Naaturel/ANN-framework
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Fix plotting

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This commit is contained in:
Laurent
2026-05-11 20:23:25 +02:00
parent 9f4a76d9e5
commit 2f37d72efd
3 changed files with 30 additions and 17 deletions
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6
config.json
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@@ -1,14 +1,14 @@
{ {
"model": { "model": {
"new": true, "new": true,
"parameters": [1], "parameters": [10, 5, 1],
"path": "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/snapshots/snapshot-test.json" "path": "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/snapshots/snapshot-test.json"
}, },
"training" : { "training" : {
"learning_rate" : 1.0, "learning_rate" : 0.002,
"max_epoch" : 5000 "max_epoch" : 5000
}, },
"dataset" : { "dataset" : {
"path" : "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and.csv" "path" : "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_2_10.csv"
} }
} }

21
src/main/java/com/naaturel/ANN/Main.java
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@@ -21,7 +21,7 @@ public class Main {
public static void main(String[] args) throws Exception { public static void main(String[] args) throws Exception {
String[] types = {"Simple perceptron", "Gradient descent", "Adaline", "Gradient retro-propagation"}; String[] types = {"Simple perceptron", "Gradient descent", "Adaline", "Gradient backpropagation"};
Scanner sc = new Scanner(System.in); Scanner sc = new Scanner(System.in);
for (int i = 0; i < types.length; i++) { for (int i = 0; i < types.length; i++) {
@@ -77,22 +77,22 @@ public class Main {
if(dataset.getNbrInputs() != 2) return; if(dataset.getNbrInputs() != 2) return;
GraphVisualizer visualizer = new GraphVisualizer(); GraphVisualizer visualizer = new GraphVisualizer();
float min = -10F; float min = 0F;
float max = 10F; float max = 15F;
float step = Math.abs(max - min)/300; float step = Math.abs(max - min)/300;
boolean isOneHot = dataset.getNbrLabels() > 1; boolean isOneHot = dataset.getNbrLabels() > 1;
//plot labels //plot labels
for (DataSetEntry entry : dataset) { for (DataSetEntry entry : dataset) {
List<Float> label = dataset.getLabelsAsFloat(entry); List<Float> labels = dataset.getLabelsAsFloat(entry);
int expectedClass = 0; int expectedClass = 0;
if (isOneHot) { if (isOneHot) {
for (int i = 1; i < label.size(); i++) { for (int i = 1; i < labels.size(); i++) {
if (label.get(i) > label.get(expectedClass)) expectedClass = i; if (labels.get(i) > labels.get(expectedClass)) expectedClass = i;
} }
} else { } else {
expectedClass = Math.round(label.getFirst()); expectedClass = Math.round(labels.getFirst());
} }
visualizer.addPoint("Label " + expectedClass, visualizer.addPoint("Label " + expectedClass,
entry.getData().get(0).getValue(), entry.getData().get(1).getValue()); entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
@@ -105,12 +105,7 @@ public class Main {
List<Float> predictions = new ArrayList<>(); List<Float> predictions = new ArrayList<>();
for (float p : network.predict(new float[]{x, y})) predictions.add(p); for (float p : network.predict(new float[]{x, y})) predictions.add(p);
int predictedClass; int predictedClass = dataset.getPredictedClass(predictions);
if(isOneHot){
predictedClass = predictions.indexOf(Collections.max(predictions));
} else {
predictedClass = Math.round(predictions.getFirst());
}
visualizer.addPoint("Class " + predictedClass, x, y); visualizer.addPoint("Class " + predictedClass, x, y);
} }

20
src/main/java/com/naaturel/ANN/infrastructure/dataset/DataSet.java
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@@ -36,7 +36,6 @@ public class DataSet implements Iterable<DataSetEntry>{
return firstEntry.map(inputs -> this.data.get(inputs).getValues().size()).orElse(0); return firstEntry.map(inputs -> this.data.get(inputs).getValues().size()).orElse(0);
} }
public int size() { public int size() {
return data.size(); return data.size();
} }
@@ -57,6 +56,25 @@ public class DataSet implements Iterable<DataSetEntry>{
return this.data.get(entry).getValues(); return this.data.get(entry).getValues();
} }
public int getPredictedClass(List<Float> values) {
if (nbrLabels == 1) {
// single value — round to nearest known class
float value = values.getFirst();
return data.values().stream()
.map(l -> l.getValues().getFirst())
.distinct()
.min(Comparator.comparingDouble(l -> Math.abs(l - value)))
.map(Math::round)
.orElseThrow();
} else {
// one-hot — return index of max value
int max = 0;
for (int i = 1; i < values.size(); i++)
if (values.get(i) > values.get(max)) max = i;
return max;
}
}
public DataSet toNormalized() { public DataSet toNormalized() {
List<DataSetEntry> entries = this.getData(); List<DataSetEntry> entries = this.getData();