Import Upstream version 0.7.0

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diff --git a/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/PolynomialKernelFunction.java b/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/PolynomialKernelFunction.java
deleted file mode 100644
index a726003c..00000000
--- a/src/de/lmu/ifi/dbs/elki/distance/similarityfunction/kernel/PolynomialKernelFunction.java
+++ /dev/null
@@ -1,160 +0,0 @@
-package de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel;
-
-/*
- 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.database.query.DistanceSimilarityQuery;
-import de.lmu.ifi.dbs.elki.database.query.distance.PrimitiveDistanceSimilarityQuery;
-import de.lmu.ifi.dbs.elki.database.relation.Relation;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractNumberVectorDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.similarityfunction.AbstractVectorSimilarityFunction;
-import de.lmu.ifi.dbs.elki.math.MathUtil;
-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;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
-
-/**
- * Polynomial Kernel function that computes a similarity between the two feature
- * vectors V1 and V2 defined by (V1^T*V2)^degree.
- * 
- * @author Simon Paradies
- */
-public class PolynomialKernelFunction extends AbstractVectorSimilarityFunction implements PrimitiveDistanceFunction<NumberVector> {
-  /**
-   * The default degree.
-   */
-  public static final int DEFAULT_DEGREE = 2;
-
-  /**
-   * Degree of the polynomial kernel function.
-   */
-  private final int degree;
-
-  /**
-   * Bias of the similarity function.
-   */
-  private final double bias;
-
-  /**
-   * Constructor.
-   * 
-   * @param degree Kernel degree
-   * @param bias Bias offset
-   */
-  public PolynomialKernelFunction(int degree, double bias) {
-    super();
-    this.degree = degree;
-    this.bias = bias;
-  }
-
-  /**
-   * Constructor.
-   * 
-   * @param degree Kernel degree
-   */
-  public PolynomialKernelFunction(int degree) {
-    this(degree, 0.);
-  }
-
-  @Override
-  public double similarity(NumberVector o1, NumberVector o2) {
-    final int dim = AbstractNumberVectorDistanceFunction.dimensionality(o1, o2);
-    double sim = 0.;
-    for(int i = 0; i < dim; i++) {
-      sim += o1.doubleValue(i) * o2.doubleValue(i);
-    }
-    return MathUtil.powi(sim + bias, degree);
-  }
-
-  @Override
-  public boolean isMetric() {
-    return true;
-  }
-
-  @Override
-  public double distance(NumberVector fv1, NumberVector fv2) {
-    return Math.sqrt(similarity(fv1, fv1) + similarity(fv2, fv2) - 2 * similarity(fv1, fv2));
-  }
-
-  @Override
-  public <T extends NumberVector> DistanceSimilarityQuery<T> instantiate(Relation<T> database) {
-    return new PrimitiveDistanceSimilarityQuery<>(database, this, this);
-  }
-
-  /**
-   * Parameterization class.
-   * 
-   * @author Erich Schubert
-   * 
-   * @apiviz.exclude
-   */
-  public static class Parameterizer extends AbstractParameterizer {
-    /**
-     * Degree parameter.
-     */
-    public static final OptionID DEGREE_ID = new OptionID("kernel.polynomial.degree", "The degree of the polynomial kernel function. Default: " + DEFAULT_DEGREE);
-
-    /**
-     * Bias parameter.
-     */
-    public static final OptionID BIAS_ID = new OptionID("kernel.polynomial.bias", "The bias of the polynomial kernel, a constant that is added to the scalar product.");
-
-    /**
-     * Degree of the polynomial kernel function.
-     */
-    protected int degree = 0;
-
-    /**
-     * Bias parameter.
-     */
-    protected double bias = 0.;
-
-    @Override
-    protected void makeOptions(Parameterization config) {
-      super.makeOptions(config);
-      final IntParameter degreeP = new IntParameter(DEGREE_ID, DEFAULT_DEGREE);
-      degreeP.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
-      if(config.grab(degreeP)) {
-        degree = degreeP.intValue();
-      }
-      final DoubleParameter biasP = new DoubleParameter(BIAS_ID);
-      biasP.setOptional(true);
-      if(config.grab(biasP)) {
-        bias = biasP.doubleValue();
-      }
-    }
-
-    @Override
-    protected PolynomialKernelFunction makeInstance() {
-      if(degree == 1 && (bias == 0.)) {
-        return LinearKernelFunction.STATIC;
-      }
-      return new PolynomialKernelFunction(degree, bias);
-    }
-  }
-}