/**
* 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]];
}
}
}
/**
* <p>
* Solves for x in the following equation:<br>
* <br>
* A*x = b
* </p>
*
* <p>
* If the system could not be solved then false is returned. If it returns true
* that just means the algorithm finished operating, but the results could still be bad
* because 'A' is singular or nearly singular.
* </p>
*
* <p>
* If repeat calls to solve are being made then one should consider using {@link LinearSolverFactory_DSCC}
* instead.
* </p>
*
* <p>
* It is ok for 'b' and 'x' to be the same matrix.
* </p>
*
* @param a (Input) A matrix that is m by n. Not modified.
* @param b (Input) A matrix that is n by k. Not modified.
* @param x (Output) A matrix that is m by k. Modified.
*
* @return true if it could invert the matrix false if it could not.
*/
public static bool solve(DMatrixSparseCSC a,
DMatrixRMaj b,
DMatrixRMaj x)
{
LinearSolverSparse <DMatrixSparseCSC, DMatrixRMaj> solver;
if (a.numRows > a.numCols)
{
solver = LinearSolverFactory_DSCC.qr(FillReducing.NONE); // todo specify a filling that makes sense
}
else
{
solver = LinearSolverFactory_DSCC.lu(FillReducing.NONE);
}
// Ensure that the input isn't modified
if (solver.modifiesA())
{
a = (DMatrixSparseCSC)a.copy();
}
if (solver.modifiesB())
{
b = (DMatrixRMaj)b.copy();
}
// decompose then solve the matrix
if (!solver.setA(a))
{
return(false);
}
solver.solve(b, x);
return(true);
}
/**
* Computes the quality of a triangular matrix, where the quality of a matrix
* is defined in {@link LinearSolverDense#quality()}. In
* this situation the quality os the absolute value of the product of
* each diagonal element divided by the magnitude of the largest diagonal element.
* If all diagonal elements are zero then zero is returned.
*
* @param T A matrix.
* @return the quality of the system.
*/
public static double qualityTriangular(DMatrixSparseCSC T)
{
int N = Math.Min(T.numRows, T.numCols);
double max = T.unsafe_get(0, 0);
for (int i = 1; i < N; i++)
{
max = Math.Max(max, Math.Abs(T.unsafe_get(i, i)));
}
if (max == 0.0)
{
return(0.0);
}
double quality = 1.0;
for (int i = 0; i < N; i++)
{
quality *= T.unsafe_get(i, i) / max;
}
return(Math.Abs(quality));
}
public static void removeZeros(DMatrixSparseCSC A, double tol)
{
int offset = 0;
for (int i = 0; i < A.numCols; i++)
{
int idx0 = A.col_idx[i] + offset;
int idx1 = A.col_idx[i + 1];
for (int j = idx0; j < idx1; j++)
{
double val = A.nz_values[j];
if (Math.Abs(val) > tol)
{
A.nz_rows[j - offset] = A.nz_rows[j];
A.nz_values[j - offset] = val;
}
else
{
offset++;
}
}
A.col_idx[i + 1] -= offset;
}
A.nz_length -= offset;
}
/**
* <p>Determines which elements in 'X' will be non-zero when the system below is solved for.</p>
* G*X = B
*
* <p>xi will contain a list of ordered row indexes in B which will be modified starting at xi[top] to xi[n-1]. top
* is the value returned by this function.</p>
*
* <p>See cs_reach in dsparse library to understand the algorithm. This code follow the spirit but not
* the details because of differences in the contract.</p>
*
* @param G (Input) Lower triangular system matrix. Diagonal elements are assumed to be not zero. Not modified.
* @param B (Input) Matrix B. Not modified.
* @param colB Column in B being solved for
* @param pinv (Input, Optional) Column pivots in G. Null if no pivots.
* @param xi (Output) List of row indices in B which are non-zero in graph order. Must have length B.numRows
* @param gwork workspace array used internally. Can be null.
* @return Returns the index of the first element in the xi list. Also known as top.
