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< > BotCompany Repo | #1001011 // Token prediction, multiple predictors (adding zip support)

JavaX source code [tags: use-pretranspiled] - run with: x30.jar

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!747
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abstract class P {
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  abstract S read(L<S> tok);
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  abstract P derive(); // clone & reset counter for actual use
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}
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m {
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  static S corpusID = "#1001010";
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  static int numSnippets = 3000;
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  static boolean showGUI = true;
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  static int maxCharsGUI = 500000;
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  static boolean allTokens = true;
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  static Collector collector;
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  static L<S> tok;
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  static Set<int> predicted;
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  static class Chain extends P {
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    new L<P> list;
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    *() {}
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    *(L<P> *list) {}
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    *(P... a) { list = asList(a); }
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    void add(P p) { list.add(p); }
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    S read(L<S> tok) {
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      for (P p : list) {
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        S s = p.read(tok);
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        if (s != null) return s;
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      }
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      return null;
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    }
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    P derive() {
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      new Chain c;
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      for (P p : list)
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        c.add(p.derive());
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      return c;
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    }
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  }
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  static class Tuples extends P {
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    Map<L<S>,S> map = new HashMap<L<S>,S>();
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    int n, seen;
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    *(int *n) {
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    }
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    S read(L<S> tok) {
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      while (tok.size() > seen) {
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        ++seen;
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        if (seen > n)
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          map.put(new ArrayList<S>(tok.subList(seen-n-1, seen-1)), tok.get(seen-1));
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      }
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      if (tok.size() >= n)
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        return map.get(new ArrayList<S>(tok.subList(tok.size()-n, tok.size())));
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      return null;
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    }
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    // slow...
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    P oldDerive() {
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      Tuples t = new Tuples(n);
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      t.map.putAll(map);
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      // t.seen == 0 which is ok
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      return t;
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    }
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    // fast!
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    P derive() {
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      Tuples t = new Tuples(n);
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      t.map = new DerivedHashMap<L<S>,S>(map);
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      return t;
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    }
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  }
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  static class DerivedHashMap<A, B> extends AbstractMap<A, B> {
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    Map<A, B> base;
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    new HashMap<A, B> additions;
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    *(Map<A, B> *base) {}
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    public B get(Object key) {
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      B b = additions.get(key);
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      if (b != null) return b;
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      return base.get(key);
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    }
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    public B put(A key, B value) {
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      return additions.put(key, value);
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    }
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    public Set<Map.Entry<A,B>> entrySet() {
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      throw fail();
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    }
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  }
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  // TODO: Put NewX back in
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  p {
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    tok = makeCorpusJavaTok();
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    print("Tokens in corpus: " + tok.size());
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    print("Learning...");
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    collector = new Collector;
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    /*test(new Tuples(1));
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    test(new Tuples(2));
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    test(new Tuples(3));
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    test(new Tuples(4));
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    test(new Chain(new Tuples(2), new Tuples(1)));*/
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    test(new Chain(new Tuples(4), new Tuples(3), new Tuples(2), new Tuples(1)));
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    print("Learning done.");
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    if (collector.winner != null && showGUI) {
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      predicted = collector.predicted;
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      showColoredText();
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    }
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  }
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  // test a predictor
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  static void test(P p) {
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    predicted = new TreeSet<int>();
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    int points = 0, total = 0, lastPercent = 0;
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    new L<S> history;
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    for (int i = allTokens ? 0 : 1; i < tok.size(); i += allTokens ? 1 : 2) {
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      S t = tok.get(i);
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      S x = p.read(history);
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      boolean correct = t.equals(x);
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      total += t.length();
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      if (correct) {
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        predicted.add(i);
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        points += t.length();
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      }
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      history.add(t);
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      int percent = roundUpTo(10, (int) (i*100L/tok.size()));
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      if (percent > lastPercent) {
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        print("Learning " + percent + "% done.");
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        lastPercent = percent;
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      }
