| Modifier and Type | Class and Description |
|---|---|
class |
AbstractAssociator
Abstract scheme for learning associations.
|
class |
Apriori
Class implementing an Apriori-type algorithm.
|
class |
AprioriItemSet
Class for storing a set of items.
|
class |
AssociatorEvaluation
Class for evaluating Associaters.
|
class |
CheckAssociator
Class for examining the capabilities and finding problems with associators.
|
class |
FilteredAssociator
Class for running an arbitrary associator on data
that has been passed through an arbitrary filter.
|
class |
FPGrowth
Class implementing the FP-growth algorithm for
finding large item sets without candidate generation.
|
class |
ItemSet
Class for storing a set of items.
|
class |
LabeledItemSet
Class for storing a set of items together with a class label.
|
class |
SingleAssociatorEnhancer
Abstract utility class for handling settings common to meta associators that
use a single base associator.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ASEvaluation
Abstract attribute selection evaluation class
|
class |
ASSearch
Abstract attribute selection search class.
|
class |
AttributeSelection
Attribute selection class.
|
class |
AttributeSetEvaluator
Abstract attribute set evaluator.
|
class |
BestFirst
BestFirst:
Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility. |
class |
BestFirst.Link2
Class for a node in a linked list.
|
class |
CfsSubsetEval
CfsSubsetEval :
Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. |
class |
CheckAttributeSelection
Class for examining the capabilities and finding problems with attribute
selection schemes.
|
class |
ClassifierAttributeEval
ClassifierAttributeEval :
Evaluates the worth of an attribute by using a user-specified classifier. |
class |
ClassifierSubsetEval
Classifier subset evaluator:
Evaluates attribute subsets on training data or a seperate hold out testing set. |
class |
CorrelationAttributeEval
CorrelationAttributeEval :
Evaluates the worth of an attribute by measuring the correlation (Pearson's) between it and the class. |
class |
GainRatioAttributeEval
GainRatioAttributeEval :
Evaluates the worth of an attribute by measuring the gain ratio with respect to the class. |
class |
GreedyStepwise
GreedyStepwise :
Performs a greedy forward or backward search through the space of attribute subsets. |
class |
HoldOutSubsetEvaluator
Abstract attribute subset evaluator capable of evaluating subsets with
respect to a data set that is distinct from that used to initialize/
train the subset evaluator.
|
class |
InfoGainAttributeEval
InfoGainAttributeEval :
Evaluates the worth of an attribute by measuring the information gain with respect to the class. |
class |
OneRAttributeEval
OneRAttributeEval :
Evaluates the worth of an attribute by using the OneR classifier. |
class |
PrincipalComponents
Performs a principal components analysis and
transformation of the data.
|
class |
Ranker
Ranker :
Ranks attributes by their individual evaluations. |
class |
ReliefFAttributeEval
ReliefFAttributeEval :
Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class. |
class |
SymmetricalUncertAttributeEval
SymmetricalUncertAttributeEval :
Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class. |
class |
UnsupervisedAttributeEvaluator
Abstract unsupervised attribute evaluator.
|
class |
UnsupervisedSubsetEvaluator
Abstract unsupervised attribute subset evaluator.
|
class |
WrapperSubsetEval
WrapperSubsetEval:
Evaluates attribute sets by using a learning scheme. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractClassifier
Abstract classifier.
|
class |
BVDecompose
Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
Ron Kohavi, David H. |
class |
BVDecomposeSegCVSub
This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1).
|
class |
CheckClassifier
Class for examining the capabilities and finding problems with classifiers.
|
class |
CheckSource
A simple class for checking the source generated from Classifiers
implementing the
weka.classifiers.Sourcable interface. |
class |
CostMatrix
Class for storing and manipulating a misclassification cost matrix.
|
class |
IteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from a single base learner.
|
class |
MultipleClassifiersCombiner
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from multiple classifiers.
|
class |
ParallelIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to meta classifiers that
build an ensemble in parallel from a single base learner.
|
class |
ParallelMultipleClassifiersCombiner
Abstract utility class for handling settings common to
meta classifiers that build an ensemble in parallel using multiple
classifiers.
|
class |
RandomizableClassifier
Abstract utility class for handling settings common to randomizable
classifiers.
|
class |
RandomizableIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
|
class |
RandomizableMultipleClassifiersCombiner
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from multiple classifiers based
on a given random number seed.
|
class |
RandomizableParallelIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble in parallel from a single base
learner.
|
class |
RandomizableParallelMultipleClassifiersCombiner
Abstract utility class for handling settings common to
meta classifiers that build an ensemble in parallel using multiple
classifiers based on a given random number seed.
|
class |
RandomizableSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
|
class |
SingleClassifierEnhancer
Abstract utility class for handling settings common to meta
classifiers that use a single base learner.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NaiveBayes
Class for a Naive Bayes classifier using estimator
classes.
|
class |
NaiveBayesMultinomial
Class for building and using a multinomial Naive Bayes classifier.
|
class |
NaiveBayesMultinomialText
Multinomial naive bayes for text data.
|
class |
NaiveBayesMultinomialUpdateable
Class for building and using an updateable multinomial Naive Bayes classifier.
|
class |
NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ADNode
The ADNode class implements the ADTree datastructure which increases the
speed with which sub-contingency tables can be constructed from a data set in
an Instances object.
|
class |
BayesNetGenerator
Bayes Network learning using various search
algorithms and quality measures.
|
class |
BIFReader
Builds a description of a Bayes Net classifier
stored in XML BIF 0.3 format.
|
class |
EditableBayesNet
Bayes Network learning using various search
algorithms and quality measures.
|
class |
MarginCalculator |
class |
MarginCalculator.JunctionTreeNode |
class |
MarginCalculator.JunctionTreeSeparator |
class |
ParentSet
Helper class for Bayes Network classifiers.
|
class |
VaryNode
Part of ADTree implementation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BayesNetEstimator
BayesNetEstimator is the base class for estimating
the conditional probability tables of a Bayes network once the structure has
been learned.
|
class |
BMAEstimator
BMAEstimator estimates conditional probability
tables of a Bayes network using Bayes Model Averaging (BMA).
|
class |
DiscreteEstimatorBayes
Symbolic probability estimator based on symbol counts and a prior.
|
class |
DiscreteEstimatorFullBayes
Symbolic probability estimator based on symbol counts and a prior.
|
class |
MultiNomialBMAEstimator
Multinomial BMA Estimator.
|
class |
SimpleEstimator
SimpleEstimator is used for estimating the
conditional probability tables of a Bayes network once the structure has been
learned.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SearchAlgorithm
This is the base class for all search algorithms for learning Bayes networks.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CISearchAlgorithm
The CISearchAlgorithm class supports Bayes net structure search algorithms that are based on conditional independence test (as opposed to for example score based of cross validation based search algorithms).
|
class |
ICSSearchAlgorithm
This Bayes Network learning algorithm uses
conditional independence tests to find a skeleton, finds V-nodes and applies
a set of rules to find the directions of the remaining arrows.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FromFile
The FromFile reads the structure of a Bayes net
from a file in BIFF format.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GlobalScoreSearchAlgorithm
This Bayes Network learning algorithm uses cross
validation to estimate classification accuracy.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GeneticSearch
This Bayes Network learning algorithm uses genetic
search for finding a well scoring Bayes network structure.