*/
public static int searchNzRowsInB(DMatrixSparseCSC G, DMatrixSparseCSC B, int colB, int[] pinv,
int[] xi, IGrowArray gwork)
{
if (xi.Length < B.numRows)
{
throw new ArgumentException("xi must be at least this long: " + B.numRows);
}
// this is a change from csparse. CSparse marks an entry by modifying G then reverting it. This can cause
// weird unexplained behavior when people start using threads...
int[] w = adjust(gwork, B.numRows * 2, B.numRows);
// use 'w' as a marker to know which rows in B have been examined. 0 = unexamined and 1 = examined
int idx0 = B.col_idx[colB];
int idx1 = B.col_idx[colB + 1];
int top = G.numRows;
for (int i = idx0; i < idx1; i++)
{
int rowB = B.nz_rows[i];
if (w[rowB] == 0)
{
top = searchNzRowsInB_DFS(rowB, G, top, pinv, xi, w);
}
}
return(top);
}
/**
* Computes the solution to the triangular system.
*
* @param G (Input) Lower or upper triangular matrix. diagonal elements must be non-zero. Not modified.
* @param lower true for lower triangular and false for upper
* @param B (Input) Matrix. Not modified.
* @param X (Output) Solution
* @param g_x (Optional) Storage for workspace.
* @param g_xi (Optional) Storage for workspace.
* @param g_w (Optional) Storage for workspace.
*/
public static void solve(DMatrixSparseCSC G, bool lower,
DMatrixSparseCSC B, DMatrixSparseCSC X,
DGrowArray g_x, IGrowArray g_xi, IGrowArray g_w)
{
double[] x = adjust(g_x, G.numRows);
if (g_xi == null)
{
g_xi = new IGrowArray();
}
int[] xi = adjust(g_xi, G.numRows);
X.nz_length = 0;
X.col_idx[0] = 0;
X.indicesSorted = false;
for (int colB = 0; colB < B.numCols; colB++)
{
int top = solve(G, lower, B, colB, x, null, g_xi, g_w);
int nz_count = X.numRows - top;
if (X.nz_values.Length < X.nz_length + nz_count)
{
X.growMaxLength(X.nz_length * 2 + nz_count, true);
}
for (int p = top; p < X.numRows; p++, X.nz_length++)
{
X.nz_rows[X.nz_length] = xi[p];
X.nz_values[X.nz_length] = x[xi[p]];
}
X.col_idx[colB + 1] = X.nz_length;
}
}
/**
* Initializes class data structures and parameters
*/
void initialize(DMatrixSparseCSC A)
{
m = A.numRows;
n = A.numCols;
int s = 4 * n + (ata ? (n + m + 1) : 0);
gw.reshape(s);
w = gw.data;
// compute the transpose of A
At.reshape(A.numCols, A.numRows, A.nz_length);
CommonOps_DSCC.transpose(A, At, gw);
// initialize w
//Arrays.fill(w, 0, s, -1); // assign all values in workspace to -1
for (var i = 0; i < s; i++)
{
w[i] = -1;
}
ancestor = 0;
maxfirst = n;
prevleaf = 2 * n;
first = 3 * n;
}
/**
* <p>
* Performs a matrix inversion operation that does not modify the original
* and stores the results in another matrix. The two matrices must have the
* same dimension.<br>
* <br>
* B = A<sup>-1</sup>
* </p>
*
* <p>
* If the algorithm could not invert the matrix then false is returned. If it returns true
* that just means the algorithm finished. The results could still be bad
* because the matrix is singular or nearly singular.
* </p>
*
* <p>
* For medium to large matrices there might be a slight performance boost to using
* {@link LinearSolverFactory_DSCC} instead.
* </p>
*
* @param A (Input) The matrix that is to be inverted. Not modified.
* @param inverse (Output) Where the inverse matrix is stored. Modified.
* @return true if it could invert the matrix false if it could not.
