public virtual DMatrixRMaj getU(DMatrixRMaj U, bool transpose, bool compact)
{
U = BidiagonalDecompositionRow_DDRM.handleU(U, false, compact, m, n, min);
if (compact)
{
// U = Q*U1
DMatrixRMaj Q1 = decompQRP.getQ(null, true);
DMatrixRMaj U1 = decompBi.getU(null, false, true);
CommonOps_DDRM.mult(Q1, U1, U);
}
else
{
// U = [Q1*U1 Q2]
DMatrixRMaj Q = decompQRP.getQ(U, false);
DMatrixRMaj U1 = decompBi.getU(null, false, true);
DMatrixRMaj Q1 = CommonOps_DDRM.extract(Q, 0, Q.numRows, 0, min);
DMatrixRMaj tmp = new DMatrixRMaj(Q1.numRows, U1.numCols);
CommonOps_DDRM.mult(Q1, U1, tmp);
CommonOps_DDRM.insert(tmp, Q, 0, 0);
}
if (transpose)
{
CommonOps_DDRM.transpose(U);
}
return(U);
}
/**
* Returns the Q matrix.
*/
public DMatrixRMaj getQ()
{
DMatrixRMaj Q = CommonOps_DDRM.identity(QR.numRows);
DMatrixRMaj Q_k = new DMatrixRMaj(QR.numRows, QR.numRows);
DMatrixRMaj u = new DMatrixRMaj(QR.numRows, 1);
DMatrixRMaj temp = new DMatrixRMaj(QR.numRows, QR.numRows);
int N = Math.Min(QR.numCols, QR.numRows);
// compute Q by first extracting the householder vectors from the columns of QR and then applying it to Q
for (int j = N - 1; j >= 0; j--)
{
CommonOps_DDRM.extract(QR, j, QR.numRows, j, j + 1, u, j, 0);
u.set(j, 1.0);
// A = (I - γ*u*u<sup>T</sup>)*A<br>
CommonOps_DDRM.setIdentity(Q_k);
CommonOps_DDRM.multAddTransB(-gammas[j], u, u, Q_k);
CommonOps_DDRM.mult(Q_k, Q, temp);
Q.set(temp);
}
return(Q);
}
/**
* <p>
* Computes a metric which measures the the quality of an eigen value decomposition. If a
* value is returned that is close to or smaller than 1e-15 then it is within machine precision.
* </p>
* <p>
* EVD quality is defined as:<br>
* <br>
* Quality = ||A*V - V*D|| / ||A*V||.
* </p>
*
* @param orig The original matrix. Not modified.
* @param eig EVD of the original matrix. Not modified.
* @return The quality of the decomposition.
*/
public static double quality(DMatrixRMaj orig, EigenDecomposition_F64 <DMatrixRMaj> eig)
{
DMatrixRMaj A = orig;
DMatrixRMaj V = EigenOps_DDRM.createMatrixV(eig);
DMatrixRMaj D = EigenOps_DDRM.createMatrixD(eig);
// L = A*V
DMatrixRMaj L = new DMatrixRMaj(A.numRows, V.numCols);
CommonOps_DDRM.mult(A, V, L);
// R = V*D
DMatrixRMaj R = new DMatrixRMaj(V.numRows, D.numCols);
CommonOps_DDRM.mult(V, D, R);
DMatrixRMaj diff = new DMatrixRMaj(L.numRows, L.numCols);
CommonOps_DDRM.subtract(L, R, diff);
double top = NormOps_DDRM.normF(diff);
double bottom = NormOps_DDRM.normF(L);
double error = top / bottom;
return(error);
}
//@Override
public void update(DMatrixRMaj z, DMatrixRMaj R)
{
// y = z - H x
CommonOps_DDRM.mult(H, x, y);
CommonOps_DDRM.subtract(z, y, y);
// S = H P H' + R
CommonOps_DDRM.mult(H, P, c);
CommonOps_DDRM.multTransB(c, H, S);
CommonOps_DDRM.addEquals(S, R);
// K = PH'S^(-1)
if (!solver.setA(S))
{
throw new InvalidOperationException("Invert failed");
}
solver.invert(S_inv);
CommonOps_DDRM.multTransA(H, S_inv, d);
CommonOps_DDRM.mult(P, d, K);
// x = x + Ky
CommonOps_DDRM.mult(K, y, a);
CommonOps_DDRM.addEquals(x, a);
// P = (I-kH)P = P - (KH)P = P-K(HP)
CommonOps_DDRM.mult(H, P, c);
CommonOps_DDRM.mult(K, c, b);
CommonOps_DDRM.subtractEquals(P, b);
}
public virtual bool setA(DMatrixRMaj A)
{
pinv.reshape(A.numCols, A.numRows, false);
if (!svd.decompose(A))
{
return(false);
}
svd.getU(U_t, true);
svd.getV(V, false);
double[] S = svd.getSingularValues();
int N = Math.Min(A.numRows, A.numCols);
// compute the threshold for singular values which are to be zeroed
double maxSingular = 0;
for (int i = 0; i < N; i++)
{
if (S[i] > maxSingular)
{
maxSingular = S[i];
}
}
double tau = threshold * Math.Max(A.numCols, A.numRows) * maxSingular;
// computer the pseudo inverse of A
if (maxSingular != 0.0)
{
for (int i = 0; i < N; i++)
{
double s = S[i];
if (s < tau)
{
S[i] = 0;
}
else
{
S[i] = 1.0 / S[i];
}
}
}
// V*W
for (int i = 0; i < V.numRows; i++)
{
int index = i * V.numCols;
for (int j = 0; j < V.numCols; j++)
{
V.data[index++] *= S[j];
}
}
// V*W*U^T
CommonOps_DDRM.mult(V, U_t, pinv);
return(true);
}
//@Override
public void predict()
{
// x = F x
CommonOps_DDRM.mult(F, x, a);
x.set(a);
// P = F P F' + Q
CommonOps_DDRM.mult(F, P, b);
CommonOps_DDRM.multTransB(b, F, P);
CommonOps_DDRM.addEquals(P, Q);
}
/**
* Compute the A matrix from the Q and R matrices.
