List of usage examples for com.google.common.collect ConcurrentHashMultiset create
public static <E> ConcurrentHashMultiset<E> create()
From source file:mahout.classifier.Classifier.java
public static void main(String[] args) throws Exception { if (args.length < 5) { System.out.println("Arguments: [model] [label index] [dictionnary] [document frequency] [tweet file]"); return;/* w ww. j ava2s . co m*/ } String modelPath = args[0]; String labelIndexPath = args[1]; String dictionaryPath = args[2]; String documentFrequencyPath = args[3]; String tweetsPath = args[4]; Configuration configuration = new Configuration(); // model is a matrix (wordId, labelId) => probability score NaiveBayesModel model = NaiveBayesModel.materialize(new Path(modelPath), configuration); StandardNaiveBayesClassifier classifier = new StandardNaiveBayesClassifier(model); // labels is a map label => classId Map<Integer, String> labels = BayesUtils.readLabelIndex(configuration, new Path(labelIndexPath)); Map<String, Integer> dictionary = readDictionnary(configuration, new Path(dictionaryPath)); Map<Integer, Long> documentFrequency = readDocumentFrequency(configuration, new Path(documentFrequencyPath)); // analyzer used to extract word from tweet Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_43); int labelCount = labels.size(); int documentCount = documentFrequency.get(-1).intValue(); System.out.println("Number of labels: " + labelCount); System.out.println("Number of documents in training set: " + documentCount); BufferedReader reader = new BufferedReader(new FileReader(tweetsPath)); while (true) { String line = reader.readLine(); if (line == null) { break; } String[] tokens = line.split("\t", 2); String tweetId = tokens[0]; String tweet = tokens[1]; Multiset<String> words = ConcurrentHashMultiset.create(); // extract words from tweet TokenStream ts = analyzer.tokenStream("text", new StringReader(tweet)); CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class); ts.reset(); int wordCount = 0; while (ts.incrementToken()) { if (termAtt.length() > 0) { String word = ts.getAttribute(CharTermAttribute.class).toString(); Integer wordId = dictionary.get(word); // if the word is not in the dictionary, skip it if (wordId != null) { words.add(word); wordCount++; } } } // create vector wordId => weight using tfidf Vector vector = new RandomAccessSparseVector(10000); TFIDF tfidf = new TFIDF(); for (Multiset.Entry<String> entry : words.entrySet()) { String word = entry.getElement(); int count = entry.getCount(); Integer wordId = dictionary.get(word); Long freq = documentFrequency.get(wordId); double tfIdfValue = tfidf.calculate(count, freq.intValue(), wordCount, documentCount); vector.setQuick(wordId, tfIdfValue); } // With the classifier, we get one score for each label // The label with the highest score is the one the tweet is more likely to // be associated to Vector resultVector = classifier.classifyFull(vector); double bestScore = -Double.MAX_VALUE; int bestCategoryId = -1; for (Element element : resultVector.all()) { int categoryId = element.index(); double score = element.get(); if (score > bestScore) { bestScore = score; bestCategoryId = categoryId; } } System.out.println(labels.get(bestCategoryId) + "\t" + tweet); } analyzer.close(); reader.close(); }
From source file:org.mahout.example.classifier.naivebayes.Classifier.java
public static void main(String[] args) throws Exception { if (args.length < 5) { System.out.println("Arguments: [model] [label index] [dictionnary] [document frequency] [tweet file]"); return;/*from ww w. j ava2 s . c om*/ } String modelPath = args[0]; String labelIndexPath = args[1]; String dictionaryPath = args[2]; String documentFrequencyPath = args[3]; String tweetsPath = args[4]; Configuration configuration = new Configuration(); // model is a matrix (wordId, labelId) => probability score NaiveBayesModel model = NaiveBayesModel.materialize(new Path(modelPath), configuration); StandardNaiveBayesClassifier classifier = new StandardNaiveBayesClassifier(model); // labels is a map label => classId Map<Integer, String> labels = BayesUtils.readLabelIndex(configuration, new Path(labelIndexPath)); Map<String, Integer> dictionary = readDictionnary(configuration, new