Convert index.pl to templates.

[wloh] / bayeswf.cpp

diff --git a/bayeswf.cpp b/bayeswf.cpp
index 7ee0120a592e23f1075ebef4cfc1a7cb36fbec47..cc591832a556f023ea6aa364a44ce5422a0b0740 100644 (file)
--- a/bayeswf.cpp
+++ b/bayeswf.cpp
@@ -2,6 +2,8 @@
 #include <math.h>
 #include <string.h>
 #include <stdlib.h>
+#include <Eigen/Core>
+#include <Eigen/Eigenvalues>
 
 #include <map>
 #include <vector>
@@ -9,8 +11,9 @@
 #include <algorithm>
 
 using namespace std;
+using namespace Eigen;
 
-#define PRIOR_MU 1500
+#define PRIOR_MU 500
 #define PRIOR_WEIGHT 1.0
 #define MAX_PLAYERS 4096
 #define DUMP_RAW 0
@@ -196,11 +199,15 @@ float compute_total_logl(float *mu, int num_players)
  *
  * Note that this does not depend on mu or the margin at all.
  */
-double hessian[MAX_PLAYERS][MAX_PLAYERS];
+Matrix<float, Dynamic, Dynamic> hessian;
 void construct_hessian(const float *mu, int num_players)
 {
-       memset(hessian, 0, sizeof(hessian));
+       hessian = Matrix<float, Dynamic, Dynamic>(num_players, num_players);
+       hessian.fill(0.0f);
 
+       for (int i = 0; i < num_players; ++i) {
+               hessian(i, i) += 1.0f / (prior_sigma * prior_sigma);
+       }
        for (unsigned i = 0; i < all_matches.size(); ++i) {
                const match &m = all_matches[i];
 
@@ -210,35 +217,31 @@ void construct_hessian(const float *mu, int num_players)
                double sigma_sq = global_sigma * global_sigma;
                float w = m.weight;
 
-               hessian[p1][p2] -= w / sigma_sq;
-               hessian[p2][p1] -= w / sigma_sq;
+               hessian(p1, p2) -= w / sigma_sq;
+               hessian(p2, p1) -= w / sigma_sq;
 
-               hessian[p1][p1] += w / sigma_sq;
-               hessian[p2][p2] += w / sigma_sq;
+               hessian(p1, p1) += w / sigma_sq;
+               hessian(p2, p2) += w / sigma_sq;
        }
 }
 
-// Compute uncertainty (stddev) of mu estimates, which is 1/sqrt(H_ii),
+// 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)
 {
-       memset(mu_stddev, 0, sizeof(mu_stddev));
-
-       for (unsigned i = 0; i < all_matches.size(); ++i) {
-               const match &m = all_matches[i];
-
-               int p1 = m.player;
-               int p2 = m.other_player;
-
-               double sigma_sq = global_sigma * global_sigma;
-               float w = m.weight;
-
-               // Temporarily use mu_stddev to store the diagonal of the Hessian.
-               mu_stddev[p1] += w / sigma_sq;
-               mu_stddev[p2] += w / sigma_sq;
+       // FIXME: Use pseudoinverse if applicable.
+       Matrix<float, Dynamic, Dynamic> ih = hessian.inverse();
+       for (unsigned i = 0; i < players.size(); ++i) {
+               mu_stddev[i] = sqrt(ih(i, i));
        }
-       for (int i = 0; i < num_players; ++i) {
-               mu_stddev[i] = 1.0f / sqrt(mu_stddev[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));
+               }
        }
 }
 
@@ -337,7 +340,7 @@ int main(int argc, char **argv)
                sumdiff += (global_sigma - old_global_sigma) * (global_sigma - old_global_sigma);
                if (sumdiff < EPSILON) {
                        //fprintf(stderr, "Converged after %d iterations. Stopping.\n", j);
-                       printf("%d 0 -1\n", j + 1);
+                       printf("aux_param num_iterations %d\n", j + 1);
                        break;
                }
        }
@@ -345,15 +348,14 @@ int main(int argc, char **argv)
 #if DUMP_RAW
        dump_raw(mu, num_players);
 #else
-       compute_mu_uncertainty(mu, num_players);
+       construct_hessian(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));
-       printf("%f 0 -3\n", prior_sigma);
+       printf("aux_param score_stddev %f\n", global_sigma / sqrt(2.0f));
+       printf("aux_param rating_prior_stddev %f\n", prior_sigma);
 
        float total_logl = compute_total_logl(mu, num_players);
-       printf("%f 0 -4\n", total_logl);
-
-//     construct_hessian(mu, sigma, num_players);
+       printf("aux_param total_log_likelihood %f\n", total_logl);
 #endif
 }