/// <summary>
/// Construct packed versions of input matrices, and then use sparse row/column dot
/// to compute elements of output matrix. This is faster. But still relatively expensive.
/// </summary>
void multiply_fast(SymmetricSparseMatrix M2in, ref SymmetricSparseMatrix Rin, bool bParallel)
{
int N = Rows;
if (M2in.Rows != N)
{
throw new Exception("SymmetricSparseMatrix.Multiply: matrices have incompatible dimensions");
}
if (Rin == null)
{
Rin = new SymmetricSparseMatrix();
}
SymmetricSparseMatrix R = Rin; // require alias for use in lambda below
PackedSparseMatrix M = new PackedSparseMatrix(this);
M.Sort();
PackedSparseMatrix M2 = new PackedSparseMatrix(M2in, true);
M2.Sort();
// Parallel variant is vastly faster, uses spinlock to control access to R
if (bParallel)
{
// goddamn SpinLock is in .Net 4
//SpinLock spin = new SpinLock();
gParallel.ForEach(Interval1i.Range(N), (r1i) => {
for (int c2i = r1i; c2i < N; c2i++)
{
double v = M.DotRowColumn(r1i, c2i, M2);
if (Math.Abs(v) > MathUtil.ZeroTolerance)
{
//bool taken = false;
//spin.Enter(ref taken);
//Debug.Assert(taken);
//R[r1i, c2i] = v;
//spin.Exit();
lock (R) {
R[r1i, c2i] = v;
}
}
}
});
}
else
{
for (int r1i = 0; r1i < N; r1i++)
{
for (int c2i = r1i; c2i < N; c2i++)
{
double v = M.DotRowColumn(r1i, c2i, M2);
if (Math.Abs(v) > MathUtil.ZeroTolerance)
{
R[r1i, c2i] = v;
}
}
}
}
}
/// <summary>
/// Compute dot product of this.row[r] and M.col[c], where the
/// column is stored as MTranspose.row[c]
/// </summary>
public double DotRowColumn(int r, int c, PackedSparseMatrix MTranspose)
{
Debug.Assert(Sorted && MTranspose.Sorted);
Debug.Assert(Rows.Length == MTranspose.Rows.Length);
int a = 0;
int b = 0;
nonzero[] Row = Rows[r];
nonzero[] Col = MTranspose.Rows[c];
int NA = Row.Length;
int NB = Col.Length;
double sum = 0;
while (a < NA && b < NB)
{
if (Row[a].j == Col[b].j)
{
sum += Row[a].d * Col[b].d;
a++;
b++;
}
else if (Row[a].j < Col[b].j)
{
a++;
}
else
{
b++;
}
}
return(sum);
}
/// <summary>
/// Returns this*this, as a packed sparse matrix. Computes in parallel.
/// </summary>
public PackedSparseMatrix SquarePackedParallel()
{
PackedSparseMatrix M = new PackedSparseMatrix(this);
M.Sort();
return(M.Square());
}
public PackedSparseMatrix Square()
{
if (Rows.Length != Columns)
{
throw new Exception("PackedSparseMatrix.Square: matrix is not square!");
}
int N = Columns;
var entries = new DVector <matrix_entry>();
var entries_lock = new SpinLock();
gParallel.BlockStartEnd(0, N - 1, (r_start, r_end) =>
{
for (int r1i = r_start; r1i <= r_end; r1i++)
{
// determine which entries of squared matrix might be nonzeros
var nbrs = new HashSet <int>();
nbrs.Add(r1i);
PackedSparseMatrix.nonzero[] row = Rows[r1i];
for (int k = 0; k < row.Length; ++k)
{
if (row[k].j > r1i)
{
nbrs.Add(row[k].j);
}
PackedSparseMatrix.nonzero[] row2 = Rows[row[k].j];
for (int j = 0; j < row2.Length; ++j)
{
if (row2[j].j > r1i) // only compute lower-triangular entries
{
nbrs.Add(row2[j].j);
}
}
}
// compute them!
