12 static const double int_step_size = 75.0;
14 // rating constant (see below)
15 static const double rating_constant = 455.0;
19 static double prob_score(int k, int a, double rd);
20 static double prob_score_real(int k, int a, double binomial, double rd_norm);
21 static double prodai(int k, int a);
22 static double fac(int x);
25 // probability of match ending k-a (k>a) when winnerR - loserR = RD
30 // | Poisson[lambda1, t](a) * Erlang[lambda2, k](t) dt
35 // where lambda1 = 1.0, lambda2 = 2^(rd/455)
37 // The constant of 455 is chosen carefully so to match with the
38 // Glicko/Bradley-Terry assumption that a player rated 400 points over
39 // his/her opponent will win with a probability of 10/11 =~ 0.90909.
41 static double prob_score(int k, int a, double rd)
43 return prob_score_real(k, a, prodai(k, a) / fac(k-1), rd/rating_constant);
46 // computes x^a, probably more efficiently than pow(x, a) (but requires that a
47 // is n unsigned integer)
48 static double intpow(double x, unsigned a)
63 // Same, but takes in binomial(a+k-1, k-1) as an argument in
64 // addition to a. Faster if you already have that precomputed, and assumes rd
65 // is already divided by 455.
66 static double prob_score_real(int k, int a, double binomial, double rd_norm)
68 double nom = binomial * intpow(pow(2.0, rd_norm), a);
69 double denom = intpow(1.0 + pow(2.0, rd_norm), k+a);
73 // Calculates Product(a+i, i=1..k-1) (see above).
74 static double prodai(int k, int a)
77 for (int i = 1; i < k; ++i)
82 static double fac(int x)
85 for (int i = 2; i <= x; ++i)
90 static void compute_opponent_rating_pdf(int k, int a, double mu2, double sigma2, double winfac, vector<pair<double, double> > &result)
92 double binomial_precompute = prodai(k, a) / fac(k-1);
93 winfac /= rating_constant;
95 int sz = (6000.0 - 0.0) / int_step_size;
96 double h = (6000.0 - 0.0) / sz;
99 complex<double> *func1, *func2, *res;
101 func1 = reinterpret_cast<complex<double> *>(fftw_malloc(sz*2*sizeof(complex<double>)));
102 func2 = reinterpret_cast<complex<double> *>(fftw_malloc(sz*2*sizeof(complex<double>)));
103 res = reinterpret_cast<complex<double> *>(fftw_malloc(sz*2*sizeof(complex<double>)));
104 f1 = fftw_plan_dft_1d(sz*2,
105 reinterpret_cast<fftw_complex*>(func1),
106 reinterpret_cast<fftw_complex*>(func1),
109 f2 = fftw_plan_dft_1d(sz*2,
110 reinterpret_cast<fftw_complex*>(func2),
111 reinterpret_cast<fftw_complex*>(func2),
114 b = fftw_plan_dft_1d(sz*2,
115 reinterpret_cast<fftw_complex*>(res),
116 reinterpret_cast<fftw_complex*>(res),
121 for (int i = 0; i < sz*2; ++i) {
122 func1[i].real() = func1[i].imag() = func2[i].real() = func2[i].imag() = 0.0;
125 double invsigma2 = 1.0 / sigma2;
126 for (int i = 0; i < sz; ++i) {
127 double x1 = 0.0 + h*i;
128 double z = (x1 - mu2) * invsigma2;
129 func1[i].real() = exp(-(z*z/2.0));
131 double x2 = -3000.0 + h*i;
132 func2[(i - sz/2 + sz*2)%(sz*2)].real() = prob_score_real(k, a, binomial_precompute, x2*winfac);
135 result.reserve(sz*2);
140 for (int i = 0; i < sz*2; ++i) {
141 res[i] = func1[i] * func2[i];
144 for (int i = 0; i < sz; ++i) {
146 result.push_back(make_pair(r1, abs(res[i])));
150 // normalize the curve so we know that A ~= 1
151 static void normalize(vector<pair<double, double> > &curve)
154 for (vector<pair<double, double> >::const_iterator i = curve.begin(); i != curve.end(); ++i) {
155 peak = max(peak, i->second);
158 double invpeak = 1.0 / peak;
159 for (vector<pair<double, double> >::iterator i = curve.begin(); i != curve.end(); ++i) {
160 i->second *= invpeak;
164 // computes matA^T * matB
165 static void mat_mul_trans(double *matA, unsigned ah, unsigned aw,
166 double *matB, unsigned bh, unsigned bw,
170 for (unsigned y = 0; y < bw; ++y) {
171 for (unsigned x = 0; x < aw; ++x) {
173 for (unsigned c = 0; c < ah; ++c) {
174 sum += matA[x*ah + c] * matB[y*bh + c];
176 result[y*bw + x] = sum;
181 // solves Ax = B by Gauss-Jordan elimination, where A is an NxN matrix,
182 // x is a column vector of length N and B is a row vector of length N.
