Import Upstream version 0.7.0

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diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/LPNormDistanceFunction.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/LPNormDistanceFunction.java
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+++ b/elki/src/main/java/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/LPNormDistanceFunction.java
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+package de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2015
+ Ludwig-Maximilians-Universität München
+ Lehr- und Forschungseinheit für Datenbanksysteme
+ ELKI Development Team
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;
+import de.lmu.ifi.dbs.elki.data.type.SimpleTypeInformation;
+import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractSpatialNorm;
+import de.lmu.ifi.dbs.elki.utilities.Alias;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
+
+/**
+ * LP-Norm for {@link NumberVector}s.
+ * 
+ * @author Arthur Zimek
+ * 
+ * @apiviz.landmark
+ */
+@Alias({ "lp", "minkowski", "p", "de.lmu.ifi.dbs.elki.distance.distancefunction.LPNormDistanceFunction" })
+public class LPNormDistanceFunction extends AbstractSpatialNorm {
+  /**
+   * p parameter and its inverse.
+   */
+  protected double p, invp;
+
+  /**
+   * Constructor, internal version.
+   * 
+   * @param p Parameter p
+   */
+  public LPNormDistanceFunction(double p) {
+    super();
+    this.p = p;
+    this.invp = 1. / p;
+  }
+
+  /**
+   * Compute unscaled distance in a range of dimensions.
+   * 
+   * @param v1 First object
+   * @param v2 Second object
+   * @param start First dimension
+   * @param end Exclusive last dimension
+   * @param agg Current aggregate value
+   * @return Aggregated values.
+   */
+  private final double preDistance(NumberVector v1, NumberVector v2, final int start, final int end, double agg) {
+    for(int d = start; d < end; d++) {
+      final double xd = v1.doubleValue(d), yd = v2.doubleValue(d);
+      final double delta = (xd >= yd) ? xd - yd : yd - xd;
+      agg += Math.pow(delta, p);
+    }
+    return agg;
+  }
+
+  /**
+   * Compute unscaled distance in a range of dimensions.
+   * 
+   * @param v First vector
+   * @param mbr Second MBR
+   * @param start First dimension
+   * @param end Exclusive last dimension
+   * @param agg Current aggregate value
+   * @return Aggregated values.
+   */
+  private final double preDistanceVM(NumberVector v, SpatialComparable mbr, final int start, final int end, double agg) {
+    for(int d = start; d < end; d++) {
+      final double value = v.doubleValue(d), min = mbr.getMin(d);
+      double delta = min - value;
+      if(delta < 0.) {
+        delta = value - mbr.getMax(d);
+      }
+      if(delta > 0.) {
+        agg += Math.pow(delta, p);
+      }
+    }
+    return agg;
+  }
+
+  /**
+   * Compute unscaled distance in a range of dimensions.
+   * 
+   * @param mbr1 First MBR
+   * @param mbr2 Second MBR
+   * @param start First dimension
+   * @param end Exclusive last dimension
+   * @param agg Current aggregate value
+   * @return Aggregated values.
+   */
+  private final double preDistanceMBR(SpatialComparable mbr1, SpatialComparable mbr2, final int start, final int end, double agg) {
+    for(int d = start; d < end; d++) {
+      double delta = mbr2.getMin(d) - mbr1.getMax(d);
+      if(delta < 0.) {
+        delta = mbr1.getMin(d) - mbr2.getMax(d);
+      }
+      if(delta > 0.) {
+        agg += Math.pow(delta, p);
+      }
+    }
+    return agg;
+  }
+
+  /**
+   * Compute unscaled norm in a range of dimensions.
+   * 
+   * @param v Data object
+   * @param start First dimension
+   * @param end Exclusive last dimension
+   * @param agg Current aggregate value
+   * @return Aggregated values.
+   */
+  private final double preNorm(NumberVector v, final int start, final int end, double agg) {
+    for(int d = start; d < end; d++) {
+      final double xd = v.doubleValue(d);
+      final double delta = xd >= 0. ? xd : -xd;
+      agg += Math.pow(delta, p);
+    }
+    return agg;
+  }
+
+  /**
+   * Compute unscaled norm in a range of dimensions.
+   * 
+   * @param mbr Data object
+   * @param start First dimension
+   * @param end Exclusive last dimension
+   * @param agg Current aggregate value
+   * @return Aggregated values.
