/**
* Checks to see if the provided matrix is within tolerance to an identity matrix.
*
* @param mat Matrix being examined. Not modified.
* @param tol Tolerance.
* @return True if it is within tolerance to an identify matrix.
*/
public static bool isIdentity(FMatrixRMaj mat, float tol)
{
// see if the result is an identity matrix
int index = 0;
for (int i = 0; i < mat.numRows; i++)
{
for (int j = 0; j < mat.numCols; j++)
{
if (i == j)
{
if (!(Math.Abs(mat.get(index++) - 1) <= tol))
{
return(false);
}
}
else
{
if (!(Math.Abs(mat.get(index++)) <= tol))
{
return(false);
}
}
}
}
return(true);
}
/**
* <p>
* Returns true if the matrix is symmetric within the tolerance. Only square matrices can be
* symmetric.
* </p>
* <p>
* A matrix is symmetric if:<br>
* |a<sub>ij</sub> - a<sub>ji</sub>| ≤ tol
* </p>
*
* @param m A matrix. Not modified.
* @param tol Tolerance for how similar two elements need to be.
* @return true if it is symmetric and false if it is not.
*/
public static bool isSymmetric(FMatrixRMaj m, float tol)
{
if (m.numCols != m.numRows)
{
return(false);
}
float max = CommonOps_FDRM.elementMaxAbs(m);
for (int i = 0; i < m.numRows; i++)
{
for (int j = 0; j < i; j++)
{
float a = m.get(i, j) / max;
float b = m.get(j, i) / max;
float diff = Math.Abs(a - b);
if (!(diff <= tol))
{
return(false);
}
}
}
return(true);
}
private static void swapRowOrCol(FMatrixRMaj M, bool tran, int i, int bigIndex)
{
float tmp;
if (tran)
{
// swap the rows
for (int col = 0; col < M.numCols; col++)
{
tmp = M.get(i, col);
M.set(i, col, M.get(bigIndex, col));
M.set(bigIndex, col, tmp);
}
}
else
{
// swap the columns
for (int row = 0; row < M.numRows; row++)
{
tmp = M.get(row, i);
M.set(row, i, M.get(row, bigIndex));
M.set(row, bigIndex, tmp);
}
}
}
/**
* <p>
* Adjusts the matrices so that the singular values are in descending order.
* </p>
*
* <p>
* In most implementations of SVD the singular values are automatically arranged in in descending
* order. In EJML this is not the case since it is often not needed and some computations can
* be saved by not doing that.
* </p>
*
* @param U Matrix. Modified.
* @param tranU is U transposed or not.
* @param W Diagonal matrix with singular values. Modified.
* @param V Matrix. Modified.
* @param tranV is V transposed or not.
*/
// TODO the number of copies can probably be reduced here
public static void descendingOrder(FMatrixRMaj U, bool tranU,
FMatrixRMaj W,
FMatrixRMaj V, bool tranV)
{
int numSingular = Math.Min(W.numRows, W.numCols);
checkSvdMatrixSize(U, tranU, W, V, tranV);
for (int i = 0; i < numSingular; i++)
{
float bigValue = -1;
int bigIndex = -1;
// find the smallest singular value in the submatrix
for (int j = i; j < numSingular; j++)
{
float v = W.get(j, j);
if (v > bigValue)
{
bigValue = v;
bigIndex = j;
}
}
// only swap if the current index is not the smallest
if (bigIndex == i)
{
continue;
}
if (bigIndex == -1)
{
// there is at least one uncountable singular value. just stop here
break;
}
float tmp = W.get(i, i);
W.set(i, i, bigValue);
W.set(bigIndex, bigIndex, tmp);
if (V != null)
{
swapRowOrCol(V, tranV, i, bigIndex);
}
if (U != null)
{
swapRowOrCol(U, tranU, i, bigIndex);
}
}
}
/**
* <p>
* Puts all the real eigenvectors into the columns of a matrix. If an eigenvalue is imaginary
* then the corresponding eigenvector will have zeros in its column.
* </p>
*
* @param eig An eigenvalue decomposition which has already decomposed a matrix.
