public void Predict()
{
double error = 0;
int c = 0;
var d = new DenseMatrix(2, _normalizedPredictionData.Length - WindowSize);
int count = 0;
for (int i = WindowSize; i < _normalizedPredictionData.Length; i++)
{
// calculate based on actual data
IMLData input = new BasicMLData(WindowSize);
for (int j = 0; j < input.Count; j++)
{
input.Data[j] = _normalizedPredictionData[(i - WindowSize) + j];
}
IMLData output = network.Compute(input);
double prediction = output.Data[0];
error += Math.Pow((normalizeArray.Stats.DeNormalize(prediction) - predictionData[i]) / predictionData[i],
2);
c++;
d[0, count] = predictionData[i];
d[1, count] = normalizeArray.Stats.DeNormalize(prediction);
count++;
}
error /= c;
error = Math.Pow(error, .5);
Console.WriteLine(error);
OutputData = d.Row(1).ToArray();
string[] symbols = { "actual", "predicted" };
Visualize.GeneratePredictionGraph(symbols, d, new DateTime(), new TimeSpan(24, 0, 0),
QSConstants.DEFAULT_DATA_FILEPATH + write + ".html");
}
public void Predict()
{
double error = 0;
int c = 0;
var d = new DenseMatrix(2, _normalizedPredictionData.Length);
int count = 0;
for (int i = 0; i < _normalizedPredictionData.Length; i++)
{
// calculate based on actual data
IMLData input = new BasicMLData(inputs);
for (int j = 0; j < input.Count; j++)
{
input.Data[j] = _normalizedTrainingData[j, i];
}
IMLData output = network.Compute(input);
double prediction = output.Data[0];
error +=
Math.Pow(
(normalizeArrayOutput.Stats.DeNormalize(prediction) - predictionData[i]) / predictionData[i], 2);
c++;
d[0, count] = predictionData[i];
d[1, count] = normalizeArrayOutput.Stats.DeNormalize(prediction);
count++;
}
error /= c;
error = Math.Pow(error, .5);
Console.WriteLine(error);
string[] symbols = { "actual", "predicted" };
Visualize.GeneratePredictionGraph(symbols, d, new DateTime(), new TimeSpan(24, 0, 0),
"C:\\Sangar\\resultfinal.html");
}
public void Predict(DenseMatrix newPredictData, double[] newPredictPrices)
{
double error = 0;
int c = 0;
var newNormalizedPredictData = new DenseMatrix(newPredictData.RowCount, newPredictData.ColumnCount,
double.NaN);
for (int i = 0; i < newPredictData.RowCount; i++)
{
newNormalizedPredictData.SetRow(i, normalizeArrayInput[i].Process(newPredictData.Row(i).ToArray()));
}
double[] normalizedPrices = normalizeArrayOutput.Process(newPredictPrices);
var d = new DenseMatrix(2, normalizedPrices.Length + 1, double.NaN);
int count = 0;
for (int i = 0; i < normalizedPrices.Length; i++)
{
// calculate based on actual data
IMLData input = new BasicMLData(inputs);
for (int j = 0; j < input.Count; j++)
{
input.Data[j] = newNormalizedPredictData[j, i];
}
IMLData output = network.Compute(input);
double prediction = output.Data[0];
error +=
Math.Pow(
(normalizeArrayOutput.Stats.DeNormalize(prediction) - newPredictPrices[i]) / newPredictPrices[i],
2);
c++;
d[0, count] = newPredictPrices[i];
d[1, count] = normalizeArrayOutput.Stats.DeNormalize(prediction);
count++;
}
/////////////////////////////////////////////////////////////////
IMLData input1 = new BasicMLData(inputs);
for (int j = 0; j < input1.Count; j++)
{
input1.Data[j] = newNormalizedPredictData[j, newNormalizedPredictData.ColumnCount - 1];
}
IMLData output1 = network.Compute(input1);
d[1, count] = normalizeArrayOutput.Stats.DeNormalize(output1.Data[0]);
/////////////////////////////////////////////////////////////////
error /= c;
error = Math.Pow(error, .5);
Console.WriteLine(error);
string[] symbols = { "actual", "predicted" };
Visualize.GeneratePredictionGraph(symbols, d, new DateTime(), new TimeSpan(24, 0, 0),
"C:\\Sangar\\resultfinal.html");
outputCorre =
StatisticsExtension.Correlation(d.Row(0).ToArray().Take(d.ColumnCount - 1).ToArray().RawRateOfReturn(),
d.Row(1).ToArray().Take(d.ColumnCount - 1).ToArray().RawRateOfReturn());
Console.WriteLine("ST2 Correlation: " + outputCorre);
outputRMSE = error;
Console.WriteLine("Predicted return for D+1:" +
(d[1, d.ColumnCount - 1] - d[1, d.ColumnCount - 2]) / d[1, d.ColumnCount - 2] * 100 +
" percent");
}
public void Predict(double[] newPredictData)
{
double error = 0;
int c = 0;
double[] newNormalizedData = normalizeArray.Process(newPredictData);
var d = new DenseMatrix(2, newNormalizedData.Length - WindowSize + 1, double.NaN);
int count = 0;
for (int i = WindowSize; i < newNormalizedData.Length; i++)
{
// calculate based on actual data
IMLData input = new BasicMLData(WindowSize);
for (int j = 0; j < input.Count; j++)
{
input.Data[j] = newNormalizedData[(i - WindowSize) + j];
}
IMLData output = network.Compute(input);
double prediction = output.Data[0];
error += Math.Pow((normalizeArray.Stats.DeNormalize(prediction) - newPredictData[i]) / newPredictData[i],
2);
c++;
d[0, count] = newPredictData[i];
d[1, count] = normalizeArray.Stats.DeNormalize(prediction);
count++;
}
///////////////////////////////////////////////////////////////////////////////
var lastData = new double[WindowSize];
int count1 = 0;
for (int i = newNormalizedData.Length - WindowSize; i < newNormalizedData.Length; i++)
{
lastData[count1++] = newNormalizedData[i];
}
IMLData input1 = new BasicMLData(WindowSize);
for (int j = 0; j < input1.Count; j++)
{
input1.Data[j] = lastData[j];
}
IMLData output1 = network.Compute(input1);
d[1, count] = normalizeArray.Stats.DeNormalize(output1.Data[0]);
/////////////////////////////////////////////////////////////////////////////////
error /= c;
error = Math.Pow(error, .5);
Console.WriteLine(error);
OutputData = d.Row(1).ToArray();
string[] symbols = { "actual", "predicted" };
Visualize.GeneratePredictionGraph(symbols, d, new DateTime(), new TimeSpan(24, 0, 0),
QSConstants.DEFAULT_DATA_FILEPATH + write + ".html");
Console.WriteLine("ST1 Correlation: " +
StatisticsExtension.Correlation(
d.Row(0).ToArray().Take(d.ColumnCount - 1).ToArray().RawRateOfReturn(),
d.Row(1).ToArray().Take(d.ColumnCount - 1).ToArray().RawRateOfReturn()));
}
