Author: tn
Date: Wed Sep 5 20:06:07 2012
New Revision: 1381332
URL: http://svn.apache.org/viewvc?rev=1381332&view=rev
Log:
Add throw declarations for filter package, javadoc formatting.
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/DefaultMeasurementModel.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/DefaultProcessModel.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/KalmanFilter.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/MeasurementModel.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/ProcessModel.java
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/DefaultMeasurementModel.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/DefaultMeasurementModel.java?rev=1381332&r1=1381331&r2=1381332&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/DefaultMeasurementModel.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/DefaultMeasurementModel.java
Wed Sep 5 20:06:07 2012
@@ -16,12 +16,14 @@
*/
package org.apache.commons.math3.filter;
+import org.apache.commons.math3.exception.DimensionMismatchException;
+import org.apache.commons.math3.exception.NoDataException;
+import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
/**
- * Default implementation of a {@link MeasurementModel} for the use with a
- * {@link KalmanFilter}.
+ * Default implementation of a {@link MeasurementModel} for the use with a
{@link KalmanFilter}.
*
* @since 3.0
* @version $Id$
@@ -40,13 +42,22 @@ public class DefaultMeasurementModel imp
private RealMatrix measurementNoise;
/**
- * Create a new {@link MeasurementModel}, taking double arrays as input
- * parameters for the respective measurement matrix and noise.
+ * Create a new {@link MeasurementModel}, taking double arrays as input
parameters for the
+ * respective measurement matrix and noise.
*
- * @param measMatrix the measurement matrix
- * @param measNoise the measurement noise matrix
+ * @param measMatrix
+ * the measurement matrix
+ * @param measNoise
+ * the measurement noise matrix
+ * @throws NullArgumentException
+ * if any of the input matrices is {@code null}
+ * @throws NoDataException
+ * if any row / column dimension of the input matrices is zero
+ * @throws DimensionMismatchException
+ * if any of the input matrices is non-rectangular
*/
- public DefaultMeasurementModel(final double[][] measMatrix, final
double[][] measNoise) {
+ public DefaultMeasurementModel(final double[][] measMatrix, final
double[][] measNoise)
+ throws NullArgumentException, NoDataException,
DimensionMismatchException {
this(new Array2DRowRealMatrix(measMatrix), new
Array2DRowRealMatrix(measNoise));
}
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/DefaultProcessModel.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/DefaultProcessModel.java?rev=1381332&r1=1381331&r2=1381332&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/DefaultProcessModel.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/DefaultProcessModel.java
Wed Sep 5 20:06:07 2012
@@ -16,28 +16,28 @@
*/
package org.apache.commons.math3.filter;
+import org.apache.commons.math3.exception.DimensionMismatchException;
+import org.apache.commons.math3.exception.NoDataException;
+import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
/**
- * Default implementation of a {@link ProcessModel} for the use with a
- * {@link KalmanFilter}.
+ * Default implementation of a {@link ProcessModel} for the use with a {@link
KalmanFilter}.
*
* @since 3.0
* @version $Id$
*/
public class DefaultProcessModel implements ProcessModel {
/**
- * The state transition matrix, used to advance the internal state
- * estimation each time-step.
+ * The state transition matrix, used to advance the internal state
estimation each time-step.
*/
private RealMatrix stateTransitionMatrix;
/**
- * The control matrix, used to integrate a control input into the state
- * estimation.
+ * The control matrix, used to integrate a control input into the state
estimation.
*/
private RealMatrix controlMatrix;
@@ -51,20 +51,32 @@ public class DefaultProcessModel impleme
private RealMatrix initialErrorCovMatrix;
/**
- * Create a new {@link ProcessModel}, taking double arrays as input
- * parameters.
+ * Create a new {@link ProcessModel}, taking double arrays as input
parameters.
*
- * @param stateTransition the state transition matrix
- * @param control the control matrix
- * @param processNoise the process noise matrix
- * @param initialStateEstimate the initial state estimate vector
- * @param initialErrorCovariance the initial error covariance matrix
+ * @param stateTransition
+ * the state transition matrix
+ * @param control
+ * the control matrix
+ * @param processNoise
+ * the process noise matrix
+ * @param initialStateEstimate
+ * the initial state estimate vector
+ * @param initialErrorCovariance
+ * the initial error covariance matrix
+ * @throws NullArgumentException
+ * if any of the input arrays is {@code null}
+ * @throws NoDataException
+ * if any row / column dimension of the input matrices is zero
+ * @throws DimensionMismatchException
+ * if any of the input matrices is non-rectangular
*/
public DefaultProcessModel(final double[][] stateTransition,
final double[][] control,
final double[][] processNoise,
final double[] initialStateEstimate,
- final double[][] initialErrorCovariance) {
+ final double[][] initialErrorCovariance)
+ throws NullArgumentException, NoDataException,
DimensionMismatchException {
+
this(new Array2DRowRealMatrix(stateTransition),
new Array2DRowRealMatrix(control),
new Array2DRowRealMatrix(processNoise),
@@ -73,31 +85,47 @@ public class DefaultProcessModel impleme
}
/**
- * Create a new {@link ProcessModel}, taking double arrays as input
- * parameters. The initial state estimate and error covariance are omitted
- * and will be initialized by the {@link KalmanFilter} to default values.
