private void ExcelImport(object sender, EventArgs e)
{
ExcelFile exFile = new ExcelFile();
OpenFileDialog loadFileDlg = new OpenFileDialog();
loadFileDlg.Filter = "Excel Files (.xls)| *.xls|All files (.)| *.";
if (loadFileDlg.ShowDialog() == DialogResult.OK)
{
studyPairs.Clear();
try
{
exFile.LoadXls(loadFileDlg.FileName);
}
catch (Exception)
{
MessageBox.Show("Нету файла", "ExcelImport", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
for (int i = 0; i < exFile.Worksheets.Count; i++)
{
ExcelWorksheet sheet = exFile.Worksheets[i];
StudyPair pair = new StudyPair();
int ii = 0;
int jj = 0;
for (int j = 0; j < colorGrid.GridSize.Width * colorGrid.GridSize.Height; j++)
{
if (jj >= colorGrid.GridSize.Width)
{
ii++;
jj = 0;
}
if (sheet.Cells[ii, jj].Value.ToString() == "X")
{
pair.inputs.Add(1);
}
else
{
pair.inputs.Add(0);
}
jj++;
}
studyPairs.Add(pair);
}
colorGrid.SetData(studyPairs[currentStudyPair].inputs);
ChangeNetSettings();
CreatePreview();
}
else
{
MessageBox.Show("Нету образов", "ExcelImport", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
protected virtual void RecognizePicture(object sender, EventArgs e)
{
if (pairsLoaded)
{
StudyPair pair = net.Recognize(colorGrid.Data);
string resultString = "";
for (int i = 0; i < net.recognitionResults.Count; i++)
{
resultString += string.Format("Образ : {0} Вероятность : {1}%", net.recognitionResults[i].pair.name, net.recognitionResults[i].result) + Environment.NewLine;
}
MessageBox.Show(resultString);
}
}
private StudyPair CurrentToStudyPair()
{
StudyPair pair = new StudyPair();
for (int i = 0; i < net.InputsCount; i++)
{
pair.inputs.Add(float.Parse(ParametrGrid.Rows[i].Cells[2].Value.ToString()));
}
for (int i = 0; i < net.OutputsCount; i++)
{
pair.quits.Add(float.Parse(OutputGridView.Rows[i].Cells[1].Value.ToString()));
}
return(pair);
}
private void button1_Click(object sender, EventArgs e)
{
studyPairs.Clear();
for (int i = 0; i < studyPairsBox.Lines.Length; i++)
{
if (studyPairsBox.Lines[i] == "")
{
continue;
}
studyPairs.Add(StudyPair.FromString(studyPairsBox.Lines[i]));
}
studyPairsCount.Value = studyPairs.Count;
neuronNet.OutputsCount = studyPairs[0].quits.Count;
neuronNet.InputsCount = studyPairs[0].inputs.Count;
neuronNet.StudyPairsLoaded = true;
}
private void LoadPairs(object sender, EventArgs e)
{
string[] file;
OpenFileDialog dlg = new OpenFileDialog();
studyPairs.Clear();
if (dlg.ShowDialog() == DialogResult.OK)
{
file = File.ReadAllLines(dlg.FileName, Encoding.Default);
for (int i = 0; i < file.Length; i++)
{
studyPairs.Add(StudyPair.FromString(file[i]));
}
colorGrid.SetData(studyPairs[currentStudyPair].inputs);
ChangeNetSettings();
CreatePreview();
}
}
private void AddPicture(object sender, EventArgs e)
{
StudyPair pair = new StudyPair();
ObjectNameForm form = new ObjectNameForm();
if (form.ShowDialog() == DialogResult.OK)
{
pair.name = form.NewName;
for (int i = 0; i < colorGrid.Data.GetLength(0); i++)
{
for (int j = 0; j < colorGrid.Data.GetLength(1); j++)
{
if (IsClassification)
{
pair.inputs.Add(colorGrid.Data[j, i].FloatValueClassification);
}
else
{
pair.inputs.Add(colorGrid.Data[j, i].FloatValueAssociationMemory);
}
}
}
studyPairs.Add(pair);
for (int i = 0; i < studyPairs.Count; i++)
{
studyPairs[i].quits.Clear();
for (int j = 0; j < studyPairs.Count; j++)
{
studyPairs[i].quits.Add(i == j ? 1 : 0);
}
}
CreatePreview();
}
}
private void DataBaseExport(object sender, EventArgs e)
{
LoadMenu loadMenu = new LoadMenu();
loadMenu.indexSave = 1;
loadMenu.ShowDialog();
string[] file;
if (loadMenu.fileName != null)
{
