(order, D)
R = np.dot(D2, D2) / pow(dt, 2 * order)
invR = np.linalg.inv(R)
Det_invR = np.linalg.det(invR)
Gamma = np.identity(N)
Det_Gamma = np.linalg.det(Gamma)
p_Wtilde = np.zeros((M, K), dtype=np.float64)
p_Wtilde1 = np.zeros((M, K), dtype=np.float64)
p_Wtilde[:, 1] = 1
Crit_H = 1
Crit_Z = 1
Crit_A = 1
Crit_AH = 1
AH = np.zeros((J, M, D), dtype=np.float64)
AH1 = np.zeros((J, M, D), dtype=np.float64)
Crit_FreeEnergy = 1
cA = []
cH = []
cZ = []
cAH = []
FreeEnergy_Iter = []
cTime = []
cFE = []
SUM_q_Z = [[] for m in xrange(M)]
mu1 = [[] for m in xrange(M)]
h_norm = []
h_norm2 = []
CONTRAST = np.zeros((J, len(contrasts)), dtype=np.float64)
CONTRASTVAR = np.zeros((J, len(contrasts)), dtype=np.float64)
Q_barnCond = np.zeros((M, M, D, D), dtype=np.float64)
XGamma = np.zeros((M, D, N), dtype=np.float64)
m1 = 0
for k1 in X: # Loop over the M conditions
m2 = 0
for k2 in X:
Q_barnCond[m1, m2, :, :] = np.dot(
np.dot(X[k1].transpose(), Gamma), X[k2])
m2 += 1
XGamma[m1, :, :] = np.dot(X[k1].transpose(), Gamma)
m1 += 1
if MiniVEMFlag:
logger.info("MiniVEM to choose the best initialisation...")
"""InitVar, InitMean, gamma_h = MiniVEM_CompMod(Thrf,TR,dt,beta,Y,K,
gamma,gradientStep,
MaxItGrad,D,M,N,J,S,
maxNeighbours,
neighboursIndexes,
XX,X,R,Det_invR,Gamma,
Det_Gamma,
scale,Q_barnCond,XGamma,
NbItMiniVem,
sigmaH,estimateHRF)"""
InitVar, InitMean, gamma_h = vt.MiniVEM_CompMod(Thrf, TR, dt, beta, Y, K, gamma, gradientStep, MaxItGrad, D, M, N, J, S, maxNeighbours,
neighboursIndexes, XX, X, R, Det_invR, Gamma, Det_Gamma, p_Wtilde, scale, Q_barnCond, XGamma, tau1, tau2, NbItMiniVem, sigmaH, estimateHRF)
sigmaH = Init_sigmaH
sigma_epsilone = np.ones(J)
logger.info(
"Labels are initialized by setting active probabilities to ones ...")
q_Z = np.zeros((M, K, J), dtype=np.float64)
q_Z[:, 1, :] = 1
q_Z1 = np.zeros((M, K, J), dtype=np.float64)
Z_tilde = q_Z.copy()
# TT,m_h = getCanoHRF(Thrf-dt,dt) #TODO: check
TT, m_h = getCanoHRF(Thrf, dt) # TODO: check
m_h = m_h[:D]
m_H = np.array(m_h).astype(np.float64)
m_H1 = np.array(m_h)
sigmaH1 = sigmaH
if estimateHRF:
Sigma_H = np.ones((D, D), dtype=np.float64)
else:
Sigma_H = np.zeros((D, D), dtype=np.float64)
Beta = beta * np.ones((M), dtype=np.float64)
P = vt.PolyMat(N, 4, TR)
L = vt.polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - PL
Ndrift = L.shape[0]
sigma_M = np.ones((M, K), dtype=np.float64)
sigma_M[:, 0] = 0.5
sigma_M[:, 1] = 0.6
mu_M = np.zeros((M, K), dtype=np.float64)
for k in xrange(1, K):
mu_M[:, k] = InitMean
Sigma_A = np.zeros((M, M, J), np.float64)
for j in xrange(0, J):
Sigma_A[:, :, j] = 0.01 * np.identity(M)
m_A = np.zeros((J, M), dtype=np.float64)
m_A1 = np.zeros((J, M), dtype=np.float64)
for j in xrange(0, J):
for m in xrange(0, M):
for k in xrange(0, K):
m_A[j, m] += np.random.normal(mu_M[m, k],
np.sqrt(sigma_M[m, k])) * q_Z[m, k, j]
m_A1 = m_A
t1 = time.time()
##########################################################################
# VBJDE num. iter. minimum
ni = 0
while ((ni < NitMin) or (((Crit_FreeEnergy > Thresh_FreeEnergy) or (Crit_AH > Thresh)) and (ni < NitMax))):
logger.info("------------------------------ Iteration n° " +
str(ni + 1) + " ------------------------------")
#####################
# EXPECTATION
#####################
# A
logger.info("E A step ...")
