62 lines
1.8 KiB
Java
62 lines
1.8 KiB
Java
package machine_learning.nearest_neighbour;
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import machine_learning.Vector;
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import org.junit.jupiter.api.BeforeAll;
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import org.junit.jupiter.api.Test;
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import org.junit.jupiter.api.TestInstance;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.List;
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import java.util.stream.Stream;
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import static org.junit.jupiter.api.Assertions.*;
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@TestInstance(TestInstance.Lifecycle.PER_CLASS)
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class KNearestNeighbourTest
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{
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List<Vector> positives;
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List<Vector> negatives;
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@BeforeAll
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void initLearnData()
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{
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this.positives = new ArrayList<>(List.of(
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new Vector(8d, 4d),
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new Vector(8d, 6d),
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new Vector(9d, 2d),
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new Vector(9d, 5d))
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);
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this.negatives = new ArrayList<>(List.of(
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new Vector(6d, 1d),
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new Vector(7d, 3d),
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new Vector(8d, 2d),
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new Vector(9d, 0d))
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);
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}
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@Test
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public void shouldReturnCorrectClassForVectorWithKEquals3()
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{
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var kNearestNeighbour = new KNearestNeighbour((a ,b) -> Math.abs(a.get(0) - b.get(0)) + Math.abs(a.get(1) - b.get(1)), 3);
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var vector = new Vector(8, 3.5);
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var actualClass = kNearestNeighbour.kNearestNeighbour(this.positives, this.negatives, vector);
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var expectedClass = DataClass.NEGATIVE;
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assertEquals(expectedClass, actualClass);
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}
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@Test
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public void shouldReturnCorrectClassForVectorWithKEquals5()
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{
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var kNearestNeighbour = new KNearestNeighbour((a ,b) -> Math.abs(a.get(0) - b.get(0)) + Math.abs(a.get(1) - b.get(1)), 5);
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var vector = new Vector(8, 3.5);
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var actualClass = kNearestNeighbour.kNearestNeighbour(this.positives, this.negatives, vector);
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var expectedClass = DataClass.POSITIVE;
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assertEquals(expectedClass, actualClass);
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}
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} |