1 files changed, 33 insertions, 23 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/AttributeWiseMinMaxNormalization.java b/src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/AttributeWiseMinMaxNormalization.java
index 62c0bf12..31f72660 100644
--- a/src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/AttributeWiseMinMaxNormalization.java
+++ b/src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/AttributeWiseMinMaxNormalization.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.datasource.filter.normalization;
  This file is part of ELKI:
  Environment for Developing KDD-Applications Supported by Index-Structures
 
- Copyright (C) 2012
+ Copyright (C) 2013
  Ludwig-Maximilians-Universität München
  Lehr- und Forschungseinheit für Datenbanksysteme
  ELKI Development Team
@@ -28,6 +28,7 @@ import java.util.ArrayList;
 import de.lmu.ifi.dbs.elki.data.NumberVector;
 import de.lmu.ifi.dbs.elki.data.type.SimpleTypeInformation;
 import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
+import de.lmu.ifi.dbs.elki.logging.Logging;
 import de.lmu.ifi.dbs.elki.math.linearalgebra.LinearEquationSystem;
 import de.lmu.ifi.dbs.elki.utilities.FormatUtil;
 import de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.ArrayLikeUtil;
@@ -52,6 +53,11 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Parameter;
 // TODO: extract superclass AbstractAttributeWiseNormalization
 public class AttributeWiseMinMaxNormalization<V extends NumberVector<?>> extends AbstractNormalization<V> {
   /**
+   * Class logger.
+   */
+  private static final Logging LOG = Logging.getLogger(AttributeWiseMinMaxNormalization.class);
+
+  /**
    * Parameter for minimum.
    */
   public static final OptionID MINIMA_ID = new OptionID("normalize.min", "a comma separated concatenation of the minimum values in each dimension that are mapped to 0. If no value is specified, the minimum value of the attribute range in this dimension will be taken.");
@@ -91,24 +97,24 @@ public class AttributeWiseMinMaxNormalization<V extends NumberVector<?>> extends
   @Override
   protected void prepareProcessInstance(V featureVector) {
     // First object? Then initialize.
-    if(minima.length == 0 || maxima.length == 0) {
+    if (minima.length == 0 || maxima.length == 0) {
       int dimensionality = featureVector.getDimensionality();
       minima = new double[dimensionality];
       maxima = new double[dimensionality];
-      for(int i = 0; i < dimensionality; i++) {
+      for (int i = 0; i < dimensionality; i++) {
         maxima[i] = -Double.MAX_VALUE;
         minima[i] = Double.MAX_VALUE;
       }
     }
-    if(minima.length != featureVector.getDimensionality()) {
+    if (minima.length != featureVector.getDimensionality()) {
       throw new IllegalArgumentException("FeatureVectors differ in length.");
     }
-    for(int d = 0; d < featureVector.getDimensionality(); d++) {
+    for (int d = 0; d < featureVector.getDimensionality(); d++) {
       final double val = featureVector.doubleValue(d);
-      if(val > maxima[d]) {
+      if (val > maxima[d]) {
         maxima[d] = val;
       }
-      if(val < minima[d]) {
+      if (val < minima[d]) {
         minima[d] = val;
       }
     }
@@ -117,10 +123,10 @@ public class AttributeWiseMinMaxNormalization<V extends NumberVector<?>> extends
   @Override
   protected V filterSingleObject(V featureVector) {
     double[] values = new double[featureVector.getDimensionality()];
-    if(minima.length != featureVector.getDimensionality()) {
+    if (minima.length != featureVector.getDimensionality()) {
       throw new IllegalArgumentException("FeatureVectors and given Minima/Maxima differ in length.");
     }
-    for(int d = 0; d < featureVector.getDimensionality(); d++) {
+    for (int d = 0; d < featureVector.getDimensionality(); d++) {
       values[d] = (featureVector.doubleValue(d) - minima[d]) / factor(d);
     }
     return factory.newNumberVector(values);
@@ -128,14 +134,13 @@ public class AttributeWiseMinMaxNormalization<V extends NumberVector<?>> extends
 
