stefan
/
NLMSvariants
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'master' of https://github.com/FBRDNLMS/NLMSvariants

This commit is contained in:
Friese 2018-05-11 11:32:44 +02:00
parent 0a8a81677c be5a61ac0a
commit c70c7e65cb
6 changed files with 3492 additions and 240 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
bin/NLMS_with_UI.exe Normal file
View File

Binary file not shown.

446
bin/NLMSvariants.cs Normal file
View File

@ -0,0 +1,446 @@
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using System.Windows.Forms.DataVisualization.Charting;
namespace NMLS_Graphisch
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
}
static int NumberOfSamples = 1000;
const int tracking = 40;
static Stack<double> x = new Stack<double>();
static Random rnd = new Random();
static double[] _x = new double[NumberOfSamples];
static double[,] w = new double[NumberOfSamples, NumberOfSamples];
static double learnrate = 0.2;
static double[] pixel_array;
static int windowSize = 5;
private void button1_Click(object sender, EventArgs e)
{
NumberOfSamples = Int32.Parse(comboBox2.SelectedItem.ToString());
chart1.ChartAreas[0].AxisX.Maximum = NumberOfSamples;
chart1.ChartAreas[0].AxisY.Maximum = 300;
chart1.ChartAreas[0].AxisY.Minimum = -5;
if (checkBox1.Checked)
{
for (int i = 0; i < tracking; i++)
{
for (int k = 0; k < windowSize; k++)
{
File.AppendAllText("weights.txt",
String.Format("[{0}][{1}] {2}\n", k, i, Math.Round(w[k, i], 2).ToString()),
Encoding.UTF8);
}
}
}
Series[] series = new Series[2];
switch (comboBox1.SelectedItem.ToString())
{
case "lokaler Mittelwert":
series = lokalerMittelWert();
break;
case "direkter Vorgänger":
series = direkterVorgaenger();
break;
case "differenzieller Vorgänger":
series = diffVorgaenger();
break;
default:
return;
}
foreach (Series s in series)
{
if (chart1.Series.IndexOf(s.Name) < 0)
{
chart1.Series.Add(s);
}
else
{
chart1.Series.RemoveAt(chart1.Series.IndexOf(s.Name));
chart1.Series.Add(s);
}
}
if (checkBox1.Checked)
{
for (int i = 0; i < tracking; i++)
{
for (int k = 0; k < windowSize; k++)
{
File.AppendAllText("weights_after.txt",
String.Format("[{0}][{1}] {2}\n", k, i, Math.Round(w[k, i], 2).ToString()),
Encoding.UTF8);
}
}
}
}
Series[] lokalerMittelWert()
{
int x_count = 0;
double[] x_error = new double[NumberOfSamples];
x_error[0] = 0;
//Graphischer Stuff
Series lokal_M_error = new Series("Lokaler Mittelwert Error");
Series lokal_M_predic = new Series("Lokaler Mittelwert Prediction");
lokal_M_error.ChartType = SeriesChartType.Spline;
lokal_M_predic.ChartType = SeriesChartType.Spline;
while (x_count + 1 < NumberOfSamples)
{
double[] x_part_Array = new double[x_count];
int _sourceIndex = (x_count > windowSize) ? x_count - windowSize : 0;
int _arrayLength = (x_count > windowSize) ? windowSize + 1 : (x_count > 0) ? x_count : 0;
Array.Copy(_x, _sourceIndex, x_part_Array, 0, _arrayLength);
double x_middle = (x_count > 0) ? ( x_part_Array.Sum() / _arrayLength) : 0;
double x_pred = 0.0;
double[] x_array = _x;
double x_actual = _x[x_count + 1];
for (int i = 1; i < _arrayLength; i++)
{
x_pred += (w[i, x_count] * (x_array[x_count - i] - x_middle));
}
x_pred += x_middle;
// Output Stuff
if (checkBox1.Checked)
{
File.AppendAllText("lokalerMittelwert.txt",
String.Format("{0}. X_pred {1}\n", x_count, x_pred),
Encoding.UTF8);
File.AppendAllText("lokalerMittelwert.txt",
String.Format("{0}. X_actual {1}\n", x_count, x_actual),
Encoding.UTF8);
}
x_error[x_count] = x_actual - x_pred;
double x_square = 0;
//Output Stuff
if (checkBox1.Checked)
{
