Mercurial > hg > audiodb
view sample.cpp @ 469:d3afc91d205d api-inversion
Move audioDB::query over to audioDB.cpp
At the same time, remove all the abstraction violations in
audioDB::query, which came in two flavours: use of dbH->numFiles, which
is dealt with by getting the database status instead (and is eventually
unnecessary, being only needed now because reporters are implemented in
terms of vectors indexed by ID), and use of fileTable in reporter's
report functions (dealt with by passing in the adb instead).
To actually implement reporting as of now, we continue to use stuff from
audioDB-internals.h; maybe someday we will be clean and shiny.
author | mas01cr |
---|---|
date | Wed, 31 Dec 2008 15:44:16 +0000 |
parents | 0c1c8726a79b |
children | e6dab5ed471c |
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#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; }