annotate toolboxes/FullBNT-1.0.7/Kalman/learn_kalman.m @ 0:cc4b1211e677 tip

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646 (e263d8a21543) added further path and more save "camirversion.m"

function [A, C, Q, R, initx, initV, LL] = ...
    learn_kalman(data, A, C, Q, R, initx, initV, max_iter, diagQ, diagR, ARmode, constr_fun, varargin)
% LEARN_KALMAN Find the ML parameters of a stochastic Linear Dynamical System using EM.
%
% [A, C, Q, R, INITX, INITV, LL] = LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0) fits
% the parameters which are defined as follows
%   x(t+1) = A*x(t) + w(t),  w ~ N(0, Q),  x(0) ~ N(init_x, init_V)
%   y(t)   = C*x(t) + v(t),  v ~ N(0, R)
% A0 is the initial value, A is the final value, etc.
% DATA(:,t,l) is the observation vector at time t for sequence l. If the sequences are of
% different lengths, you can pass in a cell array, so DATA{l} is an O*T matrix.
% LL is the "learning curve": a vector of the log lik. values at each iteration.
% LL might go positive, since prob. densities can exceed 1, although this probably
% indicates that something has gone wrong e.g., a variance has collapsed to 0.
%
% There are several optional arguments, that should be passed in the following order.
% LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0, MAX_ITER, DIAGQ, DIAGR, ARmode)
% MAX_ITER specifies the maximum number of EM iterations (default 10).
% DIAGQ=1 specifies that the Q matrix should be diagonal. (Default 0).
% DIAGR=1 specifies that the R matrix should also be diagonal. (Default 0).
% ARMODE=1 specifies that C=I, R=0. i.e., a Gauss-Markov process. (Default 0).
% This problem has a global MLE. Hence the initial parameter values are not important.
% 
% LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0, MAX_ITER, DIAGQ, DIAGR, F, P1, P2, ...)
% calls [A,C,Q,R,initx,initV] = f(A,C,Q,R,initx,initV,P1,P2,...) after every M step. f can be
% used to enforce any constraints on the params. 
%
% For details, see
% - Ghahramani and Hinton, "Parameter Estimation for LDS", U. Toronto tech. report, 1996
% - Digalakis, Rohlicek and Ostendorf, "ML Estimation of a stochastic linear system with the EM
%      algorithm and its application to speech recognition",
%       IEEE Trans. Speech and Audio Proc., 1(4):431--442, 1993.


%    learn_kalman(data, A, C, Q, R, initx, initV, max_iter, diagQ, diagR, ARmode, constr_fun, varargin)
if nargin < 8, max_iter = 10; end
if nargin < 9, diagQ = 0; end
if nargin < 10, diagR = 0; end
if nargin < 11, ARmode = 0; end
if nargin < 12, constr_fun = []; end
verbose = 1;
thresh = 1e-4;


if ~iscell(data)
  N = size(data, 3);
  data = num2cell(data, [1 2]); % each elt of the 3rd dim gets its own cell
else
  N = length(data);
end

N = length(data);
ss = size(A, 1);
os = size(C,1);

alpha = zeros(os, os);
Tsum = 0;
for ex = 1:N
  %y = data(:,:,ex);
  y = data{ex};
  T = length(y);
  Tsum = Tsum + T;
  alpha_temp = zeros(os, os);
  for t=1:T
    alpha_temp = alpha_temp + y(:,t)*y(:,t)';
  end
  alpha = alpha + alpha_temp;
end

previous_loglik = -inf;
loglik = 0;
converged = 0;
num_iter = 1;
LL = [];

% Convert to inline function as needed.
if ~isempty(constr_fun)
  constr_fun = fcnchk(constr_fun,length(varargin));
end


while ~converged & (num_iter <= max_iter) 

  %%% E step
  
  delta = zeros(os, ss);
  gamma = zeros(ss, ss);
  gamma1 = zeros(ss, ss);
  gamma2 = zeros(ss, ss);
  beta = zeros(ss, ss);
  P1sum = zeros(ss, ss);
  x1sum = zeros(ss, 1);
  loglik = 0;
  
  for ex = 1:N
    y = data{ex};
    T = length(y);
    [beta_t, gamma_t, delta_t, gamma1_t, gamma2_t, x1, V1, loglik_t] = ...
	Estep(y, A, C, Q, R, initx, initV, ARmode);
    beta = beta + beta_t;
    gamma = gamma + gamma_t;
    delta = delta + delta_t;
    gamma1 = gamma1 + gamma1_t;
    gamma2 = gamma2 + gamma2_t;
    P1sum = P1sum + V1 + x1*x1';
    x1sum = x1sum + x1;
    %fprintf(1, 'example %d, ll/T %5.3f\n', ex, loglik_t/T);
    loglik = loglik + loglik_t;
  end
  LL = [LL loglik];
  if verbose, fprintf(1, 'iteration %d, loglik = %f\n', num_iter, loglik); end
  %fprintf(1, 'iteration %d, loglik/NT = %f\n', num_iter, loglik/Tsum);
  num_iter =  num_iter + 1;
  
  %%% M step
  
  % Tsum =  N*T
  % Tsum1 = N*(T-1);
  Tsum1 = Tsum - N;
  A = beta * inv(gamma1);
  %A = (gamma1' \ beta')';
  Q = (gamma2 - A*beta') / Tsum1;
  if diagQ
    Q = diag(diag(Q));
  end
  if ~ARmode
    C = delta * inv(gamma);
    %C = (gamma' \ delta')';
    R = (alpha - C*delta') / Tsum;
    if diagR
      R = diag(diag(R));
    end
  end
  initx = x1sum / N;
  initV = P1sum/N - initx*initx';

  if ~isempty(constr_fun)
    [A,C,Q,R,initx,initV] = feval(constr_fun, A, C, Q, R, initx, initV, varargin{:});
  end
  
  converged = em_converged(loglik, previous_loglik, thresh);
  previous_loglik = loglik;
end



%%%%%%%%%

function [beta, gamma, delta, gamma1, gamma2, x1, V1, loglik] = ...
    Estep(y, A, C, Q, R, initx, initV, ARmode)
%
% Compute the (expected) sufficient statistics for a single Kalman filter sequence.
%

[os T] = size(y);
ss = length(A);

if ARmode
  xsmooth = y;
  Vsmooth = zeros(ss, ss, T); % no uncertainty about the hidden states
  VVsmooth = zeros(ss, ss, T);
  loglik = 0;
else
  [xsmooth, Vsmooth, VVsmooth, loglik] = kalman_smoother(y, A, C, Q, R, initx, initV);
end

delta = zeros(os, ss);
gamma = zeros(ss, ss);
beta = zeros(ss, ss);
for t=1:T
  delta = delta + y(:,t)*xsmooth(:,t)';
  gamma = gamma + xsmooth(:,t)*xsmooth(:,t)' + Vsmooth(:,:,t);
  if t>1 beta = beta + xsmooth(:,t)*xsmooth(:,t-1)' + VVsmooth(:,:,t); end
end
gamma1 = gamma - xsmooth(:,T)*xsmooth(:,T)' - Vsmooth(:,:,T);
gamma2 = gamma - xsmooth(:,1)*xsmooth(:,1)' - Vsmooth(:,:,1);

x1 = xsmooth(:,1);
V1 = Vsmooth(:,:,1);