*
* @return The A matrix.
*/
public override DMatrixRMaj getA()
{
if (A.data.Length < numRows * numCols)
{
A = new DMatrixRMaj(numRows, numCols);
}
A.reshape(numRows, numCols, false);
CommonOps_DDRM.mult(Q, R, A);
return(A);
}
/**
* Creates a newJava.Util.Random symmetric matrix that will have the specified real eigenvalues.
*
* @param num Dimension of the resulting matrix.
* @param randJava.Util.Random number generator.
* @param eigenvalues Set of real eigenvalues that the matrix will have.
* @return AJava.Util.Random matrix with the specified eigenvalues.
*/
public static DMatrixRMaj symmetricWithEigenvalues(int num, Java.Util.Random rand, double[] eigenvalues)
{
DMatrixRMaj V = RandomMatrices_DDRM.orthogonal(num, num, rand);
DMatrixRMaj D = CommonOps_DDRM.diag(eigenvalues);
DMatrixRMaj temp = new DMatrixRMaj(num, num);
CommonOps_DDRM.mult(V, D, temp);
CommonOps_DDRM.multTransB(temp, V, D);
return(D);
}
/**
* Computes the dot product of each basis vector against the sample. Can be used as a measure
* for membership in the training sample set. High values correspond to a better fit.
*
* @param sample Sample of original data.
* @return Higher value indicates it is more likely to be a member of input dataset.
*/
public double response(double[] sample)
{
if (sample.Length != A.numCols)
{
throw new ArgumentException("Expected input vector to be in sample space");
}
DMatrixRMaj dots = new DMatrixRMaj(numComponents, 1);
DMatrixRMaj s = DMatrixRMaj.wrap(A.numCols, 1, sample);
CommonOps_DDRM.mult(V_t, s, dots);
return(NormOps_DDRM.normF(dots));
}
public static double quality(DMatrixRMaj orig, DMatrixRMaj U, DMatrixRMaj W, DMatrixRMaj Vt)
{
// foundA = U*W*Vt
DMatrixRMaj UW = new DMatrixRMaj(U.numRows, W.numCols);
CommonOps_DDRM.mult(U, W, UW);
DMatrixRMaj foundA = new DMatrixRMaj(UW.numRows, Vt.numCols);
CommonOps_DDRM.mult(UW, Vt, foundA);
double normA = NormOps_DDRM.normF(foundA);
return(SpecializedOps_DDRM.diffNormF(orig, foundA) / normA);
}
private void checkMatrix(int numRows, int numCols)
{
DMatrixRMaj A = RandomMatrices_DDRM.rectangle(numRows, numCols, -1, 1, rand);
QRExampleOperations alg = new QRExampleOperations();
alg.decompose(A);
DMatrixRMaj Q = alg.getQ();
DMatrixRMaj R = alg.getR();
DMatrixRMaj A_found = new DMatrixRMaj(numRows, numCols);
CommonOps_DDRM.mult(Q, R, A_found);
Assert.IsTrue(MatrixFeatures_DDRM.isIdentical(A, A_found, UtilEjml.TEST_F64));
}
/**
* Converts a vector from sample space into eigen space.
*
* @param sampleData Sample space data.
* @return Eigen space projection.
*/
public double[] sampleToEigenSpace(double[] sampleData)
{
if (sampleData.Length != A.getNumCols())
{
throw new ArgumentException("Unexpected sample length");
}
DMatrixRMaj mean = DMatrixRMaj.wrap(A.getNumCols(), 1, this.mean);
DMatrixRMaj s = new DMatrixRMaj(A.getNumCols(), 1, true, sampleData);
DMatrixRMaj r = new DMatrixRMaj(numComponents, 1);
CommonOps_DDRM.subtract(s, mean, s);
CommonOps_DDRM.mult(V_t, s, r);
return(r.data);
}
/**
* This method computes the eigen vector with the largest eigen value by using the
* direct power method. This technique is the easiest to implement, but the slowest to converge.
* Works only if all the eigenvalues are real.
*
* @param A The matrix. Not modified.
* @return If it converged or not.
*/
public bool computeDirect(DMatrixRMaj A)
{
initPower(A);
bool converged = false;
for (int i = 0; i < maxIterations && !converged; i++)
{
// q0.print();
CommonOps_DDRM.mult(A, q0, q1);
double s = NormOps_DDRM.normPInf(q1);
CommonOps_DDRM.divide(q1, s, q2);
converged = checkConverged(A);
}
return(converged);
}
public bool extractVectors(DMatrixRMaj Q_h)
{
Array.Clear(eigenvectorTemp.data, 0, eigenvectorTemp.data.Length);
// extract eigenvectors from the shur matrix
// start at the top left corner of the matrix
bool triangular = true;
for (int i = 0; i < N; i++)
{
Complex_F64 c = _implicit.eigenvalues[N - i - 1];
if (triangular && !c.isReal())
{
triangular = false;
}
if (c.isReal() && eigenvectors[N - i - 1] == null)
{
solveEigenvectorDuplicateEigenvalue(c.real, i, triangular);
}
}
// translate the eigenvectors into the frame of the original matrix
if (Q_h != null)
{
DMatrixRMaj temp = new DMatrixRMaj(N, 1);
for (int i = 0; i < N; i++)
{
DMatrixRMaj v = eigenvectors[i];
if (v != null)
{
CommonOps_DDRM.mult(Q_h, v, temp);
eigenvectors[i] = temp;
temp = v;
}
}
}
return(true);
}
private List <DMatrixRMaj> createSimulatedMeas(DMatrixRMaj x)
{
List <DMatrixRMaj> ret = new List <DMatrixRMaj>();
DMatrixRMaj F = createF(T);
DMatrixRMaj H = createH();
// UtilEjml.print(F);
// UtilEjml.print(H);
DMatrixRMaj x_next = new DMatrixRMaj(x);
DMatrixRMaj z = new DMatrixRMaj(H.numRows, 1);
for (int i = 0; i < MAX_STEPS; i++)
{
CommonOps_DDRM.mult(F, x, x_next);
CommonOps_DDRM.mult(H, x_next, z);
ret.Add((DMatrixRMaj)z.copy());
x.set(x_next);
}
return(ret);
}
/**
* <p>
* Creates a random matrix which will have the provided singular values. The length of sv
* is assumed to be the rank of the matrix. This can be useful for testing purposes when one
* needs to ensure that a matrix is not singular but randomly generated.
