/// <summary>
/// See <see cref="IVectorView.LinearCombination(double, IVectorView, double)"/>.
/// </summary>
public IVector LinearCombination(double thisCoefficient, IVectorView otherVector, double otherCoefficient)
{
Preconditions.CheckVectorDimensions(this, otherVector);
if (otherVector is SparseVector otherSparse) // In case both matrices have the exact same index arrays
{
if (HasSameIndexer(otherSparse))
{
// Do not copy the index arrays, since they are already spread around. TODO: is this a good idea?
double[] result = new double[this.values.Length];
if (thisCoefficient == 1.0)
{
Array.Copy(this.values, result, this.values.Length);
Blas.Daxpy(values.Length, otherCoefficient, otherSparse.values, 0, 1, result, 0, 1);
}
else if (otherCoefficient == 1.0)
{
Array.Copy(otherSparse.values, result, values.Length);
Blas.Daxpy(values.Length, thisCoefficient, this.values, 0, 1, result, 0, 1);
}
else
{
Array.Copy(this.values, result, this.values.Length);
BlasExtensions.Daxpby(values.Length, otherCoefficient, otherSparse.values, 0, 1,
thisCoefficient, result, 0, 1);
}
return(new SparseVector(Length, result, indices));
}
}
// All entries must be processed. TODO: optimizations may be possible (e.g. only access the nnz in this vector)
return(DenseStrategies.LinearCombination(this, thisCoefficient, otherVector, otherCoefficient));
}
/// <summary>
/// See <see cref="IMatrixView.LinearCombination(double, IMatrixView, double)"/>.
/// </summary>
public IMatrix LinearCombination(double thisCoefficient, IMatrixView otherMatrix, double otherCoefficient)
{
if (otherMatrix is CscMatrix otherCSC) // In case both matrices have the exact same index arrays
{
if (HasSameIndexer(otherCSC))
{
// Do not copy the index arrays, since they are already spread around. TODO: is this a good idea?
double[] resultValues = new double[values.Length];
if (thisCoefficient == 1.0)
{
Array.Copy(this.values, resultValues, values.Length);
Blas.Daxpy(values.Length, otherCoefficient, otherCSC.values, 0, 1, this.values, 0, 1);
}
else if (otherCoefficient == 1.0)
{
Array.Copy(otherCSC.values, resultValues, values.Length);
Blas.Daxpy(values.Length, thisCoefficient, this.values, 0, 1, resultValues, 0, 1);
}
else
{
Array.Copy(this.values, resultValues, values.Length);
BlasExtensions.Daxpby(values.Length, otherCoefficient, otherCSC.values, 0, 1,
thisCoefficient, resultValues, 0, 1);
}
return(new CscMatrix(NumRows, NumColumns, resultValues, this.rowIndices, this.colOffsets));
}
}
// All entries must be processed. TODO: optimizations may be possible (e.g. only access the nnz in this matrix)
return(DenseStrategies.LinearCombination(this, thisCoefficient, otherMatrix, otherCoefficient));
}
/// <summary>
/// See <see cref="IEntrywiseOperableView2D{TMatrixIn, TMatrixOut}.DoEntrywise(TMatrixIn, Func{double, double, double})"/>.
