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.
352 lines
11 KiB
C#
352 lines
11 KiB
C#
// Accord Statistics 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.Statistics.Analysis
|
|
{
|
|
using System;
|
|
using System.ComponentModel;
|
|
using Accord.Statistics.Testing;
|
|
using Accord.Compat;
|
|
|
|
/// <summary>
|
|
/// Weighted confusion matrix for multi-class decision problems.
|
|
/// </summary>
|
|
///
|
|
/// <remarks>
|
|
/// <para>
|
|
/// References:
|
|
/// <list type="bullet">
|
|
/// <item><description>
|
|
/// <a href="http://uwf.edu/zhu/evr6930/2.pdf">
|
|
/// R. G. Congalton. A Review of Assessing the Accuracy of Classifications
|
|
/// of Remotely Sensed Data. Available on: http://uwf.edu/zhu/evr6930/2.pdf </a></description></item>
|
|
/// <item><description>
|
|
/// <a href="http://www.iiasa.ac.at/Admin/PUB/Documents/IR-98-081.pdf">
|
|
/// G. Banko. A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and
|
|
/// of Methods Including Remote Sensing Data in Forest Inventory. Interim report. Available on:
|
|
/// http://www.iiasa.ac.at/Admin/PUB/Documents/IR-98-081.pdf </a></description></item>
|
|
/// </list></para>
|
|
/// </remarks>
|
|
///
|
|
[Serializable]
|
|
public class WeightedConfusionMatrix : GeneralConfusionMatrix
|
|
{
|
|
|
|
private double[,] weights;
|
|
|
|
private double[] weightedRowProportion;
|
|
private double[] weightedColProportion;
|
|
|
|
private double? kappa;
|
|
private double? kappaStdError;
|
|
private double? kappaVariance;
|
|
|
|
private double? kappaStdError0;
|
|
private double? kappaVariance0;
|
|
|
|
/// <summary>
|
|
/// Gets the Weights matrix.
|
|
/// </summary>
|
|
///
|
|
public double[,] Weights
|
|
{
|
|
get { return weights; }
|
|
}
|
|
|
|
|
|
|
|
/// <summary>
|
|
/// Creates a new Confusion Matrix.
|
|
/// </summary>
|
|
///
|
|
public WeightedConfusionMatrix(double[,] matrix, double[,] weights, int samples)
|
|
: base(matrix, samples)
|
|
{
|
|
this.weights = weights;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Creates a new Confusion Matrix.
|
|
/// </summary>
|
|
///
|
|
public WeightedConfusionMatrix(int[,] matrix, double[,] weights)
|
|
: base(matrix)
|
|
{
|
|
this.weights = weights;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Creates a new Confusion Matrix.
|
|
/// </summary>
|
|
///
|
|
public WeightedConfusionMatrix(double[,] weights, int[] expected, int[] predicted)
|
|
: base(weights.GetLength(0), expected, predicted)
|
|
{
|
|
this.weights = weights;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Gets the row marginals (proportions).
|
|
/// </summary>
|
|
///
|
|
[DisplayName("Weighted Row Proportions")]
|
|
public double[] WeightedRowProportions
|
|
{
|
|
get
|
|
{
|
|
if (weightedRowProportion == null)
|
|
{
|
|
double[] rowProportion = RowProportions;
|
|
weightedRowProportion = new double[NumberOfClasses];
|
|
for (int i = 0; i < rowProportion.Length; i++)
|
|
for (int j = 0; j < rowProportion.Length; j++)
|
|
weightedRowProportion[i] += rowProportion[j] * weights[i, j];
|
|
}
|
|
|
|
return weightedRowProportion;
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Gets the column marginals (proportions).
|
|
/// </summary>
|
|
///
|
|
[DisplayName("Weighted Column Proportions")]
|
|
public double[] WeightedColumnProportions
|
|
{
|
|
get
|
|
{
|
|
if (weightedColProportion == null)
|
|
{
|
|
double[] colProportion = ColumnProportions;
|
|
weightedColProportion = new double[NumberOfClasses];
|
|
for (int i = 0; i < colProportion.Length; i++)
|
|
for (int j = 0; j < colProportion.Length; j++)
|
|
weightedColProportion[i] += colProportion[j] * weights[i, j];
|
|
}
|
|
|
|
return weightedColProportion;
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Gets the Kappa coefficient of performance.
|
|
/// </summary>
|
|
///
|
|
[DisplayName("Kappa Coefficient (κ)")]
|
|
public double WeightedKappa
|
|
{
|
|
get
|
|
{
|
|
if (kappa == null)
|
|
{
|
|
double Po = WeightedOverallAgreement;
|
|
double Pc = WeightedChanceAgreement;
|
|
|
|
kappa = (Po - Pc) / (1.0 - Pc);
|
|
}
|
|
|
|
return kappa.Value;
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Gets the standard error of the <see cref="WeightedKappa"/>
|
|
/// coefficient of performance.
