Author: erans
Date: Thu Nov 8 10:50:34 2012
New Revision: 1407010
URL: http://svn.apache.org/viewvc?rev=1407010&view=rev
Log:
MATH-887
Deprecated "protected" fields.
Created new methods (that take arguments and return a value) to
replace those that operate on protected fields.
Removed call to deprecated methods in unit tests.
Added public method "setCost" to replace the direct assignment to
a protected field.
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizer.java
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTest.java
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTestValidation.java
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizer.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizer.java?rev=1407010&r1=1407009&r2=1407010&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizer.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizer.java
Thu Nov 8 10:50:34 2012
@@ -26,7 +26,6 @@ import org.apache.commons.math3.exceptio
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.RealMatrix;
-import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.DecompositionSolver;
import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.QRDecomposition;
@@ -61,7 +60,11 @@ import org.apache.commons.math3.util.Fas
public abstract class AbstractLeastSquaresOptimizer
extends
BaseAbstractMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction>
implements DifferentiableMultivariateVectorOptimizer {
- /** Singularity threshold (cf. {@link #getCovariances(double)}). */
+ /**
+ * Singularity threshold (cf. {@link #getCovariances(double)}).
+ * @deprecated As of 3.1.
+ */
+ @Deprecated
private static final double DEFAULT_SINGULARITY_THRESHOLD = 1e-14;
/**
* Jacobian matrix of the weighted residuals.
@@ -69,19 +72,41 @@ public abstract class AbstractLeastSquar
* {@link #updateJacobian()}, but may be modified by the solver
* in the derived class (the {@link LevenbergMarquardtOptimizer
* Levenberg-Marquardt optimizer} does this).
+ * @deprecated As of 3.1. To be removed in 4.0. Please use
+ * {@link #computeJacobian(double[])} instead.
*/
+ @Deprecated
protected double[][] weightedResidualJacobian;
- /** Number of columns of the jacobian matrix. */
+ /** Number of columns of the jacobian matrix.
+ * @deprecated As of 3.1.
+ */
+ @Deprecated
protected int cols;
- /** Number of rows of the jacobian matrix. */
+ /** Number of rows of the jacobian matrix.
+ * @deprecated As of 3.1.
+ */
+ @Deprecated
protected int rows;
- /** Current point. */
+ /** Current point.
+ * @deprecated As of 3.1.
+ */
+ @Deprecated
protected double[] point;
- /** Current objective function value. */
+ /** Current objective function value.
+ * @deprecated As of 3.1.
+ */
+ @Deprecated
protected double[] objective;
- /** Weighted residuals */
+ /** Weighted residuals
+ * @deprecated As of 3.1.
+ */
+ @Deprecated
protected double[] weightedResiduals;
- /** Cost value (square root of the sum of the residuals). */
+ /** Cost value (square root of the sum of the residuals).
+ * @deprecated As of 3.1. Field to become "private" in 4.0.
+ * Please use {@link #setCost(double)}.
+ */
+ @Deprecated
protected double cost;
/** Objective function derivatives. */
private MultivariateDifferentiableVectorFunction jF;
@@ -118,23 +143,39 @@ public abstract class AbstractLeastSquar
*
* @throws DimensionMismatchException if the Jacobian dimension does not
* match problem dimension.
+ * @deprecated As of 3.1. Please use {@link #computeJacobian(double[])}
+ * instead.
*/
+ @Deprecated
protected void updateJacobian() {
+ computeJacobian(point);
+ }
+
+ /**
+ * Computes the Jacobian matrix.
+ *
+ * @param params Model parameters at which to compute the Jacobian.
+ * @return the weighted Jacobian: -(W<sup>1/2</sup> J).
+ * @throws DimensionMismatchException if the Jacobian dimension does not
+ * match problem dimension.
