Merge branch 'master' of https://github.com/FBRDNLMS/NLMSvariants
This commit is contained in:
Friese
commit
c70c7e65cb
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using System;
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using System.Collections.Generic;
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using System.ComponentModel;
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using System.Data;
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using System.Drawing;
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using System.IO;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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using System.Windows.Forms;
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using System.Windows.Forms.DataVisualization.Charting;
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namespace NMLS_Graphisch
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{
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public partial class Form1 : Form
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{
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public Form1()
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{
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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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static Random rnd = new Random();
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static double[] _x = new double[NumberOfSamples];
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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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private void button1_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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if (checkBox1.Checked)
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{
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for (int i = 0; i < tracking; i++)
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{
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for (int k = 0; k < windowSize; k++)
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{
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File.AppendAllText("weights.txt",
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String.Format("[{0}][{1}] {2}\n", k, i, Math.Round(w[k, i], 2).ToString()),
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Encoding.UTF8);
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}
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}
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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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{
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case "lokaler Mittelwert":
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series = lokalerMittelWert();
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break;
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case "direkter Vorgänger":
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series = direkterVorgaenger();
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break;
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case "differenzieller Vorgänger":
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series = diffVorgaenger();
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break;
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default:
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return;
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}
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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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{
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chart1.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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}
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}
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if (checkBox1.Checked)
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{
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for (int i = 0; i < tracking; i++)
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{
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for (int k = 0; k < windowSize; k++)
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{
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File.AppendAllText("weights_after.txt",
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String.Format("[{0}][{1}] {2}\n", k, i, Math.Round(w[k, i], 2).ToString()),
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Encoding.UTF8);
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}
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}
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}
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}
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Series[] lokalerMittelWert()
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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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while (x_count + 1 < NumberOfSamples)
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{
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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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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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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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{
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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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Encoding.UTF8);
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File.AppendAllText("lokalerMittelwert.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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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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{
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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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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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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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x_count += 1;
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}
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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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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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{
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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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}
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Series[] direkterVorgaenger()
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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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while (x_count + 1 < NumberOfSamples)
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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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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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}
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x_pred += x_array[x_count - 1];
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// Output Stuff
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if (checkBox1.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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Encoding.UTF8);
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File.AppendAllText("direkterVorgaenger.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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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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{
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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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Encoding.UTF8);
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}
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double x_square = 0;
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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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}
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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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}
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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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x_count += 1;
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}
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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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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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{
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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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}
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Series[] diffVorgaenger()
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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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//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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while (x_count + 1 < NumberOfSamples)
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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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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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}
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x_pred += x_array[x_count - 1];
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// Output Stuff
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if (checkBox1.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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Encoding.UTF8);
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File.AppendAllText("differenziellerVorgaenger.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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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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{
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File.AppendAllText("differenziellerVorgaenger.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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double x_square = 0;
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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_array[x_count - i - 1], 2);
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}
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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_array[x_count - i - 1]) / x_square);
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}
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}
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//Graphischer Stuff
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diffVorgaenger_error.Points.AddXY(x_count, x_error[x_count]);
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diffVorgaenger_predic.Points.AddXY(x_count, x_pred);
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x_count += 1;
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}
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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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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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{
|
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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[] { diffVorgaenger_error, diffVorgaenger_predic };
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}
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|
||||
// Inizialisierung von Arrays
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||||
private void Form1_Load(object sender, EventArgs e)
|
||||
{
|
||||
comboBox1.SelectedIndex = 0;
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||||
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();
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||||
//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()));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
Binary file not shown.
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|
@ -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);
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -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"
|
||||
xmlns="http://www.w3.org/2000/svg">
|
||||
<desc>NLMSvariants output graph
|
||||
|
|
@ -22,7 +19,7 @@
|
|||
</g>
|
||||
<g transform="translate(200, 400) scale(1,-1)">
|
||||
<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>
|
||||
|
|
|
|||
|
Before Width: | Height: | Size: 1.6 KiB After Width: | Height: | Size: 1.4 KiB |
Loading…
Reference in New Issue