foreach (int c2i in nbrs)
{
double v = DotRowColumn(r1i, c2i, this);
if (Math.Abs(v) > MathUtil.ZeroTolerance)
{
bool taken = false;
entries_lock.Enter(ref taken);
entries.Add(new matrix_entry()
{
r = r1i, c = c2i, value = v
});
entries_lock.Exit();
}
}
}
});
var R = new PackedSparseMatrix(entries, N, N, true);
return(R);
}
/// <summary>
/// Compute dot product of this.row[r] and M.col[c], where the
/// column is stored as MTranspose.row[c]
/// </summary>
public double DotRowColumn(int r, int c, PackedSparseMatrix MTranspose)
{
if (Sorted == false || MTranspose.Sorted == false)
{
throw new Exception("PackedSparseMatrix.DotRowColumn: matrices must be sorted!");
}
if (Rows.Length != MTranspose.Rows.Length)
{
throw new Exception("PackedSparseMatrix.DotRowColumn: matrices are not the same size!");
}
Debug.Assert(Sorted && MTranspose.Sorted);
Debug.Assert(Rows.Length == MTranspose.Rows.Length);
int ri = 0;
int ci = 0;
nonzero[] Row = Rows[r];
nonzero[] Col = MTranspose.Rows[c];
int NR = Row.Length;
int NC = Col.Length;
int last_col_j = Col[NC - 1].j;
int last_row_j = Row[NR - 1].j;
double sum = 0;
while (ri < NR && ci < NC)
{
// early out if we passed last nonzero in other array
if (Row[ri].j > last_col_j || Col[ci].j > last_row_j)
{
break;
}
if (Row[ri].j == Col[ci].j)
{
sum += Row[ri].d * Col[ci].d;
ri++;
ci++;
}
else if (Row[ri].j < Col[ci].j)
{
ri++;
}
else
{
ci++;
}
}
return(sum);
}
// returns this*this (requires less memory)
public SymmetricSparseMatrix Square(bool bParallel = true)
{
var R = new SymmetricSparseMatrix();
var M = new PackedSparseMatrix(this);
M.Sort();
// Parallel variant is vastly faster, uses spinlock to control access to R
if (bParallel)
{
// goddamn SpinLock is in .Net 4
//SpinLock spin = new SpinLock();
gParallel.ForEach(Interval1i.Range(N), (r1i) =>
{
for (int c2i = r1i; c2i < N; c2i++)
{
double v = M.DotRowColumn(r1i, c2i, M);
if (Math.Abs(v) > MathUtil.ZeroTolerance)
{
//bool taken = false;
//spin.Enter(ref taken);
//Debug.Assert(taken);
//R[r1i, c2i] = v;
//spin.Exit();
lock (R)
{
R[r1i, c2i] = v;
}
}
}
});
}
else
{
for (int r1i = 0; r1i < N; r1i++)
{
for (int c2i = r1i; c2i < N; c2i++)
{
double v = M.DotRowColumn(r1i, c2i, M);
if (Math.Abs(v) > MathUtil.ZeroTolerance)
{
R[r1i, c2i] = v;
}
}
}
}
return(R);
}
public PackedSparseMatrix(PackedSparseMatrix copy)
{
int N = copy.Rows.Length;
Rows = new nonzero[N][];
for (int r = 0; r < N; ++r)
{
Rows[r] = new nonzero[copy.Rows[r].Length];
Array.Copy(copy.Rows[r], Rows[r], Rows[r].Length);
}
Columns = copy.Columns;
Sorted = copy.Sorted;
NumNonZeros = copy.NumNonZeros;
StorageMode = copy.StorageMode;
IsSymmetric = copy.IsSymmetric;
}
/// <summary>
/// Compute dot product of this.row[r] with all columns of M,
/// where columns are stored in MTranspose rows.