|
|
/// </summary>
|
|
///
|
|
[DisplayName("Kappa (κ) Std. Error")]
|
|
public double WeightedStandardError
|
|
{
|
|
get
|
|
{
|
|
if (kappaStdError == null)
|
|
{
|
|
double se;
|
|
kappaVariance = KappaTest.AsymptoticKappaVariance(this, out se);
|
|
kappaStdError = se;
|
|
}
|
|
|
|
return kappaStdError.Value;
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Gets the variance of the <see cref="WeightedKappa"/>
|
|
/// coefficient of performance.
|
|
/// </summary>
|
|
///
|
|
[DisplayName("Kappa (κ) Variance")]
|
|
public double WeightedVariance
|
|
{
|
|
get
|
|
{
|
|
if (kappaVariance == null)
|
|
{
|
|
double se;
|
|
kappaVariance = KappaTest.AsymptoticKappaVariance(this, out se);
|
|
kappaStdError = se;
|
|
}
|
|
|
|
return kappaVariance.Value;
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Gets the variance of the <see cref="WeightedKappa"/>
|
|
/// under the null hypothesis that the underlying
|
|
/// Kappa value is 0.
|
|
/// </summary>
|
|
///
|
|
[DisplayName("Kappa (κ) H₀ Variance")]
|
|
public double WeightedVarianceUnderNull
|
|
{
|
|
get
|
|
{
|
|
if (kappaVariance0 == null)
|
|
{
|
|
double se;
|
|
kappaVariance0 = KappaTest.AsymptoticKappaVariance(this,
|
|
out se, nullHypothesis: true);
|
|
kappaStdError0 = se;
|
|
}
|
|
|
|
return kappaVariance0.Value;
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Gets the standard error of the <see cref="WeightedKappa"/>
|
|
/// under the null hypothesis that the underlying Kappa
|
|
/// value is 0.
|
|
/// </summary>
|
|
///
|
|
[DisplayName("Kappa (κ) H₀ Std. Error")]
|
|
public double WeightedStandardErrorUnderNull
|
|
{
|
|
get
|
|
{
|
|
if (kappaStdError0 == null)
|
|
{
|
|
double se;
|
|
kappaVariance0 = KappaTest.AsymptoticKappaVariance(this,
|
|
out se, nullHypothesis: true);
|
|
kappaStdError0 = se;
|
|
}
|
|
|
|
return kappaStdError0.Value;
|
|
}
|
|
}
|
|
|
|
|
|
|
|
/// <summary>
|
|
/// Overall agreement.
|
|
/// </summary>
|
|
///
|
|
[DisplayName("Overall Agreement")]
|
|
public double WeightedOverallAgreement
|
|
{
|
|
get
|
|
{
|
|
double sum = 0;
|
|
for (int i = 0; i < NumberOfClasses; i++)
|
|
for (int j = 0; j < NumberOfClasses; j++)
|
|
sum += weights[i, j] * Matrix[i, j];
|
|
|
|
return sum / (double)NumberOfSamples;
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Chance agreement.
|
|
/// </summary>
|
|
///
|
|
/// <remarks>
|
|
/// The chance agreement tells how many samples
|
|
/// were correctly classified by chance alone.
|
|
/// </remarks>
|
|
///
|
|
[DisplayName("Chance Agreement")]
|
|
public double WeightedChanceAgreement
|
|
{
|
|
get
|
|
{
|
|
double chance = 0;
|
|
for (int i = 0; i < NumberOfClasses; i++)
|
|
for (int j = 0; j < NumberOfClasses; j++)
|
|
chance += weights[i, j]
|
|
* RowTotals[i]
|
|
* ColumnTotals[j];
|
|
return chance / (NumberOfSamples * NumberOfSamples);
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Creates a new Weighted Confusion Matrix with linear weighting.
|
|
/// </summary>
|
|
///
|
|
public static WeightedConfusionMatrix LinearWeighting(int[,] matrix)
|
|
{
|
|
// Create matrix of weights with linear weighting.
|
|
int classes = matrix.GetLength(0);
|
|
|
|
double[,] weights = new double[classes, classes];
|
|
for (int i = 0; i < classes; i++)
|
|
{
|
|
for (int j = 0; j < classes; j++)
|
|
{
|
|
double num = Math.Abs(i - j);
|
|
double den = classes-1;
|
|
weights[i, j] = 1.0 - num / den;
|
|
}
|
|
}
|
|
|
|
return new WeightedConfusionMatrix(matrix, weights);
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
/// Creates a new Weighted Confusion Matrix with linear weighting.
|
|
/// </summary>
|
|
///
|
|
public static WeightedConfusionMatrix QuadraticWeighting(int[,] matrix)
|
|
{
|
|
// Create matrix of weights with quadratic weighting.
|
|
int classes = matrix.GetLength(0);
|
|
|
|
double[,] weights = new double[classes, classes];
|
|
for (int i = 0; i < classes; i++)
|
|
{
|
|
for (int j = 0; j < classes; j++)
|
|
{
|
|
double num = Math.Abs(i - j);
|
|
double den = classes - 1;
|
|
weights[i, j] = 1.0 - (num / den) * (num / den);
|
|
}
|
|
}
|
|
|
|
return new WeightedConfusionMatrix(matrix, weights);
|
|
}
|
|
}
|
|
}
|