diff --git a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov.java b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov.java index ad3d4b9..758cd86 100755 --- a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov.java +++ b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov.java @@ -33,6 +33,7 @@ public abstract class ConditionalMutualInfoCalculatorMultiVariateKraskov { protected double condMi; protected boolean condMiComputed; + protected EuclideanUtils normCalculator; // Storage for the norms from each observation to each other one protected double[][] xNorms; protected double[][] yNorms; @@ -50,6 +51,7 @@ public abstract class ConditionalMutualInfoCalculatorMultiVariateKraskov { public ConditionalMutualInfoCalculatorMultiVariateKraskov() { super(); k = 1; // by default + normCalculator = new EuclideanUtils(EuclideanUtils.NORM_MAX_NORM); } public void initialise(int dimensions1, int dimensions2, int dimensionsCond) { @@ -63,11 +65,27 @@ public abstract class ConditionalMutualInfoCalculatorMultiVariateKraskov { // No need to keep the dimensions here } + /** + * Sets properties for the calculator. + * Valid properties include: + * + * + * @param propertyName name of the property to set + * @param propertyValue value to set on that property + */ public void setProperty(String propertyName, String propertyValue) { if (propertyName.equalsIgnoreCase(PROP_K)) { k = Integer.parseInt(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_NORM_TYPE)) { - EuclideanUtils.setNormToUse(propertyValue); + normCalculator.setNormToUse(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_NORMALISE)) { normalise = Boolean.parseBoolean(propertyValue); } @@ -149,7 +167,7 @@ public abstract class ConditionalMutualInfoCalculatorMultiVariateKraskov { zNorms = new double[N][N]; for (int t = 0; t < N; t++) { // Compute the norms from t to all other time points - double[][] xyzNormsForT = EuclideanUtils.computeNorms(data1, + double[][] xyzNormsForT = normCalculator.computeNorms(data1, data2, dataCond, t); for (int t2 = 0; t2 < N; t2++) { xNorms[t][t2] = xyzNormsForT[t2][0]; diff --git a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov1.java b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov1.java index 08ecd6e..4569b2d 100755 --- a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov1.java +++ b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov1.java @@ -1,6 +1,5 @@ package infodynamics.measures.continuous.kraskov; -import infodynamics.utils.EuclideanUtils; import infodynamics.utils.MathsUtils; import infodynamics.utils.MatrixUtils; @@ -164,7 +163,7 @@ public class ConditionalMutualInfoCalculatorMultiVariateKraskov1 // Compute eps for this time step: // First get x and y norms to all neighbours // (note that norm of point t to itself will be set to infinity). - double[][] xyzNorms = EuclideanUtils.computeNorms(data1, data2, dataCond, t); + double[][] xyzNorms = normCalculator.computeNorms(data1, data2, dataCond, t); double[] jointNorm = new double[N]; for (int t2 = 0; t2 < N; t2++) { jointNorm[t2] = Math.max(xyzNorms[t2][0], Math.max(xyzNorms[t2][1], xyzNorms[t2][2])); @@ -239,7 +238,7 @@ public class ConditionalMutualInfoCalculatorMultiVariateKraskov1 // Compute eps for this time step: // First get x and y and z norms to all neighbours // (note that norm of point t to itself will be set to infinity. - double[][] xyzNorms = EuclideanUtils.computeNorms(data1, data2, dataCond, t); + double[][] xyzNorms = normCalculator.computeNorms(data1, data2, dataCond, t); double[] jointNorm = new double[N]; for (int t2 = 0; t2 < N; t2++) { jointNorm[t2] = Math.max(xyzNorms[t2][0], Math.max(xyzNorms[t2][1], xyzNorms[t2][2])); diff --git a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov2.java b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov2.java index 7a4d52b..3be935e 100755 --- a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov2.java +++ b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateKraskov2.java @@ -1,6 +1,5 @@ package infodynamics.measures.continuous.kraskov; -import infodynamics.utils.EuclideanUtils; import infodynamics.utils.FirstIndexComparatorDouble; import infodynamics.utils.MathsUtils; import infodynamics.utils.MatrixUtils; @@ -297,7 +296,7 @@ public class ConditionalMutualInfoCalculatorMultiVariateKraskov2 // Compute eps_x and eps_y and eps_z for this time step: // First get x and y and z norms to all neighbours // (note that norm of point t to itself will be set to infinity). - double[][] xyzNorms = EuclideanUtils.computeNorms(data1, data2, dataCond, t); + double[][] xyzNorms = normCalculator.computeNorms(data1, data2, dataCond, t); double[][] jointNorm = new double[N][2]; for (int t2 = 0; t2 < N; t2++) { jointNorm[t2][JOINT_NORM_VAL_COLUMN] = Math.max(xyzNorms[t2][0], @@ -400,7 +399,7 @@ public class ConditionalMutualInfoCalculatorMultiVariateKraskov2 // Compute eps_x and eps_y and eps_z for this time step: // First get x and y and z norms to all neighbours // (note that norm of point t to itself will be set to infinity). - double[][] xyzNorms = EuclideanUtils.computeNorms(data1, data2, dataCond, t); + double[][] xyzNorms = normCalculator.computeNorms(data1, data2, dataCond, t); double[][] jointNorm = new double[N][2]; for (int t2 = 0; t2 < N; t2++) { jointNorm[t2][JOINT_NORM_VAL_COLUMN] = Math.max(xyzNorms[t2][0], diff --git a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateWithDiscreteKraskov.java b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateWithDiscreteKraskov.java index 55049ff..bed9d0d 100755 --- a/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateWithDiscreteKraskov.java +++ b/java/source/infodynamics/measures/continuous/kraskov/ConditionalMutualInfoCalculatorMultiVariateWithDiscreteKraskov.java @@ -39,6 +39,7 @@ public class ConditionalMutualInfoCalculatorMultiVariateWithDiscreteKraskov impl protected double condMi; protected boolean miComputed; + protected EuclideanUtils normCalculator; // Storage for the norms from each observation to each other one protected double[][] xNorms; protected double[][] zNorms; @@ -56,6 +57,7 @@ public class ConditionalMutualInfoCalculatorMultiVariateWithDiscreteKraskov impl public ConditionalMutualInfoCalculatorMultiVariateWithDiscreteKraskov() { super(); k = 1; // by default + normCalculator = new EuclideanUtils(EuclideanUtils.NORM_MAX_NORM); } /** @@ -77,15 +79,26 @@ public class ConditionalMutualInfoCalculatorMultiVariateWithDiscreteKraskov impl } /** + * Sets properties for the calculator. + * Valid properties include: + * * - * @param propertyName name of the property to set - * @param propertyValue value to set on that property + * @param propertyName + * @param propertyValue */ public void setProperty(String propertyName, String propertyValue) { if (propertyName.equalsIgnoreCase(PROP_K)) { k = Integer.parseInt(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_NORM_TYPE)) { - EuclideanUtils.setNormToUse(propertyValue); + normCalculator.setNormToUse(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_NORMALISE)) { normalise = Boolean.parseBoolean(propertyValue); } @@ -172,8 +185,8 @@ public class ConditionalMutualInfoCalculatorMultiVariateWithDiscreteKraskov impl continue; } // Compute norm in the continuous space - xNorms[t][t2] = EuclideanUtils.norm(continuousDataX[t], continuousDataX[t2]); - zNorms[t][t2] = EuclideanUtils.norm(conditionedDataZ[t], conditionedDataZ[t2]); + xNorms[t][t2] = normCalculator.norm(continuousDataX[t], continuousDataX[t2]); + zNorms[t][t2] = normCalculator.norm(conditionedDataZ[t], conditionedDataZ[t2]); xzNorms[t][t2] = Math.max(xNorms[t][t2], zNorms[t][t2]); } } @@ -406,7 +419,7 @@ public class ConditionalMutualInfoCalculatorMultiVariateWithDiscreteKraskov impl // Compute eps_* for this time step: // First get xz norms to all neighbours // (note that norm of point t to itself will be set to infinity). - double[][] xzNorms = EuclideanUtils.computeNorms(continuousDataX, conditionedDataZ, t); + double[][] xzNorms = normCalculator.computeNorms(continuousDataX, conditionedDataZ, t); double[][] jointNorm = new double[N][2]; for (int t2 = 0; t2 < N; t2++) { jointNorm[t2][0] = Math.max(xzNorms[t2][0], diff --git a/java/source/infodynamics/measures/continuous/kraskov/MultiInfoCalculatorKraskov.java b/java/source/infodynamics/measures/continuous/kraskov/MultiInfoCalculatorKraskov.java index 0c650e8..7b8b699 100755 --- a/java/source/infodynamics/measures/continuous/kraskov/MultiInfoCalculatorKraskov.java +++ b/java/source/infodynamics/measures/continuous/kraskov/MultiInfoCalculatorKraskov.java @@ -32,6 +32,7 @@ public abstract class MultiInfoCalculatorKraskov implements protected int N; // number of observations protected int V; // number of variables + protected EuclideanUtils normCalculator; // Storage for the norms for each marginal variable from each observation to each other one protected double[][][] norms; // Keep the norms each time (making reordering very quick) @@ -46,6 +47,7 @@ public abstract class MultiInfoCalculatorKraskov implements public MultiInfoCalculatorKraskov() { super(); k = 1; // by default + normCalculator = new EuclideanUtils(EuclideanUtils.NORM_MAX_NORM); } public void initialise(int dimensions) { @@ -56,11 +58,26 @@ public abstract class MultiInfoCalculatorKraskov implements data = null; } + /** + * Sets properties for the calculator. + * Valid properties include: + * + * + * @param propertyName + * @param propertyValue + */ public void setProperty(String propertyName, String propertyValue) { if (propertyName.equalsIgnoreCase(PROP_K)) { k = Integer.parseInt(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_NORM_TYPE)) { - EuclideanUtils.setNormToUse(propertyValue); + normCalculator.setNormToUse(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_TRY_TO_KEEP_ALL_PAIRS_NORM)) { tryKeepAllPairsNorms = Boolean.parseBoolean(propertyValue); } diff --git a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov.java b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov.java index eb629a0..55c2bc1 100755 --- a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov.java +++ b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov.java @@ -29,6 +29,7 @@ public abstract class MutualInfoCalculatorMultiVariateKraskov implements protected double mi; protected boolean miComputed; + protected EuclideanUtils normCalculator; // Storage for the norms from each observation to each other one protected double[][] xNorms; protected double[][] yNorms; @@ -46,6 +47,7 @@ public abstract class MutualInfoCalculatorMultiVariateKraskov implements public MutualInfoCalculatorMultiVariateKraskov() { super(); k = 1; // by default + normCalculator = new EuclideanUtils(EuclideanUtils.NORM_MAX_NORM); } public void initialise(int dimensions1, int dimensions2) { @@ -63,8 +65,12 @@ public abstract class MutualInfoCalculatorMultiVariateKraskov implements * Valid properties include: * * @@ -75,7 +81,7 @@ public abstract class MutualInfoCalculatorMultiVariateKraskov implements if (propertyName.equalsIgnoreCase(PROP_K)) { k = Integer.parseInt(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_NORM_TYPE)) { - EuclideanUtils.setNormToUse(propertyValue); + normCalculator.setNormToUse(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_NORMALISE)) { normalise = Boolean.parseBoolean(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_TIME_DIFF)) { @@ -154,7 +160,7 @@ public abstract class MutualInfoCalculatorMultiVariateKraskov implements yNorms = new double[N][N]; for (int t = 0; t < N; t++) { // Compute the norms from t to all other time points - double[][] xyNormsForT = EuclideanUtils.computeNorms(data1, data2, t); + double[][] xyNormsForT = normCalculator.computeNorms(data1, data2, t); for (int t2 = 0; t2 < N; t2++) { xNorms[t][t2] = xyNormsForT[t2][0]; yNorms[t][t2] = xyNormsForT[t2][1]; diff --git a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov1.java b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov1.java index 010f9b4..3ad5084 100755 --- a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov1.java +++ b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov1.java @@ -1,6 +1,5 @@ package infodynamics.measures.continuous.kraskov; -import infodynamics.utils.EuclideanUtils; import infodynamics.utils.MathsUtils; import infodynamics.utils.MatrixUtils; @@ -146,7 +145,7 @@ public class MutualInfoCalculatorMultiVariateKraskov1 // Compute eps for this time step: // First get x and y norms to all neighbours // (note that norm of point t to itself will be set to infinity). - double[][] xyNorms = EuclideanUtils.computeNorms(data1, data2, t); + double[][] xyNorms = normCalculator.computeNorms(data1, data2, t); double[] jointNorm = new double[N]; for (int t2 = 0; t2 < N; t2++) { jointNorm[t2] = Math.max(xyNorms[t2][0], xyNorms[t2][1]); @@ -213,7 +212,7 @@ public class MutualInfoCalculatorMultiVariateKraskov1 // Compute eps for this time step: // First get x and y norms to all neighbours // (note that norm of point t to itself will be set to infinity. - double[][] xyNorms = EuclideanUtils.computeNorms(data1, data2, t); + double[][] xyNorms = normCalculator.computeNorms(data1, data2, t); double[] jointNorm = new double[N]; for (int t2 = 0; t2 < N; t2++) { jointNorm[t2] = Math.max(xyNorms[t2][0], xyNorms[t2][1]); diff --git a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov2.java b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov2.java index 0d013a8..2bb600f 100755 --- a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov2.java +++ b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov2.java @@ -1,6 +1,5 @@ package infodynamics.measures.continuous.kraskov; -import infodynamics.utils.EuclideanUtils; import infodynamics.utils.FirstIndexComparatorDouble; import infodynamics.utils.MathsUtils; import infodynamics.utils.MatrixUtils; @@ -246,7 +245,7 @@ public class MutualInfoCalculatorMultiVariateKraskov2 // Compute eps_x and eps_y for this time step: // First get x and y norms to all neighbours // (note that norm of point t to itself will be set to infinity). - double[][] xyNorms = EuclideanUtils.computeNorms(data1, data2, t); + double[][] xyNorms = normCalculator.computeNorms(data1, data2, t); double[][] jointNorm = new double[N][2]; for (int t2 = 0; t2 < N; t2++) { jointNorm[t2][JOINT_NORM_VAL_COLUMN] = Math.max(xyNorms[t2][0], xyNorms[t2][1]); @@ -332,7 +331,7 @@ public class MutualInfoCalculatorMultiVariateKraskov2 // Compute eps_x and eps_y for this time step: // First get x and y norms to all neighbours // (note that norm of point t to itself will be set to infinity). - double[][] xyNorms = EuclideanUtils.computeNorms(data1, data2, t); + double[][] xyNorms = normCalculator.computeNorms(data1, data2, t); double[][] jointNorm = new double[N][2]; for (int t2 = 0; t2 < N; t2++) { jointNorm[t2][JOINT_NORM_VAL_COLUMN] = Math.max(xyNorms[t2][0], xyNorms[t2][1]); diff --git a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateWithDiscreteKraskov.java b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateWithDiscreteKraskov.java index a118cb5..8e12901 100755 --- a/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateWithDiscreteKraskov.java +++ b/java/source/infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateWithDiscreteKraskov.java @@ -47,6 +47,7 @@ public class MutualInfoCalculatorMultiVariateWithDiscreteKraskov implements Mutu protected double mi; protected boolean miComputed; + protected EuclideanUtils normCalculator; // Storage for the norms from each observation to each other one protected double[][] xNorms; // Keep the norms each time (making reordering very quick) @@ -74,6 +75,7 @@ public class MutualInfoCalculatorMultiVariateWithDiscreteKraskov implements Mutu public MutualInfoCalculatorMultiVariateWithDiscreteKraskov() { super(); k = 1; // by default + normCalculator = new EuclideanUtils(EuclideanUtils.NORM_MAX_NORM); } /** @@ -95,6 +97,17 @@ public class MutualInfoCalculatorMultiVariateWithDiscreteKraskov implements Mutu } /** + * Sets properties for the calculator. + * Valid properties include: + * * * @param propertyName name of the property to set * @param propertyValue value to set on that property @@ -103,7 +116,7 @@ public class MutualInfoCalculatorMultiVariateWithDiscreteKraskov implements Mutu if (propertyName.equalsIgnoreCase(PROP_K)) { k = Integer.parseInt(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_NORM_TYPE)) { - EuclideanUtils.setNormToUse(propertyValue); + normCalculator.setNormToUse(propertyValue); } else if (propertyName.equalsIgnoreCase(PROP_NORMALISE)) { normalise = Boolean.parseBoolean(propertyValue); } @@ -184,7 +197,7 @@ public class MutualInfoCalculatorMultiVariateWithDiscreteKraskov implements Mutu continue; } // Compute norm in the continuous space - xNorms[t][t2] = EuclideanUtils.norm(continuousData[t], continuousData[t2]); + xNorms[t][t2] = normCalculator.norm(continuousData[t], continuousData[t2]); } } } @@ -363,7 +376,7 @@ public class MutualInfoCalculatorMultiVariateWithDiscreteKraskov implements Mutu