|
class |
HillClimber
This Bayes Network learning algorithm uses a hill
climbing algorithm adding, deleting and reversing arcs.
|
class |
K2
This Bayes Network learning algorithm uses a hill
climbing algorithm restricted by an order on the variables.
|
class |
LAGDHillClimber
This Bayes Network learning algorithm uses a Look
Ahead Hill Climbing algorithm called LAGD Hill Climbing.
|
class |
LocalScoreSearchAlgorithm
The ScoreBasedSearchAlgorithm class supports Bayes
net structure search algorithms that are based on maximizing scores (as
opposed to for example conditional independence based search algorithms).
|
class |
RepeatedHillClimber
This Bayes Network learning algorithm repeatedly
uses hill climbing starting with a randomly generated network structure and
return the best structure of the various runs.
|
class |
SimulatedAnnealing
This Bayes Network learning algorithm uses the
general purpose search method of simulated annealing to find a well scoring
network structure.
|
class |
TabuSearch
This Bayes Network learning algorithm uses tabu
search for finding a well scoring Bayes network structure.
|
class |
TAN
This Bayes Network learning algorithm determines
the maximum weight spanning tree and returns a Naive Bayes network augmented
with a tree.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AggregateableEvaluation
Subclass of Evaluation that provides a method for aggregating the results
stored in another Evaluation object.
|
class |
ConfusionMatrix
Cells of this matrix correspond to counts of the number (or weight) of
predictions for each actual value / predicted value combination.
|
class |
CostCurve
Generates points illustrating probablity cost tradeoffs that can be obtained
by varying the threshold value between classes.
|
class |
Evaluation
Class for evaluating machine learning models.
|
class |
EvaluationUtils
Contains utility functions for generating lists of predictions in various
manners.
|
class |
MarginCurve
Generates points illustrating the prediction margin.
|
class |
NominalPrediction
Encapsulates an evaluatable nominal prediction: the predicted probability
distribution plus the actual class value.
|
class |
NumericPrediction
Encapsulates an evaluatable numeric prediction: the predicted class value
plus the actual class value.
|
class |
ThresholdCurve
Generates points illustrating prediction tradeoffs that can be obtained by
varying the threshold value between classes.
|
class |
TwoClassStats
Encapsulates performance functions for two-class problems.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GaussianProcesses
* Implements Gaussian processes for regression without hyperparameter-tuning.
|
class |
LinearRegression
Class for using linear regression for prediction.
|
class |
Logistic
Class for building and using a multinomial logistic
regression model with a ridge estimator.
|
class |
MultilayerPerceptron
A Classifier that uses backpropagation to classify
instances.
|
class |
SGD
Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss linear regression).
|
class |
SGDText
Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
|
class |
SimpleLinearRegression
Learns a simple linear regression model.
|
class |
SimpleLogistic
Classifier for building linear logistic regression
models.
|
class |
SMO
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
|
class |
SMOreg
SMOreg implements the support vector machine for regression.
|
class |
VotedPerceptron
Implementation of the voted perceptron algorithm by Freund and Schapire.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LinearUnit
This can be used by the
neuralnode to perform all it's computations (as a Linear unit).
|
class |
NeuralConnection
Abstract unit in a NeuralNetwork.
|
class |
NeuralNode
This class is used to represent a node in the neuralnet.
|
class |
SigmoidUnit
This can be used by the
neuralnode to perform all it's computations (as a sigmoid unit).
|
| Modifier and Type | Class and Description |
|---|---|
class |
CachedKernel
Base class for RBFKernel and PolyKernel that implements a simple LRU.
|
class |
CheckKernel
Class for examining the capabilities and finding problems with kernels.
|
class |
Kernel
Abstract kernel.
|
class |
KernelEvaluation
Class for evaluating Kernels.
|
class |
NormalizedPolyKernel
The normalized polynomial kernel.
|
class |
PolyKernel
The polynomial kernel : K(x, y) = <x, y>^p or
K(x, y) = (<x, y>+1)^p
|
class |
PrecomputedKernelMatrixKernel
This kernel is based on a static kernel matrix that
is read from a file.
|
class |
Puk
The Pearson VII function-based universal kernel.
|
class |
RBFKernel
The RBF kernel : K(x, y) = exp(-gamma*(x-y)^2)
|
class |
RegOptimizer
Base class implementation for learning algorithm of SMOreg
|
class |
RegSMO
Implementation of SMO for support vector regression
as described in :
A.J. |
class |
RegSMOImproved
Learn SVM for regression using SMO with Shevade,
Keerthi, et al. adaption of the stopping criterion.
|
class |
SMOset
Stores a set of integer of a given size.
|
class |
StringKernel
Implementation of the subsequence kernel (SSK) as
described in [1] and of the subsequence kernel with lambda pruning (SSK-LP)
as described in [2].
|
| Modifier and Type | Class and Description |
|---|---|
class |
IBk
K-nearest neighbours classifier.
|
class |
KStar
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
|
class |
LWL
Locally weighted learning.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KStarCache
A class representing the caching system used to keep track of each attribute
value and its corresponding scale factor or stop parameter.
|
class |
KStarCache.CacheTable
A custom hashtable class to support the caching system.
|
class |
KStarCache.TableEntry
Hashtable collision list.
|
class |
KStarNominalAttribute
A custom class which provides the environment for computing the
transformation probability of a specified test instance nominal attribute to
a specified train instance nominal attribute.
|
class |
KStarNumericAttribute
A custom class which provides the environment for computing the
transformation probability of a specified test instance numeric attribute to
a specified train instance numeric attribute.
|
class |
KStarWrapper |
| Modifier and Type | Class and Description |
|---|---|
class |
ALVOT |
class |
CRplus |
class |
KORAomega |
| Modifier and Type | Class and Description |
|---|---|
class |
AdaBoostM1
Class for boosting a nominal class classifier using
the Adaboost M1 method.
|
class |
AdditiveRegression
Meta classifier that enhances the performance of a regression base classifier.
|
class |
AttributeSelectedClassifier
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
|
class |
Bagging
Class for bagging a classifier to reduce variance.
|
class |
ClassificationViaRegression
Class for doing classification using regression methods.
|
class |
CostSensitiveClassifier
A metaclassifier that makes its base classifier cost-sensitive.
|
class |
CVParameterSelection
Class for performing parameter selection by cross-validation for any classifier.
|
class |
FilteredClassifier
Class for running an arbitrary classifier on data
that has been passed through an arbitrary filter.
|
class |
IterativeClassifierOptimizer
Chooses the best number of iterations for an IterativeClassifier such as
LogitBoost using cross-validation or a percentage split evaluation
|
class |
LogitBoost
Class for performing additive logistic regression.
|
class |
MultiClassClassifier
A metaclassifier for handling multi-class datasets with 2-class classifiers.
