From 0ec23727a995dea4ca2ae595f39cbc177668159a Mon Sep 17 00:00:00 2001 From: Size43 Date: Thu, 21 May 2015 16:33:18 +0200 Subject: Fixed Serializable attributes & cleaned up code. --- .../main/java/org/rssin/neurons/FeedSorter.java | 145 +++++++-------------- app/src/main/java/org/rssin/neurons/Feedback.java | 23 ++-- .../java/org/rssin/neurons/MultiNeuralNetwork.java | 11 +- .../neurons/MultiNeuralNetworkPrediction.java | 23 ++-- .../main/java/org/rssin/neurons/NeuralNetwork.java | 31 ++--- .../org/rssin/neurons/NeuralNetworkPrediction.java | 10 +- app/src/main/java/org/rssin/neurons/Neuron.java | 9 +- .../org/rssin/neurons/PredictionInterface.java | 10 +- .../java/org/rssin/neurons/SentenceSplitter.java | 21 +-- .../main/java/org/rssin/neurons/TrainingCase.java | 11 +- .../java/org/rssin/rssin/FeedLoaderAndSorter.java | 12 +- 11 files changed, 129 insertions(+), 177 deletions(-) (limited to 'app/src') 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 trainingCases = new ArrayList<>(); - private MultiNeuralNetwork nn = new MultiNeuralNetwork(25, 50); - - private List trainingCases = new ArrayList<>(); - - private int[] isNthMonthInput = new int[12]; - private int[] isNthWeekDayInput = new int[7]; - private int isMorning, isAfternoon, isEvening, isNight, biasInput; - private Hashtable categoryInputs = new Hashtable(); - private Hashtable wordInputs = new Hashtable(); - private Hashtable authorInputs = new Hashtable(); + 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 categoryInputs = new Hashtable<>(); + private final Hashtable wordInputs = new Hashtable<>(); + private final Hashtable 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 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 words, Hashtable map) { + for (String word : words) { + addNewInput(word, map); } } - private void addNewTitleWordInputs(List words) { - for(String word : words) - { + private void addNewInput(String word, Hashtable 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 sortItems(List items) { - // Sort list based on something like date + nn.computeOutput() * DAY. - final List newItems = new ArrayList(items); - final Hashtable 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 newItems = new ArrayList<>(items); + final Hashtable 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() { @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 weights = new ArrayList(); + private final List 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 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 splitSentence(String sentence) { List 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()); } } -- cgit v1.2.3