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Genetic

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This commit is contained in:
Ahmed Zamil 2022-11-05 17:48:30 +03:00
parent e83f51f952
commit f1d5b0a38b
12 changed files with 746 additions and 54 deletions
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19
Esiur.Analysis.Test/Esiur.Analysis.Test.csproj Normal file
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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>WinExe</OutputType>
<TargetFramework>net6.0-windows</TargetFramework>
<Nullable>enable</Nullable>
<UseWindowsForms>true</UseWindowsForms>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="ScottPlot.WinForms" Version="4.1.58" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\Esiur.Analysis\Esiur.Analysis.csproj" />
</ItemGroup>
</Project>

72
Esiur.Analysis.Test/FMain.Designer.cs generated Normal file
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namespace Esiur.Analysis.Test
{
partial class FMain
{
/// <summary>
/// Required designer variable.
/// </summary>
private System.ComponentModel.IContainer components = null;
/// <summary>
/// Clean up any resources being used.
/// </summary>
/// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
protected override void Dispose(bool disposing)
{
if (disposing && (components != null))
{
components.Dispose();
}
base.Dispose(disposing);
}
#region Windows Form Designer generated code
/// <summary>
/// Required method for Designer support - do not modify
/// the contents of this method with the code editor.
/// </summary>
private void InitializeComponent()
{
this.formsPlot1 = new ScottPlot.FormsPlot();
this.button1 = new System.Windows.Forms.Button();
this.SuspendLayout();
//
// formsPlot1
//
this.formsPlot1.Location = new System.Drawing.Point(57, 47);
this.formsPlot1.Margin = new System.Windows.Forms.Padding(5, 4, 5, 4);
this.formsPlot1.Name = "formsPlot1";
this.formsPlot1.Size = new System.Drawing.Size(1006, 576);
this.formsPlot1.TabIndex = 0;
//
// button1
//
this.button1.Location = new System.Drawing.Point(12, 316);
this.button1.Name = "button1";
this.button1.Size = new System.Drawing.Size(85, 31);
this.button1.TabIndex = 1;
this.button1.Text = "button1";
this.button1.UseVisualStyleBackColor = true;
this.button1.Click += new System.EventHandler(this.button1_Click);
//
// FMain
//
this.AutoScaleDimensions = new System.Drawing.SizeF(8F, 20F);
this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
this.ClientSize = new System.Drawing.Size(1175, 658);
this.Controls.Add(this.button1);
this.Controls.Add(this.formsPlot1);
this.Name = "FMain";
this.Text = "PID Controller with Fuzzy";
this.Load += new System.EventHandler(this.FMain_Load);
this.ResumeLayout(false);
}
#endregion
private ScottPlot.FormsPlot formsPlot1;
private Button button1;
}
}

