updated src file
This commit is contained in:
kbecke23
parent
b3b5980e36
commit
11d45cdbab
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@ -19,6 +19,7 @@ namespace NMLS_Graphisch
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InitializeComponent();
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}
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static int NumberOfSamples = 1000;
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const int tracking = 40;
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static Stack<double> x = new Stack<double>();
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@ -27,17 +28,26 @@ namespace NMLS_Graphisch
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static double[,] w = new double[NumberOfSamples, NumberOfSamples];
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static double learnrate = 0.2;
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static double[] pixel_array;
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static int windowSize = 5;
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static int windowSize = 6;
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private void button1_Click(object sender, EventArgs e)
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/*---------------------------------------------------
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* GO_btn_click()
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*
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* starts the predefined algorithem with the specified parameters
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*
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*---------------------------------------------------*/
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private void GO_btn_Click(object sender, EventArgs e)
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{
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NumberOfSamples = Int32.Parse(comboBox2.SelectedItem.ToString());
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chart1.ChartAreas[0].AxisX.Maximum = NumberOfSamples;
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chart1.ChartAreas[0].AxisY.Maximum = 300;
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chart1.ChartAreas[0].AxisY.Minimum = -5;
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/* Initializing the chart to display all values */
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NumberOfSamples = Int32.Parse(comboBox_pixel.SelectedItem.ToString());
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chart_main.ChartAreas[0].AxisX.Maximum = NumberOfSamples;
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chart_main.ChartAreas[0].AxisY.Maximum = 300;
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chart_main.ChartAreas[0].AxisY.Minimum = -100;
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if (checkBox1.Checked)
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/* If output is checked weights befor and after the algorithem are printed to a file.*/
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if (checkBox_output.Checked)
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{
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for (int i = 0; i < tracking; i++)
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{
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@ -51,19 +61,21 @@ namespace NMLS_Graphisch
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}
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Series[] series = new Series[2];
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switch (comboBox1.SelectedItem.ToString())
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/* Deciding which algorithem is going to be calculated */
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switch (comboBox_algorithem.SelectedItem.ToString())
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{
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case "lokaler Mittelwert":
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series = lokalerMittelWert();
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series = localMean();
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break;
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case "direkter Vorgänger":
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series = direkterVorgaenger();
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series = directPredecessor();
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break;
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case "differenzieller Vorgänger":
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series = diffVorgaenger();
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series = diffPredecessor();
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break;
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default:
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@ -71,20 +83,22 @@ namespace NMLS_Graphisch
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}
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/* Adding the series from the algorithems to the chart */
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foreach (Series s in series)
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{
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if (chart1.Series.IndexOf(s.Name) < 0)
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if (chart_main.Series.IndexOf(s.Name) < 0)
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{
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chart1.Series.Add(s);
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chart_main.Series.Add(s);
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}
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else
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{
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chart1.Series.RemoveAt(chart1.Series.IndexOf(s.Name));
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chart1.Series.Add(s);
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chart_main.Series.RemoveAt(chart_main.Series.IndexOf(s.Name));
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chart_main.Series.Add(s);
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}
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}
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if (checkBox1.Checked)