/// </summary>
public IMatrix DoEntrywise(IMatrixView other, Func <double, double, double> binaryOperation)
{
if (other is SymmetricMatrix casted)
{
return(DoEntrywise(casted, binaryOperation));
}
else
{
return(DenseStrategies.DoEntrywise(this, other, binaryOperation)); //TODO: optimize this
}
}
private static void TestGetColumn()
{
var matrix = TriangularLower.CreateFromArray(LowerInvertible10by10.Matrix);
for (int j = 0; j < LowerInvertible10by10.Order; ++j)
{
Vector colExpected = DenseStrategies.GetColumn(matrix, j);
Vector colComputed = matrix.GetColumn(j);
comparer.AssertEqual(colExpected, colComputed);
}
}
private static void TestGetRow()
{
var matrix = Matrix.CreateFromArray(RectangularFullRank10by5.Matrix);
for (int i = 0; i < RectangularFullRank10by5.NumRows; ++i)
{
Vector rowExpected = DenseStrategies.GetRow(matrix, i);
Vector rowComputed = matrix.GetRow(i);
comparer.AssertEqual(rowExpected, rowComputed);
}
}
private static void TestGetColumn()
{
var matrix = Matrix.CreateFromArray(RectangularFullRank10by5.Matrix);
for (int j = 0; j < RectangularFullRank10by5.NumCols; ++j)
{
Vector colExpected = DenseStrategies.GetColumn(matrix, j);
Vector colComputed = matrix.GetColumn(j);
comparer.AssertEqual(colExpected, colComputed);
}
}
private static void TestGetRow()
{
var matrix = SymmetricMatrix.CreateFromArray(SymmPosDef10by10.Matrix);
for (int i = 0; i < SymmPosDef10by10.Order; ++i)
{
Vector rowExpected = DenseStrategies.GetRow(matrix, i);
Vector rowComputed = matrix.GetRow(i);
comparer.AssertEqual(rowExpected, rowComputed);
}
}
private static void TestGetColumn()
{
var matrix = SymmetricMatrix.CreateFromArray(SymmPosDef10by10.Matrix);
for (int j = 0; j < SymmPosDef10by10.Order; ++j)
{
Vector colExpected = DenseStrategies.GetColumn(matrix, j);
Vector colComputed = matrix.GetColumn(j);
comparer.AssertEqual(colExpected, colComputed);
}
}
private static void TestGetRow()
{
var matrix = TriangularLower.CreateFromArray(LowerInvertible10by10.Matrix);
for (int i = 0; i < LowerInvertible10by10.Order; ++i)
{
Vector rowExpected = DenseStrategies.GetRow(matrix, i);
Vector rowComputed = matrix.GetRow(i);
comparer.AssertEqual(rowExpected, rowComputed);
}
}
private static void TestGetColumn()
{
var matrix = SkylineMatrix.CreateFromArrays(SparsePosDef10by10.Order,
SparsePosDef10by10.SkylineValues, SparsePosDef10by10.SkylineDiagOffsets, true, true);
for (int j = 0; j < SparsePosDef10by10.Order; ++j)
{
Vector colExpected = DenseStrategies.GetColumn(matrix, j);
Vector colComputed = matrix.GetColumn(j);
comparer.AssertEqual(colExpected, colComputed);
}
}
private static void TestGetRow()
{
var matrix = SkylineMatrix.CreateFromArrays(SparsePosDef10by10.Order,
SparsePosDef10by10.SkylineValues, SparsePosDef10by10.SkylineDiagOffsets, true, true);
for (int i = 0; i < SparsePosDef10by10.Order; ++i)
{
Vector rowExpected = DenseStrategies.GetRow(matrix, i);
Vector rowComputed = matrix.GetRow(i);
comparer.AssertEqual(rowExpected, rowComputed);
}
}
private static void TestGetRow()
{
var matrix = CscMatrix.CreateFromArrays(SparseRectangular10by5.NumRows, SparseRectangular10by5.NumCols,
SparseRectangular10by5.CscValues, SparseRectangular10by5.CscRowIndices, SparseRectangular10by5.CscColOffsets,
true);
for (int i = 0; i < SparseRectangular10by5.NumRows; ++i)
{
Vector rowExpected = DenseStrategies.GetRow(matrix, i);
Vector rowComputed = matrix.GetRow(i);
comparer.AssertEqual(rowExpected, rowComputed);
}
}
private static void TestGetColumn()
{
var matrix = CscMatrix.CreateFromArrays(SparseRectangular10by5.NumRows, SparseRectangular10by5.NumCols,
SparseRectangular10by5.CscValues, SparseRectangular10by5.CscRowIndices, SparseRectangular10by5.CscColOffsets,
true);
for (int j = 0; j < SparseRectangular10by5.NumCols; ++j)
{
Vector colExpected = DenseStrategies.GetColumn(matrix, j);
Vector colComputed = matrix.GetColumn(j);
comparer.AssertEqual(colExpected, colComputed);
}
}
/// <summary>
/// See <see cref="IEntrywiseOperableView2D{TMatrixIn, TMatrixOut}.DoEntrywise(TMatrixIn, Func{double, double, double})"/>.