+ * Create a new {@link ProcessModel}, taking double arrays as input
parameters.
+ * <p>
+ * The initial state estimate and error covariance are omitted and will be
initialized by the
+ * {@link KalmanFilter} to default values.
*
- * @param stateTransition the state transition matrix
- * @param control the control matrix
- * @param processNoise the process noise matrix
+ * @param stateTransition
+ * the state transition matrix
+ * @param control
+ * the control matrix
+ * @param processNoise
+ * the process noise matrix
+ * @throws NullArgumentException
+ * if any of the input arrays is {@code null}
+ * @throws NoDataException
+ * if any row / column dimension of the input matrices is zero
+ * @throws DimensionMismatchException
+ * if any of the input matrices is non-rectangular
*/
public DefaultProcessModel(final double[][] stateTransition,
final double[][] control,
- final double[][] processNoise) {
+ final double[][] processNoise)
+ throws NullArgumentException, NoDataException,
DimensionMismatchException {
+
this(new Array2DRowRealMatrix(stateTransition),
new Array2DRowRealMatrix(control),
new Array2DRowRealMatrix(processNoise), null, null);
}
/**
- * Create a new {@link ProcessModel}, taking double arrays as input
- * parameters.
+ * Create a new {@link ProcessModel}, taking double arrays as input
parameters.
*
- * @param stateTransition the state transition matrix
- * @param control the control matrix
- * @param processNoise the process noise matrix
- * @param initialStateEstimate the initial state estimate vector
- * @param initialErrorCovariance the initial error covariance matrix
+ * @param stateTransition
+ * the state transition matrix
+ * @param control
+ * the control matrix
+ * @param processNoise
+ * the process noise matrix
+ * @param initialStateEstimate
+ * the initial state estimate vector
+ * @param initialErrorCovariance
+ * the initial error covariance matrix
*/
public DefaultProcessModel(final RealMatrix stateTransition,
final RealMatrix control,
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/KalmanFilter.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/KalmanFilter.java?rev=1381332&r1=1381331&r2=1381332&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/KalmanFilter.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/KalmanFilter.java
Wed Sep 5 20:06:07 2012
@@ -17,6 +17,7 @@
package org.apache.commons.math3.filter;
import org.apache.commons.math3.exception.DimensionMismatchException;
+import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.CholeskyDecomposition;
@@ -26,6 +27,7 @@ import org.apache.commons.math3.linear.M
import org.apache.commons.math3.linear.NonSquareMatrixException;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
+import org.apache.commons.math3.linear.SingularMatrixException;
import org.apache.commons.math3.util.MathUtils;
/**
@@ -43,6 +45,7 @@ import org.apache.commons.math3.util.Mat
* <i>z<sub>k</sub></i> = <b>H</b><i>x<sub>k</sub></i> + <i>v<sub>k</sub></i>.
* </pre>
*
+ * <p>
* The random variables <i>w<sub>k</sub></i> and <i>v<sub>k</sub></i> represent
* the process and measurement noise and are assumed to be independent of each
* other and distributed with normal probability (white noise).
@@ -52,8 +55,6 @@ import org.apache.commons.math3.util.Mat
* <li>predict: project the current state estimate ahead in time</li>
* <li>correct: adjust the projected estimate by an actual measurement</li>
* </ol>
- * </p>
- * <br/>
* <p>
* The Kalman filter is initialized with a {@link ProcessModel} and a
* {@link MeasurementModel}, which contain the corresponding transformation and
@@ -68,7 +69,6 @@ import org.apache.commons.math3.util.Mat
* <li>R - measurement noise covariance matrix</li>
* <li>P - error covariance matrix</li>
* </ul>
- * </p>
*
* @see <a href="http://www.cs.unc.edu/~welch/kalman/";>Kalman filter
* resources</a>
@@ -108,16 +108,19 @@ public class KalmanFilter {
* the model defining the underlying process dynamics
* @param measurement
* the model defining the given measurement characteristics
- * @throws org.apache.commons.math3.exception.NullArgumentException
- * if any of the given inputs is null (except for the control
- * matrix)
+ * @throws NullArgumentException
+ * if any of the given inputs is null (except for the control
matrix)
* @throws NonSquareMatrixException
* if the transition matrix is non square
+ * @throws DimensionMismatchException
+ * if the column dimension of the transition matrix does not
match the dimension of the
+ * initial state estimation vector
* @throws MatrixDimensionMismatchException
* if the matrix dimensions do not fit together
*/
- public KalmanFilter(final ProcessModel process,
- final MeasurementModel measurement) {
+ public KalmanFilter(final ProcessModel process, final MeasurementModel
measurement)
+ throws NullArgumentException, NonSquareMatrixException,
DimensionMismatchException,
+ MatrixDimensionMismatchException {
MathUtils.checkNotNull(process);
MathUtils.checkNotNull(measurement);
@@ -151,8 +154,7 @@ public class KalmanFilter {
// set the initial state estimate to a zero vector if it is not
// available from the process model
if (processModel.getInitialStateEstimate() == null) {
- stateEstimation =
- new ArrayRealVector(transitionMatrix.getColumnDimension());
+ stateEstimation = new
ArrayRealVector(transitionMatrix.getColumnDimension());
} else {
stateEstimation = processModel.getInitialStateEstimate();
}
@@ -279,18 +281,19 @@ public class KalmanFilter {
* @throws DimensionMismatchException
* if the dimension of the control vector does not fit
*/
- public void predict(final double[] u) {
+ public void predict(final double[] u) throws DimensionMismatchException {
predict(new ArrayRealVector(u));
}
/**
* Predict the internal state estimation one time step ahead.
