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 jde_vem_bold(graph, bold_data, onsets, durations, hrf_duration, nb_classes,
tr, beta, dt, estimate_sigma_h=True, sigma_h=0.05,
it_max=-1, it_min=0, estimate_beta=True, contrasts=None,
compute_contrasts=False, hrf_hyperprior=0, estimate_hrf=True,
constrained=False, zero_constraint=True, drifts_type="poly",
seed=6537546):
"""This is the main function that computes the VEM analysis on BOLD data.
This function uses optimized python functions.
Parameters
----------
graph : ndarray of lists
represents the neighbours indexes of each voxels index
bold_data : ndarray, shape (nb_scans, nb_voxels)
raw data
onsets : dict
dictionnary of onsets
durations : # TODO
# TODO
hrf_duration : float
hrf total time duration (in s)
nb_classes : int
the number of classes to classify the nrls. This parameter is provided
for development purposes as most of the algorithm implies two classes
tr : float
time of repetition
beta : float
the initial value of beta
dt : float
hrf temporal precision
estimate_sigma_h : bool, optional
toggle estimation of sigma H
sigma_h : float, optional
initial or fixed value of sigma H
it_max : int, optional
maximal computed iteration number
it_min : int, optional
minimal computed iteration number
estimate_beta : bool, optional
toggle the estimation of beta
contrasts : OrderedDict, optional
dict of contrasts to compute
compute_contrasts : bool, optional
if True, compute the contrasts defined in contrasts
hrf_hyperprior : float
# TODO
estimate_hrf : bool, optional
if True, estimate the HRF for each parcel, if False use the canonical HRF
constrained : bool, optional
if True, add a constrains the l2 norm of the HRF to 1
drifts_type : str, optional
set the drifts basis type used. Can be "poly" for polynomial or "cos"
for cosine
seed : int, optional
seed used by numpy to initialize random generator number
Returns
-------
loop : int
number of iterations before convergence
nrls_mean : ndarray, shape (nb_voxels, nb_conditions)
Neural response level mean value
hrf_mean : ndarray, shape (hrf_len,)
Hemodynamic response function mean value
hrf_covar : ndarray, shape (hrf_len, hrf_len)
Covariance matrix of the HRF
labels_proba : ndarray, shape (nb_conditions, nb_classes, nb_voxels)
probability of voxels being in one class
noise_var : ndarray, shape (nb_voxels,)
estimated noise variance
nrls_class_mean : ndarray, shape (nb_conditions, nb_classes)
estimated mean value of the gaussians of the classes
nrls_class_var : ndarray, shape (nb_conditions, nb_classes)
estimated variance of the gaussians of the classes
beta : ndarray, shape (nb_conditions,)
estimated beta
drift_coeffs : ndarray, shape (# TODO)
estimated coefficient of the drifts
drift : ndarray, shape (# TODO)
estimated drifts
contrasts_mean : ndarray, shape (nb_voxels, len(contrasts))
Contrasts computed from NRLs
contrasts_var : ndarray, shape (nb_voxels, len(contrasts))
Variance of the contrasts
compute_time : list
computation time of each iteration
compute_time_mean : float
computation mean time over iterations
nrls_covar : ndarray, shape (nb_conditions, nb_conditions, nb_voxels)
# TODO
stimulus_induced_signal : ndarray, shape (nb_scans, nb_voxels)
# TODO
mahalanobis_zero : float
Mahalanobis distance between estimated hrf_mean and the null vector
mahalanobis_cano : float
Mahalanobis distance between estimated hrf_mean and the canonical HRF
mahalanobis_diff : float
difference between mahalanobis_cano and mahalanobis_diff
mahalanobis_prod : float
product of mahalanobis_cano and mahalanobis_diff
ppm_a_nrl : ndarray, shape (nb_voxels,)
The posterior probability map using an alpha
ppm_g_nrl : ndarray, shape (nb_voxels,)
# TODO
ppm_a_contrasts : ndarray, shape (nb_voxels,)
# TODO
ppm_g_contrasts : ndarray, shape (nb_voxels,)
# TODO
variation_coeff : float
coefficient of variation of the HRF
free_energy : list
# TODO
Notes
-----
See `A novel definition of the multivariate coefficient of variation
<http://onlinelibrary.wiley.com/doi/10.1002/bimj.201000030/abstract>`_
article for more information about the coefficient of variation.