Path(dictionaryPath)); Map<Integer, Long> documentFrequency = readDocumentFrequency(configuration, new Path(documentFrequencyPath)); // analyzer used to extract word from tweet Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_43); int labelCount = labels.size(); int documentCount = documentFrequency.get(-1).intValue(); System.out.println("Number of labels: " + labelCount); System.out.println("Number of documents in training set: " + documentCount); BufferedReader reader = new BufferedReader(new FileReader(tweetsPath)); while (true) { String line = reader.readLine(); if (line == null) { break; } String[] tokens = line.split("\t", 2); String tweetId = tokens[0]; String tweet = tokens[1]; System.out.println("Tweet: " + tweetId + "\t" + tweet); Multiset<String> words = ConcurrentHashMultiset.create(); // extract words from tweet TokenStream ts = analyzer.tokenStream("text", new StringReader(tweet)); CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class); ts.reset(); int wordCount = 0; while (ts.incrementToken()) { if (termAtt.length() > 0) { String word = ts.getAttribute(CharTermAttribute.class).toString(); Integer wordId = dictionary.get(word); // if the word is not in the dictionary, skip it if (wordId != null) { words.add(word); wordCount++; } } } // create vector wordId => weight using tfidf Vector vector = new RandomAccessSparseVector(10000); TFIDF tfidf = new TFIDF(); for (Multiset.Entry<String> entry : words.entrySet()) { String word = entry.getElement(); int count = entry.getCount(); Integer wordId = dictionary.get(word); Long freq = documentFrequency.get(wordId); double tfIdfValue = tfidf.calculate(count, freq.intValue(), wordCount, documentCount); vector.setQuick(wordId, tfIdfValue); } // With the classifier, we get one score for each label // The label with the highest score is the one the tweet is more likely to // be associated to Vector resultVector = classifier.classifyFull(vector); double bestScore = -Double.MAX_VALUE; int bestCategoryId = -1; for (Element element : resultVector.all()) { int categoryId = element.index(); double score = element.get(); if (score > bestScore) { bestScore = score; bestCategoryId = categoryId; } System.out.print(" " + labels.get(categoryId) + ": " + score); } System.out.println(" => " + labels.get(bestCategoryId)); } analyzer.close(); reader.close(); }
From source file:com.chimpler.example.bayes.Classifier.java
public static void main(String[] args) throws Exception { if (args.length < 5) { System.out.println("Arguments: [model] [label index] [dictionnary] [document frequency] [tweet file]"); return;// w w w.ja va2s . c o m } String modelPath = args[0]; String labelIndexPath = args[1]; String dictionaryPath = args[2]; String documentFrequencyPath = args[3]; String tweetsPath = args[4]; Configuration configuration = new Configuration(); // model is a matrix (wordId, labelId) => probability score NaiveBayesModel model = NaiveBayesModel.materialize(new Path(modelPath), configuration); StandardNaiveBayesClassifier classifier = new StandardNaiveBayesClassifier(model); // labels is a map label => classId Map<Integer, String> labels = BayesUtils.readLabelIndex(configuration, new Path(labelIndexPath)); Map<String, Integer> dictionary = readDictionnary(configuration, new Path(dictionaryPath)); Map<Integer, Long> documentFrequency = readDocumentFrequency(configuration, new Path(documentFrequencyPath)); // analyzer used to extract word from tweet Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_43); int labelCount = labels.size(); int documentCount = documentFrequency.get(-1).intValue(); System.out.println("Number of labels: " + labelCount); System.out.println("Number of documents in training set: " + documentCount); BufferedReader reader = new BufferedReader(new FileReader(tweetsPath)); while (true) { String line = reader.readLine(); if (line == null) { break; } String[] tokens = line.split("\t", 2); String tweetId = tokens[0]; String tweet = tokens[1]; System.out.println("Tweet: " + tweetId + "\t" + tweet); Multiset<String> words = ConcurrentHashMultiset.create(); // extract words from tweet TokenStream ts = analyzer.tokenStream("text", new