* </p>
*
* @param numRows Number of rows in generated matrix.
* @param numCols NUmber of columns in generated matrix.
* @param rand Random number generator.
* @param sv Singular values of the matrix.
* @return A new matrix with the specified singular values.
*/
public static DMatrixRMaj singleValues(int numRows, int numCols,
IMersenneTwister rand, double[] sv)
{
DMatrixRMaj U = RandomMatrices_DDRM.orthogonal(numRows, numRows, rand);
DMatrixRMaj V = RandomMatrices_DDRM.orthogonal(numCols, numCols, rand);
DMatrixRMaj S = new DMatrixRMaj(numRows, numCols);
int min = Math.Min(numRows, numCols);
min = Math.Min(min, sv.Length);
for (int i = 0; i < min; i++)
{
S.set(i, i, sv[i]);
}
DMatrixRMaj tmp = new DMatrixRMaj(numRows, numCols);
CommonOps_DDRM.mult(U, S, tmp);
CommonOps_DDRM.multTransB(tmp, V, S);
return(S);
}
/**
* <p>
* Creates aJava.Util.Random matrix which will have the provided singular values. The Count() of sv
* is assumed to be the rank of the matrix. This can be useful for testing purposes when one
* needs to ensure that a matrix is not singular but randomly generated.
* </p>
*
* @param numRows Number of rows in generated matrix.
* @param numCols NUmber of columns in generated matrix.
* @param randJava.Util.Random number generator.
* @param sv Singular values of the matrix.
* @return A new matrix with the specified singular values.
*/
public static DMatrixRMaj singular(int numRows, int numCols,
Java.Util.Random rand, double[] sv)
{
DMatrixRMaj U, V, S;
// speed it up in compact format
if (numRows > numCols)
{
U = RandomMatrices_DDRM.orthogonal(numRows, numCols, rand);
V = RandomMatrices_DDRM.orthogonal(numCols, numCols, rand);
S = new DMatrixRMaj(numCols, numCols);
}
else
{
U = RandomMatrices_DDRM.orthogonal(numRows, numRows, rand);
V = RandomMatrices_DDRM.orthogonal(numCols, numCols, rand);
S = new DMatrixRMaj(numRows, numCols);
}
int min = Math.Min(numRows, numCols);
min = Math.Min(min, sv.Count());
for (int i = 0; i < min; i++)
{
S.set(i, i, sv[i]);
}
DMatrixRMaj tmp = new DMatrixRMaj(numRows, numCols);
CommonOps_DDRM.mult(U, S, tmp);
S.reshape(numRows, numCols);
CommonOps_DDRM.multTransB(tmp, V, S);
return(S);
}
public static void main(String[] args)
{
IMersenneTwister rand = new MersenneTwisterFast(234);
// easy to work with sparse format, but hard to do computations with
DMatrixSparseTriplet work = new DMatrixSparseTriplet(5, 4, 5);
work.addItem(0, 1, 1.2);
work.addItem(3, 0, 3);
work.addItem(1, 1, 22.21234);
work.addItem(2, 3, 6);
// convert into a format that's easier to perform math with
DMatrixSparseCSC Z = ConvertDMatrixStruct.convert(work, (DMatrixSparseCSC)null);
// print the matrix to standard out in two different formats
Z.print();
Console.WriteLine();
Z.printNonZero();
Console.WriteLine();
// Create a large matrix that is 5% filled
DMatrixSparseCSC A = RandomMatrices_DSCC.rectangle(ROWS, COLS, (int)(ROWS * COLS * 0.05), rand);
// large vector that is 70% filled
DMatrixSparseCSC x = RandomMatrices_DSCC.rectangle(COLS, XCOLS, (int)(XCOLS * COLS * 0.7), rand);
Console.WriteLine("Done generating random matrices");
// storage for the initial solution
DMatrixSparseCSC y = new DMatrixSparseCSC(ROWS, XCOLS, 0);
DMatrixSparseCSC z = new DMatrixSparseCSC(ROWS, XCOLS, 0);
// To demonstration how to perform sparse math let's multiply:
// y=A*x
// Optional storage is set to null so that it will declare it internally
long before = DateTimeHelper.CurrentTimeMilliseconds;
IGrowArray workA = new IGrowArray(A.numRows);
DGrowArray workB = new DGrowArray(A.numRows);
for (int i = 0; i < 100; i++)
{
CommonOps_DSCC.mult(A, x, y, workA, workB);
CommonOps_DSCC.add(1.5, y, 0.75, y, z, workA, workB);
}
long after = DateTimeHelper.CurrentTimeMilliseconds;
Console.WriteLine("norm = " + NormOps_DSCC.fastNormF(y) + " sparse time = " + (after - before) + " ms");
DMatrixRMaj Ad = ConvertDMatrixStruct.convert(A, (DMatrixRMaj)null);
DMatrixRMaj xd = ConvertDMatrixStruct.convert(x, (DMatrixRMaj)null);
DMatrixRMaj yd = new DMatrixRMaj(y.numRows, y.numCols);
DMatrixRMaj zd = new DMatrixRMaj(y.numRows, y.numCols);
before = DateTimeHelper.CurrentTimeMilliseconds;
for (int i = 0; i < 100; i++)
{
CommonOps_DDRM.mult(Ad, xd, yd);
CommonOps_DDRM.add(1.5, yd, 0.75, yd, zd);
}
after = DateTimeHelper.CurrentTimeMilliseconds;
Console.WriteLine("norm = " + NormOps_DDRM.fastNormF(yd) + " dense time = " + (after - before) + " ms");
}
/** Revises the CMA-ES distribution to reflect the current fitness results in the provided subpopulation. */
public void UpdateDistribution(IEvolutionState state, Subpopulation subpop)
{
// % Sort by fitness and compute weighted mean into xmean
// [arfitness, arindex] = sort(arfitness); % minimization
// xmean = arx(:,arindex(1:mu))*weights; % recombination % Eq.39
// counteval += lambda;
// only need partial sort?