"""
logger.info("VEM started.")
if not contrasts:
contrasts = OrderedDict()
np.random.seed(seed)
nb_2_norm = 1
normalizing = False
regularizing = False
if it_max <= 0:
it_max = 100
gamma = 7.5
thresh_free_energy = 1e-4
# Initialize sizes vectors
hrf_len = np.int(np.ceil(hrf_duration / dt)) + 1
nb_conditions = len(onsets)
nb_scans = bold_data.shape[0]
nb_voxels = bold_data.shape[1]
X, occurence_matrix, condition_names = vt.create_conditions(
onsets, durations, nb_conditions, nb_scans, hrf_len, tr, dt
)
neighbours_indexes = vt.create_neighbours(graph)
order = 2
if regularizing:
regularization = np.ones(hrf_len)
regularization[hrf_len//3:hrf_len//2] = 2
regularization[hrf_len//2:2*hrf_len//3] = 5
regularization[2*hrf_len//3:3*hrf_len//4] = 7
regularization[3*hrf_len//4:] = 10
# regularization[hrf_len//2:] = 10
else:
regularization = None
d2 = vt.buildFiniteDiffMatrix(order, hrf_len, regularization)
hrf_regu_prior_inv = d2.T.dot(d2) / pow(dt, 2 * order)
if estimate_hrf and zero_constraint:
hrf_len = hrf_len - 2
hrf_regu_prior_inv = hrf_regu_prior_inv[1:-1, 1:-1]
occurence_matrix = occurence_matrix[:, :, 1:-1]
noise_struct = np.identity(nb_scans)
free_energy = [1.]
free_energy_crit = [1.]
compute_time = []
noise_var = np.ones(nb_voxels)
labels_proba = np.zeros((nb_conditions, nb_classes, nb_voxels), dtype=np.float64)
logger.info("Labels are initialized by setting everything to {}".format(1./nb_classes))
labels_proba[:, :, :] = 1./nb_classes
m_h = getCanoHRF(hrf_duration, dt)[1][:hrf_len]
hrf_mean = np.array(m_h).astype(np.float64)
if estimate_hrf:
hrf_covar = np.identity(hrf_len, dtype=np.float64)
else:
hrf_covar = np.zeros((hrf_len, hrf_len), dtype=np.float64)
beta = beta * np.ones((nb_conditions), dtype=np.float64)
beta_list = []
beta_list.append(beta.copy())
if drifts_type == "poly":
drift_basis = vt.poly_drifts_basis(nb_scans, 4, tr)
elif drifts_type == "cos":
drift_basis = vt.cosine_drifts_basis(nb_scans, 64, tr)
drift_coeffs = vt.drifts_coeffs_fit(bold_data, drift_basis)
drift = drift_basis.dot(drift_coeffs)
bold_data_drift = bold_data - drift
# Parameters Gaussian mixtures
nrls_class_mean = 2 * np.ones((nb_conditions, nb_classes))
nrls_class_mean[:, 0] = 0
nrls_class_var = 0.3 * np.ones((nb_conditions, nb_classes), dtype=np.float64)
nrls_mean = (np.random.normal(
nrls_class_mean, nrls_class_var)[:, :, np.newaxis] * labels_proba).sum(axis=1).T
nrls_covar = (np.identity(nb_conditions)[:, :, np.newaxis] + np.zeros((1, 1, nb_voxels)))
start_time = time.time()
loop = 0
while (loop <= it_min or
((np.asarray(free_energy_crit[-5:]) > thresh_free_energy).any()
and loop < it_max)):
logger.info("{:-^80}".format(" Iteration n°"+str(loop+1)+" "))
logger.info("Expectation A step...")
logger.debug("Before: nrls_mean = %s, nrls_covar = %s", nrls_mean, nrls_covar)
nrls_mean, nrls_covar = vt.nrls_expectation(
hrf_mean, nrls_mean, occurence_matrix, noise_struct, labels_proba,
nrls_class_mean, nrls_class_var, nb_conditions, bold_data_drift, nrls_covar,
hrf_covar, noise_var)
logger.debug("After: nrls_mean = %s, nrls_covar = %s", nrls_mean, nrls_covar)
logger.info("Expectation Z step...")
logger.debug("Before: labels_proba = %s, labels_proba = %s", labels_proba, labels_proba)
labels_proba = vt.labels_expectation(
nrls_covar, nrls_mean, nrls_class_var, nrls_class_mean, beta,
labels_proba, neighbours_indexes, nb_conditions, nb_classes,
nb_voxels, parallel=True)
logger.debug("After: labels_proba = %s, labels_proba = %s", labels_proba, labels_proba)
if estimate_hrf:
logger.info("Expectation H step...")