345
Esiur.Analysis.Test/FMain.cs Normal file
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using Esiur.Analysis.DSP;
using Esiur.Analysis.Fuzzy;
using Esiur.Analysis.Optimization;
using Esiur.Analysis.Signals;
using Esiur.Analysis.Units;
using Microsoft.VisualBasic.Logging;
using ScottPlot;
using ScottPlot.Drawing.Colormaps;
using System.Security.Cryptography;
using Esiur.Analysis.Statistics;
using System.Diagnostics;
namespace Esiur.Analysis.Test
{
public partial class FMain : Form
{
public FMain()
{
InitializeComponent();
//var outage = Capacity.ComputeOutage(20000000, new Capacity.CSI[]
//{
// new Capacity.CSI(PowerUnit.FromDb(20), 0.1),
// new Capacity.CSI(PowerUnit.FromDb(15), 0.15),
// new Capacity.CSI(PowerUnit.FromDb(10), 0.25),
// new Capacity.CSI(PowerUnit.FromDb(5), 0.25),
// new Capacity.CSI(PowerUnit.FromDb(0), 0.15),
// new Capacity.CSI(PowerUnit.FromDb(-5), 0.1),
//});
var outage = Capacity.ComputeOutage(1, new Capacity.CSI[]
{
new Capacity.CSI(PowerUnit.FromDb(30), 0.2),
new Capacity.CSI(PowerUnit.FromDb(20), 0.3),
new Capacity.CSI(PowerUnit.FromDb(10), 0.3),
new Capacity.CSI(PowerUnit.FromDb(0), 0.2),
});
var low = new ContinuousSet(MembershipFunctions.Descending(20, 40));
var mid = new ContinuousSet(MembershipFunctions.Triangular(20, 40, 60));
var high = new ContinuousSet(MembershipFunctions.Ascending(40, 60));
var bad = new ContinuousSet(MembershipFunctions.Descending(0, 30));
var ok = new ContinuousSet(MembershipFunctions.Triangular(20, 50, 80));
var excelent = new ContinuousSet(MembershipFunctions.Ascending(70, 100));
var small = new ContinuousSet(MembershipFunctions.Descending(100, 200));
var avg = new ContinuousSet(MembershipFunctions.Triangular(100, 200, 300));
var big = new ContinuousSet(MembershipFunctions.Ascending(200, 300));
//var speedIsLowThenSmall = new FuzzyRule("Low=>Small", low, small);
double rating = 80;
for (double temp = 60; temp < 100; temp++)
{
var v = MamdaniDefuzzifier.Evaluate(new INumericalSet<double>[]
{
temp.Is(low).And(rating.Is(bad)).Then(small),
temp.Is(mid).And(rating.Is(ok)).Then(avg),
temp.Is(high).And(rating.Is(excelent)).Then(big),
}, MamdaniDefuzzifierMethod.CenterOfGravity, 100, 300, 1);
}
}
private void FMain_Load(object sender, EventArgs e)
{
var lowErr = new ContinuousSet(MembershipFunctions.Descending(-5, 4.5));
var midErr = new ContinuousSet(MembershipFunctions.Triangular(-4, 6.5, 16.5));
var highErr = new ContinuousSet(MembershipFunctions.Ascending(8, 18));
var lowAccErr = new ContinuousSet(MembershipFunctions.Descending(0, 0.02));
var midAccErr = new ContinuousSet(MembershipFunctions.Triangular(0.02, 0.04, 0.06));
var highAccErr = new ContinuousSet(MembershipFunctions.Ascending(0.04, 0.06));
var small = new ContinuousSet(MembershipFunctions.Descending(0.1, 0.5));
var avg = new ContinuousSet(MembershipFunctions.Triangular(0.1, 0.5, 1.1));
var big = new ContinuousSet(MembershipFunctions.Ascending(-10, 1.1));
var x = Enumerable.Range(0, 1000).Select(x => x * 0.01).ToArray();
var step = Enumerable.Repeat(1, 1000).Select(x => (double)x).ToArray();
step[0] = 0;
var motor = new TransferFunction(new double[] { 1, 2 }, new double[] { 1, 1, 2 }, 0.01);
var motorPID = new TransferFunction(new double[] { 1, 2 }, new double[] { 1, 1, 2 }, 0.01);
var motorFuzzyPID = new TransferFunction(new double[] { 1, 2 }, new double[] { 1, 1, 2 }, 0.01);
//double Kp = 2, Ki = 0.4, Kd = 0.4;
double Ki = -1.9181372, Kp = 18.625, Kd = 0.38281253;
var pid = new TransferFunction(new double[] { Kd, Kp, Ki }, new double[] { 1, 1 }, 0.01);
var fuzzyPID = new TransferFunction(new double[] { Kd, Kp, Ki }, new double[] { 1, 1 }, 0.01);
var sysOut = new double[step.Length];
var sysOutFuzzyPID = new double[step.Length];
var sysOutPID = new double[step.Length];
var pidOut = new double[step.Length];
var pidOutFuzzy = new double[step.Length];
var errorOutPID = new double[step.Length];
var errOut = new double[step.Length];
var errAccOut = new double[step.Length];
//var errorAccOut = new double[step.Length];
