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bayesian.c
174 lines (146 loc) · 4.35 KB
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bayesian.c
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#include <math.h>
#include <limits.h>
#include "matrix.h"
#include "bayesian.h"
#include "graph.h"
int dist_type;
bayesian_t *bayesian_alloc(int m, int n)
{
int i;
bayesian_t *game = malloc(sizeof(bayesian_t));
game->m = m;
game->n = n;
game->distribution = matrix_alloc(m, n);
game->payoffs = malloc(sizeof(matrix_t **) * (m + n));
for (i = 0; i < m; i++)
game->payoffs[i] = malloc(sizeof(matrix_t *) * n);
for (i = 0; i < n; i++)
game->payoffs[i+m] = malloc(sizeof(matrix_t *) * m);
return game;
}
void bayesian_free(bayesian_t *game)
{
matrix_free(game->distribution);
int i, j;
for (i = 0; i < game->m; ++i) {
for (j = 0; j < game->n; ++j)
matrix_free(game->payoffs[i][j]);
free(game->payoffs[i]);
}
for (i = 0; i < game->n; ++i) {
for (j = 0; j < game->m; ++j)
matrix_free(game->payoffs[i+game->m][j]);
free(game->payoffs[i+game->m]);
}
free(game->payoffs);
free(game);
}
polymatrix_t *bayesian_to_polymatrix(bayesian_t *game)
{
polymatrix_t *poly = polymatrix_alloc(game->m + game->n);
graph_fill_complete_bipartite(poly->graph, game->m, game->n);
int i, j;
double pi, pi_j;
for (i = 0; i < game->m; i++) {
pi = 0;
for (j = 0; j < game->n; j++)
pi += game->distribution->data[i][j];
for (j = 0; j < game->n; j++) {
//printf("Adding %d %d\n", i, j + game->m);
pi_j = game->distribution->data[i][j] / pi;
poly->payoffs[i][j + game->m] = matrix_mul_const(game->payoffs[i][j], pi_j);
}
}
for (i = 0; i < game->n; i++) {
pi = 0;
for (j = 0; j < game->n; j++)
pi += game->distribution->data[j][i];
for (j = 0; j < game->m; j++) {
pi_j = game->distribution->data[j][i] / pi;
poly->payoffs[i+game->m][j] = matrix_mul_const(game->payoffs[i + game->m][j], pi_j);
}
}
return poly;
}
void bayesian_random(bayesian_t *game, int s)
{
int i, j;
for (i = 0; i < game->m; i++)
for (j = 0; j < game->n; j++){
game->payoffs[i][j] = matrix_alloc(s, s);
matrix_rand(game->payoffs[i][j]);
}
for (i = 0; i < game->n; i++)
for (j = 0; j < game->m; j++) {
game->payoffs[i + game->m][j] = matrix_alloc(s, s);
matrix_rand(game->payoffs[i + game->m][j]);
}
}
void bayesian_fill_distribution_uniform(bayesian_t *game)
{
int i, j;
for (i = 0; i < game->m; i++)
for (j = 0; j < game->n; j++)
game->distribution->data[i][j] = 1.0 / (double)(game->m * game->n);
}
void bayesian_fill_distribution_rand(bayesian_t *game)
{
int i, j;
double sum = 0;
for (i = 0; i < game->m; ++i)
for (j = 0; j < game->n; j++) {
double k = (double)rand() / RAND_MAX;
sum += k;
game->distribution->data[i][j] = k;
}
for (i = 0; i < game->m; ++i)
for (j = 0; j < game->n; j++)
game->distribution->data[i][j] /= sum;
}
void bayesian_fill_distribution(bayesian_t *game)
{
if (dist_type == 0)
bayesian_fill_distribution_uniform(game);
else
bayesian_fill_distribution_rand(game);
}
void bayesian_fill_game(bayesian_t *game, int s)
{
bayesian_fill_distribution(game);
bayesian_random(game, s);
}
bayesian_t *generate_random_bayesian(int types, int actions)
{
bayesian_t *game = bayesian_alloc(types, types);
bayesian_fill_distribution(game);
bayesian_random(game, actions);
return game;
}
void bayesian_print(bayesian_t *game)
{
printf("Distributions\n");
matrix_print(game->distribution);
printf("Payoffs\n");
int i, j;
printf("L->R\n");
for (i = 0; i < game->m; i++) {
for (j = 0; j < game->n; j++){
printf("Types %d %d\n", i, j);
matrix_print(game->payoffs[i][j]);
}
}
printf("R->L\n");
for (i = 0; i < game->n; i++)
for (j = 0; j < game->m; j++){
printf("Types %d %d\n", i, j);
matrix_print(game->payoffs[i + game->m][j]);
}
}
void bayesian_set_game(bayesian_t *game, int i, int j, matrix_t **bimat)
{
game->payoffs[i][j] = matrix_copy(bimat[0]);
game->payoffs[j+game->m][i] = matrix_trans(bimat[1]);
matrix_free(bimat[0]);
matrix_free(bimat[1]);
free(bimat);
}