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Improved types and scoping for k-means
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@@ -29,7 +29,7 @@ InitCenters(Relation index, VectorArray samples, VectorArray centers, float *low
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centers->length++;
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for (j = 0; j < numSamples; j++)
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weight[j] = DBL_MAX;
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weight[j] = FLT_MAX;
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for (int i = 0; i < numCenters; i++)
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{
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@@ -181,7 +181,6 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
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float *halfcdist;
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float *newcdist;
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int closestCenter;
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double distance;
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bool rj;
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bool rjreset;
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double dxcx;
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@@ -243,7 +242,7 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
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/* Assign each x to its closest initial center c(x) = argmin d(x,c) */
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for (j = 0; j < numSamples; j++)
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{
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double minDistance = DBL_MAX;
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float minDistance = FLT_MAX;
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closestCenter = 0;
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@@ -251,7 +250,7 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
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for (k = 0; k < numCenters; k++)
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{
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/* TODO Use Lemma 1 in k-means++ initialization */
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distance = lowerBound[j * numCenters + k];
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float distance = lowerBound[j * numCenters + k];
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if (distance < minDistance)
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{
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@@ -279,7 +278,8 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
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for (k = j + 1; k < numCenters; k++)
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{
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distance = 0.5 * DatumGetFloat8(FunctionCall2Coll(procinfo, collation, PointerGetDatum(vec), PointerGetDatum(VectorArrayGet(centers, k))));
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float distance = 0.5 * DatumGetFloat8(FunctionCall2Coll(procinfo, collation, PointerGetDatum(vec), PointerGetDatum(VectorArrayGet(centers, k))));
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halfcdist[j * numCenters + k] = distance;
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halfcdist[k * numCenters + j] = distance;
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}
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@@ -288,10 +288,12 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
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/* For all centers c, compute s(c) */
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for (j = 0; j < numCenters; j++)
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{
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double minDistance = DBL_MAX;
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float minDistance = FLT_MAX;
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for (k = 0; k < numCenters; k++)
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{
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float distance;
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if (j == k)
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continue;
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@@ -423,7 +425,7 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
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{
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for (k = 0; k < numCenters; k++)
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{
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distance = lowerBound[j * numCenters + k] - newcdist[k];
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float distance = lowerBound[j * numCenters + k] - newcdist[k];
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if (distance < 0)
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distance = 0;
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