var errorOutFuzzy = new double[step.Length];
var errorOutAccFuzzy = new double[step.Length];
for (var i = 0; i < step.Length; i++)
{
sysOut[i] = motor.Evaluate(step[i]);
errOut[i] = step[i] - sysOut[i];
errAccOut[i] = errOut[i] - (i == 0 ? 0 : errOut[i - 1]);
sysOutPID[i] = motorPID.Evaluate(step[i] + (i == 0 ? 0 : pidOut[i - 1]));
sysOutFuzzyPID[i] = motorFuzzyPID.Evaluate(step[i] + (i == 0 ? 0 : pidOutFuzzy[i - 1]));
errorOutPID[i] = (step[i] - sysOutPID[i]);
errorOutFuzzy[i] = (step[i] - sysOutFuzzyPID[i]);
errorOutAccFuzzy[i] = (errorOutFuzzy[i] - (i == 0 ? 0 : errorOutFuzzy[i - 1]));
pidOut[i] = pid.Evaluate(errorOutPID[i]);
pidOutFuzzy[i] = fuzzyPID.Evaluate(errorOutFuzzy[i]);
var k = MamdaniDefuzzifier.Evaluate(new INumericalSet<double>[]
{
errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(small),
errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(small),
errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(avg),
errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(small),
errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(avg),
errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(big),
errorOutFuzzy[i].Is(highAccErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(avg),
errorOutFuzzy[i].Is(highAccErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(big),
errorOutFuzzy[i].Is(highAccErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(big),
}, MamdaniDefuzzifierMethod.CenterOfGravity, 0, 1, 0.05);
fuzzyPID.InputCoefficients[1] = k;
fuzzyPID.InputCoefficients[1] = k;
fuzzyPID.InputCoefficients[1] = k;
}
Debug.WriteLine($"Error Values Min: {errOut.Min()} Max: {errOut.Max()} ");
Debug.WriteLine($"Error Acc Values Min: {errAccOut.Min()} Max: {errAccOut.Max()} ");
formsPlot1.Plot.AddScatter(x, sysOut, Color.Red);
formsPlot1.Plot.AddScatter(x, sysOutPID, Color.Blue);
formsPlot1.Plot.AddScatter(x, sysOutFuzzyPID, Color.Green);
formsPlot1.Refresh();
}
struct KK
{
public float Ki;
public float Kp;
public float Kd;
public override string ToString()
{
return $"Ki {Ki} Kp {Kp} Kd {Kd}";
}
}
struct FuzzyChromosome
{
public sbyte KiInputErrPosition;
public sbyte KiInputErrScale;
public sbyte KiInputErrAccPosition;
public sbyte KiInputErrAccScale;
public sbyte KiOutputPosition;
public sbyte KiOutputScale;
}
private double CalculateFuzzyPIDStepError(FuzzyChromosome config, double errStart, double errEnd)
{
var errPos = config.KiInputErrPosition * 0.1;
var errScale = config.KiInputErrPosition * 0.1;
var lowErr = new ContinuousSet(MembershipFunctions.Descending(config.KiInputErrPosition * 0.1, config.kiLowStart * 0.1 + Math.Abs(config.kiLowEnd * 0.1)));
var midErr = new ContinuousSet(MembershipFunctions.Triangular(config.KiInputErrPosition * 0.1, config.kiMidStart * 0.1 + Math.Abs(config.kiMidMid * 0.1), config.kiMidStart * 0.1 + Math.Abs(config.kiMidMid * 0.1) + Math.Abs(config.kiMidEnd * 0.1)));
var highErr = new ContinuousSet(MembershipFunctions.Ascending(config.KiInputErrPosition * 0.1, config.kiHiStart * 0.1 + Math.Abs(config.kiHiEnd * 0.1)));
var lowAccErr = new ContinuousSet(MembershipFunctions.Descending(config.KiInputErrAccPosition * 0.1, Math.Abs(config.kiLowAccStart * 0.1) + Math.Abs(config.kiLowAccEnd * 0.1)));
var midAccErr = new ContinuousSet(MembershipFunctions.Triangular(config.KiInputErrAccPosition * 0.1, Math.Abs(config.kiMidAccStart * 0.1) + Math.Abs(config.kiMidAccMid * 0.1), config.kiMidAccStart * 0.1 + Math.Abs(config.kiMidAccMid * 0.1) + Math.Abs(config.kiMidAccEnd * 0.1)));
var highAccErr = new ContinuousSet(MembershipFunctions.Ascending(config.KiInputErrAccPosition * 0.1, config.kiHiAccStart * 0.1 + Math.Abs(config.kiHiAccEnd * 0.1)));
var small = new ContinuousSet(MembershipFunctions.Descending(config.kiSmallStart * 0.1, config.kiSmallStart * 0.1 + Math.Abs(config.kiSmallEnd * 0.1)));
var avg = new ContinuousSet(MembershipFunctions.Triangular(config.kiAvgStart * 0.1, config.kiAvgStart * 0.1 + Math.Abs(config.kiAvgMid * 0.1), config.kiAvgStart * 0.1 + Math.Abs(config.kiAvgMid * 0.1) + Math.Abs(config.kiAvgEnd * 0.1)));