*/
public static bool invert(DMatrixSparseCSC A, DMatrixRMaj inverse)
{
if (A.numRows != A.numCols)
{
throw new ArgumentException("A must be a square matrix");
}
if (A.numRows != inverse.numRows || A.numCols != inverse.numCols)
{
throw new ArgumentException("A and inverse must have the same shape.");
}
LinearSolverSparse <DMatrixSparseCSC, DMatrixRMaj> solver;
solver = LinearSolverFactory_DSCC.lu(FillReducing.NONE);
// Ensure that the input isn't modified
if (solver.modifiesA())
{
A = (DMatrixSparseCSC)A.copy();
}
DMatrixRMaj I = CommonOps_DDRM.identity(A.numRows);
// decompose then solve the matrix
if (!solver.setA(A))
{
return(false);
}
solver.solve(I, inverse);
return(true);
}
/**
* Converts the permutation matrix into a vector
* @param P (Input) Permutation matrix
* @param vector (Output) Permutation vector
*/
public static void permutationVector(DMatrixSparseCSC P, int[] vector)
{
if (P.numCols != P.numRows)
{
throw new ArgumentException("Expected a square matrix");
}
else if (P.nz_length != P.numCols)
{
throw new ArgumentException("Expected N non-zero elements in permutation matrix");
}
else if (vector.Length < P.numCols)
{
throw new ArgumentException("vector is too short");
}
int M = P.numCols;
for (int i = 0; i < M; i++)
{
if (P.col_idx[i + 1] != i + 1)
{
throw new ArgumentException("Unexpected number of elements in a column");
}
vector[P.nz_rows[i]] = i;
}
}
public static DMatrixRMaj convert(DMatrixSparseCSC src, DMatrixRMaj dst)
{
if (dst == null)
{
dst = new DMatrixRMaj(src.numRows, src.numCols);
}
else
{
dst.reshape(src.numRows, src.numCols);
dst.zero();
}
int idx0 = src.col_idx[0];
for (int j = 1; j <= src.numCols; j++)
{
int idx1 = src.col_idx[j];
for (int i = idx0; i < idx1; i++)
{
int row = src.nz_rows[i];
double val = src.nz_values[i];
dst.unsafe_set(row, j - 1, val);
}
idx0 = idx1;
}
return(dst);
}
public static bool isTranspose(DMatrixSparseCSC A, DMatrixSparseCSC B, double tol)
{
if (A.numCols != B.numRows || A.numRows != B.numCols)
{
return(false);
}
if (A.nz_length != B.nz_length)
{
return(false);
}
if (!A.indicesSorted)
{
throw new ArgumentException("A must have sorted indicies");
}
DMatrixSparseCSC Btran = new DMatrixSparseCSC(B.numCols, B.numRows, B.nz_length);
CommonOps_DSCC.transpose(B, Btran, null);
Btran.sortIndices(null);
for (int i = 0; i < B.nz_length; i++)
{
if (A.nz_rows[i] != Btran.nz_rows[i])
{
return(false);
}
if (Math.Abs(A.nz_values[i] - Btran.nz_values[i]) > tol)
{
return(false);
}
}
return(true);
}
/**
* Converts DMatrixSparseTriplet into a DMatrixSparseCSC. Duplicate elements in triplet will result in an
* illegal matrix in output having duplicate elements.
*
* @param src Original matrix which is to be copied. Not modified.
* @param dst Destination. Will be a copy. Modified.
* @param histStorage Workspace. Can be null.
*/
public static DMatrixSparseCSC convert(DMatrixSparseTriplet src, DMatrixSparseCSC dst,
IGrowArray histStorage)
{
dst = UtilEjml.reshapeOrDeclare(dst, src.numRows, src.numCols, src.nz_length);
int[] hist = UtilEjml.adjustClear(histStorage, src.numCols);
// compute the number of elements in each columns
for (int i = 0; i < src.nz_length; i++)
{
hist[src.nz_rowcol.data[i * 2 + 1]]++;
}
// define col_idx
dst.histogramToStructure(hist);
System.Array.Copy(dst.col_idx, 0, hist, 0, dst.numCols);
// now write the row indexes and the values
for (int i = 0; i < src.nz_length; i++)
{
int row = src.nz_rowcol.data[i * 2];
int col = src.nz_rowcol.data[i * 2 + 1];
double value = src.nz_value.data[i];
int index = hist[col]++;
dst.nz_rows[index] = row;
dst.nz_values[index] = value;
}
dst.indicesSorted = false;
return(dst);
}
/**
* Computes and applies the fill reduction permutation. Either A is returned (unmodified) or the permutated
* version of A.
* @param A Input matrix. unmodified.
* @return A permuted matrix. Might be A or a different matrix.
