/// <summary>
/// Returns a randomly initialized tensor with values draft from the
/// uniform distribution between minValue and maxValue.
/// </summary>
public static Edu.Stanford.Nlp.Neural.SimpleTensor Random(int numRows, int numCols, int numSlices, double minValue, double maxValue, Java.Util.Random rand)
{
Edu.Stanford.Nlp.Neural.SimpleTensor tensor = new Edu.Stanford.Nlp.Neural.SimpleTensor(numRows, numCols, numSlices);
for (int i = 0; i < numSlices; ++i)
{
tensor.slices[i] = SimpleMatrix.Random(numRows, numCols, minValue, maxValue, rand);
}
return(tensor);
}
/// <summary>
/// Returns a new tensor which has the values of the original tensor
/// scaled by
/// <paramref name="scaling"/>
/// . The original object is
/// unaffected.
/// </summary>
public virtual Edu.Stanford.Nlp.Neural.SimpleTensor Scale(double scaling)
{
Edu.Stanford.Nlp.Neural.SimpleTensor result = new Edu.Stanford.Nlp.Neural.SimpleTensor(numRows, numCols, numSlices);
for (int slice = 0; slice < numSlices; ++slice)
{
result.slices[slice] = slices[slice].Scale(scaling);
}
return(result);
}
/// <summary>Performs elementwise multiplication on the tensors.</summary>
/// <remarks>
/// Performs elementwise multiplication on the tensors. The original
/// objects are unaffected.
/// </remarks>
public virtual Edu.Stanford.Nlp.Neural.SimpleTensor ElementMult(Edu.Stanford.Nlp.Neural.SimpleTensor other)
{
if (other.numRows != numRows || other.numCols != numCols || other.numSlices != numSlices)
{
throw new ArgumentException("Sizes of tensors do not match. Our size: " + numRows + "," + numCols + "," + numSlices + "; other size " + other.numRows + "," + other.numCols + "," + other.numSlices);
}
Edu.Stanford.Nlp.Neural.SimpleTensor result = new Edu.Stanford.Nlp.Neural.SimpleTensor(numRows, numCols, numSlices);
for (int i = 0; i < numSlices; ++i)
{
result.slices[i] = slices[i].ElementMult(other.slices[i]);
}
return(result);
}