logger.debug("Before: hrf_mean = %s, hrf_covar = %s", hrf_mean, hrf_covar)
hrf_mean, hrf_covar = vt.hrf_expectation(
nrls_covar, nrls_mean, occurence_matrix, noise_struct,
hrf_regu_prior_inv, sigma_h, nb_voxels, bold_data_drift, noise_var)
if constrained:
hrf_mean = vt.norm1_constraint(hrf_mean, hrf_covar)
hrf_covar[:] = 0
logger.debug("After: hrf_mean = %s, hrf_covar = %s", hrf_mean, hrf_covar)
# Normalizing H at each nb_2_norm iterations:
if not constrained and normalizing:
# Normalizing is done before sigma_h, nrls_class_mean and nrls_class_var estimation
# we should not include them in the normalisation step
if (loop + 1) % nb_2_norm == 0:
hrf_norm = np.linalg.norm(hrf_mean)
hrf_mean /= hrf_norm
hrf_covar /= hrf_norm ** 2
nrls_mean *= hrf_norm
nrls_covar *= hrf_norm ** 2
if estimate_hrf and estimate_sigma_h:
logger.info("Maximization sigma_H step...")
logger.debug("Before: sigma_h = %s", sigma_h)
if hrf_hyperprior > 0:
sigma_h = vt.maximization_sigmaH_prior(hrf_len, hrf_covar,
hrf_regu_prior_inv,
hrf_mean, hrf_hyperprior)
else:
sigma_h = vt.maximization_sigmaH(hrf_len, hrf_covar,
hrf_regu_prior_inv, hrf_mean)
logger.debug("After: sigma_h = %s", sigma_h)
logger.info("Maximization (mu,sigma) step...")
logger.debug("Before: nrls_class_mean = %s, nrls_class_var = %s",
nrls_class_mean, nrls_class_var)
nrls_class_mean, nrls_class_var = vt.maximization_class_proba(
labels_proba, nrls_mean, nrls_covar
)
logger.debug("After: nrls_class_mean = %s, nrls_class_var = %s",
nrls_class_mean, nrls_class_var)
logger.info("Maximization L step...")
logger.debug("Before: drift_coeffs = %s", drift_coeffs)
drift_coeffs = vt.maximization_drift_coeffs(
bold_data, nrls_mean, occurence_matrix, hrf_mean, noise_struct, drift_basis
)
logger.debug("After: drift_coeffs = %s", drift_coeffs)
drift = drift_basis.dot(drift_coeffs)
bold_data_drift = bold_data - drift
if estimate_beta:
logger.info("Maximization beta step...")
for cond_nb in xrange(0, nb_conditions):
beta[cond_nb], success = vt.beta_maximization(
beta[cond_nb]*np.ones((1,)), labels_proba[cond_nb, :, :],
neighbours_indexes, gamma
)
beta_list.append(beta.copy())
logger.debug("beta = %s", str(beta))
logger.info("Maximization sigma noise step...")
noise_var = vt.maximization_noise_var(
occurence_matrix, hrf_mean, hrf_covar, nrls_mean, nrls_covar,
noise_struct, bold_data_drift, nb_scans
)
#### Computing Free Energy ####
free_energy.append(vt.free_energy_computation(
nrls_mean, nrls_covar, hrf_mean, hrf_covar, hrf_len, labels_proba,
bold_data_drift, occurence_matrix, noise_var, noise_struct, nb_conditions,
nb_voxels, nb_scans, nb_classes, nrls_class_mean, nrls_class_var, neighbours_indexes,
beta, sigma_h, np.linalg.inv(hrf_regu_prior_inv), hrf_regu_prior_inv, gamma, hrf_hyperprior
))
free_energy_crit.append(abs((free_energy[-2] - free_energy[-1]) /
free_energy[-2]))
logger.info("Convergence criteria: %f (Threshold = %f)",
free_energy_crit[-1], thresh_free_energy)
loop += 1
compute_time.append(time.time() - start_time)
compute_time_mean = compute_time[-1] / loop
mahalanobis_zero = np.nan
mahalanobis_cano = np.nan
mahalanobis_diff = np.nan
mahalanobis_prod = np.nan
variation_coeff = np.nan
if estimate_hrf and not constrained and not normalizing:
hrf_norm = np.linalg.norm(hrf_mean)
hrf_mean /= hrf_norm
hrf_covar /= hrf_norm ** 2
sigma_h /= hrf_norm ** 2
nrls_mean *= hrf_norm
nrls_covar *= hrf_norm ** 2
nrls_class_mean *= hrf_norm
nrls_class_var *= hrf_norm ** 2
mahalanobis_zero = mahalanobis(hrf_mean, np.zeros_like(hrf_mean),
np.linalg.inv(hrf_covar))
mahalanobis_cano = mahalanobis(hrf_mean, m_h, np.linalg.inv(hrf_covar))
mahalanobis_diff = mahalanobis_cano - mahalanobis_zero
mahalanobis_prod = mahalanobis_cano * mahalanobis_zero
variation_coeff = np.sqrt((hrf_mean.T.dot(hrf_covar).dot(hrf_mean))
/(hrf_mean.T.dot(hrf_mean))**2)
if estimate_hrf and zero_constraint:
hrf_mean = np.concatenate(([0], hrf_mean, [0]))
