Mercurial > hg > audiodb
view sample.cpp @ 622:695651b8c1a3
added a query hook. Should compile a run, but I haven't exhaustively tested the various input parameters yet.
As such, there may well be some ways to get to the api calls that bring the module down.
Let me know if you find any.
The actual query call is a bit of a mess, but will be more intuitive from the native python layer (to be written)...
so the python bindings now have a complete path:
>>import _pyadb
>>aDB = _pyadb._pyadb_create("test.adb", 0,0,0)
>>_pyadb._pyadb_status(aDB)
>>_pyadb._pyadb_insertFromFile(aDB, "someFeats.mfcc12")
...(add some more data)
>>result = _pyadb._pyadb_queryFromKey(aDB, "a Key in aDB", [options])
and then result has a nice dict of your results.
author | map01bf |
---|---|
date | Mon, 21 Sep 2009 17:42:52 +0000 |
parents | e6dab5ed471c |
children | 536cfa209e7f |
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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()"); } 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; }