| public UpdateDataSeries ( string name, double data ) : void | ||
| name | string | Data series name to update. |
| data | double | Data series values. |
| 리턴 | void | |
// Clear current solution
private void ClearSolution()
{
errorChart.UpdateDataSeries("error", null);
weightsList.Items.Clear();
currentIterationBox.Text = string.Empty;
currentErrorBox.Text = string.Empty;
}
// Update chart
private void UpdateChart()
{
// update chart range
chart.RangeX = userFunction.Range;
double[,] data = null;
if (chart.RangeX.Length > 0)
{
// prepare data
data = new double[501, 2];
double minX = userFunction.Range.Min;
double length = userFunction.Range.Length;
for (int i = 0; i <= 500; i++)
{
data[i, 0] = minX + length * i / 500;
data[i, 1] = userFunction.OptimizationFunction(data[i, 0]);
}
}
// update chart series
chart.UpdateDataSeries("function", data);
}
public void ProgressUpdate(KeyValuePair <int, AccordGenetic.Wrap.Result> kvp)
{
// update info
chart.UpdateDataSeries("solution", kvp.Value.Output);
SetText(currentIterationBox, kvp.Key.ToString());
SetText(currentValueBox, kvp.Value.BestSolution);
}
// Show training data on chart
private void ShowTrainingData()
{
int class1Size = 0;
int class2Size = 0;
// calculate number of samples in each class
for (int i = 0, n = samples; i < n; i++)
{
if (classes[i] == 0)
{
class1Size++;
}
else
{
class2Size++;
}
}
// allocate classes arrays
double[,] class1 = new double[class1Size, 2];
double[,] class2 = new double[class2Size, 2];
// fill classes arrays
for (int i = 0, c1 = 0, c2 = 0; i < samples; i++)
{
if (classes[i] == 0)
{
// class 1
class1[c1, 0] = data[i, 0];
class1[c1, 1] = data[i, 1];
c1++;
}
else
{
// class 2
class2[c2, 0] = data[i, 0];
class2[c2, 1] = data[i, 1];
c2++;
}
}
// updata chart control
chart.UpdateDataSeries("class1", class1);
chart.UpdateDataSeries("class2", class2);
}
// Load data
private void loadDataButton_Click(object sender, System.EventArgs e)
{
// show file selection dialog
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
StreamReader reader = null;
// read maximum 50 points
double[] tempData = new double[50];
try
{
// open selected file
reader = File.OpenText(openFileDialog.FileName);
string str = null;
int i = 0;
// read the data
while ((i < 50) && ((str = reader.ReadLine()) != null))
{
// parse the value
tempData[i] = double.Parse(str);
i++;
}
// allocate and set data
data = new double[i];
dataToShow = new double[i, 2];
Array.Copy(tempData, 0, data, 0, i);
for (int j = 0; j < i; j++)
{
dataToShow[j, 0] = j;
dataToShow[j, 1] = data[j];
}
}
catch (Exception)
{
MessageBox.Show("Failed reading the file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
finally
{
// close file
if (reader != null)
{
reader.Close();
}
}
// update list and chart
UpdateDataListView();
chart.RangeX = new Range(0, data.Length - 1);
chart.UpdateDataSeries("data", dataToShow);
chart.UpdateDataSeries("solution", null);
// set delimiters
UpdateDelimiters();
// enable "Start" button
startButton.Enabled = true;
}
}
// Generate new map for the Traivaling Salesman problem
private void GenerateMap()
{
Random rand = new Random((int)DateTime.Now.Ticks);
// create coordinates array
map = new double[citiesCount, 2];
for (int i = 0; i < citiesCount; i++)
{
map[i, 0] = rand.Next(1001);
map[i, 1] = rand.Next(1001);
}
// set the map
mapControl.UpdateDataSeries("map", map);
// erase path if it is
mapControl.UpdateDataSeries("path", null);
}
// Load data
private void loadDataButton_Click(object sender, System.EventArgs e)
{
// show file selection dialog
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
data = null;
try
{
// open selected file
using (TextReader stream = new StreamReader(openFileDialog.FileName))
using (CsvReader reader = new CsvReader(stream, false))
{
data = reader.ToTable().ToMatrix(CultureInfo.InvariantCulture).GetColumn(0);
}
}
catch (Exception)
{
MessageBox.Show("Failed reading the file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
dataToShow = Matrix.Stack(Vector.Range(0, data.Length).ToDouble(), data).Transpose();
// update list and chart
UpdateDataListView();
chart.RangeX = new Range(0, data.Length - 1);
chart.UpdateDataSeries("data", dataToShow);
chart.UpdateDataSeries("solution", null);
// set delimiters
UpdateDelimiters();
// enable "Start" button
startButton.Enabled = true;
}
}
// Load data
private void loadDataButton_Click(object sender, System.EventArgs e)
{
// show file selection dialog
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
data = null;
try
{
// open selected file
using (TextReader stream = new StreamReader(openFileDialog.FileName))
using (CsvReader reader = new CsvReader(stream, false))
{
data = reader.ToTable().ToMatrix(CultureInfo.InvariantCulture);
}
}
catch (Exception)
{
MessageBox.Show("Failed reading the file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
DoubleRange[] ranges = data.GetRange(dimension: 0);
xRange = ranges[0];
yRange = ranges[1];
// update list and chart
