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source: stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/Util.cs @ 15298

Last change on this file since 15298 was 14186, checked in by swagner, 8 years ago

#2526: Updated year of copyrights in license headers

File size: 4.7 KB
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[8401]1#region License Information
2/* HeuristicLab
[14186]3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[8401]4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
[8323]21
[8612]22using System;
[8323]23using System.Collections.Generic;
24using System.Linq;
25
[8371]26namespace HeuristicLab.Algorithms.DataAnalysis {
[8612]27  internal static class Util {
[13981]28    public static double ScalarProd(double[] v, double[] u) {
29      if (v.Length != u.Length) throw new InvalidOperationException();
30      double prod = 0.0;
31      for (int i = 0; i < v.Length; i++)
32        prod += v[i] * u[i];
33      return prod;
[8323]34    }
35
[8562]36    public static double SqrDist(IEnumerable<double> x, IEnumerable<double> y) {
37      return x.Zip(y, (a, b) => (a - b) * (a - b)).Sum();
38    }
39
[8323]40    public static double SqrDist(double x, double y) {
41      double d = x - y;
[8463]42      return d * d;
[8323]43    }
44
[13981]45    public static double SqrDist(double[,] x, int i, int j, int[] columnIndices, double scale = 1.0) {
46      return SqrDist(x, i, x, j, columnIndices, scale);
[8491]47    }
48
[13981]49    public static double SqrDist(double[,] x, int i, double[,] xt, int j, int[] columnIndices, double scale = 1.0) {
[8491]50      double ss = 0.0;
[13981]51      if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1)).ToArray();
52      for (int c = 0; c < columnIndices.Length; c++) {
53        var columnIndex = columnIndices[c];
[8933]54        double d = x[i, columnIndex] - xt[j, columnIndex];
[8491]55        ss += d * d;
56      }
57      return scale * scale * ss;
58    }
[8562]59
[13981]60    public static double SqrDist(double[,] x, int i, int j, double[] scale, int[] columnIndices) {
[8827]61      return SqrDist(x, i, x, j, scale, columnIndices);
[8491]62    }
63
[13981]64    public static double SqrDist(double[,] x, int i, double[,] xt, int j, double[] scale, int[] columnIndices) {
[8491]65      double ss = 0.0;
[8827]66      int scaleIndex = 0;
[13981]67      for (int c = 0; c < columnIndices.Length; c++) {
68        var columnIndex = columnIndices[c];
[8933]69        double d = x[i, columnIndex] - xt[j, columnIndex];
[8827]70        ss += d * d * scale[scaleIndex] * scale[scaleIndex];
71        scaleIndex++;
[8491]72      }
[8933]73      // must be at the end of scale after iterating over columnIndices
74      if (scaleIndex != scale.Length)
75        throw new ArgumentException("Lengths of scales and covariance functions does not match.");
[8491]76      return ss;
77    }
[13981]78    public static double ScalarProd(double[,] x, int i, int j, int[] columnIndices, double scale = 1.0) {
79      return ScalarProd(x, i, x, j, columnIndices, scale);
[8562]80    }
[8491]81
[13981]82    public static double ScalarProd(double[,] x, int i, double[,] xt, int j, int[] columnIndices, double scale = 1.0) {
[8562]83      double sum = 0.0;
[13981]84      for (int c = 0; c < columnIndices.Length; c++) {
85        var columnIndex = columnIndices[c];
[8933]86        sum += x[i, columnIndex] * xt[j, columnIndex];
[8562]87      }
88      return scale * scale * sum;
89    }
[13981]90    public static double ScalarProd(double[,] x, int i, int j, double[] scale, int[] columnIndices) {
[8678]91      return ScalarProd(x, i, x, j, scale, columnIndices);
[8562]92    }
93
[13981]94    public static double ScalarProd(double[,] x, int i, double[,] xt, int j, double[] scale, int[] columnIndices) {
[8562]95      double sum = 0.0;
[8827]96      int scaleIndex = 0;
[13981]97      for (int c = 0; c < columnIndices.Length; c++, scaleIndex++) {
98        var columnIndex = columnIndices[c];
[8933]99        sum += x[i, columnIndex] * scale[scaleIndex] * xt[j, columnIndex] * scale[scaleIndex];
[8562]100      }
[8933]101      // must be at the end of scale after iterating over columnIndices
102      if (scaleIndex != scale.Length)
103        throw new ArgumentException("Lengths of scales and covariance functions does not match.");
104
[8562]105      return sum;
106    }
107
[8323]108    public static IEnumerable<double> GetRow(double[,] x, int r) {
109      int cols = x.GetLength(1);
[8982]110      return GetRow(x, r, Enumerable.Range(0, cols));
[8323]111    }
[8982]112    public static IEnumerable<double> GetRow(double[,] x, int r, IEnumerable<int> columnIndices) {
113      return columnIndices.Select(c => x[r, c]);
114    }
[8366]115    public static IEnumerable<double> GetCol(double[,] x, int c) {
116      int rows = x.GetLength(0);
117      return Enumerable.Range(0, rows).Select(r => x[r, c]);
118    }
[8323]119  }
120}
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