((List <Individual>)subpop.Individuals).Sort();
SimpleMatrixD artmp = new SimpleMatrixD(GenomeSize, mu);
SimpleMatrixD xold = xmean;
xmean = new SimpleMatrixD(GenomeSize, 1);
for (int i = 0; i < mu; i++)
{
DoubleVectorIndividual dvind = (DoubleVectorIndividual)subpop.Individuals[i];
// won't modify the genome
SimpleMatrixD arz = new SimpleMatrixD(GenomeSize, 1, true, dvind.genome);
arz = (arz.minus(xold).divide(sigma));
for (int j = 0; j < GenomeSize; j++)
{
xmean.set(j, 0, xmean.get(j, 0) + weights[i] * dvind.genome[j]);
artmp.set(j, i, arz.get(j, 0));
}
}
// % Cumulation: Update evolution paths
SimpleMatrixD y = xmean.minus(xold).divide(sigma);
SimpleMatrixD bz = invsqrtC.mult(y);
SimpleMatrixD bz_scaled = bz.scale(Math.Sqrt(cs * (2.0 - cs) * mueff));
ps = ps.scale(1.0 - cs).plus(bz_scaled);
double h_sigma_value =
((ps.dot(ps) / (1.0 - Math.Pow(1.0 - cs, 2.0 * (state.Generation + 1)))) / GenomeSize);
int hsig = (h_sigma_value < (2.0 + (4.0 / (GenomeSize + 1)))) ? 1 : 0;
SimpleMatrixD y_scaled = y.scale(hsig * Math.Sqrt(cc * (2.0 - cc) * mueff));
pc = pc.scale(1.0 - cc).plus(y_scaled);
// % Adapt covariance matrix C
c = c.scale(1.0 - c1 - cmu);
c = c.plus(pc.mult(pc.transpose()).plus(c.scale((1.0 - hsig) * cc * (2.0 - cc))).scale(c1));
c = c.plus((artmp.mult(SimpleMatrixD.diag(weights).mult(artmp.transpose()))).scale(cmu));
// % Adapt step-size sigma
sigma = sigma * Math.Exp((cs / damps) * (ps.normF() / chiN - 1.0));
// % Update B and D from C
if ((state.Generation - lastEigenDecompositionGeneration) > 1.0 / ((c1 + cmu) * GenomeSize * 10.0))
{
lastEigenDecompositionGeneration = state.Generation;
// make sure the matrix is symmetric (it should be already)
// not sure if this is necessary
for (int i = 0; i < GenomeSize; i++)
{
for (int j = 0; j < i; j++)
{
c.set(j, i, c.get(i, j));
}
}
// this copy gets modified by the decomposition
DMatrixRMaj copy = c.copy().getMatrix();
EigenDecomposition <DMatrixRMaj> eig = DecompositionFactory_DDRM.eig(GenomeSize, true, true);
if (eig.decompose(copy))
{
SimpleMatrixD dinv = new SimpleMatrixD(GenomeSize, GenomeSize);
for (int i = 0; i < GenomeSize; i++)
{
double eigrt = Math.Sqrt(eig.getEigenValue(i).real);
d.set(i, i, eigrt);
dinv.set(i, i, 1 / eigrt);
CommonOps_DDRM.insert(eig.getEigenVector(i), b.getMatrix(), 0, i);
}
invsqrtC = b.mult(dinv.mult(b.transpose()));
CommonOps_DDRM.mult(b.getMatrix(), d.getMatrix(), bd);
}
else
{
state.Output.Fatal("CMA-ES eigendecomposition failed. ");
}
}
CommonOps_DDRM.scale(sigma, bd, sbd);
// % Break, if fitness is good enough or condition exceeds 1e14, better termination methods are advisable
// if arfitness(1) <= stopfitness || max(D) > 1e7 * min(D)
// break;
// end
if (useAltTermination && CommonOps_DDRM.elementMax(d.diag().getMatrix()) >
1e7 * CommonOps_DDRM.elementMin(d.diag().getMatrix()))
{
state.Evaluator.SetRunCompleted("CMAESSpecies: Stopped because matrix condition exceeded limit.");
}
}
public virtual void mult(DMatrixRMaj A, DMatrixRMaj B, DMatrixRMaj output)
{
CommonOps_DDRM.mult(A, B, output);
}
public override Individual NewIndividual(IEvolutionState state, int thread)
{
Individual newind = base.NewIndividual(state, thread);
IMersenneTwister random = state.Random[thread];
if (!(newind is DoubleVectorIndividual)) // uh oh
{
state.Output.Fatal(
"To use CMAESSpecies, the species must be initialized with a DoubleVectorIndividual. But it contains a " +
newind);
}
DoubleVectorIndividual dvind = (DoubleVectorIndividual)(newind);
DMatrixRMaj genome = DMatrixRMaj.wrap(GenomeSize, 1, dvind.genome);
DMatrixRMaj temp = new DMatrixRMaj(GenomeSize, 1);
// arz(:,k) = randn(N,1); % standard normally distributed vector
// arx(:,k) = xmean + sigma*(B*D*arz(:,k));
int tries = 0;
while (true)
{
for (int i = 0; i < GenomeSize; i++)
{
dvind.genome[i] = random.NextGaussian();
}
CommonOps_DDRM.mult(sbd, genome, temp); // temp = sigma*b*d*genome;
CommonOps_DDRM.add(temp, xmean.getMatrix(), genome); // genome = temp + xmean;
bool invalid_value = false;
for (int i = 0; i < GenomeSize; i++)
{
if (dvind.genome[i] < MinGenes[i] || dvind.genome[i] > MaxGenes[i])
{
if (useAltGenerator && tries > altGeneratorTries)
{
// instead of just failing, we're going to select uniformly from
// possible values for this particular gene.