|
class |
MultiClassClassifierUpdateable
A metaclassifier for handling multi-class datasets with 2-class classifiers.
|
class |
MultiScheme
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
|
class |
RandomCommittee
Class for building an ensemble of randomizable base classifiers.
|
class |
RandomizableFilteredClassifier
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
|
class |
RandomSubSpace
This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
|
class |
RegressionByDiscretization
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
|
class |
Stacking
Combines several classifiers using the stacking method.
|
class |
Vote
Class for combining classifiers.
|
class |
WeightedInstancesHandlerWrapper
Generic wrapper around any classifier to enable weighted instances support.
|
| Modifier and Type | Class and Description |
|---|---|
class |
InputMappedClassifier
Wrapper classifier that addresses incompatible
training and test data by building a mapping between the training data that a
classifier has been built with and the incoming test instances' structure.
|
class |
SerializedClassifier
A wrapper around a serialized classifier model.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GeneralRegression
Class implementing import of PMML General Regression model.
|
class |
NeuralNetwork
Class implementing import of PMML Neural Network model.
|
class |
PMMLClassifier
Abstract base class for all PMML classifiers.
|
class |
Regression
Class implementing import of PMML Regression model.
|
class |
RuleSetModel
Class implementing import of PMML RuleSetModel.
|
class |
SupportVectorMachineModel
Implements a PMML SupportVectorMachineModel
|
class |
TreeModel
Class implementing import of PMML TreeModel.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DecisionTable
Class for building and using a simple decision
table majority classifier.
|
class |
DecisionTableHashKey
Class providing hash table keys for DecisionTable
|
class |
JRip
This class implements a propositional rule learner,
Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was
proposed by William W.
|
class |
JRip.Antd
The single antecedent in the rule, which is composed of an attribute and
the corresponding value.
|
class |
JRip.NominalAntd
The antecedent with nominal attribute
|
class |
JRip.NumericAntd
The antecedent with numeric attribute
|
class |
JRip.RipperRule
This class implements a single rule that predicts specified class.
|
class |
M5Rules
Generates a decision list for regression problems using separate-and-conquer.
|
class |
OneR
Class for building and using a 1R classifier; in
other words, uses the minimum-error attribute for prediction, discretizing
numeric attributes.
|
class |
PART
Class for generating a PART decision list.
|
class |
Rule
Abstract class of generic rule
|
class |
RuleStats
This class implements the statistics functions used in the propositional rule
learner, from the simpler ones like count of true/false positive/negatives,
filter data based on the ruleset, etc. to the more sophisticated ones such as
MDL calculation and rule variants generation for each rule in the ruleset.
|
class |
ZeroR
Class for building and using a 0-R classifier.
|
| Modifier and Type | Class and Description |
|---|---|
class |
C45PruneableDecList
Class for handling a partial tree structure pruned using C4.5's pruning
heuristic.
|
class |
ClassifierDecList
Class for handling a rule (partial tree) for a decision list.
|
class |
MakeDecList
Class for handling a decision list.
|
class |
PruneableDecList
Class for handling a partial tree structure that can be pruned using a
pruning set.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DecisionStump
Class for building and using a decision stump.
|
class |
HoeffdingTree
A Hoeffding tree (VFDT) is an incremental, anytime
decision tree induction algorithm that is capable of learning from massive
data streams, assuming that the distribution generating examples does not
change over time.
|
class |
J48
Class for generating a pruned or unpruned C4.5
decision tree.
|
class |
LMT
Classifier for building 'logistic model trees',
which are classification trees with logistic regression functions at the
leaves.
|
class |
M5P
M5Base.
|
class |
RandomForest
Class for constructing a forest of random trees.
|
class |
RandomTree
Class for constructing a tree that considers K
randomly chosen attributes at each node.
|
class |
REPTree
Fast decision tree learner.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BinC45ModelSelection
Class for selecting a C4.5-like binary (!)
|
class |
BinC45Split
Class implementing a binary C4.5-like split on an attribute.
|
class |
C45ModelSelection
Class for selecting a C4.5-type split for a given dataset.
|
class |
C45PruneableClassifierTree
Class for handling a tree structure that can
be pruned using C4.5 procedures.
|
class |
C45Split
Class implementing a C4.5-type split on an attribute.
|
class |
ClassifierSplitModel
Abstract class for classification models that can be used
recursively to split the data.
|
class |
ClassifierTree
Class for handling a tree structure used for classification.
|
class |
Distribution
Class for handling a distribution of class values.
|
class |
EntropyBasedSplitCrit
"Abstract" class for computing splitting criteria
based on the entropy of a class distribution.
|
class |
EntropySplitCrit
Class for computing the entropy for a given distribution.
|
class |
GainRatioSplitCrit
Class for computing the gain ratio for a given distribution.
|
class |
InfoGainSplitCrit
Class for computing the information gain for a given distribution.
|
class |
ModelSelection
Abstract class for model selection criteria.
|
class |
NBTreeClassifierTree
Class for handling a naive bayes tree structure used for classification.
|
class |
NBTreeModelSelection
Class for selecting a NB tree split.
|
class |
NBTreeNoSplit
Class implementing a "no-split"-split (leaf node) for naive bayes
trees.
|
class |
NBTreeSplit
Class implementing a NBTree split on an attribute.
|
class |
NoSplit
Class implementing a "no-split"-split.
|
class |
PruneableClassifierTree
Class for handling a tree structure that can
be pruned using a pruning set.
|
class |
SplitCriterion
Abstract class for computing splitting criteria
with respect to distributions of class values.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LMTNode
Class for logistic model tree structure.
|
class |
LogisticBase
Base/helper class for building logistic regression models with the LogitBoost
algorithm.
|
class |
ResidualModelSelection
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the
splitting criterion based on residuals.
|
class |
ResidualSplit
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the
splitting criterion based on residuals of the LogitBoost algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CorrelationSplitInfo
Finds split points using correlation.
|
class |
Impurity
Class for handling the impurity values when spliting the instances
|
class |
M5Base
M5Base.
|
class |
PreConstructedLinearModel
This class encapsulates a linear regression function.
|
class |
RuleNode
Constructs a node for use in an m5 tree or rule
|
class |
Values
Stores some statistics.
|
class |
YongSplitInfo
Stores split information.
|
| Modifier and Type | Class and Description |
|---|---|
class |
XMLClassifier
This class serializes and deserializes a Classifier instance to and
fro XML.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractClusterer
Abstract clusterer.
|
class |
AbstractDensityBasedClusterer
Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie. a probability distribution).
|
class |
Canopy
Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
|
class |
CheckClusterer
Class for examining the capabilities and finding problems with clusterers.
|
class |
ClusterEvaluation
Class for evaluating clustering models.
|
class |
Cobweb
Class implementing the Cobweb and Classit clustering algorithms.
|
class |
Cobweb.CNode
Inner class handling node operations for Cobweb.
|
class |
EM
Simple EM (expectation maximisation) class.
|
class |
FarthestFirst
Cluster data using the FarthestFirst algorithm.
|
class |
FilteredClusterer
Class for running an arbitrary clusterer on data
that has been passed through an arbitrary filter.
