/// <summary>
/// Applies matrix dot product operation for
/// all matrices in tensors
///
/// O(N^3)
/// </summary>
public static GenTensor <T> TensorMatrixMultiply(GenTensor <T> a,
GenTensor <T> b)
{
#if ALLOW_EXCEPTIONS
if (a.Shape.Count < 2 || b.Shape.Count < 2)
{
throw new InvalidShapeException($"Arguments should be at least matrices while their shapes are {a.Shape} and {b.Shape}");
}
if (a.Shape.SubShape(0, 2) != b.Shape.SubShape(0, 2))
{
throw new InvalidShapeException("Other dimensions of tensors should be equal");
}
#endif
var oldShape = a.Shape.SubShape(0, 2).ToArray();
var newShape = new int[oldShape.Length + 2];
for (int i = 0; i < oldShape.Length; i++)
{
newShape[i] = oldShape[i];
}
newShape[newShape.Length - 2] = a.Shape[a.Shape.Length - 2];
newShape[newShape.Length - 1] = b.Shape[b.Shape.Length - 1];
var resTensor = new GenTensor <T>(newShape);
foreach (var subDimensions in a.IterateOverMatrices())
{
var product = MatrixMultiply(a.GetSubtensor(subDimensions), b.GetSubtensor(subDimensions));
resTensor.SetSubtensor(product, subDimensions);
}
return(resTensor);
}
/// <summary>
/// Applies scalar product to every vector in a tensor so that
/// you will get a one-reduced dimensional tensor
/// (e. g. TensorVectorDotProduct([4 x 3 x 2], [4 x 3 x 2]) -> [4 x 3]
///
/// O(V)
/// </summary>
public static GenTensor <T> TensorVectorDotProduct(GenTensor <T> a,
GenTensor <T> b)
{
#if ALLOW_EXCEPTIONS
if (a.Shape.SubShape(0, 1) != b.Shape.SubShape(0, 1))
{
throw new InvalidShapeException("Other dimensions of tensors should be equal");
}
#endif
var resTensor = new GenTensor <T>(a.Shape.SubShape(0, 1));
foreach (var index in resTensor.IterateOverElements())
{
var scal = VectorDotProduct(a.GetSubtensor(index), b.GetSubtensor(index));
resTensor.SetValueNoCheck(scal, index);
}
return(resTensor);
}
public static GenTensor <T> Concat(GenTensor <T> a, GenTensor <T> b)
{
#if ALLOW_EXCEPTIONS
if (a.Shape.SubShape(1, 0) != b.Shape.SubShape(1, 0))
{
throw new InvalidShapeException("Excluding the first dimension, all others should match");
}
#endif
if (a.IsVector)
{
var resultingVector = GenTensor <T> .CreateVector(a.Shape.shape[0] + b.Shape.shape[0]);
for (int i = 0; i < a.Shape.shape[0]; i++)
{
resultingVector.SetValueNoCheck(ConstantsAndFunctions <T> .Forward(a.GetValueNoCheck(i)), i);
}
for (int i = 0; i < b.Shape.shape[0]; i++)
{
resultingVector.SetValueNoCheck(ConstantsAndFunctions <T> .Forward(b.GetValueNoCheck(i)), i + a.Shape.shape[0]);
}
return(resultingVector);
}
else
{
var newShape = a.Shape.Copy();
newShape.shape[0] = a.Shape.shape[0] + b.Shape.shape[0];
var res = new GenTensor <T>(newShape);
for (int i = 0; i < a.Shape.shape[0]; i++)
{
res.SetSubtensor(a.GetSubtensor(i), i);
}
for (int i = 0; i < b.Shape.shape[0]; i++)
{
res.SetSubtensor(b.GetSubtensor(i), i + a.Shape.shape[0]);
}
return(res);
}
}
/// <summary>
/// Calls VectorCrossProduct for every vector in the tensor
/// </summary>
public static GenTensor <T> TensorVectorCrossProduct(GenTensor <T> a,
GenTensor <T> b)
{
#if ALLOW_EXCEPTIONS
if (a.Shape != b.Shape)
{
throw new InvalidShapeException($"Pre-shapes of {nameof(a)} and {nameof(b)} should be equal");
}
#endif
var res = new GenTensor <T>(a.Shape);
foreach (var index in a.IterateOverVectors())
{
res.SetSubtensor(
VectorCrossProduct(a.GetSubtensor(index), b.GetSubtensor(index)),
index
);
}
return(res);
}
public static GenTensor <T> TensorMatrixDivide(GenTensor <T> a, GenTensor <T> b)
{
#if ALLOW_EXCEPTIONS
InvalidShapeException.NeedTensorSquareMatrix(a);
InvalidShapeException.NeedTensorSquareMatrix(b);
if (a.Shape != b.Shape)
{
throw new InvalidShapeException("Should be of the same shape");
}
#endif
var res = new GenTensor <T>(a.Shape);
foreach (var ind in res.IterateOverMatrices())
{
res.SetSubtensor(
MatrixDivide(
a.GetSubtensor(ind),
b.GetSubtensor(ind)
), ind);
}
return(res);
}
