Public

<init>

size

A helper function that helps you to perform cross validation. It splits the data
in to n_fold Pairs,

splitData

Dictionary

Finding current android device location

A ConnectException is thrown if a connection cannot be established to a remote
host on a specific po

java.net.ConnectException

An URLConnection for HTTP (RFC 2616 [http://tools.ietf.org/html/rfc2616]) used
to send and receive d

java.net.HttpURLConnection

This class implements a vector of bits that grows as needed. Each component of
the bit set has a boo

java.util.BitSet

This class implements the Set interface, backed by a hash table (actually a 
HashMap instance). It m

java.util.HashSet

A collection designed for holding elements prior to processing. Besides basic
java.util.Collection o

java.util.Queue

A NavigableSet implementation based on a TreeMap. The elements are ordered using
their Comparable, o

java.util.TreeSet

getContentResolver

Sets the general request property. If a property with the key already exists,
overwrite its value wi

setRequestProperty

Creates and executes a periodic action that becomes enabled first after the
given initial delay, and

scheduleAtFixedRate

/**
	 * A helper function that helps you to split the training data
	 * 
	 * @param n_train
	 *            The number of the training examples.
	 * @return A {@link Pair} of the Binary Problem. The first one represents
	 *         the training set (which has n_train examples). The second one
	 *         represents the testing set
	 */

/**
	 * A helper function that helps you to perform cross validation. It splits
	 * the data in to n_fold {@link Pair}s, and each pair contains the (Training
	 * and Testing) split.
	 * 
	 * @param n_fold
	 *            The number of fold you wish to performance cross validations.
	 *            It equals to the length of the returned list.
	 * @param rnd
	 *            A random number generator. If you use the same seed, you will
	 *            generate the same split. It makes the comparisons between
	 *            different algorithms easier.
	 * @return
	 */

// System.out.println(labeledRerankingIns.getFeatureVector());

/**
	 * The parallel version of
	 * {@link L2LossJLISLearner#trainStructuredSVM(AbstractLossSensitiveStructureFinder, StructuredProblem, JLISParameters)}
	 * <p>
	 * 
	 * The only difference now is that we need to create more than one inference
	 * solver. The number of the inference solvers will determine how many
	 * threads we will use.
	 * 
	 * @param struct_finder_list
	 *            The list of the inference solvers. It determines how many
	 *            threads the learner will use.
	 * @param sp
	 * @param para
	 * @return
	 * @throws Exception
	 */

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