function [Ep,Cp,Eh,F,L,dFdp,dFdpp] = spm_nlsi_GN(M,U,Y)
% Bayesian inversion of nonlinear models - Gauss-Newton/Variational Laplace
% FORMAT [Ep,Cp,Eh,F] = spm_nlsi_GN(M,U,Y)
%
% [Dynamic] MIMO models
%__________________________________________________________________________
%
% M.IS - function name f(P,M,U) - generative model
%        This function specifies the nonlinear model:
%          y = Y.y = IS(P,M,U) + X0*P0 + e
%        where e ~ N(0,C). For dynamic systems this would be an integration
%        scheme (e.g. spm_int). spm_int expects the following:
%
%     M.f  - f(x,u,P,M)
%     M.g  - g(x,u,P,M)
%     M.h  - h(x,u,P,M)
%       x  - state variables
%       u  - inputs or causes
%       P  - free parameters
%       M  - fixed functional forms and parameters in M
%
% M.FS - function name f(y,M)   - feature selection
%        This [optional] function performs feature selection assuming the
%        generalized model y = FS(y,M) = FS(IS(P,M,U),M) + X0*P0 + e
%
% M.P  - starting estimates for model parameters [optional]
%
% M.pE - prior expectation      - E{P}   of model parameters
% M.pC - prior covariance       - Cov{P} of model parameters
%
% M.hE - prior expectation      - E{h}   of log-precision parameters
% M.hC - prior covariance       - Cov{h} of log-precision parameters
%
% U.u  - inputs (or just U)
% U.dt - sampling interval
%
% Y.y  - outputs (samples x observations x ...)
% Y.dt - sampling interval for outputs
% Y.X0 - confounds or null space      (over size(y,1) samples or all vec(y))
% Y.Q  - q error precision components (over size(y,1) samples or all vec(y))
%
%
% Parameter estimates
%--------------------------------------------------------------------------
% Ep  - (p x 1)         conditional expectation    E{P|y}
% Cp  - (p x p)         conditional covariance     Cov{P|y}
% Eh  - (q x 1)         conditional log-precisions E{h|y}
%
% log evidence
%--------------------------------------------------------------------------
% F   - [-ve] free energy F = log evidence = p(y|f,g,pE,pC) = p(y|m)
%
%__________________________________________________________________________
% Returns the moments of the posterior p.d.f. of the parameters of a
% nonlinear model specified by IS(P,M,U) under Gaussian assumptions.
% Usually, IS is an integrator of a dynamic MIMO input-state-output model
%
%              dx/dt = f(x,u,P)
%              y     = g(x,u,P)  + X0*P0 + e
%
% A static nonlinear observation model with fixed input or causes u
% obtains when x = []. i.e.
%
%              y     = g([],u,P) + X0*P0e + e
%
% but static nonlinear models are specified more simply using
%
%              y     = IS(P,M,U) + X0*P0 + e
%
% Priors on the free parameters P are specified in terms of expectation pE
% and covariance pC. The E-Step uses a Fisher-Scoring scheme and a Laplace
% approximation to estimate the conditional expectation and covariance of P
% If the free-energy starts to increase,  an abbreviated descent is
% invoked.  The M-Step estimates the precision components of e, in terms
% of log-precisions.  Although these two steps can be thought of in
% terms of E and N steps they are in fact variational steps of a full
% variational Laplace scheme that accommodates conditional uncertainty
% over both parameters and log precisions (c.f. hyperparameters with hyper
% priors)
%
% An optional feature selection can be specified with parameters M.FS.
%
% For generic aspects of the scheme see:
%
% Friston K, Mattout J, Trujillo-Barreto N, Ashburner J, Penny W.
% Variational free energy and the Laplace approximation.
% NeuroImage. 2007 Jan 1;34(1):220-34.
%
% This scheme handels complex data along the lines originally described in:
%
% Sehpard RJ, Lordan BP, and Grant EH.
% Least squares analysis of complex data with applications to permittivity
% measurements.
% J. Phys. D. Appl. Phys 1970 3:1759-1764.
%
%__________________________________________________________________________
% Copyright (C) 2001-2015 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: spm_nlsi_GN.m 7279 2018-03-10 21:22:44Z karl $

% options
%--------------------------------------------------------------------------
try, M.nograph; catch, M.nograph = 0;   end
try, M.noprint; catch, M.noprint = 0;   end
try, M.Nmax;    catch, M.Nmax    = 128; end