183 // Destroys its input in the process.
185 static void solve_matrix(double *A, double *x, double *B)
187 for (int i = 0; i < N; ++i) {
188 for (int j = i+1; j < N; ++j) {
189 // row j -= row i * (a[i,j] / a[i,i])
190 double f = A[j+i*N] / A[i+i*N];
193 for (int k = i+1; k < N; ++k) {
194 A[j+k*N] -= A[i+k*N] * f;
202 for (int i = N; i --> 0; ) {
203 for (int j = i; j --> 0; ) {
204 // row j -= row i * (a[j,j] / a[j,i])
205 double f = A[i+j*N] / A[j+j*N];
213 for (int i = 0; i < N; ++i) {
214 x[i] = B[i] / A[i+i*N];
218 // Give an OK starting estimate for the least squares, by numerical integration
219 // of statistical moments.
220 static void estimate_musigma(vector<pair<double, double> > &curve, double &mu_result, double &sigma_result)
222 double h = (curve.back().first - curve.front().first) / (curve.size() - 1);
224 double area = curve.front().second;
225 double ex = curve.front().first * curve.front().second;
226 double ex2 = curve.front().first * curve.front().first * curve.front().second;
228 for (unsigned i = 1; i < curve.size() - 1; i += 2) {
229 double x = curve[i].first;
230 double y = curve[i].second;
233 ex2 += 4.0 * x * x * y;
235 for (unsigned i = 2; i < curve.size() - 1; i += 2) {
236 double x = curve[i].first;
237 double y = curve[i].second;
240 ex2 += 2.0 * x * x * y;
243 area += curve.back().second;
244 ex += curve.back().first * curve.back().second;
245 ex2 += curve.back().first * curve.back().first * curve.back().second;
247 area = (h/3.0) * area;
248 ex = (h/3.0) * ex / area;
249 ex2 = (h/3.0) * ex2 / area;
252 sigma_result = sqrt(ex2 - ex * ex);
255 // Find best fit of the data in curves to a Gaussian pdf, based on the
256 // given initial estimates. Works by nonlinear least squares, iterating
257 // until we're below a certain threshold.
