Java Code Examples for org.apache.commons.math3.exception.util.LocalizedFormats#UNABLE_TO_SOLVE_SINGULAR_PROBLEM
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Example 1
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Override
protected RealVector solve(final RealMatrix jacobian,
final RealVector residuals) {
try {
final Pair<RealMatrix, RealVector> normalEquation =
computeNormalMatrix(jacobian, residuals);
final RealMatrix normal = normalEquation.getFirst();
final RealVector jTr = normalEquation.getSecond();
return new CholeskyDecomposition(
normal, SINGULARITY_THRESHOLD, SINGULARITY_THRESHOLD)
.getSolver()
.solve(jTr);
} catch (NonPositiveDefiniteMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
}
}
Example 2
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 5 votes |
|
@Override
protected RealVector solve(final RealMatrix jacobian,
final RealVector residuals) {
try {
final Pair<RealMatrix, RealVector> normalEquation =
computeNormalMatrix(jacobian, residuals);
final RealMatrix normal = normalEquation.getFirst();
final RealVector jTr = normalEquation.getSecond();
return new CholeskyDecomposition(
normal, SINGULARITY_THRESHOLD, SINGULARITY_THRESHOLD)
.getSolver()
.solve(jTr);
} catch (NonPositiveDefiniteMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
}
}
Example 3
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
final ConvergenceChecker<PointVectorValuePair> checker
= getConvergenceChecker();
// Computation will be useless without a checker (see "for-loop").
if (checker == null) {
throw new NullArgumentException();
}
final double[] targetValues = getTarget();
final int nR = targetValues.length; // Number of observed data.
final RealMatrix weightMatrix = getWeight();
// Diagonal of the weight matrix.
final double[] residualsWeights = new double[nR];
for (int i = 0; i < nR; i++) {
residualsWeights[i] = weightMatrix.getEntry(i, i);
}
final double[] currentPoint = getStartPoint();
final int nC = currentPoint.length;
// iterate until convergence is reached
PointVectorValuePair current = null;
int iter = 0;
for (boolean converged = false; !converged;) {
++iter;
// evaluate the objective function and its jacobian
PointVectorValuePair previous = current;
// Value of the objective function at "currentPoint".
final double[] currentObjective = computeObjectiveValue(currentPoint);
final double[] currentResiduals = computeResiduals(currentObjective);
final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
current = new PointVectorValuePair(currentPoint, currentObjective);
// build the linear problem
final double[] b = new double[nC];
final double[][] a = new double[nC][nC];
for (int i = 0; i < nR; ++i) {
final double[] grad = weightedJacobian.getRow(i);
final double weight = residualsWeights[i];
final double residual = currentResiduals[i];
// compute the normal equation
final double wr = weight * residual;
for (int j = 0; j < nC; ++j) {
b[j] += wr * grad[j];
}
// build the contribution matrix for measurement i
for (int k = 0; k < nC; ++k) {
double[] ak = a[k];
double wgk = weight * grad[k];
for (int l = 0; l < nC; ++l) {
ak[l] += wgk * grad[l];
}
}
}
try {
// solve the linearized least squares problem
RealMatrix mA = new BlockRealMatrix(a);
DecompositionSolver solver = useLU ?
new LUDecomposition(mA).getSolver() :
new QRDecomposition(mA).getSolver();
final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
// update the estimated parameters
for (int i = 0; i < nC; ++i) {
currentPoint[i] += dX[i];
}
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
}
// Check convergence.
if (previous != null) {
converged = checker.converged(iter, previous, current);
if (converged) {
cost = computeCost(currentResiduals);
// Update (deprecated) "point" field.
point = current.getPoint();
return current;
}
}
}
// Must never happen.