* @return An m by m matrix containing eigenvectors in its columns.
*/
public static FMatrixRMaj createMatrixV(EigenDecomposition_F32 <FMatrixRMaj> eig)
{
int N = eig.getNumberOfEigenvalues();
FMatrixRMaj V = new FMatrixRMaj(N, N);
for (int i = 0; i < N; i++)
{
Complex_F32 c = eig.getEigenvalue(i);
if (c.isReal())
{
FMatrixRMaj v = eig.getEigenVector(i);
if (v != null)
{
for (int j = 0; j < N; j++)
{
V.set(j, i, v.get(j, 0));
}
}
}
}
return(V);
}
private void replaceZeros(FSubmatrixD1 subU)
{
int N = Math.Min(A.blockLength, subU.col1 - subU.col0);
for (int i = 0; i < N; i++)
{
// save the zeros
for (int j = 0; j <= i; j++)
{
subU.set(i, j, zerosM.get(i, j));
}
// save the one
if (subU.col0 + i + 1 < subU.original.numCols)
{
subU.set(i, i + 1, zerosM.get(i, i + 1));
}
}
}
/**
* Extracts the tridiagonal matrix found in the decomposition.
*
* @param T If not null then the results will be stored here. Otherwise a new matrix will be created.
* @return The extracted T matrix.
*/
public FMatrixRMaj getT(FMatrixRMaj T)
{
T = UtilDecompositons_FDRM.checkZeros(T, N, N);
T.data[0] = QT.data[0];
T.data[1] = QT.data[1];
for (int i = 1; i < N - 1; i++)
{
T.set(i, i, QT.get(i, i));
T.set(i, i + 1, QT.get(i, i + 1));
T.set(i, i - 1, QT.get(i - 1, i));
}
T.data[(N - 1) * N + N - 1] = QT.data[(N - 1) * N + N - 1];
T.data[(N - 1) * N + N - 2] = QT.data[(N - 2) * N + N - 1];
return(T);
}
/**
* Checks to see if all the diagonal elements in the matrix are positive.
*
* @param a A matrix. Not modified.
* @return true if all the diagonal elements are positive, false otherwise.
*/
public static bool isDiagonalPositive(FMatrixRMaj a)
{
for (int i = 0; i < a.numRows; i++)
{
if (!(a.get(i, i) >= 0))
{
return(false);
}
}
return(true);
}
public override void solve(FMatrixRMaj B, FMatrixRMaj X)
{
if (X.numRows != numCols)
{
throw new ArgumentException("Unexpected dimensions for X");
}
else if (B.numRows != numRows || B.numCols != X.numCols)
{
throw new ArgumentException("Unexpected dimensions for B");
}
int BnumCols = B.numCols;
// get the pivots and transpose them
int[] pivots = decomposition.getColPivots();
// solve each column one by one
for (int colB = 0; colB < BnumCols; colB++)
{
x_basic.reshape(numRows, 1);
Y.reshape(numRows, 1);
// make a copy of this column in the vector
for (int i = 0; i < numRows; i++)
{
Y.data[i] = B.get(i, colB);
}
// Solve Q*a=b => a = Q'*b
CommonOps_FDRM.multTransA(Q, Y, x_basic);
// solve for Rx = b using the standard upper triangular solver
TriangularSolver_FDRM.solveU(R11.data, x_basic.data, rank);
// finish the basic solution by filling in zeros
x_basic.reshape(numCols, 1, true);
for (int i = rank; i < numCols; i++)
{
x_basic.data[i] = 0;
}
if (norm2Solution && rank < numCols)
{
upgradeSolution(x_basic);
}
// save the results
for (int i = 0; i < numCols; i++)
{
X.set(pivots[i], colB, x_basic.data[i]);
}
}
}
/**
* Computes the likelihood of the random draw
*
* @return The likelihood.