string query = "select Graph from SaveGraphs where Name=" + "\"" + loadMenu.fileName + "\"";
databaseSQLite.OpenConnection();
SQLiteCommand myCommand = new SQLiteCommand(query, databaseSQLite.myConnection);
using (SQLiteDataReader reader = myCommand.ExecuteReader())
{
if (reader.HasRows) // если есть данные
{
while (reader.Read()) // построчно считываем данные
{
string data = (string)reader.GetValue(0);
File.WriteAllText("temp.txt", data);
}
}
}
databaseSQLite.CloseConnection();
studyPairs.Clear();
FileStream fs = new FileStream("temp.txt", FileMode.Open, FileAccess.Read);
file = File.ReadAllLines("temp.txt", Encoding.Default);
for (int i = 0; i < file.Length; i++)
{
studyPairs.Add(StudyPair.FromString(file[i]));
}
colorGrid.SetData(studyPairs[currentStudyPair].inputs);
ChangeNetSettings();
CreatePreview();
fs.Close();
}
}
private void LoadPairs(object sender, EventArgs e)
{
OpenFileDialog dlg = new OpenFileDialog();
string[] strings;
if (dlg.ShowDialog() == DialogResult.OK)
{
strings = File.ReadAllLines(dlg.FileName);
studyPairs.Clear();
for (int i = 0; i < strings.Length; i++)
{
studyPairs.Add(StudyPair.FromString(strings[i]));
}
studyPairsCount.Value = studyPairs.Count;
neuronNet.OutputsCount = studyPairs[0].quits.Count;
neuronNet.InputsCount = studyPairs[0].inputs.Count;
// DialogResult = DialogResult.OK;
neuronNet.StudyPairsLoaded = true;
ShowStudyPairs();
}
}
private void BuildNet(object sender, EventArgs e)
{
if (type == NetType.LINEAR && classes.Count == 0)
{
return;
}
if (type == NetType.KOHONEN && currentClass.values.Count == 0)
{
return;
}
switch (type)
{
case NetType.LINEAR:
linearNet.InputsCount = 2;
linearNet.SetNeuronsCount(linearNet.NeuronGroups.Count - 1, classes.Count);
linearNet.GraphicsNeuron.Refresh();
int count = GetObjectsCount();
linearNet.StudyPairs.Clear();
StudyPair pair;
for (int i = 0; i < classes.Count; i++)
{
for (int j = 0; j < classes[i].values.Count; j++)
{
pair = new StudyPair();
pair.inputs.Add(classes[i].values[j].X);
pair.inputs.Add(classes[i].values[j].Y);
for (int k = 0; k < classes.Count; k++)
{
pair.quits.Add(i == k ? 1 : 0);
}
linearNet.StudyPairs.Add(pair);
}
}
linearNet.StudyPairsLoaded = true;
break;
case NetType.KOHONEN:
kohonenNet.InputsCount = 2;
kohonenNet.StudyPairs.Clear();
kohonenNet.GraphicsNeuron.Refresh();
foreach (PointF obj in currentClass.values)
{
pair = new StudyPair();
pair.inputs.Add(obj.X);
pair.inputs.Add(obj.Y);
kohonenNet.StudyPairs.Add(pair);
}
UpdateCenterClasses();
break;
}
}
public RecognitionResult(StudyPair studyPair, int res)
{
pair = studyPair;
result = res;
}
public static StudyPair FromString(string str)
{
string tmp = "";
StudyPair pair = new StudyPair();
bool output = false;
bool name = false;
pair.inputs.Clear();
for (int i = 0; i < str.Length; i++)
{
switch (str[i])
{
case '-':
case ',':
case '.': tmp += str[i]; break;
case '|':
if (!output)
{
pair.inputs.Add(Convert.ToSingle(tmp));
}
else
{
pair.quits.Add(Convert.ToSingle(tmp));
pair.realQuits.Add(0);
}
tmp = "";
break;
case ';':
if (output)
{
pair.quits.Add(Convert.ToSingle(tmp));
}
else
{
pair.inputs.Add(Convert.ToSingle(tmp));
pair.realQuits.Add(0);
}
output = true;
tmp = "";
break;
case '&':
if (name)
{
pair.name = tmp;
}
name = true;
break;
case '#': return(pair);
default:
if (char.IsDigit(str[i]))
{
tmp += str[i];
}
if (name)
{
tmp += str[i];
}
break;
}
}
return(pair);
}
private void BuildNet(object sender, EventArgs e)
{
if (!pointsCreated)
{
return;
}
StudyPair pair;
int pointCount = Int32.Parse(PointCountX.Text);
switch (type)
{
case FunctionType.ONE_ARGUMENT_FUNCTION:
