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

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gurkenhabicht 2018-05-22 15:22:13 +02:00
parent c9ae60d383 6ff71dddba
commit 3b0c9ac3fc
5 changed files with 166 additions and 32 deletions
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bin/README.md Normal file
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# README
### Short explenation of the c# .exe
![alt text](https://github.com/FBRDNLMS/NLMSvariants/blob/master/img/CS_exe_screenshot_1.PNG "C# .exe with loaded picture")
1. Choose the algorithm u want to calculate on and press "start" or "enter"
2. Choose the number of pixels you want to go for
3. Clears the screen and reprints the actual values of your picture or predefined values
4. Choose the learnrate and window size you want, learnrate has to be over 0.0 and under or even 1.0.
The window size has to be an integer.
5. Loads a picture, it has to be BMP, GIF, EXIF, JPG, PNG or TIFF
6. Check this for generating outputfiles in the same direktory you started your .exe in
Outputfiles are : weights.txt, weights_after.txt, localMean|directPredecessorer|differentialPredecessorer.txt, results.txt
7. Chart where the algorithems output is displayed
![alt text](https://github.com/FBRDNLMS/NLMSvariants/blob/master/img/CS_exe_screenshot_2.PNG "C# .exe with loaded picture and calculated")
1. Displays the average error of the calculated algorithm
2. Displays the variance of the error of the calculated algorithm
3. The history of graphs displayed in the chart, it is possible to have graphs of all 3 LMS algorithems in the history.
There is no way to hide graphs, simply use "Clear" and then calculate again.

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src/c_sharp_implementation/NLMSvariants.cs
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/*
===========================================================================
Created by Kevin Becker on 19.04.2018
===========================================================================
*/
using System;
using System.Collections.Generic;
using System.ComponentModel;
@ -37,11 +44,52 @@ namespace NMLS_Graphisch
*
*---------------------------------------------------*/
private void GO_btn_Click(object sender, EventArgs e)
private void Start_btn_Click(object sender, EventArgs e)
{
/* Initializing the chart to display all values */
/* Initializing NumberOfSamples with the choosen pixelcount */
NumberOfSamples = Int32.Parse(comboBox_pixel.SelectedItem.ToString());
/* Initializing the weights */
w = new double[windowSize, NumberOfSamples];
for (int i = 0; i < NumberOfSamples; i++)
{
for (int k = 0; k < windowSize; k++)
{
w[k, i] = rnd.NextDouble();
}
}
/* Initializing the learnrate with the choosen one */
txtBox_learnrate.Text = txtBox_learnrate.Text.Replace(".", ",");
try
{
learnrate = Double.Parse(txtBox_learnrate.Text);
}
catch(Exception exep)
{
MessageBox.Show("Please choose a learnrate between >0.0 and <=1.0");
return;
}
while(learnrate <= 0.0 || learnrate > 1.0)
{
MessageBox.Show("Please choose a learnrate between >0.0 and <=1.0");
var myValue = Microsoft.VisualBasic.Interaction.InputBox("Choose a learnrate between >0.0 and <=1.0", "Learnrate", "0,8");
if(myValue == "")
{
return;
}
else
{
learnrate = Double.Parse(myValue);
}
}
/* Initializing the windowsize with the choosen one*/
/* Initializing the chart to display all values */
chart_main.ChartAreas[0].AxisX.Maximum = NumberOfSamples;
chart_main.ChartAreas[0].AxisY.Maximum = 300;
chart_main.ChartAreas[0].AxisY.Minimum = -100;
@ -53,7 +101,7 @@ namespace NMLS_Graphisch
{
for (int k = 0; k < windowSize; k++)
{
File.AppendAllText("weights.txt",
File.AppendAllText(String.Format("weights_{0}.txt", comboBox_algorithem.SelectedItem.ToString().Replace(" ","")),
String.Format("[{0}][{1}] {2}\n", k, i, Math.Round(w[k, i], 2).ToString()),
Encoding.UTF8);
}
@ -66,15 +114,15 @@ namespace NMLS_Graphisch
switch (comboBox_algorithem.SelectedItem.ToString())
{
case "lokaler Mittelwert":
case "local mean":
series = localMean();
break;
case "direkter Vorgänger":
case "direct predecessor":