+ * @since 3.1
+ */
+ protected RealMatrix computeJacobian(double[] params) {
++jacobianEvaluations;
- DerivativeStructure[] dsPoint = new DerivativeStructure[point.length];
- for (int i = 0; i < point.length; ++i) {
- dsPoint[i] = new DerivativeStructure(point.length, 1, i, point[i]);
- }
- DerivativeStructure[] dsValue = jF.value(dsPoint);
- if (dsValue.length != rows) {
- throw new DimensionMismatchException(dsValue.length, rows);
+ final DerivativeStructure[] dsPoint = new
DerivativeStructure[params.length];
+ final int nC = params.length;
+ for (int i = 0; i < nC; ++i) {
+ dsPoint[i] = new DerivativeStructure(nC, 1, i, params[i]);
}
+ final DerivativeStructure[] dsValue = jF.value(dsPoint);
final int nR = getTarget().length;
- final int nC = point.length;
+ if (dsValue.length != nR) {
+ throw new DimensionMismatchException(dsValue.length, nR);
+ }
final double[][] jacobianData = new double[nR][nC];
for (int i = 0; i < nR; ++i) {
- int[] orders = new int[point.length];
+ int[] orders = new int[nC];
for (int j = 0; j < nC; ++j) {
orders[j] = 1;
jacobianData[i][j] = dsValue[i].getPartialDerivative(orders);
@@ -142,8 +183,15 @@ public abstract class AbstractLeastSquar
}
}
- weightedResidualJacobian
- =
weightMatrixSqrt.multiply(MatrixUtils.createRealMatrix(jacobianData)).scalarMultiply(-1).getData();
+ // XXX What is the purpose of the multiplication by -1?
+ final RealMatrix weightedJacobian
+ =
weightMatrixSqrt.multiply(MatrixUtils.createRealMatrix(jacobianData)).scalarMultiply(-1);
+
+ // XXX For backwards-compatibility (field "weightedResidualJacobian"
+ // must be removed in 4.0).
+ weightedResidualJacobian = weightedJacobian.getData();
+
+ return weightedJacobian;
}
/**
@@ -152,19 +200,37 @@ public abstract class AbstractLeastSquar
* problem dimension.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
+ * @deprecated As of 3.1. Please use {@link #computeResiduals(double[])},
+ * {@link #computeObjectiveValue(double[])} and {@link
#computeCost(double[])}
+ * instead.
*/
+ @Deprecated
protected void updateResidualsAndCost() {
- final double[] res = computeResidual(point);
- final ArrayRealVector residuals = new ArrayRealVector(res);
- final RealMatrix weight = getWeight();
+ objective = computeObjectiveValue(point);
+ final double[] res = computeResiduals(objective);
// Compute cost.
- cost = FastMath.sqrt(residuals.dotProduct(weight.operate(residuals)));
- // Compute weighted residuals.
+ cost = computeCost(res);
+
+ // Compute weighted residuals. XXX To be moved to
"LevenbergMarquardtOptimizer".
+ final ArrayRealVector residuals = new ArrayRealVector(res);
weightedResiduals = weightMatrixSqrt.operate(residuals).toArray();
}
/**
+ * Computes the cost.
+ *
+ * @param residuals Residuals.
+ * @return the cost.
+ * @see #computeResiduals(double[])
+ * @since 3.1
+ */
+ protected double computeCost(double[] residuals) {
+ final ArrayRealVector r = new ArrayRealVector(residuals);
+ return FastMath.sqrt(r.dotProduct(getWeight().operate(r)));
+ }
+
+ /**
* Get the Root Mean Square value.
* Get the Root Mean Square value, i.e. the root of the arithmetic
* mean of the square of all weighted residuals. This is related to the
@@ -189,14 +255,26 @@ public abstract class AbstractLeastSquar
}
/**
+ * Sets the cost.
+ *
+ * @param cost Cost value.
+ * @since 3.1
+ */
+ public void setCost(double cost) {
+ this.cost = cost;
+ }
+
+ /**
* Get the covariance matrix of the optimized parameters.
*
* @return the covariance matrix.
* @throws org.apache.commons.math3.linear.SingularMatrixException
* if the covariance matrix cannot be computed (singular problem).
- *
* @see #getCovariances(double)
+ * @deprecated As of 3.1. Please use {@link
#computeCovariances(double[],double)}
+ * instead.
*/
+ @Deprecated
public double[][] getCovariances() {
return getCovariances(DEFAULT_SINGULARITY_THRESHOLD);
}
@@ -215,14 +293,37 @@ public abstract class AbstractLeastSquar
* @return the covariance matrix.
* @throws org.apache.commons.math3.linear.SingularMatrixException
* if the covariance matrix cannot be computed (singular problem).
