Fixed Serializable attributes & cleaned up code.

11 files changed, 129 insertions, 177 deletions

diff --git a/app/src/main/java/org/rssin/neurons/FeedSorter.java b/app/src/main/java/org/rssin/neurons/FeedSorter.java
index 28d45c1..8549fcf 100755
--- a/app/src/main/java/org/rssin/neurons/FeedSorter.java
+++ b/app/src/main/java/org/rssin/neurons/FeedSorter.java
@@ -1,56 +1,41 @@
 package org.rssin.neurons;

 

-import android.gesture.Prediction;

-

 import org.rssin.rss.FeedItem;

 

-import java.io.IOException;

 import java.io.Serializable;

 import java.util.ArrayList;

 import java.util.Arrays;

 import java.util.Calendar;

-import java.util.Collection;

 import java.util.Collections;

 import java.util.Comparator;

-import java.util.HashMap;

 import java.util.Hashtable;

 import java.util.List;

 import java.util.TimeZone;

 

 /**

- * Created by Jos on 14-5-2015.

+ * @author Jos.

  */

-public class FeedSorter implements Serializable{

+public class FeedSorter implements Serializable {

     private static final long serialVersionUID = 0;

 

-    private final int MAX_TRAINING_HISTORY = 250;

-    private final int SECONDS_IN_DAY = 24 * 60 * 60;

     private final SentenceSplitter splitter = new SentenceSplitter();

+    private final MultiNeuralNetwork nn = new MultiNeuralNetwork(25, 50);

+    private final List<TrainingCase> trainingCases = new ArrayList<>();

 

-    private MultiNeuralNetwork nn = new MultiNeuralNetwork(25, 50);

-

-    private List<TrainingCase> trainingCases = new ArrayList<>();

-

-    private int[] isNthMonthInput = new int[12];

-    private int[] isNthWeekDayInput = new int[7];

-    private int isMorning, isAfternoon, isEvening, isNight, biasInput;

-    private Hashtable<String, Integer> categoryInputs = new Hashtable<String, Integer>();

-    private Hashtable<String, Integer> wordInputs = new Hashtable<String, Integer>();

-    private Hashtable<String, Integer> authorInputs = new Hashtable<String, Integer>();

+    private final int[] isNthMonthInput = new int[12];

+    private final int[] isNthWeekDayInput = new int[7];

+    private final int isMorning, isAfternoon, isEvening, isNight, biasInput;

+    private final Hashtable<String, Integer> categoryInputs = new Hashtable<>();

+    private final Hashtable<String, Integer> wordInputs = new Hashtable<>();

+    private final Hashtable<String, Integer> authorInputs = new Hashtable<>();

 

     public FeedSorter() {

-        createNewNetwork();

-    }

-

-    private void createNewNetwork() {

         biasInput = nn.addInput();

-        for(int i = 0; i < 12; i++)

-        {

+        for (int i = 0; i < 12; i++) {

             isNthMonthInput[i] = nn.addInput();

         }

 

-        for(int i = 0; i < 7; i++)

-        {

+        for (int i = 0; i < 7; i++) {

             isNthWeekDayInput[i] = nn.addInput();

         }

 

@@ -63,48 +48,40 @@ public class FeedSorter implements Serializable{
     private PredictionInterface getPrediction(FeedItem item) {

         List<String> words = splitter.splitSentence(item.getTitle());

 

-        addNewCategoryInputs(item);

-        addNewTitleWordInputs(words);

-        addNewAuthorInputs(item);

+        addNewInputs(item.getCategory(), categoryInputs);

+        addNewInputs(words, wordInputs);

+        addNewInput(item.getAuthor(), authorInputs);

 

         double[] inputs = newArrayInitializedToNegativeOne();

         inputs[biasInput] = 1;

 

         Calendar cal = Calendar.getInstance(TimeZone.getTimeZone("UTC"));

 

-        //Set month

+        //Set month & weekday

         inputs[isNthMonthInput[cal.get(Calendar.MONTH) - cal.getMinimum(Calendar.MONTH)]] = 1;

