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* - citation info, typos * - typo * Update README.md; Cite Me with Saskia as co-author, beta release info "no warranty" not after UniQC, but before all beta toolboxes. * Update CHANGELOG.md; PhysIO v8.0 (Hilbert RVT, resp preprocessing) * Update CITATION.cff; typos Saskia * - HUGE: release 202104 * - HUGE: file permissions * - release: update changelog * Update CITATION.cff Co-authored-by: Lars Kasper <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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@@ -2,4 +2,3 @@ | |
| /*.mex* | ||
| /trash | ||
| /@tapas_Huge/*.asv | ||
| /spm | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,177 @@ | ||
| function [ obj ] = mh_init( obj ) | ||
| % Initialize Markov chain state for Metropolized Gibbs sampling. | ||
| % Requires obj.dcm and obj.prior to be intialized. | ||
| % | ||
| % This is a protected method of the tapas_Huge class. It cannot be called | ||
| % from outside the class. | ||
| % | ||
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| % fMRI forward model | ||
| obj.options.fncBold = @bold_gen; | ||
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| %% priors | ||
| if isvector(obj.prior.tau_0) | ||
| obj.prior.tau_0 = diag(obj.prior.tau_0); | ||
| end | ||
| % adapt obj.prior to collapsed HUGE | ||
| prior = struct(); | ||
| prior.alpha_0 = obj.prior.alpha_0; | ||
| prior.m_0 = obj.prior.m_0; | ||
| prior.T_0 = inv(obj.prior.S_0).*obj.prior.tau_0; | ||
| prior.s_0 = - log(diag(obj.prior.S_0)'); | ||
| prior.nu_0 = obj.prior.nu_0; | ||
| if isscalar(prior.nu_0) | ||
| prior.nu_0 = repmat(prior.nu_0, obj.idx.P_c, 1); | ||
| end | ||
| prior.Pi_h = inv(obj.prior.Sigma_h); | ||
| prior.mu_h = obj.prior.mu_h; | ||
| % lambda: BOLD-to-Noise ratio in log-space | ||
| prior.lambda_0 = repmat(log(obj.prior.mu_lambda), 1, obj.R); | ||
| prior.omega_0 = repmat(4/(log(obj.prior.s2_lambda).^2), obj.R, 1); | ||
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| obj.prior = prior; | ||
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| %% starting values | ||
| init.pi = ones(1, obj.K)./obj.K; | ||
| init.mu = obj.options.start.clusters; | ||
| init.kappa = repmat(obj.prior.s_0, obj.K, 1); | ||
| init.theta_c = obj.options.start.subjects(:, 1:obj.idx.P_c); | ||
| init.theta_h = obj.options.start.subjects(:, obj.idx.P_c+1:end); | ||
| init.lambda = repmat(obj.prior.lambda_0, obj.N, 1); | ||
| % randomizing starting values | ||
| if obj.options.nvp.randomize | ||
| tmp = obj.options.start.gmm; | ||
| if obj.options.mh.nSteps.weights | ||
| init.pi = init.pi + exp(randn(size(init.pi))*tmp); %%% TODO draw from prior | ||
| init.pi = init.pi./sum(init.pi); %%% requires samples from dirichlet | ||
| end | ||
| if obj.options.mh.nSteps.clusters | ||
| init.kappa = init.kappa + randn(size(init.kappa))*tmp; | ||
| end | ||
| tmp = obj.options.start.dcm; | ||
| if obj.options.mh.nSteps.dcm | ||
| init.lambda = init.lambda + randn(size(init.lambda))*tmp; | ||
| end | ||
| end | ||
|
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| %% switch to single precision | ||
| if obj.options.bSinglePrec | ||
| init.pi = single(init.pi); | ||
| init.mu = single(init.mu); | ||
| init.kappa = single(init.kappa); | ||
| init.theta_c = single(init.theta_c); | ||
| init.theta_h = single(init.theta_h); | ||
| init.lambda = single(init.lambda); | ||
| end | ||
|
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| %% initialize state of Markov chain | ||
| obj.aux.l2pi = -.5*obj.idx.P_c*log(2*pi); | ||
| % step size | ||
| obj.aux.step = obj.options.mh.stepSize; | ||
| obj.aux.step.mu = repmat(obj.aux.step.mu, obj.K, 1); | ||
| % obj.aux.step.mu = ones(obj.K, 1); | ||
| obj.aux.step.kappa = repmat(obj.aux.step.kappa, obj.K, 1); | ||
| obj.aux.step.theta = repmat(obj.aux.step.theta, obj.N, 1); | ||
| % obj.aux.step.theta = ones(obj.N, 1); | ||
| obj.aux.step.lambda = repmat(obj.aux.step.lambda, obj.N, 1); | ||
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| % transforming proposal distribution | ||
| obj.aux.transform = struct(); | ||
| obj.aux.transform.mu = repmat(eye(obj.idx.P_c), 1, 1, obj.K); | ||
| obj.aux.logdet.mu = zeros(obj.K, 1); | ||
| % obj.aux.transform.theta = repmat(eye(obj.idx.P_c + obj.idx.P_h), 1, 1, obj.N); | ||
| obj.aux.transform.theta = eye(obj.idx.P_c + obj.idx.P_h); | ||
