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package dag.auxiliary;

import org.apache.commons.math3.distribution.NormalDistribution;
import org.jgrapht.DirectedGraph;
import org.jgrapht.Graph;
import org.jgrapht.VertexFactory;
import org.jgrapht.generate.GraphGenerator;
import org.jgrapht.graph.AbstractBaseGraph;

import java.util.HashMap;
import java.util.Map;
import java.util.Random;



/**
 *
 * random graph generator : layer by layer generaotr
 * In the academic paper, it is shown below,
 * Algorithm:  Layer-by-Layer method.
 * Require: n, k, p ∈ N.
 * Distribute n vertices between k different sets enumerated as L1, . . . , Lk.
 * Let layer(v) be the layer assigned to vertex v.
 * Let M be an adjacency matrix n×n initialized as the zero matrix.
 * for all i = 1 to n do
 *     for all j = 1 to n do
 *         if layer(j) > layer(i) then
 *             if Random() < p then
 *                 M[i][j] = 1
 *             else
 *                 M[i][j] = 0
 * return a random DAG with k layers and n nodes
 *
 * the original algorithm indicates that a node in layer 2 may connect to nodes in layer 3
 * or layer 4 and so on.
 * I make some changes that the node only can connect to the nodes in the next layer.
 * for example, a node in layer 2 only can connect to the nodes in layer 3.
 *
 *     graph features:
 * 1. there is only one node in the first and the last layer, respectively.
 * 2. the only one node in the first layer connects to every node in the second layer.
 * 3. every node in the second to last layer connects to the only one node in the last layer.
 * 4. layers except the first layer and the last layer are called middle layers.
 * 5. Every node connect to the each node which is in next layer according to some probability.
 * 6. Every node in the middle layers has at least one predecessor and one successor.
 *
 * for example, there is a allocaion of nodes expressed in array.
 * int[][] allocation = {{0},                      // layer 0
 *                       {11, 12, 13, 14}},        // layer 1
 *                       {21, 22, 23, 24, 25},     // layer 2
 *                       {4}};                     // layer 3
 * the number of each entry is the node's name,
 * every one-dimensional array represents the content expressing the corresponding layer
 * the edges are like that,
 * 0 --> 11, 0 --> 12, 0 --> 13, 0 --> 14,
 * 11 --> 21, 11 --> 24,    (according to some probability)
 * 12 --> 22, 12 --> 23, 12 --> 25,     (according to some probability)
 * 13 --> 24, 13 --> 25       (according to some probability)
 * 14 --> 21, 14 --> 23     (according to some probability)
 * 21 --> 4,
 * 22 --> 4,
 * 23 --> 4,
 * 24 --> 4,
 * 25 --> 4,
 *
 */



/**
 * @param <V>
 * @param <E>
 */

public class LayerByLayerGenerator<V, E> implements GraphGenerator<V, E, V> {
    private static final boolean DEFAULT_ALLOW_LOOPS = false;


    private final int num_layer;    // 层数  the number of layer
    private final Random rng;
    private final int n;        // 节点数  the number of node
    private final double p;
    private final boolean loops;
    private HashMap<Integer, Integer> vertex_cost = new HashMap<>();    // every node has its cost
    private int total_cost = 0;
    // 每一层有哪些节点
    private int[][] detail;     // allocation of nodes

    /**
     * Create a new Layer-by-Layer random graph generator. The generator does not create self-loops.
     *
     * @param n the number of nodes
     * @param p the edge probability
     */
    public LayerByLayerGenerator(int n, double p, int num_layer) {
        this(n, p, new Random(), DEFAULT_ALLOW_LOOPS, num_layer);
    }


    /**
     * Create a new Layer-by-Layer random graph generator. The generator does not create self-loops.
     *
     * @param n    the number of nodes
     * @param p    the edge probability
     * @param seed seed for the random number generator
     */
    public LayerByLayerGenerator(int n, double p, long seed, int num_layer) {
        this(n, p, new Random(seed), DEFAULT_ALLOW_LOOPS, num_layer);
    }

    /**
     * Create a new Layer-by-Layer random graph generator.
     *
     * @param n     the number of nodes
     * @param p     the edge probability
     * @param seed  seed for the random number generator
     * @param loops whether the generated graph may create loops
     */
    public LayerByLayerGenerator(int n, double p, long seed, boolean loops, int num_layer) {
        this(n, p, new Random(seed), loops, num_layer);
    }

    /**
     * Create a new Layer-by-Layer random graph generator.
     *
     * @param n         the number of nodes
     * @param p         the edge probability
     * @param rng       the random number generator to use
     * @param loops     whether the generated graph may create loops
     * @param num_layer the number of layer
     */
    public LayerByLayerGenerator(int n, double p, Random rng, boolean loops, int num_layer) {
        if (n < 6) {
            // the number of node can not smaller than 6
            throw new IllegalArgumentException("节点个数不能小于 6 ");
        }
        this.n = n;
        if (p < 0.0 || p > 1.0) {
            throw new IllegalArgumentException("概率P在 (0, 1) 之间");
        }
        if (num_layer < 3 || num_layer > n) {

            throw new IllegalArgumentException("层数不能小于3,且层数不能大于节点数");
        }
        this.num_layer = num_layer;
        this.p = p;
        this.rng = rng;
        this.loops = loops;
    }

    /**
     * Generates a random graph based on the Layer-by-Layer model.
     *
     * @param target        the target graph
     * @param vertexFactory the vertex factory
     * @param resultMap     not used by this generator, can be null
     */
    @Override
    public void generateGraph(Graph<V, E> target, VertexFactory<V> vertexFactory, Map<String, V> resultMap) {
        // special case
        if (n == 0) {
            return;
        }

        // check whether to also create loops
        boolean createLoops = loops;
        if (createLoops) {
            if (target instanceof AbstractBaseGraph<?, ?>) {
                AbstractBaseGraph<V, E> abg = (AbstractBaseGraph<V, E>) target;
                if (!abg.isAllowingLoops()) {
                    throw new IllegalArgumentException(
                            "Provided graph does not support self-loops");
                }
            } else {
                createLoops = false;
            }
        }

        /**
         *  创建节点
         *  create vertices
          */
        // 节点从 0 开始计数
        // name of node start from number 0