dvind.genome[i] = state.Random[thread].NextDouble() * (MaxGenes[i] - MinGenes[i]) +
MinGenes[i];
}
else
{
invalid_value = true;
break;
}
}
}
if (invalid_value)
{
if (++tries > MAX_TRIES_BEFORE_WARNING)
{
state.Output.WarnOnce(
"CMA-ES may be slow because many individuals are being generated which\n" +
"are outside the min/max gene bounds. If an individual violates a single\n" +
"gene bounds, it is rejected, so as the number of genes grows, the\n" +
"probability of this happens increases exponentially. You can deal\n" +
"with this by decreasing sigma. Alternatively you can use set\n" +
"pop.subpop.0.alternative-generation=true (see the manual).\n" +
"Finally, if this is happening during initialization, you might also\n" +
"change pop.subpop.0.species.covariance=scaled.\n");
}
continue;
}
return(newind);
}
}
public override void Setup(IEvolutionState state, IParameter paramBase)
{
base.Setup(state, paramBase);
IMersenneTwister random = state.Random[0];
IParameter def = DefaultBase;
IParameter subpopBase = paramBase.Pop();
IParameter subpopDefaultBase = ECDefaults.ParamBase.Push(Subpopulation.P_SUBPOPULATION);
if (!state.Parameters.ParameterExists(paramBase.Push(P_SIGMA), def.Push(P_SIGMA)))
{
state.Output.Message("CMA-ES sigma was not provided, defaulting to 1.0");
sigma = 1.0;
}
else
{
sigma = state.Parameters.GetDouble(paramBase.Push(P_SIGMA), def.Push(P_SIGMA), 0.0);
if (sigma <= 0)
{
state.Output.Fatal("If CMA-ES sigma is provided, it must be > 0.0", paramBase.Push(P_SIGMA),
def.Push(P_SIGMA));
}
}
double[] cvals = new double[GenomeSize];
string covarianceInitialization =
state.Parameters.GetStringWithDefault(paramBase.Push(P_COVARIANCE), def.Push(P_COVARIANCE), V_IDENTITY);
string covs = "Initial Covariance: <";
for (int i = 0; i < GenomeSize; i++)
{
if (i > 0)
{
covs += ", ";
}
if (covarianceInitialization.Equals(V_SCALED))
{
cvals[i] = (MaxGenes[i] - MinGenes[i]);
}
else if (covarianceInitialization.Equals(V_IDENTITY))
{
cvals[i] = 1.0;
}
else
{
state.Output.Fatal("Invalid covariance initialization type " + covarianceInitialization,
paramBase.Push(P_COVARIANCE), def.Push(P_COVARIANCE));
}
// cvals is standard deviations, so we change them to variances now
cvals[i] *= cvals[i];
covs += cvals[i];
}
state.Output.Message(covs + ">");
// set myself up and define my initial distribution here
int n = GenomeSize;
b = SimpleMatrixD.identity(n);
c = new SimpleMatrixD(CommonOps_DDRM.diag(cvals));
d = SimpleMatrixD.identity(n);
bd = CommonOps_DDRM.identity(n, n);
sbd = CommonOps_DDRM.identity(n, n);
invsqrtC = SimpleMatrixD.identity(n);
// Here we do one FIRST round of eigendecomposition, because newIndividual needs
// a valid version of sbd. If c is initially the identity matrix (and sigma = 1),
// then sbd is too, and we're done. But if c is scaled in any way, we need to compute
// the proper value of sbd. Along the way we'll wind up computing b, d, bd, and invsqrtC
EigenDecomposition <DMatrixRMaj> eig = DecompositionFactory_DDRM.eig(GenomeSize, true, true);
if (eig.decompose(c.copy().getMatrix()))
{
SimpleMatrixD dinv = new SimpleMatrixD(GenomeSize, GenomeSize);
for (int i = 0; i < GenomeSize; i++)
{
double eigrt = Math.Sqrt(eig.getEigenValue(i).real);
d.set(i, i, eigrt);
dinv.set(i, i, 1 / eigrt);
CommonOps_DDRM.insert(eig.getEigenVector(i), b.getMatrix(), 0, i);
}
invsqrtC = b.mult(dinv.mult(b.transpose()));
CommonOps_DDRM.mult(b.getMatrix(), d.getMatrix(), bd);
}
else
{
state.Output.Fatal("CMA-ES eigendecomposition failed. ");
}
CommonOps_DDRM.scale(sigma, bd, sbd);
// End FIRST round of eigendecomposition
// Initialize dynamic (internal) strategy parameters and constants
pc = new SimpleMatrixD(n, 1);
ps = new SimpleMatrixD(n, 1); // evolution paths for C and sigma
chiN = Math.Sqrt(n) *
(1.0 - 1.0 / (4.0 * n) + 1.0 / (21.0 * n * n)); // expectation of ||N(0,I)|| == norm(randn(N,1))
xmean = new SimpleMatrixD(GenomeSize, 1);
bool meanSpecified = false;
string val = state.Parameters.GetString(paramBase.Push(P_MEAN), def.Push(P_MEAN));
