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Posted to commits@commons.apache.org by tn...@apache.org on 2012/02/09 00:31:11 UTC
svn commit: r1242174 - in
/commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter:
DefaultMeasurementModel.java DefaultProcessModel.java KalmanFilter.java
MeasurementModel.java ProcessModel.java
Author: tn
Date: Wed Feb 8 23:31:10 2012
New Revision: 1242174
URL: http://svn.apache.org/viewvc?rev=1242174&view=rev
Log:
Added since tag for filter package, minor javadoc fixes.
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/DefaultMeasurementModel.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/DefaultProcessModel.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/KalmanFilter.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/MeasurementModel.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/ProcessModel.java
Modified: commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/DefaultMeasurementModel.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/DefaultMeasurementModel.java?rev=1242174&r1=1242173&r2=1242174&view=diff
==============================================================================
--- commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/DefaultMeasurementModel.java (original)
+++ commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/DefaultMeasurementModel.java Wed Feb 8 23:31:10 2012
@@ -23,6 +23,7 @@ import org.apache.commons.math.linear.Re
* Default implementation of a {@link MeasurementModel} for the use with a
* {@link KalmanFilter}.
*
+ * @since 3.0
* @version $Id$
*/
public class DefaultMeasurementModel implements MeasurementModel {
@@ -42,10 +43,8 @@ public class DefaultMeasurementModel imp
* 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
*/
public DefaultMeasurementModel(final double[][] measMatrix,
final double[][] measNoise) {
@@ -57,10 +56,8 @@ public class DefaultMeasurementModel imp
* Create a new {@link MeasurementModel}, taking {@link RealMatrix} objects
* 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
*/
public DefaultMeasurementModel(final RealMatrix measMatrix,
final RealMatrix measNoise) {
@@ -68,16 +65,12 @@ public class DefaultMeasurementModel imp
this.measurementNoise = measNoise;
}
- /**
- * {@inheritDoc}
- */
+ /** {@inheritDoc} */
public RealMatrix getMeasurementMatrix() {
return measurementMatrix;
}
- /**
- * {@inheritDoc}
- */
+ /** {@inheritDoc} */
public RealMatrix getMeasurementNoise() {
return measurementNoise;
}
Modified: commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/DefaultProcessModel.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/DefaultProcessModel.java?rev=1242174&r1=1242173&r2=1242174&view=diff
==============================================================================
--- commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/DefaultProcessModel.java (original)
+++ commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/DefaultProcessModel.java Wed Feb 8 23:31:10 2012
@@ -25,6 +25,7 @@ import org.apache.commons.math.linear.Re
* Default implementation of a {@link ProcessModel} for the use with a
* {@link KalmanFilter}.
*
+ * @since 3.0
* @version $Id$
*/
public class DefaultProcessModel implements ProcessModel {
@@ -40,40 +41,30 @@ public class DefaultProcessModel impleme
*/
private RealMatrix controlMatrix;
- /**
- * The process noise covariance matrix.
- */
+ /** The process noise covariance matrix. */
private RealMatrix processNoiseCovMatrix;
- /**
- * The initial state estimation of the observed process.
- */
+ /** The initial state estimation of the observed process. */
private RealVector initialStateEstimateVector;
- /**
- * The initial error covariance matrix of the observed process.