UpdateDataListView();
chart.RangeX = new Range((float)xRange.Min, (float)xRange.Max);
chart.UpdateDataSeries("data", data);
chart.UpdateDataSeries("solution", null);
// enable "Start" button
startButton.Enabled = true;
}
}
// Show training data on chart
private void ShowTrainingData()
{
double[][,] dataSeries = new double[classesCount][, ];
int[] indexes = new int[classesCount];
// allocate data arrays
for (int i = 0; i < classesCount; i++)
{
dataSeries[i] = new double[samplesPerClass[i], 2];
}
// fill data arrays
for (int i = 0; i < samples; i++)
{
// get sample's class
int dataClass = classes[i];
// copy data into appropriate array
dataSeries[dataClass][indexes[dataClass], 0] = data[i, 0];
dataSeries[dataClass][indexes[dataClass], 1] = data[i, 1];
indexes[dataClass]++;
}
// remove all previous data series from chart control
chart.RemoveAllDataSeries();
// add new data series
for (int i = 0; i < classesCount; i++)
{
string className = string.Format("class" + i);
// add data series
chart.AddDataSeries(className, dataSereisColors[i], Chart.SeriesType.Dots, 5);
chart.UpdateDataSeries(className, dataSeries[i]);
// add classifier
chart.AddDataSeries(string.Format("classifier" + i), Color.Gray, Chart.SeriesType.Line, 1, false);
}
}
// Load data
private void loadDataButton_Click(object sender, System.EventArgs e)
{
// show file selection dialog
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
StreamReader reader = null;
// read maximum 50 points
float[,] tempData = new float[50, 2];
float minX = float.MaxValue;
float maxX = float.MinValue;
try
{
// open selected file
reader = File.OpenText(openFileDialog.FileName);
string str = null;
int i = 0;
// read the data
while ((i < 50) && ((str = reader.ReadLine()) != null))
{
string[] strs = str.Split(';');
if (strs.Length == 1)
{
strs = str.Split(',');
}
// parse X
tempData[i, 0] = float.Parse(strs[0]);
tempData[i, 1] = float.Parse(strs[1]);
// search for min value
if (tempData[i, 0] < minX)
{
minX = tempData[i, 0];
}
// search for max value
if (tempData[i, 0] > maxX)
{
maxX = tempData[i, 0];
}
i++;
}
// allocate and set data
dataToShow = new double[i, 2];
Array.Copy(tempData, 0, dataToShow, 0, i * 2);
}
catch (Exception)
{
MessageBox.Show("Failed reading the file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
finally
{
// close file
if (reader != null)
{
reader.Close();
}
}
// update list and chart
UpdateDataListView();
chart.RangeX = new Accord.Range(minX, maxX);
chart.UpdateDataSeries("data", dataToShow);
chart.UpdateDataSeries("solution", null);
// enable "Start" button
startButton.Enabled = true;
}
}
// Load input data
private void loadButton_Click(object sender, System.EventArgs e)
{
// data file format:
// X1, X2, class
// load maximum 10 classes !
// show file selection dialog
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
StreamReader reader = null;
// temp buffers (for 200 samples only)
float[,] tempData = new float[200, 2];
int[] tempClasses = new int[200];
// min and max X values
float minX = float.MaxValue;
float maxX = float.MinValue;
// samples count
samples = 0;
// classes count
classesCount = 0;
samplesPerClass = new int[10];
try
{
string str = null;
// open selected file
reader = File.OpenText(openFileDialog.FileName);
// read the data
while ((samples < 200) && ((str = reader.ReadLine()) != null))
{
// split the string
string[] strs = str.Split(';');
if (strs.Length == 1)
{
strs = str.Split(',');
}
// check tokens count
if (strs.Length != 3)
{
throw new ApplicationException("Invalid file format");
}
// parse tokens
tempData[samples, 0] = float.Parse(strs[0]);
tempData[samples, 1] = float.Parse(strs[1]);
tempClasses[samples] = int.Parse(strs[2]);
// skip classes over 10, except only first 10 classes
if (tempClasses[samples] >= 10)
{
continue;
}
// count the amount of different classes
if (tempClasses[samples] >= classesCount)
{
classesCount = tempClasses[samples] + 1;
}
// count samples per class
samplesPerClass[tempClasses[samples]]++;
// search for min value
if (tempData[samples, 0] < minX)
{
minX = tempData[samples, 0];
}
// search for max value
if (tempData[samples, 0] > maxX)
{
maxX = tempData[samples, 0];
}
samples++;
}
// allocate and set data
data = new double[samples, 2];
Array.Copy(tempData, 0, data, 0, samples * 2);
classes = new int[samples];
Array.Copy(tempClasses, 0, classes, 0, samples);
// clear current result
weightsList.Items.Clear();
errorChart.UpdateDataSeries("error", null);
}
catch (Exception)
{
MessageBox.Show("Failed reading the file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
finally
{
// close file
if (reader != null)
{
reader.Close();
}
}
// update chart
chart.RangeX = new Range(minX, maxX);
ShowTrainingData();
// enable start button
startButton.Enabled = true;
}
}
// Clear current solution
private void ClearCurrentSolution()
{
chart.UpdateDataSeries("classifier", null);
errorChart.UpdateDataSeries("error", null);
weightsList.Items.Clear();
}