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Improved variable scoping

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This commit is contained in:
Andrew Kane
2024-04-11 09:41:47 -07:00
parent c3c6af8a84
commit 381216a956
1 changed files with 10 additions and 11 deletions
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21
src/ivfkmeans.c
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@@ -183,8 +183,6 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
FmgrInfo *procinfo;
FmgrInfo *normprocinfo;
Oid collation;
Vector *vec;
Vector *newCenter;
int dimensions = centers->dim;
int numCenters = centers->maxlen;
int numSamples = samples->length;
@@ -250,7 +248,8 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
newCenters = VectorArrayInit(numCenters, dimensions);
for (int64 j = 0; j < numCenters; j++)
{
vec = VectorArrayGet(newCenters, j);
Vector *vec = VectorArrayGet(newCenters, j);
SET_VARSIZE(vec, VECTOR_SIZE(dimensions));
vec->dim = dimensions;
}
@@ -297,7 +296,7 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
/* Step 1: For all centers, compute distance */
for (int64 j = 0; j < numCenters; j++)
{
vec = VectorArrayGet(centers, j);
Vector *vec = VectorArrayGet(centers, j);
for (int64 k = j + 1; k < numCenters; k++)
{
@@ -342,6 +341,7 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
for (int64 k = 0; k < numCenters; k++)
{
Vector *vec;
float dxcx;
/* Step 3: For all remaining points x and centers c */
@@ -394,7 +394,8 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
/* Step 4: For each center c, let m(c) be mean of all points assigned */
for (int64 j = 0; j < numCenters; j++)
{
vec = VectorArrayGet(newCenters, j);
Vector *vec = VectorArrayGet(newCenters, j);
for (int64 k = 0; k < dimensions; k++)
vec->x[k] = 0.0;
@@ -403,13 +404,11 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
for (int64 j = 0; j < numSamples; j++)
{
int closestCenter;
vec = VectorArrayGet(samples, j);
closestCenter = closestCenters[j];
int closestCenter = closestCenters[j];
Vector *vec = VectorArrayGet(samples, j);
Vector *newCenter = VectorArrayGet(newCenters, closestCenter);
/* Increment sum and count of closest center */
newCenter = VectorArrayGet(newCenters, closestCenter);
for (int64 k = 0; k < dimensions; k++)
newCenter->x[k] += vec->x[k];
@@ -418,7 +417,7 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
for (int64 j = 0; j < numCenters; j++)
{
vec = VectorArrayGet(newCenters, j);
Vector *vec = VectorArrayGet(newCenters, j);
if (centerCounts[j] > 0)
{