public void RBFNetType(object sender, EventArgs e)
{
loadSaveTasksForm.Net = net = new RBFNeuralNet();
type = NeuronNetType.RBF;
this.Net = net;
Refresh();
}
public void ShuffleNetType(object sender, EventArgs e)
{
loadSaveTasksForm.Net = net = new LinearSystemTask();
type = NeuronNetType.SHUFFLE;
this.Net = net;
Refresh();
}
public void HopfieldType(object sender, EventArgs e)
{
loadSaveTasksForm.Net = net = new HopfieldNeuronNet();
type = NeuronNetType.HOPFIELD;
this.Net = net;
Refresh();
}
public void KohonenType(object sender, EventArgs e)
{
loadSaveTasksForm.Net = net = new KohonenNeuronNet();
type = NeuronNetType.KOHONEN;
this.Net = net;
Refresh();
}
public void LinearNetType(object sender, EventArgs e)
{
loadSaveTasksForm.Net = net = new LinearNeuronNet();
type = NeuronNetType.LINEAR;
this.Net = net;
Refresh();
}
private void ExportExcel(object sender, EventArgs e)
{
OpenFileDialog openDlg = new OpenFileDialog();
openDlg.Filter = "CSV Files (.csv)| *.csv|All files (.)| *.";
if (openDlg.ShowDialog() == DialogResult.OK)
{
File.Delete(@"NodeJS\test.json");
Process.Start("cmd", @"/C cd " + Application.StartupPath + "/NodeJS & node ./csvtojson " + openDlg.FileName);
Thread.Sleep(3000);
string json = File.ReadAllText(@"NodeJS\test.json");
ITraceWriter writer = new MemoryTraceWriter();
JsonSerializerSettings serSettings = new JsonSerializerSettings();
serSettings.ReferenceLoopHandling = ReferenceLoopHandling.Ignore;
serSettings.ContractResolver = new CamelCasePropertyNamesContractResolver();
serSettings.TraceWriter = writer;
//var neuronNet = JsonConvert.DeserializeObject<List<string>>(json.ToString(), serSettings);
//var neuronNet = JsonConvert.DeserializeObject<List<NeuronNet>>(json.ToString(), serSettings);
var test = JsonConvert.DeserializeObject <List <Root2> >(json.ToString(), serSettings);
net = AcceptJson(test[0]);
net.AccessChangeNet = true;
//Console.WriteLine(writer);
}
}
public StudyPairModifier(List <StudyPair> pairs, NeuronNet net)
{
InitializeComponent();
studyPairs = pairs;
neuronNet = net;
ShowStudyPairs();
}
public StudyFunctionForm(NeuronNet net)
{
InitializeComponent();
this.net = net;
drawer = new Drawer(pictureBox1, pictureBox1.ClientRectangle);
drawer.GraphicBounds = new PointF(0, net.EraCount);
drawer.AddGraphic(1, net.StudyFunction, "Расписание обучения", typeView.Line);
drawer.Redraw();
}
public NetOptions(NeuronNet Net)
{
InitializeComponent();
net = Net;
inputsCount.Value = Net.InputsCount;
groupsCount.Value = Net.NeuronGroupsCount;
groupNumber.Value = 1;
neuronsCount.Value = Net.NeuronGroups[0].Neurons.Count;
}
//public RegressionForm(FunctionType type , LinearNeuronNet net)
public RegressionForm(FunctionType type, NeuronNet net)
{
InitializeComponent();
this.net = net;
this.type = type;
mainFunction = new Graphics3D.Function3D(1, Function3D);
missFunction = new Graphics3D.Function3D(2, Function3DWithMiss);
netFunction = new Graphics3D.Function3D(3, Function3DNet);
CreateVisualisationControl();
}
private void DeserializeNet(object sender, EventArgs e)
{
BinaryFormatter bf = new BinaryFormatter();
OpenFileDialog openDlg = new OpenFileDialog();
if (openDlg.ShowDialog() == DialogResult.OK)
{
FileStream fs = new FileStream(openDlg.FileName, FileMode.Open, FileAccess.Read);
Net = (NeuronNet)bf.Deserialize(fs);
net.AccessChangeNet = true;
fs.Close();
}
}
//Загрузка
private void DeserializeNet(object sender, EventArgs e)
{
BinaryFormatter bf = new BinaryFormatter();
//OpenFileDialog openDlg = new OpenFileDialog();
LoadMenu loadMenu = new LoadMenu();
loadMenu.indexSave = 0;
loadMenu.ShowDialog();
if (loadMenu.fileName != null)
{
string query = "select File from SaveFiles where Name=" + "\"" + loadMenu.fileName + "\"";
databaseSQLite.OpenConnection();
SQLiteCommand myCommand = new SQLiteCommand(query, databaseSQLite.myConnection);
List <SaveFiles> list = new List <SaveFiles>();
using (SQLiteDataReader reader = myCommand.ExecuteReader())
{
if (reader.HasRows) // если есть данные
{
while (reader.Read()) // построчно считываем данные
{
byte[] data = (byte[])reader.GetValue(0);
SaveFiles saveFile1 = new SaveFiles(data);
list.Add(saveFile1);
}
}
}
databaseSQLite.CloseConnection();
using (FileStream fd = new FileStream("temp.dat", FileMode.OpenOrCreate))
{
fd.Write(list[0].File, 0, list[0].File.Length);
}
FileStream fs = new FileStream("temp.dat", FileMode.Open, FileAccess.Read);
Net = (NeuronNet)bf.Deserialize(fs);
net.AccessChangeNet = true;
fs.Close();
}
}
public ParameterListForm(NeuronNet net)
{
InitializeComponent();
this.net = net;
FillParameterGrid();
}
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);
}