/// <summary>
/// Generate a random training set.
/// </summary>
/// <param name="seed">The seed value to use, the same seed value will always produce
/// the same results.</param>
/// <param name="count">How many training items to generate.</param>
/// <param name="inputCount">How many input numbers.</param>
/// <param name="idealCount">How many ideal numbers.</param>
/// <param name="min">The minimum random number.</param>
/// <param name="max">The maximum random number.</param>
/// <returns>The random training set.</returns>
public static BasicNeuralDataSet Generate(long seed,
int count, int inputCount,
int idealCount, double min, double max)
{
LinearCongruentialGenerator rand =
new LinearCongruentialGenerator(seed);
BasicNeuralDataSet result = new BasicNeuralDataSet();
for (int i = 0; i < count; i++)
{
INeuralData inputData = new BasicNeuralData(inputCount);
for (int j = 0; j < inputCount; j++)
{
inputData.Data[j] = rand.Range(min, max);
}
INeuralData idealData = new BasicNeuralData(idealCount);
for (int j = 0; j < idealCount; j++)
{
idealData[j] = rand.Range(min, max);
}
BasicNeuralDataPair pair = new BasicNeuralDataPair(inputData,
idealData);
result.Add(pair);
}
return result;
}
/// <summary>
/// Create a new neural data pair object of the correct size for the neural
/// network that is being trained. This object will be passed to the getPair
/// method to allow the neural data pair objects to be copied to it.
/// </summary>
/// <param name="inputSize">The size of the input data.</param>
/// <param name="idealSize">The size of the ideal data.</param>
/// <returns>A new neural data pair object.</returns>
public static INeuralDataPair CreatePair(int inputSize, int idealSize)
{
INeuralDataPair result;
if (idealSize > 0)
{
result = new BasicNeuralDataPair(new BasicNeuralData(inputSize),
new BasicNeuralData(idealSize));
}
else
{
result = new BasicNeuralDataPair(new BasicNeuralData(inputSize));
}
return(result);
}
/// <summary>
/// Clone this object.
/// </summary>
/// <returns>A clone of this object.</returns>
public object Clone()
{
Object result;
if (this.Ideal == null)
{
result = new BasicNeuralDataPair((INeuralData)this.input.Clone());
}
else
{
result = new BasicNeuralDataPair((INeuralData)this.input.Clone(),
(INeuralData)this.ideal.Clone());
}
return(result);
}
/// <summary>
/// Generate the training sets.
/// </summary>
public virtual void Generate()
{
SortPoints();
int start = CalculateStartIndex() + 1;
int setSize = CalculateActualSetSize();
int range = start
+ (setSize - _predictWindowSize - _inputWindowSize);
for (int i = start; i < range; i++)
{
BasicNeuralData input = GenerateInputNeuralData(i);
BasicNeuralData ideal = GenerateOutputNeuralData(i
+ _inputWindowSize);
var pair = new BasicNeuralDataPair(input, ideal);
base.Add(pair);
}
}
/// <summary>
/// Handle reading an item tag.
/// </summary>
/// <param name="xmlIn">The XML reader.</param>
private void HandleItem(ReadXML xmlIn)
{
IDictionary<String, String> properties = xmlIn.ReadPropertyBlock();
INeuralDataPair pair = null;
INeuralData input = new BasicNeuralData(NumberList
.FromList(CSVFormat.EG_FORMAT, properties
[BasicNeuralDataSetPersistor.TAG_INPUT]));
if (properties.ContainsKey(BasicNeuralDataSetPersistor.TAG_IDEAL))
{
// supervised
INeuralData ideal = new BasicNeuralData(NumberList
.FromList(CSVFormat.EG_FORMAT, properties
[BasicNeuralDataSetPersistor.TAG_IDEAL]));
pair = new BasicNeuralDataPair(input, ideal);
}
else
{
// unsupervised
pair = new BasicNeuralDataPair(input);
}
this.currentDataSet.Add(pair);
}
/// <summary>
/// Add supervised data to the set.
