X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;f=bayeswf.cpp;h=f94b1d33f6e213f281fc308ad111ab06a69778ac;hb=c5cf5cb78785bfd57ea74b23d72ce4e27439ee2f;hp=a691b8fe915ac8f6bb8714e62bd2349c173201b1;hpb=35021b4f76156feb48fd9420a9116da50acee3e1;p=wloh

diff --git a/bayeswf.cpp b/bayeswf.cpp
index a691b8f..f94b1d3 100644
--- 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));