        // 操作 HashMap<Integer, Integer> vertex_cost;
        // 设置 每个节点的 执行时间
        int previousVertexSetSize = target.vertexSet().size();
        HashMap<Integer, V> vertices = new HashMap<>(n);
        for (int i = 0; i < n; i++) {
            V v = vertexFactory.createVertex();
            target.addVertex(v);
            vertices.put(i, v);
            // 设置节点的 cost
            // set node's cost value
            int cost = getVertexCost();
            total_cost += cost;
            vertex_cost.put(i, cost);
        }

        if (target.vertexSet().size() != previousVertexSetSize + n) {
            throw new IllegalArgumentException(
                    "Vertex factory did not produce " + n + " distinct vertices.");
        }

        // check if graph is directed
        boolean isDirected = target instanceof DirectedGraph<?, ?>;

        // 确定各层节点数分布情况
        // specify the allocation of the number of node of each layer
        detail = quantifyAllocation(n, num_layer);


        /**
         * 创建边
         * create edge
         *
         * 一层中,一个节点至少要与下一层中的一个节点相连接
         */
        // 对每一层
        // for each layer
        for (int i = 0; i < detail.length - 1; i++) {
            // 判断当前层的下一层中的每个节点是否都至少有一个前驱节点
            // check whether the nodes in middle has at least one predecessor
            boolean[] hasPredecessor = new boolean[detail[i + 1].length];
            for (int index = 0; index < hasPredecessor.length; index++) {
                hasPredecessor[index] = false;
            }
            // 对该层中的每一个元素
            // for each entry in current layer
            for (int j = 0; j < detail[i].length; j++) {
                boolean hasSuccessor = false;
                int a = detail[i][j];
                V s = vertices.get(a);
                // 对下一层中的每一个元素
                // for each entry in next layer
                for (int k = 0; k < detail[i + 1].length; k++) {
                    int b = detail[i + 1][k];
                    V t = vertices.get(b);
                    if (a == b) {
                        if (!createLoops) {
                            // no self-loops`
                            continue;
                        }
                    }
                    if (i == 0 || i == detail.length - 2) {
                        // 第一层和第二层中的所有节点都连接
                        // there is only one node in the first layer and the last layer respectively
                        // the only node in the first layer connects to the every node in the second layer
                        // 倒数第二层和最有一层的所有节点都连接
                        // every node in the second last layer connects to the only one node in the last layer

                        target.addEdge(s, t);
                        hasSuccessor = true;
                        hasPredecessor[k] = true;
                    } else {
                        if (rng.nextDouble() < p) {
                            // 其他层中的节点按照概率进行连接
                            // nodes in other layers connect to the nodes in correspondingly next layer according to a certain probability p
                            // addEdge
                            target.addEdge(s, t);
                            hasSuccessor = true;
                            hasPredecessor[k] = true;
                        }
//                        target.addEdge(s, t);
                    }
                }
                // 如果这个节点没有连接下一层的任何一个节点
                // if the node has no successor
                if (!hasSuccessor) {
                    Random random = new Random();
                    int index = random.nextInt(detail[i + 1].length);
                    int b = detail[i + 1][index];
                    V t = vertices.get(b);
                    target.addEdge(s, t);
                }
            }
            // 处理没有前驱节点的节点
            // process the nodes which has no predecessor
            for (int index = 0; index < hasPredecessor.length; index++) {
                if (!hasPredecessor[index]) {
                    int[] current = detail[i];
                    Random r = new Random();
                    int position = r.nextInt(current.length);
                    int a = current[position];
                    V s = vertices.get(a);
                    int b = detail[i + 1][index];
                    V t = vertices.get(b);
                    target.addEdge(s, t);
                }
            }
        }

//        showGraphInfo();


    }


    /**
     * 确定每一层都有哪些节点
     * specify the allocation of nodes
     *
     * @param n
     * @param num_layer
     * @return
     */
    public int[][] quantifyAllocation(int n, int num_layer) {
        return quantifyAllocation(getNumOfNodeOfEachLayer(n, num_layer));
    }


    /**
     * 确定每一层都是哪些节点
     * determine which nodes are on each level
     *
     * @param allocation
     */
    public int[][] quantifyAllocation(int[] allocation) {
        int[][] result = new int[allocation.length][];
        int value = 0;
        for (int i = 0; i < allocation.length; i++) {
            int[] temp = new int[allocation[i]];
            for (int j = 0; j < allocation[i]; j++) {
                int store = value++;
                temp[j] = store;
            }
            result[i] = temp;
        }
        return result;
    }


    /**
     * 分层方式
     * the way of layering
     * 确定不同层中节点个数
     * specify the number of node in each layer
     * 第一层和最后一层都只有一个节点
     * only one node in the first layer
     * only one node in the last layer
     *
     * @param n
     * @param num_layer
     * @return
     */
    public int[] getNumOfNodeOfEachLayer(int n, int num_layer) {
        int[] result = new int[num_layer];
        result[0] = 1;
        result[num_layer - 1] = 1;
        int available_n = n - 2;
        int available_num_layer = num_layer - 2;
        int mean = available_n / available_num_layer;
        double standardDeviation = 2;
        NormalDistribution normalDistribution = new NormalDistribution(mean, standardDeviation);
//        System.out.println("概要\t\t" + "node : " + n + "\t layer number : " + num_layer + "\tmean : " + mean);

        // 对数组的前一半进行赋值
        // assign values to the first half of array
        for (int i = 1; i < (num_layer / 2); i++) {
            while (true) {
                int random = (int) normalDistribution.sample();
                if (random > 0 && random < mean * 2) {
                    result[i] = random;
                    break;
                }

            }
        }

        // 对数组的后一半进行赋值
        // assign values to the second half of array
        // 使用均值的2倍减去在前半部分对应位置的值,从而获得后半部分对应位置的值


        if (num_layer % 2 == 0) {
            for (int i = 1; i < num_layer / 2; i++) {
                result[num_layer - i - 1] = mean * 2 - result[i];
            }
        } else {
            for (int i = 1; i < (num_layer - 1) / 2; i++) {
                result[num_layer - i - 1] = mean * 2 - result[i];
            }
        }

        // 计算误差
        // calculate error
        int sum = 0;
        for (int c : result) {
            sum += c;
        }
        int error = n - sum;

        // 处理误差
        // deal with error
        if (num_layer % 2 == 0) {
            result[(num_layer - 2) / 2] += error;
        } else {
            result[(num_layer - 1) / 2] += error;
        }

        return result;
    }

    // 显示分层内容
    // demonstrate the result after the layering
    public void showLayerInfo() {
        System.out.println("\t\t\t分层方式  detail");
        int count = 0;
        for (int[] i : detail) {
            System.out.print("layer : " + count++ + "|\t");
            for (int each : i) {
                System.out.print(each + "\t");
            }
            System.out.println();
        }
        System.out.println();
    }