+   */
+  private final double preNormMBR(SpatialComparable mbr, final int start, final int end, double agg) {
+    for(int d = start; d < end; d++) {
+      double delta = mbr.getMin(d);
+      if(delta < 0.) {
+        delta = -mbr.getMax(d);
+      }
+      if(delta > 0.) {
+        agg += Math.pow(delta, p);
+      }
+    }
+    return agg;
+  }
+
+  @Override
+  public double distance(NumberVector v1, NumberVector v2) {
+    final int dim1 = v1.getDimensionality(), dim2 = v2.getDimensionality();
+    final int mindim = (dim1 < dim2) ? dim1 : dim2;
+    double agg = preDistance(v1, v2, 0, mindim, 0.);
+    if(dim1 > mindim) {
+      agg = preNorm(v1, mindim, dim1, agg);
+    }
+    else if(dim2 > mindim) {
+      agg = preNorm(v2, mindim, dim2, agg);
+    }
+    return Math.pow(agg, invp);
+  }
+
+  @Override
+  public double norm(NumberVector v) {
+    return Math.pow(preNorm(v, 0, v.getDimensionality(), 0.), invp);
+  }
+
+  @Override
+  public double minDist(SpatialComparable mbr1, SpatialComparable mbr2) {
+    final int dim1 = mbr1.getDimensionality(), dim2 = mbr2.getDimensionality();
+    final int mindim = (dim1 < dim2) ? dim1 : dim2;
+
+    final NumberVector v1 = (mbr1 instanceof NumberVector) ? (NumberVector) mbr1 : null;
+    final NumberVector v2 = (mbr2 instanceof NumberVector) ? (NumberVector) mbr2 : null;
+
+    double agg = 0.;
+    if(v1 != null) {
+      if(v2 != null) {
+        agg = preDistance(v1, v2, 0, mindim, agg);
+      }
+      else {
+        agg = preDistanceVM(v1, mbr2, 0, mindim, agg);
+      }
+    }
+    else {
+      if(v2 != null) {
+        agg = preDistanceVM(v2, mbr1, 0, mindim, agg);
+      }
+      else {
+        agg = preDistanceMBR(mbr1, mbr2, 0, mindim, agg);
+      }
+    }
+    // first object has more dimensions.
+    if(dim1 > mindim) {
+      if(v1 != null) {
+        agg = preNorm(v1, mindim, dim1, agg);
+      }
+      else {
+        agg = preNormMBR(v1, mindim, dim1, agg);
+      }
+    }
+    // second object has more dimensions.
+    if(dim2 > mindim) {
+      if(v2 != null) {
+        agg = preNorm(v2, mindim, dim2, agg);
+      }
+      else {
+        agg = preNormMBR(mbr2, mindim, dim2, agg);
+      }
+    }
+    return Math.pow(agg, invp);
+  }
+
+  @Override
+  public boolean isMetric() {
+    return (p >= 1.);
+  }
+
+  @Override
+  public String toString() {
+    return "L_" + p + "Norm";
+  }
+
+  /**
+   * Get the functions p parameter.
+   * 
+   * @return p
+   */
+  public double getP() {
+    return p;
+  }
+
+  @Override
+  public boolean equals(Object obj) {
+    if(obj == null) {
+      return false;
+    }
+    if(obj instanceof LPNormDistanceFunction) {
+      return this.p == ((LPNormDistanceFunction) obj).p;
+    }
+    return false;
+  }
+
+  @Override
+  public SimpleTypeInformation<? super NumberVector> getInputTypeRestriction() {
+    return TypeUtil.NUMBER_VECTOR_VARIABLE_LENGTH;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    /**
+     * OptionID for the "p" parameter
+     */
+    public static final OptionID P_ID = new OptionID("lpnorm.p", "the degree of the L-P-Norm (positive number)");
+    /**
+     * The value of p.
+     */
+    protected double p;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+      final DoubleParameter paramP = new DoubleParameter(P_ID);
+      paramP.addConstraint(CommonConstraints.GREATER_THAN_ZERO_DOUBLE);
+      if(config.grab(paramP)) {
+        p = paramP.getValue();
+      }
+    }
+
+    @Override
+    protected LPNormDistanceFunction makeInstance() {
+      if(p == 1.) {
+        return ManhattanDistanceFunction.STATIC;
+      }
+      if(p == 2.) {
+        return EuclideanDistanceFunction.STATIC;
+      }
+      if(p == Double.POSITIVE_INFINITY) {
+        return MaximumDistanceFunction.STATIC;
+      }
+      if(p == Math.round(p)) {
+        return new LPIntegerNormDistanceFunction((int) p);
+      }
+      return new LPNormDistanceFunction(p);
+    }
+  }
+}