-

-        //Set weekday

         inputs[isNthWeekDayInput[cal.get(Calendar.DAY_OF_WEEK) - cal.getMinimum(Calendar.DAY_OF_WEEK)]] = 1;

 

         //Set time

         int hourOfDay = cal.get(Calendar.HOUR_OF_DAY);

-        if(hourOfDay > 6 && hourOfDay < 12)

-        {

+        if (hourOfDay > 6 && hourOfDay < 12) {

             inputs[isMorning] = 1;

-        }else if(hourOfDay >= 12 && hourOfDay <= 6)

-        {

+        } else if (hourOfDay >= 12 && hourOfDay <= 6) {

             inputs[isAfternoon] = 1;

-        }else if(hourOfDay >= 6 && hourOfDay < 23)

-        {

+        } else if (hourOfDay >= 6 && hourOfDay < 23) {

             inputs[isEvening] = 1;

-        }else if(hourOfDay >= 23 || hourOfDay <= 6)

-        {

+        } else if (hourOfDay >= 23 || hourOfDay <= 6) {

             inputs[isNight] = 1;

         }

 

-        for(String category : item.getCategory())

-        {

+        for (String category : item.getCategory()) {

             inputs[categoryInputs.get(category.toLowerCase())] = 1;

         }

 

-        for(String word : words)

-        {

+        for (String word : words) {

             inputs[wordInputs.get(word)] = 1;

         }

 

-        if(item.getAuthor() != null) {

+        if (item.getAuthor() != null) {

             inputs[authorInputs.get(item.getAuthor().toLowerCase())] = 1;

         }

 

@@ -117,52 +94,35 @@ public class FeedSorter implements Serializable{
         return inputs;

     }

 

-    private void addNewCategoryInputs(FeedItem item) {

-        for(String category : item.getCategory())

-        {

-            category = category.toLowerCase();

-            if(!categoryInputs.containsKey(category))

-            {

-                categoryInputs.put(category, nn.addInput());

-            }

-        }

-    }

-

-    private void addNewAuthorInputs(FeedItem item)

-    {

-        if(item.getAuthor() != null) {

-            String author = item.getAuthor().toLowerCase();

-            if (!authorInputs.containsKey(author)) {

-                authorInputs.put(author, nn.addInput());

-            }

+    private void addNewInputs(Iterable<String> words, Hashtable<String, Integer> map) {

+        for (String word : words) {

+            addNewInput(word, map);

         }

     }

 

-    private void addNewTitleWordInputs(List<String> words) {

-        for(String word : words)

-        {

+    private void addNewInput(String word, Hashtable<String, Integer> map) {

+        if (word != null) {

             word = word.toLowerCase();

-            if(!wordInputs.containsKey(word))

-            {

-                wordInputs.put(word, nn.addInput());

+            if (!map.containsKey(word)) {

+                map.put(word, nn.addInput());

             }

         }

     }

 

     /**

      * Provides feedback to the neural network.

-     * @param item The feeditem.

+     *

+     * @param item     The feeditem.

      * @param feedback The feedback. Like will move these types of items up in the list,

      *                 dislike will move them down.

      */

-    public void feedback(FeedItem item, Feedback feedback)

-    {

+    public void feedback(FeedItem item, Feedback feedback) {

         PredictionInterface prediction = getPrediction(item);

         prediction.learn(feedback.toExpectedOutput());

         trainingCases.add(new TrainingCase(prediction.getInputs(), feedback));

 

-        while(trainingCases.size() > MAX_TRAINING_HISTORY)

-        {

+        final int MAX_TRAINING_HISTORY = 250;

+        while (trainingCases.size() > MAX_TRAINING_HISTORY) {

             trainingCases.remove(0);

         }

     }

@@ -170,13 +130,10 @@ public class FeedSorter implements Serializable{
     /**

      * Runs an iteration of training, using feedback that was provided previously using FeedSorter.feedback(...).

      */

-    public void train()

-    {

-        for(TrainingCase t : trainingCases)

-        {

+    public void train() {

+        for (TrainingCase t : trainingCases) {

             double[] inputs = t.getInputs();

-            if(inputs.length < nn.getInputCount())

-            {

+            if (inputs.length < nn.getInputCount()) {

                 // Resize array to fit new input size

                 inputs = Arrays.copyOf(inputs, nn.getInputCount());

             }

@@ -188,30 +145,28 @@ public class FeedSorter implements Serializable{
 

     /**

      * Returns a sorted list of all the items in the List, according to the neural network.

