mirror of
https://github.com/hanzoai/jube-fork.git
synced 2026-10-30 18:33:33 +00:00
richard-churchman
cc0a2bee21
Included open-source Accord.net library code natively to the solution given the deprecated state of that project. Accord.net will be supported inside Jube for own needs (such as upgrading to .Net 7+). Accord.net libraries modified to compile to .Net 6. Modified Accord.net libraries to better support Newtonsoft Json.net serialisation. Included annotations in the Accord.net libraries to house important dependency statistics. Serialised json can be used independently of Jube to recall a Neural Network. Removal of Binary Serializer totally as a precursor to the projects support of .Net 7+. Extensive bug fixing in crucial functions of Neural Network training and recall. While this part of the system remains in Alpha, it is substantially more fit for purpose.
478 lines
17 KiB
C#
478 lines
17 KiB
C#
// Accord Math Library
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// The Accord.NET Framework
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// http://accord-framework.net
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//
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// Copyright © César Souza, 2009-2017
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// cesarsouza at gmail.com
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//
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// This library is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 2.1 of the License, or (at your option) any later version.
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//
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// This library 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 GNU
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// Lesser General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License along with this library; if not, write to the Free Software
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// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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//
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// ======================================================================
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// This code has been generated by a tool; do not edit manually. Instead,
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// edit the T4 template Vector.Random.tt so this file can be regenerated.
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// ======================================================================
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namespace Accord.Math
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{
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using System;
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using Accord.Math;
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public static partial class Matrix
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{
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static double[,] Random(int rows, int columns)
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{
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return Random(rows, columns, 0.0, 1.0);
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static double[,] Random(int size)
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{
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return Random(size, size, 0.0, 1.0);
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static int[,] Random(int rows, int columns, int min, int max, int[,] result = null)
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{
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if (result == null)
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result = new int[rows, columns];
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var random = Accord.Math.Random.Generator.Random;
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for (int i = 0; i < rows; i++)
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for (int j = 0; j < columns; j++)
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result[i, j] = (int)random.Next((int)min, (int)max);
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static int[,] Random(int size, int min, int max, bool symmetric = false, int[,] result = null)
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{
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if (result == null)
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result = new int[size, size];
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var random = Accord.Math.Random.Generator.Random;
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if (symmetric)
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = result[j, i] = (int)random.Next((int)min, (int)max);
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}
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else
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = (int)random.Next((int)min, (int)max);
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}
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static short[,] Random(int rows, int columns, short min, short max, short[,] result = null)
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{
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if (result == null)
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result = new short[rows, columns];
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var random = Accord.Math.Random.Generator.Random;
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for (int i = 0; i < rows; i++)
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for (int j = 0; j < columns; j++)
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result[i, j] = (short)random.Next((int)min, (int)max);
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static short[,] Random(int size, short min, short max, bool symmetric = false, short[,] result = null)
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{
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if (result == null)
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result = new short[size, size];
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var random = Accord.Math.Random.Generator.Random;
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if (symmetric)
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = result[j, i] = (short)random.Next((int)min, (int)max);
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}
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else
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = (short)random.Next((int)min, (int)max);
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}
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static byte[,] Random(int rows, int columns, byte min, byte max, byte[,] result = null)
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{
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if (result == null)
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result = new byte[rows, columns];
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var random = Accord.Math.Random.Generator.Random;
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for (int i = 0; i < rows; i++)
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for (int j = 0; j < columns; j++)
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result[i, j] = (byte)random.Next((int)min, (int)max);
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static byte[,] Random(int size, byte min, byte max, bool symmetric = false, byte[,] result = null)
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{
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if (result == null)
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result = new byte[size, size];
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var random = Accord.Math.Random.Generator.Random;
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if (symmetric)
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = result[j, i] = (byte)random.Next((int)min, (int)max);
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}
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else
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = (byte)random.Next((int)min, (int)max);
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}
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static sbyte[,] Random(int rows, int columns, sbyte min, sbyte max, sbyte[,] result = null)
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{
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if (result == null)
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result = new sbyte[rows, columns];
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var random = Accord.Math.Random.Generator.Random;
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for (int i = 0; i < rows; i++)
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for (int j = 0; j < columns; j++)
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result[i, j] = (sbyte)random.Next((int)min, (int)max);
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static sbyte[,] Random(int size, sbyte min, sbyte max, bool symmetric = false, sbyte[,] result = null)
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{
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if (result == null)
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result = new sbyte[size, size];
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var random = Accord.Math.Random.Generator.Random;
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if (symmetric)
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = result[j, i] = (sbyte)random.Next((int)min, (int)max);
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}
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else
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = (sbyte)random.Next((int)min, (int)max);
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}
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static long[,] Random(int rows, int columns, long min, long max, long[,] result = null)
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{
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if (result == null)
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result = new long[rows, columns];
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var random = Accord.Math.Random.Generator.Random;
