Public

<init>

setIgnoreClass

Signify that this batch of input to the filter is finished. If the filter
requires all instances pri

batchFinished

bufferInput

Convert a single instance over. The converted instance is added to the end of
the output queue.

convertInstance

flushInput

getInputFormat

Input an instance for filtering. Filter requires all training instances be read
before producing out

input

numPendingOutput

output

push

resetQueue

runFilter

setInputFormat

setOutputFormat

FileOutputStream

Finding current android device location

The BitSet class implements abit array [http://en.wikipedia.org/wiki/Bit_array].
Each element is eit

java.util.BitSet

Breaks a string into tokens; new code should probably use String#split.>  
// Legacy code: 
StringTo

java.util.StringTokenizer

The TimerTask class represents a task to run at a specified time. The task may
be run once or repeat

java.util.TimerTask

TreeSet is an implementation of SortedSet. All optional operations (adding and
removing) are support

java.util.TreeSet

javax.swing.JTextField

Class that models an arbitrary location in a Resource.Typically used to track
the location of proble

org.springframework.beans.factory.parsing.Location

setRequestProperty

scheduleAtFixedRate

setScale

/**
   * Processes the given data (may change the provided dataset) and returns the
   * modified version. This method is called in batchFinished().
   * 
   * @param instances the data to process
   * @return the modified data
   * @throws Exception in case the processing goes wrong
   * @see #batchFinished()
   */

// save original class values if no prediction is performed

// add the mean to the class again if predictions are to be performed,

// otherwise restore original class values

/**
   * Method for building the classifier.
   *
   * @param instances the set of training instances
   * @throws Exception if the classifier can't be built successfully
   */

// Removes all the instances with weight equal to 0.

// MUST be done since condition (8) of Keerthi's paper 

// is made with the assertion Ci > 0 (See equation (3a).

"No training instances left after removing "

"instance with either a weight null or a missing class!"

// retrieve two different class values used to determine filter transformation

// degenerate case, all class values are equal

// we don't want to deal with this, too much hassle

"All class values are the same. At least two class values should be different"

// no division by zero, since y0 != y1 guaranteed => z0 != z1 ???

/**
   * Method for building the classifier.
   * 
   * @param insts the set of training instances
   * @throws Exception if the classifier can't be built successfully
   */

// determine which linear transformation has been

// Store square roots of instance m_weights

// initialize kernel matrix/covariance matrix

How to use setIgnoreClassmethodin weka.filters.unsupervised.attribute.Standardize

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How to use
setIgnoreClass
method
in
weka.filters.unsupervised.attribute.Standardize