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_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 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_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_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 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, Nit, sigmaH, estimateHRF):
# print 'InitVar =',InitVar,', InitMean =',InitMean,', gamma_h
# =',gamma_h
Init_sigmaH = sigmaH
IM_val = np.array([-5., 5.])
IV_val = np.array([0.008, 0.016, 0.032, 0.064, 0.128, 0.256, 0.512])
#IV_val = np.array([0.01,0.05,0.1,0.5])
gammah_val = np.array([1000])
MiniVemStep = IM_val.shape[0] * IV_val.shape[0] * gammah_val.shape[0]
Init_mixt_p_gammah = []
logger.info("Number of tested initialisation is %s", MiniVemStep)
t1_MiniVEM = time.time()
FE = []
for Gh in gammah_val:
for InitVar in IV_val:
for InitMean in IM_val:
Init_mixt_p_gammah += [[InitVar, InitMean, Gh]]
sigmaH = Init_sigmaH
sigma_epsilone = np.ones(J)
if 0:
logger.info(
"Labels are initialized by setting active probabilities to zeros ...")
q_Z = np.ones((M, K, J), dtype=np.float64)
q_Z[:, 1, :] = 0
if 0:
logger.info("Labels are initialized randomly ...")
q_Z = np.zeros((M, K, J), dtype=np.float64)
nbVoxInClass = J / K
for j in xrange(M):
if J % 2 == 0:
l = []
else:
l = [0]
for c in xrange(K):
l += [c] * nbVoxInClass
q_Z[j, 0, :] = np.random.permutation(l)
q_Z[j, 1, :] = 1. - q_Z[j, 0, :]
if 1:
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
# 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)
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 = PolyMat(N, 4, TR)
L = polyFit(Y, TR, 4, P)
PL = np.dot(P, L)
y_tilde = Y - PL
Ndrift = L.shape[0]
gamma_h = Gh
sigma_M = np.ones((M, K), dtype=np.float64)
sigma_M[:, 0] = 0.1
sigma_M[:, 1] = 1.0
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)
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]
for ni in xrange(0, Nit + 1):
logger.info("------------------------------ Iteration n° " +
str(ni + 1) + " ------------------------------")
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(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
m_A = np.reshape(val, (J, M))
if estimateHRF:
UtilsC.expectation_H(XGamma, Q_barnCond, sigma_epsilone, Gamma, R, Sigma_H, Y, y_tilde, m_A, m_H, Sigma_A, XX.astype(
int32), J, D, M, N, scale, sigmaH)
m_H[0] = 0
m_H[-1] = 0
UtilsC.expectation_Z_ParsiMod_3(
Sigma_A, m_A, sigma_M, Beta, p_Wtilde, mu_M, q_Z, neighboursIndexes.astype(int32), M, J, K, maxNeighbours)
val = np.reshape(q_Z, (M * K * J))
val[np.where((val <= 1e-50) & (val > 0.0))] = 0.0
q_Z = np.reshape(val, (M, K, J))
if estimateHRF:
if gamma_h > 0:
sigmaH = maximization_sigmaH_prior(
D, Sigma_H, R, m_H, gamma_h)
else:
sigmaH = maximization_sigmaH(D, Sigma_H, R, m_H)
mu_M, sigma_M = maximization_mu_sigma(
mu_M, sigma_M, q_Z, m_A, K, M, Sigma_A)
UtilsC.maximization_L(
Y, m_A, m_H, L, P, XX.astype(int32), J, D, M, Ndrift, N)
PL = np.dot(P, L)
y_tilde = Y - PL
for m in xrange(0, M):
Beta[m] = UtilsC.maximization_beta(beta, q_Z[m, :, :].astype(float64), q_Z[m, :, :].astype(
float64), J, K, neighboursIndexes.astype(int32), gamma, maxNeighbours, MaxItGrad, gradientStep)
UtilsC.maximization_sigma_noise(
Gamma, PL, sigma_epsilone, Sigma_H, Y, m_A, m_H, Sigma_A, XX.astype(int32), J, D, M, N)
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")
FE += [FreeEnergy]
max_FE, max_FE_ind = maximum(FE)
InitVar = Init_mixt_p_gammah[max_FE_ind][0]
InitMean = Init_mixt_p_gammah[max_FE_ind][1]
Initgamma_h = Init_mixt_p_gammah[max_FE_ind][2]
t2_MiniVEM = time.time()
logger.info(
"MiniVEM duration is %s", format_duration(t2_MiniVEM - t1_MiniVEM))
logger.info("Choosed initialisation is : var = %s, mean = %s, gamma_h = %s",
InitVar, InitMean, Initgamma_h)
return InitVar, InitMean, Initgamma_h
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_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