259 // Note that the algorithm blows up quite hard if the initial estimate is
260 // not good enough. Use estimate_musigma to get a reasonable starting
262 static void least_squares(vector<pair<double, double> > &curve, double mu1, double sigma1, double &mu_result, double &sigma_result)
266 double sigma = sigma1;
269 double matA[curve.size() * 3]; // N x 3
270 double dbeta[curve.size()]; // N x 1
272 // A^T * A: 3xN * Nx3 = 3x3
275 // A^T * dβ: 3xN * Nx1 = 3x1
281 //printf("A=%f mu=%f sigma=%f\n", A, mu, sigma);
283 // fill in A (depends only on x_i, A, mu, sigma -- not y_i)
284 for (unsigned i = 0; i < curve.size(); ++i) {
285 double x = curve[i].first;
288 matA[i + 0 * curve.size()] =
289 exp(-(x-mu)*(x-mu)/(2.0*sigma*sigma));
292 matA[i + 1 * curve.size()] =
293 A * (x-mu)/(sigma*sigma) * matA[i + 0 * curve.size()];
296 matA[i + 2 * curve.size()] =
297 matA[i + 1 * curve.size()] * (x-mu)/sigma;
301 for (unsigned i = 0; i < curve.size(); ++i) {
302 double x = curve[i].first;
303 double y = curve[i].second;
305 dbeta[i] = y - A * exp(- (x-mu)*(x-mu)/(2.0*sigma*sigma));
309 mat_mul_trans(matA, curve.size(), 3, matA, curve.size(), 3, matATA);
310 mat_mul_trans(matA, curve.size(), 3, dbeta, curve.size(), 1, matATdb);
313 solve_matrix<3>(matATA, dlambda, matATdb);
319 // terminate when we're down to three digits
320 if (fabs(dlambda[0]) <= 1e-3 && fabs(dlambda[1]) <= 1e-3 && fabs(dlambda[2]) <= 1e-3)
325 sigma_result = sigma;
328 static void compute_new_rating(double mu1, double sigma1, double mu2, double sigma2, int score1, int score2, double &mu, double &sigma)
330 vector<pair<double, double> > curve;
332 if (score1 > score2) {
333 compute_opponent_rating_pdf(score1, score2, mu2, sigma2, -1.0, curve);
335 compute_opponent_rating_pdf(score2, score1, mu2, sigma2, 1.0, curve);
338 // multiply in the gaussian
339 for (unsigned i = 0; i < curve.size(); ++i) {
340 double r1 = curve[i].first;
341 double z = (r1 - mu1) / sigma1;
342 double gaussian = exp(-(z*z/2.0));
343 curve[i].second *= gaussian;
346 double mu_est, sigma_est;
348 estimate_musigma(curve, mu_est, sigma_est);
349 least_squares(curve, mu_est, sigma_est, mu, sigma);
352 static 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)
354 vector<pair<double, double> > curve, newcurve;
355 double mu_t = mu3 + mu4;
356 double sigma_t = sqrt(sigma3*sigma3 + sigma4*sigma4);
358 if (score1 > score2) {
359 compute_opponent_rating_pdf(score1, score2, mu_t, sigma_t, -1.0, curve);
361 compute_opponent_rating_pdf(score2, score1, mu_t, sigma_t, 1.0, curve);
364 newcurve.reserve(curve.size());
367 double h = 3000.0 / curve.size();
368 for (unsigned i = 0; i < curve.size(); ++i) {
371 // could be anything, but this is a nice start
372 //double r1 = curve[i].first;
376 double invsigma2 = 1.0 / sigma2;
377 for (unsigned j = 0; j < curve.size(); ++j) {
378 double r1plusr2 = curve[j].first;
379 double r2 = r1plusr2 - r1;
381 double z = (r2 - mu2) * invsigma2;
382 double gaussian = exp(-(z*z/2.0));
383 sum += curve[j].second * gaussian;
386 double z = (r1 - mu1) / sigma1;
387 double gaussian = exp(-(z*z/2.0));
388 newcurve.push_back(make_pair(r1, gaussian * sum));
392 double mu_est, sigma_est;
394 estimate_musigma(newcurve, mu_est, sigma_est);
395 least_squares(newcurve, mu_est, sigma_est, mu, sigma);