*/
public float computeLikelihoodP()
{
float ret = 1.0f;
for (int i = 0; i < r.numRows; i++)
{
float a = r.get(i, 0);
ret *= (float)Math.Exp(-a * a / 2.0f);
}
return(ret);
}
/**
* Multiplied a transpose orthogonal matrix Q by the specified rotator. This is used
* to update the U and V matrices. Updating the transpose of the matrix is faster
* since it only modifies the rows.
*
*
* @param Q Orthogonal matrix
* @param m Coordinate of rotator.
* @param n Coordinate of rotator.
* @param c cosine of rotator.
* @param s sine of rotator.
*/
protected void updateRotator(FMatrixRMaj Q, int m, int n, float c, float s)
{
int rowA = m * Q.numCols;
int rowB = n * Q.numCols;
// for( int i = 0; i < Q.numCols; i++ ) {
// float a = Q.get(rowA+i);
// float b = Q.get(rowB+i);
// Q.set( rowA+i, c*a + s*b);
// Q.set( rowB+i, -s*a + c*b);
// }
// Console.WriteLine("------ AFter Update Rotator "+m+" "+n);
// Q.print();
// Console.WriteLine();
int endA = rowA + Q.numCols;
for (; rowA != endA; rowA++, rowB++)
{
float a = Q.get(rowA);
float b = Q.get(rowB);
Q.set(rowA, c * a + s * b);
Q.set(rowB, -s * a + c * b);
}
}
/**
* <p>
* Checks to see if a matrix is skew symmetric with in tolerance:<br>
* <br>
* -A = A<sup>T</sup><br>
* or<br>
* |a<sub>ij</sub> + a<sub>ji</sub>| ≤ tol
* </p>
*
* @param A The matrix being tested.
* @param tol Tolerance for being skew symmetric.
* @return True if it is skew symmetric and false if it is not.
*/
public static bool isSkewSymmetric(FMatrixRMaj A, float tol)
{
if (A.numCols != A.numRows)
{
return(false);
}
for (int i = 0; i < A.numRows; i++)
{
for (int j = 0; j < i; j++)
{
float a = A.get(i, j);
float b = A.get(j, i);
float diff = Math.Abs(a + b);
if (!(diff <= tol))
{
return(false);
}
}
}
return(true);
}
/**
* <p>
* Generates a bound for the largest eigen value of the provided matrix using Perron-Frobenius
* theorem. This function only applies to non-negative real matrices.
* </p>
*
* <p>
* For "stochastic" matrices (Markov process) this should return one for the upper and lower bound.
* </p>
*
* @param A Square matrix with positive elements. Not modified.
* @param bound Where the results are stored. If null then a matrix will be declared. Modified.
* @return Lower and upper bound in the first and second elements respectively.
*/
public static float[] boundLargestEigenValue(FMatrixRMaj A, float[] bound)
{
if (A.numRows != A.numCols)
{
throw new ArgumentException("A must be a square matrix.");
}
float min = float.MaxValue;
float max = 0;
int n = A.numRows;
for (int i = 0; i < n; i++)
{
float total = 0;
for (int j = 0; j < n; j++)
{
float v = A.get(i, j);
if (v < 0)
{
throw new ArgumentException("Matrix must be positive");
}
total += v;
}
if (total < min)
{
min = total;
}
if (total > max)
{
max = total;
}
}
if (bound == null)
{
bound = new float[2];
}
bound[0] = min;
bound[1] = max;
return(bound);
}
/**
* <p>
* Induced matrix p = infinity norm.<br>
* <br>
* ||A||<sub>∞</sub> = max(i=1 to m; sum(j=1 to n; |a<sub>ij</sub>|))
* </p>
*
* @param A A matrix.
* @return the norm.
*/
public static float inducedPInf(FMatrixRMaj A)
{
float max = 0;
int m = A.numRows;
int n = A.numCols;
for (int i = 0; i < m; i++)
{
float total = 0;
for (int j = 0; j < n; j++)
{
total += Math.Abs(A.get(i, j));
}
if (total > max)
{
max = total;
}
}
return(max);
}
public override /**/ double quality()
{
return(SpecializedOps_FDRM.qualityTriangular(R));
}
/**
* Solves for X using the QR decomposition.
*
* @param B A matrix that is n by m. Not modified.