| % obj.aux.logdet.theta = zeros(obj.N, 1); | ||
| obj.aux.logdet.theta = 0; | ||
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| obj.aux.sample = init; % obj.aux sample | ||
| obj.aux.nProp = struct(); % number of proposals | ||
| obj.aux.nAccept = struct(); % number of accepted proposals | ||
| obj.aux.lpr = struct(); % log obj.prior | ||
| obj.aux.lpr.llh = zeros(obj.N,1); % log likelihood | ||
|
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| % weights | ||
| obj.aux.nProp.pi = 0; | ||
| obj.aux.nAccept.pi = 0; | ||
| pic = fliplr(cumsum(obj.aux.sample.pi(end:-1:1))); | ||
| pis = obj.aux.sample.pi./pic; | ||
| pic = pic(2:end-1); | ||
| pis = pis(1:end-1); | ||
| obj.aux.sample.pi_u = tapas_huge_logit(pis) + log(obj.K-1:-1:1); | ||
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| obj.aux.lpr.pi = max(log(obj.aux.sample.pi)*(obj.prior.alpha_0 - 1) ... | ||
| + sum(log(pis)) + sum(log(1-pis)) + sum(log(pic)), -realmax); | ||
| % dPi = obj.aux.sample.pi_u - obj.prior.mu_pi; % Gaussian prior in | ||
| % obj.aux.lpr.pi = -dPi'.^2*obj.prior.pi_pi/2; % unconstrained space | ||
|
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| % clusters | ||
| obj.aux.nProp.mu = 0; | ||
| obj.aux.nAccept.mu = zeros(obj.K, 1); | ||
| obj.aux.nAccept.mu_rsk = zeros(obj.K, 2); | ||
| obj.aux.nProp.kappa = 0; | ||
| obj.aux.nAccept.kappa = zeros(obj.K, 1); | ||
| obj.aux.rho = zeros(obj.N, obj.K); | ||
| obj.aux.lpr.mu = zeros(1, obj.K); | ||
| obj.aux.lpr.kappa = zeros(1, obj.K); | ||
| for k = 1:obj.K | ||
| dmu_k = obj.aux.sample.mu(k,:) - obj.prior.m_0; | ||
| obj.aux.lpr.mu(k) = -.5*dmu_k*obj.prior.T_0*dmu_k'; | ||
| obj.aux.lpr.kappa(k) = -.5*(obj.aux.sample.kappa(k,:) - ... | ||
| obj.prior.s_0).^2*obj.prior.nu_0; | ||
| dtheta_c = bsxfun(@minus, obj.aux.sample.theta_c, obj.aux.sample.mu(k,:)); | ||
| obj.aux.rho(:,k) = -.5*dtheta_c.^2*exp(obj.aux.sample.kappa(k,:)') ... | ||
| + .5*sum(obj.aux.sample.kappa(k,:)) + obj.aux.l2pi; | ||
| end | ||
| obj.aux.rho_max = max(obj.aux.rho, [], 2); | ||
| obj.aux.rho = bsxfun(@minus, obj.aux.rho, obj.aux.rho_max); | ||
|
|
||
| % DCM parameters | ||
| obj.aux.nProp.theta = 0; | ||
| obj.aux.nAccept.theta = zeros(obj.N, 1); | ||
| obj.aux.lpr.theta_c = log(exp(obj.aux.rho)*obj.aux.sample.pi(:)) ... | ||
| + obj.aux.rho_max; | ||
| dtheta_h = bsxfun(@minus, obj.aux.sample.theta_h, obj.prior.mu_h); | ||
| obj.aux.lpr.theta_h = -.5*sum((dtheta_h*obj.prior.Pi_h).*dtheta_h, 2); | ||
|
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| obj.aux.lvarBold = zeros(obj.N, obj.R); % log-variance of BOLD response | ||
| obj.aux.q_r = zeros(obj.N, 1); % number of scans | ||
| obj.aux.epsilon = cell(obj.N, 1); | ||
| for n = 1:obj.N | ||
| % number of scans | ||
| obj.aux.q_r(n) = size(obj.data(n).bold, 1); | ||
| % log-variance of BOLD response (per subject and region) | ||
| obj.aux.lvarBold(n,:) = max(log(var(obj.data(n).bold)), ... | ||
| obj.const.minLogVar); | ||
| theta = [obj.aux.sample.theta_c(n,:), obj.aux.sample.theta_h(n,:)]; | ||
| epsilon = obj.bold_gen( theta, obj.data(n), obj.inputs(n), ... | ||
| obj.options.hemo, obj.R, obj.L, obj.idx ); | ||
| assert(~(any(isnan(epsilon(:))) || any(isinf(epsilon(:)))), ... | ||
| 'TAPAS:HUGE:Init:Stability',... | ||
| 'Starting values for subject %u lead to instable DCM.', n); | ||
| obj.aux.epsilon{n} = epsilon; | ||
| end | ||
|
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||
| % Noise precision (hyperparameters) | ||
| obj.aux.nProp.lambda = 0; | ||
| obj.aux.nAccept.lambda = zeros(obj.N,1); | ||
| dLambda = bsxfun(@minus, obj.aux.sample.lambda, obj.prior.lambda_0); | ||
| obj.aux.lpr.lambda = -.5*dLambda.^2*obj.prior.omega_0(:); | ||
|
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| %%% TODO add support for confounds | ||
|
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| % log-likelihood | ||
| for n = 1:obj.N | ||
| obj.aux.lpr.llh(n) = ... | ||
| -.5*sum(obj.aux.epsilon{n}.^2*exp(obj.aux.sample.lambda(n,:) ... | ||
| - obj.aux.lvarBold(n,:))') ... | ||
| +.5*obj.aux.q_r(n)*sum(obj.aux.sample.lambda(n,:)... | ||
| - obj.aux.lvarBold(n,:)); | ||
| end | ||
|
|
||
| % special proposals | ||
| obj.aux.nProp.sp = [0;0]; | ||
| obj.aux.nAccept.sp = [0;0]; | ||
|
|
||
| end | ||
|
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