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/* Output weights after algorithem */
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if (checkBox_output.Checked)
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{
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for (int i = 0; i < tracking; i++)
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{
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@ -101,39 +115,59 @@ namespace NMLS_Graphisch
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}
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Series[] lokalerMittelWert()
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/*---------------------------------------------------
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* localMean()
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*
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* the algorithem for the local Mean
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*
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* returns a array of Series with the error and prediction for
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* displaying in a chart
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*
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*---------------------------------------------------*/
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Series[] localMean()
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{
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int x_count = 0;
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double[] x_error = new double[NumberOfSamples];
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x_error[0] = 0;
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//Graphischer Stuff
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Series lokal_M_error = new Series("Lokaler Mittelwert Error");
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Series lokal_M_predic = new Series("Lokaler Mittelwert Prediction");
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lokal_M_error.ChartType = SeriesChartType.Spline;
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lokal_M_predic.ChartType = SeriesChartType.Spline;
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/* Inizilazing series for the main chart */
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Series localMeanError = new Series("Lokaler Mittelwert Error");
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Series localMeanPredict = new Series("Lokaler Mittelwert Prediction");
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localMeanError.ChartType = SeriesChartType.Spline;
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localMeanPredict.ChartType = SeriesChartType.Spline;
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while (x_count + 1 < NumberOfSamples)
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/* Main while loop for the prediction and learing */
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while (x_count < NumberOfSamples - 1)
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{
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/* Initializing a part array of the actual array to capture the last WINDOWSIZE pixels
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* for predicting and getting the average of the array*/
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double[] x_part_Array = new double[x_count];
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int _sourceIndex = (x_count > windowSize) ? x_count - windowSize : 0;
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int _arrayLength = (x_count > windowSize) ? windowSize + 1 : (x_count > 0) ? x_count : 0;
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int _arrayLength = (x_count > windowSize) ? windowSize : x_count;
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Array.Copy(_x, _sourceIndex, x_part_Array, 0, _arrayLength);
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double x_middle = (x_count > 0) ? (x_part_Array.Sum() / _arrayLength) : 0;
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Array.Copy(_x, _sourceIndex, x_part_Array, 0, _arrayLength);
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double x_middle = (x_count > 0) ? ( x_part_Array.Sum() / _arrayLength) : 0;
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double x_pred = 0.0;
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double[] x_array = _x;
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double x_actual = _x[x_count + 1];
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/* Prediction algorithem */
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for (int i = 1; i < _arrayLength; i++)
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{
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x_pred += (w[i, x_count] * (x_array[x_count - i] - x_middle));
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}
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x_pred += x_middle;
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// Output Stuff
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if (checkBox1.Checked)
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/* If output is checked a file is produced with prediction, actual and the error */
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if (checkBox_output.Checked)
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{
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File.AppendAllText("lokalerMittelwert.txt",
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String.Format("{0}. X_pred {1}\n", x_count, x_pred),
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@ -144,80 +178,112 @@ namespace NMLS_Graphisch
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Encoding.UTF8);
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}
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/* Calculating the error */
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x_error[x_count] = x_actual - x_pred;
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double x_square = 0;
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//Output Stuff
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if (checkBox1.Checked)
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/* Output */
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if (checkBox_output.Checked)
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{
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File.AppendAllText("lokalerMittelwert.txt",
