I'm trying to get k nearest neighbors using weka KDTree implementation like this: ArrayList<ArrayList<Double>> ar = new ArrayList<ArrayList<Double>>(); ArrayList<Double> d1 = new ArrayList<Double>(); d1.add(1.1); d1.add(1.1); ArrayList<Double> d2 = new ArrayList<Double>(); d2.add(2.2); d2.add(2.2); ArrayList<Double> d3 = new ArrayList<Double>(); d3.add(3.3); d3.add(3.3); ar.add(d1); ar.add(d2); ar.add(d3); Attribute a1 = new Attribute("attr1", 0); Attribute a2...

I have a table, #geo, with points in geolocation. Id geolocation 9201 0xE6100000010CE33995EB71164CC054791243B87441C0 9202 0xE6100000010C56B77A4E7A1B4CC0D15790662C6E41C0 I calculated the distance to the nearest neighbour for each data point. I have 1000 points for 24 month. Now I replicate my code with the first 19 points in a month create table #Geo...

Assume you have a n x m matrix. In this matrix, you will be randomly positioning FOUR different objects say a, b, c, d. There will be many of each. Now what is the best algorithm so that when they are randomly placed, their positions don't clash? My approach would...

I am trying to implement a k-d tree in C# and the source I'm looking at is in C++ with Boost. The function I'm trying to find is util::subtract(). I've searched all through Boost's documentation and I can't find it anywhere. Line number 94 is the function I'm trying to...

Suppose there is a point cloud having 50 000 points in the x-y-z 3D space. For every point in this cloud, what algorithms or data strictures should be implemented to find k neighbours of a given point which are within a distance of [R,r]? Naive way is to go through...

I'm working with a medium size dataset (shape=(14013L, 46L)). I want to smooth each sample with its knn. I'm training my model with: NearestNeighbors(n_neighbors, algorithm='ball_tree', metric=sklearn.metrics.pairwise.cosine_distances) And the smooth is as follows: def smooth(x,nbrs,data,alpha): """ input: alpha: the smoothing factor nbrs: trained NearestNeighbors from sklearn data: the original data (since...

I'm Trying to perform a K nearest neighbor search using spark. I have a RDD[Seq[Double]] and I'm planing to return a RDD[(Seq[Double],Seq[Seq[Double]])] with the actual row and a list of neighbors val out = data.map(row => { val neighbours = data.top(num = 3)(new Ordering[Seq[Double]] { override def compare(a:Seq[Double],b:Seq[Double]) = {...

The gotcha with this question is "arbitrary metric". If you don't know what that is, it's just the way to measure distance between points. (In the "real" world, the 1-dimensinal distance is just the absolute magnitude of the difference between the two points). Enough of the pre-lims. I'm trying to...

I've been working on a code (Py 2.7) that generates an array of elements with each node assigned some random numbers. Now, I wish to make a list of the surrounding elements, and find the index of the max value. The array size is variable (I considered col = array...