Hipparchus-Math · BryanCazabonne · May 18, 2026 · May 18, 2026
diff --git a/hipparchus-fitting/src/changes/changes.xml b/hipparchus-fitting/src/changes/changes.xml
@@ -50,6 +50,9 @@ If the output is not quite correct, check for invisible trailing spaces!
   </properties>
   <body>
     <release version="4.1" date="TBD" description="TBD">
+      <action dev="bryan" type="update" issue="issues/462">
+        Replaced double[] arrays with Fittable interface in RANSAC fitting classes.
+      </action>
       <action dev="bryan" type="add" issue="issues/424">
         Added RANSAC algorithm for estimating the parameters of a mathematical model.
       </action>

diff --git a/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/Fittable.java b/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/Fittable.java
@@ -0,0 +1,30 @@
+/*
+ * 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
+ *
+ *      https://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.hipparchus.fitting.ransac;
+
+/**
+ * Interface for data points that can be used with {@link RansacFitter}.
+ * @since 4.1
+ */
+public interface Fittable {
+
+    /**
+     * Gets the n-dimensional point.
+     * @return the point array (beware, it may be a reference to an internal array)
+     */
+    double[] getPoint();
+}
diff --git a/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/IModelFitter.java b/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/IModelFitter.java
@@ -30,7 +30,7 @@ public interface IModelFitter<M> {
      * @param points set of observed data
      * @return the fitted model parameters
      */
-    M fitModel(final List<double[]> points);
+    M fitModel(List<Fittable> points);
 
     /**
      * Computes the error between the model and an observed data.
@@ -41,5 +41,5 @@ public interface IModelFitter<M> {
      * @param point observed data
      * @return the error between the model and the observed data
      */
-    double computeModelError(final M model, final double[] point);
+    double computeModelError(M model, Fittable point);
 }
diff --git a/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/PolynomialModelFitter.java b/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/PolynomialModelFitter.java
@@ -86,7 +86,7 @@ public PolynomialModelFitter(final int degree) {
 
     /** {@inheritDoc} */
     @Override
-    public Model fitModel(final List<double[]> points) {
+    public Model fitModel(final List<Fittable> points) {
         // Reference: Wikipedia page "Polynomial regression"
         final int size = points.size();
         checkSampleSize(size);
@@ -95,8 +95,9 @@ public Model fitModel(final List<double[]> points) {
         final double[][] x = new double[size][degree + 1];
         final double[] y = new double[size];
         for (int i = 0; i < size; i++) {
-            final double currentX = points.get(i)[0];
-            final double currentY = points.get(i)[1];
+            final double[] point = points.get(i).getPoint();
+            final double currentX = point[0];
+            final double currentY = point[1];
             double value = 1.0;
             for (int j = 0; j <= degree; j++) {
                 x[i][j] = value;
@@ -117,8 +118,8 @@ public Model fitModel(final List<double[]> points) {
 
     /** {@inheritDoc}. */
     @Override
-    public double computeModelError(final Model model, final double[] point) {
-        return FastMath.abs(point[1] - model.predict(point[0]));
+    public double computeModelError(final Model model, final Fittable point) {
+        return FastMath.abs(point.getPoint()[1] - model.predict(point.getPoint()[0]));
     }
 
     /**

diff --git a/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/RansacFitter.java b/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/RansacFitter.java
@@ -91,19 +91,19 @@ public RansacFitter(final IModelFitter<M> fitter, final int sampleSize,
      * @param points set of observed data
      * @return a java class containing the best estimate of the model parameters
      */
-    public RansacFitterOutputs<M> fit(final List<double[]> points) {
+    public RansacFitterOutputs<M> fit(final List<Fittable> points) {
 
         // Initialize the best model data
-        final List<double[]> data = new ArrayList<>(points);
+        final List<Fittable> data = new ArrayList<>(points);
         Optional<M> bestModel = Optional.empty();
-        List<double[]> bestInliers = new ArrayList<>();
+        List<Fittable> bestInliers = new ArrayList<>();
 
         // Iterative loop to determine the best model
         for (int iteration = 0; iteration < maxIterations; iteration++) {
 
             // Random permute the set of observed data and determine the inliers
             Collections.shuffle(data, random);
-            final List<double[]> inliers = determineCurrentInliersFromRandomlyPermutedPoints(data);
+            final List<Fittable> inliers = determineCurrentInliersFromRandomlyPermutedPoints(data);
 
             // Verifies if the current inliers are fit better the model than the previous ones
             if (isCurrentInliersSetBetterThanPreviousOne(inliers, bestInliers)) {
@@ -122,8 +122,8 @@ public RansacFitterOutputs<M> fit(final List<double[]> points) {
      * @param permutedPoints randomly permuted data
      * @return the list of inliers
      */
-    private List<double[]> determineCurrentInliersFromRandomlyPermutedPoints(final List<double[]> permutedPoints) {
-        M model = fitter.fitModel(permutedPoints.subList(0, sampleSize));
+    private List<Fittable> determineCurrentInliersFromRandomlyPermutedPoints(final List<Fittable> permutedPoints) {
+        final M model = fitter.fitModel(permutedPoints.subList(0, sampleSize));
         return permutedPoints.stream().filter(point -> fitter.computeModelError(model, point) < threshold).collect(Collectors.toList());
     }
 