throw new MathInternalError();
}
Example 4
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
final ConvergenceChecker<PointVectorValuePair> checker
= getConvergenceChecker();
// iterate until convergence is reached
PointVectorValuePair current = null;
int iter = 0;
for (boolean converged = false; !converged;) {
++iter;
// evaluate the objective function and its jacobian
PointVectorValuePair previous = current;
updateResidualsAndCost();
updateJacobian();
current = new PointVectorValuePair(point, objective);
final double[] targetValues = getTargetRef();
final double[] residualsWeights = getWeightRef();
// build the linear problem
final double[] b = new double[cols];
final double[][] a = new double[cols][cols];
for (int i = 0; i < rows; ++i) {
final double[] grad = weightedResidualJacobian[i];
final double weight = residualsWeights[i];
final double residual = objective[i] - targetValues[i];
// compute the normal equation
final double wr = weight * residual;
for (int j = 0; j < cols; ++j) {
b[j] += wr * grad[j];
}
// build the contribution matrix for measurement i
for (int k = 0; k < cols; ++k) {
double[] ak = a[k];
double wgk = weight * grad[k];
for (int l = 0; l < cols; ++l) {
ak[l] += wgk * grad[l];
}
}
}
try {
// solve the linearized least squares problem
RealMatrix mA = new BlockRealMatrix(a);
DecompositionSolver solver = useLU ?
new LUDecomposition(mA).getSolver() :
new QRDecomposition(mA).getSolver();
final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
// update the estimated parameters
for (int i = 0; i < cols; ++i) {
point[i] += dX[i];
}
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
}
// check convergence
if (checker != null) {
if (previous != null) {
converged = checker.converged(iter, previous, current);
}
}
}
// we have converged
return current;
}
Example 5
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
final ConvergenceChecker<PointVectorValuePair> checker
= getConvergenceChecker();
// iterate until convergence is reached
PointVectorValuePair current = null;
int iter = 0;
for (boolean converged = false; !converged;) {
++iter;
// evaluate the objective function and its jacobian
PointVectorValuePair previous = current;
updateResidualsAndCost();
updateJacobian();
current = new PointVectorValuePair(point, objective);
final double[] targetValues = getTargetRef();
final double[] residualsWeights = getWeightRef();
// build the linear problem
final double[] b = new double[cols];
final double[][] a = new double[cols][cols];
for (int i = 0; i < rows; ++i) {
final double[] grad = weightedResidualJacobian[i];
final double weight = residualsWeights[i];
final double residual = objective[i] - targetValues[i];
// compute the normal equation
final double wr = weight * residual;
for (int j = 0; j < cols; ++j) {
b[j] += wr * grad[j];
}
// build the contribution matrix for measurement i
for (int k = 0; k < cols; ++k) {
double[] ak = a[k];
double wgk = weight * grad[k];
for (int l = 0; l < cols; ++l) {
ak[l] += wgk * grad[l];
}
}
}
try {
// solve the linearized least squares problem
RealMatrix mA = new BlockRealMatrix(a);
DecompositionSolver solver = useLU ?
new LUDecomposition(mA).getSolver() :
new QRDecomposition(mA).getSolver();
final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
// update the estimated parameters
for (int i = 0; i < cols; ++i) {
point[i] += dX[i];
}
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
}
// check convergence
if (checker != null) {
if (previous != null) {
converged = checker.converged(iter, previous, current);
}
}
}
// we have converged
return current;
}
Example 6
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
final ConvergenceChecker<PointVectorValuePair> checker
= getConvergenceChecker();
// Computation will be useless without a checker (see "for-loop").
if (checker == null) {
throw new NullArgumentException();
}
final double[] targetValues = getTarget();
final int nR = targetValues.length; // Number of observed data.
final RealMatrix weightMatrix = getWeight();
// Diagonal of the weight matrix.
final double[] residualsWeights = new double[nR];
for (int i = 0; i < nR; i++) {
residualsWeights[i] = weightMatrix.getEntry(i, i);
}
final double[] currentPoint = getStartPoint();
final int nC = currentPoint.length;
// iterate until convergence is reached
PointVectorValuePair current = null;
int iter = 0;
for (boolean converged = false; !converged;) {
++iter;
// evaluate the objective function and its jacobian
PointVectorValuePair previous = current;
// Value of the objective function at "currentPoint".
