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package machine_learning.perceptron;
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import machine_learning.DataClass;
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import machine_learning.Vector;
import org.junit.jupiter.api.*;
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import java.util.ArrayList;
import java.util.List;
@TestInstance(TestInstance.Lifecycle.PER_CLASS)
class PerceptronTest
{
List<Vector> positives;
List<Vector> negatives;
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Perceptron perceptron;
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@BeforeAll
void initLearnData()
{
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double biasUnit = 1d;
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this.positives = new ArrayList<>(List.of(
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new Vector(8d, 4d, biasUnit),
new Vector(8d, 6d, biasUnit),
new Vector(9d, 2d, biasUnit),
new Vector(9d, 5d, biasUnit))
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);
this.negatives = new ArrayList<>(List.of(
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new Vector(6d, 1d, biasUnit),
new Vector(7d, 3d, biasUnit),
new Vector(8d, 2d, biasUnit),
new Vector(9d, 0d, biasUnit))
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);
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this.perceptron = new Perceptron();
this.perceptron.learn(this.positives, this.negatives);
}
@Test
void shouldClassifyVectorCorrectAsNegative()
{
var vector = new Vector(0d, 0d, 1d);
var actualClass = this.perceptron.classify(vector);
var expectedClass = DataClass.NEGATIVE;
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Assertions.assertEquals(expectedClass, actualClass);
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}
@Test
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void shouldClassifyVectorCorrectAsPositive()
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{
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var vector = new Vector(9d, 3d, 1d);
var actualClass = this.perceptron.classify(vector);
var expectedClass = DataClass.POSITIVE;
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Assertions.assertEquals(expectedClass, actualClass);
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}
}