    // 显示所有节点的 cost
    // show all nodes' cost
    public void showAllCost() {
        for (Integer key : vertex_cost.keySet()) {
            System.out.println("vertex : cost\t\t\t" + key + " : " + vertex_cost.get(key));
        }
        System.out.println();
    }





    // 获取一个节点的 cost
    // get a node's cost
    private static int getVertexCost() {
        int period = getPeriod();
        int n = getNumOfVertex();
        int max = period / n;
        while (true) {
            int result = (int) (Math.random() * max);
            if (result > 0) {
                return result;
            }
        }
    }


    // 获取节点数, [1, 30]
    // get the number of node
    private static int getNumOfVertex() {
        int max = 30;
        while (true) {
            int n = (int) (Math.random() * max);
            if (n > 5) {
                return n;
            }
        }
    }


    // 获取周期, [100, 1000]
    // get the period value
    private static int getPeriod() {
        while (true) {
            int a = (int) (Math.random() * 1000);
            if (a >= 100 && a <= 1000) {
                return a;
            }
        }
    }


    // 获取 HashMap<Integer, Integer> vertex_cost;
    // get HashMap<Integer, Integer> vertex_cost;
    public HashMap<Integer, Integer> getVertexCostMap() {
        return vertex_cost;
    }


    // 获取总的 cost
    // get all nodes' cost
    public int getToalCost() {
        return total_cost;
    }


    // 显示 graph 的信息
    // show the graph information
    public void showGraphInfo() {
        System.out.println("------------ graph info from generator -------------------");
        showLayerInfo();
        showAllCost();
        System.out.println("------------- end graph info -----------------");
        System.out.println();
    }



}

HannesWell and others added 30 commits March 17, 2021 13:24
…936)

* [Tour] simplifications and improvements of HamiltonianCycleAlgorithms

TwoOptHeuristicTSP:
	- use tour-copy free path swap
	- use VertexToIntegerMapping

NearestNeighborHeuristicTSP:
	- use array based data structure

PalmerHamiltonianCycle:
	- use array of vertices instead of left and right indices.
	- makes implementation more comprehensible, saves memory and makes it
faster

* Reduce number of changes

TwoOptHeuristicTSP: revert changes in fields and initialization
The intended change was already addressed with another PR.

NearestNeighborHeuristicTSP:
Also adjust the variable-names in NearestNeighborHeuristicTSP

* Add ArrayUtil class
* [SCC] Improvements of StrongConnectivityInspector-algorithms

GabowStrongConnectivityInspector:
- replace Vector by ArrayList
- create HashSets holding the SCC's vertices with the size of each SCC
and use Collections.singleton() for single vertex SCCs

- cosmetic changes:
  - simplify method signatures (just use graph field)
  - rename field "stack" to "S" to comply to paper of Gabow
  - use stack-methods (push()/pop()/peek()) instead of Dequeue methods
  - inline methods of VertexNumber (also could reduce Object creation)

KosarajuStrongConnectivityInspector:
- replace Vector by ArrayList

AbstractStrongConnectivityInspector:
- use edge-supplier
- create HashMap with expected size

StrongConnectivityAlgorithmTest:
- use parameterized JUnit runner to test different implementations
- simplify/unify graph creation and assertions/checks of computed SCCs
- add more test cases

* [Gabow-SCC] avoid Integer-objects and left behind initial node

- use one-based vertex numbers instead of zero-based. Using zero-based
numbers caused the first node of a DFS to be on the stacks twice and
also to remain on stack S and B after DFS has completed, if the first
node is part of an SCC. This also had the effect that cycles that
contain the initial node are detected not before a successor of the
initial node was explored. The example of a three vertex directed ring,
makes this issue clear.

- using one-based numbers allows to use VertexNumber objects in stack B
too. This avoids creation of Integer-objects (which are only cached up
to 127 by default. See Integer$IntegerCache). This is possible because
each vertex is on stack S (and B) at most once and has a unique number
(as long as it is on S).

* [GabowStrongConnectivityInspector] prefix stack-fields with "stack"

AbstractStrongConnectivityInspector use the class-argument constructor
again. The SupplierUtil now uses a performant and a proper
(serializable) supplier.

* Create HashSet with expected size and make constructor protected
* updated dependency versions

* deleted deprecated code

* fixed few javadoc issues

* updated history

* updated release doc

Co-authored-by: Joris Kinable <kinable@amazon.com>
* Fix warnings in tests

Replace deprecated code in order to fix deprecation warnings:
- use hamcreset.MatcherAssert.assertThat instead of
junit.Assert.asserThat
- use assertThrows instead of ExpectedException
- and many more like new Integer(int) oder new ModifiableInteger()

Use assertEquals(expected, actual) instead of
assertTrue(actual==expected) and fix argument order in calls
assertEquals (actual and expected was swapped)

Fix unused warnings by either removing the unused method/variable or by
adding an suppress warning annotation, if removal would disturb the code
structure.

* Revert fix that accidentally broke the tests
… part 1) (#1066)

* [Checkstyle] update to version 8.41 and update DTD-file links

* [CheckStyle] enforce Variable/Method/Type-name conventions for non API

Add variable/method/type naming-convention checkstyle rules.

Fix all variable/method/type naming-convention violations that do not
require modifications of the public API.

* [CheckStyle] suppress Variable/Method/Type-name rules in API elements

Suppress all Variable/Method/Type name violations on API elements.
Fixing those rules requires API modifications.

In case of (static) fields, the fields are already deprecated and a
replacement is provided.

org.jgrapht.nio.DefaultAttribute : make static field 'NULL' final. This
is actually an API break, but I assume it was never intended to make it
modifiable and it was not-final by accident.
* [TSPLIBImporter] Consider multi-space delimiters (#1060)

+ adjust the tests to cover this case

+ use FileReader in GraphImporter

* [TSPLIBImporter] ignore everything after whitespace for metadata values

* Fix accidentally wrong input-data for tests

* Replace trim() by strip() to remove all leading/trailing whitespace

trim() only removes spaces, strip() removes all whitespace.
Allowed are the characters in the Unicode-blocks BASIC_LATIN (equal to
ASCII) and LATIN_1_SUPPLEMENT.
jkinable and others added 30 commits April 13, 2026 10:41
* fix(io): Resolve 3 compiler warnings in jgrapht-io

- Remove 'transitive' from ANTLR requires in module-info.java
- Replace this.setParameter() with direct field access in GEXFExporter
  constructor to avoid this-escape warning
- Make implicit int-to-byte cast explicit in Graph6Sparse6Exporter

* refactor(demo): Extract KnightTour and TourType to own source file

Move KnightTour class and TourType enum (now nested inside KnightTour)
out of WarnsdorffRuleKnightTourHeuristic.java into KnightTour.java to
resolve auxiliary-class-access and module-accessibility warnings.

