toString for neural network

README.md

@@ -21,6 +21,6 @@ Add following snippets to your ``pom.xml`` file:
         <dependency>    
             <groupId>neuralnetwork</groupId>    
             <artifactId>neural-network-repo</artifactId>    
-            <version>1.4</version>    
+            <version>1.5</version>    
         </dependency>    
     </dependencies>    

neuralnetwork/neural-network-repo/1.5/neural-network-repo-1.5-javadoc.jar.md5

@@ -0,0 +1 @@
+90ce733e2d3af43f745b6240cf6244e8

neuralnetwork/neural-network-repo/1.5/neural-network-repo-1.5-javadoc.jar.sha1

@@ -0,0 +1 @@
+c3e01fe28a9fb81133ad0f5c68306dbe716c3b34

neuralnetwork/neural-network-repo/1.5/neural-network-repo-1.5-sources.jar.md5

@@ -0,0 +1 @@
+8b84ce67aa39a1d7ad497ea05625ab40

neuralnetwork/neural-network-repo/1.5/neural-network-repo-1.5-sources.jar.sha1

@@ -0,0 +1 @@
+e5d1a9ed557d37e0e9829433546cecf7b7a84a91

neuralnetwork/neural-network-repo/1.5/neural-network-repo-1.5.jar.md5

@@ -0,0 +1 @@
+2ee03fdaac4b983835432e947683be66

neuralnetwork/neural-network-repo/1.5/neural-network-repo-1.5.jar.sha1

@@ -0,0 +1 @@
+6dbbcebd0009036fa7ec90b98a3fbdc5ea3bb5f0

neuralnetwork/neural-network-repo/1.5/neural-network-repo-1.5.pom

@@ -0,0 +1,51 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>neuralnetwork</groupId>
+    <artifactId>neural-network-repo</artifactId>
+    <version>1.5</version>
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <version>2.3.2</version>
+                <configuration>
+                    <source>1.8</source>
+                    <target>1.8</target>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+    <dependencies>
+        <dependency>
+            <groupId>junit</groupId>
+            <artifactId>junit</artifactId>
+            <version>4.13</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.jetbrains</groupId>
+            <artifactId>annotations</artifactId>
+            <version>20.1.0</version>
+            <scope>compile</scope>
+        </dependency>
+    </dependencies>
+
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <github.repo>file:///C:/Users/Lena Papailiou/git/neuralnetwork/</github.repo>
+    </properties>
+
+    <distributionManagement>
+        <repository>
+            <id>neural-network-repo</id>
+            <url>${github.repo}</url>
+        </repository>
+    </distributionManagement>
+
+
+</project>

neuralnetwork/neural-network-repo/1.5/neural-network-repo-1.5.pom.md5

@@ -0,0 +1 @@
+152df5e00b40c715e237b3dbfbc9fe04

neuralnetwork/neural-network-repo/1.5/neural-network-repo-1.5.pom.sha1

@@ -0,0 +1 @@
+23033143be7679a8b3f3f685a03f438a969614a5

neuralnetwork/neural-network-repo/maven-metadata.xml

@@ -3,12 +3,13 @@
   <groupId>neuralnetwork</groupId>
   <artifactId>neural-network-repo</artifactId>
   <versioning>
-    <release>1.4</release>
+    <release>1.5</release>
     <versions>
       <version>1.2</version>
       <version>1.3</version>
       <version>1.4</version>
+      <version>1.5</version>
     </versions>
-    <lastUpdated>20201002132431</lastUpdated>
+    <lastUpdated>20201002202431</lastUpdated>
   </versioning>
 </metadata>

neuralnetwork/neural-network-repo/maven-metadata.xml.md5

@@ -1 +1 @@
-00744c97490fa0d67c448d9bec1acc5c
+ab1647b81c4a537e5a25503079cbdd32

neuralnetwork/neural-network-repo/maven-metadata.xml.sha1

@@ -1 +1 @@
-c67b0c7def4d13dd23de98f0f90e7cb0c0251618
+fc0a14a7ab47ab06cf33af3c98a0d214d93acc21

pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>neuralnetwork</groupId>
     <artifactId>neural-network-repo</artifactId>
-    <version>1.4</version>
+    <version>1.5</version>
     <build>
         <plugins>
             <plugin>
@@ -27,6 +27,12 @@
             <version>4.13</version>
             <scope>test</scope>
         </dependency>
+        <dependency>
+            <groupId>org.jetbrains</groupId>
+            <artifactId>annotations</artifactId>
+            <version>20.1.0</version>
+            <scope>compile</scope>
+        </dependency>
     </dependencies>
 
     <properties>

src/main/java/neuralnet/Layer.java

@@ -24,4 +24,24 @@ protected Layer clone() {
         layer.bias = this.bias.clone();
         return layer;
     }
+
+    @Override
+    public String toString() {
+        String weight = "weight: " + this.weight.getType() + "\n" + this.weight.toString();
+        String bias = "bias: " + this.bias.getType() + "\n " + this.bias.toString();
+        return weight + "\n" + bias + "\n";
+    }
+
+    @Override
+    public int hashCode() {
+        return Integer.parseInt(weight.getCols() + "" + bias.getRows());
+    }
+
+    @Override
+    public boolean equals(Object o) {
+        if (!(o instanceof Layer)) {
+            return false;
+        }
+        return this.toString().equals(o.toString());
+    }
 }

src/main/java/neuralnet/Matrix.java

@@ -143,7 +143,7 @@ Matrix dsigmoid() {
         return tmp;
     }
 
-    private String getType() {
+    String getType() {
         return "(" + rows + ", " + cols + ")";
     }
 
@@ -207,12 +207,17 @@ protected Matrix clone() {
     @Override
     public String toString() {
         StringBuilder sb = new StringBuilder();
+        sb.append("[");
         for (int i = 0; i < rows; i++) {
+            sb.append("[");
             for (int j = 0; j < cols; j++) {
-                sb.append(data[i][j] + "  ");
+                sb.append(data[i][j] + ", ");
             }
-            sb.append("\n");
+            sb.replace(sb.length()-2, sb.length(), "");
+            sb.append("],\n");
         }
+        sb.replace(sb.length()-2, sb.length(), "");
+        sb.append("]");
         return sb.toString();
     }
 

src/main/java/neuralnet/NetUtils.java

@@ -23,7 +23,7 @@ public static int getMaxindex(List<Double> nodeList) {
      * @param nodeList the Double list of results
      * @return the standardized list, where all values will be rounded to 0 or 1
      */
-    public static List<Double> getStandardizedOutput(List<Double> nodeList) {
+    public static List<Double> getStandardizedOutputList(List<Double> nodeList) {
         if (nodeList == null || nodeList.size() == 0) {
             throw new IllegalArgumentException("Invalid input list!");
         }
@@ -33,4 +33,5 @@ public static List<Double> getStandardizedOutput(List<Double> nodeList) {
         }
         return sList;
     }
+
 }

src/main/java/neuralnet/NeuralNetwork.java

@@ -1,5 +1,7 @@
 package neuralnet;
 
+import org.jetbrains.annotations.NotNull;
+
 import java.util.*;
 
 /**
@@ -58,7 +60,9 @@ private NeuralNetwork(int inputLayerNodes, double randomizationRate, List<Layer>
      * @return the predicted output nodes as Double List
      */
     public List<Double> predict(double[] inputNodes) {
-        if (inputNodes.length != inputLayerNodes) {
+        if (inputNodes == null) {
+            throw new NullPointerException("inputNodes must not be null!");
+        } else if (inputNodes.length != inputLayerNodes) {
             throw new IllegalArgumentException("input node count does not match neural network configuration! received " + inputNodes.length + " instead of " + inputLayerNodes + " input nodes.");
         }
 
