118 lines
4.5 KiB
C#
118 lines
4.5 KiB
C#
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/*
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* SVM.NET Library
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* Copyright (C) 2008 Matthew Johnson
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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using System;
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using System.Collections.Generic;
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namespace SVM.BotSharp.MachineLearning
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{
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/// <summary>
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/// Class encapsulating a precomputed kernel, where each position indicates the similarity score for two items in the training data.
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/// </summary>
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[Serializable]
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public class PrecomputedKernel
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{
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private float[,] _similarities;
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private int _rows;
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private int _columns;
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/// <summary>
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/// Constructor.
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/// </summary>
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/// <param name="similarities">The similarity scores between all items in the training data</param>
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public PrecomputedKernel(float[,] similarities)
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{
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_similarities = similarities;
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_rows = _similarities.GetLength(0);
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_columns = _similarities.GetLength(1);
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}
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/// <summary>
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/// Constructor.
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/// </summary>
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/// <param name="nodes">Nodes for self-similarity analysis</param>
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/// <param name="param">Parameters to use when computing similarities</param>
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public PrecomputedKernel(List<Node[]> nodes, Parameter param)
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{
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_rows = nodes.Count;
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_columns = _rows;
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_similarities = new float[_rows, _columns];
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for (int r = 0; r < _rows; r++)
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{
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for (int c = 0; c < r; c++)
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_similarities[r, c] = _similarities[c, r];
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_similarities[r, r] = 1;
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for (int c = r + 1; c < _columns; c++)
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_similarities[r, c] = (float)Kernel.KernelFunction(nodes[r], nodes[c], param);
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}
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}
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/// <summary>
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/// Constructor.
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/// </summary>
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/// <param name="rows">Nodes to use as the rows of the matrix</param>
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/// <param name="columns">Nodes to use as the columns of the matrix</param>
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/// <param name="param">Parameters to use when compute similarities</param>
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public PrecomputedKernel(List<Node[]> rows, List<Node[]> columns, Parameter param)
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{
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_rows = rows.Count;
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_columns = columns.Count;
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_similarities = new float[_rows, _columns];
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for (int r = 0; r < _rows; r++)
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for (int c = 0; c < _columns; c++)
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_similarities[r, c] = (float)Kernel.KernelFunction(rows[r], columns[c], param);
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}
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/// <summary>
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/// Constructs a <see cref="Problem"/> object using the labels provided. If a label is set to "0" that item is ignored.
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/// </summary>
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/// <param name="rowLabels">The labels for the row items</param>
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/// <param name="columnLabels">The labels for the column items</param>
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/// <returns>A <see cref="Problem"/> object</returns>
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public Problem Compute(double[] rowLabels, double[] columnLabels)
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{
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List<Node[]> X = new List<Node[]>();
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List<double> Y = new List<double>();
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int maxIndex = 0;
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for (int i = 0; i < columnLabels.Length; i++)
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if (columnLabels[i] != 0)
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maxIndex++;
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maxIndex++;
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for (int r = 0; r < _rows; r++)
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{
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if (rowLabels[r] == 0)
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continue;
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List<Node> nodes = new List<Node>();
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nodes.Add(new Node(0, X.Count + 1));
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for (int c = 0; c < _columns; c++)
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{
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if (columnLabels[c] == 0)
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continue;
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double value = _similarities[r, c];
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nodes.Add(new Node(nodes.Count, value));
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}
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X.Add(nodes.ToArray());
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Y.Add(rowLabels[r]);
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}
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return new Problem(X.Count, Y.ToArray(), X.ToArray(), maxIndex);
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}
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}
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}
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