series = directPredecessor();
break;
case "differenzieller Vorgänger":
case "differential predecessor":
series = diffPredecessor();
break;
@ -104,7 +152,7 @@ namespace NMLS_Graphisch
{
for (int k = 0; k < windowSize; k++)
{
File.AppendAllText("weights_after.txt",
File.AppendAllText(String.Format("weights_after_{0}.txt", comboBox_algorithem.SelectedItem.ToString().Replace(" ", "")),
String.Format("[{0}][{1}] {2}\n", k, i, Math.Round(w[k, i], 2).ToString()),
Encoding.UTF8);
}
@ -133,8 +181,8 @@ namespace NMLS_Graphisch
x_error[0] = 0;
/* Inizilazing series for the main chart */
Series localMeanError = new Series("Lokaler Mittelwert Error");
Series localMeanPredict = new Series("Lokaler Mittelwert Prediction");
Series localMeanError = new Series("Local Mean Error");
Series localMeanPredict = new Series("Local Mean Prediction");
localMeanError.ChartType = SeriesChartType.Spline;
localMeanPredict.ChartType = SeriesChartType.Spline;
@ -155,7 +203,7 @@ namespace NMLS_Graphisch
double x_pred = 0.0;
double[] x_array = _x;
double x_actual = _x[x_count + 1];
double x_actual = _x[x_count];
/* Prediction algorithem */
@ -169,11 +217,11 @@ namespace NMLS_Graphisch
if (checkBox_output.Checked)
{
File.AppendAllText("lokalerMittelwert.txt",
File.AppendAllText("localMean.txt",
String.Format("{0}. X_pred {1}\n", x_count, x_pred),
Encoding.UTF8);
File.AppendAllText("lokalerMittelwert.txt",
File.AppendAllText("localMean.txt",
String.Format("{0}. X_actual {1}\n", x_count, x_actual),
Encoding.UTF8);
}
@ -187,7 +235,7 @@ namespace NMLS_Graphisch
/* Output */
if (checkBox_output.Checked)
{
File.AppendAllText("lokalerMittelwert.txt",
File.AppendAllText("localMean.txt",
String.Format("{0}. X_error {1}\n\n", x_count, x_error[x_count]),
Encoding.UTF8);
}
@ -234,8 +282,8 @@ namespace NMLS_Graphisch
/* Output the result */
if (checkBox_output.Checked)
{
File.AppendAllText("ergebnisse.txt",
String.Format("Quadratische Varianz(x_error): {0}\n Mittelwert(x_error): {1}\n\n", varianz, mittel),
File.AppendAllText("results_localMean.txt",
String.Format("Squared variance(x_error): {0}\n Average(x_error): {1}\n\n", varianz, mittel),
Encoding.UTF8);
}
@ -259,8 +307,8 @@ namespace NMLS_Graphisch
int x_count = 0;
/* Inizilazing series for the main chart */
Series directPredecessorError = new Series("Direkter Vorgänger Error");
Series directPredecessorPrediction = new Series("Direkter Vorgänger Prediction");
Series directPredecessorError = new Series("Direct predecessor Error");
Series directPredecessorPrediction = new Series("Direct predecessor Prediction");
directPredecessorError.ChartType = SeriesChartType.Spline;
directPredecessorPrediction.ChartType = SeriesChartType.Spline;
@ -268,7 +316,7 @@ namespace NMLS_Graphisch
while (x_count < NumberOfSamples - 1)
{
double x_pred = 0.0;
double x_actual = _x[x_count + 1];
double x_actual = _x[x_count];
if (x_count > 0)
{
@ -285,11 +333,11 @@ namespace NMLS_Graphisch
/* If output is checked a file is produced with prediction, actual and the error */
if (checkBox_output.Checked)
{
File.AppendAllText("direkterVorgaenger.txt",
File.AppendAllText("directpredecessor.txt",
String.Format("{0}. X_pred {1}\n", x_count, x_pred),
Encoding.UTF8);
File.AppendAllText("direkterVorgaenger.txt",
File.AppendAllText("directpredecessor.txt",
String.Format("{0}. X_actual {1}\n", x_count, x_actual),
Encoding.UTF8);
}
@ -300,7 +348,7 @@ namespace NMLS_Graphisch
/* Output */
if (checkBox_output.Checked)
{
File.AppendAllText("direkterVorgaenger.txt",
File.AppendAllText("directpredecessor.txt",
String.Format("{0}. X_error {1}\n\n", x_count, x_error[x_count]),
Encoding.UTF8);
}
@ -350,8 +398,8 @@ namespace NMLS_Graphisch
/* Output the result */
if (checkBox_output.Checked)
{
File.AppendAllText("ergebnisse.txt",
String.Format("Quadratische Varianz(x_error): {0}\n Mittelwert(x_error): {1}\n\n", varianz, mittel),
File.AppendAllText("results_directpredecessor.txt",
String.Format("Squared variance(x_error): {0}\n Average(x_error): {1}\n\n", varianz, mittel),
Encoding.UTF8);
}
@ -375,8 +423,8 @@ namespace NMLS_Graphisch
int x_count = 0;
/* Inizilazing series for the main chart */