+ * @deprecated As of 3.1. Please use {@link
#computeCovariances(double[],double)}
+ * instead.
*/
+ @Deprecated
public double[][] getCovariances(double threshold) {
- // Set up the jacobian.
- updateJacobian();
+ return computeCovariances(point, threshold);
+ }
+
+ /**
+ * Get the covariance matrix of the optimized parameters.
+ * <br/>
+ * Note that this operation involves the inversion of the
+ * <code>J<sup>T</sup>J</code> matrix, where {@code J} is the
+ * Jacobian matrix.
+ * The {@code threshold} parameter is a way for the caller to specify
+ * that the result of this computation should be considered meaningless,
+ * and thus trigger an exception.
+ *
+ * @param params Model parameters.
+ * @param threshold Singularity threshold.
+ * @return the covariance matrix.
+ * @throws org.apache.commons.math3.linear.SingularMatrixException
+ * if the covariance matrix cannot be computed (singular problem).
+ */
+ public double[][] computeCovariances(double[] params,
+ double threshold) {
+ // Set up the Jacobian.
+ final RealMatrix j = computeJacobian(params);
// Compute transpose(J)J.
- final RealMatrix wrj = new
Array2DRowRealMatrix(weightedResidualJacobian);
- final RealMatrix jTj = wrj.transpose().multiply(wrj);
+ final RealMatrix jTj = j.transpose().multiply(j);
// Compute the covariances matrix.
final DecompositionSolver solver
@@ -255,9 +356,9 @@ public abstract class AbstractLeastSquar
* @throws NumberIsTooSmallException if the number of degrees of freedom
is not
* positive, i.e. the number of measurements is less or equal to the
number of
* parameters.
- * @deprecated as of version 3.1, {@link #getSigma()} should be used
- * instead. It should be emphasized that {@link #guessParametersErrors()}
and
- * {@link #getSigma()} are <em>not</em> strictly equivalent.
+ * @deprecated as of version 3.1, {@link #computeSigma(double[],double)}
should be used
+ * instead. It should be emphasized that {@code guessParametersErrors} and
+ * {@code computeSigma} are <em>not</em> strictly equivalent.
*/
@Deprecated
public double[] guessParametersErrors() {
@@ -267,7 +368,7 @@ public abstract class AbstractLeastSquar
}
double[] errors = new double[cols];
final double c = FastMath.sqrt(getChiSquare() / (rows - cols));
- double[][] covar = getCovariances();
+ double[][] covar = computeCovariances(point, 1e-14);
for (int i = 0; i < errors.length; ++i) {
errors[i] = FastMath.sqrt(covar[i][i]) * c;
}
@@ -275,22 +376,25 @@ public abstract class AbstractLeastSquar
}
/**
- * <p>
- * Returns an estimate of the standard deviation of the parameters. The
+ * Computes an estimate of the standard deviation of the parameters. The
* returned values are the square root of the diagonal coefficients of the
* covariance matrix, {@code sd(a[i]) ~= sqrt(C[i][i])}, where {@code a[i]}
* is the optimized value of the {@code i}-th parameter, and {@code C} is
* the covariance matrix.
- * </p>
*
+ * @param params Model parameters.
+ * @param covarianceSingularityThreshold Singularity threshold (see
+ * {@link #computeCovariances(double[],double) computeCovariances}).
* @return an estimate of the standard deviation of the optimized
parameters
* @throws org.apache.commons.math3.linear.SingularMatrixException
* if the covariance matrix cannot be computed.
*/
- public double[] getSigma() {
- final double[] sig = new double[cols];
- final double[][] cov = getCovariances();
- for (int i = 0; i < sig.length; ++i) {
+ public double[] computeSigma(double[] params,
+ double covarianceSingularityThreshold) {
+ final int nC = params.length;
+ final double[] sig = new double[nC];
+ final double[][] cov = computeCovariances(params,
covarianceSingularityThreshold);
+ for (int i = 0; i < nC; ++i) {
sig[i] = FastMath.sqrt(cov[i][i]);
}
return sig;
@@ -410,43 +514,43 @@ public abstract class AbstractLeastSquar
}
/**
- * Computes the square-root of the weight matrix.
- *
- * @param m Symmetric, positive-definite (weight) matrix.
