changeset 270:9636040ff503 sampling

Parametrize nsamples (though not on the command-line yet)

Compute the mean number of possible sequences in tracks, and display
that.

Compute the track xthreshold as in TASLP paper.  (Not quite getting the
same answers, though).

--- a/sample.cpp	Mon Jun 16 11:57:25 2008 +0000
+++ b/sample.cpp	Mon Jun 16 11:59:43 2008 +0000
@@ -63,12 +63,15 @@
 
   unsigned *propTable = new unsigned[dbH->numFiles];
   unsigned total = 0;
+  unsigned count = 0;
 
   for (unsigned int i = 0; i < dbH->numFiles; i++) {
     /* what kind of a stupid language doesn't have binary max(), let
        alone nary? */
     unsigned int prop = trackTable[i] - sequenceLength + 1;
     prop = prop > 0 ? prop : 0;
+    if (prop > 0) 
+      count++;
     propTable[i] = prop;
     total += prop;
   }
@@ -85,8 +88,9 @@
   double sumdist = 0;
   double sumlogdist = 0;
 
-  /* 1037 samples for now */
-  for (unsigned int i = 0; i < 1037;) {
+  unsigned int nsamples = 2049;
+
+  for (unsigned int i = 0; i < nsamples;) {
     /* FIXME: in Real Life we'll want to initialize the RNG using
        /dev/random or the current time or something.  */
     unsigned track1 = random_track(propTable, total);
@@ -137,18 +141,30 @@
     }
   }
 
-  double sigma2 = (sumdist / (sequenceLength * dbH->dim * 1037));
-  double d = 2 * yinv(log(sumdist/1037) - sumlogdist/1037);
+  /* FIXME: the mean isn't really what we should be reporting here */
+  unsigned meanN = total / count;
+
+  double sigma2 = sumdist / (sequenceLength * dbH->dim * nsamples);
+  double d = 2 * yinv(log(sumdist/nsamples) - sumlogdist/nsamples);
 
   std::cout << "Summary statistics" << std::endl;
-  std::cout << "number of samples: " << 1037 << std::endl;
+  std::cout << "number of samples: " << nsamples << std::endl;
   std::cout << "sum of distances (S): " << sumdist << std::endl;
   std::cout << "sum of log distances (L): " << sumlogdist << std::endl;
+  std::cout << "mean number of applicable sequences (N): " << meanN << std::endl;
   std::cout << std::endl;
   std::cout << "Estimated parameters" << std::endl;
   std::cout << "sigma^2: " << sigma2 << std::endl;
   std::cout << "d: " << d << std::endl;
-  std::cout << "check: " << yfun(d/2) << std::endl;
+
+  double logw = (2 / d) * gsl_sf_log(-gsl_sf_log(0.99));
+  double logxthresh = gsl_sf_log(sumdist / nsamples) + logw
+    - (2 / d) * gsl_sf_log(meanN)
+    - gsl_sf_log(d/2)
+    - (2 / d) * gsl_sf_log(2 / d)
+    + (2 / d) * gsl_sf_lngamma(d / 2);
+
+  std::cout << "track xthresh: " << exp(logxthresh) << std::endl;
 
   /* FIXME: we'll also want some summary statistics based on
      propTable, for the minimum-of-X estimate */