79 lines
2.9 KiB
Java
79 lines
2.9 KiB
Java
package com.naaturel.ANN;
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import com.naaturel.ANN.domain.model.neuron.Neuron;
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import com.naaturel.ANN.domain.abstraction.Trainer;
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import com.naaturel.ANN.implementation.gradientDescent.Linear;
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import com.naaturel.ANN.implementation.multiLayers.Sigmoid;
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import com.naaturel.ANN.implementation.multiLayers.TanH;
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import com.naaturel.ANN.implementation.training.GradientBackpropagationTraining;
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import com.naaturel.ANN.infrastructure.dataset.DataSet;
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import com.naaturel.ANN.infrastructure.dataset.DataSetEntry;
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import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
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import com.naaturel.ANN.domain.model.neuron.*;
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import com.naaturel.ANN.infrastructure.graph.GraphVisualizer;
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import java.io.Console;
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import java.util.*;
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public class Main {
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public static void main(String[] args){
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int nbrClass = 1;
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DataSet dataset = new DatasetExtractor()
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.extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_2_9.csv", nbrClass);
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int[] neuronPerLayer = new int[]{10, 5, 5, dataset.getNbrLabels()};
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int nbrInput = dataset.getNbrInputs();
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List<Layer> layers = new ArrayList<>();
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for (int i = 0; i < neuronPerLayer.length; i++){
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List<Neuron> neurons = new ArrayList<>();
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for (int j = 0; j < neuronPerLayer[i]; j++){
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int nbrSyn = i == 0 ? nbrInput: neuronPerLayer[i-1];
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List<Synapse> syns = new ArrayList<>();
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for (int k=0; k < nbrSyn; k++){
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syns.add(new Synapse(new Input(0), new Weight()));
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}
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Bias bias = new Bias(new Weight());
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Neuron n = new Neuron(syns.toArray(new Synapse[0]), bias, new TanH());
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neurons.add(n);
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}
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Layer layer = new Layer(neurons.toArray(new Neuron[0]));
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layers.add(layer);
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}
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FullyConnectedNetwork network = new FullyConnectedNetwork(layers.toArray(new Layer[0]));
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Trainer trainer = new GradientBackpropagationTraining();
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trainer.train(0.0005F, 15000, network, dataset);
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GraphVisualizer visualizer = new GraphVisualizer();
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for (DataSetEntry entry : dataset) {
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List<Float> label = dataset.getLabelsAsFloat(entry);
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visualizer.addPoint("Label " + label.getFirst(), entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
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}
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float min = 0F;
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float max = 15F;
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float step = 0.03F;
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for (float x = min; x < max; x+=step){
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for (float y = min; y < max; y+=step){
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float prediction = network.predict(List.of(new Input(x), new Input(y))).getFirst();
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float predSeries = prediction > 0.5F ? 1 : -1;
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visualizer.addPoint(Float.toString(predSeries), x, y);
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}
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}
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visualizer.buildScatterGraph((int)min-1, (int)max+1);
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}
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}
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