File.AppendAllText("lokalerMittelwert.txt",
String.Format("{0}. X_error {1}\n\n", x_count, x_error[x_count]),
Encoding.UTF8);
}
for (int i = 1; i < _arrayLength; i++)
{
x_square += Math.Pow(x_array[x_count - i] - x_middle, 2);
}
for (int i = 1; i < _arrayLength; i++)
{
w[i, x_count + 1] = w[i, x_count] + learnrate * x_error[x_count] * ((x_array[x_count - i] - x_middle) / x_square);
}
// Graphischer Stuff
lokal_M_error.Points.AddXY(x_count, x_error[x_count]);
lokal_M_predic.Points.AddXY(x_count, x_pred);
x_count += 1;
}
double mittel = x_error.Where(d => !double.IsNaN(d)).Sum() / x_error.Length;
double varianz = 0.0;
foreach (double x_e in x_error)
{
if(!double.IsNaN(x_e))
varianz += Math.Pow(x_e - mittel, 2);
}
varianz /= x_error.Length;
if (checkBox1.Checked)
{
File.AppendAllText("ergebnisse.txt",
String.Format("Quadratische Varianz(x_error): {0}\n Mittelwert(x_error): {1}\n\n", varianz, mittel),
Encoding.UTF8);
}
return new Series[] { lokal_M_predic, lokal_M_error };
}
Series[] direkterVorgaenger()
{
double[] x_error = new double[NumberOfSamples];
x_error[0] = 0;
int x_count = 0;
// Graphischer Stuff
Series direkterVorgaenger_error = new Series("Direkter Vorgänger Error");
Series direkterVorgaenger_predic = new Series("Direkter Vorgänger Prediction");
direkterVorgaenger_error.ChartType = SeriesChartType.Spline;
direkterVorgaenger_predic.ChartType = SeriesChartType.Spline;
while (x_count + 1 < NumberOfSamples)
{
double x_pred = 0.0;
double[] x_array = _x;
double x_actual = _x[x_count + 1];
if (x_count > 0)
{
int _arrayLength = (x_count > windowSize) ? windowSize + 1 : x_count;
for (int i = 1; i < _arrayLength; i++)
{
x_pred += (w[i, x_count] * (x_array[x_count - 1] - x_array[x_count - i - 1]));
}
x_pred += x_array[x_count - 1];
// Output Stuff
if (checkBox1.Checked)
{
File.AppendAllText("direkterVorgaenger.txt",
String.Format("{0}. X_pred {1}\n", x_count, x_pred),
Encoding.UTF8);
File.AppendAllText("direkterVorgaenger.txt",
String.Format("{0}. X_actual {1}\n", x_count, x_actual),
Encoding.UTF8);
}
x_error[x_count] = x_actual - x_pred;
// Output Stuff
if (checkBox1.Checked)
{
File.AppendAllText("direkterVorgaenger.txt",
String.Format("{0}. X_error {1}\n\n", x_count, x_error[x_count]),
Encoding.UTF8);
}
double x_square = 0;
for (int i = 1; i < _arrayLength; i++)
{
x_square += Math.Pow(x_array[x_count - 1] - x_array[x_count - i - 1], 2);
}
for (int i = 1; i < _arrayLength; i++)
{
w[i, x_count + 1] = w[i, x_count] + learnrate * x_error[x_count] * ((x_array[x_count - 1] - x_array[x_count - i - 1]) / x_square);
}
}
//Graphischer Stuff
direkterVorgaenger_error.Points.AddXY(x_count, x_error[x_count]);
direkterVorgaenger_predic.Points.AddXY(x_count, x_pred);
x_count += 1;
}
double mittel = x_error.Where(d => !double.IsNaN(d)).Sum() / x_error.Length;
double varianz = 0.0;
foreach (double x_e in x_error)
{
if (!double.IsNaN(x_e))
varianz += Math.Pow(x_e - mittel, 2);
}
varianz /= x_error.Length;
if (checkBox1.Checked)
{
File.AppendAllText("ergebnisse.txt",
String.Format("Quadratische Varianz(x_error): {0}\n Mittelwert(x_error): {1}\n\n", varianz, mittel),
Encoding.UTF8);
}
return new Series[] { direkterVorgaenger_error, direkterVorgaenger_predic };
}
Series[] diffVorgaenger()
{
double[] x_error = new double[NumberOfSamples];
x_error[0] = 0;
int x_count = 1;
//Graphischer Stuff
Series diffVorgaenger_error = new Series("Differenzieller Vorgänger Error");
Series diffVorgaenger_predic = new Series("Differenzieller Vorgänger Prediction");
diffVorgaenger_error.ChartType = SeriesChartType.Spline;
