protected List <PredictionOfCurrencyLearnResult> FitLstm(bool saveLearnResult = false)
{
InitData();
InitLstm();
var result = new List <PredictionOfCurrencyLearnResult>();
for (var i = 0; i < DataManager.LearnData.Data.Count - 1; i += Chunk)
{
var chunk = Chunk;
if (i + Chunk > DataManager.LearnData.Data.Count - 1)
{
chunk = DataManager.LearnData.Data.Count - 1 - i;
}
var input = new Vector[chunk];
var ideal = new Vector[chunk];
for (var j = i; j < i + chunk && j < DataManager.LearnData.Data.Count - 1; j++)
{
input[j - i] = DataManager.LearnData[j].Vector;
ideal[j - i] = new double[] { DataManager.LearnData[j + 1].Vector.Values.Last() };
}
var(output, error) = Lstm.Learn(input, ideal);
for (var j = 0; j < chunk; j++)
{
result.Add(new PredictionOfCurrencyLearnResult {
Date = DataManager.LearnData.Data[i + j].Date,
Error = error[j],
Output = DataManager.ConvertOutput(output[j]),
Ideal = DataManager.ConvertOutput(ideal[j]),
Input = DataManager.ConvertInput(input[j]),
});
}
}
Lstm.Save(NeuralNetworkName);
if (saveLearnResult)
{
SaveLearnProgress(result);
}
return(result);
}
public override void Run()
{
var rnd = new Random();
var lstm = new Lstm(2, 1, new RecurentParameters(0.5, 1, 0.5),
new RecurentCellParameters(2, 4),
new RecurentCellParameters(4, 1));
/*new RecurentParameters {
* ActivationCoefficient = 0.5,
* LearnSpeed = 1,
* LengthOfInput = 2,
* LengthOfOutput = 1,
* LengthOfOutputSequence = 3,
* LayerCount = 3,
* Cells = new RecurentCellParameters[] {
* new RecurentCellParameters(2, 4),
* new RecurentCellParameters(4, 1)
* }
* }*/
/*lstm.GatesForLayers.ForgetLayer = new double[][] {
* new [] { 0.0097627, 0.04303787, 0.02055268, 0.00897664, -0.01526904 },
* new [] { 0.02917882, -0.01248256, 0.0783546, 0.09273255, -0.0233117 }
* };s
* lstm.GatesForLayers.InputLayer = new double[][] {
* new [] { 0.0097627, 0.04303787, 0.02055268, 0.00897664, -0.01526904 },
* new [] { 0.02917882, -0.01248256, 0.0783546, 0.09273255, -0.0233117 }
* };
* lstm.GatesForLayers.OutputLayer = new double[][] {
* new [] { 0.0097627, 0.04303787, 0.02055268, 0.00897664, -0.01526904 },
* new [] { 0.02917882, -0.01248256, 0.0783546, 0.09273255, -0.0233117 }
* };
* lstm.GatesForLayers.TanhLayer = new double[][] {
* new [] { 0.0097627, 0.04303787, 0.02055268, 0.00897664, -0.01526904 },
* new [] { 0.02917882, -0.01248256, 0.0783546, 0.09273255, -0.0233117 }
* };
*
* lstm.GatesForLayers.BiasForgetLayer = new [] { 0.0097627, 0.04303787 };
* lstm.GatesForLayers.BiasInputLayer = new [] { 0.0097627, 0.04303787 };
* lstm.GatesForLayers.BiasOutputLayer = new [] { 0.0097627, 0.04303787 };
* lstm.GatesForLayers.BiasTanhLayer = new [] { 0.0097627, 0.04303787 };*/
//lstm.BaseLayerLstm.Forget = new Vector(2, () => rnd.NextDouble());
//lstm.BaseLayerLstm.ForgetFromPreviousLayer = new Vector(2, () => rnd.NextDouble());
//lstm.BaseLayerLstm.OutputFromPreviousLayer = new Vector(2, () => rnd.NextDouble());
var learn = new Vector[] {
new [] { 0.018088, 0.01591 },
new [] { 0.0248, -0.00912 },
new [] { -0.013727, 0.00502 },
new [] { -0.023491, 0.007678 },
new [] { -0.011982, 0.025521 },
new [] { 0.00835, -0.0316 },
new [] { 0.041049, -0.041505 },
new [] { 0.050914, -0.046292 },
/*new [] { 0.076138, -0.106684 },
* new [] { 0.131035, -0.092031 },
* new [] { 0.206694, -0.209201 },*/
};
var ideal1 = new Vector[] {
new [] { 0.0248, -0.00912 },
new [] { -0.013727, 0.00502 },
new [] { -0.023491, 0.007678 },
new [] { -0.011982, 0.025521 },
new [] { 0.00835, -0.0316 },
new [] { 0.041049, -0.041505 },
new [] { 0.050914, -0.046292 },
new [] { 0.076138, -0.106684 },
/*new [] { 0.131035, -0.092031 },
* new [] { 0.206694, -0.209201 },
* new [] { 0.168238, -0.211099 }*/
};
var ideal = new Vector[] {
new [] { -0.4 },
new [] { 0.1 },
new [] { 0.4 }
};
var input = new Vector[] {
new [] { -0.3, -0.1 },
new [] { 0.7, 0.8 },
new [] { 0.2, 0.1 }
};
var errorCommon = 0.0;
var minError = double.MaxValue;
var t = new TimeSpan(0);
for (var i = 0; i < 10000000; i++)
{
var perf = new Stopwatch();
perf.Start();
var(outputs, errors) = lstm.Learn(input, ideal);
perf.Stop();
t += perf.Elapsed;
if (outputs.ToList().Any(x => double.IsNaN(x[0])))
{
Console.WriteLine("NaN");
for (var h = 0; h < 5; h++)
{
Console.Beep(1000, 700);
Thread.Sleep(100);
}
Console.ReadLine();
Console.ReadLine();
Console.ReadLine();
}
var error = errors.Sum(x => x[0]);
if (error <= minError)
{
minError = error;
}
if (error < 0.000000000000000000001)
{
Console.WriteLine("End \t" + lstm.Epoch);
Console.ReadLine();
}
/*else {
* Console.WriteLine("Increath");
* Console.ReadLine();
* }*/
errorCommon += error;
/*Console.WriteLine("Learn:\t" + i + "\t time = " + (t / (i + 1)));
* for (var j = 0; j < outputs.Length; j++) {
* Console.WriteLine("\tOutput:\t" + j);
* Console.WriteLine("\t\tI:\t" + ideal[j]);
* Console.WriteLine("\t\tO:\t" + outputs[j]);
* Console.WriteLine("\t\tE:\t" + errors[j]);
* }
* Console.WriteLine("Common error:\t" + errorCommon / lstm.Epoch);
* Console.WriteLine("Min error:\t" + minError);*/
Console.WriteLine("Error:\t" + error);
//Console.ReadKey();
}
}