*/
public DMatrixSparseCSC apply(DMatrixSparseCSC A)
{
if (fillReduce == null)
{
return(A);
}
fillReduce.process(A);
IGrowArray gp = fillReduce.getRow();
if (pinv.Length < gp.Length)
{
pinv = new int[gp.Length];
}
CommonOps_DSCC.permutationInverse(gp.data, pinv, gp.Length);
if (symmetric)
{
CommonOps_DSCC.permuteSymmetric(A, pinv, Aperm, gw);
}
else
{
CommonOps_DSCC.permuteRowInv(pinv, A, Aperm);
}
return(Aperm);
}
/**
* Performs the performing operation x = x + A(:,i)*alpha
*
* <p>NOTE: This is the same as cs_scatter() in csparse.</p>
*/
public static void multAddColA(DMatrixSparseCSC A, int colA,
double alpha,
DMatrixSparseCSC C, int mark,
double[] x, int[] w)
{
int idxA0 = A.col_idx[colA];
int idxA1 = A.col_idx[colA + 1];
for (int j = idxA0; j < idxA1; j++)
{
int row = A.nz_rows[j];
if (w[row] < mark)
{
if (C.nz_length >= C.nz_rows.Length)
{
C.growMaxLength(C.nz_length * 2 + 1, true);
}
w[row] = mark;
C.nz_rows[C.nz_length] = row;
C.col_idx[mark] = ++C.nz_length;
x[row] = A.nz_values[j] * alpha;
}
else
{
x[row] += A.nz_values[j] * alpha;
}
}
}
public static void mult(DMatrixSparseCSC A, DMatrixRMaj B, DMatrixRMaj C)
{
C.zero();
// C(i,j) = sum_k A(i,k) * B(k,j)
for (int k = 0; k < A.numCols; k++)
{
int idx0 = A.col_idx[k];
int idx1 = A.col_idx[k + 1];
for (int indexA = idx0; indexA < idx1; indexA++)
{
int i = A.nz_rows[indexA];
double valueA = A.nz_values[indexA];
int indexB = k * B.numCols;
int indexC = i * C.numCols;
int end = indexB + B.numCols;
// for (int j = 0; j < B.numCols; j++) {
while (indexB < end)
{
C.data[indexC++] += valueA * B.data[indexB++];
}
}
}
}
public static DMatrixSparseTriplet convert(DMatrixSparseCSC src, DMatrixSparseTriplet dst)
{
if (dst == null)
{
dst = new DMatrixSparseTriplet(src.numRows, src.numCols, src.nz_length);
}
else
{
dst.reshape(src.numRows, src.numCols);
}
int i0 = src.col_idx[0];
for (int col = 0; col < src.numCols; col++)
{
int i1 = src.col_idx[col + 1];
for (int i = i0; i < i1; i++)
{
int row = src.nz_rows[i];
dst.addItem(row, col, src.nz_values[i]);
}
i0 = i1;
}
return(dst);
}
/**
* <p>
* Checks to see if a matrix is orthogonal or isometric.
* </p>
*
* @param Q The matrix being tested. Not modified.
* @param tol Tolerance.
* @return True if it passes the test.
*/
public static bool isOrthogonal(DMatrixSparseCSC Q, double tol)
{
if (Q.numRows < Q.numCols)
{
throw new ArgumentException("The number of rows must be more than or equal to the number of columns");
}
IGrowArray gw = new IGrowArray();
DGrowArray gx = new DGrowArray();
for (int i = 0; i < Q.numRows; i++)
{
for (int j = i + 1; j < Q.numCols; j++)
{
double val = CommonOps_DSCC.dotInnerColumns(Q, i, Q, j, gw, gx);
if (!(Math.Abs(val) <= tol))
{
return(false);
}
}
}
return(true);
}
public static bool isIdentity(DMatrixSparseCSC A, double tol)
{
if (A.numCols != A.numRows)
{
return(false);
}
if (A.nz_length != A.numCols)
{
return(false);
}
for (int i = 1; i <= A.numCols; i++)
{
if (A.col_idx[i] != i)
{
return(false);
}
if (Math.Abs(A.nz_values[i - 1] - 1) > tol)
{
return(false);
}
}
return(true);
}
/**
* Checks to see if row indicies are sorted into ascending order. O(N)
* @return true if sorted and false if not
*/
public static bool checkIndicesSorted(DMatrixSparseCSC A)
{
for (int j = 0; j < A.numCols; j++)
{
int idx0 = A.col_idx[j];
int idx1 = A.col_idx[j + 1];
if (idx0 != idx1 && A.nz_rows[idx0] >= A.numRows)
{
return(false);
}
for (int i = idx0 + 1; i < idx1; i++)
{
int row = A.nz_rows[i];
if (A.nz_rows[i - 1] >= row)
{
return(false);
}
if (row >= A.numRows)
{
return(false);
}
}
}
return(true);
}
/**
* Applies the row permutation specified by the vector to the input matrix and save the results
* in the output matrix. output[perm[j],:] = input[j,:]
*
* @param permInv (Input) Inverse permutation vector. Specifies new order of the rows.
