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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.
350 lines
11 KiB
C#
350 lines
11 KiB
C#
// Accord Statistics 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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// AForge Math Library
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// AForge.NET framework
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// http://www.aforgenet.com/framework/
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//
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// Copyright © AForge.NET, 2005-2011
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// [email protected]
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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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namespace Accord.Statistics
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{
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using Accord;
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using Accord.Math;
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using System;
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public static partial class Measures
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{
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/// <summary>
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/// Gets the minimum value in the histogram.
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/// </summary>
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///
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/// <param name="values">Histogram array.</param>
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///
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/// <returns>The minimum value in the histogram.</returns>
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///
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public static int HistogramMin(this int[] values)
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{
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int min = values.Length;
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for (int i = 0; i < values.Length; i++)
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if (i < min)
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min = i;
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return min;
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}
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/// <summary>
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/// Gets the maximum value in the histogram.
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/// </summary>
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///
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/// <param name="values">Histogram array.</param>
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///
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/// <returns>The maximum value in the histogram.</returns>
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///
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public static int HistogramMax(this int[] values)
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{
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int max = 0;
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for (int i = 0; i < values.Length; i++)
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if (i > max)
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max = i;
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return max;
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}
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/// <summary>
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/// Calculates the total number of samples in a histogram.
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/// </summary>
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///
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/// <param name="values">The histogram array.</param>
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///
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/// <returns>The total number of samples in the histogram.</returns>
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///
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public static long HistogramSum(this int[] values)
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{
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long sum = 0;
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for (int i = 0; i < values.Length; i++)
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sum += values[i];
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return sum;
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}
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/// <summary>
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/// Calculate mean value of an histogram.
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/// </summary>
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///
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/// <param name="values">Histogram array.</param>
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///
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/// <returns>Returns mean value.</returns>
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///
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/// <remarks><para>The input array is treated as histogram, i.e. its
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/// indexes are treated as values of stochastic function, but
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/// array values are treated as "probabilities" (total amount of
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/// hits).</para>
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/// </remarks>
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///
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public static double HistogramMean(this int[] values)
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{
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double total = 0;
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double mean = 0;
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for (int i = 0; i < values.Length; i++)
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{
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int hits = values[i];
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mean += (double)i * hits;
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total += hits;
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}
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return (total == 0) ? 0 : mean / total;
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}
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/// <summary>
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/// Calculate standard deviation of an histogram.
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/// </summary>
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///
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/// <param name="values">Histogram array.</param>
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///
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/// <returns>Returns value of standard deviation.</returns>
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///
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/// <remarks><para>The input array is treated as histogram, i.e. its
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/// indexes are treated as values of stochastic function, but
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/// array values are treated as "probabilities" (total amount of
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/// hits).</para>
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/// </remarks>
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///
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public static double HistogramStandardDeviation(this int[] values)
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{
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return StandardDeviation(values, Mean(values));
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}
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/// <summary>
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/// Calculate standard deviation of an histogram.
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/// </summary>
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///
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/// <param name="values">Histogram array.</param>
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/// <param name="mean">Mean value of the histogram.</param>
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///
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/// <returns>Returns value of standard deviation.</returns>
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///
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/// <remarks><para>The input array is treated as histogram, i.e. its
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/// indexes are treated as values of stochastic function, but
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/// array values are treated as "probabilities" (total amount of
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/// hits).</para>
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///
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/// <para>The method is an equivalent to the <see cref="HistogramStandardDeviation(int[])"/> method,
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/// but it relies on the passed mean value, which is previously calculated
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/// using <see cref="HistogramMean"/> method.</para>
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/// </remarks>
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///
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public static double HistogramStandardDeviation(this int[] values, double mean)
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{
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double stddev = 0;
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int total = 0;
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// for all values
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for (int i = 0; i < values.Length; i++)
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{
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int hits = values[i];
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double diff = (double)i - mean;
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stddev += diff * diff * hits;
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total += hits;
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}
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return (total == 0) ? 0 : Math.Sqrt(stddev / total);
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}
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/// <summary>
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/// Calculate median value of an histogram.
