Try to use FastKNN CRAN package (although it is not well documented). It offers k.nearest.neighbors function where an arbitrary distance matrix can be given. Below you have an example that computes the matrix you need. # arbitrary data train <- matrix(sample(c(“a”,”b”,”c”),12,replace=TRUE), ncol=2) # n x 2 n = dim(train)[1] distMatrix <- matrix(runif(n^2,0,1),ncol=n) # n x … Read more
ChartFactory.createScatterPlot() instantiates an XYLineAndShapeRenderer. You can replace the renderer with one that lets you selectively replace the Shape returned by getItemShape(), as shown below. xyPlot.setRenderer(new XYLineAndShapeRenderer(false, true) { @Override public Shape getItemShape(int row, int col) { if (row == 0 & col == N) { return ShapeUtilities.createDiagonalCross(5, 2); } else { return super.getItemShape(row, col); } … Read more
The basis of the K-Nearest Neighbour (KNN) algorithm is that you have a data matrix that consists of N rows and M columns where N is the number of data points that we have, while M is the dimensionality of each data point. For example, if we placed Cartesian co-ordinates inside a data matrix, this … Read more
Million points is a small number. The most straightforward approach works here (code based on KDTree is slower (for querying only one point)). Brute-force approach (time ~1 second) #!/usr/bin/env python import numpy NDIM = 3 # number of dimensions # read points into array a = numpy.fromfile(‘million_3D_points.txt’, sep=’ ‘) a.shape = a.size / NDIM, NDIM … Read more