Add support for loading and saving Neural Network

src/main/groovy/net/zomis/machlearn/neural/NeuralNetwork.groovy

@@ -1,5 +1,6 @@
 package net.zomis.machlearn.neural
 
+import java.nio.file.Path
 import java.util.stream.Stream
 
 class NeuralNetwork {
@@ -44,6 +45,53 @@ class NeuralNetwork {
         println()
     }
 
+    void save(OutputStream output) {
+        new DataOutputStream(output).withCloseable {
+            it.writeInt(layerCount)
+            for (NeuronLayer layer : layers) {
+                it.writeInt(layer.size())
+                it.writeInt(layer.name.length())
+                it.writeBytes(layer.name)
+            }
+            for (NeuronLayer layer : layers) {
+                for (Neuron neuron : layer) {
+                    for (NeuronLink link : neuron.inputs) {
+                        it.writeDouble(link.weight)
+                    }
+                }
+            }
+        }
+    }
+
+    static NeuralNetwork load(InputStream input) {
+        def network = new NeuralNetwork()
+        new DataInputStream(input).withCloseable {
+            int layers = it.readInt()
+            for (int i = 0; i < layers; i++) {
+                int size = it.readInt()
+                int nameLength = it.readInt()
+                StringBuilder name = new StringBuilder()
+                for (int nameIndex = 0; nameIndex < nameLength; nameIndex++) {
+                    name.append((char) it.readByte())
+                }
+                NeuronLayer layer = network.createLayer(name.toString())
+                size.times { layer.createNeuron() }
+            }
+            for (int i = 0; i < layers; i++) {
+                NeuronLayer layer = network.getLayer(i)
+                if (i > 0) {
+                    layer.neurons.each {it.addInputs(network.getLayer(i - 1))}
+                }
+                for (Neuron neuron : layer) {
+                    for (NeuronLink link : neuron.inputs) {
+                        link.weight = it.readDouble()
+                    }
+                }
+            }
+        }
+        network
+    }
+
     double[] run(double[] input) {
         double[] output = new double[outputLayer.size()]
         assert input.length == inputLayer.size()

src/test/groovy/net/zomis/machlearn/neural/LoadSaveTest.groovy

@@ -0,0 +1,56 @@
+package net.zomis.machlearn.neural
+
+import org.junit.Test
+
+class LoadSaveTest {
+
+    @Test
+    void simpleLoadSave() {
+        List<LearningData> examples = Arrays.asList(
+                new LearningData([0, 0] as double[], [0] as double[]),
+                new LearningData([0, 1] as double[], [1] as double[]),
+                new LearningData([1, 0] as double[], [1] as double[]),
+                new LearningData([1, 1] as double[], [0] as double[]),
+        )
+        def network = new NeuralNetwork()
+        def inputLayer = network.createLayer('IN')
+        inputLayer.createNeuron()
+        inputLayer.createNeuron()
+
+        def middleLayer = network.createLayer('MIDDLE')
+        middleLayer.createNeuron().addInputs(inputLayer)
+        middleLayer.createNeuron().addInputs(inputLayer)
+
+        def outputLayer = network.createLayer('OUT')
+        outputLayer.createNeuron().addInputs(middleLayer)
+        new BackPropagation(0.2, 100000).backPropagationLearning(examples, network, new Random(42))
+
+        def savedNetwork = new ByteArrayOutputStream()
+        network.save(savedNetwork)
+
+        assert savedNetwork.toByteArray() == LoadSaveTest.class.getClassLoader()
+            .getResource('simplenetwork.network').bytes
+    }
+
+    @Test
+    void loadTest() {
+        def network = NeuralNetwork.load(LoadSaveTest.class.getClassLoader()
+            .getResourceAsStream('simplenetwork.network'))
+        assert network.layerCount == 3
+        assert network.getLayer(0).size() == 2
+        assert network.getLayer(1).size() == 2
+        assert network.getLayer(2).size() == 1
+
+        assert network.getLayer(0).name == 'IN'
+        assert network.getLayer(1).name == 'MIDDLE'
+        assert network.getLayer(2).name == 'OUT'
+        def loadedNetwork = new ByteArrayOutputStream()
+        network.save(loadedNetwork)
+
+        def runResult = network.run([1, 0] as double[])
+        println runResult
+        assert loadedNetwork.toByteArray() == LoadSaveTest.class.getClassLoader()
+                .getResource('simplenetwork.network').bytes
+    }
+
+}