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/// </summary>
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///
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/// <param name="values">Histogram array.</param>
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///
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/// <returns>Returns value of median.</returns>
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///
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/// <remarks>
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/// <para>The input array is treated as histogram, i.e. its
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/// indexes are treated as values of stochastic function, but
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/// array values are treated as "probabilities" (total amount of
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/// hits).</para>
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///
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/// <para><note>The median value is calculated accumulating histogram's
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/// values starting from the <b>left</b> point until the sum reaches 50% of
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/// histogram's sum.</note></para>
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/// </remarks>
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///
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public static int HistogramMedian(this int[] values)
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{
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int total = 0;
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// for all values
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for (int i = 0; i < values.Length; i++)
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{
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// accumulate total
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total += values[i];
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}
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int halfTotal = total / 2;
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int median = 0;
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int v = 0;
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// find median value
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for (; median < values.Length; median++)
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{
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v += values[median];
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if (v >= halfTotal)
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break;
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}
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return median;
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}
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/// <summary>
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/// Get range around median of an histogram containing specified percentage of values.
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/// </summary>
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///
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/// <param name="values">Histogram array.</param>
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/// <param name="percent">Values percentage around median.</param>
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///
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/// <returns>Returns the range which contains specified percentage
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/// of values.</returns>
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///
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/// <remarks>
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/// <para>The input array is treated as histogram, i.e. its
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/// indexes are treated as values of stochastic function, but
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/// array values are treated as "probabilities" (total amount of
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/// hits).</para>
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///
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/// <para>The method calculates range of stochastic variable, which summary probability
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/// comprises the specified percentage of histogram's hits.</para>
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/// </remarks>
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///
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public static IntRange GetHistogramRange(this int[] values, double percent)
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{
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int total = 0;
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// for all values
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for (int i = 0; i < values.Length; i++)
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{
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// accumulate total
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total += values[i];
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}
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int min, max, hits;
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int h = (int)(total * (percent + (1 - percent) / 2));
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// get range min value
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for (min = 0, hits = total; min < values.Length; min++)
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{
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hits -= values[min];
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if (hits < h)
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break;
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}
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// get range max value
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for (max = values.Length - 1, hits = total; max >= 0; max--)
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{
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hits -= values[max];
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if (hits < h)
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break;
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}
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return new IntRange(min, max);
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}
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/// <summary>
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/// Calculate entropy value of an histogram.
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/// </summary>
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///
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/// <param name="values">Histogram array.</param>
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///
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/// <returns>Returns entropy value of the specified histogram array.</returns>
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///
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/// <remarks><para>The input array is treated as histogram, i.e. its
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/// indexes are treated as values of stochastic function, but
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/// array values are treated as "probabilities" (total amount of
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/// hits).</para>
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/// </remarks>
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///
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public static double HistogramEntropy(this int[] values)
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{
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int total = 0;
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double entropy = 0;
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double p;
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// calculate total amount of hits
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for (int i = 0; i < values.Length; i++)
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total += values[i];
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if (total != 0)
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{
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// for all values
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for (int i = 0; i < values.Length; i++)
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{
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// get item's probability
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p = (double)values[i] / total;
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// calculate entropy
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if (p != 0)
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entropy += (-p * Math.Log(p, 2));
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}
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}
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return entropy;
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}
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/// <summary>
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/// Calculate mode value of an histogram.
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/// </summary>
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///
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/// <param name="values">Histogram array.</param>
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///
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/// <returns>Returns mode value of the histogram array.</returns>
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///
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/// <remarks>
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/// <para>The input array is treated as histogram, i.e. its
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/// indexes are treated as values of stochastic function, but
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/// array values are treated as "probabilities" (total amount of
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/// hits).</para>
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///
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/// <para><note>Returns the minimum mode value if the specified histogram is multimodal.</note></para>
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/// </remarks>
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///
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public static int HistogramMode(this int[] values)
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{
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int mode = 0, curMax = 0;
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for (int i = 0; i < values.Length; i++)
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{
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if (values[i] > curMax)
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{
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curMax = values[i];
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mode = i;
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}
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}
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return mode;
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}
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}
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}
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