continue; } // Compute norm in the continuous space - norms[t2] = EuclideanUtils.norm(continuousData[t], continuousData[t2]); + norms[t2] = normCalculator.norm(continuousData[t], continuousData[t2]); } // Then find the k closest neighbours in the same discrete bin @@ -530,7 +543,7 @@ public class MutualInfoCalculatorMultiVariateWithDiscreteKraskov implements Mutu double[] norms = new double[continuousData.length]; for (int t2 = 0; t2 < continuousData.length; t2++) { // Compute norm in the continuous space - norms[t2] = EuclideanUtils.norm(continuousNewStates[t], continuousData[t2]); + norms[t2] = normCalculator.norm(continuousNewStates[t], continuousData[t2]); } // Then find the k closest neighbours in the same discrete bin diff --git a/java/source/infodynamics/utils/EuclideanUtils.java b/java/source/infodynamics/utils/EuclideanUtils.java index 632969d..033c584 100755 --- a/java/source/infodynamics/utils/EuclideanUtils.java +++ b/java/source/infodynamics/utils/EuclideanUtils.java @@ -17,10 +17,19 @@ public class EuclideanUtils { public static final String NORM_EUCLIDEAN_NORMALISED_STRING = "EUCLIDEAN_NORMALISED"; public static final int NORM_MAX_NORM = 2; public static final String NORM_MAX_NORM_STRING = "MAX_NORM"; - private static int normToUse = 0; + // Track which norm we should use here + private int normToUse = 0; - public EuclideanUtils() { - super(); + /** + * Construct a EuclideanUtils object, to take norms of the given type + * + * @param normToUse norm type, one of + * {@link #NORM_EUCLIDEAN_STRING}, + * {@link #NORM_EUCLIDEAN_NORMALISED_STRING}, + * or {@link #NORM_MAX_NORM_STRING} + */ + public EuclideanUtils(int normToUse) { + this.normToUse = normToUse; } public static double[] computeMinEuclideanDistances(double[][] observations) { @@ -144,7 +153,17 @@ public class EuclideanUtils { return minDistance; } - public static double maxJointSpaceNorm(double[] x1, double[] y1, + /** + * Return the max norm out of the two norms (x1:x2) and (y1:y2), + * using the configured norm type + * + * @param x1 + * @param y1 + * @param x2 + * @param y2 + * @return the max of the two norms + */ + public double maxJointSpaceNorm(double[] x1, double[] y1, double[] x2, double[] y2) { return Math.max(norm(x1, x2), norm(y1,y2)); } @@ -156,7 +175,7 @@ public class EuclideanUtils { * @param x2 * @return */ - public static double norm(double[] x1, double[] x2) { + public double norm(double[] x1, double[] x2) { switch (normToUse) { case NORM_EUCLIDEAN_NORMALISED: return euclideanNorm(x1, x2) / Math.sqrt(x1.length); @@ -207,7 +226,7 @@ public class EuclideanUtils { } /** - * Compute the x and y norms of all other points from + * Compute the x and y configured norms of all other points from * the data points at time step t. * Puts norms of t from itself as infinity, which is useful * when counting the number of points closer than epsilon say. @@ -216,7 +235,7 @@ public class EuclideanUtils { * @param mvTimeSeries2 * @return */ - public static double[][] computeNorms(double[][] mvTimeSeries1, + public double[][] computeNorms(double[][] mvTimeSeries1, double[][] mvTimeSeries2, int t) { int timeSteps = mvTimeSeries1.length; @@ -246,7 +265,7 @@ public class EuclideanUtils { * @param mvTimeSeries3 * @return */ - public static double[][] computeNorms(double[][] mvTimeSeries1, + public double[][] computeNorms(double[][] mvTimeSeries1, double[][] mvTimeSeries2, double[][] mvTimeSeries3, int t) { int timeSteps = mvTimeSeries1.length; @@ -302,8 +321,8 @@ public class EuclideanUtils { * * @param normType */ - public static void setNormToUse(int normType) { - normToUse = normType; + public void setNormToUse(int normType) { + this.normToUse = normType; } /** @@ -311,7 +330,7 @@ public class EuclideanUtils { * * @param normType */ - public static void setNormToUse(String normType) { + public void setNormToUse(String normType) { if (normType.equalsIgnoreCase(NORM_EUCLIDEAN_NORMALISED_STRING)) { normToUse = NORM_EUCLIDEAN_NORMALISED; } else if (normType.equalsIgnoreCase(NORM_MAX_NORM_STRING)) { @@ -321,7 +340,7 @@ public class EuclideanUtils { } } - public static String getNormInUse() { + public String getNormInUse() { switch (normToUse) { case NORM_EUCLIDEAN_NORMALISED: return NORM_EUCLIDEAN_NORMALISED_STRING;