Import Upstream version 0.7.0

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diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/datasource/filter/normalization/instancewise/InstanceMinMaxNormalization.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/datasource/filter/normalization/instancewise/InstanceMinMaxNormalization.java
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+++ b/elki/src/main/java/de/lmu/ifi/dbs/elki/datasource/filter/normalization/instancewise/InstanceMinMaxNormalization.java
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+package de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise;
+
+/*
+ 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.type.SimpleTypeInformation;
+import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
+import de.lmu.ifi.dbs.elki.data.type.VectorTypeInformation;
+import de.lmu.ifi.dbs.elki.datasource.filter.normalization.AbstractStreamNormalization;
+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.LessGlobalConstraint;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
+
+/**
+ * Normalize vectors such that the smallest attribute is 0, the largest is 1.
+ * 
+ * @author Erich Schubert
+ * 
+ * @param <V> vector type
+ */
+public class InstanceMinMaxNormalization<V extends NumberVector> extends AbstractStreamNormalization<V> {
+  /**
+   * Minimum and maximum values.
+   */
+  private double min, max;
+
+  /**
+   * Multiplicity of the vector.
+   */
+  private int multiplicity;
+
+  /**
+   * Constructor.
+   * 
+   * @param min Desired minimum value
+   * @param max Desired maximum value
+   */
+  public InstanceMinMaxNormalization(double min, double max) {
+    super();
+    this.min = min;
+    this.max = max;
+  }
+
+  /**
+   * Constructor, normalizing to {@code [0;1]}
+   */
+  public InstanceMinMaxNormalization() {
+    this(0., 1.);
+  }
+
+  @Override
+  protected V filterSingleObject(V featureVector) {
+    double[] raw = featureVector.getColumnVector().getArrayRef();
+    // Multivariate codepath:
+    if(multiplicity > 1) {
+      assert (raw.length % multiplicity == 0) : "Vector length is not divisible by multiplicity?";
+      double[] mi = new double[multiplicity], ma = new double[multiplicity];
+      for(int i = 0; i < multiplicity; i++) {
+        mi[i] = Double.POSITIVE_INFINITY;
+        ma[i] = Double.NEGATIVE_INFINITY;
+      }
+      for(int i = 0, j = 0; i < raw.length; ++i, j = ++j % multiplicity) {
+        final double v = raw[i];
+        if(v != v) { // NaN guard
+          continue;
+        }
+        mi[j] = (mi[j] < v) ? mi[j] : v;
+        ma[j] = (ma[j] > v) ? ma[j] : v;
+      }
+      for(int j = 0; j < multiplicity; j++) {
+        if(mi[j] < ma[j]) {
+          final double s = (max - min) / (ma[j] - mi[j]);
+          for(int i = 0; i < raw.length; i += multiplicity) {
+            raw[i] = (raw[i] - mi[j]) * s + min;
+          }
+        }
+      }
+      return factory.newNumberVector(raw);
+    }
+    // Default codepath
+    double mi = Double.POSITIVE_INFINITY, ma = Double.NEGATIVE_INFINITY;
+    for(int i = 0; i < raw.length; ++i) {
+      final double v = raw[i];
+      if(v != v) { // NaN guard
+        continue;
+      }
+      mi = (mi < v) ? mi : v;
+      ma = (ma > v) ? ma : v;
+    }
+    if(mi < ma) {
+      final double s = (max - min) / (ma - mi);
+      for(int i = 0; i < raw.length; ++i) {
+        raw[i] = (raw[i] - mi) * s + min;
+      }
+    }
+    return factory.newNumberVector(raw);
+  }
+
+  @Override
+  protected void initializeOutputType(SimpleTypeInformation<V> type) {
+    super.initializeOutputType(type);
+    multiplicity = ((VectorTypeInformation<?>) type).getMultiplicity();
+  }
+
+  @Override
+  protected SimpleTypeInformation<? super V> getInputTypeRestriction() {
+    return TypeUtil.NUMBER_VECTOR_VARIABLE_LENGTH;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer<V extends NumberVector> extends AbstractParameterizer {
+    /**
+     * Option ID for minimum value.
+     */
+    public static final OptionID MIN_ID = new OptionID("normalization.min", "Minimum value to assign to objects.");
+
+    /**
+     * Option ID for maximum value.
+     */
+    public static final OptionID MAX_ID = new OptionID("normalization.max", "Maximum value to assign to objects.");
+
+    /**
+     * Minimum and maximum values.
+     */
+    private double min, max;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+      DoubleParameter minP = new DoubleParameter(MIN_ID, 0.) //
+      .setOptional(true);
+      if(config.grab(minP)) {
+        min = minP.doubleValue();
+      }
+      DoubleParameter maxP = new DoubleParameter(MAX_ID, 1.) //
+      .setOptional(true);
+      if(config.grab(maxP)) {
+        max = maxP.doubleValue();
+      }
+      config.checkConstraint(new LessGlobalConstraint<>(minP, maxP));
+    }
+
+    @Override
+    protected InstanceMinMaxNormalization<V> makeInstance() {
+      return new InstanceMinMaxNormalization<>(min, max);
+    }
+  }
+}
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