UtilsC.expectation_A(q_Z, mu_M, sigma_M, PL, sigma_epsilone, Gamma,
Sigma_H, Y, y_tilde, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N, K)
val = np.reshape(m_A, (M * J))
val[np.where((val <= 1e-50) & (val > 0.0))] = 0.0
val[np.where((val >= -1e-50) & (val < 0.0))] = 0.0
# crit. A
DIFF = np.reshape(m_A - m_A1, (M * J))
# To avoid numerical problems
DIFF[np.where((DIFF < 1e-50) & (DIFF > 0.0))] = 0.0
# To avoid numerical problems
DIFF[np.where((DIFF > -1e-50) & (DIFF < 0.0))] = 0.0
Crit_A = (
np.linalg.norm(DIFF) / np.linalg.norm(np.reshape(m_A1, (M * J)))) ** 2
cA += [Crit_A]
m_A1[:, :] = m_A[:, :]
# HRF h
if estimateHRF:
################################
# HRF ESTIMATION
################################
UtilsC.expectation_H(XGamma, Q_barnCond, sigma_epsilone, Gamma, R, Sigma_H, Y,
y_tilde, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N, scale, sigmaH)
import cvxpy as cvx
m, n = Sigma_H.shape
Sigma_H_inv = np.linalg.inv(Sigma_H)
zeros_H = np.zeros_like(m_H[:, np.newaxis])
# Construct the problem. PRIMAL
h = cvx.Variable(n)
expression = cvx.quad_form(h - m_H[:, np.newaxis], Sigma_H_inv)
objective = cvx.Minimize(expression)
#constraints = [h[0] == 0, h[-1]==0, h >= zeros_H, cvx.square(cvx.norm(h,2))<=1]
constraints = [
h[0] == 0, h[-1] == 0, cvx.square(cvx.norm(h, 2)) <= 1]
prob = cvx.Problem(objective, constraints)
result = prob.solve(verbose=0, solver=cvx.CVXOPT)
# Now we update the mean of h
m_H_old = m_H
Sigma_H_old = Sigma_H
m_H = np.squeeze(np.array((h.value)))
Sigma_H = np.zeros_like(Sigma_H)
h_norm += [np.linalg.norm(m_H)]
# print 'h_norm = ', h_norm
# Plotting HRF
if PLOT and ni >= 0:
import matplotlib.pyplot as plt
plt.figure(M + 1)
plt.plot(m_H)
plt.hold(True)
else:
if TrueHrfFlag:
#TrueVal, head = read_volume(HrfFilename)
TrueVal, head = read_volume(HrfFilename)[:, 0, 0, 0]
print TrueVal
print TrueVal.shape
m_H = TrueVal
# crit. h
Crit_H = (np.linalg.norm(m_H - m_H1) / np.linalg.norm(m_H1)) ** 2
cH += [Crit_H]
m_H1[:] = m_H[:]
# crit. AH
for d in xrange(0, D):
AH[:, :, d] = m_A[:, :] * m_H[d]
DIFF = np.reshape(AH - AH1, (M * J * D))
# To avoid numerical problems
DIFF[np.where((DIFF < 1e-50) & (DIFF > 0.0))] = 0.0
# To avoid numerical problems
DIFF[np.where((DIFF > -1e-50) & (DIFF < 0.0))] = 0.0
if np.linalg.norm(np.reshape(AH1, (M * J * D))) == 0:
Crit_AH = 1000000000.
else:
Crit_AH = (
np.linalg.norm(DIFF) / np.linalg.norm(np.reshape(AH1, (M * J * D)))) ** 2
cAH += [Crit_AH]
AH1[:, :, :] = AH[:, :, :]
# Z labels
if estimateLabels:
logger.info("E Z step ...")
