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体验新版 GitCode,发现更多精彩内容 >>
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56dc18db
编写于
4月 17, 2008
作者:
M
Marius Muja
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FLANN - Fast Library for Approximate Nearest neighbors
======================================================
This is a library for fast approximate nearest neighbor matching.
See the doc/manual.pdf file for information on how to use the library.
src/algo/KMeansTree.d
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@@ -353,6 +353,69 @@ class KMeansTree(T) : NNIndex
return
centers
[
0.
.
index
];
}
/+
vector<Point> chooseSmartCenters(const vector<Point> &data, int numCenters, int numLocalTries) {
ASSERT( numCenters > 0 && numCenters <= (int)data.size() );
int i;
Scalar currentPot = 0;
vector<Point> centers;
vector<Scalar> closestDistSq;
// Choose one random center and set the closestDistSq values
int index = (int)(getRandomScalar() * data.size());
centers.push_back(data[index]);
for (i = 0; i < (int)data.size(); i++) {
closestDistSq.push_back( distSq(data[i], data[index]) );
currentPot += closestDistSq[i];
}
// Choose each center
for (int centerCount = 1; centerCount < numCenters; centerCount++) {
// Repeat several trials
Scalar bestNewPot = -1;
int bestNewIndex;
for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {
// Choose our center - have to be slightly careful to return a valid answer even accounting
// for possible rounding errors
Scalar randVal = getRandomScalar() * currentPot;
for (index = 0; index < (int)data.size()-1; index++) {
if (randVal <= closestDistSq[index])
break;
else
randVal -= closestDistSq[index];
}
// Compute the new potential
Scalar newPot = 0;
for (i = 0; i < (int)data.size(); i++)
newPot += min( distSq(data[i], data[index]), closestDistSq[i] );
// Store the best result
if (bestNewPot < 0 || newPot < bestNewPot) {
bestNewPot = newPot;
bestNewIndex = index;
}
}
// Add the appropriate center
centers.push_back(data[bestNewIndex]);
currentPot = bestNewPot;
for (i = 0; i < (int)data.size(); i++)
closestDistSq[i] = min( distSq(data[i], data[bestNewIndex]), closestDistSq[i] );
}
return centers;
}
+/
/**
* Builds the index
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