Java/src/test/java/com/thealgorithms/machinelearning/KMeansTest.java at c093785ec43203d709620cec7114af6468ebc4c3 · TheAlgorithms/Java · GitHub

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package com.thealgorithms.machinelearning;

import static org.junit.jupiter.api.Assertions.assertArrayEquals;
import static org.junit.jupiter.api.Assertions.assertThrows;
import org.junit.jupiter.api.Test;

class KMeansTest {

    @Test
    void testSimpleClustering() {

        double[][] points = {{1.0, 1.0}, {1.2, 1.1}, {8.0, 8.0}, {8.2, 8.1}};

        double[][] centroids = {{1.0, 1.0}, {8.0, 8.0}};

        int[] expected = {0, 0, 1, 1};

        assertArrayEquals(expected, KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testNullCentroids() {
        double[][] points = {{1.0, 1.0}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, null, 100, 0.0001));
    }

    @Test
    void testEmptyDataset() {
        double[][] points = {};
        double[][] centroids = {{1.0, 1.0}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testEmptyPoints() {
        double[][] points = {};
        double[][] centroids = {{1.0, 1.0}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testNoCentroids() {
        double[][] points = {{1.0, 1.0}};

        double[][] centroids = {};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testNonPositiveMaxIterations() {
        double[][] points = {{1.0, 1.0}};

        double[][] centroids = {{1.0, 1.0}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 0, 0.0001));
    }

    @Test
    void testNegativeTolerance() {
        double[][] points = {{1.0, 1.0}};
        double[][] centroids = {{1.0, 1.0}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, -1.0));
    }

    @Test
    void testTooManyCentroids() {
        double[][] points = {{1.0, 1.0}};

        double[][] centroids = {{1.0, 1.0}, {2.0, 2.0}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testDimensionMismatchInPoints() {
        double[][] points = {{1.0, 1.0}, {2.0}};

        double[][] centroids = {{1.0, 1.0}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testDimensionMismatchInCentroids() {
        double[][] points = {{1.0, 1.0}};

        double[][] centroids = {{1.0}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testZeroDimensionPoints() {
        double[][] points = {{}};

        double[][] centroids = {{}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testSingleCluster() {
        double[][] points = {{1.0, 1.0}, {2.0, 2.0}, {3.0, 3.0}};

        double[][] centroids = {{2.0, 2.0}};

        int[] expected = {0, 0, 0};

        assertArrayEquals(expected, KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testEmptyClusterHandling() {
        double[][] points = {{0.0, 0.0}, {0.1, 0.1}, {10.0, 10.0}};

        double[][] centroids = {{0.0, 0.0}, {100.0, 100.0}};

        int[] result = KMeans.cluster(points, centroids, 100, 0.0001);

        assertArrayEquals(new int[]{0, 0, 0}, result);
    }

    @Test
    void testImmediateConvergence() {
        double[][] points = {{1.0, 1.0}, {9.0, 9.0}};

        double[][] centroids = {{1.0, 1.0}, {9.0, 9.0}};

        int[] expected = {0, 1};

        assertArrayEquals(expected, KMeans.cluster(points, centroids, 100, 0.000001));
    }

    @Test
    void testFirstPointNull() {
        double[][] points = {null};

        double[][] centroids = {{1.0, 1.0}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testNullPointInDataset() {
        double[][] points = {{1.0, 1.0}, null};

        double[][] centroids = {{1.0, 1.0}};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, 0.0001));
    }

    @Test
    void testNullCentroidRow() {
        double[][] points = {{1.0, 1.0}};

        double[][] centroids = {null};

        assertThrows(IllegalArgumentException.class, () -> KMeans.cluster(points, centroids, 100, 0.0001));
    }
}