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Improved variable scoping [skip ci]

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This commit is contained in:
Andrew Kane
2024-04-11 09:38:54 -07:00
parent d45f561d75
commit c3c6af8a84
1 changed files with 22 additions and 24 deletions
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46
src/ivfkmeans.c
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@@ -185,8 +185,6 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
Oid collation;
Vector *vec;
Vector *newCenter;
int64 j;
int64 k;
int dimensions = centers->dim;
int numCenters = centers->maxlen;
int numSamples = samples->length;
@@ -250,7 +248,7 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
newcdist = palloc(newcdistSize);
newCenters = VectorArrayInit(numCenters, dimensions);
for (j = 0; j < numCenters; j++)
for (int64 j = 0; j < numCenters; j++)
{
vec = VectorArrayGet(newCenters, j);
SET_VARSIZE(vec, VECTOR_SIZE(dimensions));
@@ -265,13 +263,13 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
InitCenters(index, samples, centers, lowerBound);
/* Assign each x to its closest initial center c(x) = argmin d(x,c) */
for (j = 0; j < numSamples; j++)
for (int64 j = 0; j < numSamples; j++)
{
float minDistance = FLT_MAX;
int closestCenter = 0;
/* Find closest center */
for (k = 0; k < numCenters; k++)
for (int64 k = 0; k < numCenters; k++)
{
/* TODO Use Lemma 1 in k-means++ initialization */
float distance = lowerBound[j * numCenters + k];
@@ -297,11 +295,11 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
CHECK_FOR_INTERRUPTS();
/* Step 1: For all centers, compute distance */
for (j = 0; j < numCenters; j++)
for (int64 j = 0; j < numCenters; j++)
{
vec = VectorArrayGet(centers, j);
for (k = j + 1; k < numCenters; k++)
for (int64 k = j + 1; k < numCenters; k++)
{
float distance = 0.5 * DatumGetFloat8(FunctionCall2Coll(procinfo, collation, PointerGetDatum(vec), PointerGetDatum(VectorArrayGet(centers, k))));
@@ -311,11 +309,11 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
}
/* For all centers c, compute s(c) */
for (j = 0; j < numCenters; j++)
for (int64 j = 0; j < numCenters; j++)
{
float minDistance = FLT_MAX;
for (k = 0; k < numCenters; k++)
for (int64 k = 0; k < numCenters; k++)
{
float distance;
@@ -332,7 +330,7 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
rjreset = iteration != 0;
for (j = 0; j < numSamples; j++)
for (int64 j = 0; j < numSamples; j++)
{
bool rj;
@@ -342,7 +340,7 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
rj = rjreset;
for (k = 0; k < numCenters; k++)
for (int64 k = 0; k < numCenters; k++)
{
float dxcx;
@@ -394,16 +392,16 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
}
/* Step 4: For each center c, let m(c) be mean of all points assigned */
for (j = 0; j < numCenters; j++)
for (int64 j = 0; j < numCenters; j++)
{
vec = VectorArrayGet(newCenters, j);
for (k = 0; k < dimensions; k++)
for (int64 k = 0; k < dimensions; k++)
vec->x[k] = 0.0;
centerCounts[j] = 0;
}
for (j = 0; j < numSamples; j++)
for (int64 j = 0; j < numSamples; j++)
{
int closestCenter;
@@ -412,13 +410,13 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
/* Increment sum and count of closest center */
newCenter = VectorArrayGet(newCenters, closestCenter);
for (k = 0; k < dimensions; k++)
for (int64 k = 0; k < dimensions; k++)
newCenter->x[k] += vec->x[k];
centerCounts[closestCenter] += 1;
}
for (j = 0; j < numCenters; j++)
for (int64 j = 0; j < numCenters; j++)
{
vec = VectorArrayGet(newCenters, j);
@@ -426,19 +424,19 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
{
/* Double avoids overflow, but requires more memory */
/* TODO Update bounds */
for (k = 0; k < dimensions; k++)
for (int64 k = 0; k < dimensions; k++)
{
if (isinf(vec->x[k]))
vec->x[k] = vec->x[k] > 0 ? FLT_MAX : -FLT_MAX;
}
for (k = 0; k < dimensions; k++)
for (int64 k = 0; k < dimensions; k++)
vec->x[k] /= centerCounts[j];
}
else
{
/* TODO Handle empty centers properly */
for (k = 0; k < dimensions; k++)
for (int64 k = 0; k < dimensions; k++)
vec->x[k] = RandomDouble();
}
@@ -448,12 +446,12 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
}
/* Step 5 */
for (j = 0; j < numCenters; j++)
for (int64 j = 0; j < numCenters; j++)
newcdist[j] = DatumGetFloat8(FunctionCall2Coll(procinfo, collation, PointerGetDatum(VectorArrayGet(centers, j)), PointerGetDatum(VectorArrayGet(newCenters, j))));
for (j = 0; j < numSamples; j++)
for (int64 j = 0; j < numSamples; j++)
{
for (k = 0; k < numCenters; k++)
for (int64 k = 0; k < numCenters; k++)
{
float distance = lowerBound[j * numCenters + k] - newcdist[k];
@@ -466,11 +464,11 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
/* Step 6 */
/* We reset r(x) before Step 3 in the next iteration */
for (j = 0; j < numSamples; j++)
for (int64 j = 0; j < numSamples; j++)
upperBound[j] += newcdist[closestCenters[j]];
/* Step 7 */
for (j = 0; j < numCenters; j++)
for (int64 j = 0; j < numCenters; j++)
VectorArraySet(centers, j, VectorArrayGet(newCenters, j));
if (changes == 0 && iteration != 0)