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String.Format("{0}. X_error {1}\n\n", x_count, x_error[x_count]),
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Encoding.UTF8);
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}
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/* Calulating the LMS value */
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for (int i = 1; i < _arrayLength; i++)
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{
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x_square += Math.Pow(x_array[x_count - i] - x_middle, 2);
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}
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/* x_square == 0.0, drives to undefined weights therefor we set it to 1 */
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if (x_square == 0.0)
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x_square = 1.0;
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/* Learning algorithem */
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for (int i = 1; i < _arrayLength; i++)
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{
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w[i, x_count + 1] = w[i, x_count] + learnrate * x_error[x_count] * ((x_array[x_count - i] - x_middle) / x_square);
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}
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// Graphischer Stuff
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lokal_M_error.Points.AddXY(x_count, x_error[x_count]);
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lokal_M_predic.Points.AddXY(x_count, x_pred);
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/* Printing error and prediction on the series */
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localMeanError.Points.AddXY(x_count, x_error[x_count]);
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localMeanPredict.Points.AddXY(x_count, x_pred);
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x_count += 1;
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}
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/* Calculating the avearage of the error and the variance of the error */
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double mittel = x_error.Where(d => !double.IsNaN(d)).Sum() / x_error.Length;
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double varianz = 0.0;
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foreach (double x_e in x_error)
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{
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if(!double.IsNaN(x_e))
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if (!double.IsNaN(x_e))
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varianz += Math.Pow(x_e - mittel, 2);
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}
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varianz /= x_error.Length;
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if (checkBox1.Checked)
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/* Displayes both on labels */
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label3.Text = mittel.ToString();
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label5.Text = varianz.ToString();
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/* Output the result */
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if (checkBox_output.Checked)
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{
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File.AppendAllText("ergebnisse.txt",
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String.Format("Quadratische Varianz(x_error): {0}\n Mittelwert(x_error): {1}\n\n", varianz, mittel),
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Encoding.UTF8);
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}
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return new Series[] { lokal_M_predic, lokal_M_error };
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return new Series[] { localMeanPredict, localMeanError };
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}
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Series[] direkterVorgaenger()
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/*---------------------------------------------------
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* directPredecessor()
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*
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* the algorithem for the direct predecessor
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*
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* returns a array of Series with the error and prediction for
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* displaying in a chart
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*
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*---------------------------------------------------*/
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Series[] directPredecessor()
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{
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double[] x_error = new double[NumberOfSamples];
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x_error[0] = 0;
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int x_count = 0;
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// Graphischer Stuff
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Series direkterVorgaenger_error = new Series("Direkter Vorgänger Error");
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Series direkterVorgaenger_predic = new Series("Direkter Vorgänger Prediction");
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direkterVorgaenger_error.ChartType = SeriesChartType.Spline;
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direkterVorgaenger_predic.ChartType = SeriesChartType.Spline;
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/* Inizilazing series for the main chart */
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Series directPredecessorError = new Series("Direkter Vorgänger Error");
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Series directPredecessorPrediction = new Series("Direkter Vorgänger Prediction");
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directPredecessorError.ChartType = SeriesChartType.Spline;
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directPredecessorPrediction.ChartType = SeriesChartType.Spline;
