private void menuPredict_Click(object sender, EventArgs e)
{
try
{
int i = lbStocks.SelectedIndex;
if (i < 0)
{
return;
}
StockState state = stockDictionary[Stocks[i]];
state.predict();
}
catch (Exception ex)
{
invoke_AppendLogText(ex.Message);
}
}
protected void initial()
{
Stocks = (List <String>)lbStocks.Tag;
Task task = Job.getInstance().addTask(Stocks.Count);
for (int i = 0; i < Stocks.Count; i++)
{
StockState stockState = new StockState(Stocks[i]);
stockState.loadHistoryData(System.Windows.Forms.Application.StartupPath + Path.DirectorySeparatorChar + Stocks[i] + ".csv");
//BP bpNetwork = new BP(Constants.Input_Days * 4, Constants.Hidden_Layor_Count, Constants.Output_Days * 4);
//stockState.neuralMatrix = bpNetwork;
String filename = System.Windows.Forms.Application.StartupPath + Path.DirectorySeparatorChar + Stocks[i] + ".data";
stockState.loadNeuralMatrixData(filename);
stockDictionary.Add(Stocks[i], stockState);
task.process();
}
isInitializing = false;
}
private void lbStocks_MouseUp(object sender, MouseEventArgs e)
{
if (isInitializing)
{
return;
}
int i = lbStocks.SelectedIndex;
if (i < 0)
{
return;
}
StockState state = stockDictionary[Stocks[i]];
if (state.predictData != null)
{
drawDailyK(state.predictData);
}
}
private void menuTrain_Click(object sender, EventArgs e)
{
try
{
int i = lbStocks.SelectedIndex;
if (i < 0)
{
return;
}
StockState state = stockDictionary[Stocks[i]];
state.initial();
Thread thrd = new Thread(state.training);
thrd.Start();
}
catch (Exception ex)
{
invoke_AppendLogText(ex.Message);
}
}