diffVorgaenger_predic.ChartType = SeriesChartType.Spline;
while (x_count + 1 < NumberOfSamples)
{
double x_pred = 0.0;
double[] x_array = _x;
double x_actual = _x[x_count + 1];
if (x_count > 0)
{
int _arrayLength = (x_count > windowSize) ? windowSize + 1 : x_count;
for (int i = 1; i < _arrayLength; i++)
{
x_pred += (w[i, x_count] * (x_array[x_count - i] - x_array[x_count - i - 1]));
}
x_pred += x_array[x_count - 1];
// Output Stuff
if (checkBox1.Checked)
{
File.AppendAllText("differenziellerVorgaenger.txt",
String.Format("{0}. X_pred {1}\n", x_count, x_pred),
Encoding.UTF8);
File.AppendAllText("differenziellerVorgaenger.txt",
String.Format("{0}. X_actual {1}\n", x_count, x_actual),
Encoding.UTF8);
}
x_error[x_count] = x_actual - x_pred;
// Output Stuff
if (checkBox1.Checked)
{
File.AppendAllText("differenziellerVorgaenger.txt",
String.Format("{0}. X_error {1}\n\n", x_count, x_error[x_count]),
Encoding.UTF8);
}
double x_square = 0;
for (int i = 1; i < _arrayLength; i++)
{
x_square += Math.Pow(x_array[x_count - i] - x_array[x_count - i - 1], 2);
}
for (int i = 1; i < _arrayLength; i++)
{
w[i, x_count + 1] = w[i, x_count] + learnrate * x_error[x_count] * ((x_array[x_count - i] - x_array[x_count - i - 1]) / x_square);
}
}
//Graphischer Stuff
diffVorgaenger_error.Points.AddXY(x_count, x_error[x_count]);
diffVorgaenger_predic.Points.AddXY(x_count, x_pred);
x_count += 1;
}
double mittel = x_error.Where(d => !double.IsNaN(d)).Sum() / x_error.Length;
double varianz = 0.0;
foreach (double x_e in x_error)
{
if (!double.IsNaN(x_e))
varianz += Math.Pow(x_e - mittel, 2);
}
varianz /= x_error.Length;
if (checkBox1.Checked)
{
File.AppendAllText("ergebnisse.txt",
String.Format("Quadratische Varianz(x_error): {0}\n Mittelwert(x_error): {1}\n\n", varianz, mittel),
Encoding.UTF8);
}
return new Series[] { diffVorgaenger_error, diffVorgaenger_predic };
}
// Inizialisierung von Arrays
private void Form1_Load(object sender, EventArgs e)
{
comboBox1.SelectedIndex = 0;
comboBox2.SelectedIndex = 0;
chart1.Series.Clear();
Series x_actual = new Series("Actual x Value");
x_actual.ChartType = SeriesChartType.Spline;
for (int i = 0; i < NumberOfSamples; i++)
{
_x[i] += ((255.0 / NumberOfSamples) * i);
for (int k = 0; k < windowSize; k++)
{
w[k, i] = rnd.NextDouble();
//Console.WriteLine(String.Format("Weight[{0}, {1}]: {2}",k,i, w[k, i]));
}
x_actual.Points.AddXY(i, _x[i]);
}
chart1.Series.Add(x_actual);
}
// Graphen Clearen
private void button2_Click(object sender, EventArgs e)
{
chart1.Series.Clear();
Series x_actual = new Series("Actual x Value");
x_actual.ChartType = SeriesChartType.Spline;
for (int i = 0; i < NumberOfSamples; i++)
{
x_actual.Points.AddXY(i, _x[i]);
}
chart1.Series.Add(x_actual);
}
// Bild Laden
private void button3_Click(object sender, EventArgs e)
{
OpenFileDialog openFileDialog = new OpenFileDialog();
if(openFileDialog.ShowDialog() == DialogResult.OK)
{
try
{
Bitmap img = new Bitmap(openFileDialog.FileName);
pixel_array = new double[img.Width * img.Height];
for (int i = 1; i < img.Width; i++)
{
for (int j = 1; j < img.Height; j++)
{
Color pixel = img.GetPixel(i, j);
pixel_array[j*i] = pixel.R;
}
}
NumberOfSamples = (img.Width * img.Height) / 2;
comboBox2.Items.Add(NumberOfSamples);
_x = pixel_array;
w = new double[NumberOfSamples, NumberOfSamples];
for (int i = 0; i < NumberOfSamples; i++)
{
for (int k = 1; k < NumberOfSamples; k++)
{
w[k, i] = rnd.NextDouble();
}
}
}
catch(Exception exep)
{
MessageBox.Show("Konnte Bild nicht laden.");
MessageBox.Show(String.Format("{0}", exep.ToString()));
}
}
}
}
}

