/// <summary>
/// Creates a new feature.
/// </summary>
/// <returns>An object which implements <see cref="T:IFeature" /></returns>
public IFeature <T, float[]> Create()
{
int index1 = ThreadsafeRandom.Next(0, _length);
int index2 = ThreadsafeRandom.Next(0, _length);
while (index2 == index1)
{
index2 = ThreadsafeRandom.Next(0, _length);
}
switch (_combo)
{
case BinaryCombination.Add:
return(new Add(index1, index2, _modifier));
case BinaryCombination.Divide:
return(new Divide(index1, index2, _modifier));
case BinaryCombination.Log:
return(new Log(index1, index2, _modifier));
case BinaryCombination.Multiply:
return(new Multiply(index1, index2, _modifier));
case BinaryCombination.Subtract:
default:
return(new Subtract(index1, index2, _modifier));
}
}
public IEnumerable <int> Receive(int minMessageCount, int maxMessageCount)
{
var returnedMessages = ThreadsafeRandom.Next(minMessageCount, maxMessageCount + 1);
for (var i = 0; i < returnedMessages; i++)
{
yield return(i);
}
}
public static string CreateAlphanumeric(int length)
{
var buffer = new char[length];
for (var i = 0; i < length; i++)
{
buffer[i] = _alphanumeric[ThreadsafeRandom.Next(_alphanumeric.Length)];
}
return(new string(buffer));
}
/// <summary>
/// Creates a new feature.
/// </summary>
/// <returns>An object which implements <see cref="T:IFeature" /></returns>
public IFeature <ImageDataPoint <float>, float[]> Create()
{
int row, column;
row = ThreadsafeRandom.Next(-_boxRows, _boxRows);
column = ThreadsafeRandom.Next(-_boxColumns, _boxColumns);
int channel = ThreadsafeRandom.Next(0, _numChannels);
return(new UnaryFeature(row, column, channel, _modifier));
}
/// <summary>
/// Creates a new feature.
/// </summary>
/// <returns>An object which implements <see cref="T:IFeature" /></returns>
public IFeature <ImageDataPoint <float>, float[]> Create()
{
int row = ThreadsafeRandom.Next(-BoxSize, BoxSize);
int column = ThreadsafeRandom.Next(-BoxSize, BoxSize);
int rows = ThreadsafeRandom.Next(1, MaxRows);
int columns = ThreadsafeRandom.Next(1, MaxColumns);
int channel = ThreadsafeRandom.Next(ChannelCount);
return(new RectangleFeature(new Rectangle {
R = row, C = column, Rows = rows, Columns = columns
}, channel));
}
/// <summary>
/// Creates a new part feature.
/// </summary>
/// <returns>The part feature</returns>
public IFeature <ImageDataPoint <float>, float[]> Create()
{
Part[] parts = new Part[_numParts];
for (int i = 0; i < _numParts; i++)
{
parts[i] = new Part(
ThreadsafeRandom.Next(-_boxSize, _boxSize),
ThreadsafeRandom.Next(-_boxSize, _boxSize),
ThreadsafeRandom.Next(0, _numChannels),
1f - ThreadsafeRandom.NextFloat(2)
);
}
return(new PartFeature(parts));
}
private short getLabel(short label, LabelSet labels)
{
switch (_supervisionMode)
{
case SupervisionMode.Full:
return(label);
case SupervisionMode.Part:
return(labels.SelectRandom());
case SupervisionMode.None:
return((short)ThreadsafeRandom.Next(20));
}
return(0);
}
/// <summary>
/// Creates a new feature.
/// </summary>
/// <returns>An object which implements <see cref="T:IFeature" /></returns>
public IFeature <T, float[]> Create()
{
int index;
if (_random)
{
index = ThreadsafeRandom.Next(0, _length);
}
else
{
index = _currentIndex++;
if (_currentIndex == _length)
{
_currentIndex = 0;
}
}
return(new UnaryFeature(index, _modifier));
}
/// <summary>
/// Creates a new feature.
/// </summary>
/// <returns>An object which implements <see cref="T:IFeature" /></returns>
public IFeature <ImageDataPoint <float>, float[]> Create()
{
int index = ThreadsafeRandom.Next(0, HaarFeatures.FEATURES.Length);
Rectangle[] rectangles = HaarFeatures.FEATURES[index];
int[] channels = new int[rectangles.Length];
if (_mixChannels)
{
for (int i = 0; i < channels.Length; i++)
{
channels[i] = ThreadsafeRandom.Next(0, _numChannels);
}
}
else
{
int channel = ThreadsafeRandom.Next(0, _numChannels);
for (int i = 0; i < channels.Length; i++)
{
channels[i] = channel;
}
}
return(new HaarFeature(HaarFeatures.FEATURES[index], channels));
}
/// <summary>
/// Creates a new feature.
/// </summary>
/// <returns>An object which implements <see cref="T:IFeature" /></returns>
public IFeature <ImageDataPoint <float>, float[]> Create()
{
int row1, row2, column1, column2, channel1, channel2;
row1 = randomRow();
row2 = randomRow();
column1 = randomColumn();
column2 = randomColumn();
channel1 = ThreadsafeRandom.Next(0, _numChannels);
if (_mixChannels)
{
channel2 = ThreadsafeRandom.Next(0, _numChannels);
}
else
{
channel2 = channel1;
}
switch (_combo)
{
case BinaryCombination.Add:
return(new Add(row1, column1, channel1, row2, column2, channel2, _modifier));
case BinaryCombination.Divide:
return(new Divide(row1, column1, channel1, row2, column2, channel2, _modifier));
case BinaryCombination.Log:
return(new Log(row1, column1, channel1, row2, column2, channel2, _modifier));
case BinaryCombination.Multiply:
return(new Multiply(row1, column1, channel1, row2, column2, channel2, _modifier));
case BinaryCombination.Subtract:
default:
return(new Subtract(row1, column1, channel1, row2, column2, channel2, _modifier));
}
}
private int randomColumn()
{
return(ThreadsafeRandom.Next(-_boxColumns, _boxColumns));
}
private int randomRow()
{
return(ThreadsafeRandom.Next(-_boxRows, _boxRows));
}