+ const int maxiterations = 1000;
+ const double enough = 100.0; // sets the number of decimals we are happy with
+ const double stepchange = 0.5; // reduction of the step size when finding middle
+ // 0.5 sems to be magical, much better than 0.4 or 0.6
+ struct norm_client *norm;
+ for ( norm = rel->norm; norm; norm = norm->next )
+ {
+ yaz_log(YLOG_LOG,"Normalizing client %d: scorefield=%d count=%d range=%f %f = %f",
+ norm->num, norm->scorefield, norm->count, norm->min,
+ norm->max, norm->max-norm->min);
+ norm->a = 1.0; // default normalizing factors, no change
+ norm->b = 0.0;
+ if ( norm->scorefield != scorefield_none &&
+ norm->scorefield != scorefield_position )
+ { // have something to normalize
+ double range = norm->max - norm->min;
+ int it = 0;
+ double a,b; // params to optimize
+ double as,bs; // step sizes
+ double chi;
+ char *branch = "?";
+ // initial guesses for the parameters
+ // Rmax = a * rmax + b # want to be 1.0
+ // Rmin = a * rmin + b # want to be 0.0
+ // Rmax - Rmin = a ( rmax - rmin ) # subtracting equations
+ // 1.0 - 0.0 = a ( rmax - rmin )
+ // a = 1 / range
+ // Rmin = a * rmin + b
+ // b = Rmin - a * rmin
+ // = 0.0 - 1/range * rmin
+ // = - rmin / range
+
+ if ( range < 1e-6 ) // practically zero
+ range = norm->max;
+ a = 1.0 / range;
+ b = -1.0 * norm->min / range;
+ // b = fabs(norm->min) / range;
+ as = a / 10;
+ bs = fabs(b) / 10;
+ chi = squaresum( norm->records, a,b);
+ yaz_log(YLOG_LOG,"Initial done: it=%d: a=%f / %f b=%f / %f chi = %f",
+ 0, a, as, b, bs, chi );
+ while (it++ < maxiterations) // safeguard against things not converging
+ {
+ double aplus = squaresum(norm->records, a+as, b);
+ double aminus= squaresum(norm->records, a-as, b);
+ double bplus = squaresum(norm->records, a, b+bs);
+ double bminus= squaresum(norm->records, a, b-bs);
+ double prevchi = chi;
+ if ( aplus < chi && aplus < aminus && aplus < bplus && aplus < bminus)
+ {
+ a = a + as;
+ chi = aplus;
+ as = as * (1.0 + stepchange);
+ branch = "aplus ";
+ }
+ else if ( aminus < chi && aminus < aplus && aminus < bplus && aminus < bminus)
+ {
+ a = a - as;
+ chi = aminus;
+ as = as * (1.0 + stepchange);
+ branch = "aminus";
+ }
+ else if ( bplus < chi && bplus < aplus && bplus < aminus && bplus < bminus)
+ {
+ b = b + bs;
+ chi = bplus;
+ bs = bs * (1.0 + stepchange);
+ branch = "bplus ";
+ }
+ else if ( bminus < chi && bminus < aplus && bminus < bplus && bminus < aminus)
+ {
+ b = b - bs;
+ chi = bminus;
+ branch = "bminus";
+ bs = bs * (1.0+stepchange);
+ }
+ else
+ { // a,b is the best so far, adjust one step size
+ // which one? The one that has the greatest effect to chi
+ // That is, the average of plus and minus is further away from chi
+ double adif = 0.5 * ( aplus + aminus ) - prevchi;
+ double bdif = 0.5 * ( bplus + bminus ) - prevchi;
+ if ( fabs(adif) > fabs(bdif) )
+ {
+ as = as * ( 1.0 - stepchange);
+ branch = "step a";
+ }
+ else
+ {
+ bs = bs * ( 1.0 - stepchange);
+ branch = "step b";
+ }
+ }
+ yaz_log(YLOG_LOG,"Fitting %s it=%d: a=%g %g b=%g %g chi=%g ap=%g am=%g, bp=%g bm=%g p=%g",
+ branch, it, a, as, b, bs, chi,
+ aplus, aminus, bplus, bminus, prevchi );
+ norm->a = a;
+ norm->b = b;
+ if ( fabs(as) * enough < fabs(a) &&
+ fabs(bs) * enough < fabs(b) ) {
+ break; // not changing much any more
+
+ }
+ }
+ yaz_log(YLOG_LOG,"Fitting done: it=%d: a=%g / %g b=%g / %g chi = %g",
+ it-1, a, as, b, bs, chi );
+ }
+
+ if ( norm->scorefield != scorefield_none )
+ { // distribute the normalized scores to the records
+ struct norm_record *nr = norm->records;
+ for ( ; nr ; nr = nr->next ) {
+ double r = nr->score;
+ r = norm->a * r + norm -> b;
+ nr->clust->relevance_score = 10000 * r;
+ nr->record->score = r;
+ yaz_log(YLOG_LOG,"Normalized %f * %f + %f = %f",
+ nr->score, norm->a, norm->b, r );
+ // TODO - This keeps overwriting the cluster score in random order!
+ // Need to merge results better
+ }
+ }
+ } // client loop
+}