+     *

      * @param items The list of items.

      * @return A new, sorted, list of items. The parameter items is not modified.

      */

     public List<FeedItem> sortItems(List<FeedItem> items) {

-        // Sort list based on something like date + nn.computeOutput() * DAY.

-        final List<FeedItem> newItems = new ArrayList<FeedItem>(items);

-        final Hashtable<FeedItem, PredictionInterface> predictions = new Hashtable<>();

-

-        for(FeedItem feed : newItems)

-        {

-            PredictionInterface prediction = getPrediction(feed);

-            predictions.put(feed, prediction);

+        final int SECONDS_IN_DAY = 24 * 60 * 60;

+

+        final List<FeedItem> newItems = new ArrayList<>(items);

+        final Hashtable<FeedItem, Long> predictions = new Hashtable<>();

+

+        for (FeedItem item : newItems) {

+            PredictionInterface prediction = getPrediction(item);

+            predictions.put(item, (long) (prediction.getOutput() * SECONDS_IN_DAY));

         }

 

         Collections.sort(newItems, new Comparator<FeedItem>() {

             @Override

             public int compare(FeedItem lhs, FeedItem rhs) {

-                PredictionInterface lPrediction = predictions.get(lhs),

-                        rPrediction = predictions.get(rhs);

-

-                long lhsSeconds = (long)(lhs.getPubDate().getTime() / 1000 + lPrediction.getOutput() * SECONDS_IN_DAY);

-                long rhsSeconds = (long)(rhs.getPubDate().getTime() / 1000 + rPrediction.getOutput() * SECONDS_IN_DAY);

+                long lhsScore = lhs.getPubDate().getTime() / 1000 + predictions.get(lhs);

+                long rhsScore = rhs.getPubDate().getTime() / 1000 + predictions.get(rhs);

 

-                return (int)Math.signum(rhsSeconds - lhsSeconds);

+                return (int) Math.signum(rhsScore - lhsScore);

             }

         });

 

diff --git a/app/src/main/java/org/rssin/neurons/Feedback.java b/app/src/main/java/org/rssin/neurons/Feedback.java
index 9652b2b..fe59b1b 100755
--- a/app/src/main/java/org/rssin/neurons/Feedback.java
+++ b/app/src/main/java/org/rssin/neurons/Feedback.java
@@ -1,21 +1,18 @@
 package org.rssin.neurons;

 

 /**

- * Created by Jos on 19-5-2015.

+ * @author Jos.

  */

 public enum Feedback {

-    Like, Dislike;

+    Like(1.0d), Dislike(-1.0d);

 

-    double toExpectedOutput()

-    {

-        switch(this)

-        {

-            case Like:

-                return 1;

-            case Dislike:

-                return -1;

-            default:

-                throw new IllegalArgumentException();

-        }

+    private final double expectedOutput;

+

+    private Feedback(double expectedOutput) {

+        this.expectedOutput = expectedOutput;

+    }

+

+    double toExpectedOutput() {

+        return expectedOutput;

     }

 }

diff --git a/app/src/main/java/org/rssin/neurons/MultiNeuralNetwork.java b/app/src/main/java/org/rssin/neurons/MultiNeuralNetwork.java
index 68ff390..03ad2d1 100755
--- a/app/src/main/java/org/rssin/neurons/MultiNeuralNetwork.java
+++ b/app/src/main/java/org/rssin/neurons/MultiNeuralNetwork.java
@@ -3,12 +3,12 @@ package org.rssin.neurons;
 import java.io.Serializable;

 

 /**

- * Created by Jos on 14-5-2015.

- * Is used to migitate the problem of neural networks ending up in the wrong local minimum.

+ * @author Jos

+ *         Is used to migitate the problem of neural networks ending up in the wrong local minimum.