StringReader(tweet)); CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class); ts.reset(); int wordCount = 0; while (ts.incrementToken()) { if (termAtt.length() > 0) { String word = ts.getAttribute(CharTermAttribute.class).toString(); Integer wordId = dictionary.get(word); // if the word is not in the dictionary, skip it if (wordId != null) { words.add(word); wordCount++; } } } // Fixed error : close ts:TokenStream ts.end(); ts.close(); // create vector wordId => weight using tfidf Vector vector = new RandomAccessSparseVector(10000); TFIDF tfidf = new TFIDF(); for (Multiset.Entry<String> entry : words.entrySet()) { String word = entry.getElement(); int count = entry.getCount(); Integer wordId = dictionary.get(word); Long freq = documentFrequency.get(wordId); double tfIdfValue = tfidf.calculate(count, freq.intValue(), wordCount, documentCount); vector.setQuick(wordId, tfIdfValue); } // With the classifier, we get one score for each label // The label with the highest score is the one the tweet is more likely to // be associated to Vector resultVector = classifier.classifyFull(vector); double bestScore = -Double.MAX_VALUE; int bestCategoryId = -1; for (Element element : resultVector.all()) { int categoryId = element.index(); double score = element.get(); if (score > bestScore) { bestScore = score; bestCategoryId = categoryId; } System.out.print(" " + labels.get(categoryId) + ": " + score); } System.out.println(" => " + labels.get(bestCategoryId)); } analyzer.close(); reader.close(); }
From source file:com.umaircheema.mahout.utils.classifiers.NaiveBayesClassifier.java
public static void main(String[] args) throws Exception { if (args.length < 5) { System.out.println("Mahout Naive Bayesian Classifier"); System.out.println(//from w w w.ja v a 2 s . com "Classifies input text document into a class given a model, dictionary, document frequency and input file"); System.out.println( "Arguments: [model] [label_index] [dictionary] [document-frequency] [input-text-file]"); return; } String modelPath = args[0]; String labelIndexPath = args[1]; String dictionaryPath = args[2]; String documentFrequencyPath = args[3]; String inputFilePath = args[4]; Configuration configuration = new Configuration(); // model is a matrix (wordId, labelId) => probability score NaiveBayesModel model = NaiveBayesModel.materialize(new Path(modelPath), configuration); StandardNaiveBayesClassifier classifier = new StandardNaiveBayesClassifier(model); // labels is a map label => classId Map<Integer, String> labels = BayesUtils.readLabelIndex(configuration, new Path(labelIndexPath)); Map<String, Integer> dictionary = readDictionnary(configuration, new Path(dictionaryPath)); Map<Integer, Long> documentFrequency = readDocumentFrequency(configuration, new Path(documentFrequencyPath)); // analyzer used to extract word from input file Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_36); int labelCount = labels.size(); int documentCount = documentFrequency.get(-1).intValue(); System.out.println("Number of labels: " + labelCount); System.out.println("Number of documents in training set: " + documentCount); BufferedReader reader = new BufferedReader(new FileReader(inputFilePath)); StringBuilder stringBuilder = new StringBuilder(); String lineSeparator = System.getProperty("line.separator"); String line = null; while ((line = reader.readLine()) != null) { stringBuilder.append(line); stringBuilder.append(lineSeparator); } // Close the reader I/O reader.close(); Multiset<String> words = ConcurrentHashMultiset.create(); // extract words from input file TokenStream ts = analyzer.tokenStream("text", new StringReader(stringBuilder.toString())); CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class); ts.reset(); int wordCount = 0; while (ts.incrementToken()) { if (termAtt.length() > 0) { String word = ts.getAttribute(CharTermAttribute.class).toString(); Integer wordId = dictionary.get(word); // if the word is not in the dictionary, skip it if (wordId != null) { words.add(word); wordCount++; } } } // Fixed error : close ts:TokenStream ts.end(); ts.close(); // create vector wordId => weight using tfidf Vector vector = new RandomAccessSparseVector(10000); TFIDF tfidf = new TFIDF(); for (Multiset.Entry<String> entry : words.entrySet()) { String