* fix(demo): Add private constructors to demo utility classes

Prevents default constructors from being exposed in the module's
public API. Affects: CompleteGraphDemo, DependencyDemo,
DirectedGraphDemo, GraphBuilderDemo, GraphMLDemo, LabeledEdges,
PerformanceDemo.

* fix(demo): Migrate JGraphXAdapterDemo from JApplet to JFrame

JApplet is deprecated for removal since Java 9. Rewrite the demo
as a standalone JFrame application. Also make KnightTour and its
inner classes public to match their use in public API signatures.
Update TourType references in test file.

* Fixed a ton of compile warnings

* Fixed javadoc

* Fixed more javadoc

---------

Co-authored-by: kinable <kinable@amazon.com>
Bumps [org.apfloat:apfloat](https://github.com/mtommila/apfloat) from 1.14.0 to 1.15.0.
- [Commits](mtommila/apfloat@1.14.0...1.15.0)

---
updated-dependencies:
- dependency-name: org.apfloat:apfloat
  dependency-version: 1.15.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Bumps [org.apache.maven.plugins:maven-compiler-plugin](https://github.com/apache/maven-compiler-plugin) from 3.14.1 to 3.15.0.
- [Release notes](https://github.com/apache/maven-compiler-plugin/releases)
- [Commits](apache/maven-compiler-plugin@maven-compiler-plugin-3.14.1...maven-compiler-plugin-3.15.0)

---
updated-dependencies:
- dependency-name: org.apache.maven.plugins:maven-compiler-plugin
  dependency-version: 3.15.0
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
…1322)

Bumps [org.apache.maven.plugins:maven-shade-plugin](https://github.com/apache/maven-shade-plugin) from 3.6.1 to 3.6.2.
- [Release notes](https://github.com/apache/maven-shade-plugin/releases)
- [Commits](apache/maven-shade-plugin@maven-shade-plugin-3.6.1...maven-shade-plugin-3.6.2)

---
updated-dependencies:
- dependency-name: org.apache.maven.plugins:maven-shade-plugin
  dependency-version: 3.6.2
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
…1323)

Bumps [org.apache.maven.plugins:maven-jar-plugin](https://github.com/apache/maven-jar-plugin) from 3.4.2 to 3.5.0.
- [Release notes](https://github.com/apache/maven-jar-plugin/releases)
- [Commits](apache/maven-jar-plugin@maven-jar-plugin-3.4.2...maven-jar-plugin-3.5.0)

---
updated-dependencies:
- dependency-name: org.apache.maven.plugins:maven-jar-plugin
  dependency-version: 3.5.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Bumps `maven-surefire-plugin.version` from 3.5.4 to 3.5.5.

Updates `org.apache.maven.surefire:surefire-junit-platform` from 3.5.4 to 3.5.5

Updates `org.apache.maven.plugins:maven-surefire-plugin` from 3.5.4 to 3.5.5
- [Release notes](https://github.com/apache/maven-surefire/releases)
- [Commits](apache/maven-surefire@surefire-3.5.4...surefire-3.5.5)

Updates `org.apache.maven.plugins:maven-failsafe-plugin` from 3.5.4 to 3.5.5
- [Release notes](https://github.com/apache/maven-surefire/releases)
- [Commits](apache/maven-surefire@surefire-3.5.4...surefire-3.5.5)

---
updated-dependencies:
- dependency-name: org.apache.maven.surefire:surefire-junit-platform
  dependency-version: 3.5.5
  dependency-type: direct:production
  update-type: version-update:semver-patch
- dependency-name: org.apache.maven.plugins:maven-surefire-plugin
  dependency-version: 3.5.5
  dependency-type: direct:production
  update-type: version-update:semver-patch
- dependency-name: org.apache.maven.plugins:maven-failsafe-plugin
  dependency-version: 3.5.5
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Bumps [org.apache.commons:commons-text](https://github.com/apache/commons-text) from 1.14.0 to 1.15.0.
- [Changelog](https://github.com/apache/commons-text/blob/master/RELEASE-NOTES.txt)
- [Commits](apache/commons-text@rel/commons-text-1.14.0...rel/commons-text-1.15.0)

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#1327)

Bumps [org.apache.maven.plugins:maven-release-plugin](https://github.com/apache/maven-release) from 3.1.1 to 3.3.1.
- [Release notes](https://github.com/apache/maven-release/releases)
- [Commits](apache/maven-release@maven-release-3.1.1...maven-release-3.3.1)

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Bumps [org.xmlunit:xmlunit-core](https://github.com/xmlunit/xmlunit) from 2.10.3 to 2.11.0.
- [Release notes](https://github.com/xmlunit/xmlunit/releases)
- [Changelog](https://github.com/xmlunit/xmlunit/blob/main/RELEASE_NOTES.md)
- [Commits](xmlunit/xmlunit@v2.10.3...v2.11.0)

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* Bump com.google.guava:guava from 33.3.1-jre to 33.6.0-jre

Bumps [com.google.guava:guava](https://github.com/google/guava) from 33.3.1-jre to 33.6.0-jre.
- [Release notes](https://github.com/google/guava/releases)
- [Commits](https://github.com/google/guava/commits)

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* Update pom.xml to remove failureaccess exclusion

Removed exclusion for 'failureaccess' from dependencies.

* Update pom.xml to add new exclusions

Added exclusions for error_prone_annotations and jsap in pom.xml.