/// In theory more efficient than doing DotRowColumn(r,c) for each c,
/// however so far the difference is negligible...perhaps because
/// there are quite a few more branches in the inner loop
/// </summary>
public void DotRowAllColumns(int r, double[] sums, int[] col_indices, PackedSparseMatrix MTranspose)
{
Debug.Assert(Sorted && MTranspose.Sorted);
Debug.Assert(Rows.Length == MTranspose.Rows.Length);
int N = Rows.Length;
int a = 0;
nonzero[] Row = Rows[r];
int NA = Row.Length;
Array.Clear(sums, 0, N);
Array.Clear(col_indices, 0, N);
while (a < NA)
{
int aj = Row[a].j;
for (int ci = 0; ci < N; ++ci)
{
nonzero[] Col = MTranspose.Rows[ci];
int b = col_indices[ci];
if (b >= Col.Length)
{
continue;
}
while (b < Col.Length && Col[b].j < aj)
{
b++;
}
if (b < Col.Length && aj == Col[b].j)
{
sums[ci] += Row[a].d * Col[b].d;
b++;
}
col_indices[ci] = b;
}
a++;
}
}
public void Initialize()
{
ToMeshV = new int[Mesh.MaxVertexID];
ToIndex = new int[Mesh.MaxVertexID];
N = 0;
foreach (int vid in Mesh.VertexIndices())
{
ToMeshV[N] = vid;
ToIndex[vid] = N;
N++;
}
Px = new double[N];
Py = new double[N];
Pz = new double[N];
nbr_counts = new int[N];
M = new SymmetricSparseMatrix();
for (int i = 0; i < N; ++i)
{
int vid = ToMeshV[i];
Vector3d v = Mesh.GetVertex(vid);
Px[i] = v.x; Py[i] = v.y; Pz[i] = v.z;
nbr_counts[i] = Mesh.GetVtxEdgeCount(vid);
}
// construct laplacian matrix
for (int i = 0; i < N; ++i)
{
int vid = ToMeshV[i];
int n = nbr_counts[i];
double sum_w = 0;
foreach (int nbrvid in Mesh.VtxVerticesItr(vid))
{
int j = ToIndex[nbrvid];
int n2 = nbr_counts[j];
// weight options
//double w = -1;
double w = -1.0 / Math.Sqrt(n + n2);
//double w = -1.0 / n;
M.Set(i, j, w);
sum_w += w;
}
sum_w = -sum_w;
M.Set(vid, vid, sum_w);
}
// transpose(L) * L, but matrix is symmetric...
if (UseSoftConstraintNormalEquations)
{
//M = M.Multiply(M);
M = M.Square(); // only works if M is symmetric
}
// construct packed version of M matrix
PackedM = new PackedSparseMatrix(M);
// compute laplacian vectors of initial mesh positions
MLx = new double[N];
MLy = new double[N];
MLz = new double[N];
M.Multiply(Px, MLx);
M.Multiply(Py, MLy);
M.Multiply(Pz, MLz);
// zero out...
for (int i = 0; i < Px.Length; ++i)
{
MLx[i] = 0;
MLy[i] = 0;
MLz[i] = 0;
}
// allocate memory for internal buffers
Preconditioner = new DiagonalMatrix(N);
WeightsM = new DiagonalMatrix(N);
Cx = new double[N]; Cy = new double[N]; Cz = new double[N];
Bx = new double[N]; By = new double[N]; Bz = new double[N];
Sx = new double[N]; Sy = new double[N]; Sz = new double[N];
UpdateForSolve();
}
public void Initialize()
{
ToMeshV = new int[Mesh.MaxVertexID];
ToIndex = new int[Mesh.MaxVertexID];
N = 0;
foreach (int vid in Mesh.VertexIndices())
{
ToMeshV[N] = vid;
ToIndex[vid] = N;
N++;
}
Px = new double[N];
Py = new double[N];
Pz = new double[N];
nbr_counts = new int[N];
SymmetricSparseMatrix M = new SymmetricSparseMatrix();
for (int i = 0; i < N; ++i)
{
int vid = ToMeshV[i];
Vector3d v = Mesh.GetVertex(vid);
Px[i] = v.x; Py[i] = v.y; Pz[i] = v.z;
nbr_counts[i] = Mesh.GetVtxEdgeCount(vid);
}
// construct laplacian matrix
for (int i = 0; i < N; ++i)
{
int vid = ToMeshV[i];
int n = nbr_counts[i];
double sum_w = 0;
foreach (int nbrvid in Mesh.VtxVerticesItr(vid))
{
int j = ToIndex[nbrvid];
int n2 = nbr_counts[j];
// weight options
//double w = -1;
double w = -1.0 / Math.Sqrt(n + n2);
//double w = -1.0 / n;
M.Set(i, j, w);
sum_w += w;
}
sum_w = -sum_w;
// TODO: Investigate: is this ia bug?
