Examples of Alphabet

cc.mallet.types.Alphabet
A mapping between integers and objects where the mapping in each direction is efficient. Integers are assigned consecutively, starting at zero, as objects are added to the Alphabet. Objects can not be deleted from the Alphabet and thus the integers are never reused.
The most common use of an alphabet is as a dictionary of feature names associated with a {@link cc.mallet.types.FeatureVector} in an{@link cc.mallet.types.Instance}. In a simple document classification usage, each unique word in a document would be a unique entry in the Alphabet with a unique integer associated with it. FeatureVectors rely on the integer part of the mapping to efficiently represent the subset of the Alphabet present in the FeatureVector. @see FeatureVector @see Instance @see cc.mallet.pipe.Pipe
lexer.Alphabet
org.jboss.test.kernel.config.support.SimpleBean.Alphabet
org.jsmpp.bean.Alphabet
types.Alphabet
This class is used to map from some descriptive features (e.g. words in text) to an index in an array. @author kuzman

Examples of org.jsmpp.bean.Alphabet

      e.printStackTrace();
    }


    // configure variables acording to if message contains national
    // characters
    Alphabet alphabet = null;
    int maximumSingleMessageSize = 0;
    int maximumMultipartMessageSegmentSize = 0;
    byte[] byteSingleMessage = null;
    if (Gsm0338.isEncodeableInGsm0338(messageBody)) {
      byteSingleMessage = messageBody.getBytes();

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Examples of org.jsmpp.bean.Alphabet


  @Override
  public boolean sendMessage(OutboundMessage msg) throws TimeoutException,
      GatewayException, IOException, InterruptedException {
    
    Alphabet encoding=Alphabet.ALPHA_DEFAULT;
    
    switch (msg.getEncoding()){
    case ENC8BIT:
      encoding=Alphabet.ALPHA_8_BIT;
      break;

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Examples of types.Alphabet

public class InternetAds {


  public static void main(String[] args) throws IOException {
    // read in the data.
    ArrayList<ClassificationInstance> allData = (new InternetAdReader(
        new Alphabet(), new Alphabet())).readFile(args[0]);
    StaticUtils.shuffle(allData, 0);
    // randomly split data into training and testing part
    ArrayList<ClassificationInstance>[] tmp = StaticUtils.split(allData,
        200);
    ArrayList<ClassificationInstance> train = tmp[0];
    ArrayList<ClassificationInstance> test = tmp[1];
    Alphabet xA = allData.get(0).xAlphabet;
    Alphabet yA = allData.get(0).yAlphabet;
    System.out.println("num Features = " + allData.get(0).xAlphabet.size());
    LinearClassifier h;
    h = trainAdaBoost(50, train, xA, yA);
    System.out.println("Boost  Train Accuracy = "
        + StaticUtils.computeAccuracy(h, train));

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