1 files changed, 231 insertions, 0 deletions
diff --git a/src/main/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java b/src/main/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java
new file mode 100644
index 0000000..c5d0dd3
--- /dev/null
+++ b/src/main/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java
@@ -0,0 +1,231 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.commons.math.estimation;
+
+import java.io.Serializable;
+
+import org.apache.commons.math.exception.util.LocalizedFormats;
+import org.apache.commons.math.linear.InvalidMatrixException;
+import org.apache.commons.math.linear.LUDecompositionImpl;
+import org.apache.commons.math.linear.MatrixUtils;
+import org.apache.commons.math.linear.RealMatrix;
+import org.apache.commons.math.linear.RealVector;
+import org.apache.commons.math.linear.ArrayRealVector;
+import org.apache.commons.math.util.FastMath;
+
+/**
+ * This class implements a solver for estimation problems.
+ *
+ * <p>This class solves estimation problems using a weighted least
+ * squares criterion on the measurement residuals. It uses a
+ * Gauss-Newton algorithm.</p>
+ *
+ * @version $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $
+ * @since 1.2
+ * @deprecated as of 2.0, everything in package org.apache.commons.math.estimation has
+ * been deprecated and replaced by package org.apache.commons.math.optimization.general
+ *
+ */
+@Deprecated
+public class GaussNewtonEstimator extends AbstractEstimator implements Serializable {
+
+    /** Serializable version identifier */
+    private static final long serialVersionUID = 5485001826076289109L;
+
+    /** Default threshold for cost steady state detection. */
+    private static final double DEFAULT_STEADY_STATE_THRESHOLD = 1.0e-6;
+
+    /** Default threshold for cost convergence. */
+    private static final double DEFAULT_CONVERGENCE = 1.0e-6;
+
+    /** Threshold for cost steady state detection. */
+    private double steadyStateThreshold;
+
+    /** Threshold for cost convergence. */
+    private double convergence;
+
+    /** Simple constructor with default settings.
+     * <p>
+     * The estimator is built with default values for all settings.
+     * </p>
+     * @see #DEFAULT_STEADY_STATE_THRESHOLD
+     * @see #DEFAULT_CONVERGENCE
+     * @see AbstractEstimator#DEFAULT_MAX_COST_EVALUATIONS
+     */
+    public GaussNewtonEstimator() {
+        this.steadyStateThreshold = DEFAULT_STEADY_STATE_THRESHOLD;
+        this.convergence          = DEFAULT_CONVERGENCE;
+    }
+
+    /**
+     * Simple constructor.
+     *
+     * <p>This constructor builds an estimator and stores its convergence
+     * characteristics.</p>
+     *
+     * <p>An estimator is considered to have converged whenever either
+     * the criterion goes below a physical threshold under which
+     * improvements are considered useless or when the algorithm is
+     * unable to improve it (even if it is still high). The first
+     * condition that is met stops the iterations.</p>
+     *
+     * <p>The fact an estimator has converged does not mean that the
+     * model accurately fits the measurements. It only means no better
+     * solution can be found, it does not mean this one is good. Such an
+     * analysis is left to the caller.</p>
+     *
+     * <p>If neither conditions are fulfilled before a given number of
+     * iterations, the algorithm is considered to have failed and an
+     * {@link EstimationException} is thrown.</p>
+     *
+     * @param maxCostEval maximal number of cost evaluations allowed
+     * @param convergence criterion threshold below which we do not need
+     * to improve the criterion anymore
+     * @param steadyStateThreshold steady state detection threshold, the
+     * problem has converged has reached a steady state if
+     * <code>FastMath.abs(J<sub>n</sub> - J<sub>n-1</sub>) &lt;
+     * J<sub>n</sub> &times convergence</code>, where <code>J<sub>n</sub></code>
+     * and <code>J<sub>n-1</sub></code> are the current and preceding criterion
+     * values (square sum of the weighted residuals of considered measurements).
+     */
+    public GaussNewtonEstimator(final int maxCostEval, final double convergence,
+                                final double steadyStateThreshold) {
+        setMaxCostEval(maxCostEval);
+        this.steadyStateThreshold = steadyStateThreshold;
+        this.convergence          = convergence;
+    }
+
+    /**
+     * Set the convergence criterion threshold.