var big = new ContinuousSet(MembershipFunctions.Ascending(config.kiBigStart * 0.1, config.kiBigStart * 0.1 + Math.Abs(config.kiBigEnd * 0.1)));
double Ki = -1.9181372, Kp = 18.625, Kd = 0.38281253;
var step = Enumerable.Repeat(1, 1000).Select(x => (double)x).ToArray();
step[0] = 0;
var motor = new TransferFunction(new double[] { 1, 2 }, new double[] { 1, 1, 2 }, 0.01);
var fuzzyPID = new TransferFunction(new double[] { Kd, Kp, Ki }, new double[] { 1, 1 }, 0.01);
var sysOutFuzzyPID = new double[step.Length];
var pidOut = new double[step.Length];
var pidOutFuzzy = new double[step.Length];
var errorOutFuzzy = new double[step.Length];
var errorOutAccFuzzy = new double[step.Length];
for (var i = 0; i < step.Length; i++)
{
sysOutFuzzyPID[i] = motor.Evaluate(step[i] + (i == 0 ? 0 : pidOutFuzzy[i - 1]));
errorOutFuzzy[i] = (step[i] - sysOutFuzzyPID[i]);
errorOutAccFuzzy[i] = (errorOutFuzzy[i] - (i == 0 ? 0 : errorOutFuzzy[i - 1]));
pidOutFuzzy[i] = fuzzyPID.Evaluate(errorOutFuzzy[i]);
var k = MamdaniDefuzzifier.Evaluate(new INumericalSet<double>[]
{
errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(small),
errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(small),
errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(avg),
errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(small),
errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(avg),
errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(big),
errorOutFuzzy[i].Is(highAccErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(avg),
errorOutFuzzy[i].Is(highAccErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(big),
errorOutFuzzy[i].Is(highAccErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(big),
}, MamdaniDefuzzifierMethod.CenterOfGravity, -100, 100, 0.5);
fuzzyPID.InputCoefficients[1] = k;
//fuzzyPID.InputCoefficients[1] = k;
//fuzzyPID.InputCoefficients[1] = k;
}
return errorOutFuzzy.RMS();
}
private double CalculatePIDStepError(double Kd, double Kp, double Ki)
{
var step = Enumerable.Repeat(1, 1000).Select(x => (double)x).ToArray();
step[0] = 0;
var motor = new TransferFunction(new double[] { 1, 2 }, new double[] { 1, 1, 2 }, 0.01);
var sysOutPID = new double[step.Length];
var pidOut = new double[step.Length];
var errorOutPID = new double[step.Length];
var pid = new TransferFunction(new double[] { Kd, Kp, Ki }, new double[] { 1, 1 }, 0.01);
for (var i = 0; i < step.Length; i++)
{
sysOutPID[i] = motor.Evaluate(step[i] + (i == 0 ? 0 : pidOut[i - 1]));
if (double.IsInfinity(sysOutPID[i]))
Console.WriteLine();
errorOutPID[i] = (step[i] - sysOutPID[i]);
if (double.IsNaN(errorOutPID[i]))
Console.WriteLine();
pidOut[i] = pid.Evaluate(errorOutPID[i]);
if (double.IsInfinity(pidOut[i]))
Console.WriteLine();
}
return errorOutPID.RMS();
}
private void button1_Click(object sender, EventArgs e)
{
//var gen = new Genetic<KK>(100, k =>
//{
// if (float.IsNaN(k.Ki) || float.IsNaN(k.Kp) || float.IsNaN(k.Kd))
// return (double.MaxValue);
// var r = CalculatePIDStepError(k.Kd, k.Kp, k.Ki);
// if (double.IsNaN(r))
// Console.WriteLine();
// return r;
//});
var gen = new Genetic<KKFF>(100, k =>
{
if (float.IsNaN(k.kiAvgEnd)
|| float.IsNaN(k.kiAvgMid)
|| float.IsNaN(k.kiAvgStart)
|| float.IsNaN(k.kiBigEnd)
|| float.IsNaN(k.kiBigStart)
|| float.IsNaN(k.kiHiAccEnd)
|| float.IsNaN(k.kiHiAccStart)
|| float.IsNaN(k.kiHiEnd)
|| float.IsNaN(k.kiHiStart)
|| float.IsNaN(k.kiLowAccEnd)
|| float.IsNaN(k.kiLowAccStart)
|| float.IsNaN(k.kiLowEnd)
|| float.IsNaN(k.kiLowStart)
|| float.IsNaN(k.kiMidAccEnd)
|| float.IsNaN(k.kiMidAccMid)
|| float.IsNaN(k.kiMidAccStart)
|| float.IsNaN(k.kiMidEnd)
|| float.IsNaN(k.kiMidMid)
|| float.IsNaN(k.kiMidStart)
|| float.IsNaN(k.kiSmallEnd)
|| float.IsNaN(k.kiSmallStart))
return (double.MaxValue);
var r = CalculateFuzzyPIDStepError(k);
if (double.IsNaN(r))
Console.WriteLine();
return r;
});
var ev = gen.Evaluate(1000);
Console.WriteLine(ev);
}
}
}