  */

-class MultiNeuralNetwork implements Serializable{

+class MultiNeuralNetwork implements Serializable {

     private static final long serialVersionUID = 0;

-    private NeuralNetwork[] networks;

+    private final NeuralNetwork[] networks;

 

     public MultiNeuralNetwork(int numNetworks, int numHiddenNodes) {

         networks = new NeuralNetwork[numNetworks];

@@ -28,8 +28,7 @@ class MultiNeuralNetwork implements Serializable{
 

     public PredictionInterface computeOutput(double[] inputs) {

         PredictionInterface[] predictions = new PredictionInterface[networks.length];

-        for(int i = 0; i < predictions.length; i++)

-        {

+        for (int i = 0; i < predictions.length; i++) {

             predictions[i] = networks[i].computeOutput(inputs);

         }

 

diff --git a/app/src/main/java/org/rssin/neurons/MultiNeuralNetworkPrediction.java b/app/src/main/java/org/rssin/neurons/MultiNeuralNetworkPrediction.java
index dc85261..f618749 100755
--- a/app/src/main/java/org/rssin/neurons/MultiNeuralNetworkPrediction.java
+++ b/app/src/main/java/org/rssin/neurons/MultiNeuralNetworkPrediction.java
@@ -1,22 +1,20 @@
 package org.rssin.neurons;

 

 /**

- * Created by Jos on 14-5-2015.

+ * @author Jos.

  */

 class MultiNeuralNetworkPrediction implements PredictionInterface {

-    private PredictionInterface[] predictions;

-    MultiNeuralNetworkPrediction(PredictionInterface[] predictions)

-    {

-        if(predictions.length <= 0)

-        {

+    private final PredictionInterface[] predictions;

+

+    MultiNeuralNetworkPrediction(PredictionInterface[] predictions) {

+        if (predictions.length <= 0) {

             throw new IllegalArgumentException("predictions");

         }

 

         this.predictions = predictions;

     }

 

-    public double getOutput()

-    {

+    public double getOutput() {

         double average = 0;

         for (PredictionInterface prediction : predictions) {

             average += prediction.getOutput();

@@ -25,16 +23,13 @@ class MultiNeuralNetworkPrediction implements PredictionInterface {
         return average / (double) predictions.length;

     }

 

-    public void learn(double expectedOutput)

-    {

-        for(PredictionInterface prediction : predictions)

-        {

+    public void learn(double expectedOutput) {

+        for (PredictionInterface prediction : predictions) {

             prediction.learn(expectedOutput);

         }

     }

 

-    public double[] getInputs()

-    {

+    public double[] getInputs() {

         return predictions[0].getInputs();

     }

 }

diff --git a/app/src/main/java/org/rssin/neurons/NeuralNetwork.java b/app/src/main/java/org/rssin/neurons/NeuralNetwork.java
index 7fe003f..0acfda7 100755
--- a/app/src/main/java/org/rssin/neurons/NeuralNetwork.java
+++ b/app/src/main/java/org/rssin/neurons/NeuralNetwork.java
@@ -1,18 +1,19 @@
 package org.rssin.neurons;

 

+import android.annotation.SuppressLint;

+

 import java.io.Serializable;

 

 /**

- * Created by Jos on 14-5-2015.

+ * @author Jos.

  */

-class NeuralNetwork implements Serializable{

+class NeuralNetwork implements Serializable {

     private static final long serialVersionUID = 0;

-    private Neuron[] hiddenNodes;

-    private Neuron outputNode;

+    private final Neuron[] hiddenNodes;

+    private final Neuron outputNode;

 

-    public NeuralNetwork(int numHiddenNodes) {

-        if(numHiddenNodes < 1)

-        {

+    NeuralNetwork(int numHiddenNodes) {

+        if (numHiddenNodes < 1) {

             throw new IllegalArgumentException("numHiddenNodes must be > 0");

         }

 

@@ -25,7 +26,8 @@ class NeuralNetwork implements Serializable{
         outputNode = new Neuron(numHiddenNodes + 1);

     }

 

-    public int addInput() {

+    @SuppressLint("Assert")

+    int addInput() {

         assert hiddenNodes.length > 0;

 

         int result = 0;

