comparison toolboxes/FullBNT-1.0.7/Kalman/eval_AR_perf.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [ypred, ll, mse] = eval_AR_perf(coef, C, y, model)
+% Evaluate the performance of an AR model.
+% 
+% Inputs
+% coef(:,:,k,m) - coef. matrix to use for k steps back, model m
+% C(:,:,m)      - cov. matrix for model m
+% y(:,t)        - observation at time t
+% model(t)      - which model to use at time t (defaults to 1 if not specified)
+%
+% Outputs
+% ypred(:,t)    - the predicted value of y at t based on the evidence thru t-1.
+% ll            - log likelihood
+% mse           - mean squared error = sum_t d_t . d_t, where d_t = pred(y_t) - y(t)
+
+[s T] = size(y);
+k = size(coef, 3);
+M = size(coef, 4);
+
+if nargin<4, model = ones(1, T); end
+
+ypred = zeros(s, T);
+ypred(:, 1:k) = y(:, 1:k);
+mse = 0;
+ll = 0;
+for j=1:M
+  c(j) = log(normal_coef(C(:,:,j)));
+  invC(:,:,j) = inv(C(:,:,j));
+end
+coef = reshape(coef, [s s*k M]);
+
+for t=k+1:T
+  m = model(t-k);
+  past = y(:,t-1:-1:t-k);
+  ypred(:,t) = coef(:, :, m) * past(:);
+  d = ypred(:,t) - y(:,t);
+  mse = mse + d' * d;
+  ll = ll + c(m) - 0.5*(d' * invC(:,:,m) * d);
+end
+mse = mse / (T-k+1);
+