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    }
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    double score = points*100.0/total;
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    collector.add(p, score);
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  }
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  static void showColoredText() ctex {
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    JFrame jf = new JFrame("Predicted = green");
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    Container cp = jf.getContentPane();
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    JTextPane pane = new JTextPane();
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    //pane.setFont(loadFont("#1000993", 24));
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    Document doc = pane.getStyledDocument();
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    int i = tok.size(), len = 0;
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    while (len <= maxCharsGUI && i > 0) {
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      --i;
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      len += tok.get(i).length();
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    }
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    for (; i < tok.size(); i++) {
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      if (tok.get(i).length() == 0) continue;
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      boolean green = predicted.contains(i);
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      SimpleAttributeSet set = new SimpleAttributeSet();
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      StyleConstants.setForeground(set, green ? Color.green : Color.gray);
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      doc.insertString(doc.getLength(), tok.get(i), set);
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    }
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    JScrollPane scrollPane = new JScrollPane(pane);
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    cp.add(scrollPane, BorderLayout.CENTER);
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    jf.setBounds(100, 100, 600, 600);
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    jf.setVisible(true);
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  }
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  !include #1000989 // SnippetDB
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  static L<S> makeCorpusJavaTok() {
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    S name = getSnippetTitle(corpusID);
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    if (name.toLowerCase().indexOf(".zip") >= 0)
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      return makeCorpus_zip();
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    else
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      return makeCorpus_mysqldump();
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  }
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  static L<S> makeCorpus_zip() ctex {
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    ZipFile zipFile = new ZipFile(loadLibrary(corpusID));
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    Enumeration entries = zipFile.entries();
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    new L<S> tok;
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    while (entries.hasMoreElements()) {
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      ZipEntry entry = (ZipEntry) entries.nextElement(); 
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      //System.out.println("File found: " + entry.getName());
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      InputStream fin = zipFile.getInputStream(entry);
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      // TODO: try to skip binary files?
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      InputStreamReader reader = new InputStreamReader(fin, "UTF-8");
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      new StringBuilder builder;
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      BufferedReader bufferedReader = new BufferedReader(reader);
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      String line;
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      while ((line = bufferedReader.readLine()) != null)
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        builder.append(line).append('\n');
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      fin.close();
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      new StringBuilder buf;
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      buf.append("\n== File: " + entry.getName());
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      buf.append("\n==\n");
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      buf.append(builder.toString()).append("\n");
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      if (tok.size() != 0) tok.remove(tok.size()-1);
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      tok.addAll(javaTok(buf.toString()));
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    }
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    zipFile.close();
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    return internAll(tok);
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  }
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  static L<S> makeCorpus_mysqldump() {
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    SnippetDB db = new SnippetDB(corpusID);
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    List<List<S>> rows = db.rowsOrderedBy("sn_created");
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    new L<S> tok;
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    for (int i = 0; i < Math.min(rows.size(), numSnippets); i++) {
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      new StringBuilder buf;
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      S id = db.getField(rows.get(i), "sn_id");
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      S title = db.getField(rows.get(i), "sn_title");
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      S text = db.getField(rows.get(i), "sn_text");
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      buf.append("\n== ID: " + id);
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      buf.append("\n== Title: " + title);
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      buf.append("\n==\n");
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      buf.append(text).append("\n");
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      if (tok.size() != 0) tok.remove(tok.size()-1);
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      tok.addAll(javaTok(buf.toString()));
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      ++i;
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    }
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    return internAll(tok);
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  }
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  static L<S> internAll(L<S> tok) {
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    new L<S> l;
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    for (S t : tok)
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      l.add(t.intern());
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    return l;
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  }
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  static class Collector {
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    P winner;
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    double bestScore = -1;
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    Set<int> predicted;
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    void add(P p, double score) {
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      if (winner == null || score > bestScore) {
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        winner = p;
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        bestScore = score;
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        //S name = shorten(structure(p), 100);
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        S name = p.getClass().getName();
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        print("New best score: " + formatDouble(score, 2) + "% (" + name + ")");
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        this.predicted = main.predicted;
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      }
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    }
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  }
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}

Author comment

Began life as a copy of #1001000

download  show line numbers  debug dex  old transpilations   

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Snippet ID: #1001011
Snippet name: Token prediction, multiple predictors (adding zip support)
Eternal ID of this version: #1001011/1
Text MD5: 942f3ed24c4432b998f1e22ebdd4e9fe
Transpilation MD5: 78b9d92dea6ca60c5e4296d797492666
Author: stefan
Category:
Type: JavaX source code
Public (visible to everyone): Yes
Archived (hidden from active list): No
Created/modified: 2015-09-16 00:58:02
Source code size: 7026 bytes / 262 lines
Pitched / IR pitched: No / Yes
Views / Downloads: 627 / 957
Referenced in: [show references]