   @Override
   public V restore(V featureVector) throws NonNumericFeaturesException {
-    if(featureVector.getDimensionality() == maxima.length && featureVector.getDimensionality() == minima.length) {
+    if (featureVector.getDimensionality() == maxima.length && featureVector.getDimensionality() == minima.length) {
       double[] values = new double[featureVector.getDimensionality()];
-      for(int d = 0; d < featureVector.getDimensionality(); d++) {
+      for (int d = 0; d < featureVector.getDimensionality(); d++) {
         values[d] = (featureVector.doubleValue(d) * (factor(d)) + minima[d]);
       }
       return factory.newNumberVector(values);
-    }
-    else {
+    } else {
       throw new NonNumericFeaturesException("Attributes cannot be resized: current dimensionality: " + featureVector.getDimensionality() + " former dimensionality: " + maxima.length);
     }
   }
@@ -161,10 +166,10 @@ public class AttributeWiseMinMaxNormalization<V extends NumberVector<?>> extends
     int[] row = linearEquationSystem.getRowPermutations();
     int[] col = linearEquationSystem.getColumnPermutations();
 
-    for(int i = 0; i < coeff.length; i++) {
-      for(int r = 0; r < coeff.length; r++) {
+    for (int i = 0; i < coeff.length; i++) {
+      for (int r = 0; r < coeff.length; r++) {
         double sum = 0.0;
-        for(int c = 0; c < coeff[0].length; c++) {
+        for (int c = 0; c < coeff[0].length; c++) {
           sum += minima[c] * coeff[row[r]][col[c]] / factor(c);
           coeff[row[r]][col[c]] = coeff[row[r]][col[c]] / factor(c);
         }
@@ -186,12 +191,17 @@ public class AttributeWiseMinMaxNormalization<V extends NumberVector<?>> extends
     result.append("normalization maxima: ").append(FormatUtil.format(maxima));
     return result.toString();
   }
-  
+
   @Override
   protected SimpleTypeInformation<? super V> getInputTypeRestriction() {
     return TypeUtil.NUMBER_VECTOR_FIELD;
   }
 
+  @Override
+  protected Logging getLogger() {
+    return LOG;
+  }
+
   /**
    * Parameterization class.
    * 
@@ -214,20 +224,20 @@ public class AttributeWiseMinMaxNormalization<V extends NumberVector<?>> extends
     protected void makeOptions(Parameterization config) {
       super.makeOptions(config);
       DoubleListParameter minimaP = new DoubleListParameter(MINIMA_ID, true);
-      if(config.grab(minimaP)) {
+      if (config.grab(minimaP)) {
         minima = ArrayLikeUtil.toPrimitiveDoubleArray(minimaP.getValue());
       }
       DoubleListParameter maximaP = new DoubleListParameter(MAXIMA_ID, true);
-      if(config.grab(maximaP)) {
+      if (config.grab(maximaP)) {
         maxima = ArrayLikeUtil.toPrimitiveDoubleArray(maximaP.getValue());
       }
 
-      ArrayList<Parameter<?>> global_1 = new ArrayList<Parameter<?>>();
+      ArrayList<Parameter<?>> global_1 = new ArrayList<>();
       global_1.add(minimaP);
       global_1.add(maximaP);
       config.checkConstraint(new AllOrNoneMustBeSetGlobalConstraint(global_1));
 
-      ArrayList<ListParameter<?>> global = new ArrayList<ListParameter<?>>();
+      ArrayList<ListParameter<?>> global = new ArrayList<>();
       global.add(minimaP);
       global.add(maximaP);
       config.checkConstraint(new EqualSizeGlobalConstraint(global));
@@ -235,7 +245,7 @@ public class AttributeWiseMinMaxNormalization<V extends NumberVector<?>> extends
 
     @Override
     protected AttributeWiseMinMaxNormalization<V> makeInstance() {
-      return new AttributeWiseMinMaxNormalization<V>(minima, maxima);
+      return new AttributeWiseMinMaxNormalization<>(minima, maxima);
     }
   }
-}
\ No newline at end of file
+}