# WARNING!!! ParsiMod gives better results, but we need the other
# one.
if MFapprox:
UtilsC.expectation_Z(Sigma_A, m_A, sigma_M, Beta, Z_tilde, mu_M, q_Z, neighboursIndexes.astype(
np.int32), M, J, K, maxNeighbours)
if not MFapprox:
UtilsC.expectation_Z_ParsiMod_RVM_and_CompMod(
Sigma_A, m_A, sigma_M, Beta, mu_M, q_Z, neighboursIndexes.astype(np.int32), M, J, K, maxNeighbours)
else:
logger.info("Using True Z ...")
TrueZ = read_volume(LabelsFilename)
for m in xrange(M):
q_Z[m, 1, :] = np.reshape(TrueZ[0][:, :, :, m], J)
q_Z[m, 0, :] = 1 - q_Z[m, 1, :]
# crit. Z
val = np.reshape(q_Z, (M * K * J))
val[np.where((val <= 1e-50) & (val > 0.0))] = 0.0
DIFF = np.reshape(q_Z - q_Z1, (M * K * J))
# To avoid numerical problems
DIFF[np.where((DIFF < 1e-50) & (DIFF > 0.0))] = 0.0
# To avoid numerical problems
DIFF[np.where((DIFF > -1e-50) & (DIFF < 0.0))] = 0.0
if np.linalg.norm(np.reshape(q_Z1, (M * K * J))) == 0:
Crit_Z = 1000000000.
else:
Crit_Z = (
np.linalg.norm(DIFF) / np.linalg.norm(np.reshape(q_Z1, (M * K * J)))) ** 2
cZ += [Crit_Z]
q_Z1 = q_Z
#####################
# MAXIMIZATION
#####################
# HRF: Sigma_h
if estimateHRF:
if estimateSigmaH:
logger.info("M sigma_H step ...")
if gamma_h > 0:
sigmaH = vt.maximization_sigmaH_prior(
D, Sigma_H_old, R, m_H_old, gamma_h)
else:
sigmaH = vt.maximization_sigmaH(D, Sigma_H, R, m_H)
logger.info('sigmaH = %s', str(sigmaH))
# (mu,sigma)
logger.info("M (mu,sigma) step ...")
mu_M, sigma_M = vt.maximization_mu_sigma(
mu_M, sigma_M, q_Z, m_A, K, M, Sigma_A)
for m in xrange(M):
SUM_q_Z[m] += [sum(q_Z[m, 1, :])]
mu1[m] += [mu_M[m, 1]]
# Drift L
UtilsC.maximization_L(
Y, m_A, m_H, L, P, XX.astype(np.int32), J, D, M, Ndrift, N)
PL = np.dot(P, L)
y_tilde = Y - PL
# Beta
if estimateBeta:
logger.info("estimating beta")
for m in xrange(0, M):
if MFapprox:
Beta[m] = UtilsC.maximization_beta(beta, q_Z[m, :, :].astype(np.float64), Z_tilde[m, :, :].astype(
np.float64), J, K, neighboursIndexes.astype(np.int32), gamma, maxNeighbours, MaxItGrad, gradientStep)
if not MFapprox:
#Beta[m] = UtilsC.maximization_beta(beta,q_Z[m,:,:].astype(np.float64),q_Z[m,:,:].astype(np.float64),J,K,neighboursIndexes.astype(int32),gamma,maxNeighbours,MaxItGrad,gradientStep)
Beta[m] = UtilsC.maximization_beta_CB(beta, q_Z[m, :, :].astype(
np.float64), J, K, neighboursIndexes.astype(np.int32), gamma, maxNeighbours, MaxItGrad, gradientStep)
logger.info("End estimating beta")
logger.info(Beta)
# Sigma noise
logger.info("M sigma noise step ...")