# when using the zero constraint the hrf covariance is fill with
# arbitrary zeros around the matrix, this is maybe a bad idea if we need
# it for later computation...
hrf_covar = np.concatenate(
(np.zeros((hrf_covar.shape[0], 1)), hrf_covar, np.zeros((hrf_covar.shape[0], 1))),
axis=1
)
hrf_covar = np.concatenate(
(np.zeros((1, hrf_covar.shape[1])), hrf_covar, np.zeros((1, hrf_covar.shape[1]))),
axis=0
)
if estimate_hrf:
(delay_of_response, delay_of_undershoot, dispersion_of_response,
dispersion_of_undershoot, ratio_resp_under, delay) = vt.fit_hrf_two_gammas(
hrf_mean, dt, hrf_duration
)
else:
(delay_of_response, delay_of_undershoot, dispersion_of_response,
dispersion_of_undershoot, ratio_resp_under, delay) = (None, None, None,
None, None, None)
ppm_a_nrl, ppm_g_nrl = vt.ppms_computation(
nrls_mean, np.diagonal(nrls_covar), nrls_class_mean, nrls_class_var,
threshold_a="intersect"
)
#+++++++++++++++++++++++ calculate contrast maps and variance +++++++++++++++++++++++#
nb_contrasts = len(contrasts)
if compute_contrasts and nb_contrasts > 0:
logger.info('Computing contrasts ...')
(contrasts_mean,
contrasts_var,
contrasts_class_mean,
contrasts_class_var) = vt.contrasts_mean_var_classes(
contrasts, condition_names, nrls_mean, nrls_covar,
nrls_class_mean, nrls_class_var, nb_contrasts, nb_classes, nb_voxels
)
ppm_a_contrasts, ppm_g_contrasts = vt.ppms_computation(
contrasts_mean, contrasts_var, contrasts_class_mean, contrasts_class_var
)
logger.info('Done computing contrasts.')
else:
(contrasts_mean, contrasts_var, contrasts_class_mean,
contrasts_class_var, ppm_a_contrasts, ppm_g_contrasts) = (None, None,
None, None,
None, None)
#+++++++++++++++++++++++ calculate contrast maps and variance +++++++++++++++++++++++#
logger.info("Nb iterations to reach criterion: %d", loop)
logger.info("Computational time = %s min %s s",
*(str(int(x)) for x in divmod(compute_time[-1], 60)))
logger.debug('nrls_class_mean: %s', nrls_class_mean)
logger.debug('nrls_class_var: %s', nrls_class_var)
logger.debug("sigma_H = %s", str(sigma_h))
logger.debug("beta = %s", str(beta))
stimulus_induced_signal = vt.computeFit(hrf_mean, nrls_mean, X, nb_voxels, nb_scans)
snr = 20 * np.log(
np.linalg.norm(bold_data.astype(np.float))
/ np.linalg.norm((bold_data - stimulus_induced_signal - drift).astype(np.float))
)
snr /= np.log(10.)
logger.info('snr comp = %f', snr)
# ,FreeEnergyArray
return (loop, nrls_mean, hrf_mean, hrf_covar, labels_proba, noise_var,
nrls_class_mean, nrls_class_var, beta, drift_coeffs, drift,
contrasts_mean, contrasts_var, compute_time[2:], compute_time_mean,
nrls_covar, stimulus_induced_signal, mahalanobis_zero,
mahalanobis_cano, mahalanobis_diff, mahalanobis_prod, ppm_a_nrl,
ppm_g_nrl, ppm_a_contrasts, ppm_g_contrasts, variation_coeff,
free_energy[1:], free_energy_crit[1:], beta_list[1:],
delay_of_response, delay_of_undershoot, dispersion_of_response,
dispersion_of_undershoot, ratio_resp_under, delay)
def jde_vem_bold(graph,
bold_data,
onsets,
durations,
hrf_duration,
nb_classes,
tr,
beta,
dt,
estimate_sigma_h=True,
sigma_h=0.05,
it_max=-1,
it_min=0,
estimate_beta=True,
contrasts=None,
compute_contrasts=False,
hrf_hyperprior=0,
estimate_hrf=True,
constrained=False,
zero_constraint=True,
drifts_type="poly",
seed=6537546):
"""This is the main function that computes the VEM analysis on BOLD data.
