More cleanups, and statify.

diff --git a/foosrank.cpp b/foosrank.cpp
index b711f0015384f4f6db7b16efbf6ea45ec513b24e..bdb54f2324b3e6a95e626cf035474fb9c1f97b1f 100644 (file)
--- a/foosrank.cpp
+++ b/foosrank.cpp
@@ -16,10 +16,10 @@ static const double rating_constant = 455.0;
 
 using namespace std;
 
-double prob_score(int k, int a, double rd);
-double prob_score_real(int k, int a, double binomial, double rd_norm);
-double prodai(int k, int a);
-double fac(int x);
+static double prob_score(int k, int a, double rd);
+static double prob_score_real(int k, int a, double binomial, double rd_norm);
+static double prodai(int k, int a);
+static double fac(int x);
 
 
 // probability of match ending k-a (k>a) when winnerR - loserR = RD
@@ -38,14 +38,14 @@ double fac(int x);
 // Glicko/Bradley-Terry assumption that a player rated 400 points over
 // his/her opponent will win with a probability of 10/11 =~ 0.90909. 
 //
-double prob_score(int k, int a, double rd)
+static double prob_score(int k, int a, double rd)
 {
        return prob_score_real(k, a, prodai(k, a) / fac(k-1), rd/rating_constant);
 }
 
 // computes x^a, probably more efficiently than pow(x, a) (but requires that a
 // is n unsigned integer)
-double intpow(double x, unsigned a)
+static double intpow(double x, unsigned a)
 {
        double result = 1.0;
 
@@ -63,7 +63,7 @@ double intpow(double x, unsigned a)
 // Same, but takes in binomial(a+k-1, k-1) as an argument in
 // addition to a. Faster if you already have that precomputed, and assumes rd
 // is already divided by 455.
-double prob_score_real(int k, int a, double binomial, double rd_norm)
+static double prob_score_real(int k, int a, double binomial, double rd_norm)
 {
        double nom = binomial * intpow(pow(2.0, rd_norm), a); 
        double denom = intpow(1.0 + pow(2.0, rd_norm), k+a);
@@ -71,7 +71,7 @@ double prob_score_real(int k, int a, double binomial, double rd_norm)
 }
 
 // Calculates Product(a+i, i=1..k-1) (see above).
-double prodai(int k, int a)
+static double prodai(int k, int a)
 {
        double prod = 1.0;
        for (int i = 1; i < k; ++i)
@@ -79,7 +79,7 @@ double prodai(int k, int a)
        return prod;
 }
 
-double fac(int x)
+static double fac(int x)
 {
        double prod = 1.0;
        for (int i = 2; i <= x; ++i)
@@ -87,11 +87,7 @@ double fac(int x)
        return prod;
 }
 
-void convolve(int size)
-{
-}
-
-void compute_opponent_rating_pdf(int k, int a, double mu2, double sigma2, double winfac, vector<pair<double, double> > &result)
+static void compute_opponent_rating_pdf(int k, int a, double mu2, double sigma2, double winfac, vector<pair<double, double> > &result)
 {
        double binomial_precompute = prodai(k, a) / fac(k-1);
        winfac /= rating_constant;
@@ -151,7 +147,7 @@ void compute_opponent_rating_pdf(int k, int a, double mu2, double sigma2, double
 }
 
 // normalize the curve so we know that A ~= 1
-void normalize(vector<pair<double, double> > &curve)
+static void normalize(vector<pair<double, double> > &curve)
 {
        double peak = 0.0;
        for (vector<pair<double, double> >::const_iterator i = curve.begin(); i != curve.end(); ++i) {
@@ -164,27 +160,10 @@ void normalize(vector<pair<double, double> > &curve)
        }
 }
 
-// computes matA * matB
-void mat_mul(double *matA, unsigned ah, unsigned aw,
-             double *matB, unsigned bh, unsigned bw,
-            double *result)
-{
-       assert(aw == bh);
-       for (unsigned y = 0; y < bw; ++y) {
-               for (unsigned x = 0; x < ah; ++x) {
-                       double sum = 0.0;
-                       for (unsigned c = 0; c < aw; ++c) {
-                               sum += matA[c*ah + x] * matB[y*bh + c];
-                       }
-                       result[y*bw + x] = sum;
-               }
-       }
-}
-               
 // computes matA^T * matB
-void mat_mul_trans(double *matA, unsigned ah, unsigned aw,
-                   double *matB, unsigned bh, unsigned bw,
-                  double *result)
+static void mat_mul_trans(double *matA, unsigned ah, unsigned aw,
+                          double *matB, unsigned bh, unsigned bw,
+                         double *result)
 {
        assert(ah == bh);
        for (unsigned y = 0; y < bw; ++y) {
@@ -198,24 +177,10 @@ void mat_mul_trans(double *matA, unsigned ah, unsigned aw,
        }
 }
 
-void print3x3(double *M)
-{
-       printf("%f %f %f\n", M[0], M[3], M[6]);
-       printf("%f %f %f\n", M[1], M[4], M[7]);
-       printf("%f %f %f\n", M[2], M[5], M[8]);
-}
-
-void print3x1(double *M)
-{
-       printf("%f\n", M[0]);
-       printf("%f\n", M[1]);
-       printf("%f\n", M[2]);
-}
-
 // solves Ax = B by Gauss-Jordan elimination, where A is a 3x3 matrix,
 // x is a column vector of length 3 and B is a row vector of length 3.
 // Destroys its input in the process.
-void solve3x3(double *A, double *x, double *B)
+static void solve3x3(double *A, double *x, double *B)
 {
        // row 1 -= row 0 * (a1/a0)
        {
@@ -280,7 +245,7 @@ void solve3x3(double *A, double *x, double *B)
 
 // Give an OK starting estimate for the least squares, by numerical integration
 // of statistical moments.
-void estimate_musigma(vector<pair<double, double> > &curve, double &mu_result, double &sigma_result)
+static void estimate_musigma(vector<pair<double, double> > &curve, double &mu_result, double &sigma_result)
 {
        double h = (curve.back().first - curve.front().first) / (curve.size() - 1);
 
@@ -322,7 +287,7 @@ void estimate_musigma(vector<pair<double, double> > &curve, double &mu_result, d
 // Note that the algorithm blows up quite hard if the initial estimate is
 // not good enough. Use estimate_musigma to get a reasonable starting
 // estimate.
-void least_squares(vector<pair<double, double> > &curve, double mu1, double sigma1, double &mu_result, double &sigma_result)
+static void least_squares(vector<pair<double, double> > &curve, double mu1, double sigma1, double &mu_result, double &sigma_result)
 {
        double A = 1.0;
        double mu = mu1;
@@ -388,7 +353,7 @@ void least_squares(vector<pair<double, double> > &curve, double mu1, double sigm
        sigma_result = sigma;
 }
 
-void compute_new_rating(double mu1, double sigma1, double mu2, double sigma2, int score1, int score2, double &mu, double &sigma)
+static void compute_new_rating(double mu1, double sigma1, double mu2, double sigma2, int score1, int score2, double &mu, double &sigma)
 {
        vector<pair<double, double> > curve;
 
@@ -412,7 +377,7 @@ void compute_new_rating(double mu1, double sigma1, double mu2, double sigma2, in
        least_squares(curve, mu_est, sigma_est, mu, sigma);
 }
 
-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)
+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)
 {
        vector<pair<double, double> > curve, newcurve;
        double mu_t = mu3 + mu4;