Files
jube-fork/Accord.Math/Vector/Vector.Random.cs
richard-churchman cc0a2bee21 Extensive modifications to the serialisation of Neural Network models to support Newtonsoft Json.net rather than Binary Serializer.
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.
2023-12-24 10:22:22 +01:00

60 lines
2.3 KiB
C#

// Accord Math Library
// The Accord.NET Framework
// http://accord-framework.net
//
// Copyright © César Souza, 2009-2017
// cesarsouza at gmail.com
//
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
//
// This library 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
// Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License along with this library; if not, write to the Free Software
// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
//
namespace Accord.Math
{
using System;
using System.Collections.Generic;
public static partial class Vector
{
/// <summary>
/// Creates a vector with uniformly distributed random data.
/// </summary>
///
public static double[] Random(int size)
{
return Random(size, 0.0, 1.0);
}
/// <summary>
/// Draws a random sample from a group of observations, with or without repetitions.
/// </summary>
///
/// <typeparam name="T">The type of the observations.</typeparam>
///
/// <param name="values">The observation vector.</param>
/// <param name="size">The size of the sample to be drawn (how many samples to get).</param>
/// <param name="replacement">Whether to sample with replacement (repeating values) or without replacement (non-repeating values).</param>
///
/// <returns>A vector containing the samples drawn from <paramref name="values"/>.</returns>
///
public static T[] Sample<T>(this T[] values, int size, bool replacement = false)
{
int[] idx = replacement ? Vector.Random(size, 0, values.Length) : Vector.Sample(size, values.Length);
System.Diagnostics.Debug.Assert(idx.Length == size);
return values.Get(idx);
}
}
}