This function uses optimized python functions.
Parameters
----------
graph : ndarray of lists
represents the neighbours indexes of each voxels index
bold_data : ndarray, shape (nb_scans, nb_voxels)
raw data
onsets : dict
dictionnary of onsets
durations : # TODO
# TODO
hrf_duration : float
hrf total time duration (in s)
nb_classes : int
the number of classes to classify the nrls. This parameter is provided
for development purposes as most of the algorithm implies two classes
tr : float
time of repetition
beta : float
the initial value of beta
dt : float
hrf temporal precision
estimate_sigma_h : bool, optional
toggle estimation of sigma H
sigma_h : float, optional
initial or fixed value of sigma H
it_max : int, optional
maximal computed iteration number
it_min : int, optional
minimal computed iteration number
estimate_beta : bool, optional
toggle the estimation of beta
contrasts : OrderedDict, optional
dict of contrasts to compute
compute_contrasts : bool, optional
if True, compute the contrasts defined in contrasts
hrf_hyperprior : float
# TODO
estimate_hrf : bool, optional
if True, estimate the HRF for each parcel, if False use the canonical HRF
constrained : bool, optional
if True, add a constrains the l2 norm of the HRF to 1
zero_constraint : bool, optional
if True, add zeros to the beginning and the end of the estimated HRF.
drifts_type : str, optional
set the drifts basis type used. Can be "poly" for polynomial or "cos"
for cosine
seed : int, optional
seed used by numpy to initialize random generator number
Returns
-------
loop : int
number of iterations before convergence
nrls_mean : ndarray, shape (nb_voxels, nb_conditions)
Neural response level mean value
hrf_mean : ndarray, shape (hrf_len,)
Hemodynamic response function mean value
hrf_covar : ndarray, shape (hrf_len, hrf_len)
Covariance matrix of the HRF
labels_proba : ndarray, shape (nb_conditions, nb_classes, nb_voxels)
probability of voxels being in one class
noise_var : ndarray, shape (nb_voxels,)
estimated noise variance
nrls_class_mean : ndarray, shape (nb_conditions, nb_classes)
estimated mean value of the gaussians of the classes
nrls_class_var : ndarray, shape (nb_conditions, nb_classes)
estimated variance of the gaussians of the classes
beta : ndarray, shape (nb_conditions,)
estimated beta
drift_coeffs : ndarray, shape (# TODO)
estimated coefficient of the drifts
drift : ndarray, shape (# TODO)
estimated drifts
contrasts_mean : ndarray, shape (nb_voxels, len(contrasts))
Contrasts computed from NRLs
contrasts_var : ndarray, shape (nb_voxels, len(contrasts))
Variance of the contrasts
compute_time : list
computation time of each iteration
compute_time_mean : float
computation mean time over iterations
nrls_covar : ndarray, shape (nb_conditions, nb_conditions, nb_voxels)
# TODO
stimulus_induced_signal : ndarray, shape (nb_scans, nb_voxels)
# TODO
mahalanobis_zero : float
Mahalanobis distance between estimated hrf_mean and the null vector
mahalanobis_cano : float
Mahalanobis distance between estimated hrf_mean and the canonical HRF
mahalanobis_diff : float
difference between mahalanobis_cano and mahalanobis_diff
mahalanobis_prod : float
product of mahalanobis_cano and mahalanobis_diff
ppm_a_nrl : ndarray, shape (nb_voxels,)
The posterior probability map using an alpha
ppm_g_nrl : ndarray, shape (nb_voxels,)
# TODO
ppm_a_contrasts : ndarray, shape (nb_voxels,)
# TODO
ppm_g_contrasts : ndarray, shape (nb_voxels,)
# TODO
variation_coeff : float
coefficient of variation of the HRF
free_energy : list
# TODO
Notes
-----
See `A novel definition of the multivariate coefficient of variation
<http://onlinelibrary.wiley.com/doi/10.1002/bimj.201000030/abstract>`_
article for more information about the coefficient of variation.