|
class |
HierarchicalClusterer
Hierarchical clustering class.
|
class |
MakeDensityBasedClusterer
Class for wrapping a Clusterer to make it return a
distribution and density.
|
class |
RandomizableClusterer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
RandomizableDensityBasedClusterer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
RandomizableSingleClustererEnhancer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
SimpleKMeans
Cluster data using the k means algorithm.
|
class |
SingleClustererEnhancer
Meta-clusterer for enhancing a base clusterer.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractInstance
Abstract class providing common functionality for the original instance
implementations.
|
class |
AlgVector
Class for performing operations on an algebraic vector
of floating-point values.
|
class |
AllJavadoc
Applies all known Javadoc-derived classes to a source file
|
class |
Attribute
Class for handling an attribute.
|
class |
AttributeLocator
This class locates and records the indices of a certain type of attributes,
recursively in case of Relational attributes.
|
class |
AttributeMetaInfo |
class |
AttributeStats
A Utility class that contains summary information on an
the values that appear in a dataset for a particular attribute.
|
class |
BinarySparseInstance
Class for storing a binary-data-only instance as a sparse vector.
|
class |
Capabilities
A class that describes the capabilites (e.g., handling certain types of
attributes, missing values, types of classes, etc.) of a specific classifier.
|
class |
ChebyshevDistance
Implements the Chebyshev distance.
|
class |
Check
Abstract general class for testing in Weka.
|
class |
CheckGOE
Simple command line checking of classes that are editable in the GOE.
|
class |
CheckOptionHandler
Simple command line checking of classes that implement OptionHandler.
|
class |
CheckScheme
Abstract general class for testing schemes in Weka.
|
static class |
CheckScheme.PostProcessor
a class for postprocessing the test-data
|
class |
ClassCache
A singleton that stores all classes on the classpath.
|
class |
ClassDiscovery
This class is used for discovering classes that implement a certain interface
or a derived from a certain class.
|
static class |
ClassDiscovery.StringCompare
compares two strings.
|
class |
ClassloaderUtil
Utility class that can add jar files to the classpath dynamically.
|
class |
ConjugateGradientOptimization
This subclass of Optimization.java implements conjugate gradient descent
rather than BFGS updates, by overriding findArgmin(), with the same tests for
convergence, and applies the same line search code.
|
class |
ContingencyTables
Class implementing some statistical routines for contingency tables.
|
class |
Debug
A helper class for debug output, logging, clocking, etc.
|
static class |
Debug.Clock
A little helper class for clocking and outputting times.
|
static class |
Debug.DBO
contains debug methods
|
static class |
Debug.Log
A helper class for logging stuff.
|
static class |
Debug.Random
This extended Random class enables one to print the generated random
numbers etc., before they are returned.
|
static class |
Debug.SimpleLog
A little, simple helper class for logging stuff.
|
static class |
Debug.Timestamp
A class that can be used for timestamps in files, The toString() method
simply returns the associated Date object in a timestamp format.
|
class |
DenseInstance
Class for handling an instance.
|
class |
Environment
This class encapsulates a map of all environment and java system properties.
|
class |
EuclideanDistance
Implementing Euclidean distance (or similarity) function.
|
class |
FastVector<E>
Deprecated.
|
class |
FindWithCapabilities
Locates all classes with certain capabilities.
|
class |
GlobalInfoJavadoc
Generates Javadoc comments from the class's globalInfo method.
|
class |
HVDM1 |
class |
HVDM2 |
class |
HVDM3 |
class |
InstanceComparator
A comparator for the Instance class. it can be used with or without the
class label.
|
class |
Instances
Class for handling an ordered set of weighted instances.
|
class |
Javadoc
Abstract superclass for classes that generate Javadoc comments and replace
the content between certain comment tags.
|
class |
ListOptions
Lists the options of an OptionHandler
|
class |
ManhattanDistance
Implements the Manhattan distance (or Taxicab geometry).
|
class |
Memory
A little helper class for Memory management.
|
class |
MinkowskiDistance
Implementing Minkowski distance (or similarity)
function.
|
class |
NormalizableDistance
Represents the abstract ancestor for normalizable distance functions, like
Euclidean or Manhattan distance.
|
class |
Optimization
Implementation of Active-sets method with BFGS update to solve optimization
problem with only bounds constraints in multi-dimensions.
|
class |
Option
Class to store information about an option.
|
class |
OptionHandlerJavadoc
Generates Javadoc comments from the OptionHandler's options.
|
class |
PropertyPath
A helper class for accessing properties in nested objects, e.g., accessing
the "getRidge" method of a LinearRegression classifier part of
MultipleClassifierCombiner, e.g., Vote.
|
static class |
PropertyPath.Path
Contains a (property) path structure
|
static class |
PropertyPath.PathElement
Represents a single element of a property path
|
class |
ProtectedProperties
Simple class that extends the Properties class so that the properties are
unable to be modified.
|
class |
Queue
Class representing a FIFO queue.
|
class |
RandomVariates
Class implementing some simple random variates generator.
|
class |
Range
Class representing a range of cardinal numbers.
|
class |
RelationalLocator
This class locates and records the indices of relational attributes,
|
class |
SelectedTag
Represents a selected value from a finite set of values, where each
value is a Tag (i.e. has some string associated with it).
|
class |
SerializationHelper
A helper class for determining serialVersionUIDs and checking whether classes
contain one and/or need one.
|
class |
SerializedObject
Class for storing an object in serialized form in memory.
|
class |
SimilarityMeasure
Implementing Euclidean distance (or similarity) function.
|
class |
SingleIndex
Class representing a single cardinal number.
|
class |
SparseInstance
Class for storing an instance as a sparse vector.
|
class |
SpecialFunctions
Class implementing some mathematical functions.
|
class |
Statistics
Class implementing some distributions, tests, etc.
|
class |
Stopwords
Class that can test whether a given string is a stop word.
|
class |
StringLocator
This class locates and records the indices of String attributes, recursively
in case of Relational attributes.
|
class |
SystemInfo
This class prints some information about the system setup, like Java version,
JVM settings etc.
|
class |
Tag
A
Tag simply associates a numeric ID with a String description. |
class |
TechnicalInformation
Used for paper references in the Javadoc and for BibTex generation.
|
class |
TechnicalInformationHandlerJavadoc
Generates Javadoc comments from the TechnicalInformationHandler's data.
|
class |
Tee
This class pipelines print/println's to several PrintStreams.
|
class |
TestInstances
Generates artificial datasets for testing.
|
class |
Trie
A class representing a Trie data structure for strings.
|
static class |
Trie.TrieIterator
Represents an iterator over a trie
|
static class |
Trie.TrieNode
Represents a node in the trie.
|
class |
Utils
Class implementing some simple utility methods.
|
class |
Version
This class contains the version number of the current WEKA release and some
methods for comparing another version string.
|
class |
WekaEnumeration<E>
Class for enumerating an array list's elements.
|
| Modifier and Type | Method and Description |
|---|---|
static String |
RevisionUtils.extract(RevisionHandler handler)
Extracts the revision string returned by the RevisionHandler.