Series diffPredecessorError = new Series("Differenzieller Vorgänger Error");
Series diffPredecessorPrediction = new Series("Differenzieller Vorgänger Prediction");
Series diffPredecessorError = new Series("Differential predecessor Error");
Series diffPredecessorPrediction = new Series("Differential predecessor Prediction");
diffPredecessorError.ChartType = SeriesChartType.Spline;
diffPredecessorPrediction.ChartType = SeriesChartType.Spline;
@ -384,7 +432,7 @@ namespace NMLS_Graphisch
while (x_count < NumberOfSamples - 1)
{
double x_pred = 0.0;
double x_actual = _x[x_count + 1];
double x_actual = _x[x_count];
if (x_count > 0)
{
@ -401,11 +449,11 @@ namespace NMLS_Graphisch
/* If output is checked a file is produced with prediction, actual and the error */
if (checkBox_output.Checked)
{
File.AppendAllText("differenziellerVorgaenger.txt",
File.AppendAllText("differentialpredecessor .txt",
String.Format("{0}. X_pred {1}\n", x_count, x_pred),
Encoding.UTF8);
File.AppendAllText("differenziellerVorgaenger.txt",
File.AppendAllText("differentialpredecessor.txt",
String.Format("{0}. X_actual {1}\n", x_count, x_actual),
Encoding.UTF8);
}
@ -416,7 +464,7 @@ namespace NMLS_Graphisch
/* Output */
if (checkBox_output.Checked)
{
File.AppendAllText("differenziellerVorgaenger.txt",
File.AppendAllText("differentialpredecessor.txt",
String.Format("{0}. X_error {1}\n\n", x_count, x_error[x_count]),
Encoding.UTF8);
}
@ -468,8 +516,8 @@ namespace NMLS_Graphisch
/* Output the result */
if (checkBox_output.Checked)
{
File.AppendAllText("ergebnisse.txt",
String.Format("Quadratische Varianz(x_error): {0}\n Mittelwert(x_error): {1}\n\n", varianz, mittel),
File.AppendAllText("results_differentialpredecessor.txt",
String.Format("Squared variance(x_error): {0}\n Average(x_error): {1}\n\n", varianz, mittel),
Encoding.UTF8);
}
@ -484,15 +532,24 @@ namespace NMLS_Graphisch
*---------------------------------------------------*/
private void Form1_Load(object sender, EventArgs e)
{
/* Initializing some diffrent combo-/textboxes and the main chart */
comboBox_algorithem.SelectedIndex = 0;
comboBox_pixel.SelectedIndex = 0;
// sets an eventhandler on the gotFocus event
txtBox_learnrate.GotFocus += new EventHandler(txtBox_learnrate_gotFocus);
txtBox_learnrate.Text = ">0.0 & <=1.0";
// sets an eventhandler on the gotFocus event
txtBox_windowSize.GotFocus += new EventHandler(txtBox_windowSize_gotFocus);
chart_main.Series.Clear();
Series x_actual = new Series("Actual x Value");
x_actual.ChartType = SeriesChartType.Spline;
// sets the accept button to Start_btn
this.AcceptButton = Start_btn;
/* Initializing weights and actual values
In case no picture is loaded, actual values are generated
And printing them on a chart */
Then printing them on a chart */
for (int i = 0; i < NumberOfSamples; i++)
{
_x[i] += ((255.0 / NumberOfSamples) * i);
@ -505,6 +562,30 @@ namespace NMLS_Graphisch
chart_main.Series.Add(x_actual);
}
/*--------------------------------------------------
* txtBox_learnrate_gotFocus()
*
* sets the default text to "" if gotFocus is raised
*
* -------------------------------------------------*/
protected void txtBox_learnrate_gotFocus(Object sender, EventArgs e)
{
txtBox_learnrate.Text = "";
}
/*--------------------------------------------------
* txtBox_windowSize_gotFocus()
*
* sets the default text to "" if gotFocus is raised
*
* -------------------------------------------------*/
protected void txtBox_windowSize_gotFocus(Object sender, EventArgs e)
{
txtBox_windowSize.Text = "";
}
/*---------------------------------------------------
* Clear_btn_Click()
*
@ -594,6 +675,28 @@ namespace NMLS_Graphisch
}
}
}
/*---------------------------------------------------
* txtBox_windowSize_TextChanged()
*
* proofs if window size textbox is a valid number
*
*---------------------------------------------------*/
private void txtBox_windowSize_TextChanged(object sender, EventArgs e)
{
try
{
if(txtBox_windowSize.Text.Length > 0)
{
windowSize = Int32.Parse(txtBox_windowSize.Text);
}
}catch(Exception excep)
{
MessageBox.Show("Not a valid window size");
txtBox_windowSize.Text = "";
}
}
}
}