public void Execute()
{
var nnSet = new Stage1NeuralNetwork[vectors.Length];
int trainLength = 2000;
int validationLength = 500;
int predictLength = 500;
int useLength = 3000;
var totalData = new DenseMatrix(vectors.Length, useLength);
var outputData = new DenseMatrix(vectors.Length, validationLength - window);
/////////////////populate the actual price data we want to predict
var pricingData = new double[useLength];
for (int i = 2; i < 2 + useLength; i++)
{
pricingData[i - 2] = (double)data[useLength + 3 - i, 5];
}
double[] returnpricingData = pricingData.RawRateOfReturn();
for (int i = 0; i < returnpricingData.Length; i++)
{
pricingData[i + 1] = returnpricingData[i];
}
pricingData[0] = 0;
////////////////////////training and validation////////////////////////
for (int i = 2; i < 2 + useLength; i++)
{
for (int j = 0; j < vectors.Length; j++)
{
totalData[j, i - 2] = (double)data[useLength + 3 - i, vectors[j]];
}
}
for (int j = 0; j < vectors.Length; j++)
{
double[] train = totalData.Row(j).ToArray().Take(trainLength).ToArray();
double[] validate =
totalData.Row(j).ToArray().Skip(trainLength).ToArray().Take(validationLength).ToArray();
nnSet[j] = new Stage1NeuralNetwork(window, cycles, train, validate);
nnSet[j].Execute(j);
outputData.SetRow(j, nnSet[j].OutputData);
}
var s1 = new Stage2NeuralNetwork(vectors.Length, cycles, outputData,
pricingData.Skip(trainLength).ToArray().Take(validationLength).ToArray().Skip(window).ToArray());
s1.Execute();
//////////////////////////////////////////////////////////////////////////
//////////////////////////////////prediction/////////////////////////////
var predictedData = new DenseMatrix(vectors.Length, predictLength - window + 1);
var lastPredData = new double[vectors.Length];
for (int j = 0; j < vectors.Length; j++)
{
double[] predictData =
totalData.Row(j)
.ToArray()
.Skip(trainLength + validationLength)
.ToArray()
.Take(predictLength)
.ToArray();
nnSet[j].Predict(predictData);
predictedData.SetRow(j, nnSet[j].OutputData);
lastPredData[j] = nnSet[j].NextPrediction;
}
s1.Predict(predictedData,
pricingData.ToArray()
.Skip(trainLength + validationLength)
.ToArray()
.Take(predictLength)
.ToArray()
.Skip(window)
.ToArray());
correlation = s1.outputCorre;
RMSE = s1.outputRMSE;
}
public void Execute()
{
var nnSet = new Stage1NeuralNetwork[vectors.Length];
int trainLength = 2000;
int validationLength = 500;
int predictLength = 500;
int useLength = 3000;
var totalData = new DenseMatrix(vectors.Length, useLength);
var outputData = new DenseMatrix(vectors.Length, validationLength - window);
/////////////////populate the actual price data we want to predict
var pricingData = new double[useLength];
for (int i = 2; i < 2 + useLength; i++)
{
pricingData[i - 2] = (double) data[useLength + 3 - i, 5];
}
double[] returnpricingData = pricingData.RawRateOfReturn();
for (int i = 0; i < returnpricingData.Length; i++) pricingData[i + 1] = returnpricingData[i];
pricingData[0] = 0;
////////////////////////training and validation////////////////////////
for (int i = 2; i < 2 + useLength; i++)
{
for (int j = 0; j < vectors.Length; j++)
{
totalData[j, i - 2] = (double) data[useLength + 3 - i, vectors[j]];
}
}
for (int j = 0; j < vectors.Length; j++)
{
double[] train = totalData.Row(j).ToArray().Take(trainLength).ToArray();
double[] validate =
totalData.Row(j).ToArray().Skip(trainLength).ToArray().Take(validationLength).ToArray();
nnSet[j] = new Stage1NeuralNetwork(window, cycles, train, validate);
nnSet[j].Execute(j);
outputData.SetRow(j, nnSet[j].OutputData);
}
var s1 = new Stage2NeuralNetwork(vectors.Length, cycles, outputData,
pricingData.Skip(trainLength).ToArray().Take(validationLength).ToArray().Skip(window).ToArray());
s1.Execute();
//////////////////////////////////////////////////////////////////////////
//////////////////////////////////prediction/////////////////////////////
var predictedData = new DenseMatrix(vectors.Length, predictLength - window + 1);
var lastPredData = new double[vectors.Length];
for (int j = 0; j < vectors.Length; j++)
{
double[] predictData =
totalData.Row(j)
.ToArray()
.Skip(trainLength + validationLength)
.ToArray()
.Take(predictLength)
.ToArray();
nnSet[j].Predict(predictData);
predictedData.SetRow(j, nnSet[j].OutputData);
lastPredData[j] = nnSet[j].NextPrediction;
}
s1.Predict(predictedData,
pricingData.ToArray()
.Skip(trainLength + validationLength)
.ToArray()
.Take(predictLength)
.ToArray()
.Skip(window)
.ToArray());
correlation = s1.outputCorre;
RMSE = s1.outputRMSE;
}
