Serialized Form


Package weka.classifiers.misc

Class weka.classifiers.misc.InputMappedClassifier extends SingleClassifierEnhancer implements Serializable

serialVersionUID: 4901630631723287761L

Serialized Fields

m_modelPath

java.lang.String m_modelPath

m_modelHeader

Instances m_modelHeader

m_trim

boolean m_trim

m_ignoreCase

boolean m_ignoreCase

m_suppressMappingReport

boolean m_suppressMappingReport

m_initialTestStructureKnown

boolean m_initialTestStructureKnown

m_vals

double[] m_vals

Class weka.classifiers.misc.OSDL extends OSDLCore implements Serializable

serialVersionUID: -4534219825732505381L

Class weka.classifiers.misc.SerializedClassifier extends AbstractClassifier implements Serializable

serialVersionUID: 4599593909947628642L

Serialized Fields

m_ModelFile

java.io.File m_ModelFile

Package weka.classifiers.misc.monotone

Class weka.classifiers.misc.monotone.Coordinates extends java.lang.Object implements Serializable

serialVersionUID: 2319016195345994738L

Serialized Fields

m_coord

int[] m_coord
The internal weka values of the attributes of the instance, minus the class attribute.


m_hashCode

int m_hashCode
The hashcode of this object. Calculated during construction.

Class weka.classifiers.misc.monotone.CumulativeDiscreteDistribution extends java.lang.Object implements Serializable

serialVersionUID: -2959806903004453176L

Serialized Fields

m_cdf

double[] m_cdf
The cumulative probabilities

Class weka.classifiers.misc.monotone.DiscreteDistribution extends java.lang.Object implements Serializable

serialVersionUID: 1954630934425689828L

Serialized Fields

m_dd

double[] m_dd
the array of probabilities

Class weka.classifiers.misc.monotone.OSDLCore extends AbstractClassifier implements Serializable

serialVersionUID: -9209888846680062897L

Serialized Fields

m_ctype

int m_ctype
The classification type, by default set to CT_MEDIAN.


m_train

Instances m_train
The training examples.


m_estimatedDistributions

java.util.Map<K,V> m_estimatedDistributions
Collection of (Coordinates,DiscreteEstimator) pairs. This Map is build from the training examples. The DiscreteEstimator is over the classes. Each DiscreteEstimator indicates how many training examples there are with the specified classes.


m_estimatedCumulativeDistributions

java.util.Map<K,V> m_estimatedCumulativeDistributions
Collection of (Coordinates,CumulativeDiscreteDistribution) pairs. This Map is build from the training examples, and more specifically from the previous map.


m_s

double m_s
The interpolationparameter s. By default set to 1/2.


m_sLower

double m_sLower
Lower bound for the interpolationparameter s. Default value is 0.


m_sUpper

double m_sUpper
Upper bound for the interpolationparameter s. Default value is 1.


m_sNrParts

int m_sNrParts
The number of parts the interval [m_sLower,m_sUpper] is divided in, while searching for the best parameter s. This thus determines the granularity of the search. m_sNrParts + 1 values of the interpolationparameter will be tested.


m_tuneInterpolationParameter

boolean m_tuneInterpolationParameter
Indicates whether the interpolationparameter is to be tuned using leave-one-out cross validation. true if this is the case (default is false ).


m_interpolationParameterValid

boolean m_interpolationParameterValid
Indicates whether the current value of the interpolationparamter is valid. More specifically if m_tuneInterpolationParameter == true , and m_InterpolationParameter == false , this means that the current interpolation parameter is not valid. This parameter is only relevant if m_tuneInterpolationParameter == true . If m_tuneInterpolationParameter and m_interpolationParameterValid are both true , then m_s should always be between m_sLower and m_sUpper .


m_balanced

boolean m_balanced
Constant to switch between balanced and unbalanced OSDL. true means that one chooses balanced OSDL (default: false ).


m_weighted

boolean m_weighted
Constant to choose the weighted variant of the OSDL algorithm.


smallestElement

Coordinates smallestElement
Coordinates representing the smallest element of the data space.


biggestElement

Coordinates biggestElement
Coordinates representing the biggest element of the data space.