BIN
bin/NMLS_Graphisch.exe
View File

Binary file not shown.

232
bin/NMLS_c_sharp_variante.cs
View File

@ -1,232 +0,0 @@
// Variable mit der anzahl Punkten zwischen 0 - 255
static int M = 1000;
// Variable zum tracken der Gewichte
const int tracking = 40;
// C# only zum erstellen von Randomzahlen
static Random rnd = new Random();
// Array mit den Testwerten
static double[] _x = new double[M];
// Array mit den Gewichten
static double[,] w = new double[M, M];
// Lernrate
static double learnrate = 1;
/**************************************************************
main() des Programms
***************************************************************/
int main(){
// Initialisierung des Test Array + Gewichte
for (int i = 0; i < M; i++)
{
_x[i] += ((255.0 / M) * i);
for (int k = 1; k < M; k++)
{
w[k, i] = rnd.NextDouble();
//Console.WriteLine(String.Format("Weight: {0}", w[k, i]));
}
}
// Zum erstellen eines Files mit den Gewichten vor den Updates
for (int i = 0; i < tracking; i++)
{
for (int k = 1; k < tracking; k++)
{
File.AppendAllText("weights.txt",
String.Format("[{0}][{1}] {2}\n", k, i, Math.Round(w[k, i], 2).ToString()),
Encoding.UTF8);
}
}
// Variante die Ausgeführt werden soll
lokalerMittelWert();
// Zum erstellen eines Files mit den Gewichten nach den Updates
for (int i = 0; i < tracking; i++)
{
for (int k = 1; k < tracking; k++)
{
File.AppendAllText("weights_after.txt",
String.Format("[{0}][{1}] {2}\n", k, i, Math.Round(w[k, i], 2).ToString()),
Encoding.UTF8);
}
}
}
/**************************************************************
Errechnet die 1. Variante, mit abziehen des lokalen Mittelwertes
***************************************************************/
void lokalerMittelWert()
{
//Array in dem e(n) gespeichert wird
double[] x_error = new double[M];
//Laufzeitvariable
int x_count = 0;
// x_count + 1 da x_count > 0 sein muss
while (x_count + 1 < M)
{
//Erstellt ein neues Array mit allen werten bis zur Laufzeitvariable
double[] x_part_Array = new double[x_count];
Array.Copy(_x, 0, x_part_Array, 0, x_count);
double x_middle = (x_count > 0) ? ( x_part_Array.Sum() / x_count) : 0;
// Variable für die errechnete Zahl
double x_pred = 0.0;
// Variable mit der eigentlichen Zahl
double x_actual = _x[x_count + 1];
for (int i = 1; i < x_count; i++)
{
x_pred += (w[i, x_count] * (_x[x_count - i] - x_middle));
}
x_pred += x_middle;
//Console.WriteLine(String.Format("X_sum: {0}", x_middle));
//Console.WriteLine(String.Format("X_pred: {0}", x_pred));
//Console.WriteLine(String.Format("X_actual: {0}", x_actual));
x_error[x_count] = x_actual - x_pred;
// Funktion zum berechnen des Quadrates
double x_square = 0;
for (int i = 1; i <= x_count; i++)
{
x_square += Math.Pow(_x[x_count - i], 2);
}
// Funktion zum updaten der Gewichte
for (int i = 1; i < x_count; i++)
{
w[i, x_count + 1] = w[i, x_count] + learnrate * x_error[x_count] * (_x[x_count - i] / x_square);
}
// Laufzeitvariable hochzählen
x_count += 1;
}
// Berechenen des mittleren Fehlers
double mittel = x_error.Sum() / x_error.Length;
// Berechenen der varianz des Fehlers
double varianz = 0.0;
foreach (double x_e in x_error)
{
varianz += Math.Pow(x_e - mittel, 2);
}
varianz /= x_error.Length;
// Hängt dem Angegebenen File den Vorgegebenen String an
File.AppendAllText("ergebnisse.txt",
String.Format("Quadratische Varianz(x_error): {0}\n Mittelwert(x_error): {1}\n\n", varianz, mittel),
Encoding.UTF8);
}
/**************************************************************
Errechnet die 2. Variante, mit abziehen des direkten Vorgängers
***************************************************************/
void direkterVorgaenger()
{
//Array in dem e(n) gespeichert wird
double[] x_error = new double[M];
//Laufzeitvariable
int x_count = 0;
// x_count + 1 da x_count > 0 sein muss
while (x_count + 1 < M)
{
// Variable für die errechnete Zahl
double x_pred = 0.0;
// Variable mit der eigentlichen Zahl
double x_actual = _x[x_count + 1];
// Funktion fürs berechnen der Vorhersagezahl
for (int i = 1; i < x_count; i++)
{
x_pred += (w[i, x_count] * (_x[x_count - i] - _x[x_count - i - 1]));
}
x_pred += _x[x_count - 1];
//Console.WriteLine(String.Format("X_pred: {0}", x_pred));
// Hängt dem Angegebenen File den Vorgegebenen String an
File.AppendAllText("direkterVorgaenger.txt",
String.Format("{0}. X_pred {1}\n", x_count, x_pred),
Encoding.UTF8);
//Console.WriteLine(String.Format("X_actual: {0}", x_actual));
// Hängt dem Angegebenen File den Vorgegebenen String an
File.AppendAllText("direkterVorgaenger.txt",
String.Format("{0}. X_actual {1}\n", x_count, x_actual),
Encoding.UTF8);
// Berechnung des Fehlers
x_error[x_count] = x_actual - x_pred;
//Console.WriteLine(String.Format("X_error: {0}", x_error));
// Hängt dem Angegebenen File den Vorgegebenen String an
File.AppendAllText("direkterVorgaenger.txt",
String.Format("{0}. X_error {1}\n\n", x_count, x_error),
Encoding.UTF8);
// Funktion zum berechnen des Quadrates
double x_square = 0;
for (int i = 1; i < x_count; i++)
{
x_square += Math.Pow(_x[x_count - i] - _x[x_count - i - 1], 2);
}
//Console.WriteLine(String.Format("X_square: {0}", x_square));
// Hängt dem Angegebenen File den Vorgegebenen String an
//File.AppendAllText("direkterVorgaenger.txt",
// String.Format("{0}. X_square {1}\n", x_count, x_square),
// Encoding.UTF8);
// Funktion zum updaten der Gewichte
for (int i = 1; i < x_count; i++)
{
w[i, x_count + 1] = w[i, x_count] + learnrate * x_error[x_count] * ((_x[x_count - i] - _x[x_count - i - 1]) / x_square);
}
// Laufzeitvariable hochzählen
x_count += 1;
}
// Berechenen des mittleren Fehlers
double mittel = x_error.Sum() / x_error.Length;
// Berechenen der varianz des Fehlers
double varianz = 0.0;
foreach (double x_e in x_error)
{
varianz += Math.Pow(x_e - mittel, 2);
}
varianz /= x_error.Length;
// Hängt dem Angegebenen File den Vorgegebenen String an
File.AppendAllText("ergebnisse.txt",
String.Format("Quadratische Varianz(x_error): {0}\n Mittelwert(x_error): {1}\n\n", varianz, mittel),
Encoding.UTF8);
}