if (val != null)
{
meanSpecified = true;
if (val.Equals(V_CENTER))
{
for (int i = 0; i < GenomeSize; i++)
{
xmean.set(i, 0, (MaxGenes[i] + MinGenes[i]) / 2.0);
}
}
else if (val.Equals(V_ZERO))
{
for (int i = 0; i < GenomeSize; i++)
{
xmean.set(i, 0, 0); // it is this anyway
}
}
else if (val.Equals(V_RANDOM))
{
for (int i = 0; i < GenomeSize; i++)
{
xmean.set(i, 0,
state.Random[0].NextDouble(true, true) * (MaxGenes[i] - MinGenes[i]) + MinGenes[i]);
}
}
else
{
state.Output.Fatal("Unknown mean value specified: " + val, paramBase.Push(P_MEAN), def.Push(P_MEAN));
}
}
else
{
state.Output.Fatal("No default mean value specified. Loading full mean from parameters.",
paramBase.Push(P_MEAN), def.Push(P_MEAN));
}
bool nonDefaultMeanSpecified = false;
for (int i = 0; i < GenomeSize; i++)
{
double m_i = 0;
try
{
m_i = state.Parameters.GetDouble(paramBase.Push(P_MEAN).Push("" + i), def.Push(P_MEAN).Push("" + i));
xmean.set(i, 0, m_i);
nonDefaultMeanSpecified = true;
}
catch (FormatException e)
{
if (!meanSpecified)
{
state.Output.Error(
"No default mean value was specified, but CMA-ES mean index " + i +
" is missing or not a number.", paramBase.Push(P_MEAN).Push("" + i),
def.Push(P_MEAN).Push("" + i));
}
}
}
state.Output.ExitIfErrors();
if (nonDefaultMeanSpecified && meanSpecified)
{
state.Output.Warning("A default mean value was specified, but certain mean values were overridden.");
}
string mes = "Initial Mean: <";
for (int i = 0; i < GenomeSize - 1; i++)
{
mes = mes + xmean.get(i, 0) + ", ";
}
mes = mes + xmean.get(GenomeSize - 1, 0) + ">";
state.Output.Message(mes);
if (!state.Parameters.ParameterExists(paramBase.Push(P_LAMBDA), def.Push(P_LAMBDA)))
{
lambda = 4 + (int)Math.Floor(3 * Math.Log(n));
}
else
{
lambda = state.Parameters.GetInt(paramBase.Push(P_LAMBDA), def.Push(P_LAMBDA), 1);
if (lambda <= 0)
{
state.Output.Fatal("If the CMA-ES lambda parameter is provided, it must be a valid integer > 0",
paramBase.Push(P_LAMBDA), def.Push(P_LAMBDA));
}
}
if (!state.Parameters.ParameterExists(paramBase.Push(P_MU), def.Push(P_MU)))
{
mu = (int)(Math.Floor(lambda / 2.0));
}
else
{
mu = state.Parameters.GetInt(paramBase.Push(P_MU), def.Push(P_MU), 1);
if (mu <= 0)
{
state.Output.Fatal("If the CMA-ES mu parameter is provided, it must be a valid integer > 0",
paramBase.Push(P_MU), def.Push(P_MU));
}
}
if (mu > lambda) // uh oh
{
state.Output.Fatal("CMA-ES mu must be <= lambda. Presently mu=" + mu + " and lambda=" + lambda);
}
weights = new double[mu];
bool weightsSpecified = false;
for (int i = 0; i < mu; i++)
{
if (state.Parameters.ParameterExists(paramBase.Push(P_WEIGHTS).Push("" + i), def.Push(P_WEIGHTS).Push("" + i)))
{
state.Output.Message("CMA-ES weight index " + i +
" specified. Loading all weights from parameters.");
weightsSpecified = true;
break;
}
}
if (weightsSpecified)
{
for (int i = 0; i < mu; i++)
{
double m_i = 0;
try
{
weights[i] = state.Parameters.GetDouble(paramBase.Push(P_WEIGHTS).Push("" + i),
def.Push(P_WEIGHTS).Push("" + i));
}
catch (FormatException e)
{
state.Output.Error("CMA-ES weight index " + i + " missing or not a number.",
paramBase.Push(P_WEIGHTS).Push("" + i), def.Push(P_WEIGHTS).Push("" + i));
}
}
state.Output.ExitIfErrors();
}
else
{
for (int i = 0; i < mu; i++)
{
weights[i] = Math.Log((lambda + 1.0) / (2.0 * (i + 1)));
}
}
// normalize
double sum = 0.0;
for (int i = 0; i < mu; i++)
{
sum += weights[i];
}
for (int i = 0; i < mu; i++)
{
weights[i] /= sum;
}
// compute mueff
double sumSqr = 0.0;
for (int i = 0; i < mu; i++)
{
sumSqr += weights[i] * weights[i];
}
mueff = 1.0 / sumSqr;
mes = "Weights: <";
for (int i = 0; i < weights.Length - 1; i++)
{
mes = mes + weights[i] + ", ";
}
mes = mes + (weights.Length - 1) + ">";
state.Output.Message(mes);
useAltTermination = state.Parameters.GetBoolean(paramBase.Push(P_ALTERNATIVE_TERMINATION),
def.Push(P_ALTERNATIVE_TERMINATION), false);
useAltGenerator = state.Parameters.GetBoolean(paramBase.Push(P_ALTERNATIVE_GENERATOR),
