/**
* Performs matrix addition:<br>
* C = αA + βB
*
* @param alpha scalar value multiplied against A
* @param A Matrix
* @param beta scalar value multiplied against B
* @param B Matrix
* @param C Output matrix.
* @param gw (Optional) Storage for internal workspace. Can be null.
* @param gx (Optional) Storage for internal workspace. Can be null.
*/
public static void add(double alpha, DMatrixSparseCSC A, double beta, DMatrixSparseCSC B, DMatrixSparseCSC C,
IGrowArray gw, DGrowArray gx)
{
double[] x = TriangularSolver_DSCC.adjust(gx, A.numRows);
int[] w = TriangularSolver_DSCC.adjust(gw, A.numRows, A.numRows);
C.indicesSorted = false;
C.nz_length = 0;
for (int col = 0; col < A.numCols; col++)
{
C.col_idx[col] = C.nz_length;
ImplSparseSparseMult_DSCC.multAddColA(A, col, alpha, C, col + 1, x, w);
ImplSparseSparseMult_DSCC.multAddColA(B, col, beta, C, col + 1, x, w);
// take the values in the dense vector 'x' and put them into 'C'
int idxC0 = C.col_idx[col];
int idxC1 = C.col_idx[col + 1];
for (int i = idxC0; i < idxC1; i++)
{
C.nz_values[i] = x[C.nz_rows[i]];
}
}
}
/**
* Performs matrix multiplication. C = A*B<sup>T</sup></sup>
*
* @param A Matrix
* @param B Matrix
* @param C Storage for results. Data length is increased if increased if insufficient.
* @param gw (Optional) Storage for internal workspace. Can be null.
* @param gx (Optional) Storage for internal workspace. Can be null.
*/
public static void multTransB(DMatrixSparseCSC A, DMatrixSparseCSC B, DMatrixSparseCSC C,
IGrowArray gw, DGrowArray gx)
{
if (!B.isIndicesSorted())
{
throw new ArgumentException("B must have its indices sorted.");
}
else if (!CommonOps_DSCC.checkIndicesSorted(B))
{
throw new ArgumentException("Crap. Not really sorted");
}
double[] x = TriangularSolver_DSCC.adjust(gx, A.numRows);
int[] w = TriangularSolver_DSCC.adjust(gw, A.numRows + B.numCols, A.numRows);
C.growMaxLength(A.nz_length + B.nz_length, false);
C.indicesSorted = false;
C.nz_length = 0;
C.col_idx[0] = 0;
// initialize w is the first index in each column of B
int locationB = A.numRows;
Array.Copy(B.col_idx, 0, w, locationB, B.numCols);
for (int colC = 0; colC < B.numRows; colC++)
{
C.col_idx[colC + 1] = C.nz_length; // needs a value of B has nothing in the row
// find the column in the transposed B
int mark = colC + 1;
for (int colB = 0; colB < B.numCols; colB++)
{
int bi = w[locationB + colB];
if (bi < B.col_idx[colB + 1])
{
int row = B.nz_rows[bi];
if (row == colC)
{
multAddColA(A, colB, B.nz_values[bi], C, mark, x, w);
w[locationB + colB]++;
}
}
}
// take the values in the dense vector 'x' and put them into 'C'
int idxC0 = C.col_idx[colC];
int idxC1 = C.col_idx[colC + 1];
for (int i = idxC0; i < idxC1; i++)
{
C.nz_values[i] = x[C.nz_rows[i]];
}
}
}
/**
* Performs element-wise multiplication:<br>
* C_ij = A_ij * B_ij
*
* @param A (Input) Matrix
* @param B (Input) Matrix
* @param C (Output) matrix.
* @param gw (Optional) Storage for internal workspace. Can be null.
* @param gx (Optional) Storage for internal workspace. Can be null.
*/
public static void elementMult(DMatrixSparseCSC A, DMatrixSparseCSC B, DMatrixSparseCSC C,
IGrowArray gw, DGrowArray gx)
{
double[] x = TriangularSolver_DSCC.adjust(gx, A.numRows);
int[] w = TriangularSolver_DSCC.adjust(gw, A.numRows);
//Arrays.fill(w, 0, A.numRows, -1); // fill with -1. This will be a value less than column
for (var i = 0; i < A.numRows; i++)
{
w[i] = -1;
}
C.indicesSorted = false; // Hmm I think if B is storted then C will be sorted...