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while (x_count + 1 < NumberOfSamples)
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/* Main while loop for the prediction and learing */
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while (x_count < NumberOfSamples - 1)
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{
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double x_pred = 0.0;
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double[] x_array = _x;
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double x_actual = _x[x_count + 1];
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if (x_count > 0)
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{
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int _arrayLength = (x_count > windowSize) ? windowSize + 1 : x_count;
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/* Initializing the windowsize */
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int _arrayLength = (x_count > windowSize) ? windowSize : x_count;
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/* Prediction algorithem */
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for (int i = 1; i < _arrayLength; i++)
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{
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x_pred += (w[i, x_count] * (x_array[x_count - 1] - x_array[x_count - i - 1]));
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x_pred += (w[i, x_count] * (_x[x_count - 1] - _x[x_count - i - 1]));
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}
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x_pred += x_array[x_count - 1];
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x_pred += _x[x_count - 1];
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// Output Stuff
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if (checkBox1.Checked)
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/* If output is checked a file is produced with prediction, actual and the error */
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if (checkBox_output.Checked)
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{
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File.AppendAllText("direkterVorgaenger.txt",
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String.Format("{0}. X_pred {1}\n", x_count, x_pred),
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@ -228,11 +294,11 @@ namespace NMLS_Graphisch
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Encoding.UTF8);
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}
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/* Calculating the error */
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x_error[x_count] = x_actual - x_pred;
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// Output Stuff
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if (checkBox1.Checked)
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/* Output */
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if (checkBox_output.Checked)
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{
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File.AppendAllText("direkterVorgaenger.txt",
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String.Format("{0}. X_error {1}\n\n", x_count, x_error[x_count]),
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@ -240,23 +306,34 @@ namespace NMLS_Graphisch
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}
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double x_square = 0;
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/* Calulating the LMS value */
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for (int i = 1; i < _arrayLength; i++)
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{
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x_square += Math.Pow(x_array[x_count - 1] - x_array[x_count - i - 1], 2);
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x_square += Math.Pow(_x[x_count - 1] - _x[x_count - i - 1], 2);
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}
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/* x_square == 0.0, drives to undefined weights therefor we set it to 1 */
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if (x_square == 0.0)
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{
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x_square = 1;
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}
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/* Learning algorithem */
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for (int i = 1; i < _arrayLength; i++)
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{
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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);
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w[i, x_count + 1] = w[i, x_count] + learnrate * x_error[x_count] * ((_x[x_count - 1] - _x[x_count - i - 1]) / x_square);
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}
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}
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//Graphischer Stuff
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direkterVorgaenger_error.Points.AddXY(x_count, x_error[x_count]);
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direkterVorgaenger_predic.Points.AddXY(x_count, x_pred);
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/* Printing error and prediction on the series */
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directPredecessorError.Points.AddXY(x_count, x_error[x_count]);
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directPredecessorPrediction.Points.AddXY(x_count, x_pred);
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x_count += 1;
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}
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/* Calculating the avearage of the error and the variance of the error */
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double mittel = x_error.Where(d => !double.IsNaN(d)).Sum() / x_error.Length;
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double varianz = 0.0;
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foreach (double x_e in x_error)
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@ -265,45 +342,64 @@ namespace NMLS_Graphisch
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varianz += Math.Pow(x_e - mittel, 2);
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}