final double[] currentObjective = computeObjectiveValue(currentPoint);
final double[] currentResiduals = computeResiduals(currentObjective);
final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
current = new PointVectorValuePair(currentPoint, currentObjective);
// build the linear problem
final double[] b = new double[nC];
final double[][] a = new double[nC][nC];
for (int i = 0; i < nR; ++i) {
final double[] grad = weightedJacobian.getRow(i);
final double weight = residualsWeights[i];
final double residual = currentResiduals[i];
// compute the normal equation
final double wr = weight * residual;
for (int j = 0; j < nC; ++j) {
b[j] += wr * grad[j];
}
// build the contribution matrix for measurement i
for (int k = 0; k < nC; ++k) {
double[] ak = a[k];
double wgk = weight * grad[k];
for (int l = 0; l < nC; ++l) {
ak[l] += wgk * grad[l];
}
}
}
try {
// solve the linearized least squares problem
RealMatrix mA = new BlockRealMatrix(a);
DecompositionSolver solver = useLU ?
new LUDecomposition(mA).getSolver() :
new QRDecomposition(mA).getSolver();
final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
// update the estimated parameters
for (int i = 0; i < nC; ++i) {
currentPoint[i] += dX[i];
}
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
}
// Check convergence.
if (previous != null) {
converged = checker.converged(iter, previous, current);
if (converged) {
cost = computeCost(currentResiduals);
// Update (deprecated) "point" field.
point = current.getPoint();
return current;
}
}
}
// Must never happen.
throw new MathInternalError();
}
Example 7
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
final ConvergenceChecker<PointVectorValuePair> checker
= getConvergenceChecker();
// iterate until convergence is reached
PointVectorValuePair current = null;
int iter = 0;
for (boolean converged = false; !converged;) {
++iter;
// evaluate the objective function and its jacobian
PointVectorValuePair previous = current;
updateResidualsAndCost();
updateJacobian();
current = new PointVectorValuePair(point, objective);
final double[] targetValues = getTargetRef();
final double[] residualsWeights = getWeightRef();
// build the linear problem
final double[] b = new double[cols];
final double[][] a = new double[cols][cols];
for (int i = 0; i < rows; ++i) {
final double[] grad = weightedResidualJacobian[i];
final double weight = residualsWeights[i];
final double residual = objective[i] - targetValues[i];
// compute the normal equation
final double wr = weight * residual;
for (int j = 0; j < cols; ++j) {
b[j] += wr * grad[j];
}
// build the contribution matrix for measurement i
for (int k = 0; k < cols; ++k) {
double[] ak = a[k];
double wgk = weight * grad[k];
for (int l = 0; l < cols; ++l) {
ak[l] += wgk * grad[l];
}
}
}
try {
// solve the linearized least squares problem
RealMatrix mA = new BlockRealMatrix(a);
DecompositionSolver solver = useLU ?
new LUDecomposition(mA).getSolver() :
new QRDecomposition(mA).getSolver();
final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
// update the estimated parameters
for (int i = 0; i < cols; ++i) {
point[i] += dX[i];
}
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
}
// check convergence
if (checker != null) {
if (previous != null) {
converged = checker.converged(iter, previous, current);
}
}
}
// we have converged
return current;
}
Example 8
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
final ConvergenceChecker<PointVectorValuePair> checker
= getConvergenceChecker();
// Computation will be useless without a checker (see "for-loop").
if (checker == null) {
throw new NullArgumentException();
}
final double[] targetValues = getTarget();
final int nR = targetValues.length; // Number of observed data.
final RealMatrix weightMatrix = getWeight();
// Diagonal of the weight matrix.
final double[] residualsWeights = new double[nR];
for (int i = 0; i < nR; i++) {
residualsWeights[i] = weightMatrix.getEntry(i, i);
}
final double[] currentPoint = getStartPoint();
final int nC = currentPoint.length;
// iterate until convergence is reached
PointVectorValuePair current = null;
int iter = 0;
for (boolean converged = false; !converged;) {
++iter;
// evaluate the objective function and its jacobian
PointVectorValuePair previous = current;
// Value of the objective function at "currentPoint".
