/// <summary>
/// Metoda testująca znalezioną najlepszą sieć neuronową.
/// </summary>
public void TestNet()
{
StringReader reader;
try
{
reader = new StringReader(File.ReadAllText(Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location) + "\\networks\\LowestMSENetwork.xml"));
}
catch
{
return;
}
XmlSerializer serializer = new XmlSerializer(typeof(NetworkMSE));
NetworkMSE lowesMSE = (NetworkMSE)serializer.Deserialize(reader);
NeuralNet net = new NeuralNet();
if (!net.CreateFromFile("networks\\" + lowesMSE.NetworkFileName))
{
return;
}
uint numberOfInputs = net.GetNumInput();
uint numberOfPeriods = (numberOfInputs + 1) / 2;
DateTime date = new DateTime(2009, 12, 13);
DateTime iterationDate = date;
date = DataProvider.Instance.GetNextExchangeQuotationDate(date, 0);
DateTime endDate = new DateTime(2011, 12 , 13);
Trend up = new Trend(), down = new Trend(), side = new Trend();
int predictionDownCount = 0;
int predictionSidewaysCount = 0;
int predictionUpCount = 0;
int predictionDownTrue = 0;
int predictionSidewaysTrue = 0;
int predictionUpTrue = 0;
while (date <= endDate)
{
List<ExchangePeriod> periods = DataProvider.Instance.GetExchangePeriodsMergedByMovementDirectoryFromEndDate((int)numberOfPeriods, date, this.firstExchangeQuotationDate, 1);
List<ExchangePeriod> testPeriods = DataProvider.Instance.GetExchangePeriodsMergedByMovementDirectoryFromStartDate((int)numberOfPeriods, DataProvider.Instance.GetNextExchangeQuotationDate(DateTime.Now, 0), date, 1);
TrendDirection truTrend = GetTrendDirection(testPeriods, 0);
double[] inputs = new double[numberOfInputs];
for (int i = 0; i < numberOfPeriods; i += 2)
{
inputs[i] = periods[i].PercentageChange;
if (i + 1 < periods.Count)
{
inputs[i + 1] = (periods[i + 1].PublicTrading - periods[i].PublicTrading) / periods[i].PublicTrading;
}
}
double[] result = net.Run(inputs);
double sum = 0;
foreach (double r in result)
{
if (r > 0)
{
sum += r;
}
}
for (int i = 0; i < result.Length; i++)
{
TrendDirectionWithPropability td = new TrendDirectionWithPropability();
if (result[i] > 0)
{
result[i] = result[i] * 100 / sum;
}
else
{
result[i] = 0;
}
}
switch (result.ToList().IndexOf(result.Max()))
{
case 0:
predictionDownCount++;
if (truTrend == TrendDirection.Down)
{
predictionDownTrue++;
}
break;
case 1:
predictionSidewaysCount++;
if (truTrend == TrendDirection.Sideways)
{
predictionSidewaysTrue++;
}
break;
case 2:
predictionUpCount++;
if (truTrend == TrendDirection.Up)
{
predictionUpTrue++;
}
break;
}
switch (truTrend)
{
case TrendDirection.Down:
down.count++;
down.avgDown += result[0];
down.avgSide += result[1];
down.avgUp += result[2];
break;
case TrendDirection.Sideways:
side.count++;
side.avgDown += result[0];
side.avgSide += result[1];
side.avgUp += result[2];
break;
case TrendDirection.Up:
up.count++;
up.avgDown += result[0];
up.avgSide += result[1];
up.avgUp += result[2];
break;
}
// iterationDate = iterationDate.AddDays(7);
// date = DataProvider.Instance.GetNextExchangeQuotationDate(iterationDate, 0);
date = DataProvider.Instance.GetNextExchangeQuotationDate(date, 1);
}
down.avgUp = down.avgUp/(double) down.count;
down.avgDown = down.avgDown/(double) down.count;
down.avgSide = down.avgSide / (double)down.count;
up.avgUp = up.avgUp / (double)up.count;
up.avgDown = up.avgDown / (double)up.count;
up.avgSide = up.avgSide / (double)up.count;
side.avgUp = side.avgUp / (double)side.count;
side.avgDown = side.avgDown / (double)side.count;
side.avgSide = side.avgSide / (double)side.count;
List<string> fileContent = new List<string>();
