Import Upstream version 0.7.0

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diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/PearsonCorrelationDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/PearsonCorrelationDistanceFunction.java
deleted file mode 100644
index c8f9d487..00000000
--- a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/PearsonCorrelationDistanceFunction.java
+++ /dev/null
@@ -1,105 +0,0 @@
-package de.lmu.ifi.dbs.elki.distance.distancefunction.correlation;
-
-/*
- This file is part of ELKI:
- Environment for Developing KDD-Applications Supported by Index-Structures
-
- Copyright (C) 2014
- 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.distance.distancefunction.AbstractNumberVectorDistanceFunction;
-import de.lmu.ifi.dbs.elki.math.MathUtil;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
-
-/**
- * Pearson correlation distance function for feature vectors.
- * 
- * The Pearson correlation distance is computed from the Pearson correlation
- * coefficient <code>r</code> as: <code>1-r</code>. Hence, possible values of
- * this distance are between 0 and 2.
- * 
- * The distance between two vectors will be low (near 0), if their attribute
- * values are dimension-wise strictly positively correlated, it will be high
- * (near 2), if their attribute values are dimension-wise strictly negatively
- * correlated. For Features with uncorrelated attributes, the distance value
- * will be intermediate (around 1).
- * 
- * @author Arthur Zimek
- */
-public class PearsonCorrelationDistanceFunction extends AbstractNumberVectorDistanceFunction {
-  /**
-   * Static instance.
-   */
-  public static final PearsonCorrelationDistanceFunction STATIC = new PearsonCorrelationDistanceFunction();
-
-  /**
-   * Constructor - use {@link #STATIC} instead.
-   * 
-   * @deprecated Use static instance!
-   */
-  @Deprecated
-  public PearsonCorrelationDistanceFunction() {
-    super();
-  }
-
-  /**
-   * Computes the Pearson correlation distance for two given feature vectors.
-   * 
-   * The Pearson correlation distance is computed from the Pearson correlation
-   * coefficient <code>r</code> as: <code>1-r</code>. Hence, possible values of
-   * this distance are between 0 and 2.
-   * 
-   * @param v1 first feature vector
-   * @param v2 second feature vector
-   * @return the Pearson correlation distance for two given feature vectors v1
-   *         and v2
-   */
-  @Override
-  public double distance(NumberVector v1, NumberVector v2) {
-    return 1 - MathUtil.pearsonCorrelationCoefficient(v1, v2);
-  }
-
-  @Override
-  public String toString() {
-    return "PearsonCorrelationDistance";
-  }
-  
-  @Override
-  public boolean equals(Object obj) {
-    if(obj == null) {
-      return false;
-    }
-    return this.getClass().equals(obj.getClass());
-  }
-
-  /**
-   * Parameterization class.
-   * 
-   * @author Erich Schubert
-   * 
-   * @apiviz.exclude
-   */
-  public static class Parameterizer extends AbstractParameterizer {
-    @Override
-    protected PearsonCorrelationDistanceFunction makeInstance() {
-      return PearsonCorrelationDistanceFunction.STATIC;
-    }
-  }
-}
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