view sample.cpp @ 395:bc7a821004bb api-inversion

Invert audioDB::status / audiodb_status().

To do that without breaking abstractions, we actually need a new field
in the status structure, storing the size of the data region.
Previously, this was computed in the audioDB::status request from the
database header, but I'm assuming that "user" code doesn't have access
to such internals.  While we're at it, name some intermediate values in
audioDB::status() so that I don't get confused.

Here's the thing, though: we need to make sure that the adb_t * that we
have from audiodb_open() or audiodb_create() is propagated all the way
through into the C++ routines that implement library functions -- in
particular those which actually write to the database; otherwise we
won't have a consistent view in memory of the header on-disk (as the adb
header that will have been written to disk won't be the same as the one
in memory).

We can do that, by altering the "API" audioDB constructors to take the
adb_t * argument, and setting the adb field in the audioDB object that
we've already introduced to that.  But now we need to be careful a
couple of times: if we have one, then audioDB::initTables() mustn't
stomp on it; also, if we're only constructing an audioDB instance to
fulfil an API request, we mustn't audiodb_close() the one we have when
we destroy the audioDB object, because the adb_t * is the one we have
passed in and are going to reuse in later calls to the API.

The good news is that we can be careful in just these ways with minimal
code.  The really good news is that once the inversion is complete, all
of this horribleness will automatically go away (as there will be no
code which constructs audioDB objects to fulfil API functions).  Hooray!
It's almost like it was all planned this way.

#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");
  }
    
  // build track offset table (FIXME: cut'n'pasted from query.cpp)
  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;
}