60
Esiur.Analysis.Test/FMain.resx Normal file
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<root>
<xsd:schema id="root" xmlns="" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:msdata="urn:schemas-microsoft-com:xml-msdata">
<xsd:import namespace="http://www.w3.org/XML/1998/namespace" />
<xsd:element name="root" msdata:IsDataSet="true">
<xsd:complexType>
<xsd:choice maxOccurs="unbounded">
<xsd:element name="metadata">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" />
</xsd:sequence>
<xsd:attribute name="name" use="required" type="xsd:string" />
<xsd:attribute name="type" type="xsd:string" />
<xsd:attribute name="mimetype" type="xsd:string" />
<xsd:attribute ref="xml:space" />
</xsd:complexType>
</xsd:element>
<xsd:element name="assembly">
<xsd:complexType>
<xsd:attribute name="alias" type="xsd:string" />
<xsd:attribute name="name" type="xsd:string" />
</xsd:complexType>
</xsd:element>
<xsd:element name="data">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
<xsd:element name="comment" type="xsd:string" minOccurs="0" msdata:Ordinal="2" />
</xsd:sequence>
<xsd:attribute name="name" type="xsd:string" use="required" msdata:Ordinal="1" />
<xsd:attribute name="type" type="xsd:string" msdata:Ordinal="3" />
<xsd:attribute name="mimetype" type="xsd:string" msdata:Ordinal="4" />
<xsd:attribute ref="xml:space" />
</xsd:complexType>
</xsd:element>
<xsd:element name="resheader">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
</xsd:sequence>
<xsd:attribute name="name" type="xsd:string" use="required" />
</xsd:complexType>
</xsd:element>
</xsd:choice>
</xsd:complexType>
</xsd:element>
</xsd:schema>
<resheader name="resmimetype">
<value>text/microsoft-resx</value>
</resheader>
<resheader name="version">
<value>2.0</value>
</resheader>
<resheader name="reader">
<value>System.Resources.ResXResourceReader, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089</value>
</resheader>
<resheader name="writer">
<value>System.Resources.ResXResourceWriter, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089</value>
</resheader>
</root>

