private void PrintOutputGraph_1stOfEach()
{
var input = new PointCollection();
var output = new PointCollection();
var expected = new PointCollection();
for (var i = 0; i < _trainingData["Inputs"].Count; i++)
{
input.Add(new Point(i, _trainingData["Inputs"][i][0]));
output.Add(new Point(i, _nn.GenerateOutput(_trainingData["Inputs"][i])[0]));
expected.Add(new Point(i, _trainingData["Outputs"][i][0]));
}
OutputPlot.LineThickness = 2.5;
OutputPlot.ScaleXAxis = true;
OutputPlot.Add("Input", input, Colors.Black);
OutputPlot.Add("Expected", expected, Colors.White);
OutputPlot.Add("Output", output, Colors.Purple);
}
private void TestNN()
{
var testingData = new List <List <double> >();
var testingDataJson = Common.GetJsonFromFile();
foreach (JArray testSet in testingDataJson["Inputs"])
{
var ts = new List <double>();
for (var i = 0; i < testSet.Count(); ++i)
{
ts.Add(testSet[i].Value <double>());
}
testingData.Add(ts);
}
_trainingDataMeta = testingDataJson["Meta"] as JObject;
var outputOffset = _trainingDataMeta["OutputOffset"].Value <int>();
var outputSize = _trainingDataMeta["OutputSize"].Value <int>();
var inputSize = _trainingDataMeta["InputSize"].Value <int>();
var inputPoints = new PointCollection();
var outputPoints = new PointCollection();
for (var i = 0; i < testingData.Count; i++)
{
for (var j = 0; j < inputSize; ++j)
{
var input = testingData[i][j];
var output = j < outputSize?_nn.GenerateOutput(testingData[i])[j] : 0;
inputPoints.Add(new Point(i * inputSize + j, input));
outputPoints.Add(new Point(i * inputSize + j + outputOffset, output));
}
}
OutputPlot.Clear();
OutputPlot.LineThickness = 2.5;
OutputPlot.ScaleXAxis = true;
OutputPlot.Add("Input", inputPoints, Colors.White);
OutputPlot.Add("Output", outputPoints, Colors.Purple);
}
private void XOnDataAvailable(object sender, WaveInEventArgs e)
{
var spectrum_cp = new PointCollection();
var buff = new double[1024];
for (var i = 0; i < buff.Length; ++i)
{
buff[i] = e.Buffer[i];
}
var spectrum = DSP.FourierTransform.Spectrum(ref buff);
for (var i = 0; i < spectrum.Length; ++i)
{
spectrum_cp.Add(new Point(i, spectrum[i]));
}
OutputPlot.Add("Spectrum", spectrum_cp, Colors.White);
}
private async Task ShowStock(string stock)
{
try
{
var mongo = new MongoClient("mongodb://localhost:27017");
var db = mongo.GetDatabase("traiding_data");
var stockCollections = new List <string>();
using (var cursor = await db.ListCollectionNamesAsync())
{
await cursor.ForEachAsync(d => stockCollections.Add(d.ToString()));
}
if (!stockCollections.Contains(stock))
{
MessageBox.Show($"{stock} collection doesn't exist!");
return;
}
var collection = db.GetCollection <StockData>(stock);
var res = (from c in collection.AsQueryable() orderby c.Date descending select c).ToList();
var pc = new PointCollection();
for (var i = 0; i < res.Count; ++i)
{
pc.Add(new Point(i, res[i].Close - res[i].Low));
}
OutputPlot.Clear();
OutputPlot.Add(stock, pc, Colors.White);
MainTabControl_tc.SelectedItem = OutputGraph_Tab_ti;
}
catch (Exception e)
{
Console.WriteLine(e);
throw;
}
}
private void PrintOutputGraph_AllOfEach()
{
var outputOffset = _trainingDataMeta["OutputOffset"].Value <int>();
var input = new PointCollection();
var output = new PointCollection();
var expected = new PointCollection();
var sampleCount = _trainingData["Inputs"].Count;
for (var i = 0; i < sampleCount; i++)
{
var inputSize = _trainingData["Inputs"][i].Count;
var outputSize = _trainingData["Outputs"][i].Count;
for (var j = 0; j < inputSize; ++j)
{
input.Add(new Point(i * inputSize + j, _trainingData["Inputs"][i][j]));
}
for (var j = 0; j < inputSize; ++j)
{
output.Add(new Point(i * inputSize + j + outputOffset,
j < outputSize ? _nn.GenerateOutput(_trainingData["Inputs"][i])[j] : 0));
}
for (var j = 0; j < inputSize; j++)
{
expected.Add(new Point(i * inputSize + j + outputOffset,
j < outputSize ? _trainingData["Outputs"][i][j] : 0));
}
}
OutputPlot.LineThickness = 2.5;
OutputPlot.ScaleXAxis = true;
OutputPlot.Add("Input", input, Colors.Black);
OutputPlot.Add("Expected", expected, Colors.White);
OutputPlot.Add("Output", output, Colors.Purple);
}
