Error_t
VectorMinVI(float* value, unsigned* index, const float* src, unsigned length)
{
#ifdef __APPLE__
// Use the Accelerate framework if we have it
vDSP_minvi(src, 1, value, (unsigned long*)index, length);
#else
float res = src[0];
for (unsigned i = 0; i < length; ++i)
{
if (src[i] < res)
{
*value = res = src[i];
*index = i;
}
}
#endif
return NOERR;
}
uint_t
fvec_min_elem (fvec_t * s)
{
#ifndef HAVE_ACCELERATE
uint_t j, pos = 0.;
smpl_t tmp = s->data[0];
for (j = 0; j < s->length; j++) {
pos = (tmp < s->data[j]) ? pos : j;
tmp = (tmp < s->data[j]) ? tmp : s->data[j];
}
#else
smpl_t tmp = 0.;
uint_t pos = 0.;
#if !HAVE_AUBIO_DOUBLE
vDSP_minvi(s->data, 1, &tmp, (vDSP_Length *)&pos, s->length);
#else
vDSP_minviD(s->data, 1, &tmp, (vDSP_Length *)&pos, s->length);
#endif
#endif
return pos;
}
Float32 DTW32_getSimilarityScore(DTW32 *self,
Float32 *inputData,
size_t inputRowCount,
Float32 *comparisonData,
size_t comparisonDataRowCount,
size_t currentColumnCount)
{
// Float32_printContiguous2DArray(inputData, inputRowCount, currentColumnCount, "input");
// Float32_printContiguous2DArray(comparisonData, comparisonDataRowCount, currentColumnCount, "compare");
Float32 similarity = INFINITY;
self->currentInputRowCount = inputRowCount;
self->currentComparisonDataRowCount = comparisonDataRowCount;
vDSP_vsq(inputData, 1, self->inputTemp, 1, inputRowCount * currentColumnCount);
vDSP_vsq(comparisonData, 1, self->comparisonDataTemp, 1, comparisonDataRowCount * currentColumnCount);
vDSP_mtrans(self->inputTemp, 1, self->multiplicationTemp, 1, currentColumnCount, inputRowCount);
vDSP_mmul(self->ones, 1, self->multiplicationTemp, 1, self->inputTemp, 1, 1, inputRowCount, currentColumnCount);
vDSP_mtrans(self->comparisonDataTemp, 1, self->multiplicationTemp, 1, currentColumnCount, comparisonDataRowCount);
vDSP_mmul(self->ones, 1, self->multiplicationTemp, 1, self->comparisonDataTemp, 1, 1, comparisonDataRowCount, currentColumnCount);
const int elementCountInput = (int)inputRowCount;
const int elementCountComparisonData = (int)comparisonDataRowCount;
vvsqrtf(self->inputTemp, self->inputTemp, &elementCountInput);
vvsqrtf(self->comparisonDataTemp, self->comparisonDataTemp, &elementCountComparisonData);
cblas_sgemm(CblasRowMajor,
CblasNoTrans,
CblasTrans,
(SInt32)inputRowCount,
(SInt32)comparisonDataRowCount,
1,
1.0,
self->inputTemp,
1,
self->comparisonDataTemp,
1,
0.,
self->normalisationMatrix,
(SInt32)comparisonDataRowCount);
cblas_sgemm(CblasRowMajor,
CblasNoTrans,
CblasTrans,
(SInt32)inputRowCount,
(SInt32)comparisonDataRowCount,
(SInt32)currentColumnCount,
1.0,
inputData,
(SInt32)currentColumnCount,
comparisonData,
(SInt32)currentColumnCount,
0.,
self->distanceMatrix,
(SInt32)comparisonDataRowCount);
vDSP_vdiv(self->normalisationMatrix, 1, self->distanceMatrix, 1, self->distanceMatrix, 1, inputRowCount * comparisonDataRowCount);
// Float32_printContiguous2DArray(self->distanceMatrix, inputRowCount, comparisonDataRowCount, "norm");
Float32 minusOne = -1.f;
vDSP_vsma(self->distanceMatrix, 1, &minusOne, self->ones, 1, self->distanceMatrix, 1, inputRowCount * comparisonDataRowCount);
// Float32_printContiguous2DArray(self->distanceMatrix, inputRowCount, comparisonDataRowCount, "norm");
vDSP_mmov(self->distanceMatrix, &self->globalDistanceMatrix[comparisonDataRowCount + 2], comparisonDataRowCount, inputRowCount, comparisonDataRowCount, comparisonDataRowCount + 1);
Float32 nan = NAN;
vDSP_vfill(&nan, &self->globalDistanceMatrix[1], 1, comparisonDataRowCount);
vDSP_vfill(&nan, &self->globalDistanceMatrix[comparisonDataRowCount + 1], comparisonDataRowCount + 1, inputRowCount);
Float32 comparisonArray[3] = {0};
for (size_t i = 0; i < inputRowCount; ++i) {
for (size_t j = 0; j < comparisonDataRowCount; ++j) {
comparisonArray[0] = self->globalDistanceMatrix[i * (comparisonDataRowCount + 1) + j];
comparisonArray[1] = self->globalDistanceMatrix[i * (comparisonDataRowCount + 1) + (j + 1)];
comparisonArray[2] = self->globalDistanceMatrix[(i + 1) * (comparisonDataRowCount + 1) + j];
Float32 dmax;
size_t index;
vDSP_minvi(comparisonArray, 1, &dmax, &index, 3);
self->phi[(i * comparisonDataRowCount) + j] = index + 1;
self->globalDistanceMatrix[(i + 1) * (comparisonDataRowCount + 1) + (j + 1)] = self->globalDistanceMatrix[(i + 1) * (comparisonDataRowCount + 1) + (j + 1)] + dmax;
}
}
similarity = self->globalDistanceMatrix[(comparisonDataRowCount + 1) * (inputRowCount + 1) - 1];
return similarity;
}