*
- * @param u the control vector
- * @throws DimensionMismatchException if the dimension of the control
- * vector does not fit
+ * @param u
+ * the control vector
+ * @throws DimensionMismatchException
+ * if the dimension of the control vector does not match
*/
- public void predict(final RealVector u) {
+ public void predict(final RealVector u) throws DimensionMismatchException {
// sanity checks
if (u != null &&
u.getDimension() != controlMatrix.getColumnDimension()) {
@@ -317,26 +320,35 @@ public class KalmanFilter {
/**
* Correct the current state estimate with an actual measurement.
*
- * @param z the measurement vector
+ * @param z
+ * the measurement vector
+ * @throws NullArgumentException
+ * if the measurement vector is {@code null}
* @throws DimensionMismatchException
- * if the dimension of the measurement vector does not fit
- * @throws org.apache.commons.math3.linear.SingularMatrixException
- * if the covariance matrix could not be inverted
+ * if the dimension of the measurement vector does not fit
+ * @throws SingularMatrixException
+ * if the covariance matrix could not be inverted
*/
- public void correct(final double[] z) {
+ public void correct(final double[] z)
+ throws NullArgumentException, DimensionMismatchException,
SingularMatrixException {
correct(new ArrayRealVector(z));
}
/**
* Correct the current state estimate with an actual measurement.
*
- * @param z the measurement vector
- * @throws DimensionMismatchException if the dimension of the
- * measurement vector does not fit
- * @throws org.apache.commons.math3.linear.SingularMatrixException
- * if the covariance matrix could not be inverted
+ * @param z
+ * the measurement vector
+ * @throws NullArgumentException
+ * if the measurement vector is {@code null}
+ * @throws DimensionMismatchException
+ * if the dimension of the measurement vector does not fit
+ * @throws SingularMatrixException
+ * if the covariance matrix could not be inverted
*/
- public void correct(final RealVector z) {
+ public void correct(final RealVector z)
+ throws NullArgumentException, DimensionMismatchException,
SingularMatrixException {
+
// sanity checks
MathUtils.checkNotNull(z);
if (z.getDimension() != measurementMatrix.getRowDimension()) {
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/MeasurementModel.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/MeasurementModel.java?rev=1381332&r1=1381331&r2=1381332&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/MeasurementModel.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/MeasurementModel.java
Wed Sep 5 20:06:07 2012
@@ -33,10 +33,9 @@ public interface MeasurementModel {
RealMatrix getMeasurementMatrix();
/**
- * Returns the measurement noise matrix. This method is called by the
- * {@link KalmanFilter} every correct step, so implementations of this
- * interface may return a modified measurement noise depending on current
- * iteration step.
+ * Returns the measurement noise matrix. This method is called by the
{@link KalmanFilter} every
+ * correction step, so implementations of this interface may return a
modified measurement noise
+ * depending on the current iteration step.
*
* @return the measurement noise matrix
* @see KalmanFilter#correct(double[])
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/ProcessModel.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/ProcessModel.java?rev=1381332&r1=1381331&r2=1381332&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/ProcessModel.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/filter/ProcessModel.java
Wed Sep 5 20:06:07 2012
@@ -41,10 +41,9 @@ public interface ProcessModel {
RealMatrix getControlMatrix();
/**
- * Returns the process noise matrix. This method is called by the
- * {@link KalmanFilter} every predict step, so implementations of this
- * interface may return a modified process noise depending on current
- * iteration step.
+ * Returns the process noise matrix. This method is called by the {@link
KalmanFilter} every
+ * prediction step, so implementations of this interface may return a
modified process noise
+ * depending on the current iteration step.
*
* @return the process noise matrix
* @see KalmanFilter#predict()
@@ -56,9 +55,8 @@ public interface ProcessModel {
/**
* Returns the initial state estimation vector.
* <p>
- * Note: if the return value is zero, the Kalman filter will initialize the
+ * <b>Note:</b> if the return value is zero, the Kalman filter will
initialize the
* state estimation with a zero vector.
- * </p>
*
* @return the initial state estimation vector
*/
@@ -67,9 +65,8 @@ public interface ProcessModel {
/**
* Returns the initial error covariance matrix.
* <p>
- * Note: if the return value is zero, the Kalman filter will initialize the
+ * <b>Note:</b> if the return value is zero, the Kalman filter will
initialize the
* error covariance with the process noise matrix.
- * </p>
*
* @return the initial error covariance matrix
*/