net.StudyPairs.Clear();
net.InputsCount = 1;
net.OutputsCount = 1;
PointF [] pts = drawer2D.GetPoints(1);
for (int i = 0; i < pts.Length; i++)
{
pair = new StudyPair();
pair.inputs.Add(pts[i].X);
pair.quits.Add(pts[i].Y);
net.StudyPairs.Add(pair);
}
net.LinearNormalizeInputs();
net.LinearNormalizeOutputs();
drawer2D.AddGraphic(3, Function2DNet, "Выход сети", typeView.Line);
drawer2D.Functions[2].NeedCreateBuffer = true;
drawer2D.Redraw();
break;
case FunctionType.TWO_ARGUMENT_FUNCTION:
int pointCountX = Int32.Parse(PointCountX.Text);
int pointCountY = Int32.Parse(PointCountZ.Text);
net.StudyPairs.Clear();
net.InputsCount = 2;
net.OutputsCount = 1;
Vector3[] p = drawer3D.GetGraphicPoints(1, pointCountX, pointCountY);
for (int i = 0; i < p.Length; i++)
{
pair = new StudyPair();
pair.inputs.Add(p[i].X);
pair.inputs.Add(p[i].Z);
pair.quits.Add(p[i].Y);
net.StudyPairs.Add(pair);
}
net.LinearNormalizeInputs();
net.LinearNormalizeOutputs();
drawer3D.AddGraphic(netFunction);
drawer3D.EnableCreateBuffers();
drawer3D.RecreateGraphic();
break;
}
net.StudyPairsLoaded = true;
netCreated = true;
}
private NeuronNet AcceptJson(Root2 root2)
{
NeuronNet net = new NeuronNet();
for (int i = 0; i < root2.inputss.Count; i++)
{
PointF pointF = new PointF();
pointF.X = (float)root2.inputss[i].Position.X;
pointF.Y = (float)root2.inputss[i].Position.Y;
NeuronInput neuron = new NeuronInput((float)root2.inputss[i].value, root2.inputss[i].Name, pointF);
neuron.positionChanged = root2.inputss[i].positionChanged;
neuron.wasPainted = root2.inputss[i].wasPainted;
net.inputss.Add(neuron);
}
for (int i = 0; i < root2.NeuronGroups.Count; i++)
{
NeuronGroup neurongroup = new NeuronGroup();
neurongroup.Neurons = root2.NeuronGroups[i].Neurons;
neurongroup.SecondActivate = root2.NeuronGroups[i].SecondActivate;
neurongroup.SumForSoftMax = (float)root2.NeuronGroups[i].SumForSoftMax;
neurongroup.allNeuronsWasPainted = root2.NeuronGroups[i].allNeuronsWasPainted;
net.NeuronGroups.Add(neurongroup);
}
for (int i = 0; i < root2.studyPairss.Count; i++)
{
StudyPair studyPair = new StudyPair();
for (int j = 0; j < root2.studyPairss[i].inputs.Count; j++)
{
studyPair.inputs.Add((float)root2.studyPairss[i].inputs[j]);
}
for (int j = 0; j < root2.studyPairss[i].quits.Count; j++)
{
studyPair.quits.Add((float)root2.studyPairss[i].quits[j]);
}
for (int j = 0; j < root2.studyPairss[i].realQuits.Count; j++)
{
studyPair.realQuits.Add((float)root2.studyPairss[i].realQuits[j]);
}
net.studyPairss.Add(studyPair);
}
net.E = (float)root2.E;
net.moment = (float)root2.moment;
for (int i = 0; i < root2.errors.Count; i++)
{
PointF pointF = new PointF();
pointF.X = (float)root2.errors[i].X;
pointF.Y = (float)root2.errors[i].Y;
net.errors.Add(pointF);
}
for (int i = 0; i < root2.normalizedErrors.Count; i++)
{
PointF pointF = new PointF();
pointF.X = (float)root2.normalizedErrors[i].X;
pointF.Y = (float)root2.normalizedErrors[i].Y;
net.normalizedErrors.Add(pointF);
}
net.EraCount = root2.EraCount;
net.currentSelection = new NeuronGroup(0);
net.recognitionResults = new List <RecognitionResult>();
net.StudyPairsLoaded = root2.StudyPairsLoaded;
net.InputsSum = (float)root2.InputsSum;
net.allInputsWasPainted = root2.allInputsWasPainted;
net.minError = (float)root2.minError;
net.NormalizeOutputValue = (float)root2.NormalizeOutputValue;
net.biasX = (float[])root2.biasX;
net.biasY = (float[])root2.biasY;
net.scaleX = (float[])root2.scaleX;
net.scaleY = (float[])root2.scaleY;
net.StudyLimit = (float)root2.StudyLimit;
net.AccessChangeNet = root2.AccessChangeNet;
return(net);
}