// Source https://github.com/ZelimDamian/geometry3Sharp/commit/7a50d8de10faad762e726e60956acc4bdc5456b5
// makes the following line M.Set(i, i, sum_w);
M.Set(vid, vid, sum_w);
}
// transpose(L) * L, but matrix is symmetric...
if (UseSoftConstraintNormalEquations)
{
//M = M.Multiply(M);
// only works if M is symmetric!!
PackedM = M.SquarePackedParallel();
}
else
{
PackedM = new PackedSparseMatrix(M);
}
// compute laplacian vectors of initial mesh positions
MLx = new double[N];
MLy = new double[N];
MLz = new double[N];
PackedM.Multiply(Px, MLx);
PackedM.Multiply(Py, MLy);
PackedM.Multiply(Pz, MLz);
// allocate memory for internal buffers
Preconditioner = new DiagonalMatrix(N);
WeightsM = new DiagonalMatrix(N);
Cx = new double[N]; Cy = new double[N]; Cz = new double[N];
Bx = new double[N]; By = new double[N]; Bz = new double[N];
Sx = new double[N]; Sy = new double[N]; Sz = new double[N];
need_solve_update = true;
UpdateForSolve();
}
public void Initialize()
{
int NV = Curve.VertexCount;
ToCurveV = new int[NV];
ToIndex = new int[NV];
N = 0;
for (int k = 0; k < NV; k++)
{
int vid = k;
ToCurveV[N] = vid;
ToIndex[vid] = N;
N++;
}
Px = new double[N];
Py = new double[N];
Pz = new double[N];
nbr_counts = new int[N];
SymmetricSparseMatrix M = new SymmetricSparseMatrix();
for (int i = 0; i < N; ++i)
{
int vid = ToCurveV[i];
Vector3d v = Curve.GetVertex(vid);
Px[i] = v.x; Py[i] = v.y; Pz[i] = v.z;
nbr_counts[i] = (i == 0 || i == N - 1) ? 1 : 2;
}
// construct laplacian matrix
for (int i = 0; i < N; ++i)
{
int vid = ToCurveV[i];
int n = nbr_counts[i];
Index2i nbrs = Curve.Neighbours(vid);
double sum_w = 0;
for (int k = 0; k < 2; ++k)
{
int nbrvid = nbrs[k];
if (nbrvid == -1)
{
continue;
}
int j = ToIndex[nbrvid];
int n2 = nbr_counts[j];
// weight options
double w = -1;
//double w = -1.0 / Math.Sqrt(n + n2);
//double w = -1.0 / n;
M.Set(i, j, w);
sum_w += w;
}
sum_w = -sum_w;
M.Set(vid, vid, sum_w);
}
// transpose(L) * L, but matrix is symmetric...
if (UseSoftConstraintNormalEquations)
{
//M = M.Multiply(M);
// only works if M is symmetric!!
PackedM = M.SquarePackedParallel();
}
else
{
PackedM = new PackedSparseMatrix(M);
}
// compute laplacian vectors of initial mesh positions
MLx = new double[N];
MLy = new double[N];
MLz = new double[N];
PackedM.Multiply(Px, MLx);
PackedM.Multiply(Py, MLy);
PackedM.Multiply(Pz, MLz);
// allocate memory for internal buffers
Preconditioner = new DiagonalMatrix(N);
WeightsM = new DiagonalMatrix(N);
Cx = new double[N]; Cy = new double[N]; Cz = new double[N];
Bx = new double[N]; By = new double[N]; Bz = new double[N];
Sx = new double[N]; Sy = new double[N]; Sz = new double[N];
need_solve_update = true;
UpdateForSolve();
}