|
static RevisionUtils.Type |
RevisionUtils.getType(RevisionHandler handler)
Determines the type of a (sanitized) revision string returned by the
RevisionHandler.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Loader
Interface to something that can load Instances from an input source in some
format.
|
interface |
Saver
Interface to something that can save Instances to an output destination in some
format.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractFileLoader
Abstract superclass for all file loaders.
|
class |
AbstractFileSaver
Abstract class for Savers that save to a file
Valid options are:
-i input arff file
The input filw in arff format. |
class |
AbstractLoader
Abstract class gives default implementation of setSource methods.
|
class |
AbstractSaver
Abstract class for Saver
|
class |
ArffLoader
Reads a source that is in arff (attribute relation
file format) format.
|
static class |
ArffLoader.ArffReader
Reads data from an ARFF file, either in incremental or batch mode.
|
class |
ArffSaver
Writes to a destination in arff text format.
|
class |
C45Loader
Reads a file that is C45 format.
|
class |
C45Saver
Writes to a destination that is in the format used
by the C4.5 algorithm.
|
class |
ConverterUtils
Utility routines for the converter package.
|
static class |
ConverterUtils.DataSink
Helper class for saving data to files.
|
static class |
ConverterUtils.DataSource
Helper class for loading data from files and URLs.
|
class |
CSVLoader
Reads a source that is in comma separated format (the default).
|
class |
CSVSaver
Writes to a destination that is in CSV
(comma-separated values) format.
|
class |
DatabaseConnection
Connects to a database.
|
class |
DatabaseLoader
Reads Instances from a Database.
|
class |
DatabaseSaver
Writes to a database (tested with MySQL, InstantDB,
HSQLDB).
|
class |
DictionarySaver
Writes a dictionary constructed from string
attributes in incoming instances to a destination.
|
class |
JSONLoader
Reads a source that is in the JSON format.
|
class |
JSONSaver
Writes to a destination that is in JSON format.
|
class |
LibSVMLoader
Reads a source that is in libsvm format.
|
class |
LibSVMSaver
Writes to a destination that is in libsvm format.
|
class |
MatlabLoader
Reads a Matlab file containing a single matrix in ASCII format.
|
class |
MatlabSaver
Writes Matlab ASCII files, in single or double
precision format.
|
class |
SerializedInstancesLoader
Reads a source that contains serialized Instances.
|
class |
SerializedInstancesSaver
Serializes the instances to a file with extension bsi.
|
class |
StreamTokenizerUtils
Helper class for using stream tokenizers
|
class |
SVMLightLoader
Reads a source that is in svm light format.
|
class |
SVMLightSaver
Writes to a destination that is in svm light
format.
|
class |
TextDirectoryLoader
Loads all text files in a directory and uses the
subdirectory names as class labels.
|
class |
XRFFLoader
Reads a source that is in the XML version of the ARFF format.
|
class |
XRFFSaver
Writes to a destination that is in the XML version
of the ARFF format.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ConsoleLogger
A simple logger that outputs the logging information in the console.
|
class |
FileLogger
A simple file logger, that just logs to a single file.
|
class |
Logger
Abstract superclass for all loggers.
|
class |
OutputLogger
A logger that logs all output on stdout and stderr to a file.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CholeskyDecomposition
Cholesky Decomposition.
|
class |
DoubleVector
A vector specialized on doubles.
|
class |
EigenvalueDecomposition
Eigenvalues and eigenvectors of a real matrix.
|
class |
ExponentialFormat |
class |
FlexibleDecimalFormat |
class |
FloatingPointFormat
Class for the format of floating point numbers
|
class |
IntVector
A vector specialized on integers.
|
class |
LUDecomposition
LU Decomposition.
|
class |
Maths
Utility class.
|
class |
Matrix
Jama = Java Matrix class.
|
class |
QRDecomposition
QR Decomposition.
|
class |
SingularValueDecomposition
Singular Value Decomposition.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BallTree
Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search.
|
class |
CoverTree
Class implementing the CoverTree datastructure.
|
class |
CoverTree.CoverTreeNode
class representing a node of the cover tree.
|
class |
FilteredNeighbourSearch
Applies the given filter before calling the given neighbour search method.
|
class |
KDTree
Class implementing the KDTree search algorithm for nearest neighbour search.
|
class |
LinearNNSearch
Class implementing the brute force search algorithm for nearest neighbour search.
|
class |
NearestNeighbourSearch
Abstract class for nearest neighbour search.
|
class |
PerformanceStats
The class that measures the performance of a nearest
neighbour search (NNS) algorithm.
|
class |
TreePerformanceStats
The class that measures the performance of a tree based
nearest neighbour search algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BallNode
Class representing a node of a BallTree.
|
class |
BallSplitter
Abstract class for splitting a ball tree's BallNode.
|
class |
BallTreeConstructor
Abstract class for constructing a BallTree .
|
class |
BottomUpConstructor
The class that constructs a ball tree bottom up.
|
class |
MedianDistanceFromArbitraryPoint
Class that splits a BallNode of a ball tree using
Uhlmann's described method.
|
class |
MiddleOutConstructor
The class that builds a BallTree middle out.
|
class |
PointsClosestToFurthestChildren
Implements the Moore's method to split a node of a
ball tree.
|
class |
TopDownConstructor
The class implementing the TopDown construction
method of ball trees.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Stack<T>
Class implementing a stack.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KDTreeNode
A class representing a KDTree node.
|
class |
KDTreeNodeSplitter
Class that splits up a KDTreeNode.
|
class |
KMeansInpiredMethod
The class that splits a node into two such that the
overall sum of squared distances of points to their centres on both sides of
the (axis-parallel) splitting plane is minimum.
|
class |
MedianOfWidestDimension
The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread.
|
class |
MidPointOfWidestDimension
The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.
|
class |
SlidingMidPointOfWidestSide
The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Groovy
A helper class for Groovy.
|
class |
Jython
A helper class for Jython.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Stemmer
Interface for all stemming algorithms.
|
| Modifier and Type | Class and Description |
|---|---|
class |
IteratedLovinsStemmer
An iterated version of the Lovins stemmer.
|
class |
LovinsStemmer
A stemmer based on the Lovins stemmer, described here:
Julie Beth Lovins (1968). |
class |
NullStemmer
A dummy stemmer that performs no stemming at all.
|
class |
SnowballStemmer
A wrapper class for the Snowball stemmers.
|
class |
Stemming
A helper class for using the stemmers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AlphabeticTokenizer
Alphabetic string tokenizer, tokens are to be
formed only from contiguous alphabetic sequences.
|
class |
CharacterDelimitedTokenizer
Abstract superclass for tokenizers that take characters as delimiters.
|
class |
CharacterNGramTokenizer
Splits a string into an n-gram with min and max
grams.
|
class |
NGramTokenizer
Splits a string into an n-gram with min and max
grams.
|
class |
Tokenizer
A superclass for all tokenizer algorithms.
|
class |
WordTokenizer
A simple tokenizer that is using the
java.util.StringTokenizer class to tokenize the strings.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KOML
This class is a helper class for XML serialization using KOML .
