updated with standard NLMS
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kbecke23
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4d70cb8261
commit
b79b26d1a3
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@ -114,6 +114,10 @@ namespace NMLS_Graphisch
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switch (comboBox_algorithem.SelectedItem.ToString())
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{
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case "standard NLMS":
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series = standardNLMS();
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break;
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case "local mean":
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series = localMean();
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break;
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@ -163,6 +167,125 @@ namespace NMLS_Graphisch
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}
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/*---------------------------------------------------
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* standardNLMS()
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*
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* the algorithem for the standard NLMS
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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[] standardNLMS()
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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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/* Inizilazing series for the main chart */
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Series standardNLMSError = new Series("Standard NLMS Error");
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Series standardNLMSPredict = new Series("Standard NLMS Prediction");
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standardNLMSError.ChartType = SeriesChartType.Spline;
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standardNLMSPredict.ChartType = SeriesChartType.Spline;
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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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int _sourceIndex = (x_count > windowSize) ? x_count - windowSize : 0;
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int _arrayLength = (x_count > windowSize) ? windowSize : x_count;
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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];
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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]));
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}
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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("standardNLMS.txt",
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String.Format("{0}. X_pred {1}\n", x_count, x_pred),
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Encoding.UTF8);
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File.AppendAllText("standardNLMS.txt",
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String.Format("{0}. X_actual {1}\n", x_count, x_actual),
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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 */
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if (checkBox_output.Checked)
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{
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File.AppendAllText("standardNLMS.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], 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_square);
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}
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/* Printing error and prediction on the series */
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standardNLMSError.Points.AddXY(x_count, x_error[x_count]);
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standardNLMSPredict.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)).Skip(windowSize).Sum() / x_error.Length;
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double varianz = 0.0;
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foreach (double x_e in x_error.Skip(windowSize))
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{
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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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/* 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("results_standardNLMS.txt",
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String.Format("Squared variance(x_error): {0}\n Average(x_error): {1}\n\n", varianz, mittel),
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Encoding.UTF8);
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}
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return new Series[] { standardNLMSPredict, standardNLMSError };
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}
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/*---------------------------------------------------
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* localMean()
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*
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@ -253,7 +376,7 @@ namespace NMLS_Graphisch
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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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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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/* Printing error and prediction on the series */
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@ -626,6 +749,9 @@ namespace NMLS_Graphisch
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Bitmap img = new Bitmap(openFileDialog.FileName);
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pixel_array = new double[img.Width * img.Height];
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Console.WriteLine("imgWidth: {0}", img.Width);
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Console.WriteLine("imgHeight: {0}", img.Height);
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for (int i = 1; i < img.Width; i++)
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{
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for (int j = 1; j < img.Height; j++)
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@ -636,7 +762,7 @@ namespace NMLS_Graphisch
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}
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}
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Console.WriteLine("pixle_array length: {0}", pixel_array.Length);
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_x = pixel_array;
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/* Recreating the weights with the new size of NumberOfSamples */
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