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for (int i = 0; i < rows; i++)
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for (int j = 0; j < columns; j++)
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result[i, j] = (long)random.Next((int)min, (int)max);
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static long[,] Random(int size, long min, long max, bool symmetric = false, long[,] result = null)
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{
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if (result == null)
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result = new long[size, size];
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var random = Accord.Math.Random.Generator.Random;
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if (symmetric)
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = result[j, i] = (long)random.Next((int)min, (int)max);
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}
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else
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = (long)random.Next((int)min, (int)max);
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}
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static ulong[,] Random(int rows, int columns, ulong min, ulong max, ulong[,] result = null)
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{
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if (result == null)
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result = new ulong[rows, columns];
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var random = Accord.Math.Random.Generator.Random;
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for (int i = 0; i < rows; i++)
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for (int j = 0; j < columns; j++)
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result[i, j] = (ulong)random.Next((int)min, (int)max);
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static ulong[,] Random(int size, ulong min, ulong max, bool symmetric = false, ulong[,] result = null)
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{
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if (result == null)
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result = new ulong[size, size];
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var random = Accord.Math.Random.Generator.Random;
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if (symmetric)
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = result[j, i] = (ulong)random.Next((int)min, (int)max);
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}
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else
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = (ulong)random.Next((int)min, (int)max);
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}
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static ushort[,] Random(int rows, int columns, ushort min, ushort max, ushort[,] result = null)
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{
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if (result == null)
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result = new ushort[rows, columns];
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var random = Accord.Math.Random.Generator.Random;
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for (int i = 0; i < rows; i++)
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for (int j = 0; j < columns; j++)
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result[i, j] = (ushort)random.Next((int)min, (int)max);
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static ushort[,] Random(int size, ushort min, ushort max, bool symmetric = false, ushort[,] result = null)
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{
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if (result == null)
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result = new ushort[size, size];
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var random = Accord.Math.Random.Generator.Random;
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if (symmetric)
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = result[j, i] = (ushort)random.Next((int)min, (int)max);
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}
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else
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = (ushort)random.Next((int)min, (int)max);
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}
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static float[,] Random(int rows, int columns, float min, float max, float[,] result = null)
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{
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if (result == null)
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result = new float[rows, columns];
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var random = Accord.Math.Random.Generator.Random;
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for (int i = 0; i < rows; i++)
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for (int j = 0; j < columns; j++)
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result[i, j] = (float)random.NextDouble() * (max - min) + min;
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static float[,] Random(int size, float min, float max, bool symmetric = false, float[,] result = null)
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{
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if (result == null)
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result = new float[size, size];
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var random = Accord.Math.Random.Generator.Random;
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if (symmetric)
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = result[j, i] = (float)random.NextDouble() * (max - min) + min;
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}
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else
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = (float)random.NextDouble() * (max - min) + min;
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}
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static double[,] Random(int rows, int columns, double min, double max, double[,] result = null)
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{
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if (result == null)
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result = new double[rows, columns];
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var random = Accord.Math.Random.Generator.Random;
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for (int i = 0; i < rows; i++)
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for (int j = 0; j < columns; j++)
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result[i, j] = (double)random.NextDouble() * (max - min) + min;
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static double[,] Random(int size, double min, double max, bool symmetric = false, double[,] result = null)
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{
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if (result == null)
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result = new double[size, size];
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var random = Accord.Math.Random.Generator.Random;
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if (symmetric)
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = result[j, i] = (double)random.NextDouble() * (max - min) + min;
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}
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else
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = (double)random.NextDouble() * (max - min) + min;
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}
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static decimal[,] Random(int rows, int columns, decimal min, decimal max, decimal[,] result = null)
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{
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if (result == null)
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result = new decimal[rows, columns];
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var random = Accord.Math.Random.Generator.Random;
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for (int i = 0; i < rows; i++)
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for (int j = 0; j < columns; j++)
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result[i, j] = (decimal)random.NextDouble() * (max - min) + min;
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return result;
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}
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/// <summary>
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/// Creates a matrix with uniformly distributed random data.
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/// </summary>
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///
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public static decimal[,] Random(int size, decimal min, decimal max, bool symmetric = false, decimal[,] result = null)
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{
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if (result == null)
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result = new decimal[size, size];
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var random = Accord.Math.Random.Generator.Random;
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if (symmetric)
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = result[j, i] = (decimal)random.NextDouble() * (max - min) + min;
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}
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else
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{
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for (int i = 0; i < size; i++)
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for (int j = i; j < size; j++)
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result[i, j] = (decimal)random.NextDouble() * (max - min) + min;
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}
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return result;
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}
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}
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} |