* @param input (Input) Matrix which is to be permuted
* @param output (Output) Matrix which has the permutation stored in it. Is reshaped.
*/
public static void permuteRowInv(int[] permInv, DMatrixSparseCSC input, DMatrixSparseCSC output)
{
if (input.numRows > permInv.Length)
{
throw new ArgumentException("permutation vector must have at least as many elements as input has rows");
}
output.reshape(input.numRows, input.numCols, input.nz_length);
output.nz_length = input.nz_length;
output.indicesSorted = false;
Array.Copy(input.nz_values, 0, output.nz_values, 0, input.nz_length);
Array.Copy(input.col_idx, 0, output.col_idx, 0, input.numCols + 1);
int idx0 = 0;
for (int i = 0; i < input.numCols; i++)
{
int idx1 = output.col_idx[i + 1];
for (int j = idx0; j < idx1; j++)
{
output.nz_rows[j] = permInv[input.nz_rows[j]];
}
idx0 = idx1;
}
}
/**
* Creates a submatrix by extracting the specified rows from A. rows = {row0 %le; i %le; row1}.
* @param A (Input) matrix
* @param row0 First row. Inclusive
* @param row1 Last row+1.
* @param out (Output, Option) Storage for output matrix
* @return The submatrix
*/
public static DMatrixSparseCSC extractRows(DMatrixSparseCSC A, int row0, int row1, DMatrixSparseCSC output)
{
if (output == null)
{
output = new DMatrixSparseCSC(1, 1, 1);
}
output.reshape(row1 - row0, A.numCols, A.nz_length);
// output.col_idx[0] = 0;
// output.nz_length = 0;
for (int col = 0; col < A.numCols; col++)
{
int idx0 = A.col_idx[col];
int idx1 = A.col_idx[col + 1];
for (int i = idx0; i < idx1; i++)
{
int row = A.nz_rows[i];
if (row >= row0 && row < row1)
{
output.nz_values[output.nz_length] = A.nz_values[i];
output.nz_rows[output.nz_length++] = row - row0;
}
}
output.col_idx[col + 1] = output.nz_length;
}
return(output);
}
/// <summary>
/// Create a simple matrix of the specified type
/// </summary>
/// <param name="numRows"> The number of rows in the matrix. </param>
/// <param name="numCols"> The number of columns in the matrix. </param>
/// <param name="type"> The matrix type </param>
public SimpleMatrix(int numRows, int numCols, MatrixType type)
{
switch (type.tipo)
{
case Types.DDRM:
mat = new DMatrixRMaj(numRows, numCols);
break;
case Types.FDRM:
//mat = new FMatrixRMaj(numRows, numCols);
break;
case Types.ZDRM:
mat = new ZMatrixRMaj(numRows, numCols);
break;
case Types.CDRM:
//mat = new CMatrixRMaj(numRows, numCols);
break;
case Types.DSCC:
mat = new DMatrixSparseCSC(numRows, numCols);
break;
case Types.FSCC:
//mat = new FMatrixSparseCSC(numRows, numCols);
break;
default:
throw new Exception("Unknown matrix type");
}
}
/**
* Converts the permutation vector into a matrix. B = P*A. B[p[i],:] = A[i,:]
* @param p (Input) Permutation vector
* @param inverse (Input) If it is the inverse. B[i,:] = A[p[i],:)
* @param P (Output) Permutation matrix
*/
public static DMatrixSparseCSC permutationMatrix(int[] p, bool inverse, int N, DMatrixSparseCSC P)
{
if (P == null)
{
P = new DMatrixSparseCSC(N, N, N);
}
else
{
P.reshape(N, N, N);
}
P.indicesSorted = true;
P.nz_length = N;
// each column should have one element inside of it
if (!inverse)
{
for (int i = 0; i < N; i++)
{
P.col_idx[i + 1] = i + 1;
P.nz_rows[p[i]] = i;
P.nz_values[i] = 1;
}
}
else
{
for (int i = 0; i < N; i++)
{
P.col_idx[i + 1] = i + 1;
P.nz_rows[i] = p[i];
P.nz_values[i] = 1;
}
}
return(P);
}
/**
* Randomly generates matrix with the specified number of non-zero elements filled with values from min to max.