"""
logger.info("VEM started.")
if not contrasts:
contrasts = OrderedDict()
np.random.seed(seed)
nb_2_norm = 1
normalizing = False
regularizing = False
if it_max <= 0:
it_max = 100
gamma = 7.5
# Initialize sizes vectors
hrf_len = np.int(np.ceil(hrf_duration / dt)) + 1
nb_conditions = len(onsets)
nb_scans = bold_data.shape[0]
nb_voxels = bold_data.shape[1]
X, occurence_matrix, condition_names = vt.create_conditions(
onsets, durations, nb_conditions, nb_scans, hrf_len, tr, dt)
neighbours_indexes = vt.create_neighbours(graph)
order = 2
if regularizing:
regularization = np.ones(hrf_len)
regularization[hrf_len // 3:hrf_len // 2] = 2
regularization[hrf_len // 2:2 * hrf_len // 3] = 5
regularization[2 * hrf_len // 3:3 * hrf_len // 4] = 7
regularization[3 * hrf_len // 4:] = 10
# regularization[hrf_len//2:] = 10
else:
regularization = None
d2 = vt.buildFiniteDiffMatrix(order, hrf_len, regularization)
hrf_regu_prior_inv = d2.T.dot(d2) / pow(dt, 2 * order)
if estimate_hrf and zero_constraint:
hrf_len = hrf_len - 2
hrf_regu_prior_inv = hrf_regu_prior_inv[1:-1, 1:-1]
occurence_matrix = occurence_matrix[:, :, 1:-1]
noise_struct = np.identity(nb_scans)
noise_var = np.ones(nb_voxels)
if nb_classes != 2:
logger.warn('The number of classes is different to two.')
labels_proba = np.zeros((nb_conditions, nb_classes, nb_voxels),
dtype=np.float64)
logger.info("Labels are initialized by setting everything to {}".format(
1. / nb_classes))
labels_proba[:, :, :] = 1. / nb_classes
m_h = getCanoHRF(hrf_duration, dt)[1][:hrf_len]
hrf_mean = np.array(m_h).astype(np.float64)
if estimate_hrf:
hrf_covar = np.identity(hrf_len, dtype=np.float64)
else:
hrf_covar = np.zeros((hrf_len, hrf_len), dtype=np.float64)
beta = beta * np.ones(nb_conditions, dtype=np.float64)
beta_list = [beta.copy()]
if drifts_type == "poly":
drift_basis = vt.poly_drifts_basis(nb_scans, 4, tr)
elif drifts_type == "cos":
drift_basis = vt.cosine_drifts_basis(nb_scans, 64, tr)
else:
raise Exception('drift type "%s" is not supported' % drifts_type)
drift_coeffs = vt.drifts_coeffs_fit(bold_data, drift_basis)
drift = drift_basis.dot(drift_coeffs)
bold_data_drift = bold_data - drift
# Parameters Gaussian mixtures
nrls_class_mean = 2 * np.ones((nb_conditions, nb_classes))
nrls_class_mean[:, 0] = 0
nrls_class_var = 0.3 * np.ones(
(nb_conditions, nb_classes), dtype=np.float64)
nrls_mean = (
np.random.normal(nrls_class_mean, nrls_class_var)[:, :, np.newaxis] *
labels_proba).sum(axis=1).T
nrls_covar = np.identity(nb_conditions)[:, :, np.newaxis] + np.zeros(
(1, 1, nb_voxels))
thresh_free_energy = 1e-4
free_energy = [1.]
free_energy_crit = [1.]
compute_time = []
start_time = time.time()
loop = 0
while (loop <= it_min
or ((np.asarray(free_energy_crit[-5:]) > thresh_free_energy).any()
and loop < it_max)):
logger.info("{:-^80}".format(" Iteration n°" + str(loop + 1) + " "))
logger.info("Expectation A step...")
logger.debug("Before: nrls_mean = %s, nrls_covar = %s", nrls_mean,
nrls_covar)
nrls_mean, nrls_covar = vt.nrls_expectation(
hrf_mean, nrls_mean, occurence_matrix, noise_struct, labels_proba,
nrls_class_mean, nrls_class_var, nb_conditions, bold_data_drift,
nrls_covar, hrf_covar, noise_var)
logger.debug("After: nrls_mean = %s, nrls_covar = %s", nrls_mean,
nrls_covar)
logger.info("Expectation Z step...")
logger.debug("Before: labels_proba = %s, labels_proba = %s",
labels_proba, labels_proba)
labels_proba = vt.labels_expectation(nrls_covar,
nrls_mean,
nrls_class_var,
nrls_class_mean,
beta,
labels_proba,
neighbours_indexes,
nb_conditions,
nb_classes,
nb_voxels,
parallel=True)
logger.debug("After: labels_proba = %s, labels_proba = %s",
labels_proba, labels_proba)
if estimate_hrf:
logger.info("Expectation H step...")