398 int main(int argc, char **argv)
400 FILE *fp = fopen("fftw-wisdom", "rb");
402 fftw_import_wisdom_from_file(fp);
406 double mu1 = atof(argv[1]);
407 double sigma1 = atof(argv[2]);
408 double mu2 = atof(argv[3]);
409 double sigma2 = atof(argv[4]);
412 double mu3 = atof(argv[5]);
413 double sigma3 = atof(argv[6]);
414 double mu4 = atof(argv[7]);
415 double sigma4 = atof(argv[8]);
416 int score1 = atoi(argv[9]);
417 int score2 = atoi(argv[10]);
419 compute_new_double_rating(mu1, sigma1, mu2, sigma2, mu3, sigma3, mu4, sigma4, score1, score2, mu, sigma);
420 printf("%f %f\n", mu, sigma);
421 } else if (argc > 8) {
422 double mu3 = atof(argv[5]);
423 double sigma3 = atof(argv[6]);
424 double mu4 = atof(argv[7]);
425 double sigma4 = atof(argv[8]);
426 int k = atoi(argv[9]);
428 // assess all possible scores
429 for (int i = 0; i < k; ++i) {
430 double newmu1_1, newmu1_2, newmu2_1, newmu2_2;
431 double newsigma1_1, newsigma1_2, newsigma2_1, newsigma2_2;
432 compute_new_double_rating(mu1, sigma1, mu2, sigma2, mu3, sigma3, mu4, sigma4, k, i, newmu1_1, newsigma1_1);
433 compute_new_double_rating(mu2, sigma2, mu1, sigma1, mu3, sigma3, mu4, sigma4, k, i, newmu1_2, newsigma1_2);
434 compute_new_double_rating(mu3, sigma3, mu4, sigma4, mu1, sigma1, mu2, sigma2, i, k, newmu2_1, newsigma2_1);
435 compute_new_double_rating(mu4, sigma4, mu3, sigma3, mu1, sigma1, mu2, sigma2, i, k, newmu2_2, newsigma2_2);
436 printf("%u-%u,%f,%+f,%+f,%+f,%+f\n",
437 k, i, prob_score(k, i, mu3+mu4-(mu1+mu2)), newmu1_1-mu1, newmu1_2-mu2,
438 newmu2_1-mu3, newmu2_2-mu4);
440 for (int i = k; i --> 0; ) {
441 double newmu1_1, newmu1_2, newmu2_1, newmu2_2;
442 double newsigma1_1, newsigma1_2, newsigma2_1, newsigma2_2;
443 compute_new_double_rating(mu1, sigma1, mu2, sigma2, mu3, sigma3, mu4, sigma4, i, k, newmu1_1, newsigma1_1);
444 compute_new_double_rating(mu2, sigma2, mu1, sigma1, mu3, sigma3, mu4, sigma4, i, k, newmu1_2, newsigma1_2);
445 compute_new_double_rating(mu3, sigma3, mu4, sigma4, mu1, sigma1, mu2, sigma2, k, i, newmu2_1, newsigma2_1);
446 compute_new_double_rating(mu4, sigma4, mu3, sigma3, mu1, sigma1, mu2, sigma2, k, i, newmu2_2, newsigma2_2);
447 printf("%u-%u,%f,%+f,%+f,%+f,%+f\n",
448 i, k, prob_score(k, i, mu1+mu2-(mu3+mu4)), newmu1_1-mu1, newmu1_2-mu2,
449 newmu2_1-mu3, newmu2_2-mu4);
451 } else if (argc > 6) {
452 int score1 = atoi(argv[5]);
453 int score2 = atoi(argv[6]);
455 compute_new_rating(mu1, sigma1, mu2, sigma2, score1, score2, mu, sigma);
456 printf("%f %f\n", mu, sigma);
458 int k = atoi(argv[5]);
460 // assess all possible scores
461 for (int i = 0; i < k; ++i) {
462 double newmu1, newmu2, newsigma1, newsigma2;
463 compute_new_rating(mu1, sigma1, mu2, sigma2, k, i, newmu1, newsigma1);
464 compute_new_rating(mu2, sigma2, mu1, sigma1, i, k, newmu2, newsigma2);
465 printf("%u-%u,%f,%+f,%+f\n",
466 k, i, prob_score(k, i, mu2-mu1), newmu1-mu1, newmu2-mu2);
468 for (int i = k; i --> 0; ) {
469 double newmu1, newmu2, newsigma1, newsigma2;
470 compute_new_rating(mu1, sigma1, mu2, sigma2, i, k, newmu1, newsigma1);
471 compute_new_rating(mu2, sigma2, mu1, sigma1, k, i, newmu2, newsigma2);
472 printf("%u-%u,%f,%+f,%+f\n",
473 i, k, prob_score(k, i, mu1-mu2), newmu1-mu1, newmu2-mu2);
477 fp = fopen("fftw-wisdom", "wb");
479 fftw_export_wisdom_to_file(fp);