Java Code Examples for java.io.InvalidClassException#printStackTrace()

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Example 1
Source File: StorageManager.java    From LoboBrowser with MIT License 6 votes vote down vote up 
public Serializable retrieveSettings(final String name, final ClassLoader classLoader) throws IOException, ClassNotFoundException {
  final File dir = this.getSettingsDirectory();
  if (!dir.exists()) {
    return null;
  }
  final File file = new File(dir, name);
  if (!file.exists()) {
    return null;
  }
  try (
      final InputStream in = new FileInputStream(file);
      final BufferedInputStream bin = new BufferedInputStream(in);
      final ObjectInputStream ois = new ClassLoaderObjectInputStream(bin, classLoader)) {
    return (Serializable) ois.readObject();
  } catch (final InvalidClassException ice) {
    ice.printStackTrace();
    return null;
  }
}
Example 2
Source File: SingleLinkClusterer.java    From Ngram-Graphs with Apache License 2.0 5 votes vote down vote up 
/** Calculates the similarity between two clusters. In this algorithm the 
 * maximum similarity over all pairs of the two clusters is used.
 *@param sClusterOne The first cluster.
 *@param sClusterTwo The second cluster.
 *@param clDistanceCalculator The calculator of distance between set elements.
 *@return The similarity between the clusters.
 */
protected double getSimilarityBetweenClusters(Set sClusterOne, Set sClusterTwo, 
        SimilarityComparatorListener clDistanceCalculator) {
    Distribution dDistances = new Distribution();
    
    // For every object in cluster one
    Iterator iFirstCluster = sClusterOne.iterator();
    int iCnt = 0;
    
    while (iFirstCluster.hasNext()) {
        Object oFirst = iFirstCluster.next();

        // For every object in cluster two
        Iterator iSecondCluster = sClusterTwo.iterator();
        while (iSecondCluster.hasNext()) {
            Object oSecond = iSecondCluster.next();
            ISimilarity sSimil;
            // Compare the objects
            try {
                synchronized (oFirst) {
                    synchronized (oSecond) {
                        sSimil = clDistanceCalculator.getSimilarityBetween(oFirst, 
                                oSecond);
                    }
                }
            } catch (InvalidClassException ex) {
                System.err.println("Cannot compare " + oFirst.toString() + " to " + 
                        oSecond.toString() + ". Cause:");
                ex.printStackTrace(System.err);
                continue;
            }
            // Put id of pair and their similarity in distance distribution
            dDistances.setValue(iCnt++, sSimil.getOverallSimilarity());                    
        }
    }
    
    // Return the maximum similarity
    return dDistances.maxValue();
}
Example 3
Source File: AverageLinkClusterer.java    From Ngram-Graphs with Apache License 2.0 5 votes vote down vote up 
/** Calculates the similarity between two clusters. In this algorithm the 
 * average similarity between all pairs of the two clusters is used.
 *@param sClusterOne The first cluster.
 *@param sClusterTwo The second cluster.
 *@param clDistanceCalculator The calculator of distance between set elements.
 *@return The similarity between the clusters.
 */
protected double getSimilarityBetweenClusters(Set sClusterOne, Set sClusterTwo, 
        SimilarityComparatorListener clDistanceCalculator) {
    Distribution dDistances = new Distribution();
    
    // For every object in cluster one
    Iterator iFirstCluster = sClusterOne.iterator();
    int iCnt = 0;
    while (iFirstCluster.hasNext()) {
        Object oFirst = iFirstCluster.next();

        // For every object in cluster two
        Iterator iSecondCluster = sClusterTwo.iterator();
        while (iSecondCluster.hasNext()) {
            Object oSecond = iSecondCluster.next();
            ISimilarity sSimil;
            // Compare the objects
            try {
                synchronized (oFirst) {
                    synchronized (oSecond) {
                        sSimil = clDistanceCalculator.getSimilarityBetween(oFirst, 
                                oSecond);
                    }
                }
            } catch (InvalidClassException ex) {
                System.err.println("Cannot compare " + oFirst.toString() + " to " + 
                        oSecond.toString() + ". Cause:");
                ex.printStackTrace(System.err);
                continue;
            }
            // Put id of pair and their similarity in distance distribution
            dDistances.setValue(iCnt++, sSimil.getOverallSimilarity());                    
        }
    }
    