C.nz_length = 0;
for (int col = 0; col < A.numCols; col++)
{
int idxA0 = A.col_idx[col];
int idxA1 = A.col_idx[col + 1];
int idxB0 = B.col_idx[col];
int idxB1 = B.col_idx[col + 1];
// compute the maximum number of elements that there can be in this row
int maxInRow = Math.Min(idxA1 - idxA0, idxB1 - idxB0);
// make sure there are enough non-zero elements in C
if (C.nz_length + maxInRow > C.nz_values.Length)
{
C.growMaxLength(C.nz_values.Length + maxInRow, true);
}
// update the structure of C
C.col_idx[col] = C.nz_length;
// mark the rows that appear in A and save their value
for (int i = idxA0; i < idxA1; i++)
{
int row = A.nz_rows[i];
w[row] = col;
x[row] = A.nz_values[i];
}
// If a row appears in A and B, multiply and set as an element in C
for (int i = idxB0; i < idxB1; i++)
{
int row = B.nz_rows[i];
if (w[row] == col)
{
C.nz_values[C.nz_length] = x[row] * B.nz_values[i];
C.nz_rows[C.nz_length++] = row;
}
}
}
C.col_idx[C.numCols] = C.nz_length;
}
/**
* Adds the results of adding a column in A and B as a new column in C.<br>
* C(:,end+1) = α*A(:,colA) + β*B(:,colB)
*
* @param alpha scalar
* @param A matrix
* @param colA column in A
* @param beta scalar
* @param B matrix
* @param colB column in B
* @param C Column in C
* @param gw workspace
*/
public static void addColAppend(double alpha, DMatrixSparseCSC A, int colA, double beta, DMatrixSparseCSC B,
int colB,
DMatrixSparseCSC C, IGrowArray gw)
{
if (A.numRows != B.numRows || A.numRows != C.numRows)
{
throw new ArgumentException("Number of rows in A, B, and C do not match");
}
int idxA0 = A.col_idx[colA];
int idxA1 = A.col_idx[colA + 1];
int idxB0 = B.col_idx[colB];
int idxB1 = B.col_idx[colB + 1];
C.growMaxColumns(++C.numCols, true);
C.growMaxLength(C.nz_length + idxA1 - idxA0 + idxB1 - idxB0, true);
int[] w = TriangularSolver_DSCC.adjust(gw, A.numRows);
//Arrays.fill(w, 0, A.numRows, -1);
for (var i = 0; i < A.numRows; i++)
{
w[i] = -1;
}
for (int i = idxA0; i < idxA1; i++)
{
int row = A.nz_rows[i];
C.nz_rows[C.nz_length] = row;
C.nz_values[C.nz_length] = alpha * A.nz_values[i];
w[row] = C.nz_length++;
}
for (int i = idxB0; i < idxB1; i++)
{
int row = B.nz_rows[i];
if (w[row] != -1)
{
C.nz_values[w[row]] += beta * B.nz_values[i];
}
else
{
C.nz_values[C.nz_length] = beta * B.nz_values[i];
C.nz_rows[C.nz_length++] = row;
}
}
C.col_idx[C.numCols] = C.nz_length;
}
/**
* Performs matrix multiplication. C = A*B
*
* @param A Matrix
* @param B Matrix
* @param C Storage for results. Data length is increased if increased if insufficient.
* @param gw (Optional) Storage for internal workspace. Can be null.
* @param gx (Optional) Storage for internal workspace. Can be null.
*/
public static void mult(DMatrixSparseCSC A, DMatrixSparseCSC B, DMatrixSparseCSC C,
IGrowArray gw, DGrowArray gx)
{
double[] x = TriangularSolver_DSCC.adjust(gx, A.numRows);
int[] w = TriangularSolver_DSCC.adjust(gw, A.numRows, A.numRows);
C.growMaxLength(A.nz_length + B.nz_length, false);
C.indicesSorted = false;
C.nz_length = 0;
// C(i,j) = sum_k A(i,k) * B(k,j)
int idx0 = B.col_idx[0];
for (int bj = 1; bj <= B.numCols; bj++)
{
int colB = bj - 1;
int idx1 = B.col_idx[bj];
C.col_idx[bj] = C.nz_length;
if (idx0 == idx1)
{
continue;
}
// C(:,j) = sum_k A(:,k)*B(k,j)
for (int bi = idx0; bi < idx1; bi++)
{
int rowB = B.nz_rows[bi];
double valB = B.nz_values[bi]; // B(k,j) k=rowB j=colB
multAddColA(A, rowB, valB, C, colB + 1, x, w);
}
// take the values in the dense vector 'x' and put them into 'C'
int idxC0 = C.col_idx[colB];
int idxC1 = C.col_idx[colB + 1];
for (int i = idxC0; i < idxC1; i++)
{
C.nz_values[i] = x[C.nz_rows[i]];
}
idx0 = idx1;
}
}
/**
* Computes the inner product of two column vectors taken from the input matrices.