word = entry.getElement(); int count = entry.getCount(); Integer wordId = dictionary.get(word); Long freq = documentFrequency.get(wordId); double tfIdfValue = tfidf.calculate(count, freq.intValue(), wordCount, documentCount); vector.setQuick(wordId, tfIdfValue); } // With the classifier, we get one score for each label // The label with the highest score is the one the email is more likely // to // be associated to double bestScore = -Double.MAX_VALUE; int bestCategoryId = -1; Vector resultVector = classifier.classifyFull(vector); for (Element element : resultVector) { int categoryId = element.index(); double score = element.get(); if (score > bestScore) { bestScore = score; bestCategoryId = categoryId; } } System.out.println(" Class Labe: => " + labels.get(bestCategoryId)); System.out.println(" Score: => " + bestScore); analyzer.close(); }
From source file:PostgresClassifier.java
public static void main(String[] args) throws Exception { if (args.length < 5) { System.out.println(//w w w .j a v a2 s . com "Arguments: [model] [label index] [dictionnary] [document frequency] [input postgres table]"); return; } String modelPath = args[0]; String labelIndexPath = args[1]; String dictionaryPath = args[2]; String documentFrequencyPath = args[3]; String tablename = args[4]; Configuration configuration = new Configuration(); // model is a matrix (wordId, labelId) => probability score NaiveBayesModel model = NaiveBayesModel.materialize(new Path(modelPath), configuration); StandardNaiveBayesClassifier classifier = new StandardNaiveBayesClassifier(model); // labels is a map label => classId Map<Integer, String> labels = BayesUtils.readLabelIndex(configuration, new Path(labelIndexPath)); Map<String, Integer> dictionary = readDictionnary(configuration, new Path(dictionaryPath)); Map<Integer, Long> documentFrequency = readDocumentFrequency(configuration, new Path(documentFrequencyPath)); // analyzer used to extract word from tweet Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_43); int labelCount = labels.size(); int documentCount = documentFrequency.get(-1).intValue(); System.out.println("Number of labels: " + labelCount); System.out.println("Number of documents in training set: " + documentCount); Connection c = null; Statement stmt = null; Statement stmtU = null; try { Class.forName("org.postgresql.Driver"); c = DriverManager.getConnection("jdbc:postgresql://192.168.50.170:5432/uzeni", "postgres", "dbwpsdkdl"); c.setAutoCommit(false); System.out.println("Opened database successfully"); stmt = c.createStatement(); stmtU = c.createStatement(); ResultSet rs = stmt.executeQuery("SELECT * FROM " + tablename + " WHERE rep is null"); while (rs.next()) { String seq = rs.getString("seq"); //String rep = rs.getString("rep"); String body = rs.getString("body"); //String category = rep; String id = seq; String message = body; //System.out.println("Doc: " + id + "\t" + message); Multiset<String> words = ConcurrentHashMultiset.create(); // extract words from tweet TokenStream ts = analyzer.tokenStream("text", new StringReader(message)); CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class); ts.reset(); int wordCount = 0; while (ts.incrementToken()) { if (termAtt.length() > 0) { String word = ts.getAttribute(CharTermAttribute.class).toString(); Integer wordId = dictionary.get(word); // if the word is not in the dictionary, skip it if (wordId != null) { words.add(word); wordCount++; } } } // Mark : Modified ts.end(); ts.close(); // create vector wordId => weight using tfidf Vector vector = new RandomAccessSparseVector(10000); TFIDF tfidf = new TFIDF(); for (Multiset.Entry<String> entry : words.entrySet()) { String word = entry.getElement(); int count = entry.getCount(); Integer wordId = dictionary.get(word); Long freq = documentFrequency.get(wordId); double tfIdfValue = tfidf.calculate(count, freq.intValue(), wordCount, documentCount); vector.setQuick(wordId, tfIdfValue); } // With the classifier, we get one score for each label // The label with the highest score is the one the tweet is more likely to // be associated to Vector resultVector = classifier.classifyFull(vector); double bestScore = -Double.MAX_VALUE; int bestCategoryId = -1; for (Element element : resultVector.all()) { int categoryId = element.index(); double score = element.get(); if (score > bestScore) { bestScore = score; bestCategoryId = categoryId; } //System.out.print(" " + labels.get(categoryId) + ": " + score); } //System.out.println(" => " + labels.get(bestCategoryId)); //System.out.println("UPDATE " + tablename + " SET rep = '" + labels.get(bestCategoryId) + "' WHERE seq = " + id ); stmtU.executeUpdate("UPDATE " + tablename + " SET rep = '" + labels.get(bestCategoryId) + "' WHERE seq = " + id); } rs.close(); stmt.close(); stmtU.close(); c.commit(); c.close(); analyzer.close(); } catch (Exception e) { System.err.println(e.getClass().getName() + ": " + e.getMessage()); System.exit(0); } }
From source file:edu.stanford.rad.naivebayes.ClassifyLines.java
public static void main(String[] args) throws Exception { // if (args.length < 5) { // System.out.println("Arguments: [model] [label index] [dictionnary] [document frequency] [tweet file]"); // return; // }/*from w ww .ja v a2 s . c om*/ // String modelPath = args[0]; // String labelIndexPath = args[1]; // String dictionaryPath = args[2]; // String documentFrequencyPath = args[3]; // String tweetsPath = args[4]; String modelPath = "/Users/saeedhp/Dropbox/Stanford/Code/NER/files/stride/ectopicPregnancy/classification/nb"; String labelIndexPath = "/Users/saeedhp/Dropbox/Stanford/Code/NER/files/stride/ectopicPregnancy/classification/nb/labelindex"; String dictionaryPath = "/Users/saeedhp/Dropbox/Stanford/Code/NER/files/stride/ectopicPregnancy/vectors/TFIDFsparseSeqdir/dictionary.file-0"; String documentFrequencyPath = "/Users/saeedhp/Dropbox/Stanford/Code/NER/files/stride/ectopicPregnancy/vectors/TFIDFsparseSeqdir/df-count/part-r-00000"; String tweetsPath = "/Users/saeedhp/Desktop/tweet/tweet.txt"; Configuration configuration = new Configuration(); // model is a matrix (wordId, labelId) => probability score NaiveBayesModel model = NaiveBayesModel.materialize(new Path(modelPath), configuration); StandardNaiveBayesClassifier classifier = new StandardNaiveBayesClassifier(model); // labels is a map label => classId Map<Integer, String> labels = BayesUtils.readLabelIndex(configuration, new Path(labelIndexPath)); Map<String, Integer> dictionary = readDictionnary(configuration, new Path(dictionaryPath)); Map<Integer, Long> documentFrequency = readDocumentFrequency(configuration, new Path(documentFrequencyPath)); // analyzer used to extract word from tweet Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_46); int labelCount = labels.size(); int documentCount = documentFrequency.get(-1).intValue(); System.out.println("Number of labels: " + labelCount); System.out.println("Number of documents in training set: " + documentCount); BufferedReader reader = new BufferedReader(new FileReader(tweetsPath)); while (true) { String line = reader.readLine(); if (line == null) { break; } String[] tokens = line.split("\t", 2); String tweetId = tokens[0]; String tweet = tokens[1]; System.out.println("Tweet: " + tweetId + "\t" + tweet); Multiset<String> words = ConcurrentHashMultiset.create(); // extract words from tweet TokenStream ts = analyzer.tokenStream("text", new StringReader(tweet)); CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class); ts.reset(); int wordCount = 0; while (ts.incrementToken()) { if (termAtt.length() > 0) { String word = ts.getAttribute(CharTermAttribute.class).toString(); Integer wordId = dictionary.get(word); // if the word is not in the dictionary, skip it if (wordId != null) { words.add(word); wordCount++; } } } // Fixed error : close ts:TokenStream ts.end(); ts.close(); // create vector wordId => weight using tfidf Vector vector = new RandomAccessSparseVector(10000); TFIDF tfidf = new TFIDF(); for (Multiset.Entry<String> entry : words.entrySet()) { String word = entry.getElement(); int count = entry.getCount(); Integer wordId = dictionary.get(word); Long freq = documentFrequency.get(wordId); double tfIdfValue = tfidf.calculate(count, freq.intValue(), wordCount, documentCount); vector.setQuick(wordId, tfIdfValue); } // With the classifier, we get one score for each label // The label with the highest score is the one the tweet is more likely to // be associated to Vector resultVector = classifier.classifyFull(vector); double bestScore = -Double.MAX_VALUE; int bestCategoryId = -1; for (Element element : resultVector.all()) { int categoryId = element.index(); double score = element.get(); if (score > bestScore) { bestScore = score; bestCategoryId = categoryId; } System.out.print(" " + labels.get(categoryId) + ": " + score); } System.out.println(" => " + labels.get(bestCategoryId)); } analyzer.close(); reader.close(); }