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Co-authored-by: John Sichi <jsichi@gmail.com>
Bumps [com.puppycrawl.tools:checkstyle](https://github.com/checkstyle/checkstyle) from 13.4.0 to 13.4.2.
- [Release notes](https://github.com/checkstyle/checkstyle/releases)
- [Commits](checkstyle/checkstyle@checkstyle-13.4.0...checkstyle-13.4.2)

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…#1330)

Bumps [org.apache.maven.plugins:maven-source-plugin](https://github.com/apache/maven-source-plugin) from 3.3.1 to 3.4.0.
- [Release notes](https://github.com/apache/maven-source-plugin/releases)
- [Commits](apache/maven-source-plugin@maven-source-plugin-3.3.1...maven-source-plugin-3.4.0)

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Bumps [org.sonatype.central:central-publishing-maven-plugin](https://github.com/sonatype/central-publishing-maven-plugin) from 0.9.0 to 0.10.0.
- [Commits](https://github.com/sonatype/central-publishing-maven-plugin/commits)

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…5.0 (#1332)

Bumps [org.apache.maven.plugins:maven-resources-plugin](https://github.com/apache/maven-resources-plugin) from 3.3.1 to 3.5.0.
- [Release notes](https://github.com/apache/maven-resources-plugin/releases)
- [Commits](apache/maven-resources-plugin@maven-resources-plugin-3.3.1...maven-resources-plugin-3.5.0)

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Bumps [org.apfloat:apfloat](https://github.com/mtommila/apfloat) from 1.15.0 to 1.16.0.
- [Commits](mtommila/apfloat@1.15.0...1.16.0)

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…gable spur engines (#1338)

Add a new exact Yen-family implementation, BoundedPrunedYenKShortestPath,
alongside the existing YenKShortestPath, with two separable optimisations
behind a single public class:

1. SpurShortestPathEngine abstraction with two adapter back-ends:
   - DijkstraSpurEngine: thin adapter delegating to DijkstraShortestPath
     via MaskSubgraph for ban handling.
   - AStarSpurEngine: thin adapter delegating to AStarShortestPath via
     MaskSubgraph + an AStarAdmissibleHeuristic built from a one-time
     reverse-distance precomputation. The heuristic is admissible by
     construction (reverse distances are computed on the original graph,
     and removing vertices/edges only increases shortest-path distances),
     so A* remains exact under bans. No shortest-path logic is duplicated.

2. BoundedPrunedYenKShortestPath: bounded-pruned Yen driver that defers
   each spur as a SpurTask keyed by an admissible lower bound and only
   materialises tasks that could still beat the cheapest known candidate.
   Includes an exact impossible-spur skip that eliminates doomed tasks
   before any spur shortest-path query is issued (resolves the path-chain
   pathology cleanly: every spur on a single-path chain has its only
   outgoing edge banned by the Yen rule, so all n-1 spurs are skipped).

Defaults: the no-engine constructor uses DijkstraSpurEngine (mirrors the
spur step of the existing YenKShortestPath); AStarSpurEngine is opt-in
via the two-argument constructor and is recommended for dense graphs or
larger k. The existing YenKShortestPath is unchanged.

Tests (BoundedPrunedYenKShortestPathTest, 24 cases, JUnit 5):
- hand-built edge cases: negative-k, k=0, unreachable sink, source==sink,
  zero-weight edges, single-edge graph, k larger than total paths,
  large-k stress, negative-weight rejection
- explicit cross-engine smoke (Dijkstra + A* vs YenKShortestPath)
- property-style fuzz suites (~115 random graph configurations across
  random DAGs, random cyclic digraphs, and grids), every case asserting
  the same ordered path-weight sequence as YenKShortestPath
- impossible-spur skip behaviour assertions

Benchmark: BoundedPrunedYenKShortestPathPerformance is a JMH benchmark
at the canonical jgrapht-core/src/test/java/org/jgrapht/perf/shortestpath
path, in the same style as KShortestPathsPerformance. On Gnp random
digraphs, BoundedPrunedYenKShortestPath with AStarSpurEngine measures
10.9x faster than YenKShortestPath at n=500 and 27.3x faster at n=1500
(tight measurement: -f 2 -wi 2 -i 8, Cnt=16). The Dijkstra-backed
bounded variant is slower on this workload, so the dense-random-graph
win comes from the A* spur engine; the bounded scheduling layer is
workload-conditional.

References (in BoundedPrunedYenKShortestPath javadoc):
- Yen, J. Y. (1971). Finding the k shortest loopless paths in a network.
- Martins, E. Q. V., & Pascoal, M. M. B. (2003). A new implementation
  of Yen's ranking loopless paths algorithm.
- Aljazzar, H., & Leue, S. (2011). K*: A heuristic search algorithm for
  finding the k shortest paths.

Discussed on jgrapht-dev before opening this PR:
https://groups.google.com/g/jgrapht-dev/c/JHFs5n7dMpI

Co-authored-by: Claude Sonnet 4.7 <noreply@anthropic.com>
Bumps [nokogiri](https://github.com/sparklemotion/nokogiri) from 1.19.1 to 1.19.3.
- [Release notes](https://github.com/sparklemotion/nokogiri/releases)
- [Changelog](https://github.com/sparklemotion/nokogiri/blob/main/CHANGELOG.md)
- [Commits](sparklemotion/nokogiri@v1.19.1...v1.19.3)

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…1341)

* perf(shortestpath): avoid unused pathVertices set and use ArrayDeque in AllDirectedPaths

In AllDirectedPaths.generatePaths the per-pop pathVertices HashSet is
only consulted by the simple-path self-intersection filter, but it was
rebuilt unconditionally on every iteration of the main loop. In
non-simple-paths mode the rebuild is wasted O(path length) work; guard
it behind the simplePathsOnly check. Java short-circuit evaluation
keeps the dereference safe.

Also switch the incompletePaths queue from LinkedList to ArrayDeque to
remove the per-node linked-list overhead. The queue is only used through
the Deque interface (poll / addFirst / add / isEmpty), which ArrayDeque
supports identically with better allocation behavior.

Pure refactor; no behavioral difference. Already covered by
testCycleBehavior on AllDirectedPathsTest, which exercises both
simplePathsOnly modes on a cyclic toy graph.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* perf(shortestpath): add JMH bench for AllDirectedPaths non-simple mode

* test(shortestpath): expand AllDirectedPaths non-simple-mode coverage with brute-force oracle

---------

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
…(V) to run one source search (#1340)

* perf(shortestpath): use one Dijkstra in DijkstraManyToManyShortestPaths.getPaths

Override getPaths(V) on DijkstraManyToManyShortestPaths to run a single
DijkstraClosestFirstIterator over the source via getShortestPathsTree,
instead of inheriting the BaseManyToManyShortestPaths fallback that calls
getPath(source, v) once per vertex of the graph (which re-runs Dijkstra
from the same source via getManyToManyPaths each time, an
O(|V| * (V log V + E)) cost for what should be O(V log V + E)).

The base-class fallback is preserved unchanged for
DefaultManyToManyShortestPaths (which would otherwise bypass the
user-supplied algorithm function) and CHManyToManyShortestPaths (which
would otherwise bypass its contraction-hierarchy preprocessing).

