/// <summary>
/// Write an array.
/// </summary>
/// <param name="data">The data to write.</param>
/// <param name="inputCount">How much of the data is input.</param>
public void Write(double[] data, int inputCount)
{
if (_idealCount == 0)
{
var inputData = new BasicMLData(data);
_dataset.Add(inputData);
}
else
{
var inputData = new BasicMLData(
_inputCount);
var idealData = new BasicMLData(
_idealCount);
int index = 0;
for (int i = 0; i < _inputCount; i++)
{
inputData[i] = data[index++];
}
for (int i = 0; i < _idealCount; i++)
{
idealData[i] = data[index++];
}
_dataset.Add(inputData, idealData);
}
}
/// <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>
/// 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));
}
}
/// <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);
}
}