- * @return the square-root of the weight matrix.
- */
- private RealMatrix squareRoot(RealMatrix m) {
- final EigenDecomposition dec = new EigenDecomposition(m);
- return dec.getSquareRoot();
- }
-
- /**
* Computes the residuals.
* The residual is the difference between the observed (target)
- * values and the model (objective function) value, for the given
- * parameters.
+ * values and the model (objective function) value.
* There is one residual for each element of the vector-valued
* function.
*
- * @param params Parameters of the model.
+ * @param objectiveValue Value of the the objective function. This is
+ * the value returned from a call to
+ * {@link #computeObjectiveValue(double[]) computeObjectiveValue}
+ * (whose array argument contains the model parameters).
* @return the residuals.
* @throws DimensionMismatchException if {@code params} has a wrong
* length.
*/
- private double[] computeResidual(double[] params) {
- if (params.length != getStartPoint().length) {
- throw new DimensionMismatchException(params.length,
- getStartPoint().length);
- }
-
- objective = computeObjectiveValue(params);
+ protected double[] computeResiduals(double[] objectiveValue) {
final double[] target = getTarget();
+ if (objectiveValue.length != target.length) {
+ throw new DimensionMismatchException(target.length,
+ objectiveValue.length);
+ }
final double[] residuals = new double[target.length];
for (int i = 0; i < target.length; i++) {
- residuals[i] = target[i] - objective[i];
+ residuals[i] = target[i] - objectiveValue[i];
}
return residuals;
}
+
+ /**
+ * Computes the square-root of the weight matrix.
+ *
+ * @param m Symmetric, positive-definite (weight) matrix.
+ * @return the square-root of the weight matrix.
+ */
+ private RealMatrix squareRoot(RealMatrix m) {
+ final EigenDecomposition dec = new EigenDecomposition(m);
+ return dec.getSquareRoot();
+ }
}
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java?rev=1407010&r1=1407009&r2=1407010&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerAbstractTest.java
Thu Nov 8 10:50:34 2012
@@ -364,14 +364,11 @@ public abstract class AbstractLeastSquar
Assert.assertEquals(69.96016176931406, circle.getRadius(center),
1.0e-6);
Assert.assertEquals(96.07590211815305, center.getX(),
1.0e-6);
Assert.assertEquals(48.13516790438953, center.getY(),
1.0e-6);
- double[][] cov = optimizer.getCovariances();
+ double[][] cov = optimizer.computeCovariances(optimum.getPoint(),
1e-14);
Assert.assertEquals(1.839, cov[0][0], 0.001);
Assert.assertEquals(0.731, cov[0][1], 0.001);
Assert.assertEquals(cov[0][1], cov[1][0], 1.0e-14);
Assert.assertEquals(0.786, cov[1][1], 0.001);
- double[] errors = optimizer.guessParametersErrors();
- Assert.assertEquals(1.384, errors[0], 0.001);
- Assert.assertEquals(0.905, errors[1], 0.001);
// add perfect measurements and check errors are reduced
double r = circle.getRadius(center);
@@ -382,15 +379,12 @@ public abstract class AbstractLeastSquar
Arrays.fill(target, 0.0);
double[] weights = new double[circle.getN()];
Arrays.fill(weights, 2.0);
- optimizer.optimize(100, circle, target, weights, new double[] {
98.680, 47.345 });
- cov = optimizer.getCovariances();
+ optimum = optimizer.optimize(100, circle, target, weights, new
double[] { 98.680, 47.345 });
+ cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
Assert.assertEquals(0.0016, cov[0][0], 0.001);
Assert.assertEquals(3.2e-7, cov[0][1], 1.0e-9);
Assert.assertEquals(cov[0][1], cov[1][0], 1.0e-14);
Assert.assertEquals(0.0016, cov[1][1], 0.001);
- errors = optimizer.guessParametersErrors();
- Assert.assertEquals(0.004, errors[0], 0.001);
- Assert.assertEquals(0.004, errors[1], 0.001);
}
@Test
@@ -481,16 +475,11 @@ public abstract class AbstractLeastSquar
optimum = optimizer.optimize(100, problem, data[1], w, initial);
final double[] actual = optimum.getPoint();
- final double[] actualSig = optimizer.guessParametersErrors();