* @param X An n by m matrix where the solution is written to. Modified.
*/
public override void solve(FMatrixRMaj B, FMatrixRMaj X)
{
if (X.numRows != numCols)
{
throw new ArgumentException("Unexpected dimensions for X");
}
else if (B.numRows != numRows || B.numCols != X.numCols)
{
throw new ArgumentException("Unexpected dimensions for B");
}
int BnumCols = B.numCols;
Y.reshape(numRows, 1, false);
Z.reshape(numRows, 1, false);
// solve each column one by one
for (int colB = 0; colB < BnumCols; colB++)
{
// make a copy of this column in the vector
for (int i = 0; i < numRows; i++)
{
Y.data[i] = B.get(i, colB);
}
// Solve Qa=b
// a = Q'b
CommonOps_FDRM.multTransA(Q, Y, Z);
// solve for Rx = b using the standard upper triangular solver
TriangularSolver_FDRM.solveU(R.data, Z.data, numCols);
// save the results
for (int i = 0; i < numCols; i++)
{
X.set(i, colB, Z.data[i]);
}
}
}
/**
* Checks to see if the two matrices are inverses of each other.
*
* @param a A matrix. Not modified.
* @param b A matrix. Not modified.
*/
public static bool isInverse(FMatrixRMaj a, FMatrixRMaj b, float tol)
{
if (a.numRows != b.numRows || a.numCols != b.numCols)
{
return(false);
}
int numRows = a.numRows;
int numCols = a.numCols;
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
float total = 0;
for (int k = 0; k < numCols; k++)
{
total += a.get(i, k) * b.get(k, j);
}
if (i == j)
{
if (!(Math.Abs(total - 1) <= tol))
{
return(false);
}
}
else if (!(Math.Abs(total) <= tol))
{
return(false);
}
}
}
return(true);
}
public override void solve(FMatrixRMaj B, FMatrixRMaj X)
{
if (X.numRows != numCols)
{
throw new ArgumentException("Unexpected dimensions for X");
}
else if (B.numRows != numRows || B.numCols != X.numCols)
{
throw new ArgumentException("Unexpected dimensions for B");
}
int BnumCols = B.numCols;
// get the pivots and transpose them
int[] pivots = decomposition.getColPivots();
float[][] qr = decomposition.getQR();
float[] gammas = decomposition.getGammas();
// solve each column one by one
for (int colB = 0; colB < BnumCols; colB++)
{
x_basic.reshape(numRows, 1);
Y.reshape(numRows, 1);
// make a copy of this column in the vector
for (int i = 0; i < numRows; i++)
{
x_basic.data[i] = B.get(i, colB);
}
// Solve Q*x=b => x = Q'*b
// Q_n*b = (I-gamma*u*u^T)*b = b - u*(gamma*U^T*b)
for (int i = 0; i < rank; i++)
{
float[] u = qr[i];
float vv = u[i];
u[i] = 1;
QrHelperFunctions_FDRM.rank1UpdateMultR(x_basic, u, gammas[i], 0, i, numRows, Y.data);
u[i] = vv;
}
// solve for Rx = b using the standard upper triangular solver
TriangularSolver_FDRM.solveU(R11.data, x_basic.data, rank);
// finish the basic solution by filling in zeros
x_basic.reshape(numCols, 1, true);
for (int i = rank; i < numCols; i++)
{
x_basic.data[i] = 0;
}
if (norm2Solution && rank < numCols)
{
upgradeSolution(x_basic);
}
// save the results
for (int i = 0; i < numCols; i++)
{
X.set(pivots[i], colB, x_basic.data[i]);
}
}
}
public bool isZero(int x1, int x2)
{
// this provides a relative threshold for when dealing with very large/small numbers
float target = Math.Abs(A.get(x1, x2));
float above = Math.Abs(A.get(x1 - 1, x2));
// according to Matrix Computations page 352 this is what is done in Eispack
float right = Math.Abs(A.get(x1, x2 + 1));
return(target <= 0.5f * UtilEjml.F_EPS * (above + right));
}