% figure (unless disabled)
%--------------------------------------------------------------------------
if ~M.nograph
    Fsi = spm_figure('GetWin','SI');
end

% check integrator
%--------------------------------------------------------------------------
try
    M.IS;
catch
    M.IS = 'spm_int';
end

% composition of feature selection and prediction (usually an integrator)
%--------------------------------------------------------------------------
try
    y  = Y.y;
catch
    y  = Y;
end

try
    
    % try FS(y,M)
    %----------------------------------------------------------------------
    try
        y  = feval(M.FS,y,M);
        IS = inline([M.FS '(' M.IS '(P,M,U),M)'],'P','M','U');
        
        % try FS(y)
        %------------------------------------------------------------------
    catch
        y  = feval(M.FS,y);
        IS = inline([M.FS '(' M.IS '(P,M,U))'],'P','M','U');
    end
    
catch
    
    % otherwise FS(y) = y
    %----------------------------------------------------------------------
    try
        IS = inline([M.IS '(P,M,U)'],'P','M','U');
    catch
        IS = M.IS;
    end
end

% converted to function handle
%--------------------------------------------------------------------------
IS  = spm_funcheck(IS);

% paramter update eqation
%--------------------------------------------------------------------------
if isfield(M,'f'), M.f = spm_funcheck(M.f);  end
if isfield(M,'g'), M.g = spm_funcheck(M.g);  end
if isfield(M,'h'), M.h = spm_funcheck(M.h);  end


% size of data (samples x response component x response component ...)
%--------------------------------------------------------------------------
if iscell(y)
    ns = size(y{1},1);
else
    ns = size(y,1);
end
ny   = length(spm_vec(y));          % total number of response variables
nr   = ny/ns;                       % number response components
M.ns = ns;                          % number of samples M.ns

% initial states
%--------------------------------------------------------------------------
try
    M.x;
catch
    if ~isfield(M,'n'), M.n = 0;    end
    M.x = sparse(M.n,1);
end

% input
%--------------------------------------------------------------------------
try
    U;
catch
    U = [];
end

% initial parameters
%--------------------------------------------------------------------------
try
    spm_vec(M.P) - spm_vec(M.pE);
    fprintf('\nParameter initialisation successful\n')
catch
    M.P = M.pE;
end

% time-step
%--------------------------------------------------------------------------
try
    dt = Y.dt;
catch
    dt = 1;
end

% precision components Q
%--------------------------------------------------------------------------
try
    Q = Y.Q;
    if isnumeric(Q), Q = {Q}; end
catch
    Q = spm_Ce(ns*ones(1,nr));
end
nh    = length(Q);                  % number of precision components
nq    = ny/length(Q{1});            % for compact Kronecker form of M-step


% prior moments (assume uninformative priors if not specifed)
%--------------------------------------------------------------------------
pE       = M.pE;
try
    pC   = M.pC;
catch
    np   = spm_length(M.pE);
    pC   = speye(np,np)*exp(16);
end

% confounds (if specified)
%--------------------------------------------------------------------------
try
    nb   = size(Y.X0,1);            % number of bins
    nx   = ny/nb;                   % number of blocks
    dfdu = kron(speye(nx,nx),Y.X0);
catch
    dfdu = sparse(ny,0);
end
if isempty(dfdu), dfdu = sparse(ny,0); end


% hyperpriors - expectation (and initialize hyperparameters)
%--------------------------------------------------------------------------
try
    hE  = M.hE;
    if length(hE) ~= nh
        hE = hE + sparse(nh,1);
    end
catch
    hE  = sparse(nh,1) - log(var(spm_vec(y))) + 4;
end
h       = hE;

% hyperpriors - covariance
%--------------------------------------------------------------------------
try
    ihC = spm_inv(M.hC);
    if length(ihC) ~= nh
        ihC = ihC*speye(nh,nh);
    end
catch
    ihC = speye(nh,nh)*exp(4);
end


% unpack covariance
%--------------------------------------------------------------------------
if isstruct(pC);
    pC = spm_diag(spm_vec(pC));
end

% dimension reduction of parameter space
%--------------------------------------------------------------------------
V     = spm_svd(pC,0);
nu    = size(dfdu,2);                 % number of parameters (confounds)
np    = size(V,2);                    % number of parameters (effective)
ip    = (1:np)';
iu    = (1:nu)' + np;

% second-order moments (in reduced space)
%--------------------------------------------------------------------------
pC    = V'*pC*V;
uC    = speye(nu,nu)/1e-8;
ipC   = inv(spm_cat(spm_diag({pC,uC})));

