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+/*
+ * 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.math3.fitting.leastsquares;
+
+import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation;
+import org.apache.commons.math3.linear.ArrayRealVector;
+import org.apache.commons.math3.linear.DecompositionSolver;
+import org.apache.commons.math3.linear.QRDecomposition;
+import org.apache.commons.math3.linear.RealMatrix;
+import org.apache.commons.math3.linear.RealVector;
+import org.apache.commons.math3.util.FastMath;
+
+/**
+ * An implementation of {@link Evaluation} that is designed for extension. All of the
+ * methods implemented here use the methods that are left unimplemented.
+ * <p/>
+ * TODO cache results?
+ *
+ * @since 3.3
+ */
+public abstract class AbstractEvaluation implements Evaluation {
+
+    /** number of observations */
+    private final int observationSize;
+
+    /**
+     * Constructor.
+     *
+     * @param observationSize the number of observation. Needed for {@link
+     *                        #getRMS()}.
+     */
+    AbstractEvaluation(final int observationSize) {
+        this.observationSize = observationSize;
+    }
+
+    /** {@inheritDoc} */
+    public RealMatrix getCovariances(double threshold) {
+        // Set up the Jacobian.
+        final RealMatrix j = this.getJacobian();
+
+        // Compute transpose(J)J.
+        final RealMatrix jTj = j.transpose().multiply(j);
+
+        // Compute the covariances matrix.
+        final DecompositionSolver solver
+                = new QRDecomposition(jTj, threshold).getSolver();
+        return solver.getInverse();
+    }
+
+    /** {@inheritDoc} */
+    public RealVector getSigma(double covarianceSingularityThreshold) {
+        final RealMatrix cov = this.getCovariances(covarianceSingularityThreshold);
+        final int nC = cov.getColumnDimension();
+        final RealVector sig = new ArrayRealVector(nC);
+        for (int i = 0; i < nC; ++i) {
+            sig.setEntry(i, FastMath.sqrt(cov.getEntry(i,i)));
+        }
+        return sig;
+    }
+
+    /** {@inheritDoc} */
+    public double getRMS() {
+        final double cost = this.getCost();
+        return FastMath.sqrt(cost * cost / this.observationSize);
+    }
+
+    /** {@inheritDoc} */
+    public double getCost() {
+        final ArrayRealVector r = new ArrayRealVector(this.getResiduals());
+        return FastMath.sqrt(r.dotProduct(r));
+    }
+
+}