Adds regression tests:
- DijkstraManyToManyShortestPathsTest: oracle agreement vs DijkstraShortestPath
  on reachable/unreachable targets; precondition.
- DefaultManyToManyShortestPathsTest: function-spy proving the user-supplied
  algorithm function is consulted on getPaths(source).
- CHManyToManyShortestPathsTest: oracle agreement on getPaths(source) for
  every vertex of the test graph.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* perf(shortestpath): add JMH bench for DijkstraManyToManyShortestPaths.getPaths

* test(shortestpath): expand DijkstraManyToManyShortestPaths.getPaths coverage with fuzz + edge cases

* test(shortestpath): factor SingleSourcePaths assertCorrectPaths into shared base

Per code review on #1340: extract the SingleSourcePaths
oracle-agreement check into a new BaseManyToManyShortestPathsTest
.assertCorrectPaths(graph, paths, source, targets) helper, alongside
the existing ManyToManyShortestPaths variant.

Replaces the inline DijkstraShortestPath oracle loops in:
- CHManyToManyShortestPathsTest.testGetPathsMatchesDijkstraOracle
- DefaultManyToManyShortestPathsTest
  .testGetPathsConsultsProvidedAlgorithmFunction
- DijkstraManyToManyShortestPathsTest:
    * testGetPathsSingleSourceMatchesDijkstra
    * testGetPathsSingleVertexGraph
    * testGetPathsSourceHasNoOutgoingEdges
    * testGetPathsDisconnectedGraph
    * testGetPathsCyclicGraphWithSelfLoopOnSource
    * testGetPathsFuzzAgainstDijkstraOracle

The helper handles the unreachable-target contract (null path /
+Inf weight) so callers no longer hand-roll the per-target
null/non-null branches. testGetPathsCalledTwiceReturnsConsistentResults
keeps its bespoke pair-of-calls comparison since it does not verify
against an oracle.

Pure refactor. 40/40 m2m tests still pass; full jgrapht-core suite
6885/6885 green; checkstyle + javadoc clean.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
* perf(shortestpath): add forward-pruning preprocessing to AllDirectedPaths

Before this change, `edgeMinDistancesBackwards` walked back from every target
and decorated every edge whose head was backward-reachable to a target within
the budget, including edges whose tail is not forward-reachable from any source
within the budget. Such edges cannot lie on any feasible source -> target walk
and would never be traversed by the forward enumeration in `generatePaths` in
either simple or non-simple mode, so decorating them is wasted preprocessing
work that the subsequent enumeration pays for again per outgoing edge.

This change adds a forward BFS from the source set (`vertexMinDistancesForwards`)
and threads its result into `edgeMinDistancesBackwards`. An edge (u, v) is
retained only when u is forward-reachable from some source and the sandwich
inequality holds:

    dF(u) + 1 + dB(v) <= maxPathLength

where dF(u) is the forward BFS distance from the source set to u and 1 + dB(v)
is the backward BFS distance from v to a target through this edge. Edges that
fail the sandwich cannot appear on any feasible source -> target walk within
the budget. The inequality is written as
`forwardOfSource > maxPathLength - childDistance` to avoid integer overflow at
extreme `maxPathLength` values; the BFS bounds guarantee
`childDistance <= maxPathLength` when `maxPathLength` is set, so the right-hand
side is non-negative.

The optimisation is enabled by default. A setter on the existing instance
exposes the toggle:

    public void setForwardPruning(boolean forwardPruning)
    public boolean isForwardPruning()

The two existing public constructors, `AllDirectedPaths(Graph)` and
`AllDirectedPaths(Graph, PathValidator)`, are unchanged. Callers that want the
historical backward-only preprocessing behaviour exactly can recover it with
`setForwardPruning(false)`. The prune is exact - it never drops an edge that
could lie on a feasible walk - so the produced path set is identical in both
modes. On graphs where the prune never fires (e.g. small dense strongly-
connected digraphs), enabling it adds the cost of one extra O(V + E) BFS per
`getAllPaths` call, which is dominated by the enumeration itself.

The class-level Javadoc is expanded to describe the two-phase preprocessing /
enumeration design and the forward-pruning preprocessing, so the IDE-visible
documentation explains when the toggle is useful without requiring the reader
to find the setter first.

Tests in `AllDirectedPathsTest` are extended with cases that exercise both
`forwardPruning=true` and `forwardPruning=false`:

  - `testTargetReachableButSourceUnreachableBranchIsIgnored` -
    canonical garden-vertex case
  - `testMultipleSourcesPartialGarden` - one source reaches target,
    another is isolated
  - `testUnboundedSimplePathsWithUnreachableGarden` -
    `maxPathLength=null` boundary
  - `testSourceEqualsTargetInDisconnectedGraph` - trivial zero-length walk
  - `testDenseStronglyConnectedLossCase` - bounded overhead, identical results
  - `testFuzzAgainstBruteForce` - 8 seeded random graphs, both modes, simple
    and non-simple, compared as path sets against a brute-force enumeration

Refs: https://groups.google.com/g/jgrapht-dev/c/fhhJk9FVFoo

Co-Authored-By: Claude <noreply@anthropic.com>

* perf(shortestpath): add JMH bench for AllDirectedPaths forward pruning

Two cell families, each parameterised over `forwardPruning` so the cost of
the optimisation is directly comparable to the historical preprocessing
path in the same JVM:

  - Win case: forward chain of `chainLen` vertices 0 -> 1 -> ... -> T plus a
    `gardenSize`-vertex source-disconnected garden whose every vertex has an
    edge into T. Simple-paths mode, `maxPathLength = chainLen + 5`. The
    garden is reachable backwards from T but no garden vertex is reachable
    forwards from the source, so when forward pruning is off the backward
    sweep marks every garden vertex.
  - Loss case: small dense strongly-connected digraph where F = B = V, so
    the forward BFS is pure overhead and the prune never drops anything.
    Bounds the regression cost when forward pruning is enabled on a graph
    it cannot help.

