Store covariance matrix when training model.

[wloh] / bayeswf.cpp

diff --git a/bayeswf.cpp b/bayeswf.cpp
index a691b8fe915ac8f6bb8714e62bd2349c173201b1..f94b1d33f6e213f281fc308ad111ab06a69778ac 100644 (file)
--- a/bayeswf.cpp
+++ b/bayeswf.cpp
@@ -227,13 +227,22 @@ void construct_hessian(const float *mu, int num_players)
 
 // Compute uncertainty (stddev) of mu estimates, which is sqrt((H^-1)_ii),
 // where H is the Hessian (see construct_hessian()).
-void compute_mu_uncertainty(const float *mu, int num_players)
+void compute_mu_uncertainty(const float *mu, const vector<string> &players)
 {
        // FIXME: Use pseudoinverse if applicable.
        Matrix<float, Dynamic, Dynamic> ih = hessian.inverse();
-       for (int i = 0; i < num_players; ++i) {
+       for (unsigned i = 0; i < players.size(); ++i) {
                mu_stddev[i] = sqrt(ih(i, i));
        }
+
+       for (unsigned i = 0; i < players.size(); ++i) {
+               for (unsigned j = 0; j < players.size(); ++j) {
+                       printf("covariance %s %s %f\n",
+                              players[i].c_str(),
+                              players[j].c_str(),
+                              ih(i, j));
+               }
+       }
 }
 
 int main(int argc, char **argv)
@@ -340,7 +349,7 @@ int main(int argc, char **argv)
        dump_raw(mu, num_players);
 #else
        construct_hessian(mu, num_players);
-       compute_mu_uncertainty(mu, num_players);
+       compute_mu_uncertainty(mu, players);
        dump_scores(players, mu, mu_stddev, num_players);
        //fprintf(stderr, "Optimal sigma: %f (two-player: %f)\n", sigma[0], sigma[0] * sqrt(2.0f));
        printf("%f 0 -2\n", global_sigma / sqrt(2.0f));