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varianz /= x_error.Length;
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if (checkBox1.Checked)
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/* Displayes both on labels */
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label3.Text = mittel.ToString();
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label5.Text = varianz.ToString();
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/* Output the result */
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if (checkBox_output.Checked)
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{
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File.AppendAllText("ergebnisse.txt",
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String.Format("Quadratische Varianz(x_error): {0}\n Mittelwert(x_error): {1}\n\n", varianz, mittel),
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Encoding.UTF8);
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}
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return new Series[] { direkterVorgaenger_error, direkterVorgaenger_predic };
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return new Series[] { directPredecessorError, directPredecessorPrediction };
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}
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Series[] diffVorgaenger()
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/*---------------------------------------------------
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* diffPredecessor()
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*
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* the algorithem for the differential predecessor
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*
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* returns a array of Series with the error and prediction for
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* displaying in a chart
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*
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*---------------------------------------------------*/
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Series[] diffPredecessor()
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{
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double[] x_error = new double[NumberOfSamples];
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x_error[0] = 0;
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int x_count = 1;
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int x_count = 0;
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//Graphischer Stuff
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Series diffVorgaenger_error = new Series("Differenzieller Vorgänger Error");
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Series diffVorgaenger_predic = new Series("Differenzieller Vorgänger Prediction");
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diffVorgaenger_error.ChartType = SeriesChartType.Spline;
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diffVorgaenger_predic.ChartType = SeriesChartType.Spline;
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/* Inizilazing series for the main chart */
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Series diffPredecessorError = new Series("Differenzieller Vorgänger Error");
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Series diffPredecessorPrediction = new Series("Differenzieller Vorgänger Prediction");
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diffPredecessorError.ChartType = SeriesChartType.Spline;
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diffPredecessorPrediction.ChartType = SeriesChartType.Spline;
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while (x_count + 1 < NumberOfSamples)
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/* Main while loop for the prediction and learing */
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while (x_count < NumberOfSamples - 1)
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{
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double x_pred = 0.0;
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double[] x_array = _x;
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double x_actual = _x[x_count + 1];
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if (x_count > 0)
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{
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int _arrayLength = (x_count > windowSize) ? windowSize + 1 : x_count;
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/* Initializing the windowsize */
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int _arrayLength = (x_count > windowSize) ? windowSize : x_count;
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/* Prediction algorithem */
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for (int i = 1; i < _arrayLength; i++)
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{
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x_pred += (w[i, x_count] * (x_array[x_count - i] - x_array[x_count - i - 1]));
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x_pred += (w[i, x_count] * (_x[x_count - i] - _x[x_count - i - 1]));
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}
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x_pred += x_array[x_count - 1];
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x_pred += _x[x_count - 1];
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// Output Stuff
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if (checkBox1.Checked)
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/* If output is checked a file is produced with prediction, actual and the error */
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if (checkBox_output.Checked)
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{
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File.AppendAllText("differenziellerVorgaenger.txt",
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String.Format("{0}. X_pred {1}\n", x_count, x_pred),
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@ -314,11 +410,11 @@ namespace NMLS_Graphisch
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Encoding.UTF8);
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}
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/* Calculating the error */
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x_error[x_count] = x_actual - x_pred;
