function [alpha, beta, gamma, loglik, xi, gamma2] = fwdback_twoslice(engine, init_state_distrib, transmat, obslik, varargin)% FWDBACK Compute the posterior probs. in an HMM using the forwards backwards algo.%% [alpha, beta, gamma, loglik, xi, gamma2] = fwdback(init_state_distrib, transmat, obslik, ...)%% Notation:% Y(t) = observation, Q(t) = hidden state, M(t) = mixture variable (for MOG outputs)% A(t) = discrete input (action) (for POMDP models)%% INPUT:% init_state_distrib(i) = Pr(Q(1) = i)% transmat(i,j) = Pr(Q(t) = j | Q(t-1)=i)%  or transmat{a}(i,j) = Pr(Q(t) = j | Q(t-1)=i, A(t-1)=a) if there are discrete inputs% obslik(i,t) = Pr(Y(t)| Q(t)=i)%   (Compute obslik using eval_pdf_xxx on your data sequence first.)%% Optional parameters may be passed as 'param_name', param_value pairs.% Parameter names are shown below; default values in [] - if none, argument is mandatory.%% For HMMs with MOG outputs: if you want to compute gamma2, you must specify% 'obslik2' - obslik(i,j,t) = Pr(Y(t)| Q(t)=i,M(t)=j)  []% 'mixmat' - mixmat(i,j) = Pr(M(t) = j | Q(t)=i)  []%% For HMMs with discrete inputs:% 'act' - act(t) = action performed at step t%% Optional arguments:% 'fwd_only' - if 1, only do a forwards pass and set beta=[], gamma2=[]  [0]% 'scaled' - if 1,  normalize alphas and betas to prevent underflow [1]% 'maximize' - if 1, use max-product instead of sum-product [0]%% OUTPUTS:% alpha(i,t) = p(Q(t)=i | y(1:t)) (or p(Q(t)=i, y(1:t)) if scaled=0)% beta(i,t) = p(y(t+1:T) | Q(t)=i)*p(y(t+1:T)|y(1:t)) (or p(y(t+1:T) | Q(t)=i) if scaled=0)% gamma(i,t) = p(Q(t)=i | y(1:T))% loglik = log p(y(1:T))% xi(i,j,t-1)  = p(Q(t-1)=i, Q(t)=j | y(1:T))% gamma2(j,k,t) = p(Q(t)=j, M(t)=k | y(1:T)) (only for MOG  outputs)%% If fwd_only = 1, these become% alpha(i,t) = p(Q(t)=i | y(1:t))% beta = []% gamma(i,t) = p(Q(t)=i | y(1:t))% xi(i,j,t-1)  = p(Q(t-1)=i, Q(t)=j | y(1:t))% gamma2 = []%% Note: we only compute xi if it is requested as a return argument, since it can be very large.% Similarly, we only compute gamma2 on request (and if using MOG outputs).%% Examples:%% [alpha, beta, gamma, loglik] = fwdback(pi, A, multinomial_prob(sequence, B));%% [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat);% [alpha, beta, gamma, loglik, xi, gamma2] = fwdback(pi, A, B, 'obslik2', B2, 'mixmat', mixmat);if nargout >= 5, compute_xi = 1; else compute_xi = 0; endif nargout >= 6, compute_gamma2 = 1; else compute_gamma2 = 0; end[obslik2, mixmat, fwd_only, scaled, act, maximize, compute_xi, compute_gamma2] = process_options(varargin, 'obslik2', [], 'mixmat', [], 'fwd_only', 0, 'scaled', 1, 'act', [], 'maximize', 0, 'compute_xi', compute_xi, 'compute_gamma2', compute_gamma2);[Q T] = size(obslik);if isempty(obslik2)  compute_gamma2 = 0;endif isempty(act)  act = ones(1,T);  transmat = { transmat } ;endscale = ones(1,T);% scale(t) = Pr(O(t) | O(1:t-1)) = 1/c(t) as defined by Rabiner (1989).% Hence prod_t scale(t) = Pr(O(1)) Pr(O(2)|O(1)) Pr(O(3) | O(1:2)) = Pr(O(1), ... ,O(T))% or log P = sum_t log scale(t).