|
class |
MethodHandler
This class handles relationships between display names of properties (or
classes) and Methods that are associated with them.
|
class |
PropertyHandler
This class stores information about properties to ignore or properties that
are allowed for a certain class.
|
class |
SerialUIDChanger
This class enables one to change the UID of a serialized object and therefore
not losing the data stored in the binary format.
|
class |
XMLBasicSerialization
This serializer contains some read/write methods for common classes that are
not beans-conform.
|
class |
XMLDocument
This class offers some methods for generating, reading and writing
XML documents.
|
class |
XMLInstances
XML representation of the Instances class.
|
class |
XMLOptions
A class for transforming options listed in XML to a regular WEKA command line
string.
|
class |
XMLSerialization
With this class objects can be serialized to XML instead into a binary
format.
|
class |
XMLSerializationMethodHandler
This class handles relationships between display names of properties (or
classes) and Methods that are associated with them.
|
class |
XStream
This class is a helper class for XML serialization using XStream .
|
| Modifier and Type | Class and Description |
|---|---|
class |
ClassificationGenerator
Abstract class for data generators for classifiers.
|
class |
ClusterDefinition
Ancestor to all ClusterDefinitions, i.e., subclasses that handle their own
parameters that the cluster generator only passes on.
|
class |
ClusterGenerator
Abstract class for cluster data generators.
|
class |
DataGenerator
Abstract superclass for data generators that generate data for classifiers
and clusterers.
|
class |
RegressionGenerator
Abstract class for data generators for regression classifiers.
|
class |
Test
Class to represent a test.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Agrawal
Generates a people database and is based on the
paper by Agrawal et al.
|
class |
BayesNet
Generates random instances based on a Bayes network.
|
class |
LED24
This generator produces data for a display with 7
LEDs.
|
class |
RandomRBF
RandomRBF data is generated by first creating a
random set of centers for each class.
|
class |
RDG1
A data generator that produces data randomly by
producing a decision list.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Expression
A data generator for generating y according to a
given expression out of randomly generated x.
|
class |
MexicanHat
A data generator for the simple 'Mexian Hat'
function:
y = sin|x| / |x| In addition to this simple function, the amplitude can be changed and gaussian noise can be added. |
| Modifier and Type | Class and Description |
|---|---|
class |
BIRCHCluster
Cluster data generator designed for the BIRCH
System
Dataset is generated with instances in K clusters. |
class |
SubspaceCluster
A data generator that produces data points in
hyperrectangular subspace clusters.
|
class |
SubspaceClusterDefinition
A single cluster for the SubspaceCluster
datagenerator
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ConditionalEstimator
Interface for conditional probability estimators.
|
interface |
UnivariateDensityEstimator
Interface that can be implemented by simple weighted univariate
density estimators.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CheckEstimator
Class for examining the capabilities and finding problems with estimators.
|
static class |
CheckEstimator.AttrTypes
class that contains info about the attribute types the estimator can
estimate estimator work on one attribute only
|
static class |
CheckEstimator.EstTypes
public class that contains info about the chosen attribute type estimator
work on one attribute only
|
class |
CheckEstimator.PostProcessor
a class for postprocessing the test-data
|
class |
DDConditionalEstimator
Conditional probability estimator for a discrete domain conditional upon
a discrete domain.
|
class |
DiscreteEstimator
Simple symbolic probability estimator based on symbol counts.
|
class |
DKConditionalEstimator
Conditional probability estimator for a discrete domain conditional upon
a numeric domain.
|
class |
DNConditionalEstimator
Conditional probability estimator for a discrete domain conditional upon
a numeric domain.
|
class |
Estimator
Abstract class for all estimators.
|
class |
EstimatorUtils
Contains static utility functions for Estimators.
|
class |
KDConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon
a discrete domain (utilises separate kernel estimators for each discrete
conditioning value).
|
class |
KernelEstimator
Simple kernel density estimator.
|
class |
KKConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon
a numeric domain.
|
class |
MahalanobisEstimator
Simple probability estimator that places a single normal distribution
over the observed values.
|
class |
NDConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon
a discrete domain (utilises separate normal estimators for each discrete
conditioning value).
|
class |
NNConditionalEstimator
Conditional probability estimator for a numeric domain conditional upon a
numeric domain (using Mahalanobis distance).
|
class |
NormalEstimator
Simple probability estimator that places a single normal distribution over
the observed values.
|
class |
PoissonEstimator
Simple probability estimator that places a single Poisson distribution
over the observed values.
|
class |
UnivariateEqualFrequencyHistogramEstimator
Simple histogram density estimator.
|
class |
UnivariateKernelEstimator
Simple weighted kernel density estimator.
|
class |
UnivariateMixtureEstimator
Simple weighted mixture density estimator.
|
class |
UnivariateNormalEstimator
Simple weighted normal density estimator.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AveragingResultProducer
Takes the results from a ResultProducer and submits
the average to the result listener.
|
class |
ClassifierSplitEvaluator
A SplitEvaluator that produces results for a
classification scheme on a nominal class attribute.
|
class |
CostSensitiveClassifierSplitEvaluator
SplitEvaluator that produces results for a
classification scheme on a nominal class attribute, including weighted
misclassification costs.
|
class |
CrossValidationResultProducer
Generates for each run, carries out an n-fold
cross-validation, using the set SplitEvaluator to generate some results.
|
class |
CrossValidationSplitResultProducer
Carries out one split of a repeated k-fold
cross-validation, using the set SplitEvaluator to generate some results.
|
class |
CSVResultListener
Takes results from a result producer and assembles
them into comma separated value form.
|
class |
DatabaseResultListener
Takes results from a result producer and sends them
to a database.
|
class |
DatabaseResultProducer
Examines a database and extracts out the results
produced by the specified ResultProducer and submits them to the specified
ResultListener.
|
class |
DatabaseUtils
DatabaseUtils provides utility functions for accessing the experiment
database.
|
class |
DensityBasedClustererSplitEvaluator
A SplitEvaluator that produces results for a
density based clusterer.
|
class |
Experiment
Holds all the necessary configuration information for a standard type
experiment.
|
class |
ExplicitTestsetResultProducer
Loads the external test set and calls the
appropriate SplitEvaluator to generate some results.
|
class |
InstanceQuery
Convert the results of a database query into instances.
|
class |
InstancesResultListener
Outputs the received results in arff format to a
Writer.
|
class |
LearningRateResultProducer
Tells a sub-ResultProducer to reproduce the current
run for varying sized subsamples of the dataset.
|
class |
OutputZipper
OutputZipper writes output to either gzipped files or to a
multi entry zip file.
|
class |
PairedCorrectedTTester
Behaves the same as PairedTTester, only it uses the corrected resampled
t-test statistic.
|
class |
PairedStats
A class for storing stats on a paired comparison (t-test and correlation)
|
class |
PairedStatsCorrected
A class for storing stats on a paired comparison.
|
class |
PairedTTester
Calculates T-Test statistics on data stored in a set of instances.
|
class |
PropertyNode
Stores information on a property of an object: the class of the object with
the property; the property descriptor, and the current value.