UtilsC.maximization_sigma_noise(
Gamma, PL, sigma_epsilone, Sigma_H, Y, m_A, m_H, Sigma_A, XX.astype(np.int32), J, D, M, N)
#### Computing Free Energy ####
if ni > 0:
FreeEnergy1 = FreeEnergy
"""FreeEnergy = vt.Compute_FreeEnergy(y_tilde,m_A,Sigma_A,mu_M,sigma_M,
m_H,Sigma_H,R,Det_invR,sigmaH,
p_Wtilde,q_Z,neighboursIndexes,
maxNeighbours,Beta,sigma_epsilone,
XX,Gamma,Det_Gamma,XGamma,J,D,M,
N,K,S,"CompMod")"""
FreeEnergy = vt.Compute_FreeEnergy(y_tilde, m_A, Sigma_A, mu_M, sigma_M, m_H, Sigma_H, R, Det_invR, sigmaH, p_Wtilde, tau1,
tau2, q_Z, neighboursIndexes, maxNeighbours, Beta, sigma_epsilone, XX, Gamma, Det_Gamma, XGamma, J, D, M, N, K, S, "CompMod")
if ni > 0:
Crit_FreeEnergy = (FreeEnergy1 - FreeEnergy) / FreeEnergy1
FreeEnergy_Iter += [FreeEnergy]
cFE += [Crit_FreeEnergy]
# Update index
ni += 1
t02 = time.time()
cTime += [t02 - t1]
t2 = time.time()
##########################################################################
# PLOTS and SNR computation
FreeEnergyArray = np.zeros((ni), dtype=np.float64)
for i in xrange(ni):
FreeEnergyArray[i] = FreeEnergy_Iter[i]
SUM_q_Z_array = np.zeros((M, ni), dtype=np.float64)
mu1_array = np.zeros((M, ni), dtype=np.float64)
h_norm_array = np.zeros((ni), dtype=np.float64)
for m in xrange(M):
for i in xrange(ni):
SUM_q_Z_array[m, i] = SUM_q_Z[m][i]
mu1_array[m, i] = mu1[m][i]
h_norm_array[i] = h_norm[i]
if PLOT and 0:
import matplotlib.pyplot as plt
import matplotlib
font = {'size': 15}
matplotlib.rc('font', **font)
plt.savefig('./HRF_Iter_CompMod.png')
plt.hold(False)
plt.figure(2)
plt.plot(cAH[1:-1], 'lightblue')
plt.hold(True)
plt.plot(cFE[1:-1], 'm')
plt.hold(False)
#plt.legend( ('CA','CH', 'CZ', 'CAH', 'CFE') )
plt.legend(('CAH', 'CFE'))
plt.grid(True)
plt.savefig('./Crit_CompMod.png')
plt.figure(3)
plt.plot(FreeEnergyArray)
plt.grid(True)
plt.savefig('./FreeEnergy_CompMod.png')
plt.figure(4)
for m in xrange(M):
plt.plot(SUM_q_Z_array[m])
plt.hold(True)
plt.hold(False)
#plt.legend( ('m=0','m=1', 'm=2', 'm=3') )
#plt.legend( ('m=0','m=1') )
plt.savefig('./Sum_q_Z_Iter_CompMod.png')
plt.figure(5)
for m in xrange(M):
plt.plot(mu1_array[m])
plt.hold(True)
plt.hold(False)
plt.savefig('./mu1_Iter_CompMod.png')
plt.figure(6)
plt.plot(h_norm_array)
plt.savefig('./HRF_Norm_CompMod.png')
Data_save = xndarray(h_norm_array, ['Iteration'])
Data_save.save('./HRF_Norm_Comp.nii')
CompTime = t2 - t1
cTimeMean = CompTime / ni
sigma_M = np.sqrt(np.sqrt(sigma_M))
logger.info("Nb iterations to reach criterion: %d", ni)
logger.info("Computational time = %s min %s s", str(
int(CompTime // 60)), str(int(CompTime % 60)))
# print "Computational time = " + str(int( CompTime//60 ) ) + " min " + str(int(CompTime%60)) + " s"
# print "sigma_H = " + str(sigmaH)
logger.info('mu_M: %f', mu_M)
logger.info('sigma_M: %f', sigma_M)
logger.info("sigma_H = %s" + str(sigmaH))
logger.info("Beta = %s" + str(Beta))
StimulusInducedSignal = vt.computeFit(m_H, m_A, X, J, N)
SNR = 20 * \
np.log(
np.linalg.norm(Y) / np.linalg.norm(Y - StimulusInducedSignal - PL))
SNR /= np.log(10.)
logger.info("SNR = %d", SNR)
return ni, m_A, m_H, q_Z, sigma_epsilone, mu_M, sigma_M, Beta, L, PL, CONTRAST, CONTRASTVAR, cA[2:], cH[2:], cZ[2:], cAH[2:], cTime[2:], cTimeMean, Sigma_A, StimulusInducedSignal, FreeEnergyArray
