#include <vector>
#include <algorithm>
+#include <complex>
+#include <fftw3.h>
+
// step sizes
static const double int_step_size = 75.0;
-static const double pdf_step_size = 15.0;
// rating constant (see below)
static const double rating_constant = 455.0;
double prodai(int k, double a);
double fac(int x);
-// Numerical integration using Simpson's rule
-template<class T>
-double simpson_integrate(const T &evaluator, double from, double to, double step)
-{
- int n = int((to - from) / step + 0.5);
- double h = (to - from) / n;
- double sum = evaluator(from);
-
- for (int i = 1; i < n; i += 2) {
- sum += 4.0 * evaluator(from + i * h);
- }
- for (int i = 2; i < n; i += 2) {
- sum += 2.0 * evaluator(from + i * h);
- }
- sum += evaluator(to);
-
- return (h/3.0) * sum;
-}
// probability of match ending k-a (k>a) when winnerR - loserR = RD
//
// The Gaussian is not normalized.
//
// Set the last parameter to 1.0 if player 1 won, or -1.0 if player 2 won.
-// In the latter case, ProbScore will be given (r1-r2) instead of (r2-r1).
+// In the latter case, ProbScore will be given (r2-r1) instead of (r1-r2).
//
class ProbScoreEvaluator {
private:
}
};
-double opponent_rating_pdf(int k, double a, double r1, double mu2, double sigma2, double winfac)
+void convolve(int size)
+{
+}
+
+void compute_opponent_rating_pdf(int k, double a, double mu2, double sigma2, double winfac, vector<pair<double, double> > &result)
{
double binomial_precompute = prodai(k, a) / fac(k-1);
winfac /= rating_constant;
- return simpson_integrate(ProbScoreEvaluator(k, a, binomial_precompute, r1, mu2, sigma2, winfac), 0.0, 6000.0, int_step_size);
+ int sz = (6000.0 - 0.0) / int_step_size;
+ double h = (6000.0 - 0.0) / sz;
+
+ fftw_plan f1, f2, b;
+ complex<double> *func1, *func2, *res;
+
+ func1 = reinterpret_cast<complex<double> *>(fftw_malloc(sz*2*sizeof(complex<double>)));
+ func2 = reinterpret_cast<complex<double> *>(fftw_malloc(sz*2*sizeof(complex<double>)));
+ res = reinterpret_cast<complex<double> *>(fftw_malloc(sz*2*sizeof(complex<double>)));
+ f1 = fftw_plan_dft_1d(sz*2,
+ reinterpret_cast<fftw_complex*>(func1),
+ reinterpret_cast<fftw_complex*>(func1),
+ FFTW_FORWARD,
+ FFTW_MEASURE);
+ f2 = fftw_plan_dft_1d(sz*2,
+ reinterpret_cast<fftw_complex*>(func2),
+ reinterpret_cast<fftw_complex*>(func2),
+ FFTW_FORWARD,
+ FFTW_MEASURE);
+ b = fftw_plan_dft_1d(sz*2,
+ reinterpret_cast<fftw_complex*>(res),
+ reinterpret_cast<fftw_complex*>(res),
+ FFTW_BACKWARD,
+ FFTW_MEASURE);
+
+ // start off by zero
+ for (int i = 0; i < sz*2; ++i) {
+ func1[i].real() = func1[i].imag() = func2[i].real() = func2[i].imag() = 0.0;
+ }
+
+ for (int i = 0; i < sz; ++i) {
+ double x1 = 0.0 + h*i;
+ double z = (x1 - mu2)/sigma2;
+ func1[i].real() = exp(-(z*z/2.0));
+
+ double x2 = -3000.0 + h*i;
+ func2[(i - sz/2 + sz*2)%(sz*2)].real() = prob_score_real(k, a, binomial_precompute, x2*winfac);
+ }
+
+ result.reserve(sz*2);
+
+ // convolve
+ fftw_execute(f1);
+ fftw_execute(f2);
+ for (int i = 0; i < sz*2; ++i) {
+ res[i] = func1[i] * func2[i];
+ }
+ fftw_execute(b);
+ for (int i = 0; i < sz; ++i) {
+ double r1 = i*h;
+ result.push_back(make_pair(r1, abs(res[i])));
+ }
}
// normalize the curve so we know that A ~= 1
vector<pair<double, double> > curve;
if (score1 > score2) {
- for (double r1 = 0.0; r1 < 3000.0; r1 += pdf_step_size) {
- double z = (r1 - mu1) / sigma1;
- double gaussian = exp(-(z*z/2.0));
- curve.push_back(make_pair(r1, gaussian * opponent_rating_pdf(score1, score2, r1, mu2, sigma2, -1.0)));
- }
+ compute_opponent_rating_pdf(score1, score2, mu2, sigma2, -1.0, curve);
} else {
- for (double r1 = 0.0; r1 < 3000.0; r1 += pdf_step_size) {
- double z = (r1 - mu1) / sigma1;
- double gaussian = exp(-(z*z/2.0));
