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Add multi-layer support

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Laurent
2026-03-26 21:21:31 +01:00
parent 3dd4404f51
commit 64bc830f18
30 changed files with 228 additions and 172 deletions
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7
src/test/java/adaline/AdalineTest.java
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@@ -1,7 +1,7 @@
package adaline;
import com.naaturel.ANN.domain.abstraction.Neuron;
import com.naaturel.ANN.domain.model.neuron.Neuron;
import com.naaturel.ANN.domain.abstraction.TrainingStep;
import com.naaturel.ANN.domain.model.dataset.DataSet;
import com.naaturel.ANN.domain.model.dataset.DatasetExtractor;
@@ -9,7 +9,6 @@ import com.naaturel.ANN.domain.model.neuron.*;
import com.naaturel.ANN.domain.model.training.TrainingPipeline;
import com.naaturel.ANN.implementation.adaline.AdalineTrainingContext;
import com.naaturel.ANN.implementation.gradientDescent.*;
import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
import com.naaturel.ANN.implementation.simplePerceptron.SimpleCorrectionStrategy;
import com.naaturel.ANN.implementation.simplePerceptron.SimpleDeltaStrategy;
import com.naaturel.ANN.implementation.simplePerceptron.SimpleErrorRegistrationStrategy;
@@ -37,7 +36,7 @@ public class AdalineTest {
@BeforeEach
public void init(){
dataset = new DatasetExtractor()
.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and-gradient.csv");
.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and-gradient.csv", 1);
List<Synapse> syns = new ArrayList<>();
syns.add(new Synapse(new Input(0), new Weight(0)));
@@ -45,7 +44,7 @@ public class AdalineTest {
bias = new Bias(new Weight(0));
Neuron neuron = new SimplePerceptron(syns, bias, new Linear());
Neuron neuron = new Neuron(syns, bias, new Linear());
Layer layer = new Layer(List.of(neuron));
network = new Network(List.of(layer));

8
src/test/java/gradientDescent/GradientDescentTest.java
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@@ -1,13 +1,12 @@
package gradientDescent;
import com.naaturel.ANN.domain.abstraction.Neuron;
import com.naaturel.ANN.domain.model.neuron.Neuron;
import com.naaturel.ANN.domain.abstraction.TrainingStep;
import com.naaturel.ANN.domain.model.dataset.DataSet;
import com.naaturel.ANN.domain.model.dataset.DatasetExtractor;
import com.naaturel.ANN.domain.model.neuron.*;
import com.naaturel.ANN.domain.model.training.TrainingPipeline;
import com.naaturel.ANN.implementation.gradientDescent.*;
import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
import com.naaturel.ANN.implementation.simplePerceptron.*;
import com.naaturel.ANN.implementation.training.steps.*;
import org.junit.jupiter.api.BeforeEach;
@@ -15,7 +14,6 @@ import org.junit.jupiter.api.Test;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.atomic.AtomicInteger;
import static org.junit.jupiter.api.Assertions.*;
@@ -34,7 +32,7 @@ public class GradientDescentTest {
@BeforeEach
public void init(){
dataset = new DatasetExtractor()
.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and-gradient.csv");
.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and-gradient.csv", 1);
List<Synapse> syns = new ArrayList<>();
syns.add(new Synapse(new Input(0), new Weight(0)));
@@ -42,7 +40,7 @@ public class GradientDescentTest {
bias = new Bias(new Weight(0));
Neuron neuron = new SimplePerceptron(syns, bias, new Linear());
Neuron neuron = new Neuron(syns, bias, new Linear());
Layer layer = new Layer(List.of(neuron));
network = new Network(List.of(layer));

7
src/test/java/perceptron/SimplePerceptronTest.java
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@@ -1,12 +1,11 @@
package perceptron;
import com.naaturel.ANN.domain.abstraction.Neuron;
import com.naaturel.ANN.domain.model.neuron.Neuron;
import com.naaturel.ANN.domain.abstraction.TrainingStep;
import com.naaturel.ANN.domain.model.dataset.DataSet;
import com.naaturel.ANN.domain.model.dataset.DatasetExtractor;
import com.naaturel.ANN.domain.model.neuron.*;
import com.naaturel.ANN.domain.model.training.TrainingPipeline;
import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
import com.naaturel.ANN.implementation.simplePerceptron.*;
import com.naaturel.ANN.implementation.training.steps.*;
import org.junit.jupiter.api.BeforeEach;
@@ -32,7 +31,7 @@ public class SimplePerceptronTest {
@BeforeEach
public void init(){
dataset = new DatasetExtractor()
.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and.csv");
.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and.csv", 1);
List<Synapse> syns = new ArrayList<>();
syns.add(new Synapse(new Input(0), new Weight(0)));
@@ -40,7 +39,7 @@ public class SimplePerceptronTest {
bias = new Bias(new Weight(0));
Neuron neuron = new SimplePerceptron(syns, bias, new Heaviside());
Neuron neuron = new Neuron(syns, bias, new Heaviside());
Layer layer = new Layer(List.of(neuron));
network = new Network(List.of(layer));