def.Push(P_ALTERNATIVE_GENERATOR), false);
altGeneratorTries = state.Parameters.GetIntWithDefault(paramBase.Push(P_ALTERNATIVE_GENERATOR_TRIES),
def.Push(P_ALTERNATIVE_GENERATOR_TRIES), DEFAULT_ALT_GENERATOR_TRIES);
if (altGeneratorTries < 1)
{
state.Output.Fatal(
"If specified (the default is " + DEFAULT_ALT_GENERATOR_TRIES +
"), alt-generation-tries must be >= 1",
paramBase.Push(P_ALTERNATIVE_GENERATOR_TRIES), def.Push(P_ALTERNATIVE_GENERATOR_TRIES));
}
if (!state.Parameters.ParameterExists(paramBase.Push(P_CC), def.Push(P_CC)))
{
cc = (4.0 + mueff / n) / (n + 4.0 + 2.0 * mueff / n); // time constant for cumulation for C
}
else
{
cc = state.Parameters.GetDoubleWithMax(paramBase.Push(P_CC), def.Push(P_CC), 0.0, 1.0);
if (cc < 0.0)
{
state.Output.Fatal(
"If the CMA-ES cc parameter is provided, it must be a valid number in the range [0,1]",
paramBase.Push(P_CC), def.Push(P_CC));
}
}
if (!state.Parameters.ParameterExists(paramBase.Push(P_CS), def.Push(P_CS)))
{
cs = (mueff + 2.0) / (n + mueff + 5.0); // t-const for cumulation for sigma control
}
else
{
cs = state.Parameters.GetDoubleWithMax(paramBase.Push(P_CS), def.Push(P_CS), 0.0, 1.0);
if (cs < 0.0)
{
state.Output.Fatal(
"If the CMA-ES cs parameter is provided, it must be a valid number in the range [0,1]",
paramBase.Push(P_CS), def.Push(P_CS));
}
}
if (!state.Parameters.ParameterExists(paramBase.Push(P_C1), def.Push(P_C1)))
{
c1 = 2.0 / ((n + 1.3) * (n + 1.3) + mueff); // learning rate for rank-one update of C
}
else
{
c1 = state.Parameters.GetDouble(paramBase.Push(P_C1), def.Push(P_C1), 0.0);
if (c1 < 0)
{
state.Output.Fatal("If the CMA-ES c1 parameter is provided, it must be a valid number >= 0.0",
paramBase.Push(P_C1), def.Push(P_C1));
}
}
if (!state.Parameters.ParameterExists(paramBase.Push(P_CMU), def.Push(P_CMU)))
{
cmu = Math.Min(1.0 - c1, 2.0 * (mueff - 2.0 + 1.0 / mueff) / ((n + 2.0) * (n + 2.0) + mueff));
}
else
{
cmu = state.Parameters.GetDouble(paramBase.Push(P_CMU), def.Push(P_CMU), 0.0);
if (cmu < 0)
{
state.Output.Fatal("If the CMA-ES cmu parameter is provided, it must be a valid number >= 0.0",
paramBase.Push(P_CMU), def.Push(P_CMU));
}
}
if (c1 > (1 - cmu)) // uh oh
{
state.Output.Fatal("CMA-ES c1 must be <= 1 - cmu. You are using c1=" + c1 + " and cmu=" + cmu);
}
if (cmu > (1 - c1)) // uh oh
{
state.Output.Fatal("CMA-ES cmu must be <= 1 - c1. You are using cmu=" + cmu + " and c1=" + c1);
}
if (!state.Parameters.ParameterExists(paramBase.Push(P_DAMPS), def.Push(P_DAMPS)))
{
damps = 1.0 + 2.0 * Math.Max(0.0, Math.Sqrt((mueff - 1.0) / (n + 1.0)) - 1.0) + cs; // damping for sigma
}
else
{
damps = state.Parameters.GetDouble(paramBase.Push(P_DAMPS), def.Push(P_DAMPS), 0.0);
if (damps <= 0)
{
state.Output.Fatal("If the CMA-ES damps parameter is provided, it must be a valid number > 0.0",
paramBase.Push(P_DAMPS), def.Push(P_DAMPS));
}
}
double damps_min = 0.5;
double damps_max = 2.0;
if (damps > damps_max || damps < damps_min)
{
state.Output.Warning("CMA-ES damps ought to be close to 1. You are using damps = " + damps);
}
state.Output.Message("lambda: " + lambda);
state.Output.Message("mu: " + mu);
state.Output.Message("mueff: " + mueff);
state.Output.Message("cmu: " + cmu);
state.Output.Message("c1: " + c1);
state.Output.Message("cc: " + cc);
state.Output.Message("cs: " + cs);
state.Output.Message("damps: " + damps);
}
/**
* <p>
* Given an eigenvalue it computes an eigenvector using inverse iteration:
* <br>
* for i=1:MAX {<br>
* (A - μI)z<sup>(i)</sup> = q<sup>(i-1)</sup><br>
* q<sup>(i)</sup> = z<sup>(i)</sup> / ||z<sup>(i)</sup>||<br>
* λ<sup>(i)</sup> = q<sup>(i)</sup><sup>T</sup> A q<sup>(i)</sup><br>
* }<br>
* </p>
* <p>
* NOTE: If there is another eigenvalue that is very similar to the provided one then there
* is a chance of it converging towards that one instead. The larger a matrix is the more
* likely this is to happen.
* </p>
* @param A Matrix whose eigenvector is being computed. Not modified.
