/// <summary>
/// Copy the data stored in 'this' Value object to the buffers representing a sequence in CSC sparse format.
/// The sequence buffer will be resized if necessary.
/// The Value should have the same tensor shape as outputVariable.
/// On return, the sequenceLength is set to the length of the sequence stored in 'this' Value,
/// and the colStarts, rowIndices and nonZeroValues contain the data of column indexes, row indexes and non-zero values,
/// and the numNonZeroValues is set to number of non-zero values contained in 'this' Value.
/// </summary>
/// <typeparam name="T">dat type</typeparam>
/// <param name="outputVariable">source variable</param>
/// <param name="sequenceLength">legnth of the sequence</param>
/// <param name="colStarts">column start indices</param>
/// <param name="rowIndices">row indices</param>
/// <param name="nonZeroValues">sparse values</param>
/// <param name="numNonZeroValues">number of sparse values</param>
public void GetSparseData <T>(Variable outputVariable,
out int sequenceLength,
out IList <int> colStarts,
out IList <int> rowIndices,
out IList <T> nonZeroValues,
out int numNonZeroValues)
{
var colStartVec = new IntVector();
var rowIndicesVec = new IntVector();
int[] n1 = new int[1];
int[] n2 = new int[1];
if (typeof(T).Equals(typeof(float)))
{
if (_GetDataType() != DataType.Float)
{
throw new ArgumentException("The value type does not match the list type.");
}
var nonZeroValuesVec = new FloatVector();
_CopyVariableValueToFloat(outputVariable, n1, colStartVec,
rowIndicesVec, nonZeroValuesVec, n2);
nonZeroValues = nonZeroValuesVec as IList <T>;
}
else if (typeof(T).Equals(typeof(double)))
{
if (_GetDataType() != DataType.Double)
{
throw new ArgumentException("The value type does not match the list type.");
}
var nonZeroValuesVec = new DoubleVector();
_CopyVariableValueToDouble(outputVariable, n1, colStartVec,
rowIndicesVec, nonZeroValuesVec, n2);
nonZeroValues = nonZeroValuesVec as IList <T>;
}
else
{
throw new ArgumentException("The value type does not match the list type.");
}
sequenceLength = n1[0];
numNonZeroValues = n2[0];
colStarts = colStartVec;
rowIndices = rowIndicesVec;
}
/// <summary>
/// Helper function to create a FloatVector from IEnumerable.
/// </summary>
/// <typeparam name="T">The input data type.</typeparam>
/// <param name="input">Elements to be stored in the FloatVector.</param>
/// <returns>A FloatVector containing all elements from the IEnumerable input.</returns>
internal static FloatVector AsFloatVector <T>(IEnumerable <T> input)
{
if (typeof(T).Equals(typeof(float)))
{
var inputVector = new FloatVector();
IEnumerable <float> inputInType = input as IEnumerable <float>;
if (inputInType == null)
{
throw new ArgumentNullException("The parameter cannot be cast as IEnumerable<float>.");
}
foreach (var element in inputInType)
{
inputVector.Add(element);
}
return(inputVector);
}
else
{
throw new ArgumentException("The data type " + typeof(T).ToString() + " is not supported. Only float or double is supported by CNTK.");
}
}
// Create Value based on dense input
// Todo: could this be a extension to Value class??
// Todo: use Variable instead of varName. VarName as extension method
// Todo: List can have maximal 2^31-1, enough? Otherwise need to go to array which supports 64bit size
public Value CreateValue <T>(string varName, List <List <T> > sequences, DeviceDescriptor computeDevice)
{
var variable = getVariableByName(varName);
var dim = variable.Shape.TotalSize;
if (typeof(T).Equals(typeof(float)))
{
var inputSeqVector = new FloatVectorVector();
foreach (var seq in sequences)
{
if (seq.Count() % dim != 0)
{
throw new InvalidDataException("the number of data in sequences does not match the input dimension");
}
var samples = new FloatVector(seq);
inputSeqVector.Add(samples);
}
var inputValue = Value.CreateDenseFloat(variable.Shape, inputSeqVector, computeDevice);
return(inputValue);
}
else if (typeof(T).Equals(typeof(double)))
{
var inputSeqVector = new DoubleVectorVector();
foreach (var seq in sequences)
{
if (seq.Count() % dim != 0)
{
throw new InvalidDataException("the number of data in sequences does not match the input dimension");
}
var samples = new DoubleVector(seq);
inputSeqVector.Add(samples);
}
var inputValue = Value.CreateDenseDouble(variable.Shape, inputSeqVector, computeDevice);
return(inputValue);
}
else
{
throw new InvalidDataException("The data type " + typeof(T).ToString() + " is not supported. Only float or double is supported by CNTK.");
}
}