/// </summary>
public IMatrix DoEntrywise(IMatrixView other, Func <double, double, double> binaryOperation)
{
if (other is CscMatrix otherCSC) // In case both matrices have the exact same index arrays
{
if (HasSameIndexer(otherCSC))
{
// Do not copy the index arrays, since they are already spread around. TODO: is this a good idea?
double[] resultValues = new double[values.Length];
for (int i = 0; i < values.Length; ++i)
{
resultValues[i] = binaryOperation(this.values[i], otherCSC.values[i]);
}
return(new CscMatrix(NumRows, NumColumns, resultValues, rowIndices, colOffsets));
}
}
// All entries must be processed. TODO: optimizations may be possible (e.g. only access the nnz in this matrix)
return(DenseStrategies.DoEntrywise(this, other, binaryOperation));
}
/// <summary>
/// See <see cref="IEntrywiseOperableView1D{TVectorIn, TVectorOut}.DoEntrywise(TVectorIn, Func{double, double, double})"/>.
/// </summary>
public IVector DoEntrywise(IVectorView otherVector, Func <double, double, double> binaryOperation)
{
Preconditions.CheckVectorDimensions(this, otherVector);
if (otherVector is SparseVector otherSparse) // In case both matrices have the exact same index arrays
{
if (HasSameIndexer(otherSparse))
{
// Do not copy the index arrays, since they are already spread around. TODO: is this a good idea?
double[] resultValues = new double[values.Length];
for (int i = 0; i < values.Length; ++i)
{
resultValues[i] = binaryOperation(this.values[i], otherSparse.values[i]);
}
return(new SparseVector(Length, resultValues, indices));
}
}
// All entries must be processed. TODO: optimizations may be possible (e.g. only access the nnz in this vector)
return(DenseStrategies.DoEntrywise(this, otherVector, binaryOperation));
}
/// <summary>
/// See <see cref="IVectorView.Axpy(IVectorView, double)"/>.
/// </summary>
public IVector Axpy(IVectorView otherVector, double otherCoefficient)
{
Preconditions.CheckVectorDimensions(this, otherVector);
if (otherVector is SparseVector otherSparse) // In case both matrices have the exact same index arrays
{
if (HasSameIndexer(otherSparse))
{
// Do not copy the index arrays, since they are already spread around. TODO: is this a good idea?
double[] result = new double[this.values.Length];
Array.Copy(this.values, result, this.values.Length);
Blas.Daxpy(values.Length, otherCoefficient, otherSparse.values, 0, 1, result, 0, 1);
return(new SparseVector(Length, result, indices));
}
}
else if (otherVector is Vector otherDense)
{
double[] result = otherDense.Scale(otherCoefficient).RawData;
SparseBlas.Daxpyi(this.indices.Length, 1.0, this.values, this.indices, 0, result, 0);
return(Vector.CreateFromArray(result, false));
}
// All entries must be processed. TODO: optimizations may be possible (e.g. only access the nnz in this vector)
return(DenseStrategies.LinearCombination(this, 1.0, otherVector, otherCoefficient));
}
/// <summary> /// Returns true if this[i] <= <paramref name="tolerance"/> for 0 <= i < this.<see cref="Length"/>. /// Otherwise false is returned. /// </summary> /// <param name="tolerance">The tolerance under which a vector entry is considered to be 0. It can be set to 0, to check /// if the entries are exactly 0.</param> public bool IsZero(double tolerance) => DenseStrategies.IsZero(data, tolerance);
/// <summary> /// Copies the entries of the matrix into a 2-dimensional array. The returned array has length(0) = <see cref="NumRows"/> /// and length(1) = <see cref="NumColumns"/>. /// </summary> public double[,] CopyToArray2D() => DenseStrategies.CopyToArray2D(this);
/// <summary>
/// See <see cref="IIndexable2D.Equals(IIndexable2D, double)"/>.