@@ -133,7 +133,7 @@ private List<double[]> determineCurrentInliersFromRandomlyPermutedPoints(final L
      * @param previous previous inliers
      * @return true is the current inlier are better than the previous ones
      */
-    private boolean isCurrentInliersSetBetterThanPreviousOne(final List<double[]> current, final List<double[]> previous) {
+    private boolean isCurrentInliersSetBetterThanPreviousOne(final List<Fittable> current, final List<Fittable> previous) {
         return current.size() > previous.size() && current.size() >= minInliers;
     }
 

diff --git a/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/RansacFitterOutputs.java b/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/RansacFitterOutputs.java
@@ -36,15 +36,15 @@ public class RansacFitterOutputs<M> {
     private final Optional<M> bestModel;
 
     /** List of points used to determine the best model parameters. */
-    private final List<double[]> bestInliers;
+    private final List<Fittable> bestInliers;
 
     /**
      * Constructor.
      * @param bestModel best model parameters
      * @param bestInliers list of points used to determine the best model parameters
      * @param fitter mathematical model fitter used by RANSAC algorithm
      */
-    public RansacFitterOutputs(final Optional<M> bestModel, final List<double[]> bestInliers, final IModelFitter<M> fitter) {
+    public RansacFitterOutputs(final Optional<M> bestModel, final List<Fittable> bestInliers, final IModelFitter<M> fitter) {
         this.bestModel = bestModel;
         this.bestInliers = new ArrayList<>(bestInliers);
         this.fitter = fitter;
@@ -62,7 +62,7 @@ public Optional<M> getBestModel() {
      * Get the list of points used to determine the best model parameters.
      * @return the list of points used to determine the best model parameters
      */
-    public List<double[]> getBestInliers() {
+    public List<Fittable> getBestInliers() {
         return new ArrayList<>(bestInliers);
     }
 
@@ -73,7 +73,7 @@ public List<double[]> getBestInliers() {
      * @return the list of points below the given threshold based on the computed best model parameters
      *         (can be empty if the all points are above the threshold or if no best model has been found)
      */
-    public List<double[]> filterPointsBelowThreshold(final List<double[]> points, final double threshold) {
+    public List<Fittable> filterPointsBelowThreshold(final List<Fittable> points, final double threshold) {
         return bestModel.map(model -> points.stream().filter(point -> fitter.computeModelError(model, point) < threshold).collect(Collectors.toList()))
                         .orElse(Collections.emptyList());
     }

diff --git a/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/package-info.java b/hipparchus-fitting/src/main/java/org/hipparchus/fitting/ransac/package-info.java
@@ -16,6 +16,9 @@
  */
 /**
  * Random sample consensus (RANSAC) algorithm implementation.
+ * <p>
+ * Data points to be fitted must implement the {@link org.hipparchus.fitting.ransac.Fittable} interface.
+ * </p>
  * @since 4.1
  */
 package org.hipparchus.fitting.ransac;
diff --git a/hipparchus-fitting/src/site/markdown/fitting.md b/hipparchus-fitting/src/site/markdown/fitting.md
@@ -102,15 +102,17 @@ RANSAC-based fitting of specific functions are provided through the following cl
 * call the fit method of [RansacFitter](../apidocs/org/hipparchus/fitting/ransac/RansacFitter.html) with a List of observed data points as argument, which will return 
   a java class containing the parameters that best fit the given data points.
 
+Data points must implement the [Fittable](../apidocs/org/hipparchus/fitting/ransac/Fittable.html) interface which provides a `getPoint()` method returning the point coordinates as a `double[]`.
+
 The following example shows how to fit data with a polynomial model of degree 2.
 
     // Collect data.
-    final List<double[]> obs = new ArrayList<>();
-    obs.add(0.0, -61.422);
-    obs.add(2.0, -42.28700013);
-    obs.add(4.0, -58.97612903);
+    final List<Fittable> obs = new ArrayList<>();
+    obs.add(new SimpleFittable(0.0, -61.422));
+    obs.add(new SimpleFittable(2.0, -42.28700013));
+    obs.add(new SimpleFittable(4.0, -58.97612903));
     // ... Lots of lines omitted ...
-    obs.add(498.0, -67.39);
+    obs.add(new SimpleFittable(498.0, -67.39));
 