- */
+ /** The initial error covariance matrix of the observed process. */
private RealMatrix initialErrorCovMatrix;
/**
* 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 double[][] stateTransition,
- final double[][] control, final double[][] processNoise,
- final double[] initialStateEstimate,
- final double[][] initialErrorCovariance) {
+ final double[][] control,
+ final double[][] processNoise,
+ final double[] initialStateEstimate,
+ final double[][] initialErrorCovariance) {
this(new Array2DRowRealMatrix(stateTransition),
new Array2DRowRealMatrix(control),
new Array2DRowRealMatrix(processNoise),
@@ -86,15 +77,13 @@ public class DefaultProcessModel impleme
* parameters. 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
*/
public DefaultProcessModel(final double[][] stateTransition,
- final double[][] control, final double[][] processNoise) {
+ final double[][] control,
+ final double[][] processNoise) {
this(new Array2DRowRealMatrix(stateTransition),
new Array2DRowRealMatrix(control),
new Array2DRowRealMatrix(processNoise), null, null);
@@ -104,21 +93,17 @@ public class DefaultProcessModel impleme
* 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, final RealMatrix processNoise,
- final RealVector initialStateEstimate,
- final RealMatrix initialErrorCovariance) {
+ final RealMatrix control,
+ final RealMatrix processNoise,
+ final RealVector initialStateEstimate,
+ final RealMatrix initialErrorCovariance) {
this.stateTransitionMatrix = stateTransition;
this.controlMatrix = control;
this.processNoiseCovMatrix = processNoise;
@@ -126,37 +111,27 @@ public class DefaultProcessModel impleme
this.initialErrorCovMatrix = initialErrorCovariance;
}
- /**
- * {@inheritDoc}
- */
+ /** {@inheritDoc} */
public RealMatrix getStateTransitionMatrix() {
return stateTransitionMatrix;
}
- /**
- * {@inheritDoc}
- */
+ /** {@inheritDoc} */
public RealMatrix getControlMatrix() {
return controlMatrix;
}
- /**
- * {@inheritDoc}
- */
+ /** {@inheritDoc} */
public RealMatrix getProcessNoise() {
return processNoiseCovMatrix;
}
- /**
- * {@inheritDoc}
- */
+ /** {@inheritDoc} */
public RealVector getInitialStateEstimate() {
return initialStateEstimateVector;
}
- /**
- * {@inheritDoc}
- */
+ /** {@inheritDoc} */
public RealMatrix getInitialErrorCovariance() {
return initialErrorCovMatrix;
}
Modified: commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/KalmanFilter.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/KalmanFilter.java?rev=1242174&r1=1242173&r2=1242174&view=diff
==============================================================================
--- commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/KalmanFilter.java (original)
+++ commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/KalmanFilter.java Wed Feb 8 23:31:10 2012
@@ -78,6 +78,7 @@ import org.apache.commons.math.util.Math
* Kalman filter example by Dan Simon</a>
* @see ProcessModel
* @see MeasurementModel
+ * @since 3.0
* @version $Id$
*/
public class KalmanFilter {
@@ -286,10 +287,9 @@ public class KalmanFilter {
/**
* 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 fit
*/
public void predict(final RealVector u) {
// sanity checks
@@ -318,12 +318,11 @@ public class KalmanFilter {
/**
* Correct the current state estimate with an actual measurement.
*
- * @param z
- * the measurement vector
+ * @param z the measurement vector
* @throws DimensionMismatchException
- * if the dimension of the measurement vector does not fit
+ * if the dimension of the measurement vector does not fit
* @throws org.apache.commons.math.linear.SingularMatrixException
- * if the covariance matrix could not be inverted
+ * if the covariance matrix could not be inverted
*/
public void correct(final double[] z) {
correct(new ArrayRealVector(z));
@@ -332,12 +331,11 @@ public class KalmanFilter {
/**
* 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
+ * @param z the measurement vector
+ * @throws DimensionMismatchException if the dimension of the
+ * measurement vector does not fit
* @throws org.apache.commons.math.linear.SingularMatrixException
- * if the covariance matrix could not be inverted
+ * if the covariance matrix could not be inverted
*/
public void correct(final RealVector z) {
// sanity checks
Modified: commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/MeasurementModel.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/MeasurementModel.java?rev=1242174&r1=1242173&r2=1242174&view=diff
==============================================================================
--- commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/MeasurementModel.java (original)
+++ commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/MeasurementModel.java Wed Feb 8 23:31:10 2012
@@ -21,6 +21,7 @@ import org.apache.commons.math.linear.Re
/**
* Defines the measurement model for the use with a {@link KalmanFilter}.
*
+ * @since 3.0
* @version $Id$
*/
public interface MeasurementModel {
Modified: commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/ProcessModel.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/ProcessModel.java?rev=1242174&r1=1242173&r2=1242174&view=diff
==============================================================================
--- commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/ProcessModel.java (original)
+++ commons/proper/math/trunk/src/main/java/org/apache/commons/math/filter/ProcessModel.java Wed Feb 8 23:31:10 2012
@@ -22,6 +22,7 @@ import org.apache.commons.math.linear.Re
/**
* Defines the process dynamics model for the use with a {@link KalmanFilter}.
*
+ * @since 3.0
* @version $Id$
*/
public interface ProcessModel {