/// </summary>
public bool Equals(IIndexable2D other, double tolerance = 1E-13)
{
return(DenseStrategies.AreEqual(this, other, tolerance));
}
/// <summary> /// See <see cref="IVector.AddIntoThisNonContiguouslyFrom(int[], IVectorView)"/> /// </summary> public void AddIntoThisNonContiguouslyFrom(int[] thisIndices, IVectorView otherVector) => DenseStrategies.AddNonContiguouslyFrom(this, thisIndices, otherVector);
/// <summary> /// See <see cref="IVector.CopyNonContiguouslyFrom(IVectorView, int[])"/> /// </summary> public void CopyNonContiguouslyFrom(IVectorView otherVector, int[] otherIndices) => DenseStrategies.CopyNonContiguouslyFrom(this, otherVector, otherIndices);
public Matrix CopyToFullMatrix() => DenseStrategies.CopyToFullMatrix(this);
public IMatrix LinearCombination(double thisCoefficient, IMatrixView otherMatrix, double otherCoefficient) => DenseStrategies.LinearCombination(this, thisCoefficient, otherMatrix, otherCoefficient);
/// <summary>
/// See <see cref="IEntrywiseOperableView2D{TMatrixIn, TMatrixOut}.DoEntrywise(TMatrixIn, Func{double, double, double})"/>.
/// </summary>
public IMatrix DoEntrywise(IMatrixView matrix, Func <double, double, double> binaryOperation)
{
return(DenseStrategies.DoEntrywise(this, matrix, binaryOperation)); //TODO: this can be optimized.
}
/// <summary>
/// See <see cref="IEntrywiseOperableView2D{TMatrixIn, TMatrixOut}.DoEntrywise(TMatrixIn, Func{double, double, double})"/>.
/// </summary>
public IMatrix DoEntrywise(IMatrixView matrix, Func <double, double, double> binaryOperation)
{
return(DenseStrategies.DoEntrywise(this, matrix, binaryOperation));
}
public Matrix MultiplyRight(IMatrixView other, bool transposeThis = false, bool transposeOther = false)
{
return(DenseStrategies.Multiply(this, other, transposeThis, transposeOther));
}
/// <summary> /// See <see cref="ISliceable2D.GetSubmatrix(int, int, int, int)"/>. /// </summary> public IMatrix GetSubmatrix(int rowStartInclusive, int rowEndExclusive, int colStartInclusive, int colEndExclusive) => DenseStrategies.GetSubmatrix(this, rowStartInclusive, rowEndExclusive, colStartInclusive, colEndExclusive);
/// <summary> /// See <see cref="ISliceable2D.GetSubmatrix(int[], int[])"/>. /// </summary> public IMatrix GetSubmatrix(int[] rowIndices, int[] colIndices) => DenseStrategies.GetSubmatrix(this, rowIndices, colIndices);
/// <summary> /// See <see cref="IIndexable2D.Equals(IIndexable2D, double)"/>. /// </summary> public bool Equals(IIndexable2D other, double tolerance = 1e-13) => DenseStrategies.AreEqual(this, other, tolerance);
/// <summary>
/// See <see cref="IMatrixView.MultiplyLeft(IMatrixView, bool, bool)"/>.
/// </summary>
public Matrix MultiplyLeft(IMatrixView matrix, bool transposeThis = false, bool transposeOther = false)
{
return(DenseStrategies.Multiply(matrix, this, transposeOther, transposeThis));
}
