little refactoring

README.md

@@ -21,6 +21,6 @@ Add following snippets to your ``pom.xml`` file:
         <dependency>    
             <groupId>neuralnetwork</groupId>    
             <artifactId>neural-network-repo</artifactId>    
-            <version>1.2</version>    
+            <version>1.3</version>    
         </dependency>    
     </dependencies>    

neuralnetwork/neural-network-repo/1.3/neural-network-repo-1.3-javadoc.jar.md5

@@ -0,0 +1 @@
+6d9669496470d0f40065e56e5b05638f

neuralnetwork/neural-network-repo/1.3/neural-network-repo-1.3-javadoc.jar.sha1

@@ -0,0 +1 @@
+71ab343cbfd35dc29e93dc7b1a7834400b0b1053

neuralnetwork/neural-network-repo/1.3/neural-network-repo-1.3-sources.jar.md5

@@ -0,0 +1 @@
+d1438876433d10c01beb353ec582c042

neuralnetwork/neural-network-repo/1.3/neural-network-repo-1.3-sources.jar.sha1

@@ -0,0 +1 @@
+8b76326df2915ba517c4d915ccc9b16c3ac6a269

neuralnetwork/neural-network-repo/1.3/neural-network-repo-1.3.jar.md5

@@ -0,0 +1 @@
+e0be3cbad31a571c624bda7c623e622f

neuralnetwork/neural-network-repo/1.3/neural-network-repo-1.3.jar.sha1

@@ -0,0 +1 @@
+acfa91fb768019864c36b261a4999c098982d6aa

neuralnetwork/maven-repo/1.2/maven-repo-1.2.pom → neuralnetwork/neural-network-repo/1.3/neural-network-repo-1.3.pom

@@ -5,8 +5,8 @@
     <modelVersion>4.0.0</modelVersion>
 
     <groupId>neuralnetwork</groupId>
-    <artifactId>maven-repo</artifactId>
-    <version>1.2</version>
+    <artifactId>neural-network-repo</artifactId>
+    <version>1.3</version>
     <build>
         <plugins>
             <plugin>

neuralnetwork/neural-network-repo/1.3/neural-network-repo-1.3.pom.md5

@@ -0,0 +1 @@
+bf5477529b4c166f1249978fc8f4eec0

neuralnetwork/neural-network-repo/1.3/neural-network-repo-1.3.pom.sha1

@@ -0,0 +1 @@
+6afe5087628ecc234ffc7eb8aa26f339135ecca0

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

@@ -3,10 +3,11 @@
   <groupId>neuralnetwork</groupId>
   <artifactId>neural-network-repo</artifactId>
   <versioning>
-    <release>1.2</release>
+    <release>1.3</release>
     <versions>
       <version>1.2</version>
+      <version>1.3</version>
     </versions>
-    <lastUpdated>20201001171913</lastUpdated>
+    <lastUpdated>20201001172512</lastUpdated>
   </versioning>
 </metadata>

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

@@ -1 +1 @@
-0b9330f090628a8493b16390b532c5c3
+10880088d0704b0db8f512b2a7c3bee1

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

@@ -1 +1 @@
-1176a7481ff1257b9ebf91238afa9e600b38ae79
+6a4b574b0ca9261e06b842e2799cd939099c5cf9

pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>neuralnetwork</groupId>
     <artifactId>neural-network-repo</artifactId>
-    <version>1.2</version>
+    <version>1.3</version>
     <build>
         <plugins>
             <plugin>

src/main/java/neuralnet/NeuralNetwork.java

@@ -54,15 +54,15 @@ private NeuralNetwork(int inputLayerNodes, double randomizationRate, List<Layer>
     /**
      * This method will take input nodes as parameter and return the predicted output nodes.
      * The neural net will not be modified. This method can be used for testing or the unsupervised machine learning approach.
-     * @param input the input nodes as double array
+     * @param inputNodes the input nodes as double array
      * @return the predicted output nodes as Double List
      */
-    public List<Double> predict(double[] input) {
-        if (input.length != inputLayerNodes) {
-            throw new IllegalArgumentException("input node count does not match neural network configuration!");
+    public List<Double> predict(double[] inputNodes) {
+        if (inputNodes.length != inputLayerNodes) {
+            throw new IllegalArgumentException("input node count does not match neural network configuration! received " + inputNodes.length + " instead of " + inputLayerNodes + " input nodes.");
         }
 
-        Matrix tmp = Matrix.fromArray(input);
+        Matrix tmp = Matrix.fromArray(inputNodes);
 
         for (Layer layer : layers) {
             tmp = Matrix.multiply(layer.weight, tmp);
@@ -82,7 +82,7 @@ public List<Double> predict(double[] input) {
      */
     public List<Double> learn(double[] inputNodes, double[] expectedOutputNodes) {
         if (inputNodes.length != inputLayerNodes) {
-            throw new IllegalArgumentException("input node count does not match neural network configuration!");
+            throw new IllegalArgumentException("input node count does not match neural network configuration! received " + inputNodes.length + " instead of " + inputLayerNodes + " input nodes.");
         }
 
         Matrix input = Matrix.fromArray(inputNodes);