#define PRIOR_WEIGHT 1.0
#define MAX_PLAYERS 4096
#define DUMP_RAW 0
+#define USE_DB 1
+
+#if USE_DB
+#include <pqxx/connection>
+#include <pqxx/tablewriter>
+#include <pqxx/transaction>
+#endif
float mu[MAX_PLAYERS];
float mu_stddev[MAX_PLAYERS];
-float global_sigma = 70.0f;
-float prior_sigma = 70.0f;
+float global_sigma;
+float prior_sigma;
+
+// Data waiting for insertion into the database.
+
+struct RatingDBTuple {
+ int player;
+ float mu, mu_stddev;
+};
+struct CovarianceDBTuple {
+ int player1, player2;
+ float covariance;
+};
+vector<RatingDBTuple> rating_db_tuples;
+vector<CovarianceDBTuple> covariance_db_tuples;
+map<pair<string, string>, float> aux_params;
#define EPSILON 1e-3
map<int, vector<match> > matches_for_player;
vector<match> all_matches;
-void dump_scores(const vector<string> &players, const float *mu, const float *mu_stddev, int num_players)
+void dump_scores(const vector<int> &players, const float *mu, const float *mu_stddev, int num_players)
{
-#if 0
- for (int i = 0; i < num_players; ++i) {
- printf("%s=[%5.1f, %4.1f] ", players[i].c_str(), mu[i], sigma[i]);
- }
- printf("\n");
-#elif 0
+#if USE_DB
for (int i = 0; i < num_players; ++i) {
- printf("%5.1f ", mu[i]);
+ RatingDBTuple tuple = { players[i], mu[i], mu_stddev[i] };
+ rating_db_tuples.push_back(tuple);
}
- printf("\n");
#else
for (int i = 0; i < num_players; ++i) {
- printf("%f %f %s\n", mu[i], mu_stddev[i], players[i].c_str());
+ printf("%f %f %d\n", mu[i], mu_stddev[i], players[i]);
}
#endif
}
// 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, const vector<string> &players)
+void compute_mu_uncertainty(const float *mu, const vector<int> &players)
{
// FIXME: Use pseudoinverse if applicable.
Matrix<float, Dynamic, Dynamic> ih = hessian.inverse();
mu_stddev[i] = sqrt(ih(i, i));
}
+#if USE_DB
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(),
+ CovarianceDBTuple tuple;
+ tuple.player1 = players[i];
+ tuple.player2 = players[j];
+ tuple.covariance = ih(i, j);
+ covariance_db_tuples.push_back(tuple);
+ }
+ }
+#else
+ for (unsigned i = 0; i < players.size(); ++i) {
+ for (unsigned j = 0; j < players.size(); ++j) {
+ printf("covariance %d %d %f\n",
+ players[i],
+ players[j],
ih(i, j));
}
}
+#endif
}
-int main(int argc, char **argv)
+void process_file(const char *filename)
{
+ global_sigma = 70.0f;
+ prior_sigma = 70.0f;
+ matches_for_player.clear();
+ all_matches.clear();
+
+ FILE *fp = fopen(filename, "r");
+ if (fp == NULL) {
+ perror(filename);
+ exit(1);
+ }
+
+ char locale[256];
+ if (fscanf(fp, "%s", locale) != 1) {
+ fprintf(stderr, "Could't read number of players\n");
+ exit(1);
+ }
+
int num_players;
- if (scanf("%d", &num_players) != 1) {
+ if (fscanf(fp,"%d", &num_players) != 1) {
fprintf(stderr, "Could't read number of players\n");
exit(1);
}
exit(1);
}
- vector<string> players;
- map<string, int> player_map;
+ vector<int> players;
+ map<int, int> player_map;
for (int i = 0; i < num_players; ++i) {
char buf[256];
- if (scanf("%s", buf) != 1) {
+ if (fscanf(fp, "%s", buf) != 1) {
fprintf(stderr, "Couldn't read player %d\n", i);
exit(1);
}
- players.push_back(buf);
- player_map[buf] = i;
+ players.push_back(atoi(buf));
+ player_map[atoi(buf)] = i;
}
int num_matches = 0;
for ( ;; ) {
- char pl1[256], pl2[256];
+ int pl1, pl2;
int score1, score2;
float weight;
- if (scanf("%s %s %d %d %f", pl1, pl2, &score1, &score2, &weight) != 5) {
+ if (fscanf(fp, "%d %d %d %d %f", &pl1, &pl2, &score1, &score2, &weight) != 5) {
//fprintf(stderr, "Read %d matches.\n", num_matches);
break;
}
++num_matches;
if (player_map.count(pl1) == 0) {
- fprintf(stderr, "Unknown player '%s'\n", pl1);
+ fprintf(stderr, "Unknown player '%d'\n", pl1);
exit(1);
}
if (player_map.count(pl2) == 0) {
- fprintf(stderr, "Unknown player '%s'\n", pl2);
+ fprintf(stderr, "Unknown player '%d'\n", pl2);
exit(1);
}
all_matches.push_back(m1);
}
+
+ fclose(fp);