for (int i = 0; i < actual.length; i++) {
double expected = dataset.getParameter(i);
double delta = FastMath.abs(errParams * expected);
Assert.assertEquals(dataset.getName() + ", param #" + i,
expected, actual[i], delta);
- expected = dataset.getParameterStandardDeviation(i);
- delta = FastMath.abs(errParamsSd * expected);
- Assert.assertEquals(dataset.getName() + ", sd of param #" + i,
- expected, actualSig[i], delta);
}
}
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTest.java?rev=1407010&r1=1407009&r2=1407010&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTest.java
Thu Nov 8 10:50:34 2012
@@ -29,9 +29,10 @@ public class AbstractLeastSquaresOptimiz
@Override
protected PointVectorValuePair doOptimize() {
- updateResidualsAndCost();
- updateJacobian();
- return null;
+ final double[] params = getStartPoint();
+ final double[] res =
computeResiduals(computeObjectiveValue(params));
+ setCost(computeCost(res));
+ return new PointVectorValuePair(params, null);
}
};
}
@@ -75,7 +76,7 @@ public class AbstractLeastSquaresOptimiz
}
@Test
- public void testGetSigma() throws IOException {
+ public void testComputeSigma() throws IOException {
final StatisticalReferenceDataset dataset;
dataset = StatisticalReferenceDatasetFactory.createKirby2();
final AbstractLeastSquaresOptimizer optimizer;
@@ -85,14 +86,14 @@ public class AbstractLeastSquaresOptimiz
final double[] w = new double[y.length];
Arrays.fill(w, 1.0);
- final int dof = y.length-a.length;
- optimizer.optimize(1, dataset.getLeastSquaresProblem(), y, w, a);
- final double[] sig = optimizer.getSigma();
+ final int dof = y.length - a.length;
+ final PointVectorValuePair optimum = optimizer.optimize(1,
dataset.getLeastSquaresProblem(), y, w, a);
+ final double[] sig = optimizer.computeSigma(optimum.getPoint(), 1e-14);
final double[] expected = dataset.getParametersStandardDeviations();
for (int i = 0; i < sig.length; i++) {
- final double actual =
FastMath.sqrt(optimizer.getChiSquare()/dof)*sig[i];
+ final double actual = FastMath.sqrt(optimizer.getChiSquare() /
dof) * sig[i];
Assert.assertEquals(dataset.getName() + ", parameter #" + i,
- expected[i], actual, 1.3e-8 * expected[i]);
+ expected[i], actual, 1e-7 * expected[i]);
}
}
}
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTestValidation.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTestValidation.java?rev=1407010&r1=1407009&r2=1407010&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTestValidation.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizerTestValidation.java
Thu Nov 8 10:50:34 2012
@@ -115,9 +115,9 @@ public class AbstractLeastSquaresOptimiz
// Estimation of the standard deviation (diagonal elements of the
// covariance matrix).
- optim.optimize(Integer.MAX_VALUE,
+ final PointVectorValuePair optimum =
optim.optimize(Integer.MAX_VALUE,
problem, problem.target(), problem.weight(), init);
- final double[] sigma = optim.getSigma();
+ final double[] sigma = optim.computeSigma(optimum.getPoint(),
1e-14);
// Accumulate statistics.
for (int i = 0; i < numParams; i++) {
@@ -220,7 +220,7 @@ public class AbstractLeastSquaresOptimiz
// Get chi-square of the best parameters set for the given set of
// observations.
final double bestChi2N = getChi2N(optim, problem, regress);
- final double[] sigma = optim.getSigma();
+ final double[] sigma = optim.computeSigma(regress, 1e-14);
// Monte-Carlo (generates a grid of parameters).
final int mcRepeat = MONTE_CARLO_RUNS;
@@ -312,10 +312,9 @@ class DummyOptimizer extends AbstractLea
*/
@Override
public PointVectorValuePair doOptimize() {
- // In order to be able to access the chi-square.
- updateResidualsAndCost();
-
- // Dummy value.
- return null;
+ final double[] params = getStartPoint();
+ final double[] res = computeResiduals(computeObjectiveValue(params));
+ setCost(computeCost(res));
+ return new PointVectorValuePair(params, null);
}
}