3049
bin/graphResults.html
View File

File diff suppressed because it is too large Load Diff

5
bin/template.svg
View File

@ -1,6 +1,3 @@
<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
<svg height="1200" viewBox="100 50 400 -400" width="3000" version="1.1" <svg height="1200" viewBox="100 50 400 -400" width="3000" version="1.1"
xmlns="http://www.w3.org/2000/svg"> xmlns="http://www.w3.org/2000/svg">
<desc>NLMSvariants output graph <desc>NLMSvariants output graph
@ -22,7 +19,7 @@
</g> </g>
<g transform="translate(200, 400) scale(1,-1)"> <g transform="translate(200, 400) scale(1,-1)">
<path d="M0 0 <path d="M0 0
" fill="none" stroke="red" stroke-width="0.4px"/> " fill="none" stroke="red" stroke-width="0.4px" onclick="clicksvg(this)"/>
<text class="t36 t-mid bold f-black" x="50" y="50">+ +</text> <text class="t36 t-mid bold f-black" x="50" y="50">+ +</text>
<text class="t36 t-mid bold f-black" x="-50" y="50">- +</text> <text class="t36 t-mid bold f-black" x="-50" y="50">- +</text>
<text class="t36 t-mid bold f-black" x="50" y="-50">+ -</text> <text class="t36 t-mid bold f-black" x="50" y="-50">+ -</text>

Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 1.6 KiB

After

Width:  |  Height:  |  Size: 1.4 KiB