17
Esiur.Analysis.Test/Program.cs Normal file
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namespace Esiur.Analysis.Test
{
internal static class Program
{
/// <summary>
/// The main entry point for the application.
/// </summary>
[STAThread]
static void Main()
{
// To customize application configuration such as set high DPI settings or default font,
// see https://aka.ms/applicationconfiguration.
ApplicationConfiguration.Initialize();
Application.Run(new FMain());
}
}
}

10
Esiur.Analysis/Signals/DSP.cs → Esiur.Analysis/DSP/Functions.cs
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@ -2,9 +2,9 @@
using System.Collections.Generic;
using System.Text;
namespace Esiur.Analysis.Signals
namespace Esiur.Analysis.DSP
{
public static class DSP
public static class Functions
{
public static double[] ConvolveMany(params double[][] signals)
{
@ -27,9 +27,11 @@ namespace Esiur.Analysis.Signals
for (var i = 0; i < length; i++)
{
for (var j = 0; j < signal.Length && i - j >= 0 && i - j < filter.Length; j++)
for (var j = 0; j < signal.Length; j++)
{
rt[i] = signal[j] * filter[i - j];
if (i - j >= 0 && i - j < filter.Length)
rt[i] += signal[j] * filter[i - j];
}
}

73
Esiur.Analysis/DSP/TransferFunction.cs Normal file
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using System;
using System.Collections.Generic;
using System.Linq;
using System.Net.WebSockets;
using System.Security.Cryptography.X509Certificates;
using System.Text;
using System.Xml.Schema;
namespace Esiur.Analysis.DSP
{
public class TransferFunction
{
public double Step { get; set; }
double[] inputs;
double[] outputs;
public double[] InputCoefficients { get; set; }
public double[] OutputCoefficients { get; set; }
public TransferFunction(double[] numerator, double[] denominator, double step = 0.01)
{
inputs = new double[numerator.Length];
outputs = new double[denominator.Length];
InputCoefficients = numerator.Reverse().ToArray();
OutputCoefficients = denominator.Reverse().ToArray();
Step = step;
}
public double Evaluate(double value)
{
var xs = new double[inputs.Length];
xs[0] = value;// * InputCoefficients.Last();
// diffrentiate
for(var i = 1; i < xs.Length; i++)
xs[i] = (xs[i - 1] - inputs[i - 1]) / Step;
var ys = new double[outputs.Length];
// integrate
for (var i = outputs.Length - 2; i >= 0; i--)
{
var iy = outputs[i] + (Step * outputs[i + 1]);
if (double.IsNaN(iy) || double.IsInfinity(iy))
ys[i] = outputs[i];
else
ys[i] = iy;
}
var v = xs.Zip(InputCoefficients, (x, c) => x * c).Sum() - ys.Zip(OutputCoefficients, (y, c) => y * c).Sum();
if (double.IsNaN(v) || double.IsInfinity(v))
ys[ys.Length - 1] = outputs[ys.Length - 1];
else
ys[ys.Length - 1] = v;
inputs = xs;
outputs = ys;
return ys[0];
}
public double[] Evaluate(double[] value)
{
return value.Select(x => Evaluate(x)).ToArray();
}
}
}

6
Esiur.Analysis/Esiur.Analysis.csproj
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@ -3,6 +3,7 @@
<PropertyGroup>
<TargetFramework>netstandard2.1</TargetFramework>
<Nullable>enable</Nullable>
<AllowUnsafeBlocks>True</AllowUnsafeBlocks>
</PropertyGroup>
<ItemGroup>
@ -12,11 +13,14 @@
<ItemGroup>
<Folder Include="Processing\" />
<Folder Include="Signals\Modulation\" />
<Folder Include="Optimization\" />
</ItemGroup>
<ItemGroup>
<None Include="Fuzzy\FuzzyRule.cs" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\Esiur\Esiur.csproj" />
</ItemGroup>
</Project>