From source file:ClassifierHD.java
public static void main(String[] args) throws Exception { if (args.length < 5) { System.out.println(//from w w w. j a va 2 s .c o m "Arguments: [model] [label index] [dictionnary] [document frequency] [postgres table] [hdfs dir] [job_id]"); return; } String modelPath = args[0]; String labelIndexPath = args[1]; String dictionaryPath = args[2]; String documentFrequencyPath = args[3]; String tablename = args[4]; String inputDir = args[5]; Configuration configuration = new Configuration(); // model is a matrix (wordId, labelId) => probability score NaiveBayesModel model = NaiveBayesModel.materialize(new Path(modelPath), configuration); StandardNaiveBayesClassifier classifier = new StandardNaiveBayesClassifier(model); // labels is a map label => classId Map<Integer, String> labels = BayesUtils.readLabelIndex(configuration, new Path(labelIndexPath)); Map<String, Integer> dictionary = readDictionnary(configuration, new Path(dictionaryPath)); Map<Integer, Long> documentFrequency = readDocumentFrequency(configuration, new Path(documentFrequencyPath)); // analyzer used to extract word from tweet Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_43); int labelCount = labels.size(); int documentCount = documentFrequency.get(-1).intValue(); System.out.println("Number of labels: " + labelCount); System.out.println("Number of documents in training set: " + documentCount); Connection conn = null; PreparedStatement pstmt = null; try { Class.forName("org.postgresql.Driver"); conn = DriverManager.getConnection("jdbc:postgresql://192.168.50.170:5432/uzeni", "postgres", "dbwpsdkdl"); conn.setAutoCommit(false); String sql = "INSERT INTO " + tablename + " (id,gtime,wtime,target,num,link,body,rep) VALUES (?,?,?,?,?,?,?,?);"; pstmt = conn.prepareStatement(sql); FileSystem fs = FileSystem.get(configuration); FileStatus[] status = fs.listStatus(new Path(inputDir)); BufferedWriter bw = new BufferedWriter( new OutputStreamWriter(fs.create(new Path(inputDir + "/rep.list"), true))); for (int i = 0; i < status.length; i++) { BufferedReader br = new BufferedReader(new InputStreamReader(fs.open(status[i].getPath()))); if (new String(status[i].getPath().getName()).equals("rep.list")) { continue; } int lv_HEAD = 1; int lv_cnt = 0; String lv_gtime = null; String lv_wtime = null; String lv_target = null; BigDecimal lv_num = null; String lv_link = null; String[] lv_args; String lv_line; StringBuilder lv_txt = new StringBuilder(); while ((lv_line = br.readLine()) != null) { if (lv_cnt < lv_HEAD) { lv_args = lv_line.split(","); lv_gtime = lv_args[0]; lv_wtime = lv_args[1]; lv_target = lv_args[2]; lv_num = new BigDecimal(lv_args[3]); lv_link = lv_args[4]; } else { lv_txt.append(lv_line + '\n'); } lv_cnt++; } br.close(); String id = status[i].getPath().getName(); String message = lv_txt.toString(); Multiset<String> words = ConcurrentHashMultiset.create(); TokenStream ts = analyzer.tokenStream("text", new StringReader(message)); CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class); ts.reset(); int wordCount = 0; while (ts.incrementToken()) { if (termAtt.length() > 0) { String word = ts.getAttribute(CharTermAttribute.class).toString(); Integer wordId = dictionary.get(word); if (wordId != null) { words.add(word); wordCount++; } } } ts.end(); ts.close(); Vector vector = new RandomAccessSparseVector(10000); TFIDF tfidf = new TFIDF(); for (Multiset.Entry<String> entry : words.entrySet()) { String word = entry.getElement(); int count = entry.getCount(); Integer wordId = dictionary.get(word); Long freq = documentFrequency.get(wordId); double tfIdfValue = tfidf.calculate(count, freq.intValue(), wordCount, documentCount); vector.setQuick(wordId, tfIdfValue); } Vector resultVector = classifier.classifyFull(vector); double bestScore = -Double.MAX_VALUE; int bestCategoryId = -1; for (Element element : resultVector.all()) { int categoryId = element.index(); double score = element.get(); if (score > bestScore) { bestScore = score; bestCategoryId = categoryId; } } //System.out.println(message); //System.out.println(" => "+ lv_gtime + lv_wtime + lv_link + id + ":" + labels.get(bestCategoryId)); pstmt.setString(1, id); pstmt.setString(2, lv_gtime); pstmt.setString(3, lv_wtime); pstmt.setString(4, lv_target); pstmt.setBigDecimal(5, lv_num); pstmt.setString(6, lv_link); pstmt.setString(7, message.substring(1, Math.min(50, message.length()))); pstmt.setString(8, labels.get(bestCategoryId)); pstmt.addBatch(); bw.write(id + "\t" + labels.get(bestCategoryId) + "\n"); } pstmt.executeBatch(); //pstmt.clearParameters(); pstmt.close(); conn.commit(); conn.close(); bw.close(); } catch (Exception e) { System.err.println(e.getClass().getName() + ": " + e.getMessage()); System.exit(0); } analyzer.close(); }
From source file:com.DSC.client.SecureChannel.java
/** * @param args//from w ww .j a va 2 s . c om * @throws InterruptedException * @throws IOException */ public static void main(String[] args) throws InterruptedException, IOException { /* Create their private & public keys */ ECKey key = new ECKey(); key.init(); ProgramState.publicKey = (ECPublicKeyParameters) key.getPublic(); ProgramState.privateKey = (ECPrivateKeyParameters) key.getPrivate(); /* Create the IV engine */ byte[] seed = new byte[64]; // 512 bit seed SecureRandom random = new SecureRandom(); random.nextBytes(seed); ProgramState.IVEngine = new ISAACRandomGenerator(new ISAACEngine()); ProgramState.IVEngine.init(seed); /* Create the blacklist and trusted contacts */ ProgramState.blacklist = ConcurrentHashMultiset.create(); ProgramState.trustedKeys = new ConcurrentHashMap<String, Address>(); /* Set the time for the client accurately using a NTP server */ DateTimeUtils.setCurrentMillisOffset(getTimeOffset()); ProgramState.fmt = DateTimeFormat.forPattern("HH:mm:ss"); /* Set the default nick as anonymous */ ProgramState.nick = "anonymous"; /* Initialize ISAACRandomGenerator, set ProgramState.IVEngine */ receiveController = new ReceiveController(); sendController = new SendController(); /* Start input event handler loop */ eventLoop(); }
From source file:tv.floe.metronome.io.records.RCV1RecordFactory.java
public static void ScanFile(String file, int debug_break_cnt) throws IOException { BufferedReader reader = null; int line_count = 0; Multiset<String> class_count = ConcurrentHashMultiset.create(); Multiset<String> namespaces = ConcurrentHashMultiset.create(); try {//from w ww . j a v a2 s . co m reader = new BufferedReader(new FileReader(file)); String line = reader.readLine(); while (line != null && line.length() > 0) { String[] parts = line.split(" "); class_count.add(parts[0]); namespaces.add(parts[1]); line = reader.readLine(); line_count++; Vector v = new RandomAccessSparseVector(FEATURES); for (int x = 2; x < parts.length; x++) { String[] feature = parts[x].split(":"); int index = Integer.parseInt(feature[0]) % FEATURES; double val = Double.parseDouble(feature[1]); System.out.println(feature[1] + " = " + val); if (index < FEATURES) { v.set(index, val); } else { System.out.println("Could Hash: " + index + " to " + (index % FEATURES)); } } System.out.println("###"); if (line_count > debug_break_cnt) { break; } } System.out.println("Total Rec Count: " + line_count); System.out.println("-------------------- "); System.out.println("Classes"); for (String word : class_count.elementSet()) { System.out.println("Class " + word + ": " + class_count.count(word) + " "); } System.out.println("-------------------- "); System.out.println("NameSpaces:"); for (String word : namespaces.elementSet()) { System.out.println("Namespace " + word + ": " + namespaces.count(word) + " "); } } finally { reader.close(); } }
From source file:com.publictransitanalytics.scoregenerator.scoring.CountScoreCard.java
public CountScoreCard(final int taskCount, final SetMultimap<PointLocation, Sector> pointSectorMap) { super(taskCount); locations = ConcurrentHashMultiset.create(); this.pointSectorMap = pointSectorMap; }