- curve.push_back(make_pair(r1, gaussian * opponent_rating_pdf(score2, score1, r1, mu2, sigma2, 1.0)));
- }
+ compute_opponent_rating_pdf(score2, score1, mu2, sigma2, 1.0, curve);
+ }
+
+ // multiply in the gaussian
+ for (unsigned i = 0; i < curve.size(); ++i) {
+ double r1 = curve[i].first;
+ double z = (r1 - mu1) / sigma1;
+ double gaussian = exp(-(z*z/2.0));
+ curve[i].second *= gaussian;
}
double mu_est, sigma_est;
least_squares(curve, mu_est, sigma_est, mu, sigma);
}
-// int(normpdf[mu2, sigma2](t2) * ..., t2=0..3000);
-class OuterIntegralEvaluator {
-private:
- double theta1, mu2, sigma2, mu_t, sigma_t;
- int score1, score2;
- double winfac;
-
-public:
- OuterIntegralEvaluator(double theta1, double mu2, double sigma2, double mu3, double sigma3, double mu4, double sigma4, int score1, int score2, double winfac)
- : theta1(theta1), mu2(mu2), sigma2(sigma2), mu_t(mu3 + mu4), sigma_t(sqrt(sigma3*sigma3 + sigma4*sigma4)), score1(score1), score2(score2), winfac(winfac) {}
-
- double operator() (double theta2) const
- {
- double z = (theta2 - mu2) / sigma2;
- double gaussian = exp(-(z*z/2.0));
- double r1 = theta1 + theta2;
- return gaussian * opponent_rating_pdf(score1, score2, r1, mu_t, sigma_t, winfac);
- }
-};
-
void compute_new_double_rating(double mu1, double sigma1, double mu2, double sigma2, double mu3, double sigma3, double mu4, double sigma4, int score1, int score2, double &mu, double &sigma)
{
- vector<pair<double, double> > curve;
-
+ vector<pair<double, double> > curve, newcurve;
+ double mu_t = mu3 + mu4;
+ double sigma_t = sqrt(sigma3*sigma3 + sigma4*sigma4);
+
if (score1 > score2) {
- for (double r1 = 0.0; r1 < 3000.0; r1 += pdf_step_size) {
- double z = (r1 - mu1) / sigma1;
- double gaussian = exp(-(z*z/2.0));
- curve.push_back(make_pair(r1, gaussian * simpson_integrate(OuterIntegralEvaluator(r1,mu2,sigma2,mu3,sigma3,mu4,sigma4,score1,score2,-1.0), 0.0, 3000.0, int_step_size)));
- }
+ compute_opponent_rating_pdf(score1, score2, mu_t, sigma_t, -1.0, curve);
} else {
- for (double r1 = 0.0; r1 < 3000.0; r1 += pdf_step_size) {
- double z = (r1 - mu1) / sigma1;
+ compute_opponent_rating_pdf(score2, score1, mu_t, sigma_t, 1.0, curve);
+ }
+
+ // iterate over r1
+ double h = 3000.0 / curve.size();
+ for (unsigned i = 0; i < curve.size(); ++i) {
+ double sum = 0.0;
+
+ // could be anything, but this is a nice start
+ //double r1 = curve[i].first;
+ double r1 = i * h;
+
+ // iterate over r2
+ for (unsigned j = 0; j < curve.size(); ++j) {
+ double r1plusr2 = curve[j].first;
+ double r2 = r1plusr2 - r1;
+
+ double z = (r2 - mu2) / sigma2;
double gaussian = exp(-(z*z/2.0));
- curve.push_back(make_pair(r1, gaussian * simpson_integrate(OuterIntegralEvaluator(r1,mu2,sigma2,mu3,sigma3,mu4,sigma4,score2,score1,1.0), 0.0, 3000.0, int_step_size)));
+ sum += curve[j].second * gaussian;
}
+
+ double z = (r1 - mu1) / sigma1;
+ double gaussian = exp(-(z*z/2.0));
+ newcurve.push_back(make_pair(r1, gaussian * sum));
}
+
double mu_est, sigma_est;
- normalize(curve);
- estimate_musigma(curve, mu_est, sigma_est);
- least_squares(curve, mu_est, sigma_est, mu, sigma);
+ normalize(newcurve);
+ estimate_musigma(newcurve, mu_est, sigma_est);
+ least_squares(newcurve, mu_est, sigma_est, mu, sigma);
}
int main(int argc, char **argv)
{
+ FILE *fp = fopen("fftw-wisdom", "rb");
+ if (fp != NULL) {
+ fftw_import_wisdom_from_file(fp);
+ fclose(fp);
+ }
+
double mu1 = atof(argv[1]);
double sigma1 = atof(argv[2]);
double mu2 = atof(argv[3]);
i, k, prob_score(k, i, mu1-mu2), newmu1-mu1, newmu2-mu2);
}
}
+
+ fp = fopen("fftw-wisdom", "wb");
+ if (fp != NULL) {
+ fftw_export_wisdom_to_file(fp);
+ fclose(fp);
+ }
}
projects / foosball / commitdiff