|
class |
RandomSplitResultProducer
Generates a single train/test split and calls the
appropriate SplitEvaluator to generate some results.
|
class |
RegressionSplitEvaluator
A SplitEvaluator that produces results for a
classification scheme on a numeric class attribute.
|
class |
RemoteEngine
A general purpose server for executing Task objects sent via RMI.
|
class |
RemoteExperiment
Holds all the necessary configuration information for a distributed
experiment.
|
class |
RemoteExperimentSubTask
Class to encapsulate an experiment as a task that can be executed on a remote
host.
|
class |
ResultMatrix
This matrix is a container for the datasets and classifier setups and their
statistics.
|
class |
ResultMatrixCSV
Generates the matrix in CSV ('comma-separated values') format.
|
class |
ResultMatrixGnuPlot
Generates output for a data and script file for GnuPlot.
|
class |
ResultMatrixHTML
Generates the matrix output as HTML.
|
class |
ResultMatrixLatex
Generates the matrix output in LaTeX-syntax.
|
class |
ResultMatrixPlainText
Generates the output as plain text (for fixed width
fonts).
|
class |
ResultMatrixSignificance
Only outputs the significance indicators.
|
class |
Stats
A class to store simple statistics.
|
class |
TaskStatusInfo
A class holding information for tasks being executed
on RemoteEngines.
|
| Modifier and Type | Class and Description |
|---|---|
class |
XMLExperiment
This class serializes and deserializes an Experiment instance to and fro XML.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AllFilter
A simple instance filter that passes all instances directly
through.
|
class |
Filter
An abstract class for instance filters: objects that take instances as input,
carry out some transformation on the instance and then output the instance.
|
class |
MultiFilter
Applies several filters successively.
|
class |
RenameRelation
A simple filter that allows the relation name of a set of instances to be
altered in various ways.
|
class |
SimpleBatchFilter
This filter is a superclass for simple batch filters.
|
class |
SimpleFilter
This filter contains common behavior of the SimpleBatchFilter and the
SimpleStreamFilter.
|
class |
SimpleStreamFilter
This filter is a superclass for simple stream filters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AddClassification
A filter for adding the classification, the class
distribution and an error flag to a dataset with a classifier.
|
class |
ClassConditionalProbabilities
Converts the values of nominal and/or numeric attributes into class conditional probabilities.
|
class |
ClassOrder
Changes the order of the classes so that the class
values are no longer of in the order specified in the header.
|
class |
MergeNominalValues
Merges values of all nominal attributes among the
specified attributes, excluding the class attribute, using the CHAID method,
but without considering re-splitting of merged subsets.
|
class |
PartitionMembership
* A filter that uses a PartitionGenerator to generate partition membership values; filtered instances are composed of these values plus the class attribute (if set in the input data) and rendered as sparse instances.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ClassBalancer
Reweights the instances in the data so that each class has the same total weight.
|
class |
SpreadSubsample
Produces a random subsample of a dataset.
|
class |
StratifiedRemoveFolds
This filter takes a dataset and outputs a specified
fold for cross validation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractTimeSeries
An abstract instance filter that assumes instances form time-series data and
performs some merging of attribute values in the current instance with
attribute attribute values of some previous (or future) instance.
|
class |
Add
An instance filter that adds a new attribute to the
dataset.
|
class |
AddCluster
A filter that adds a new nominal attribute
representing the cluster assigned to each instance by the specified
clustering algorithm.
|
class |
AddExpression
An instance filter that creates a new attribute by
applying a mathematical expression to existing attributes.
|
class |
AddID
An instance filter that adds an ID attribute to the
dataset.
|
class |
AddNoise
An instance filter that changes a percentage of a
given attribute's values.
|
class |
AddUserFields
A filter that adds new attributes with user
specified type and constant value.
|
class |
AddValues
Adds the labels from the given list to an attribute
if they are missing.
|
class |
CartesianProduct
A filter for performing the Cartesian product of a set of nominal attributes.
|
class |
Center
Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
|
class |
ChangeDateFormat
Changes the date format used by a date attribute.
|
class |
ClassAssigner
Filter that can set and unset the class index.
|
class |
ClusterMembership
A filter that uses a density-based clusterer to
generate cluster membership values; filtered instances are composed of these
values plus the class attribute (if set in the input data).
|
class |
Copy
An instance filter that copies a range of
attributes in the dataset.
|
class |
DateToNumeric
A filter for turning date attributes into numeric ones.
|
class |
Discretize
An instance filter that discretizes a range of
numeric attributes in the dataset into nominal attributes.
|
class |
FirstOrder
This instance filter takes a range of N numeric
attributes and replaces them with N-1 numeric attributes, the values of which
are the difference between consecutive attribute values from the original
instance. eg:
Original attribute values 0.1, 0.2, 0.3, 0.1, 0.3 New attribute values 0.1, 0.1, -0.2, 0.2 The range of attributes used is taken in numeric order. |
class |
FixedDictionaryStringToWordVector
Converts String attributes into a set of attributes
representing word occurrence (depending on the tokenizer) information from
the text contained in the strings.
|
class |
InterquartileRange
A filter for detecting outliers and extreme values
based on interquartile ranges.
|
class |
KernelFilter
Converts the given set of data into
a kernel matrix.
|
class |
MakeIndicator
A filter that creates a new dataset with a Boolean
attribute replacing a nominal attribute.
|
class |
MathExpression
Modify numeric attributes according to a given
mathematical expression.
|
class |
MergeInfrequentNominalValues
Merges all values of the specified nominal attributes that are insufficiently frequent.
|
class |
MergeManyValues
Merges many values of a nominal attribute into one
value.
|
class |
MergeTwoValues
Merges two values of a nominal attribute into one
value.
|
class |
NominalToBinary
Converts all nominal attributes into binary numeric
attributes.
|
class |
NominalToString
Converts a nominal attribute (i.e. set number of
values) to string (i.e. unspecified number of values).
|
class |
Normalize
Normalizes all numeric values in the given dataset
(apart from the class attribute, if set).
|
class |
NumericCleaner
A filter that 'cleanses' the numeric data from
values that are too small, too big or very close to a certain value,
and sets these values to a pre-defined default.
|
class |
NumericToBinary
Converts all numeric attributes into binary
attributes (apart from the class attribute, if set): if the value of the
numeric attribute is exactly zero, the value of the new attribute will be
zero.
|
class |
NumericToDate
A filter for turning numeric attributes into date attributes.
|
class |
NumericToNominal
A filter for turning numeric attributes into
nominal ones.
|
class |
NumericTransform
Transforms numeric attributes using a given
transformation method.
|
class |
Obfuscate
A simple instance filter that renames the relation,
all attribute names and all nominal attribute values.
|
class |
OrdinalToNumeric
An attribute filter that converts ordinal nominal attributes into numeric ones
|
class |
PartitionedMultiFilter
A filter that applies filters on subsets of
attributes and assembles the output into a new dataset.
|
class |
PKIDiscretize
Discretizes numeric attributes using equal
frequency binning and forces the number of bins to be equal to the square root of
the number of values of the numeric attribute.