* @param eigenvalue The eigenvalue in the eigen pair.
* @return The eigenvector or null if none could be found.
*/
public static DEigenpair computeEigenVector(DMatrixRMaj A, double eigenvalue)
{
if (A.numRows != A.numCols)
{
throw new ArgumentException("Must be a square matrix.");
}
DMatrixRMaj M = new DMatrixRMaj(A.numRows, A.numCols);
DMatrixRMaj x = new DMatrixRMaj(A.numRows, 1);
DMatrixRMaj b = new DMatrixRMaj(A.numRows, 1);
CommonOps_DDRM.fill(b, 1);
// perturb the eigenvalue slightly so that its not an exact solution the first time
// eigenvalue -= eigenvalue*UtilEjml.EPS*10;
double origEigenvalue = eigenvalue;
SpecializedOps_DDRM.addIdentity(A, M, -eigenvalue);
double threshold = NormOps_DDRM.normPInf(A) * UtilEjml.EPS;
double prevError = double.MaxValue;
bool hasWorked = false;
LinearSolverDense <DMatrixRMaj> solver = LinearSolverFactory_DDRM.linear(M.numRows);
double perp = 0.0001;
for (int i = 0; i < 200; i++)
{
bool failed = false;
// if the matrix is singular then the eigenvalue is within machine precision
// of the true value, meaning that x must also be.
if (!solver.setA(M))
{
failed = true;
}
else
{
solver.solve(b, x);
}
// see if solve silently failed
if (MatrixFeatures_DDRM.hasUncountable(x))
{
failed = true;
}
if (failed)
{
if (!hasWorked)
{
// if it failed on the first trial try perturbing it some more
double val = i % 2 == 0 ? 1.0 - perp : 1.0 + perp;
// maybe this should be turn into a parameter allowing the user
// to configure the wise of each step
eigenvalue = origEigenvalue * Math.Pow(val, i / 2 + 1);
SpecializedOps_DDRM.addIdentity(A, M, -eigenvalue);
}
else
{
// otherwise assume that it was so accurate that the matrix was singular
// and return that result
return(new DEigenpair(eigenvalue, b));
}
}
else
{
hasWorked = true;
b.set(x);
NormOps_DDRM.normalizeF(b);
// compute the residual
CommonOps_DDRM.mult(M, b, x);
double error = NormOps_DDRM.normPInf(x);
if (error - prevError > UtilEjml.EPS * 10)
{
// if the error increased it is probably converging towards a different
// eigenvalue
// CommonOps.set(b,1);
prevError = double.MaxValue;
hasWorked = false;
double val = i % 2 == 0 ? 1.0 - perp : 1.0 + perp;
eigenvalue = origEigenvalue * Math.Pow(val, 1);
}
else
{
// see if it has converged
if (error <= threshold || Math.Abs(prevError - error) <= UtilEjml.EPS)
{
return(new DEigenpair(eigenvalue, b));
}
// update everything
prevError = error;
eigenvalue = VectorVectorMult_DDRM.innerProdA(b, A, b);
}
SpecializedOps_DDRM.addIdentity(A, M, -eigenvalue);
}
}
return(null);
}
public void mult(Matrix A, Matrix B, Matrix output)
{
CommonOps_DDRM.mult((DMatrixRMaj)A, (DMatrixRMaj)B, (DMatrixRMaj)output);
}
/**
* Computes the QR decomposition of the provided matrix.
*
* @param A Matrix which is to be decomposed. Not modified.
*/
public void decompose(DMatrixRMaj A)
{
this.QR = (DMatrixRMaj)A.copy();
int N = Math.Min(A.numCols, A.numRows);
gammas = new double[A.numCols];
DMatrixRMaj A_small = new DMatrixRMaj(A.numRows, A.numCols);
DMatrixRMaj A_mod = new DMatrixRMaj(A.numRows, A.numCols);
DMatrixRMaj v = new DMatrixRMaj(A.numRows, 1);
DMatrixRMaj Q_k = new DMatrixRMaj(A.numRows, A.numRows);
for (int i = 0; i < N; i++)
{
// reshape temporary variables
A_small.reshape(QR.numRows - i, QR.numCols - i, false);
A_mod.reshape(A_small.numRows, A_small.numCols, false);
v.reshape(A_small.numRows, 1, false);
Q_k.reshape(v.getNumElements(), v.getNumElements(), false);
// use extract matrix to get the column that is to be zeroed
CommonOps_DDRM.extract(QR, i, QR.numRows, i, i + 1, v, 0, 0);
double max = CommonOps_DDRM.elementMaxAbs(v);
if (max > 0 && v.getNumElements() > 1)
{
// normalize to reduce overflow issues
CommonOps_DDRM.divide(v, max);
// compute the magnitude of the vector
double tau = NormOps_DDRM.normF(v);
if (v.get(0) < 0)
{
tau *= -1.0;
}
double u_0 = v.get(0) + tau;
double gamma = u_0 / tau;
CommonOps_DDRM.divide(v, u_0);
v.set(0, 1.0);
// extract the submatrix of A which is being operated on
CommonOps_DDRM.extract(QR, i, QR.numRows, i, QR.numCols, A_small, 0, 0);
// A = (I - γ*u*u<sup>T</sup>)A
CommonOps_DDRM.setIdentity(Q_k);
CommonOps_DDRM.multAddTransB(-gamma, v, v, Q_k);
CommonOps_DDRM.mult(Q_k, A_small, A_mod);
// save the results
CommonOps_DDRM.insert(A_mod, QR, i, i);
CommonOps_DDRM.insert(v, QR, i, i);
QR.unsafe_set(i, i, -tau * max);
// save gamma for recomputing Q later on
gammas[i] = gamma;
}
}
}