Sample numbers on JDK 21.0.10 (8 measurement iterations, 2s each):

  Win case (chainLen=20):
    gardenSize  off (ms/op)        on (ms/op)         speedup
    500         0.043 +/- 0.001    0.011 +/- 0.001    3.9x
    1000        0.080 +/- 0.002    0.015 +/- 0.001    5.3x
    2000        0.171 +/- 0.014    0.022 +/- 0.001    7.8x

  Loss case (n=20, maxPathLength=3):
    off: 0.137 +/- 0.008 ms/op   on: 0.141 +/- 0.003 ms/op (within noise)

Co-Authored-By: Claude <noreply@anthropic.com>

---------

Co-authored-by: Claude <noreply@anthropic.com>
Bumps [faraday](https://github.com/lostisland/faraday) from 2.14.1 to 2.14.2.
- [Release notes](https://github.com/lostisland/faraday/releases)
- [Changelog](https://github.com/lostisland/faraday/blob/main/CHANGELOG.md)
- [Commits](lostisland/faraday@v2.14.1...v2.14.2)

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  dependency-version: 2.14.2
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Bumps `junit.version` from 6.0.3 to 6.1.0.

Updates `org.junit.jupiter:junit-jupiter` from 6.0.3 to 6.1.0
- [Release notes](https://github.com/junit-team/junit-framework/releases)
- [Commits](junit-team/junit-framework@r6.0.3...r6.1.0)

Updates `org.junit.platform:junit-platform-suite` from 6.0.3 to 6.1.0
- [Release notes](https://github.com/junit-team/junit-framework/releases)
- [Commits](junit-team/junit-framework@r6.0.3...r6.1.0)

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* chore(perf-bench): add generic graph-loading utilities for benchmarks

Adds a small package of reusable test-scope utilities for spinning up JMH
benchmarks against external graphs:

- WeightedEdgeListCsvReader: src,dst,weight CSV (gzip/plain) into a target
  Graph, with custom vertex parsers, header/delimiter options, and an
  add-missing-vertices toggle for callers that pre-add the vertex set.
- CoordinatesCsvReader: id,lat,lon CSV into Map<V, double[]> for use with
  geographic A* heuristics.
- HaversineHeuristic: generic AStarAdmissibleHeuristic over a coordinate
  lookup, with configurable sphere radius.
- JmhBenchRunner: helper that runs a JMH bench class with forks=0 so it
  inherits the surefire JVM module path / argLine, and writes the text
  summary to a caller-supplied path.

Each utility ships with JUnit 5 tests; pom.xml adds --add-exports for the
new perf.util package so surefire can resolve the tests.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* chore(perf-bench): add Andorra OSM loader and example benches (data not bundled)

Ships the Java side of the Andorra road-graph benchmark template:

- AndorraGraphLoader: thin wrapper over WeightedEdgeListCsvReader and
  CoordinatesCsvReader; returns the loaded graph plus a coordinate map
  that pairs with HaversineHeuristic. Adds isFixtureAvailable() and a
  helpful IllegalStateException pointing at the README when fixtures
  are missing.
- AndorraGraphLoaderSmokeTest: skips via Assumptions.assumeTrue when
  fixtures are absent, so the suite stays green on clean checkouts.
- AndorraDijkstraManyToManyShortestPathsBench: AverageTime template for
  DijkstraManyToManyShortestPaths on the Andorra graph.
- AndorraAllDirectedPathsNonSimpleBench: bounded-walk enumeration over
  a BFS ball around Andorra la Vella.
- AndorraBenchSuite: surefire entry points dispatched via JmhBenchRunner.
- scripts/andorra_to_csv.py: GPKG -> CSV preprocessor, also documented
  for use against any Geofabrik free-tier OSM extract.
- src/test/resources/perf/osm/README.md: download + preprocess
  instructions; the raw 700KB CSV.gz fixtures are not bundled because
  the data is freely re-derivable from Geofabrik (OpenStreetMap) and
  changes upstream daily.

pom.xml adds --add-exports / --add-opens for the perf.shortestpath.osm
package; .gitignore needs no allow-list because no binaries land in
src/test/resources/perf/.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* chore(perf-bench): replace Python GPKG preprocessor with Java port

Drops scripts/andorra_to_csv.py in favour of an in-repo Java preprocessor
that fits the JGraphT toolchain. The new class lives next to the generic
readers under org.jgrapht.perf.util and is invoked through Maven /
java -cp like every other test asset; no Python interpreter required.

- GpkgRoadGraphPreprocessor: reads a Geofabrik free-tier GPKG via
  sqlite-jdbc, parses the GPKG-wrapped WKB LINESTRING blobs by hand
  (the only geometry type the road layer uses), assigns dense vertex
  ids by coordinate snapping at 1e-7 deg, runs Kosaraju to pick the
  largest SCC, deduplicates parallel edges keeping the shortest weight,
  and writes the two CSVs in the exact schema the loader expects.
- pom.xml additions: sqlite-jdbc 3.50.3.0 in parent dependencyManagement
  (test scope), referenced in jgrapht-core. java.sql is added to test
  compile / test runtime via maven-compiler-plugin testCompilerArgs and
  surefire argLine respectively so the production module-info stays
  java.sql-free.
- README.md update: invocation example switched from python ... to
  mvn exec:java / java -cp.
- AndorraGraphLoader javadoc: cross-reference updated to
  GpkgRoadGraphPreprocessor.

Verified by running the Java preprocessor against the Andorra GPKG and
feeding its output through AndorraGraphLoaderSmokeTest: 36,618 vertices
/ 67,354 edges in the largest SCC, identical to the Python preprocessor
output, Dijkstra route 0 -> last vertex succeeds, Haversine heuristic
admissible.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* chore(osm): refactor OSM integration into new jgrapht-osm module

Restructures the OSM preprocessor / loader / heuristic into a new
top-level jgrapht-osm module so the OSM integration is application-
facing production code rather than jgrapht-core test infrastructure,
per dev-list feedback (John Sichi, 2026-05-23).

What moves:
- org.jgrapht.osm.GpkgRoadGraphPreprocessor (was perf.util in core)
- org.jgrapht.osm.HaversineHeuristic (was perf.util in core)
- org.jgrapht.osm.AndorraGraphLoader (was perf.shortestpath.osm in core)
- org.jgrapht.osm.AndorraGraphLoaderSmokeTest (same)
- org.jgrapht.osm.perf.AndorraDijkstraManyToManyShortestPathsBench
- org.jgrapht.osm.perf.AndorraAllDirectedPathsNonSimpleBench
- org.jgrapht.osm.perf.AndorraBenchSuite
- org.jgrapht.osm.perf.JmhBenchRunner

Production scope now: GpkgRoadGraphPreprocessor, HaversineHeuristic,
OsmCsvGraphLoader, OsmCoordinatesReader. Test scope: the Andorra
fixture helper + smoke test + the JMH bench templates.