*
* <p>dot = A(:,colA)'*B(:,colB)</p>
*
* @param A Matrix
* @param colA Column in A
* @param B Matrix
* @param colB Column in B
* @return Dot product
*/
public static double dotInnerColumns(DMatrixSparseCSC A, int colA, DMatrixSparseCSC B, int colB,
IGrowArray gw, DGrowArray gx)
{
if (A.numRows != B.numRows)
{
throw new ArgumentException("Number of rows must match.");
}
int[] w = TriangularSolver_DSCC.adjust(gw, A.numRows);
//Arrays.fill(w,0,A.numRows,-1);
for (var i = 0; i < A.numRows; i++)
{
w[i] = -1;
}
double[] x = TriangularSolver_DSCC.adjust(gx, A.numRows);
int length = 0;
int idx0 = A.col_idx[colA];
int idx1 = A.col_idx[colA + 1];
for (int i = idx0; i < idx1; i++)
{
int row = A.nz_rows[i];
x[length] = A.nz_values[i];
w[row] = length++;
}
double dot = 0;
idx0 = B.col_idx[colB];
idx1 = B.col_idx[colB + 1];
for (int i = idx0; i < idx1; i++)
{
int row = B.nz_rows[i];
if (w[row] != -1)
{
dot += x[w[row]] * B.nz_values[i];
}
}
return(dot);
}
/**
* Performs a matrix transpose.
*
* @param A Original matrix. Not modified.
* @param C Storage for transposed 'a'. Reshaped.
* @param gw (Optional) Storage for internal workspace. Can be null.
*/
public static void transpose(DMatrixSparseCSC A, DMatrixSparseCSC C, IGrowArray gw)
{
int[] work = TriangularSolver_DSCC.adjust(gw, A.numRows, A.numRows);
C.reshape(A.numCols, A.numRows, A.nz_length);
// compute the histogram for each row in 'a'
int idx0 = A.col_idx[0];
for (int j = 1; j <= A.numCols; j++)
{
int idx1 = A.col_idx[j];
for (int i = idx0; i < idx1; i++)
{
if (A.nz_rows.Length <= i)
{
throw new InvalidOperationException("Egads");
}
work[A.nz_rows[i]]++;
}
idx0 = idx1;
}
// construct col_idx in the transposed matrix
C.colsum(work);
// fill in the row indexes
idx0 = A.col_idx[0];
for (int j = 1; j <= A.numCols; j++)
{
int col = j - 1;
int idx1 = A.col_idx[j];
for (int i = idx0; i < idx1; i++)
{
int row = A.nz_rows[i];
int index = work[row]++;
C.nz_rows[index] = col;
C.nz_values[index] = A.nz_values[i];
}
idx0 = idx1;
}
}
/**
* Performs matrix multiplication. C = A<sup>T</sup></sup>*B
*
* @param A Matrix
* @param B Matrix
* @param C Storage for results. Data length is increased if increased if insufficient.
* @param gw (Optional) Storage for internal workspace. Can be null.
* @param gx (Optional) Storage for internal workspace. Can be null.
*/
public static void multTransA(DMatrixSparseCSC A, DMatrixSparseCSC B, DMatrixSparseCSC C,
IGrowArray gw, DGrowArray gx)
{
double[] x = TriangularSolver_DSCC.adjust(gx, A.numRows);
int[] w = TriangularSolver_DSCC.adjust(gw, A.numRows, A.numRows);
C.growMaxLength(A.nz_length + B.nz_length, false);
C.indicesSorted = true;
C.nz_length = 0;
C.col_idx[0] = 0;
int idxB0 = B.col_idx[0];
for (int bj = 1; bj <= B.numCols; bj++)
{
int idxB1 = B.col_idx[bj];
C.col_idx[bj] = C.nz_length;
if (idxB0 == idxB1)
{
continue;
}
// convert the column of B into a dense format and mark which rows are used
for (int bi = idxB0; bi < idxB1; bi++)
{
int rowB = B.nz_rows[bi];
x[rowB] = B.nz_values[bi];
w[rowB] = bj;
}
// C(colA,colB) = A(:,colA)*B(:,colB)
for (int colA = 0; colA < A.numCols; colA++)
{
int idxA0 = A.col_idx[colA];
int idxA1 = A.col_idx[colA + 1];
double sum = 0;
for (int ai = idxA0; ai < idxA1; ai++)
{
int rowA = A.nz_rows[ai];
if (w[rowA] == bj)
{
sum += x[rowA] * A.nz_values[ai];
}
}
if (sum != 0)
{
if (C.nz_length == C.nz_values.Length)
{
C.growMaxLength(C.nz_length * 2 + 1, true);
}
C.nz_values[C.nz_length] = sum;
C.nz_rows[C.nz_length++] = colA;
}
}
C.col_idx[bj] = C.nz_length;
idxB0 = idxB1;
}
}