Perceptron (unfinished)

src/machine_learning/perceptron/Perceptron.java

@@ -1,6 +1,7 @@
 package machine_learning.perceptron;
 
 import java.util.List;
+import java.util.stream.IntStream;
 
 public class Perceptron
 {
@@ -36,7 +37,7 @@ public class Perceptron
     }
 
     private Vector getInitializationVector(List<Vector> positives, List<Vector> negatives)
-    {/*
+    {
         var a = new Vector(positives.get(0).dimension());
         for (var x : positives)
         {
@@ -49,9 +50,7 @@ public class Perceptron
             b = b.add(x);
         }
 
-        return a.subtract(b);*/
+        return a.subtract(b);
-
-        return new Vector(positives.get(0).dimension());
     }
 
     private boolean elementsAreCorrectClassified(List<Vector> vectors, Vector weight, int expectedClass)
@@ -60,7 +59,7 @@ public class Perceptron
 
         for (var x : vectors)
         {
-            actualClass = weight.scalar(x) > 0 ? 1 : 0;
+            actualClass = scalarForThreshholdPerceptron(weight, x) > weight.get(weight.dimension()-1) ? 1 : 0;
 
             if (actualClass != expectedClass)
             {
@@ -70,4 +69,12 @@ public class Perceptron
 
         return true;
     }
+
+    private double scalarForThreshholdPerceptron(Vector a, Vector b)
+    {
+        return IntStream.range(0,
+                a.dimension()-1)
+                .mapToDouble(i -> a.get(i) * b.get(i))
+                .sum();
+    }
 }

src/machine_learning/perceptron/Vector.java

@@ -33,7 +33,7 @@ public class Vector
     public Vector add(Vector b)
     {
         return new Vector(IntStream.range(0,
-                this.values.size())
+                this.dimension())
                 .mapToObj(i -> this.values.get(i) + b.values.get(i))
                 .collect(Collectors.toCollection(ArrayList::new))
         );
@@ -43,7 +43,7 @@ public class Vector
     public Vector subtract(Vector b)
     {
         return new Vector(IntStream.range(0,
-                this.values.size())
+                this.dimension())
                 .mapToObj(i -> this.values.get(i) - b.values.get(i))
                 .collect(Collectors.toCollection(ArrayList::new))
         );
@@ -52,11 +52,16 @@ public class Vector
     public double scalar(Vector b)
     {
         return IntStream.range(0,
-                this.values.size())
+                this.dimension())
                 .mapToDouble(i -> this.values.get(i) * b.values.get(i))
                 .sum();
     }
 
+    public double get(int index)
+    {
+        return this.values.get(index);
+    }
+
     @Override
     public boolean equals(Object o)
     {

test/machine_learning/perceptron/PerceptronTest.java

@@ -16,18 +16,19 @@ class PerceptronTest
     @BeforeAll
     void initLearnData()
     {
+        double biasUnit = 1d;
         this.positives = new ArrayList<>(List.of(
-                new Vector(List.of(8d, 4d)),
+                new Vector(List.of(8d, 4d, biasUnit)),
-                new Vector(List.of(8d, 6d)),
+                new Vector(List.of(8d, 6d, biasUnit)),
-                new Vector(List.of(9d, 2d)),
+                new Vector(List.of(9d, 2d, biasUnit)),
-                new Vector(List.of(9d, 5d)))
+                new Vector(List.of(9d, 5d, biasUnit)))
         );
 
         this.negatives = new ArrayList<>(List.of(
-                new Vector(List.of(6d, 1d)),
+                new Vector(List.of(6d, 1d, biasUnit)),
-                new Vector(List.of(7d, 3d)),
+                new Vector(List.of(7d, 3d, biasUnit)),
-                new Vector(List.of(8d, 2d)),
+                new Vector(List.of(8d, 2d, biasUnit)),
-                new Vector(List.of(9d, 0d)))
+                new Vector(List.of(9d, 0d, biasUnit)))
         );
     }