float mu[MAX_PLAYERS];
mu[i] = PRIOR_MU;
}
+ int num_iterations = -1;
for (int j = 0; j < 1000; ++j) {
float old_mu[MAX_PLAYERS];
float old_global_sigma = global_sigma;
sumdiff += (global_sigma - old_global_sigma) * (global_sigma - old_global_sigma);
if (sumdiff < EPSILON) {
//fprintf(stderr, "Converged after %d iterations. Stopping.\n", j);
- printf("aux_param num_iterations %d\n", j + 1);
+ num_iterations = j + 1;
break;
}
}
-#if DUMP_RAW
- dump_raw(mu, num_players);
-#else
construct_hessian(mu, num_players);
+ aux_params[make_pair(locale, "num_iterations")] = num_iterations;
+ aux_params[make_pair(locale, "score_stddev")] = global_sigma / sqrt(2.0f);
+ aux_params[make_pair(locale, "rating_prior_stddev")] = prior_sigma;
+ aux_params[make_pair(locale, "total_log_likelihood")] = compute_total_logl(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("aux_param score_stddev %f\n", global_sigma / sqrt(2.0f));
- printf("aux_param rating_prior_stddev %f\n", prior_sigma);
+}
+
+int main(int argc, char **argv)
+{
+#if USE_DB
+ pqxx::connection conn("dbname=wloh host=127.0.0.1 user=wloh password=oto4iCh5");
+#endif
+
+ for (int i = 1; i < argc; ++i) {
+ process_file(argv[i]);
+ }
- float total_logl = compute_total_logl(mu, num_players);
- printf("aux_param total_log_likelihood %f\n", total_logl);
+#if DUMP_RAW
+ dump_raw(mu, num_players);
+ return 0;
#endif
+
+#if USE_DB
+ pqxx::work txn(conn);
+ txn.exec("SET client_min_messages TO WARNING");
+
+ // Dump aux_params.
+ {
+ txn.exec("TRUNCATE aux_params");
+ pqxx::tablewriter writer(txn, "aux_params");
+ for (map<pair<string, string>, float>::const_iterator it = aux_params.begin(); it != aux_params.end(); ++it) {
+ char str[128];
+ snprintf(str, 128, "%f", it->second);
+
+ vector<string> tuple;
+ tuple.push_back(it->first.first); // locale
+ tuple.push_back(it->first.second); // parameter name
+ tuple.push_back(str);
+ writer.push_back(tuple);
+ }
+ writer.complete();
+ }
+
+ // Dump ratings.
+ {
+ txn.exec("TRUNCATE ratings");
+ pqxx::tablewriter writer(txn, "ratings");
+ for (unsigned i = 0; i < rating_db_tuples.size(); ++i) {
+ char player_str[128], mu_str[128], mu_stddev_str[128];
+ snprintf(player_str, 128, "%d", rating_db_tuples[i].player);
+ snprintf(mu_str, 128, "%f", rating_db_tuples[i].mu);
+ snprintf(mu_stddev_str, 128, "%f", rating_db_tuples[i].mu_stddev);
+
+ vector<string> tuple;
+ tuple.push_back(player_str);
+ tuple.push_back(mu_str);
+ tuple.push_back(mu_stddev_str);
+ writer.push_back(tuple);
+ }
+ writer.complete();
+ }
+
+ // Create a table new_covariance, and dump covariance into it.
+ {
+ txn.exec("CREATE TABLE new_covariance ( player1 smallint NOT NULL, player2 smallint NOT NULL, cov float NOT NULL )");
+ pqxx::tablewriter writer(txn, "new_covariance");
+ for (unsigned i = 0; i < covariance_db_tuples.size(); ++i) {
+ char player1_str[128], player2_str[128], cov_str[128];
+ snprintf(player1_str, 128, "%d", covariance_db_tuples[i].player1);
+ snprintf(player2_str, 128, "%d", covariance_db_tuples[i].player2);
+ snprintf(cov_str, 128, "%f", covariance_db_tuples[i].covariance);
+
+ vector<string> tuple;
+ tuple.push_back(player1_str);
+ tuple.push_back(player2_str);
+ tuple.push_back(cov_str);
+ writer.push_back(tuple);
+ }
+ writer.complete();
+ }
+
+ // Create index, and rename new_covariance on top of covariance.
+ txn.exec("ALTER TABLE new_covariance ADD PRIMARY KEY ( player1, player2 );");
+ txn.exec("DROP TABLE IF EXISTS covariance");
+ txn.exec("ALTER TABLE new_covariance RENAME TO covariance");
+#else
+ //fprintf(stderr, "Optimal sigma: %f (two-player: %f)\n", sigma[0], sigma[0] * sqrt(2.0f));
+ for (map<pair<string, string>, float>::const_iterator it = aux_params.begin(); it != aux_params.end(); ++it) {
+ printf("%s: aux_param %s %f\n", it->first.first.c_str(), it->first.second.c_str(), it->second);
+ }
+#endif
+
+ txn.commit();
}
projects / wloh / blobdiff