    // Return the maximum similarity, which is actually the average of distance between elements.
    return dDistances.average(true);
}
Example 4
Source File: CompleteLinkClusterer.java    From Ngram-Graphs with Apache License 2.0 5 votes vote down vote up 
/** Calculates the similarity between two clusters. In this algorithm the 
 * minimum similarity between all pairs of the two clusters is used.
 *@param sClusterOne The first cluster.
 *@param sClusterTwo The second cluster.
 *@param clDistanceCalculator The calculator of distance between set elements.
 *@return The similarity between the clusters.
 */
protected double getSimilarityBetweenClusters(Set sClusterOne, Set sClusterTwo, 
        SimilarityComparatorListener clDistanceCalculator) {
    Distribution dDistances = new Distribution();
    
    // For every object in cluster one
    Iterator iFirstCluster = sClusterOne.iterator();
    int iCnt = 0;
    while (iFirstCluster.hasNext()) {
        Object oFirst = iFirstCluster.next();

        // For every object in cluster two
        Iterator iSecondCluster = sClusterTwo.iterator();
        while (iSecondCluster.hasNext()) {
            Object oSecond = iSecondCluster.next();
            ISimilarity sSimil;
            // Compare the objects
            try {
                sSimil = clDistanceCalculator.getSimilarityBetween(oFirst, oSecond);
            } catch (InvalidClassException ex) {
                System.err.println("Cannot compare " + oFirst.toString() + " to " + 
                        oSecond.toString() + ". Cause:");
                ex.printStackTrace(System.err);
                continue;
            }
            // Put id of pair and their similarity in distance distribution
            dDistances.setValue(iCnt++, sSimil.getOverallSimilarity());                    
        }
    }
    
    // Return the maximum similarity
    return dDistances.minValue();
}
Example 5
Source File: SingleLinkClusterer.java    From Ngram-Graphs with Apache License 2.0 5 votes vote down vote up 
/** Calculates the similarity between two clusters. In this algorithm the 
 * maximum similarity over all pairs of the two clusters is used.
 *@param sClusterOne The first cluster.
 *@param sClusterTwo The second cluster.
 *@param clDistanceCalculator The calculator of distance between set elements.
 *@return The similarity between the clusters.
 */
protected double getSimilarityBetweenClusters(Set sClusterOne, Set sClusterTwo, 
        SimilarityComparatorListener clDistanceCalculator) {
    Distribution dDistances = new Distribution();
    
    // For every object in cluster one
    Iterator iFirstCluster = sClusterOne.iterator();
    int iCnt = 0;
    
    while (iFirstCluster.hasNext()) {
        Object oFirst = iFirstCluster.next();

        // For every object in cluster two
        Iterator iSecondCluster = sClusterTwo.iterator();
        while (iSecondCluster.hasNext()) {
            Object oSecond = iSecondCluster.next();
            ISimilarity sSimil;
            // Compare the objects
            try {
                synchronized (oFirst) {
                    synchronized (oSecond) {
                        sSimil = clDistanceCalculator.getSimilarityBetween(oFirst, 
                                oSecond);
                    }
                }
            } catch (InvalidClassException ex) {
                System.err.println("Cannot compare " + oFirst.toString() + " to " + 
                        oSecond.toString() + ". Cause:");
                ex.printStackTrace(System.err);
                continue;
            }
            // Put id of pair and their similarity in distance distribution
            dDistances.setValue(iCnt++, sSimil.getOverallSimilarity());                    
        }
    }
    
    // Return the maximum similarity
    return dDistances.maxValue();
}
Example 6
Source File: AverageLinkClusterer.java    From Ngram-Graphs with Apache License 2.0 5 votes vote down vote up 
/** Calculates the similarity between two clusters. In this algorithm the 
 * average similarity between all pairs of the two clusters is used.
 *@param sClusterOne The first cluster.
 *@param sClusterTwo The second cluster.
 *@param clDistanceCalculator The calculator of distance between set elements.
 *@return The similarity between the clusters.
 */
protected double getSimilarityBetweenClusters(Set sClusterOne, Set sClusterTwo, 
        SimilarityComparatorListener clDistanceCalculator) {
    Distribution dDistances = new Distribution();
    