% Rabiner suggests multiplying beta(t) by scale(t), but we can instead% normalise beta(t) - the constants will cancel when we compute gamma.loglik = 0;alpha = zeros(Q,T);gamma = zeros(Q,T);if compute_xi  xi = zeros(Q,Q,T-1);else  xi = [];end%%%%%%%%% Forwards %%%%%%%%%%t = 1;alpha(:,1) = init_state_distrib(:) .* obslik(:,t);if scaled  %[alpha(:,t), scale(t)] = normaliseC(alpha(:,t));  [alpha(:,t), scale(t)] = normalise(alpha(:,t));endif scaled, assert(approxeq(sum(alpha(:,t)),1)), endfor t=2:T  %trans = transmat(:,:,act(t-1))';  trans = transmat{act(t-1)};  if maximize    m = max_mult(trans', alpha(:,t-1));    %A = repmat(alpha(:,t-1), [1 Q]);    %m = max(trans .* A, [], 1);  else    m = trans' * alpha(:,t-1);  end  alpha(:,t) = m(:) .* obslik(:,t);  if scaled    %[alpha(:,t), scale(t)] = normaliseC(alpha(:,t));    [alpha(:,t), scale(t)] = normalise(alpha(:,t));  end  if compute_xi & fwd_only  % useful for online EM    %xi(:,:,t-1) = normaliseC((alpha(:,t-1) * obslik(:,t)') .* trans);    xi(:,:,t-1) = normalise((alpha(:,t-1) * obslik(:,t)') .* trans);  end  if scaled, assert(approxeq(sum(alpha(:,t)),1)), endendif scaled  if any(scale==0)    loglik = -inf;  else    loglik = sum(log(scale));  endelse  loglik = log(sum(alpha(:,T)));endif fwd_only  gamma = alpha;  beta = [];  gamma2 = [];  return;end%%%%%%%%% Backwards %%%%%%%%%%beta = zeros(Q,T);if compute_gamma2  M = size(mixmat, 2);  gamma2 = zeros(Q,M,T);else  gamma2 = [];endbeta(:,T) = ones(Q,1);%gamma(:,T) = normaliseC(alpha(:,T) .* beta(:,T));gamma(:,T) = normalise(alpha(:,T) .* beta(:,T));t=T;if compute_gamma2  denom = obslik(:,t) + (obslik(:,t)==0); % replace 0s with 1s before dividing  gamma2(:,:,t) = obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M]) ./ repmat(denom, [1 M]);  %gamma2(:,:,t) = normaliseC(obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M])); % wrong!endfor t=T-1:-1:1  b = beta(:,t+1) .* obslik(:,t+1);  %trans = transmat(:,:,act(t));  trans = transmat{act(t)};  if maximize    B = repmat(b(:)', Q, 1);    beta(:,t) = max(trans .* B, [], 2);  else    beta(:,t) = trans * b;  end  if scaled    %beta(:,t) = normaliseC(beta(:,t));    beta(:,t) = normalise(beta(:,t));  end  %gamma(:,t) = normaliseC(alpha(:,t) .* beta(:,t));  gamma(:,t) = normalise(alpha(:,t) .* beta(:,t));  if compute_xi    %xi(:,:,t) = normaliseC((trans .* (alpha(:,t) * b')));    xi(:,:,t) = normalise((trans .* (alpha(:,t) * b')));    %xi(:,:,t) = (trans .* (alpha(:,t) * b'));  end  if compute_gamma2    denom = obslik(:,t) + (obslik(:,t)==0); % replace 0s with 1s before dividing    gamma2(:,:,t) = obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M]) ./ repmat(denom, [1 M]);    %gamma2(:,:,t) = normaliseC(obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M]));  endend% We now explain the equation for gamma2% Let zt=y(1:t-1,t+1:T) be all observations except y(t)% gamma2(Q,M,t) = P(Qt,Mt|yt,zt) = P(yt|Qt,Mt,zt) P(Qt,Mt|zt) / P(yt|zt)%                = P(yt|Qt,Mt) P(Mt|Qt) P(Qt|zt) / P(yt|zt)% Now gamma(Q,t) = P(Qt|yt,zt) = P(yt|Qt) P(Qt|zt) / P(yt|zt)% hence% P(Qt,Mt|yt,zt) = P(yt|Qt,Mt) P(Mt|Qt) [P(Qt|yt,zt) P(yt|zt) / P(yt|Qt)] / P(yt|zt)%                = P(yt|Qt,Mt) P(Mt|Qt) P(Qt|yt,zt) / P(yt|Qt)%