135
Esiur.Analysis/Optimization/Genetic.cs Normal file
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@ -0,0 +1,135 @@
using Esiur.Data;
using Esiur.Resource;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Reflection;
using System.Security.Cryptography.X509Certificates;
using System.Text;
namespace Esiur.Analysis.Optimization
{
public class Genetic<T> where T : unmanaged
{
Random rand = new Random();
public static unsafe byte[] Encode(in T value)
{
byte[] result = new byte[sizeof(T)];
fixed (byte* dst = result)
*(T*)dst = value;
return result;
}
public static unsafe T Decode(byte[] data)
{
fixed (byte* src = data)
return *(T*)src;
}
public List<T> Population = new List<T>();
public int PopulationSize { get; set; }
private int DataSize { get; set; }
public Func<T, double> FitnessFunction { get; set; }
public unsafe Genetic(int populationSize, Func<T, double> fitnessFunction)
{
FitnessFunction = fitnessFunction;
PopulationSize = populationSize;
DataSize = sizeof(T);
}
void GeneratePopultation()
{
for (var i = 0; i < PopulationSize; i++)
{
byte[] buffer = new byte[DataSize];
rand.NextBytes(buffer);
var record = Decode(buffer);
Population.Add(record);
}
}
KeyValuePair<T, double>[] GetFitness()
{
var rt = new List<KeyValuePair<T, double>>();
foreach (var record in Population)
rt.Add(new KeyValuePair<T, double>( record, FitnessFunction(record)));
return rt.ToArray();
}
T Mate(T parent1, T parent2)
{
var dp1 = Encode(parent1);
var dp2 = Encode(parent2);
var dc = new byte[dp1.Length];
for (var i = 0; i < dc.Length; i++)
{
var prop = rand.NextDouble();
if (prop < 0.45)
dc[i] = dp1[i];
else if (prop < 0.9)
dc[i] = dp2[i];
else
dc[i] = (byte)rand.Next(0, 255);
}
return Decode(dc);
}
public T Evaluate(int maxIterations)
{
GeneratePopultation();
var generation = 0;
T best;
do
{
var ordered = GetFitness().OrderBy(x => x.Value).ToArray();
best = ordered[0].Key;
if (ordered[0].Value == 0)
break;
// Elitism selection ( 10% of fittest population )
var eliteCount = (int)(ordered.Length * 0.1);
var neededCount = (int)(ordered.Length * 0.9);
var newGeneration = ordered.Select(x => x.Key).Take(eliteCount).ToList();
for (var i = 0; i < neededCount; i++)
{
var p1 = Population[rand.Next(0, PopulationSize / 2)];
var p2 = Population[rand.Next(0, PopulationSize / 2)];
var offspring = Mate(p1, p2);
newGeneration.Add(offspring);
}
Population = newGeneration;
Debug.WriteLine($"Gen {generation} Fittest: {ordered.First().Value} {ordered.First().Key.ToString()} ");
} while (generation++ < maxIterations);
Debug.WriteLine($"Gen {generation} Best: {best.ToString()} ");
return best;
}
}
}

8
Esiur.Analysis/Statistics/StatisticalFunctions.cs
View File

@ -33,7 +33,13 @@ namespace Esiur.Analysis.Statistics
}
public static double RMS(this double[] x) => Math.Sqrt(x.Sum(x => x * x) / x.Length);
public static double RMS(this double[] x)
{
var r = Math.Sqrt(x.Sum(x => x * x) / x.Length);
if (double.IsNaN(r))
Console.WriteLine();
return r;
}
public static double Correlation(this double[] x, double[] y)