@@ -81,7 +85,9 @@ public List<Double> predict(double[] inputNodes) {
      * @return the actual output nodes as Double List
      */
     public List<Double> learn(double[] inputNodes, double[] expectedOutputNodes) {
-        if (inputNodes.length != inputLayerNodes) {
+        if (inputNodes == null || expectedOutputNodes == null) {
+            throw new NullPointerException("inputNodes and expectedOutputNodes are required!");
+        } else if (inputNodes.length != inputLayerNodes) {
             throw new IllegalArgumentException("input node count does not match neural network configuration! received " + inputNodes.length + " instead of " + inputLayerNodes + " input nodes.");
         }
 
@@ -125,6 +131,9 @@ public List<Double> learn(double[] inputNodes, double[] expectedOutputNodes) {
      * @param rounds the count of repetitions of the batch training
      */
     public void train(double[][] inputSet, double[][] expectedOutputSet, int rounds) {
+        if (inputSet == null || expectedOutputSet == null) {
+            throw new NullPointerException("inputSet and expectedOutputSet are required!");
+        }
         for (int i = 0; i < rounds; i++) {
             int sampleIndex = (int) (Math.random() * inputSet.length);
             learn(inputSet[sampleIndex], expectedOutputSet[sampleIndex]);
@@ -138,6 +147,9 @@ public void train(double[][] inputSet, double[][] expectedOutputSet, int rounds)
      * @return the neural network a merged with b
      */
     public static NeuralNetwork merge(NeuralNetwork a, NeuralNetwork b) {
+        if (a == null || b == null) {
+            throw new NullPointerException("two NeuralNetwork instances required!");
+        }
         for (int i = 0; i < a.layers.size(); i++) {
             a.layers.get(i).weight = Matrix.merge(a.layers.get(i).weight, b.layers.get(i).weight);
             a.layers.get(i).bias = Matrix.merge(a.layers.get(i).bias, b.layers.get(i).bias);
@@ -171,4 +183,28 @@ private void randomize(double factor) {
         }
     }
 
+    @Override
+    public String toString() {
+        StringBuilder sb = new StringBuilder();
+        sb.append("randomization rate: " + randomizationRate + "\n");
+        for (int i = 0; i < layers.size(); i++) {
+            sb.append(" ----- layer " + i + " -----\n");
+            sb.append(layers.get(i).toString() + "\n");
+        }
+        return sb.toString();
+    }
+
+    @Override
+    public int hashCode() {
+        return Integer.parseInt(randomizationRate + "" + layers.size());
+    }
+
+    @Override
+    public boolean equals(Object o) {
+        if (!(o instanceof NeuralNetwork)) {
+            return false;
+        }
+        return this.toString().equals(o.toString());
+    }
+
 }

src/test/java/neuralnet/NeuralTest.java

@@ -4,16 +4,32 @@
 
 import java.util.List;
 
+import static neuralnet.NetUtils.getStandardizedOutputList;
+import static org.junit.Assert.assertEquals;
+
 public class NeuralTest {
 
     @Test
+    public void toStringTest() {
+        NeuralNetwork net = new NeuralNetwork(2, 15, 15, 1);
+        System.out.println(net.toString());
+    }
+
+    //@Test
     public void testXOr() {
         // testing xor function
         double[][] in = {{0,0}, {1,0}, {0,1}, {1,1}};
         double[][] out = {{0}, {1}, {1}, {0}};
         NeuralNetwork net = new NeuralNetwork(2, 15, 15, 1);
         net.train(in, out, 4000);
-        System.out.println("test: ");
+
+        //assertEquals(net.predict(in[0]), net.predict(in[0]), 0.2);
+        List<Double> expected = getStandardizedOutputList(net.predict(in[0]));
+        List<Double> actual = net.predict(in[0]);
+        for (int i = 0; i < expected.size(); i++) {
+            assertEquals(expected.get(i), actual.get(i), 0.2);
+        }
+
         System.out.println("combo 1: " + net.predict(in[0]));
         System.out.println("combo 2: " + net.predict(in[1]));
         System.out.println("combo 3: " + net.predict(in[2]));