% initialize conditional density
%--------------------------------------------------------------------------
Eu    = spm_pinv(dfdu)*spm_vec(y);
p     = [V'*(spm_vec(M.P) - spm_vec(M.pE)); Eu];
Ep    = spm_unvec(spm_vec(pE) + V*p(ip),pE);


% EM
%==========================================================================
criterion = [0 0 0 0];

C.F   = -Inf;                                   % free energy
v     = -4;                                     % log ascent rate
dFdh  = zeros(nh,1);
dFdhh = zeros(nh,nh);
for k = 1:M.Nmax
    
    % time
    %----------------------------------------------------------------------
    tStart = tic;
    
    % E-Step: prediction f, and gradients; dfdp
    %======================================================================
    try
        
        % gradients
        %------------------------------------------------------------------
        [dfdp,f] = spm_diff(IS,Ep,M,U,1,{V});
        dfdp     = reshape(spm_vec(dfdp),ny,np);
        
        % check for stability
        %------------------------------------------------------------------
        normdfdp = norm(dfdp,'inf');
        revert   = isnan(normdfdp) || normdfdp > exp(32);
        
    catch
        revert   = true;
    end
    
    if revert && k > 1
        for i = 1:4
            
            % reset expansion point and increase regularization
            %--------------------------------------------------------------
            v        = min(v - 2,-4);
            
            % E-Step: update
            %--------------------------------------------------------------
            p        = C.p + spm_dx(dFdpp,dFdp,{v});
            Ep       = spm_unvec(spm_vec(pE) + V*p(ip),pE);
            
            % try again
            %--------------------------------------------------------------
            try
                [dfdp,f] = spm_diff(IS,Ep,M,U,1,{V});
                dfdp     = reshape(spm_vec(dfdp),ny,np);
                
                % check for stability
                %----------------------------------------------------------
                normdfdp = norm(dfdp,'inf');
                revert   = isnan(normdfdp) || normdfdp > exp(32);
                
            catch
                revert   = true;
            end
            
            % break
            %--------------------------------------------------------------
            if ~revert, break, end
            
        end
    end
    
    % convergence failure
    %----------------------------------------------------------------------
    if revert
        error('SPM:spm_nlsi_GN','Convergence failure.');
    end
    
    
    % prediction error and full gradients
    %----------------------------------------------------------------------
    e     =  spm_vec(y) - spm_vec(f) - dfdu*p(iu);
    J     = -[dfdp dfdu];
    
    
    % M-step: Fisher scoring scheme to find h = max{F(p,h)}
    %======================================================================
    for m = 1:8
        
        % precision and conditional covariance
        %------------------------------------------------------------------
        iS    = sparse(0);
        for i = 1:nh
            iS = iS + Q{i}*(exp(-32) + exp(h(i)));
        end
        S     = spm_inv(iS);
        iS    = kron(speye(nq),iS);
        Pp    = real(J'*iS*J);
        Cp    = spm_inv(Pp + ipC);
        
        % precision operators for M-Step
        %------------------------------------------------------------------
        for i = 1:nh
            P{i}   = Q{i}*exp(h(i));
            PS{i}  = P{i}*S;
            P{i}   = kron(speye(nq),P{i});
            JPJ{i} = real(J'*P{i}*J);
        end
        
        % derivatives: dLdh = dL/dh,...
        %------------------------------------------------------------------
        for i = 1:nh
            dFdh(i,1)      =   trace(PS{i})*nq/2 ...
                - real(e'*P{i}*e)/2 ...
                - spm_trace(Cp,JPJ{i})/2;
            for j = i:nh
                dFdhh(i,j) = - spm_trace(PS{i},PS{j})*nq/2;
                dFdhh(j,i) =   dFdhh(i,j);
            end
        end
        
        % add hyperpriors
        %------------------------------------------------------------------
        d     = h     - hE;
        dFdh  = dFdh  - ihC*d;
        dFdhh = dFdhh - ihC;
        Ch    = spm_inv(real(-dFdhh));
        
        % update ReML estimate
        %------------------------------------------------------------------
        dh    = spm_dx(dFdhh,dFdh,{4});
        dh    = min(max(dh,-1),1);
        h     = h  + dh;
        
        % convergence
        %------------------------------------------------------------------
        dF    = dFdh'*dh;
        if dF < 1e-2, break, end
        
    end
    
    
    % E-Step with Levenberg-Marquardt regularization
    %======================================================================
    
