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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/Util.cs @ 8678

Last change on this file since 8678 was 8678, checked in by gkronber, 11 years ago

#1902: added masking covariance function and made necessary changes to interface and utility class.

File size: 5.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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
21
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27
28namespace HeuristicLab.Algorithms.DataAnalysis {
29  internal static class Util {
30    public static double ScalarProd(IEnumerable<double> v, IEnumerable<double> u) {
31      return v.Zip(u, (vi, ui) => vi * ui).Sum();
32    }
33
34    public static double SqrDist(IEnumerable<double> x, IEnumerable<double> y) {
35      return x.Zip(y, (a, b) => (a - b) * (a - b)).Sum();
36    }
37
38    public static double SqrDist(double x, double y) {
39      double d = x - y;
40      return d * d;
41    }
42
43    public static double SqrDist(double[,] x, int i, int j, double scale = 1.0, IEnumerable<int> columnIndices = null) {
44      return SqrDist(x, i, x, j, scale, columnIndices);
45    }
46
47    public static double SqrDist(double[,] x, int i, double[,] xt, int j, double scale = 1.0, IEnumerable<int> columnIndices = null) {
48      double ss = 0.0;
49      if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
50      foreach (int k in columnIndices) {
51        double d = x[i, k] - xt[j, k];
52        ss += d * d;
53      }
54      return scale * scale * ss;
55    }
56
57    public static double SqrDist(double[,] x, int i, int j, double[] scale, IEnumerable<int> columnIndices = null) {
58      return SqrDist(x, i, x, j, scale);
59    }
60
61    public static double SqrDist(double[,] x, int i, double[,] xt, int j, double[] scale, IEnumerable<int> columnIndices = null) {
62      double ss = 0.0;
63      if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
64      foreach (int k in columnIndices) {
65        double d = x[i, k] - xt[j, k];
66        ss += d * d * scale[k] * scale[k];
67      }
68      return ss;
69    }
70    public static double ScalarProd(double[,] x, int i, int j, double scale = 1.0, IEnumerable<int> columnIndices = null) {
71      return ScalarProd(x, i, x, j, scale, columnIndices);
72    }
73
74    public static double ScalarProd(double[,] x, int i, double[,] xt, int j, double scale = 1.0, IEnumerable<int> columnIndices = null) {
75      double sum = 0.0;
76      if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
77      foreach (int k in columnIndices) {
78        sum += x[i, k] * xt[j, k];
79      }
80      return scale * scale * sum;
81    }
82    public static double ScalarProd(double[,] x, int i, int j, double[] scale, IEnumerable<int> columnIndices = null) {
83      return ScalarProd(x, i, x, j, scale, columnIndices);
84    }
85
86    public static double ScalarProd(double[,] x, int i, double[,] xt, int j, double[] scale, IEnumerable<int> columnIndices = null) {
87      double sum = 0.0;
88      if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
89      foreach (int k in columnIndices) {
90        sum += x[i, k] * scale[k] * xt[j, k] * scale[k];
91      }
92      return sum;
93    }
94
95    public static IEnumerable<double> GetRow(double[,] x, int r) {
96      int cols = x.GetLength(1);
97      return Enumerable.Range(0, cols).Select(c => x[r, c]);
98    }
99    public static IEnumerable<double> GetCol(double[,] x, int c) {
100      int rows = x.GetLength(0);
101      return Enumerable.Range(0, rows).Select(r => x[r, c]);
102    }
103
104
105    public static void AttachValueChangeHandler<T, U>(IValueParameter<T> parameter, Action action)
106      where T : ValueTypeValue<U>
107      where U : struct {
108      parameter.ValueChanged += (sender, args) => {
109        if (parameter.Value != null) {
110          parameter.Value.ValueChanged += (s, a) => action();
111          action();
112        }
113      };
114      if (parameter.Value != null) {
115        parameter.Value.ValueChanged += (s, a) => action();
116      }
117    }
118
119    public static void AttachArrayChangeHandler<T, U>(IValueParameter<T> parameter, Action action)
120      where T : ValueTypeArray<U>
121      where U : struct {
122      parameter.ValueChanged += (sender, args) => {
123        if (parameter.Value != null) {
124          parameter.Value.ItemChanged += (s, a) => action();
125          parameter.Value.Reset += (s, a) => action();
126          action();
127        }
128      };
129      if (parameter.Value != null) {
130        parameter.Value.ItemChanged += (s, a) => action();
131        parameter.Value.Reset += (s, a) => action();
132      }
133    }
134  }
135}
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