IFD:EAI SoS21/course material/Session 4: Programming the Classifier Part1: Difference between revisions
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For that it traverses the vector of points a couple of times. Once for every class we have. In each of these traverses it only looks for points of the same class, sums them up, keeps a record of how many they were, and finally divides by the number of points it found. So we end up with the centroid for every class and save it in a vector of centroids called "_centroids". This is a private variable of our class KMeans. | For that it traverses the vector of points a couple of times. Once for every class we have. In each of these traverses it only looks for points of the same class, sums them up, keeps a record of how many they were, and finally divides by the number of points it found. So we end up with the centroid for every class and save it in a vector of centroids called "_centroids". This is a private variable of our class KMeans. | ||
Now, when we classify, we know the centroids and just need to calculate the distance from our new point to every centroid. | Now, when we classify, we know the centroids and just need to calculate the distance from our new point to every centroid. | ||
<syntaxhighlight lang="c++"> | <syntaxhighlight lang="c++"> | ||
int KMeans::classify(Point2D newPoint) | int KMeans::classify(Point2D newPoint) | ||
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} | } | ||
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The idea here is to start with a very large distance and only save a centroid as a candidate if its distance is smaller than the distance we already have recorded. We will automatically arrive at the closest centroid and the minimum distance. | |||
=Homework= | =Homework= | ||