view sample.cpp @ 562:dfeb5ef768da

Usually a commitment phobe but everybody has they're first time. Include mant structural changes and additional definition in line with what Christophe has asked for. Some of the schemas are more complicated than Id like - I will try to think of more elegant ways of specification.

#include "audioDB.h"

#include <gsl/gsl_sf.h>
#include <gsl/gsl_rng.h>

static
double yfun(double d) {
  return gsl_sf_log(d) - gsl_sf_psi(d);
}

static
double yinv(double y) {
  double a = 1.0e-5;
  double b = 1000.0;

  double ay = yfun(a);
  double by = yfun(b);

  double c = 0;
  double cy;

  /* FIXME: simple binary search; there's probably some clever solver
     in gsl somewhere which is less sucky. */
  while ((b - a) > 1.0e-5) {
    c = (a + b) / 2;
    cy = yfun(c);
    if (cy > y) {
      a = c;
      ay = cy;
    } else {
      b = c;
      by = cy;
    }
  }

  return c;
}

unsigned audioDB::random_track(unsigned *propTable, unsigned total) {
  /* FIXME: make this O(1) by using the alias-rejection method, or
     some other sensible method of sampling from a discrete
     distribution. */
  double thing = gsl_rng_uniform(rng);
  unsigned sofar = 0;
  for (unsigned int i = 0; i < dbH->numFiles; i++) {
    sofar += propTable[i];
    if (thing < ((double) sofar / (double) total)) {
      return i;
    }
  }
  error("fell through in random_track()");

  /* FIXME: decorate error's declaration so that this isn't necessary */
  return 0;
}

void audioDB::sample(const char *dbName) {
  initTables(dbName, 0);
  if(dbH->flags & O2_FLAG_LARGE_ADB){
    error("error: sample not yet supported for LARGE_ADB");
  }
    
  off_t *trackOffsetTable = new off_t[dbH->numFiles];
  unsigned cumTrack=0;
  for(unsigned int k = 0; k < dbH->numFiles; k++){
    trackOffsetTable[k] = cumTrack;
    cumTrack += trackTable[k] * dbH->dim;
  }

  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;
  }

  if (total == 0) {
    error("no sequences of this sequence length in the database", dbName);
  }

  unsigned int vlen = dbH->dim * sequenceLength;
  double *v1 = new double[vlen];
  double *v2 = new double[vlen];
  double v1norm, v2norm, v1v2;

  double sumdist = 0;
  double sumlogdist = 0;

  for (unsigned int i = 0; i < nsamples;) {
    unsigned track1 = random_track(propTable, total);
    unsigned track2 = random_track(propTable, total);

    if(track1 == track2)
      continue;

    unsigned i1 = gsl_rng_uniform_int(rng, propTable[track1]);
    unsigned i2 = gsl_rng_uniform_int(rng, propTable[track2]);

    VERB_LOG(1, "%d %d, %d %d | ", track1, i1, track2, i2);

    /* FIXME: this seeking, reading and distance calculation should
       share more code with the query loop */
    if(lseek(dbfid, dbH->dataOffset + trackOffsetTable[track1] * sizeof(double) + i1 * dbH->dim * sizeof(double), SEEK_SET) == (off_t) -1) {
      error("seek failure", "", "lseek");
    }
    CHECKED_READ(dbfid, v1, dbH->dim * sequenceLength * sizeof(double));

    if(lseek(dbfid, dbH->dataOffset + trackOffsetTable[track2] * sizeof(double) + i2 * dbH->dim * sizeof(double), SEEK_SET) == (off_t) -1) {
      error("seek failure", "", "lseek");
    }
    CHECKED_READ(dbfid, v2, dbH->dim * sequenceLength * sizeof(double));

    v1norm = 0;
    v2norm = 0;
    v1v2 = 0;

    for (unsigned int j = 0; j < vlen; j++) {
      v1norm += v1[j]*v1[j];
      v2norm += v2[j]*v2[j];
      v1v2 += v1[j]*v2[j];
    }

    /* FIXME: we must deal with infinities better than this; there
       could be all sorts of NaNs from arbitrary features.  Best
       include power thresholds or something... */
    if(isfinite(v1norm) && isfinite(v2norm) && isfinite(v1v2)) {

      VERB_LOG(1, "%f %f %f | ", v1norm, v2norm, v1v2);
      /* assume normalizedDistance == true for now */
      /* FIXME: not convinced that the statistics we calculated in
	 TASLP paper are technically valid for normalizedDistance */

      double dist = 2 - 2 * v1v2 / sqrt(v1norm * v2norm);
      // double dist = v1norm + v2norm - 2*v1v2;
      
      VERB_LOG(1, "%f %f\n", dist, log(dist));
      sumdist += dist;
      sumlogdist += log(dist);
      i++;
    } else {
      VERB_LOG(1, "infinity/NaN found: %f %f %f\n", v1norm, v2norm, v1v2);
    }
  }

  /* 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: " << nsamples << std::endl;
  std::cout << "sum of distances (S): " << sumdist << std::endl;
  std::cout << "sum of log distances (L): " << sumlogdist << std::endl;

  /* FIXME: we'll also want some more summary statistics based on
     propTable, for the minimum-of-X estimate */
  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::cout << "Msigma^2: " << sumdist / nsamples << std::endl;
  std::cout << "d: " << d << 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;

  delete[] propTable;
  delete[] v1;
  delete[] v2;
}