     // Instantiate the model to fit.
     final PolynomialModelFitter model = new PolynomialModelFitter(2);

diff --git a/hipparchus-fitting/src/test/java/org/hipparchus/fitting/ransac/RansacFitterTest.java b/hipparchus-fitting/src/test/java/org/hipparchus/fitting/ransac/RansacFitterTest.java
@@ -31,6 +31,18 @@
 
 class RansacFitterTest {
 
+    /** Simple implementation of {@link Fittable} wrapping a double array. */
+    private static final class SimpleFittable implements Fittable {
+        private final double[] point;
+        SimpleFittable(final double[] point) {
+            this.point = point.clone();
+        }
+        @Override
+        public double[] getPoint() {
+            return point.clone();
+        }
+    }
+
     @Test
     void testExceptionsOnInitialValues() {
         assertThrows(MathIllegalArgumentException.class, () -> new RansacFitter<>(mockModel(), -1, 6, 1e-6, 10, 1), "-1 is smaller than the minimum (0)");
@@ -52,7 +64,7 @@ void testCanPerfectlyFitALineWithoutNoiseButWithSmallNumberOfOutliers() {
     @Test
     void testCanFitALineWithLargeNumberOfOutliers() throws IOException {
         // This test reproduces the example provided in RANSAC wikipedia page. Results are strongly consistent
-        final List<double[]> points = loadData("line_dataset.csv");
+        final List<Fittable> points = loadData("line_dataset.csv");
         final double standardDeviation = 0.6159842899599051;
         final RansacFitterOutputs<PolynomialModelFitter.Model> fitted = new RansacFitter<>(new PolynomialModelFitter(1), 10, 100, standardDeviation / 3, 10, 1).fit(points);
         Assertions.assertNotNull(fitted);
@@ -65,7 +77,7 @@ void testCanFitALineWithLargeNumberOfOutliers() throws IOException {
     @Test
     void testCanFitAPolynomialOfDegree2WithOutliers() throws IOException {
         // Reference: https://forum.orekit.org/t/addition-of-ransac-algorithm/5102
-        final List<double[]> points = loadData("quadratic_dataset.csv");
+        final List<Fittable> points = loadData("quadratic_dataset.csv");
         final double standardDeviation = 72.59099534185657;
         final RansacFitterOutputs<PolynomialModelFitter.Model> fitted = new RansacFitter<>(new PolynomialModelFitter(2), 10, 1000, standardDeviation / 3, 10, 1).fit(points);
         Assertions.assertNotNull(fitted);
@@ -90,24 +102,24 @@ private void doTestLineFittingWithSmallNumberOfOutliers(final double slopeDelta,
         Assertions.assertEquals(numberOfTrueData, fitted.getBestInliers().size());
     }
 
-    private List<double[]> generateLine(final int seed, final double expectedSlope, final double expectedIntercept,
-                                        final int trueDataCount, final int falseDataCount, final double noiseFactor) {
+    private List<Fittable> generateLine(final int seed, final double expectedSlope, final double expectedIntercept,
+                                         final int trueDataCount, final int falseDataCount, final double noiseFactor) {
         final Random random = new Random(seed);
         final PolynomialModelFitter.Model trueModel = new PolynomialModelFitter.Model(new double[]{expectedIntercept, expectedSlope});
-        final List<double[]> points = IntStream.range(0, trueDataCount)
-                                               .mapToObj(x -> new double[]{x, trueModel.predict(x) + random.nextGaussian() * noiseFactor})
-                                               .collect(Collectors.toList());
-        points.addAll(IntStream.range(0, falseDataCount).mapToObj(x -> new double[]{x * 3, random.nextDouble() * 20}).collect(Collectors.toList()));
+        final List<Fittable> points = IntStream.range(0, trueDataCount)
+                                                .mapToObj(x -> new SimpleFittable(new double[]{x, trueModel.predict(x) + random.nextGaussian() * noiseFactor}))
+                                                .collect(Collectors.toList());
+        points.addAll(IntStream.range(0, falseDataCount).mapToObj(x -> new SimpleFittable(new double[]{x * 3, random.nextDouble() * 20})).collect(Collectors.toList()));
         return points;
     }
 
-    private List<double[]> loadData(final String fileName) {
+    private List<Fittable> loadData(final String fileName) {
         final InputStream inputStream = this.getClass().getResourceAsStream("/" + this.getClass().getSimpleName() + "/" + fileName);
         Assertions.assertNotNull(inputStream, "Could not find resource " + fileName);
         return IOUtil.readLines(inputStream)
                      .stream()
                      .map(line -> line.split(","))
-                     .map(values -> new double[]{Double.parseDouble(values[0]), Double.parseDouble(values[1])})
+                     .map(values -> new SimpleFittable(new double[]{Double.parseDouble(values[0]), Double.parseDouble(values[1])}))
                      .collect(Collectors.toList());
     }
 
@@ -124,4 +136,4 @@ private PolynomialModelFitter.Model getBestModel(final RansacFitterOutputs<Polyn
         return fitted.getBestModel().orElseThrow(() -> new RuntimeException("No model found"));
     }
 
-}
+}
diff --git a/src/changes/changes.xml b/src/changes/changes.xml
@@ -50,6 +50,9 @@ If the output is not quite correct, check for invisible trailing spaces!
   </properties>
   <body>
     <release version="4.1" date="TBD" description="TBD">
+      <action dev="bryan" type="update" issue="issues/462">
+        Replaced double[] arrays with Fittable interface in RANSAC fitting classes.
+      </action>
       <action dev="Marthym" type="update" issue="issues/460">
         Allow small numerical tolerance in SmoothStepFactory.checkBetweenZeroAndOneIncluded
       </action>