/// <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(IMLDataSetAddable training,
long seed,
int count,
double min, double max)
{
var rand
= new LinearCongruentialGenerator(seed);
int inputCount = training.InputSize;
int idealCount = training.IdealSize;
for (int i = 0; i < count; i++)
{
var inputData = new BasicMLData(inputCount);
for (int j = 0; j < inputCount; j++)
{
inputData[j] = rand.Range(min, max);
}
var idealData = new BasicMLData(idealCount);
for (int j = 0; j < idealCount; j++)
{
idealData[j] = rand.Range(min, max);
}
var pair = new BasicMLDataPair(inputData,
idealData);
training.Add(pair);
}
}
/// <summary>
/// Construct a new NeuralDataSet based on the parameters specified.
/// </summary>
/// <param name="inputCount">The input count.</param>
/// <param name="idealCount">The output count.</param>
public NormalizationStorageMLDataSet(int inputCount,
int idealCount)
{
_inputCount = inputCount;
_idealCount = idealCount;
_dataset = new BasicMLDataSet();
}
/// <summary>
/// Construct a new NeuralDataSet based on the parameters specified.
/// </summary>
/// <param name="inputCount">The input count.</param>
/// <param name="idealCount">The output count.</param>
public NormalizationStorageMLDataSet(int inputCount,
int idealCount)
{
_inputCount = inputCount;
_idealCount = idealCount;
_dataset = new BasicMLDataSet();
}
/// <summary>
/// Copy whatever dataset type is specified into a memory dataset.
/// </summary>
/// <param name="set">The dataset to copy.</param>
public BasicMLSequenceSet(IMLDataSet set)
{
_currentSequence = new BasicMLDataSet();
_sequences.Add(_currentSequence);
int inputCount = set.InputSize;
int idealCount = set.IdealSize;
foreach (IMLDataPair pair in set)
{
BasicMLData input = null;
BasicMLData ideal = null;
if (inputCount > 0)
{
input = new BasicMLData(inputCount);
pair.Input.CopyTo(input.Data, 0, pair.Input.Count);
}
if (idealCount > 0)
{
ideal = new BasicMLData(idealCount);
pair.Ideal.CopyTo(ideal.Data, 0, pair.Ideal.Count);
}
_currentSequence.Add(new BasicMLDataPair(input, ideal));
}
}
public void StartNewSequence()
{
if (_currentSequence.Count > 0)
{
_currentSequence = new BasicMLDataSet();
_sequences.Add(_currentSequence);
}
}
/// <summary>
/// Construct a normalized neural storage class to hold data.
/// </summary>
/// <param name="dataset">The data set to store to. This uses an existing data set.</param>
public NormalizationStorageMLDataSet(IMLDataSetAddable dataset)
{
_dataset = dataset;
_inputCount = _dataset.InputSize;
_idealCount = _dataset.IdealSize;
}
/// <summary>
/// Construct a data set from an already created list. Mostly used to
/// duplicate this class.
/// </summary>
/// <param name="theData">The data to use.</param>
public BasicMLSequenceSet(IList <IMLDataPair> theData)
{
_currentSequence = new BasicMLDataSet(theData);
_sequences.Add(_currentSequence);
}
/// <summary>
/// Construct a data set from an input and ideal array.
/// </summary>
/// <param name="input">The input into the machine learning method for training.</param>
/// <param name="ideal">The ideal output for training.</param>
public BasicMLSequenceSet(double[][] input, double[][] ideal)
{
_currentSequence = new BasicMLDataSet(input, ideal);
_sequences.Add(_currentSequence);
}
public BasicMLSequenceSet(BasicMLSequenceSet other)
{
_sequences = other._sequences;
_currentSequence = other._currentSequence;
}
/// <summary>
/// Default constructor.
/// </summary>
public BasicMLSequenceSet()
{
_currentSequence = new BasicMLDataSet();
_sequences.Add(_currentSequence);
}
/// <summary>
/// Construct a normalized neural storage class to hold data.
/// </summary>
/// <param name="dataset">The data set to store to. This uses an existing data set.</param>
public NormalizationStorageMLDataSet(IMLDataSetAddable dataset)
{
_dataset = dataset;
_inputCount = _dataset.InputSize;
_idealCount = _dataset.IdealSize;
}
/// <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(IMLDataSetAddable training,
long seed,
int count,
double min, double max)
{
var rand
= new LinearCongruentialGenerator(seed);
int inputCount = training.InputSize;
int idealCount = training.IdealSize;
for (int i = 0; i < count; i++)
{
var inputData = new BasicMLData(inputCount);
for (int j = 0; j < inputCount; j++)
{
inputData[j] = rand.Range(min, max);
}
var idealData = new BasicMLData(idealCount);
for (int j = 0; j < idealCount; j++)
{
idealData[j] = rand.Range(min, max);
}
var pair = new BasicMLDataPair(inputData,
idealData);
training.Add(pair);
}
}