What drops:
- WeightedEdgeListCsvReader (replaced by jgrapht-io CSVImporter; the
  preprocessor now writes headerless CSVs that CSVImporter consumes
  directly with CSVFormat.EDGE_LIST + EDGE_WEIGHTS=true).
- CoordinatesCsvReader (replaced by OsmCoordinatesReader, which is
  geographic-specific and lives in the OSM module).

What disappears from jgrapht-core:
- sqlite-jdbc dependency (only jgrapht-osm needs it; now compile scope
  there, not test scope in core).
- --add-modules java.sql / --add-reads org.jgrapht.core=java.sql in
  testCompile and surefire argLine -- the new module declares
  requires java.sql properly in its module-info.
- perf.shortestpath.osm and perf.util add-exports / add-opens from the
  surefire argLine.

The parent pom adds jgrapht-osm to the module list and
dependencyManagement. sqlite-jdbc is no longer test-scoped in
dependencyManagement so the new module can use it at compile time.

The committed test resources directory under
jgrapht-core/src/test/resources/perf/osm/ is removed; its README and
fixture-loading conventions move to jgrapht-osm/src/test/resources/perf/osm/.
Andorra CSV fixtures remain not bundled and are re-derived from
Geofabrik via the Java preprocessor.

Validation:
- mvn -pl jgrapht-osm test: 16/16 pass (Andorra smoke + 3 unit suites).
- mvn -pl jgrapht-core test: 6914/6914 pass (15 unrelated skips).
- AndorraDijkstraManyToManyShortestPathsBench smoke via JMH command
  line runs end-to-end through the new module.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* chore(osm): bundle tiny sample fixture so smoke test always runs

Per dev-list feedback (John Sichi, 2026-05-24): include a tiny sample
fixture in the module instead of skipping the smoke test when the
contributor-fetched Andorra CSVs are absent.

Adds two ~50-byte gzipped CSVs under
jgrapht-osm/src/test/resources/perf/osm/:

- sample-edges.csv.gz: 10 directed edges between 5 vertices, all of
  weight 111.1949 m (one 0.001-degree step).
- sample-nodes.csv.gz: 5 vertices placed at the equator on 0.001-degree
  grid points so the Haversine math comes out to clean values.

The graph topology is:

      2 (0.001, 0.001)
      |
 4 -- 0 -- 1 -- 2
      |
      3 (0.001, 0)

Adds SampleGraphSmokeTest: loads the fixture via OsmCsvGraphLoader and
OsmCoordinatesReader, asserts the expected counts, asserts every edge
weight matches 111.1949 m within 1e-4 tolerance, runs Dijkstra 0 -> 2
and asserts the two-hop shortest-path weight (2 * 111.1949), and
asserts the Haversine heuristic produces the expected ~157.25 m
diagonal and is admissible against the routed path. The test runs on
every checkout with no external setup.

AndorraGraphLoaderSmokeTest still skips via Assumptions.assumeTrue
when the Andorra CSVs are absent; it remains the opt-in scale check
for contributors who have fetched the Geofabrik extract.

.gitignore: adds an allow-list entry so the sample-*.csv.gz files
pass the default *.gz block, scoped to the sample-* naming convention
so the larger andorra-*.csv.gz fixtures stay gitignored.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* fix(osm): drop brittle exact-count assertions in Andorra smoke test

Reported by John Sichi against PR #1344: the smoke test failed for him
because Geofabrik refreshes the upstream OSM extract daily, so the
exact largest-SCC vertex and edge counts drift between contributors
who download the GPKG on different days. The original assertions
pinned the snapshot I generated against on 2026-05-10.

Replaces the brittle exact-count checks with:

- A sanity floor on vertex count (>= 10_000) that still catches a
  loader silently dropping most of the graph but tolerates upstream
  drift around the typical 36-37k.
- A relationship check: directed edge count >= vertex count (every
  bidirectional OSM road segment produces two directed edges, so this
  is a structural invariant for a road graph).
- coords.size() == graph.vertexSet().size() and
  coords.keySet().containsAll(graph.vertexSet()), which are real
  loader-correctness invariants regardless of snapshot.

The Dijkstra-route-through and Haversine-admissibility assertions are
kept unchanged.

Also softens the javadoc in AndorraDijkstraManyToManyShortestPathsBench
to "~36-37k vertices / ~67k edges depending on the upstream Geofabrik
snapshot" so the docs do not promise the same numbers either.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* chore(osm): wire dist + README + scope sqlite-jdbc to jgrapht-osm

Maintainer review feedback on PR #1344:

1. Move sqlite-jdbc dependency declaration off the root parent pom and
   into jgrapht-osm only. The parent pom's dependencyManagement no
   longer references sqlite-jdbc; the jgrapht-osm pom declares both
   the version property and the dependency inline. This keeps the
   dependency footprint of the parent build minimal and matches the
   pattern for module-specific deps in this repo.

2. Add a jgrapht-osm dependency to jgrapht-dist so that
   jgrapht-osm-x.y.z.jar (and sqlite-jdbc transitively) lands in the
   release zip and tar.gz archives under lib/.

3. Update the top-level README:
   - "Release Contents" section lists the new jgrapht-osm jar and the
     sqlite-jdbc dependency jar.
   - "Dependencies" section describes sqlite-jdbc and the modules that
     require it.

Verified by running mvn -pl jgrapht-dist -am package and confirming
both lib/jgrapht-osm-1.6.0-SNAPSHOT.jar and
lib/sqlite-jdbc-3.50.3.0.jar are present inside
jgrapht-1.6.0-SNAPSHOT.zip.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* chore(osm): apply DRY review feedback on PR #1344

Three small cleanups requested by John Sichi during review:

1. Haversine math now lives in a single place. HaversineHeuristic
   exposes a public static distanceMeters(lat1, lon1, lat2, lon2[,
   radiusMeters]); getCostEstimate delegates to it. The private
   haversineMeters duplicate inside GpkgRoadGraphPreprocessor is gone,
   along with the now-redundant EARTH_RADIUS_M re-export.

2. The three `new long[] { src, dst, Double.doubleToRawLongBits(weight) }`
   inline calls in the segment-ingestion lambda collapse to a single
   private static packEdge(int, int, double) helper.

3. The gzipOf test helper that was duplicated as a private static in
   OsmCsvGraphLoaderTest and OsmCoordinatesReaderTest moves to a
   package-private TestStreams class shared by both. Each test imports
   the helper via `import static org.jgrapht.osm.TestStreams.*`.

Behaviour unchanged. mvn -pl jgrapht-osm -am test still green
(17/17 osm tests + 6914/6914 core + io).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
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