    // For every object in cluster one
    Iterator iFirstCluster = sClusterOne.iterator();
    int iCnt = 0;
    while (iFirstCluster.hasNext()) {
        Object oFirst = iFirstCluster.next();

        // For every object in cluster two
        Iterator iSecondCluster = sClusterTwo.iterator();
        while (iSecondCluster.hasNext()) {
            Object oSecond = iSecondCluster.next();
            ISimilarity sSimil;
            // Compare the objects
            try {
                synchronized (oFirst) {
                    synchronized (oSecond) {
                        sSimil = clDistanceCalculator.getSimilarityBetween(oFirst, 
                                oSecond);
                    }
                }
            } catch (InvalidClassException ex) {
                System.err.println("Cannot compare " + oFirst.toString() + " to " + 
                        oSecond.toString() + ". Cause:");
                ex.printStackTrace(System.err);
                continue;
            }
            // Put id of pair and their similarity in distance distribution
            dDistances.setValue(iCnt++, sSimil.getOverallSimilarity());                    
        }
    }
    
    // Return the maximum similarity, which is actually the average of distance between elements.
    return dDistances.average(true);
}
Example 7
Source File: CompleteLinkClusterer.java    From Ngram-Graphs with Apache License 2.0 5 votes vote down vote up 
/** Calculates the similarity between two clusters. In this algorithm the 
 * minimum similarity between all pairs of the two clusters is used.
 *@param sClusterOne The first cluster.
 *@param sClusterTwo The second cluster.
 *@param clDistanceCalculator The calculator of distance between set elements.
 *@return The similarity between the clusters.
 */
protected double getSimilarityBetweenClusters(Set sClusterOne, Set sClusterTwo, 
        SimilarityComparatorListener clDistanceCalculator) {
    Distribution dDistances = new Distribution();
    
    // For every object in cluster one
    Iterator iFirstCluster = sClusterOne.iterator();
    int iCnt = 0;
    while (iFirstCluster.hasNext()) {
        Object oFirst = iFirstCluster.next();

        // For every object in cluster two
        Iterator iSecondCluster = sClusterTwo.iterator();
        while (iSecondCluster.hasNext()) {
            Object oSecond = iSecondCluster.next();
            ISimilarity sSimil;
            // Compare the objects
            try {
                sSimil = clDistanceCalculator.getSimilarityBetween(oFirst, oSecond);
            } catch (InvalidClassException ex) {
                System.err.println("Cannot compare " + oFirst.toString() + " to " + 
                        oSecond.toString() + ". Cause:");
                ex.printStackTrace(System.err);
                continue;
            }
            // Put id of pair and their similarity in distance distribution
            dDistances.setValue(iCnt++, sSimil.getOverallSimilarity());                    
        }
    }
    
    // Return the maximum similarity
    return dDistances.minValue();
}
Example 8
Source File: SimilarityBasedIndex.java    From Ngram-Graphs with Apache License 2.0 4 votes vote down vote up 
/** Returns the set of documents of the cluster that is most appropriate,
 * given a document graph.
 *@param dngCur The graph of the document used.
 *@return A {@link Set} of strings, corresponding to the document IDs in the
 *cluster that has the most similar content to the given document.
 */
@Override
public Set<String> locateSimilarDocuments(DocumentNGramGraph dngCur) {
    String sClusterLabel = null;
    // Init similarity to low value
    double dSim = 0.0;
    double dPrvSim = 0.0;
                    
    // Remove grammar
    //if (Grammar != null)
    //  dgCur = dgCur.allNotIn(Grammar);
    
    // Init current cluster to top
    Vertex vBestCandidate = null;
    Vertex vCur = getRootHierarchyNode(Hierarchy);
    
    // DEBUG LINES
    // Store index path
    LinkedList<String> lPath = new LinkedList<String>();
    lPath.add(vCur.getLabel());
    //////////////
    do {
        dPrvSim = dSim;
        
        // Get similarity of all childen of the current node to given doc
        Iterator iChildren = utils.getAdjacentIncomingVertices(Hierarchy, 
                vCur).iterator();
        vBestCandidate = vCur; // Best candidate is the current vertex
        