+     * @param convergence criterion threshold below which we do not need
+     * to improve the criterion anymore
+     */
+    public void setConvergence(final double convergence) {
+        this.convergence = convergence;
+    }
+
+    /**
+     * Set the steady state detection threshold.
+     * <p>
+     * The problem has converged has reached a steady state if
+     * <code>FastMath.abs(J<sub>n</sub> - J<sub>n-1</sub>) &lt;
+     * J<sub>n</sub> &times convergence</code>, where <code>J<sub>n</sub></code>
+     * and <code>J<sub>n-1</sub></code> are the current and preceding criterion
+     * values (square sum of the weighted residuals of considered measurements).
+     * </p>
+     * @param steadyStateThreshold steady state detection threshold
+     */
+    public void setSteadyStateThreshold(final double steadyStateThreshold) {
+        this.steadyStateThreshold = steadyStateThreshold;
+    }
+
+    /**
+     * Solve an estimation problem using a least squares criterion.
+     *
+     * <p>This method set the unbound parameters of the given problem
+     * starting from their current values through several iterations. At
+     * each step, the unbound parameters are changed in order to
+     * minimize a weighted least square criterion based on the
+     * measurements of the problem.</p>
+     *
+     * <p>The iterations are stopped either when the criterion goes
+     * below a physical threshold under which improvement are considered
+     * useless or when the algorithm is unable to improve it (even if it
+     * is still high). The first condition that is met stops the
+     * iterations. If the convergence it not reached before the maximum
+     * number of iterations, an {@link EstimationException} is
+     * thrown.</p>
+     *
+     * @param problem estimation problem to solve
+     * @exception EstimationException if the problem cannot be solved
+     *
+     * @see EstimationProblem
+     *
+     */
+    @Override
+    public void estimate(EstimationProblem problem)
+    throws EstimationException {
+
+        initializeEstimate(problem);
+
+        // work matrices
+        double[] grad             = new double[parameters.length];
+        ArrayRealVector bDecrement = new ArrayRealVector(parameters.length);
+        double[] bDecrementData   = bDecrement.getDataRef();
+        RealMatrix wGradGradT     = MatrixUtils.createRealMatrix(parameters.length, parameters.length);
+
+        // iterate until convergence is reached
+        double previous = Double.POSITIVE_INFINITY;
+        do {
+
+            // build the linear problem
+            incrementJacobianEvaluationsCounter();
+            RealVector b = new ArrayRealVector(parameters.length);
+            RealMatrix a = MatrixUtils.createRealMatrix(parameters.length, parameters.length);
+            for (int i = 0; i < measurements.length; ++i) {
+                if (! measurements [i].isIgnored()) {
+
+                    double weight   = measurements[i].getWeight();
+                    double residual = measurements[i].getResidual();
+
+                    // compute the normal equation
+                    for (int j = 0; j < parameters.length; ++j) {
+                        grad[j] = measurements[i].getPartial(parameters[j]);
+                        bDecrementData[j] = weight * residual * grad[j];
+                    }
+
+                    // build the contribution matrix for measurement i
+                    for (int k = 0; k < parameters.length; ++k) {
+                        double gk = grad[k];
+                        for (int l = 0; l < parameters.length; ++l) {
+                            wGradGradT.setEntry(k, l, weight * gk * grad[l]);
+                        }
+                    }
+
+                    // update the matrices
+                    a = a.add(wGradGradT);
+                    b = b.add(bDecrement);
+
+                }
+            }
+
+            try {
+
+                // solve the linearized least squares problem
+                RealVector dX = new LUDecompositionImpl(a).getSolver().solve(b);
+
+                // update the estimated parameters
+                for (int i = 0; i < parameters.length; ++i) {
+                    parameters[i].setEstimate(parameters[i].getEstimate() + dX.getEntry(i));
+                }
+
+            } catch(InvalidMatrixException e) {
+                throw new EstimationException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
+            }
+
+
+            previous = cost;
+            updateResidualsAndCost();
+
+        } while ((getCostEvaluations() < 2) ||
+                 (FastMath.abs(previous - cost) > (cost * steadyStateThreshold) &&
+                  (FastMath.abs(cost) > convergence)));
+
+    }
+
+}