diff --git a/java/source/infodynamics/measures/mixed/kraskov/MutualInfoCalculatorMultiVariateWithDiscreteKraskov.java b/java/source/infodynamics/measures/mixed/kraskov/MutualInfoCalculatorMultiVariateWithDiscreteKraskov.java
index 970c497..1ffffa4 100755
--- a/java/source/infodynamics/measures/mixed/kraskov/MutualInfoCalculatorMultiVariateWithDiscreteKraskov.java
+++ b/java/source/infodynamics/measures/mixed/kraskov/MutualInfoCalculatorMultiVariateWithDiscreteKraskov.java
@@ -485,92 +485,6 @@ public class MutualInfoCalculatorMultiVariateWithDiscreteKraskov implements Mutu
 		return mi;
 	}
 
-	/**
-	 * This method correctly computes the average local MI, but recomputes the x and y 
-	 *  distances between all tuples in time.
-	 * Kept here for cases where we have too many observations
-	 *  to keep the norm between all pairs, and for testing purposes.
-	 * 
-	 * @return
-	 * @throws Exception
-	 */
-	public double computeAverageLocalOfObservationsWhileComputingDistances() throws Exception {
-		int N = continuousData.length; // number of observations
-
-		// Count the average number of points within eps_x and eps_y
-		double averageDiGammas = 0;
-		double avNx = 0;
-		double avNy = 0;
-		
-		double testSum = 0.0; // Used for debugging prints
-		
-		for (int t = 0; t < N; t++) {
-			// Compute eps_x and eps_y for this time step:
-			//  First get x norms to all neighbours
-			//  (note that norm of point t to itself will be set to infinity).
-			double[] norms = new double[N]; 
-			for (int t2 = 0; t2 < N; t2++) {
-				if (t2 == t) {
-					norms[t2] = Double.POSITIVE_INFINITY;
-					continue;
-				}
-				// Compute norm in the continuous space
-				norms[t2] = normCalculator.norm(continuousData[t], continuousData[t2]);
-			}
-
-			// Then find the k closest neighbours in the same discrete bin
-			double eps_x = MatrixUtils.kthMinSubjectTo(norms, k, discreteData, discreteData[t]);			
-
-			// Count the number of points whose x distance is less
-			//  than or equal to eps_x
-			int n_x = 0;
-			for (int t2 = 0; t2 < N; t2++) {
-				if (norms[t2] <= eps_x) {
-					n_x++;
-				}
-			}
-			// n_y is number of points in that bin, minus that point
-			int n_y = counts[discreteData[t]] - 1;
-			avNx += n_x;
-			avNy += n_y;
-			// And take the digamma before adding into the 
-			//  average:
-			double localSum = MathsUtils.digamma(n_x) + MathsUtils.digamma(n_y);
-			averageDiGammas += localSum;
-
-			if (debug) {
-				// Don't need the 1/k correction here because the conditional entropy term
-				//  is taken over the continuous space only. The correction is (m-1)/k
-				//  for an entropy over m subspaces.
-				// double localValue = MathsUtils.digamma(k) - 1.0/(double)k - localSum + MathsUtils.digamma(N);
-				// Instead do:
-				double localValue = digammaK + digammaN - localSum;
-				testSum += localValue;
-				if (dimensions == 1) {
-					System.out.printf("t=%d: x=%.3f, eps_x=%.3f, n_x=%d, n_y=%d, local=%.3f, running total = %.5f\n",
-							t, continuousData[t][0], eps_x, n_x, n_y, localValue, testSum);
-				} else {
-					System.out.printf("t=%d: eps_x=%.3f, n_x=%d, n_y=%d, local=%.3f, running total = %.5f\n",
-							t, eps_x, n_x, n_y, localValue, testSum);
-				}
-			}
-		}
-		averageDiGammas /= (double) N;
-		if (debug) {
-			avNx /= (double)N;
-			avNy /= (double)N;
-			System.out.println(String.format("Average n_x=%.3f, Average n_y=%.3f", avNx, avNy));
-		}
-		
-		// Don't need the 1/k correction here because the conditional entropy term
-		//  is taken over the continuous space only. The correction is (m-1)/k
-		//  for an entropy over m subspaces.
-		// mi = MathsUtils.digamma(k) - 1.0/(double)k - averageDiGammas + MathsUtils.digamma(N);
-		// Instead do:
-		mi = digammaK + digammaN - averageDiGammas;
-		miComputed = true;
-		return mi;
-	}
 
 	/**
 	 * Compute the significance of the mutual information of the previously supplied observations.