        // If not reached leaf
        if (iChildren.hasNext())
        {                
            // For every child
            while (iChildren.hasNext()) {
                Vertex vCandidate = (Vertex)iChildren.next();
                double dCurSim = Double.NEGATIVE_INFINITY;
                try {
                    // DEBUG LINES
                    // System.out.println("Comparing to..." + vCandidate.getLabel());
                    //////////////
                    
                    // Init comparator if required
                    initComparator();
                    dCurSim = Comparator.getSimilarityBetween(
                            dngCur, 
                            getRepresentationFromCluster(vCandidate.getLabel())).getOverallSimilarity();
                } catch (InvalidClassException ex) {
                    System.err.println("Invalid document type. Ignoring...");
                    ex.printStackTrace(System.err);
                }
                // If candidate is more similar than the parent
                if (dCurSim > dSim)
                {
                    // Update best candidate
                    vBestCandidate = vCandidate;
                    // and similarity
                    dSim = dCurSim;
                }
            }
        }
        
        vCur = vBestCandidate; // Update current position
        sClusterLabel = vBestCandidate.getLabel(); // Update best cluster label
        // DEBUG LINES
        // Add current node to path
        lPath.add(sClusterLabel);
        //////////////
    } while (dPrvSim < dSim);
    
    // DEBUG LINES
    System.err.println(utils.printIterable(lPath, "->\n"));
    //////////////
    return getDocumentIDsFromCluster(sClusterLabel);
}
Example 9
Source File: SimilarityBasedIndex.java    From Ngram-Graphs with Apache License 2.0 4 votes vote down vote up 
/** Returns the set of documents of the cluster that is most appropriate,
 * given a document graph.
 *@param dngCur The graph of the document used.
 *@return A {@link Set} of strings, corresponding to the document IDs in the
 *cluster that has the most similar content to the given document.
 */
@Override
public Set<String> locateSimilarDocuments(DocumentNGramGraph dngCur) {
    String sClusterLabel = null;
    // Init similarity to low value
    double dSim = 0.0;
    double dPrvSim = 0.0;
                    
    // Remove grammar
    //if (Grammar != null)
    //  dgCur = dgCur.allNotIn(Grammar);
    
    // Init current cluster to top
    Vertex vBestCandidate = null;
    Vertex vCur = getRootHierarchyNode(Hierarchy);
    
    // DEBUG LINES
    // Store index path
    LinkedList<String> lPath = new LinkedList<String>();
    lPath.add(vCur.getLabel());
    //////////////
    do {
        dPrvSim = dSim;
        
        // Get similarity of all childen of the current node to given doc
        Iterator iChildren = utils.getAdjacentIncomingVertices(Hierarchy, 
                vCur).iterator();
        vBestCandidate = vCur; // Best candidate is the current vertex
        
        // If not reached leaf
        if (iChildren.hasNext())
        {                
            // For every child
            while (iChildren.hasNext()) {
                Vertex vCandidate = (Vertex)iChildren.next();
                double dCurSim = Double.NEGATIVE_INFINITY;
                try {
                    // DEBUG LINES
                    // System.out.println("Comparing to..." + vCandidate.getLabel());
                    //////////////
                    
                    // Init comparator if required
                    initComparator();
                    dCurSim = Comparator.getSimilarityBetween(
                            dngCur, 
                            getRepresentationFromCluster(vCandidate.getLabel())).getOverallSimilarity();
                } catch (InvalidClassException ex) {
                    System.err.println("Invalid document type. Ignoring...");
                    ex.printStackTrace(System.err);
                }
                // If candidate is more similar than the parent
                if (dCurSim > dSim)
                {
                    // Update best candidate
                    vBestCandidate = vCandidate;
                    // and similarity
                    dSim = dCurSim;
                }
            }
        }
        
        vCur = vBestCandidate; // Update current position
        sClusterLabel = vBestCandidate.getLabel(); // Update best cluster label
        // DEBUG LINES
        // Add current node to path
        lPath.add(sClusterLabel);
        //////////////
    } while (dPrvSim < dSim);
    
    // DEBUG LINES
    System.err.println(utils.printIterable(lPath, "->\n"));
    //////////////
    return getDocumentIDsFromCluster(sClusterLabel);
}