    % objective function: F(p) = log evidence - divergence
    %----------------------------------------------------------------------
    L(1) = spm_logdet(iS)*nq/2  - real(e'*iS*e)/2 - ny*log(8*atan(1))/2;            ...
    L(2) = spm_logdet(ipC*Cp)/2 - p'*ipC*p/2;
    L(3) = spm_logdet(ihC*Ch)/2 - d'*ihC*d/2;
    F    = sum(L);
    
    % record increases and reference log-evidence for reporting
    %----------------------------------------------------------------------
    try
        F0;
        if ~M.noprint
            fprintf(' actual: %.3e (%.2f sec)\n',full(F - C.F),toc(tStart))
        end
    catch
        F0 = F;
    end
    
    % if F has increased, update gradients and curvatures for E-Step
    %----------------------------------------------------------------------
    if F > C.F || k < 3
        
        % accept current estimates
        %------------------------------------------------------------------
        C.p   = p;
        C.h   = h;
        C.F   = F;
        C.L   = L;
        C.Cp  = Cp;
        
        % E-Step: Conditional update of gradients and curvature
        %------------------------------------------------------------------
        dFdp  = -real(J'*iS*e) - ipC*p;
        dFdpp = -real(J'*iS*J) - ipC;
        
        % decrease regularization
        %------------------------------------------------------------------
        v     = min(v + 1/2,4);
        str   = 'EM:(+)';
        
    else
        
        % reset expansion point
        %------------------------------------------------------------------
        p     = C.p;
        h     = C.h;
        Cp    = C.Cp;
        
        % and increase regularization
        %------------------------------------------------------------------
        v     = min(v - 2,-4);
        str   = 'EM:(-)';
        
    end
    
    % E-Step: update
    %======================================================================
    dp    = spm_dx(dFdpp,dFdp,{v});
    p     = p + dp;
    Ep    = spm_unvec(spm_vec(pE) + V*p(ip),pE);
    
    
    
    % Graphics
    %======================================================================
    if exist('Fsi', 'var')
        spm_figure('Select', Fsi)
        
        
        % reshape prediction if necessary
        %------------------------------------------------------------------
        e  = spm_vec(e);
        f  = spm_vec(f);
        try
            e  = reshape(e,ns,nr);
            f  = reshape(f,ns,nr);
        end
        
        % subplot prediction
        %------------------------------------------------------------------
        x    = (1:size(e,1))*dt;
        xLab = 'time (seconds)';
        try
            if length(M.Hz) == ns
                x    = M.Hz;
                xLab = 'Frequency (Hz)';
            end
        end
        
        
        % plot real or complex predictions
        %------------------------------------------------------------------
        tstr = sprintf('%s: %i','prediction and response: E-Step',k);
        if isreal(spm_vec(y))
            
            subplot(2,1,1)
            plot(x,real(f)), hold on
            plot(x,real(f + e),':'), hold off
            xlabel(xLab)
            title(tstr,'FontSize',16)
            grid on
            
        else
            subplot(2,2,1)
            plot(x,real(f)), hold on
            plot(x,real(f + e),':'), hold off
            xlabel(xLab)
            ylabel('real')
            title(tstr,'FontSize',16)
            grid on
            
            subplot(2,2,2)
            plot(x,imag(f)), hold on
            plot(x,imag(f + e),':'), hold off
            xlabel(xLab)
            ylabel('imaginary')
            title(tstr,'FontSize',16)
            grid on
        end
        
        % subplot parameters
        %--------------------------------------------------------------
        subplot(2,1,2)
        bar(full(V*p(ip)))
        xlabel('parameter')
        tstr = 'conditional [minus prior] expectation';
        title(tstr,'FontSize',16)
        grid on
        drawnow
        
    end
    
    % convergence
    %----------------------------------------------------------------------
    dF  = dFdp'*dp;
    if ~M.noprint
        fprintf('%-6s: %i %6s %-6.3e %6s %.3e ',str,k,'F:',full(C.F - F0),'dF predicted:',full(dF))
    end
    criterion = [(dF < 1e-1) criterion(1:end - 1)];
    if all(criterion)
        if ~M.noprint
            fprintf(' convergence\n')
        end
        break
    end
    
end

if exist('Fsi', 'var')
    spm_figure('Focus', Fsi)
end

% outputs
%--------------------------------------------------------------------------
Ep     = spm_unvec(spm_vec(pE) + V*C.p(ip),pE);
Cp     = V*C.Cp(ip,ip)*V';
Eh     = C.h;
F      = C.F;
L      = C.L;