*
* @param numRows Number of rows
* @param numCols Number of columns
* @param nz_total Total number of non-zero elements in the matrix
* @param min Minimum element value, inclusive
* @param max Maximum element value, inclusive
* @param rand Random number generator
* @return Randomly generated matrix
*/
public static DMatrixSparseCSC rectangle(int numRows, int numCols, int nz_total,
double min, double max, IMersenneTwister rand)
{
nz_total = Math.Min(numCols * numRows, nz_total);
int[] selected = UtilEjml.shuffled(numRows * numCols, nz_total, rand);
Array.Sort(selected, 0, nz_total);
DMatrixSparseCSC ret = new DMatrixSparseCSC(numRows, numCols, nz_total);
ret.indicesSorted = true;
// compute the number of elements in each column
int[] hist = new int[numCols];
for (int i = 0; i < nz_total; i++)
{
hist[selected[i] / numRows]++;
}
// define col_idx
ret.colsum(hist);
for (int i = 0; i < nz_total; i++)
{
int row = selected[i] % numRows;
ret.nz_rows[i] = row;
ret.nz_values[i] = rand.NextDouble() * (max - min) + min;
}
return(ret);
}
/**
* Checks to see if the matrix is symmetric to within tolerance.
*
* @param A Matrix being tested. Not modified.
* @param tol Tolerance that defines how similar two values must be to be considered identical
* @return true if symmetric or false if not
*/
public static bool isSymmetric(DMatrixSparseCSC A, double tol)
{
if (A.numRows != A.numCols)
{
return(false);
}
int N = A.numCols;
for (int i = 0; i < N; i++)
{
int idx0 = A.col_idx[i];
int idx1 = A.col_idx[i + 1];
for (int index = idx0; index < idx1; index++)
{
int j = A.nz_rows[index];
double value_ji = A.nz_values[index];
double value_ij = A.get(i, j);
if (Math.Abs(value_ij - value_ji) > tol)
{
return(false);
}
}
}
return(true);
}
public static bool checkSortedFlag(DMatrixSparseCSC A)
{
if (A.indicesSorted)
{
return(checkIndicesSorted(A));
}
return(true);
}
public static DMatrixSparseCSC identity(int numRows, int numCols)
{
int min = Math.Min(numRows, numCols);
DMatrixSparseCSC A = new DMatrixSparseCSC(numRows, numCols, min);
setToIdentity(A);
return(A);
}
/**
* Performs matrix multiplication. C = A*B
*
* @param A Matrix
* @param B Dense Matrix
* @param C Dense Matrix
*/
public static void mult(DMatrixSparseCSC A, DMatrixRMaj B, DMatrixRMaj C)
{
if (A.numRows != C.numRows || B.numCols != C.numCols)
{
throw new ArgumentException("Inconsistent matrix shapes");
}
ImplSparseSparseMult_DSCC.mult(A, B, C);
}
public static DMatrixSparseCSC triangleUpper(int dimen, int hessenberg, int nz_total,
double min, double max, IMersenneTwister rand)
{
DMatrixSparseCSC L = triangleLower(dimen, hessenberg, nz_total, min, max, rand);
DMatrixSparseCSC U = (DMatrixSparseCSC)L.createLike();
CommonOps_DSCC.transpose(L, U, null);
return(U);
}
/**
* Performs an element-wise multiplication.<br>
* C[i,j] = A[i,j]*B[i,j]<br>
* All matrices must have the same shape.
*
* @param A (Input) Matrix.
* @param B (Input) Matrix
* @param C (Ouptut) 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)
{
if (A.numCols != B.numCols || A.numRows != B.numRows || A.numCols != C.numCols || A.numRows != C.numRows)
{
throw new ArgumentException("All inputs must have the same number of rows and columns");
}
ImplCommonOps_DSCC.elementMult(A, B, C, gw, gx);
}