fileContent.Add("Trend Name \t Sample Count \t Up \t Side \t Down ");
fileContent.Add(TrendDirection.Up.ToString() + " \t " + up.count.ToString() + " \t " + up.avgUp.ToString() + " \t " + up.avgSide.ToString() + " \t " + up.avgDown.ToString());
fileContent.Add(TrendDirection.Sideways.ToString() + " \t " + side.count.ToString() + " \t " + side.avgUp.ToString() + " \t " + side.avgSide.ToString() + " \t " + side.avgDown.ToString());
fileContent.Add(TrendDirection.Down.ToString() + " \t " + down.count.ToString() + " \t " + down.avgUp.ToString() + " \t " + down.avgSide.ToString() + " \t " + down.avgDown.ToString());
fileContent.Add("");
fileContent.Add(TrendDirection.Up.ToString() + " \t " + (predictionUpTrue/(double)predictionUpCount).ToString());
fileContent.Add(TrendDirection.Sideways.ToString() + " \t " + (predictionSidewaysTrue / (double)predictionSidewaysCount).ToString());
fileContent.Add(TrendDirection.Down.ToString() + " \t " + (predictionDownTrue / (double)predictionDownCount).ToString());
fileContent.Add("");
File.AppendAllLines(Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location) + "\\test result.txt", fileContent);
}
/// <summary>
/// Metoda przewidująca kierunek trendu na podstawie najlepszej znalezionej sieci neuronowej.
/// </summary>
/// <param name="date">Data dla której ma się odbyć przewidywanie.</param>
/// <param name="path">Ścieżka do folderu zawierającego sieci neuronowe.</param>
/// <returns>Wyniki przewidywania.</returns>
public PredictionResult PredictTrendDirection(DateTime date, string path)
{
path = Path.GetFileName(path);
StringReader reader;
try
{
reader = new StringReader(File.ReadAllText(Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location) + "\\" + path + "\\LowestMSENetwork.xml"));
}
catch
{
return null;
}
XmlSerializer serializer = new XmlSerializer(typeof(NetworkMSE));
NetworkMSE lowesMSE = (NetworkMSE)serializer.Deserialize(reader);
NeuralNet net = new NeuralNet();
if (!net.CreateFromFile(path + "\\" + lowesMSE.NetworkFileName))
{
return null;
}
uint numberOfInputs = net.GetNumInput();
uint numberOfPeriods = (numberOfInputs + 1) / 2;
List<ExchangePeriod> periods = DataProvider.Instance.GetExchangePeriodsMergedByMovementDirectoryFromEndDate((int)numberOfPeriods, date, this.firstExchangeQuotationDate, 1);
double[] inputs = new double[numberOfInputs];
for (int i = 0; i < numberOfPeriods; i += 2)
{
inputs[i] = periods[i].PercentageChange;
if (i + 1 < periods.Count)
{
inputs[i + 1] = (periods[i + 1].PublicTrading - periods[i].PublicTrading) / periods[i].PublicTrading;
}
}
double[] result = net.Run(inputs);
PredictionResult results = new PredictionResult();
results.Trends = new List<TrendDirectionWithPropability>();
double sum = 0;
foreach (double r in result)
{
if (r > 0)
{
sum += r;
}
}
for (int i = 0; i < result.Length; i++)
{
TrendDirectionWithPropability td = new TrendDirectionWithPropability();
if (result[i] > 0)
{
td.Propability = result[i] * 100 / sum;
}
else
{
td.Propability = 0;
}
switch (i)
{
case 0:
td.Direction = TrendDirection.Down;
break;
case 1:
td.Direction = TrendDirection.Sideways;
break;
case 2:
td.Direction = TrendDirection.Up;
break;
}
results.Trends.Add(td);
}
int[] resultParam = DataProvider.Instance.GetNetParametersFromFile(Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location) + "\\" + path + "\\" + lowesMSE.NetworkFileName);
if (resultParam != null)
{
results.PeriodNo = resultParam[0];
results.PatternsNo = resultParam[1];
results.NeuronNo = resultParam[2];
results.EpochsNo = resultParam[3];
}
results.MSE = lowesMSE.MSE;
return results;
}