logger.debug("Before: hrf_mean = %s, hrf_covar = %s", hrf_mean,
hrf_covar)
hrf_mean, hrf_covar = vt.hrf_expectation(
nrls_covar, nrls_mean, occurence_matrix, noise_struct,
hrf_regu_prior_inv, sigma_h, nb_voxels, bold_data_drift,
noise_var)
if constrained:
hrf_mean = vt.norm1_constraint(hrf_mean, hrf_covar)
hrf_covar[:] = 0
logger.debug("After: hrf_mean = %s, hrf_covar = %s", hrf_mean,
hrf_covar)
# Normalizing H at each nb_2_norm iterations:
if not constrained and normalizing:
# Normalizing is done before sigma_h, nrls_class_mean and nrls_class_var estimation
# we should not include them in the normalisation step
if (loop + 1) % nb_2_norm == 0:
hrf_norm = np.linalg.norm(hrf_mean)
hrf_mean /= hrf_norm
hrf_covar /= hrf_norm**2
nrls_mean *= hrf_norm
nrls_covar *= hrf_norm**2
if estimate_hrf and estimate_sigma_h:
logger.info("Maximization sigma_H step...")
logger.debug("Before: sigma_h = %s", sigma_h)
if hrf_hyperprior > 0:
sigma_h = vt.maximization_sigmaH_prior(hrf_len, hrf_covar,
hrf_regu_prior_inv,
hrf_mean,
hrf_hyperprior)
else:
sigma_h = vt.maximization_sigmaH(hrf_len, hrf_covar,
hrf_regu_prior_inv, hrf_mean)
logger.debug("After: sigma_h = %s", sigma_h)
logger.info("Maximization (mu,sigma) step...")
logger.debug("Before: nrls_class_mean = %s, nrls_class_var = %s",
nrls_class_mean, nrls_class_var)
nrls_class_mean, nrls_class_var = vt.maximization_class_proba(
labels_proba, nrls_mean, nrls_covar)
logger.debug("After: nrls_class_mean = %s, nrls_class_var = %s",
nrls_class_mean, nrls_class_var)
logger.info("Maximization L step...")
logger.debug("Before: drift_coeffs = %s", drift_coeffs)
drift_coeffs = vt.maximization_drift_coeffs(bold_data, nrls_mean,
occurence_matrix, hrf_mean,
noise_struct, drift_basis)
logger.debug("After: drift_coeffs = %s", drift_coeffs)
drift = drift_basis.dot(drift_coeffs)
bold_data_drift = bold_data - drift
if estimate_beta:
logger.info("Maximization beta step...")
for cond_nb in xrange(0, nb_conditions):
beta[cond_nb], success = vt.beta_maximization(
beta[cond_nb] * np.ones((1, )),
labels_proba[cond_nb, :, :], neighbours_indexes, gamma)
beta_list.append(beta.copy())
logger.debug("beta = %s", str(beta))
logger.info("Maximization sigma noise step...")
noise_var = vt.maximization_noise_var(occurence_matrix, hrf_mean,
hrf_covar, nrls_mean, nrls_covar,
noise_struct, bold_data_drift,
nb_scans)
# Computing Free Energy
free_energy.append(
vt.free_energy_computation(
nrls_mean, nrls_covar, hrf_mean, hrf_covar, hrf_len,
labels_proba, bold_data_drift, occurence_matrix, noise_var,
noise_struct, nb_conditions, nb_voxels, nb_scans, nb_classes,
nrls_class_mean, nrls_class_var,
neighbours_indexes, beta, sigma_h,
np.linalg.inv(hrf_regu_prior_inv), hrf_regu_prior_inv, gamma,
hrf_hyperprior))
free_energy_crit.append(
abs((free_energy[-2] - free_energy[-1]) / free_energy[-2]))
logger.info("Convergence criteria: %f (Threshold = %f)",
free_energy_crit[-1], thresh_free_energy)
loop += 1
compute_time.append(time.time() - start_time)
compute_time_mean = compute_time[-1] / loop
mahalanobis_zero = np.nan
mahalanobis_cano = np.nan
mahalanobis_diff = np.nan
mahalanobis_prod = np.nan
variation_coeff = np.nan
if estimate_hrf and not constrained and not normalizing:
hrf_norm = np.linalg.norm(hrf_mean)
hrf_mean /= hrf_norm
hrf_covar /= hrf_norm**2
sigma_h /= hrf_norm**2
nrls_mean *= hrf_norm
nrls_covar *= hrf_norm**2
nrls_class_mean *= hrf_norm
nrls_class_var *= hrf_norm**2
mahalanobis_zero = mahalanobis(hrf_mean, np.zeros_like(hrf_mean),
np.linalg.inv(hrf_covar))
mahalanobis_cano = mahalanobis(hrf_mean, m_h, np.linalg.inv(hrf_covar))
mahalanobis_diff = mahalanobis_cano - mahalanobis_zero
mahalanobis_prod = mahalanobis_cano * mahalanobis_zero
variation_coeff = np.sqrt((hrf_mean.T.dot(hrf_covar).dot(hrf_mean)) /
(hrf_mean.T.dot(hrf_mean))**2)
if estimate_hrf and zero_constraint:
hrf_mean = np.concatenate(([0], hrf_mean, [0]))