@@ -36,7 +38,7 @@ class NeuralNetwork implements Serializable{
         return result;

     }

 

-    public PredictionInterface computeOutput(double[] inputs) {

+    PredictionInterface computeOutput(double[] inputs) {

         double[] intermediateValues = new double[outputNode.getWeightCount()];

 

         //Output of hidden neurons

@@ -60,20 +62,19 @@ class NeuralNetwork implements Serializable{
     }

 

     void learn(NeuralNetworkPrediction p, double expectedOutput) {

-        //TODO: See if adding momentum helps avoid local minima

+        //TODO: See if adding momentum helps avoid local minimum

         double actualOutput = p.getOutput();

         double[] intermediateValues = p.getIntermediateValues();

         double[] inputs = p.getInputs();

 

-        double[] hiddenGradients = new double[hiddenNodes.length];

-

-        //Calculate output gradients

+        //Calculate output gradient

         double outputDerivative = (1 - actualOutput) * (1 + actualOutput);

 

         //Derivative of HyperTan function

         double outputGradient = outputDerivative * (expectedOutput - actualOutput);

 

-        //Calulate hidden gradients

+        //Calculate hidden gradients

+        double[] hiddenGradients = new double[hiddenNodes.length];

         for (int i = 0; i < hiddenGradients.length; i++) {

             //Derivative of HyperTan function

             double hiddenDerivative = (1 - intermediateValues[i]) * (1 + intermediateValues[i]);

@@ -111,7 +112,7 @@ class NeuralNetwork implements Serializable{
         else return Math.tanh(x);

     }

 

-    public int getInputCount() {

+    int getInputCount() {

         return hiddenNodes[0].getWeightCount();

     }

 }

diff --git a/app/src/main/java/org/rssin/neurons/NeuralNetworkPrediction.java b/app/src/main/java/org/rssin/neurons/NeuralNetworkPrediction.java
index 9d6fc89..169caee 100755
--- a/app/src/main/java/org/rssin/neurons/NeuralNetworkPrediction.java
+++ b/app/src/main/java/org/rssin/neurons/NeuralNetworkPrediction.java
@@ -1,13 +1,13 @@
 package org.rssin.neurons;

 

 /**

- * Created by Jos on 14-5-2015.

+ * @author Jos.

  */

 class NeuralNetworkPrediction implements PredictionInterface {

-    private double[] inputs;

-    private double[] intermediateValues;

-    private double output;

-    private NeuralNetwork nn;

+    private final double[] inputs;

+    private final double[] intermediateValues;

+    private final double output;

+    private final NeuralNetwork nn;

 

     NeuralNetworkPrediction(NeuralNetwork nn, double[] inputs, double[] intermediateValues, double output) {

         this.inputs = inputs;

diff --git a/app/src/main/java/org/rssin/neurons/Neuron.java b/app/src/main/java/org/rssin/neurons/Neuron.java
index d668ebc..23f69e1 100755
--- a/app/src/main/java/org/rssin/neurons/Neuron.java
+++ b/app/src/main/java/org/rssin/neurons/Neuron.java
@@ -1,17 +1,18 @@
 package org.rssin.neurons;

 

+import java.io.Serializable;

 import java.util.ArrayList;

 import java.util.List;

 import java.util.Random;

 

 /**

- * Created by Jos on 14-5-2015.

+ * @author Jos.

  */

-class Neuron {

+class Neuron implements Serializable {

     private static final long serialVersionUID = 0;

-    private static Random r = new Random();

+    private static final Random r = new Random();

 

-    private List<Double> weights = new ArrayList<Double>();

+    private final List<Double> weights = new ArrayList<>();

 

     public Neuron() {

     }

diff --git a/app/src/main/java/org/rssin/neurons/PredictionInterface.java b/app/src/main/java/org/rssin/neurons/PredictionInterface.java
index 27a214f..ff46992 100755
--- a/app/src/main/java/org/rssin/neurons/PredictionInterface.java
+++ b/app/src/main/java/org/rssin/neurons/PredictionInterface.java
@@ -1,10 +1,12 @@
 package org.rssin.neurons;

 

 /**

- * Created by Jos on 14-5-2015.