final double[] currentObjective = computeObjectiveValue(currentPoint);
final double[] currentResiduals = computeResiduals(currentObjective);
final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
current = new PointVectorValuePair(currentPoint, currentObjective);
// build the linear problem
final double[] b = new double[nC];
final double[][] a = new double[nC][nC];
for (int i = 0; i < nR; ++i) {
final double[] grad = weightedJacobian.getRow(i);
final double weight = residualsWeights[i];
final double residual = currentResiduals[i];
// compute the normal equation
final double wr = weight * residual;
for (int j = 0; j < nC; ++j) {
b[j] += wr * grad[j];
}
// build the contribution matrix for measurement i
for (int k = 0; k < nC; ++k) {
double[] ak = a[k];
double wgk = weight * grad[k];
for (int l = 0; l < nC; ++l) {
ak[l] += wgk * grad[l];
}
}
}
try {
// solve the linearized least squares problem
RealMatrix mA = new BlockRealMatrix(a);
DecompositionSolver solver = useLU ?
new LUDecomposition(mA).getSolver() :
new QRDecomposition(mA).getSolver();
final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
// update the estimated parameters
for (int i = 0; i < nC; ++i) {
currentPoint[i] += dX[i];
}
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
}
// Check convergence.
if (previous != null) {
converged = checker.converged(iter, previous, current);
if (converged) {
cost = computeCost(currentResiduals);
// Update (deprecated) "point" field.
point = current.getPoint();
return current;
}
}
}
// Must never happen.
throw new MathInternalError();
}
Example 9
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
final ConvergenceChecker<PointVectorValuePair> checker
= getConvergenceChecker();
// Computation will be useless without a checker (see "for-loop").
if (checker == null) {
throw new NullArgumentException();
}
final double[] targetValues = getTarget();
final int nR = targetValues.length; // Number of observed data.
final RealMatrix weightMatrix = getWeight();
// Diagonal of the weight matrix.
final double[] residualsWeights = new double[nR];
for (int i = 0; i < nR; i++) {
residualsWeights[i] = weightMatrix.getEntry(i, i);
}
final double[] currentPoint = getStartPoint();
final int nC = currentPoint.length;
// iterate until convergence is reached
PointVectorValuePair current = null;
int iter = 0;
for (boolean converged = false; !converged;) {
++iter;
// evaluate the objective function and its jacobian
PointVectorValuePair previous = current;
// Value of the objective function at "currentPoint".
final double[] currentObjective = computeObjectiveValue(currentPoint);
final double[] currentResiduals = computeResiduals(currentObjective);
final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
current = new PointVectorValuePair(currentPoint, currentObjective);
// build the linear problem
final double[] b = new double[nC];
final double[][] a = new double[nC][nC];
for (int i = 0; i < nR; ++i) {
final double[] grad = weightedJacobian.getRow(i);
final double weight = residualsWeights[i];
final double residual = currentResiduals[i];
// compute the normal equation
final double wr = weight * residual;
for (int j = 0; j < nC; ++j) {
b[j] += wr * grad[j];
}
// build the contribution matrix for measurement i
for (int k = 0; k < nC; ++k) {
double[] ak = a[k];
double wgk = weight * grad[k];
for (int l = 0; l < nC; ++l) {
ak[l] += wgk * grad[l];
}
}
}
try {
// solve the linearized least squares problem
RealMatrix mA = new BlockRealMatrix(a);
DecompositionSolver solver = useLU ?
new LUDecomposition(mA).getSolver() :
new QRDecomposition(mA).getSolver();
final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
// update the estimated parameters
for (int i = 0; i < nC; ++i) {
currentPoint[i] += dX[i];
}
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
}
// Check convergence.
if (previous != null) {
converged = checker.converged(iter, previous, current);
if (converged) {
cost = computeCost(currentResiduals);
// Update (deprecated) "point" field.
point = current.getPoint();
return current;
}
}
}
// Must never happen.
throw new MathInternalError();
}
Example 10
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/** {@inheritDoc} */
@Override
public PointVectorValuePair doOptimize() {
final ConvergenceChecker<PointVectorValuePair> checker
= getConvergenceChecker();
// Computation will be useless without a checker (see "for-loop").
