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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;
+
+import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
+import org.apache.commons.math3.analysis.MultivariateVectorFunction;
+import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
+import org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer;
+import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem;
+import org.apache.commons.math3.fitting.leastsquares.LevenbergMarquardtOptimizer;
+
+import java.util.Collection;
+
+/**
+ * Base class that contains common code for fitting parametric univariate real functions <code>
+ * y = f(p<sub>i</sub>;x)</code>, where {@code x} is the independent variable and the <code>
+ * p<sub>i</sub></code> are the <em>parameters</em>. <br>
+ * A fitter will find the optimal values of the parameters by <em>fitting</em> the curve so it
+ * remains very close to a set of {@code N} observed points <code>(x<sub>k</sub>, y<sub>k</sub>)
+ * </code>, {@code 0 <= k < N}. <br>
+ * An algorithm usually performs the fit by finding the parameter values that minimizes the
+ * objective function
+ *
+ * <pre><code>
+ *  &sum;y<sub>k</sub> - f(x<sub>k</sub>)<sup>2</sup>,
+ * </code></pre>
+ *
+ * which is actually a least-squares problem. This class contains boilerplate code for calling the
+ * {@link #fit(Collection)} method for obtaining the parameters. The problem setup, such as the
+ * choice of optimization algorithm for fitting a specific function is delegated to subclasses.
+ *
+ * @since 3.3
+ */
+public abstract class AbstractCurveFitter {
+    /**
+     * Fits a curve. This method computes the coefficients of the curve that best fit the sample of
+     * observed points.
+     *
+     * @param points Observations.
+     * @return the fitted parameters.
+     */
+    public double[] fit(Collection<WeightedObservedPoint> points) {
+        // Perform the fit.
+        return getOptimizer().optimize(getProblem(points)).getPoint().toArray();
+    }
+
+    /**
+     * Creates an optimizer set up to fit the appropriate curve.
+     *
+     * <p>The default implementation uses a {@link LevenbergMarquardtOptimizer Levenberg-Marquardt}
+     * optimizer.
+     *
+     * @return the optimizer to use for fitting the curve to the given {@code points}.
+     */
+    protected LeastSquaresOptimizer getOptimizer() {
+        return new LevenbergMarquardtOptimizer();
+    }
+
+    /**
+     * Creates a least squares problem corresponding to the appropriate curve.
+     *
+     * @param points Sample points.
+     * @return the least squares problem to use for fitting the curve to the given {@code points}.
+     */
+    protected abstract LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> points);
+
+    /** Vector function for computing function theoretical values. */
+    protected static class TheoreticalValuesFunction {
+        /** Function to fit. */
+        private final ParametricUnivariateFunction f;
+
+        /** Observations. */
+        private final double[] points;
+
+        /**
+         * @param f function to fit.
+         * @param observations Observations.
+         */
+        public TheoreticalValuesFunction(
+                final ParametricUnivariateFunction f,
+                final Collection<WeightedObservedPoint> observations) {
+            this.f = f;
+
+            final int len = observations.size();
+            this.points = new double[len];
+            int i = 0;
+            for (WeightedObservedPoint obs : observations) {
+                this.points[i++] = obs.getX();
+            }
+        }
+
+        /**
+         * @return the model function values.
+         */
+        public MultivariateVectorFunction getModelFunction() {
+            return new MultivariateVectorFunction() {
+                /** {@inheritDoc} */
+                public double[] value(double[] p) {
+                    final int len = points.length;
+                    final double[] values = new double[len];
+                    for (int i = 0; i < len; i++) {
+                        values[i] = f.value(points[i], p);
+                    }
+
+                    return values;
+                }
+            };
+        }
+
+        /**
+         * @return the model function Jacobian.
+         */
+        public MultivariateMatrixFunction getModelFunctionJacobian() {
+            return new MultivariateMatrixFunction() {
+                /** {@inheritDoc} */
+                public double[][] value(double[] p) {
+                    final int len = points.length;
+                    final double[][] jacobian = new double[len][];
+                    for (int i = 0; i < len; i++) {
+                        jacobian[i] = f.gradient(points[i], p);
+                    }
+                    return jacobian;
+                }
+            };
+        }
+    }
+}