+ * @author Jos.

  */

 interface PredictionInterface {

-    public double getOutput();

-    public void learn(double expectedOutput);

-    public double[] getInputs();

+    double getOutput();

+

+    void learn(double expectedOutput);

+

+    double[] getInputs();

 }

diff --git a/app/src/main/java/org/rssin/neurons/SentenceSplitter.java b/app/src/main/java/org/rssin/neurons/SentenceSplitter.java
index 6fefe52..887439d 100755
--- a/app/src/main/java/org/rssin/neurons/SentenceSplitter.java
+++ b/app/src/main/java/org/rssin/neurons/SentenceSplitter.java
@@ -6,20 +6,25 @@ import java.util.regex.Matcher;
 import java.util.regex.Pattern;

 

 /**

- * Created by Jos on 21-5-2015.

+ * @author Jos.

  */

 public class SentenceSplitter {

-    private Pattern wordMatch = Pattern.compile("[\\w-]+");

-    public SentenceSplitter()

-    { }

+    private final Pattern wordMatch = Pattern.compile("[\\w-]+");//For unicode support, add the Pattern.UNICODE_CHARACTER_CLASS flag. Works only in Java 7+.

 

-    public List<String> splitSentence(String sentence)

-    {

+    public SentenceSplitter() {

+    }

+

+    /**

+     * Returns all the words in a sentence.

+     *

+     * @param sentence The sentence.

+     * @return The list of words in a sentence.

+     */

+    public List<String> splitSentence(String sentence) {

         List<String> allMatches = new ArrayList<>();

         Matcher m = wordMatch.matcher(sentence);

 

-        while (m.find())

-        {

+        while (m.find()) {

             allMatches.add(m.group().toLowerCase());

         }

 

diff --git a/app/src/main/java/org/rssin/neurons/TrainingCase.java b/app/src/main/java/org/rssin/neurons/TrainingCase.java
index 77162be..69f72cb 100755
--- a/app/src/main/java/org/rssin/neurons/TrainingCase.java
+++ b/app/src/main/java/org/rssin/neurons/TrainingCase.java
@@ -3,15 +3,14 @@ package org.rssin.neurons;
 import java.io.Serializable;

 

 /**

- * Created by Jos on 20-5-2015.

+ * @author Jos.

  */

 class TrainingCase implements Serializable {

-    private static long serialVersionID;

-    private double[] inputs;

-    private Feedback feedback;

+    private static final long serialVersionUID = 0;

+    private final double[] inputs;

+    private final Feedback feedback;

 

-    public TrainingCase(double[] inputs, Feedback feedback)

-    {

+    public TrainingCase(double[] inputs, Feedback feedback) {

         this.inputs = inputs;

         this.feedback = feedback;

     }

diff --git a/app/src/main/java/org/rssin/rssin/FeedLoaderAndSorter.java b/app/src/main/java/org/rssin/rssin/FeedLoaderAndSorter.java
index eda0526..e9d3e5d 100755
--- a/app/src/main/java/org/rssin/rssin/FeedLoaderAndSorter.java
+++ b/app/src/main/java/org/rssin/rssin/FeedLoaderAndSorter.java
@@ -28,8 +28,7 @@ public class FeedLoaderAndSorter {
         {

             FeedLoader loader = new FeedLoader(feed.getURL());

             loader.fetchXML();

-            org.rssin.rss.Feed loadedFeed = loader.getFeed();

-            for(FeedItem item : loadedFeed.getPosts())

+            for(FeedItem item : loader.getFeed().getPosts())

             {

                 if(matchesKeyword(item))

                 {

@@ -55,10 +54,9 @@ public class FeedLoaderAndSorter {
         return filter.getKeywords().size() == 0;

     }

 

-    private static boolean contains( String haystack, String needle ) {

-        haystack = haystack == null ? "" : haystack;

-        needle = needle == null ? "" : needle;

-

-        return haystack.toLowerCase().contains(needle.toLowerCase());

+    private static boolean contains(String haystack, String needle) {

+        return haystack != null

+                && needle != null

+                && haystack.toLowerCase().contains(needle.toLowerCase());

     }

 }