8
Esiur.sln
View File

@ -10,7 +10,9 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Esiur.Stores.EntityCore", "
EndProject
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Test", "Test\Test.csproj", "{331F82B6-6B90-4533-9718-F7C8090D8F19}"
EndProject
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Esiur.Analysis", "Esiur.Analysis\Esiur.Analysis.csproj", "{8D52DEEA-B7E8-4C93-9B6C-EDB9C2D25211}"
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Esiur.Analysis", "Esiur.Analysis\Esiur.Analysis.csproj", "{8D52DEEA-B7E8-4C93-9B6C-EDB9C2D25211}"
EndProject
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Esiur.Analysis.Test", "Esiur.Analysis.Test\Esiur.Analysis.Test.csproj", "{72115ABD-BD54-4E88-8DB6-B2A953F57F0A}"
EndProject
Global
GlobalSection(SolutionConfigurationPlatforms) = preSolution
@ -38,6 +40,10 @@ Global
{8D52DEEA-B7E8-4C93-9B6C-EDB9C2D25211}.Debug|Any CPU.Build.0 = Debug|Any CPU
{8D52DEEA-B7E8-4C93-9B6C-EDB9C2D25211}.Release|Any CPU.ActiveCfg = Release|Any CPU
{8D52DEEA-B7E8-4C93-9B6C-EDB9C2D25211}.Release|Any CPU.Build.0 = Release|Any CPU
{72115ABD-BD54-4E88-8DB6-B2A953F57F0A}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
{72115ABD-BD54-4E88-8DB6-B2A953F57F0A}.Debug|Any CPU.Build.0 = Debug|Any CPU
{72115ABD-BD54-4E88-8DB6-B2A953F57F0A}.Release|Any CPU.ActiveCfg = Release|Any CPU
{72115ABD-BD54-4E88-8DB6-B2A953F57F0A}.Release|Any CPU.Build.0 = Release|Any CPU
EndGlobalSection
GlobalSection(SolutionProperties) = preSolution
HideSolutionNode = FALSE

47
Test/Program.cs
View File

@ -56,53 +56,6 @@ namespace Test
{
static async Task Main(string[] args)
{
//var outage = Capacity.ComputeOutage(20000000, new Capacity.CSI[]
//{
// new Capacity.CSI(PowerUnit.FromDb(20), 0.1),
// new Capacity.CSI(PowerUnit.FromDb(15), 0.15),
// new Capacity.CSI(PowerUnit.FromDb(10), 0.25),
// new Capacity.CSI(PowerUnit.FromDb(5), 0.25),
// new Capacity.CSI(PowerUnit.FromDb(0), 0.15),
// new Capacity.CSI(PowerUnit.FromDb(-5), 0.1),
//});
var outage = Capacity.ComputeOutage(1, new Capacity.CSI[]
{
new Capacity.CSI(PowerUnit.FromDb(30), 0.2),
new Capacity.CSI(PowerUnit.FromDb(20), 0.3),
new Capacity.CSI(PowerUnit.FromDb(10), 0.3),
new Capacity.CSI(PowerUnit.FromDb(0), 0.2),
});
var low = new ContinuousSet(MembershipFunctions.Descending(20, 40));
var mid = new ContinuousSet(MembershipFunctions.Triangular(20, 40, 60));
var high = new ContinuousSet(MembershipFunctions.Ascending(40, 60));
var bad = new ContinuousSet(MembershipFunctions.Descending(0, 30));
var ok = new ContinuousSet(MembershipFunctions.Triangular(20, 50, 80));
var excelent = new ContinuousSet(MembershipFunctions.Ascending(70, 100));
var small = new ContinuousSet(MembershipFunctions.Descending(100, 200));
var avg = new ContinuousSet(MembershipFunctions.Triangular(100, 200, 300));
var big = new ContinuousSet(MembershipFunctions.Ascending(200, 300));
//var speedIsLowThenSmall = new FuzzyRule("Low=>Small", low, small);
double rating = 80;
for (double temp = 60; temp < 100; temp++)
{
var v = MamdaniDefuzzifier.Evaluate(new INumericalSet<double>[]
{
temp.Is(low).And(rating.Is(bad)).Then(small),
temp.Is(mid).And(rating.Is(ok)).Then(avg),
temp.Is(high).And(rating.Is(excelent)).Then(big),
}, MamdaniDefuzzifierMethod.CenterOfGravity, 100, 300, 1);
Console.WriteLine(temp + " " + v);
}
// Create stores to keep objects.
var system = await Warehouse.Put("sys", new MemoryStore());
var server = await Warehouse.Put("sys/server", new DistributedServer());