if (checker == null) {
throw new NullArgumentException();
}
final double[] targetValues = getTarget();
final int nR = targetValues.length; // Number of observed data.
final RealMatrix weightMatrix = getWeight();
// Diagonal of the weight matrix.
final double[] residualsWeights = new double[nR];
for (int i = 0; i < nR; i++) {
residualsWeights[i] = weightMatrix.getEntry(i, i);
}
final double[] currentPoint = getStartPoint();
final int nC = currentPoint.length;
// iterate until convergence is reached
PointVectorValuePair current = null;
int iter = 0;
for (boolean converged = false; !converged;) {
++iter;
// evaluate the objective function and its jacobian
PointVectorValuePair previous = current;
// Value of the objective function at "currentPoint".
final double[] currentObjective = computeObjectiveValue(currentPoint);
final double[] currentResiduals = computeResiduals(currentObjective);
final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
current = new PointVectorValuePair(currentPoint, currentObjective);
// build the linear problem
final double[] b = new double[nC];
final double[][] a = new double[nC][nC];
for (int i = 0; i < nR; ++i) {
final double[] grad = weightedJacobian.getRow(i);
final double weight = residualsWeights[i];
final double residual = currentResiduals[i];
// compute the normal equation
final double wr = weight * residual;
for (int j = 0; j < nC; ++j) {
b[j] += wr * grad[j];
}
// build the contribution matrix for measurement i
for (int k = 0; k < nC; ++k) {
double[] ak = a[k];
double wgk = weight * grad[k];
for (int l = 0; l < nC; ++l) {
ak[l] += wgk * grad[l];
}
}
}
try {
// solve the linearized least squares problem
RealMatrix mA = new BlockRealMatrix(a);
DecompositionSolver solver = useLU ?
new LUDecomposition(mA).getSolver() :
new QRDecomposition(mA).getSolver();
final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
// update the estimated parameters
for (int i = 0; i < nC; ++i) {
currentPoint[i] += dX[i];
}
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
}
// Check convergence.
if (previous != null) {
converged = checker.converged(iter, previous, current);
if (converged) {
cost = computeCost(currentResiduals);
// Update (deprecated) "point" field.
point = current.getPoint();
return current;
}
}
}
// Must never happen.
throw new MathInternalError();
}
Example 11
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 3 votes |
|
@Override
protected RealVector solve(final RealMatrix jacobian,
final RealVector residuals) {
try {
final Pair<RealMatrix, RealVector> normalEquation =
computeNormalMatrix(jacobian, residuals);
final RealMatrix normal = normalEquation.getFirst();
final RealVector jTr = normalEquation.getSecond();
return new LUDecomposition(normal, SINGULARITY_THRESHOLD)
.getSolver()
.solve(jTr);
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
}
}
Example 12
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 3 votes |
|
@Override
protected RealVector solve(final RealMatrix jacobian,
final RealVector residuals) {
try {
final Pair<RealMatrix, RealVector> normalEquation =
computeNormalMatrix(jacobian, residuals);
final RealMatrix normal = normalEquation.getFirst();
final RealVector jTr = normalEquation.getSecond();
return new LUDecomposition(normal, SINGULARITY_THRESHOLD)
.getSolver()
.solve(jTr);
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
}
}
Example 13
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 2 votes |
|
@Override
protected RealVector solve(final RealMatrix jacobian,
final RealVector residuals) {
try {
return new QRDecomposition(jacobian, SINGULARITY_THRESHOLD)
.getSolver()
.solve(residuals);
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
}
}
Example 14
| Source File: GaussNewtonOptimizer.java From astor with GNU General Public License v2.0 | 2 votes |
|
@Override
protected RealVector solve(final RealMatrix jacobian,
final RealVector residuals) {
try {
return new QRDecomposition(jacobian, SINGULARITY_THRESHOLD)
.getSolver()
.solve(residuals);
} catch (SingularMatrixException e) {
throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
}
}
