public void NormalizeSunspots(double lo, double hi)
{
array= new NormalizeArray { NormalizedHigh = hi, NormalizedLow = lo };
// create arrays to hold the normalized sunspots
_normalizedSunspots = array.Process(Sunspots);
_closedLoopSunspots = EngineArray.ArrayCopy(_normalizedSunspots);
}
public Stage1NeuralNetwork(int windowSize, int iterations, double[] trainingData, double[] predictData)
{
WindowSize = windowSize;
this.iterations = iterations;
this.trainingData = trainingData;
predictionData = predictData;
normalizeArray = new NormalizeArray {NormalizedHigh = normalizeHi, NormalizedLow = normalizeLo};
}
public void TestNormalize()
{
var norm = new NormalizeArray();
double[] input = {1, 5, 10};
double[] output = norm.Process(input);
Assert.AreEqual(3, output.Length);
Assert.AreEqual(-1.0, output[0]);
Assert.AreEqual(1.0, output[2]);
Assert.AreEqual(1.0, norm.Stats.ActualLow);
Assert.AreEqual(10.0, norm.Stats.ActualHigh);
}
public Stage2NeuralNetwork(int inputs, int iterations, DenseMatrix trainingData, double[] predictData)
{
this.inputs = inputs;
this.iterations = iterations;
this.trainingData = trainingData;
predictionData = predictData;
normalizeArrayInput = new NormalizeArray[trainingData.RowCount];
for (int i = 0; i < normalizeArrayInput.Length; i++)
normalizeArrayInput[i] = new NormalizeArray {NormalizedHigh = normalizeHi, NormalizedLow = normalizeLo};
normalizeArrayOutput = new NormalizeArray {NormalizedHigh = normalizeHi, NormalizedLow = normalizeLo};
}
/// <summary>
/// Send all the (unNormalized)inputs in the as the network was trained and this outputs a list of double ready for a network.compute(imldata result)).
/// </summary>
/// <param name="WindoSize"> Size of the windo. </param>
/// <param name="pparamInputs"> A variable-length parameters list containing pparam inputs. </param>
/// <returns>
/// The compute pair ready for network computes)
/// </returns>
public static Tuple<List<double>, NormalizeArray> GetReadiedComputePair(int WindoSize, params double[][] pparamInputs)
{
try
{
//We make a dic with the count of inputs being the number of double series we are sending in.
Dictionary<int, double[]> inputsDics = new Dictionary<int, double[]>(pparamInputs.Length);
int indexS = 0;
NormalizeArray Normee = new NormalizeArray(-1, 1);
// PredictionStats.NormalizationClass NormingClass = new PredictionStats.NormalizationClass();
foreach (double[] doubleSeries in pparamInputs)
{
inputsDics.Add(indexS++, Normee.Process(doubleSeries));
}
List<double> dtda = new List<double>();
int listindex = 0;
int currentindex = 0;
//count the fields -1 ,as it starts from zero.
int dicinputsCount = inputsDics.Keys.Count - 1;
foreach (double d in inputsDics[0])
{
if (currentindex++ < WindoSize)
{
dtda.Add(d);
//we put all the fields which are in the dic.
while (dicinputsCount > 0)
{
dtda.Add(inputsDics[dicinputsCount--][listindex]);
}
//We reset the field count for a later pass.
dicinputsCount = inputsDics.Keys.Count - 1;
}
if (currentindex == WindoSize)
{
return new Tuple<List<double>, NormalizeArray>(dtda, Normee);
}
//Lets increment the indexes..
listindex++;
}
return new Tuple<List<double>, NormalizeArray>(dtda, Normee);
}
catch (Exception ex)
{
throw ex;
}
}
/// <summary>
/// Normalizes an array using Normalize Array (and not DataNormalization way : Faster).
/// The high and low are the standard -1,1.
/// </summary>
/// <param name="arrays">The arrays.</param>
/// <returns>returns a tuple with the array in item1 and the normalization in item 2.</returns>
public static Tuple<double[], NormalizeArray> NormalizeArray(double[] arrays)
{
var norm = new NormalizeArray();
return new Tuple<double[], NormalizeArray>(norm.Process(arrays), norm);
}
/// <summary>
/// Normalizes an array using Normalize Array (and not DataNormalization way : Faster).
/// </summary>
/// <param name="lo">The lo.</param>
/// <param name="hi">The hi.</param>
/// <param name="arrays">The arrays.</param>
/// <returns></returns>
public static double[] NormalizeArray(double lo, double hi, double[] arrays)
{
var norm = new NormalizeArray {NormalizedHigh = hi, NormalizedLow = lo};
return norm.Process(arrays);
}
public void NormalizeForexPair(double lo, double hi)
{
array= new NormalizeArray { NormalizedHigh = hi, NormalizedLow = lo };
// create arrays to hold the normalized forex pair data
_normalizedForexPair = array.Process(ForexPair);
_closedLoopForexPair = EngineArray.ArrayCopy(_normalizedForexPair);
}
public static void normalizeSunspots(double lo, double hi)
{
NormalizeArray norm = new NormalizeArray();
norm.NormalizedHigh=( hi);
norm.NormalizedLow = lo;
// create arrays to hold the normalized sunspots
normalizedSunspots = norm.Process(SUNSPOTS);
closedLoopSunspots = EngineArray.ArrayCopy(normalizedSunspots);
}
/// <summary>
/// Loads variables inputs and one ideal double series into an imldataset.
/// </summary>
/// <param name="idealsinputs"></param>
/// <param name="WindoSize"></param>
/// <param name="pparamInputs"></param>
/// <returns></returns>
public static Tuple<IMLDataSet, NormalizeArray> Load(double[] idealsinputs, int WindoSize, params double[][] pparamInputs)
{
try
{
var finalSet = new BasicMLDataSet();
//We make a dic with the count of inputs being the number of double series we are sending in.
Dictionary<int, double[]> inputsDics = new Dictionary<int, double[]>(pparamInputs.Length);
int indexS = 0;
//We make a normalizeArray which we will return as a tuple ready for denormalization.
NormalizeArray Normer = new NormalizeArray(-1, 1);
//Process each inputs.
foreach (double[] doubleSeries in pparamInputs)
{
inputsDics.Add(indexS++, Normer.Process(doubleSeries));
}
//Process the ideals.
var idealNormed = Normer.Process(idealsinputs);
//Make a list which will hold the inputs one after the others
List<double> dtda = new List<double>();
int listindex = 0;
int currentindex = 0;
//starts from zero so count -1..
int dicinputsCount = inputsDics.Keys.Count - 1;
//Process the input normed.
foreach (double d in inputsDics[0])
{
if (currentindex++ < WindoSize)
{
dtda.Add(d);
//we put all the fields which are in the dic.
while (dicinputsCount > 0)
{
dtda.Add(inputsDics[dicinputsCount--][listindex]);
}
//We reset the field count for a later pass.
dicinputsCount = inputsDics.Keys.Count - 1;
}
if (currentindex == WindoSize)
{
//Make an imldata pair, and add it to the imldataset...reset the temp list of inputs...
var pair = new BasicMLDataPair(
new BasicMLData(dtda.ToArray()),
new BasicMLData(new double[] { idealNormed[listindex] }));
currentindex = 0;
dtda.Clear();
finalSet.Add(pair);
}
//Lets increment the indexes..
listindex++;
}
//Return the dataset and the normalization array..
return new Tuple<IMLDataSet, NormalizeArray>(finalSet,Normer);
}
catch (Exception ex)
{
Console.WriteLine("Got an error : ", ex);
throw new Exception("Error parsing points....");
}
}