# when using the zero constraint the hrf covariance is fill with
# arbitrary zeros around the matrix, this is maybe a bad idea if we need
# it for later computation...
hrf_covar = np.concatenate((np.zeros(
(hrf_covar.shape[0], 1)), hrf_covar,
np.zeros((hrf_covar.shape[0], 1))),
axis=1)
hrf_covar = np.concatenate((np.zeros(
(1, hrf_covar.shape[1])), hrf_covar,
np.zeros((1, hrf_covar.shape[1]))),
axis=0)
if estimate_hrf:
(delay_of_response, delay_of_undershoot, dispersion_of_response,
dispersion_of_undershoot, ratio_resp_under,
delay) = vt.fit_hrf_two_gammas(hrf_mean, dt, hrf_duration)
else:
(delay_of_response, delay_of_undershoot, dispersion_of_response,
dispersion_of_undershoot, ratio_resp_under,
delay) = (None, None, None, None, None, None)
ppm_a_nrl, ppm_g_nrl = vt.ppms_computation(nrls_mean,
np.diagonal(nrls_covar),
nrls_class_mean,
nrls_class_var,
threshold_a="intersect")
# Calculate contrast maps and variance
nb_contrasts = len(contrasts)
if compute_contrasts and nb_contrasts > 0:
logger.info('Computing contrasts ...')
(contrasts_mean, contrasts_var, contrasts_class_mean,
contrasts_class_var) = vt.contrasts_mean_var_classes(
contrasts, condition_names, nrls_mean, nrls_covar,
nrls_class_mean, nrls_class_var, nb_contrasts, nb_classes,
nb_voxels)
ppm_a_contrasts, ppm_g_contrasts = vt.ppms_computation(
contrasts_mean, contrasts_var, contrasts_class_mean,
contrasts_class_var)
logger.info('Done computing contrasts.')
else:
(contrasts_mean, contrasts_var, contrasts_class_mean,
contrasts_class_var, ppm_a_contrasts,
ppm_g_contrasts) = (None, None, None, None, None, None)
logger.info("Number of iterations to reach criterion: %d", loop)
logger.info("Computational time = {t[0]:.0f} min {t[1]:.0f} s".format(
t=divmod(compute_time[-1], 60)))
logger.debug('nrls_class_mean: %s', nrls_class_mean)
logger.debug('nrls_class_var: %s', nrls_class_var)
logger.debug("sigma_H = %s", str(sigma_h))
logger.debug("beta = %s", str(beta))
stimulus_induced_signal = vt.computeFit(hrf_mean, nrls_mean, X, nb_voxels,
nb_scans)
snr = 20 * np.log(
np.linalg.norm(bold_data.astype(np.float)) / np.linalg.norm(
(bold_data_drift - stimulus_induced_signal).astype(np.float)))
snr /= np.log(10.)
logger.info('SNR comp = %f', snr)
return (loop, nrls_mean, hrf_mean, hrf_covar, labels_proba, noise_var,
nrls_class_mean, nrls_class_var, beta, drift_coeffs, drift,
contrasts_mean, contrasts_var, compute_time[2:], compute_time_mean,
nrls_covar, stimulus_induced_signal, mahalanobis_zero,
mahalanobis_cano, mahalanobis_diff, mahalanobis_prod, ppm_a_nrl,
ppm_g_nrl, ppm_a_contrasts, ppm_g_contrasts, variation_coeff,
free_energy[1:], free_energy_crit[1:], beta_list[1:],
delay_of_response, delay_of_undershoot, dispersion_of_response,
dispersion_of_undershoot, ratio_resp_under, delay)