/// </summary>
/// <param name="inputData">The input data.</param>
/// <param name="idealData">The ideal data.</param>
public virtual void Add(INeuralData inputData, INeuralData idealData)
{
INeuralDataPair pair = new BasicNeuralDataPair(inputData, idealData);
this.data.Add(pair);
}
/// <summary>
/// Add the specified data to the set. Add unsupervised data.
/// </summary>
/// <param name="data1">The data to add to the set.</param>
public virtual void Add(INeuralData data1)
{
INeuralDataPair pair = new BasicNeuralDataPair(data1, null);
this.data.Add(pair);
}
/// <summary>
/// Generate random training into a training set.
/// </summary>
/// <param name="training">The training set to generate into.</param>
/// <param name="seed">The seed to use.</param>
/// <param name="count">How much data to generate.</param>
/// <param name="min">The low random value.</param>
/// <param name="max">The high random value.</param>
public static void Generate(INeuralDataSet training,
long seed,
int count,
double min, double max)
{
LinearCongruentialGenerator rand
= new LinearCongruentialGenerator(seed);
int inputCount = training.InputSize;
int idealCount = training.IdealSize;
for (int i = 0; i < count; i++)
{
INeuralData inputData = new BasicNeuralData(inputCount);
for (int j = 0; j < inputCount; j++)
{
inputData[j] = rand.Range(min, max);
}
INeuralData idealData = new BasicNeuralData(idealCount);
for (int j = 0; j < idealCount; j++)
{
idealData[j] = rand.Range(min, max);
}
BasicNeuralDataPair pair = new BasicNeuralDataPair(inputData,
idealData);
training.Add(pair);
}
}
public virtual void Generate()
{
int num;
int num2;
int num3;
int num4;
BasicNeuralData data;
BasicNeuralData data2;
this.SortPoints();
if ((((uint) num) - ((uint) num2)) <= uint.MaxValue)
{
goto Label_00C0;
}
if ((((uint) num) | 3) != 0)
{
goto Label_0083;
}
if ((((uint) num3) + ((uint) num)) <= uint.MaxValue)
{
goto Label_0089;
}
Label_0051:
if ((((uint) num) - ((uint) num2)) < 0)
{
goto Label_00E8;
}
BasicNeuralDataPair inputData = new BasicNeuralDataPair(data, data2);
base.Add(inputData);
num4++;
Label_0083:
if (num4 >= num3)
{
if ((((uint) num) + ((uint) num2)) <= uint.MaxValue)
{
return;
}
goto Label_00C0;
}
Label_0089:
data = this.GenerateInputNeuralData(num4);
data2 = this.GenerateOutputNeuralData(num4 + this._xc278386c02fd6e51);
goto Label_0051;
Label_00C0:
if ((((uint) num2) + ((uint) num4)) >= 0)
{
num = this.CalculateStartIndex() + 1;
num2 = this.CalculateActualSetSize();
}
Label_00E8:
num3 = num + ((num2 - this._xcfe8f2ba20a6a8e4) - this._xc278386c02fd6e51);
num4 = num;
goto Label_0083;
}
/// <summary>
/// Create a new neural data pair object of the correct size for the neural
/// network that is being trained. This object will be passed to the getPair
/// method to allow the neural data pair objects to be copied to it.
/// </summary>
/// <param name="inputSize">The size of the input data.</param>
/// <param name="idealSize">The size of the ideal data.</param>
/// <returns>A new neural data pair object.</returns>
public static INeuralDataPair CreatePair(int inputSize, int idealSize)
{
INeuralDataPair result;
if (idealSize > 0)
{
result = new BasicNeuralDataPair(new BasicNeuralData(inputSize),
new BasicNeuralData(idealSize));
}
else
{
result = new BasicNeuralDataPair(new BasicNeuralData(inputSize));
}
return result;
}
/// <summary>
/// Clone this object.
/// </summary>
/// <returns>A clone of this object.</returns>
public object Clone()
{
Object result;
if (this.Ideal == null)
result = new BasicNeuralDataPair((INeuralData)this.input.Clone());
else
result = new BasicNeuralDataPair((INeuralData)this.input.Clone(),
(INeuralData)this.ideal.Clone());
return result;
}