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// Output Stuff
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if (checkBox1.Checked)
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/* Output */
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if (checkBox_output.Checked)
|
||||
{
|
||||
File.AppendAllText("differenziellerVorgaenger.txt",
|
||||
String.Format("{0}. X_error {1}\n\n", x_count, x_error[x_count]),
|
||||
|
|
@ -327,24 +423,35 @@ namespace NMLS_Graphisch
|
|||
|
||||
|
||||
double x_square = 0;
|
||||
|
||||
/* Calulating the LMS value */
|
||||
for (int i = 1; i < _arrayLength; i++)
|
||||
{
|
||||
x_square += Math.Pow(x_array[x_count - i] - x_array[x_count - i - 1], 2);
|
||||
x_square += Math.Pow(_x[x_count - i] - _x[x_count - i - 1], 2);
|
||||
}
|
||||
|
||||
/* x_square == 0.0, drives to undefined weights therefor we set it to 1 */
|
||||
if (x_square == 0.0)
|
||||
{
|
||||
x_square = 1;
|
||||
}
|
||||
|
||||
/* Learning algorithem */
|
||||
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);
|
||||
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);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
//Graphischer Stuff
|
||||
diffVorgaenger_error.Points.AddXY(x_count, x_error[x_count]);
|
||||
diffVorgaenger_predic.Points.AddXY(x_count, x_pred);
|
||||
/* Printing error and prediction on the series */
|
||||
diffPredecessorError.Points.AddXY(x_count, x_error[x_count]);
|
||||
diffPredecessorPrediction.Points.AddXY(x_count, x_pred);
|
||||
|
||||
x_count += 1;
|
||||
}
|
||||
|
||||
/* Calculating the avearage of the error and the variance of the error */
|
||||
double mittel = x_error.Where(d => !double.IsNaN(d)).Sum() / x_error.Length;
|
||||
double varianz = 0.0;
|
||||
foreach (double x_e in x_error)
|
||||
|
|
@ -354,60 +461,88 @@ namespace NMLS_Graphisch
|
|||
}
|
||||
varianz /= x_error.Length;
|
||||
|
||||
if (checkBox1.Checked)
|
||||
/* Displayes both on labels */
|
||||
label3.Text = mittel.ToString();
|
||||
label5.Text = varianz.ToString();
|
||||
|
||||
/* 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),
|
||||
Encoding.UTF8);
|
||||
}
|
||||
|
||||
return new Series[] { diffVorgaenger_error, diffVorgaenger_predic };
|
||||
return new Series[] { diffPredecessorError, diffPredecessorPrediction };
|
||||
}
|
||||
|
||||
// Inizialisierung von Arrays
|
||||
/*---------------------------------------------------
|
||||
* Form1_Load()
|
||||
*
|
||||
* loads the form and initializes some variables
|
||||
*
|
||||
*---------------------------------------------------*/
|
||||
private void Form1_Load(object sender, EventArgs e)
|
||||
{
|
||||
comboBox1.SelectedIndex = 0;
|
||||
comboBox2.SelectedIndex = 0;
|
||||
chart1.Series.Clear();
|
||||
comboBox_algorithem.SelectedIndex = 0;
|
||||
comboBox_pixel.SelectedIndex = 0;
|
||||
chart_main.Series.Clear();
|
||||
Series x_actual = new Series("Actual x Value");
|
||||
x_actual.ChartType = SeriesChartType.Spline;
|
||||
|
||||
/* Initializing weights and actual values
|
||||
In case no picture is loaded, actual values are generated
|
||||
And printing them on a chart */
|
||||
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);
|
||||
chart_main.Series.Add(x_actual);
|
||||
}
|
||||
|
||||
// Graphen Clearen
|
||||
private void button2_Click(object sender, EventArgs e)
|
||||
/*---------------------------------------------------
|
||||
* Clear_btn_Click()
|
||||
*
|
||||
* clears the chart and reprints the actual values
|
||||
*
|
||||
*---------------------------------------------------*/
|
||||
|
||||
private void Clear_btn_Click(object sender, EventArgs e)
|
||||
{
|
||||
chart1.Series.Clear();
|
||||
chart_main.Series.Clear();
|
||||
Series x_actual = new Series("Actual x Value");
|
||||
x_actual.ChartType = SeriesChartType.Spline;
|
||||
for (int i = 0; i < NumberOfSamples; i++)
|
||||
for (int i = 0; i < Int32.Parse(comboBox_pixel.SelectedItem.ToString()); i++)
|
||||
{
|
||||
x_actual.Points.AddXY(i, _x[i]);
|
||||
}
|
||||
chart1.Series.Add(x_actual);
|
||||
chart_main.Series.Add(x_actual);
|
||||
}
|
||||
|
||||
// Bild Laden
|
||||
private void button3_Click(object sender, EventArgs e)
|
||||
/*---------------------------------------------------
|
||||
* Load_btn_Click()
|
||||
*
|
||||
* loads a image from a file and stores all pixels in an array
|
||||
*
|
||||
*---------------------------------------------------*/
|
||||
|
||||
private void Load_btn_Click(object sender, EventArgs e)
|
||||
{
|
||||
|
||||
|
||||
OpenFileDialog openFileDialog = new OpenFileDialog();
|
||||
|
||||
if(openFileDialog.ShowDialog() == DialogResult.OK)
|
||||
if (openFileDialog.ShowDialog() == DialogResult.OK)
|
||||
{
|
||||
/* Catch a exception if any is raised */
|
||||
try
|
||||
{
|
||||
/* Loads file into a bitmap */
|
||||
Bitmap img = new Bitmap(openFileDialog.FileName);
|
||||
pixel_array = new double[img.Width * img.Height];
|
||||
|
||||
|
|
@ -417,23 +552,41 @@ namespace NMLS_Graphisch
|
|||
{
|
||||
|
||||
Color pixel = img.GetPixel(i, j);
|
||||
pixel_array[j*i] = pixel.R;
|
||||
pixel_array[j * i] = pixel.R;
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
/* If NuberOfSamples is greater then 2147483591 its size is over
|
||||
* the limit of C# possible indexes in an array, so we have to decrease
|
||||
* the size of NumberOfSamples */
|
||||
|
||||
NumberOfSamples = (img.Width * img.Height) / 2;
|
||||
comboBox2.Items.Add(NumberOfSamples);
|
||||
|
||||
if (NumberOfSamples > 2147483591 / 5)
|
||||
NumberOfSamples = 2147483591 / 5;
|
||||
|
||||
for (int i = 1; i < 6; i++)
|
||||
comboBox_pixel.Items.Add((int)(NumberOfSamples * 0.2 * i));
|
||||
|
||||
_x = pixel_array;
|
||||
w = new double[NumberOfSamples, NumberOfSamples];
|
||||
|
||||
/* Recreating the weights with the new size of NumberOfSamples */
|
||||
|
||||
w = new double[windowSize, NumberOfSamples];
|
||||
for (int i = 0; i < NumberOfSamples; i++)
|
||||
{
|
||||
for (int k = 1; k < NumberOfSamples; k++)
|
||||
for (int k = 0; k < windowSize; k++)
|
||||
{
|
||||
w[k, i] = rnd.NextDouble();
|
||||
}
|
||||
}
|
||||
|
||||
/* Clearing the screen after loading the pixels to make the new graph occure */
|
||||
Clear_btn_Click(new object(), new EventArgs());
|
||||
|
||||
}
|
||||
catch(Exception exep)
|
||||
catch (Exception exep)
|
||||
{
|
||||
MessageBox.Show("Konnte Bild nicht laden.");
|
||||
MessageBox.Show(String.Format("{0}", exep.ToString()));
|
||||
|
|
|
|||
Loading…
Reference in New Issue