|
class |
PotentialClassIgnorer
This filter should be extended by other unsupervised attribute filters to
allow processing of the class attribute if that's required.
|
class |
RandomProjection
Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length.
|
class |
RandomSubset
Chooses a random subset of attributes, either an absolute number or a percentage.
|
class |
Remove
An filter that removes a range of attributes from
the dataset.
|
class |
RemoveByName
Removes attributes based on a regular expression
matched against their names.
|
class |
RemoveType
Removes attributes of a given type.
|
class |
RemoveUseless
This filter removes attributes that do not vary at
all or that vary too much.
|
class |
RenameAttribute
This filter is used for renaming attributes.
|
class |
RenameNominalValues
Renames the values of nominal attributes.
|
class |
Reorder
A filter that generates output with a new order of
the attributes.
|
class |
ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means
from the training data.
|
class |
ReplaceMissingWithUserConstant
Replaces all missing values for nominal, string,
numeric and date attributes in the dataset with user-supplied constant
values.
|
class |
ReplaceWithMissingValue
A filter that can be used to introduce missing values in a dataset.
|
class |
SortLabels
A simple filter for sorting the labels of nominal
attributes.
|
class |
Standardize
Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
|
class |
StringToNominal
Converts a range of string attributes (unspecified
number of values) to nominal (set number of values).
|
class |
StringToWordVector
Converts string attributes into a set of numeric attributes representing word occurrence
information from the text contained in the strings.
|
class |
SwapValues
Swaps two values of a nominal attribute.
|
class |
TimeSeriesDelta
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
|
class |
TimeSeriesTranslate
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
|
class |
Transpose
Transposes the data: instances become attributes and attributes become instances.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NonSparseToSparse
An instance filter that converts all incoming
instances into sparse format.
|
class |
Randomize
Randomly shuffles the order of instances passed
through it.
|
class |
RemoveDuplicates
Removes all duplicate instances from the first batch of data it receives.
|
class |
RemoveFolds
This filter takes a dataset and outputs a specified
fold for cross validation.
|
class |
RemoveFrequentValues
Determines which values (frequent or infrequent
ones) of an (nominal) attribute are retained and filters the instances
accordingly.
|
class |
RemoveMisclassified
A filter that removes instances which are
incorrectly classified.
|
class |
RemovePercentage
A filter that removes a given percentage of a
dataset.
|
class |
RemoveRange
A filter that removes a given range of instances of
a dataset.
|
class |
RemoveWithValues
Filters instances according to the value of an
attribute.
|
class |
Resample
Produces a random subsample of a dataset using
either sampling with replacement or without replacement.
|
class |
ReservoirSample
Produces a random subsample of a dataset using the
reservoir sampling Algorithm "R" by Vitter.
|
class |
SparseToNonSparse
An instance filter that converts all incoming sparse instances into non-sparse format.
|
class |
SubsetByExpression
Filters instances according to a user-specified expression.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FlowRunner
Small utility class for executing KnowledgeFlow flows outside of the
KnowledgeFlow application
|
| Modifier and Type | Class and Description |
|---|---|
class |
XMLBeans
This class serializes and deserializes a KnowledgeFlow setup to and fro XML.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DbUtils
A little bit extended DatabaseUtils class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
JobEnvironment
Extended Environment with support for storing results and property values to
be set at a later date on the base schemes of WekaAlgorithmWrapper steps.
|
Copyright © 2018 University of Waikato, Hamilton, NZ. All Rights Reserved.


--- # this is an empty front matter --- {% include header.html %}
Views inherit and extend functionality of the ClearPress::view
superclass. At the very minimum, application views can contain as
little as no code, just the reference to their superclass for
inheritance purposes.
package Grandkids::view::kid;
use strict;
use warnings;
use base qw(ClearPress::view);
1;
The superclass defines all of the basic functionality to support extended CRUD mechanisms (create, read, update, delete, list, edit, add) for XML, JSON, HTML and AJAX/HTML fragments.
Whilst basic functionality gets you part-way there it's often not quite enough for more complicated scenarios. To take care of these situations any method implemented in the view with the right systematic name will override the method of the same name in the superclass, e.g. list, list_xml, read, read_png, create_json etc.
Within these methods it is possible to access all of the useful bits for processing a request and buildig a response.
When processing a web request it's usually handy to have any CGI
parameters. To get hold of them grab the instance of CGI.pm:
sub list {
my $self = shift;
my $util = $self->util;
my $cgi = $util->cgi;
...
}
Almost all requests need to do something with one or more data models
so at the very least you'll need to locate the one corresponding to
this view method before you do something with it:
sub list {
my $self = shift;
my $model = $self->model;
...
Let's say you have a small cascade of related objects, let's say a
family with a child, which need to be built from the results of a
POSTed form and saved together in one transaction. Assuming we're in
Grandkids::view::family...
sub create {
my $self = shift;
my $util = $self->util;
my $cgi = $util->cgi;
my $family = $self->model;
my $f_name = $cgi->param('name');
$family->name($f_name);
my $c_name = $cgi->param('child_name');
my $child = Grandkids::model::child->new({name => $c_name});
$family->children($child);
return $family->create();
}
This makes quite a lot of other assumptions too - the form posting to
this code has to contain both name and child_name
fields, and only one of each and the family model's create
method needs to check for children to save as part of its
transaction.


Vivo U20 vs Realme 5s: Price in India, features, camera, specs
by BusinessToday.InVivo U20 vs Realme 5s: The new Vivo U20 costs around Rs 1,000 more than the Realme 5s. So is the new Vivo U20 worth the extra cash?
Vivo U20 vs Realme 5s: Vivo India has launched its Vivo U20 and Realme launched Realme 5s in India last week. Vivo U20 was launched in less than two months after the debut of its predecessor Vivo U10. The latest Vivo smartphone retains some features from its predecessor, like a triple rear camera setup and 5,000mAh battery.
These new budget smartphones from Vivo and Realme come with their own perks and features. However, the new Vivo U20 costs around Rs 1,000 more than the Realme 5s. So is the new Vivo U20 worth the extra cash?
Here's a quick comparison between Vivo U20 and Realme 5s on the basis of their prices, features, and specifications.
Display
Vivo U20 sports a 6.53-inch full-HD+ display with 1080 x 2340 pixels, peak brightness of 480 nits and over 90 per cent screen-to-body ratio. On the other hand, Realme 5s features a 6.5-inch HD+ display with 720 x 1600 pixels, Corning Gorilla Glass 3+ protection and 89 per cent screen-to-body ratio.
Connectivity and OS
Both the smartphone runs on Android Pie with different custom skins. Vivo U20 gets Funtouch OS 9 skin on top, while the Realme 5s has ColorOS 6 skin on top. Both phones feature dual-SIM connectivity.
The new Vivo U20 comes with 64GB storage, expandable up to 256GB via a microSD card. The phone includes 4G LTE, Wi-Fi, Bluetooth 5.0, GPS, BeiDou, GLONASS, and Galileo. The phone also supports the company's proprietary Dual-Engine fast charge technology and comes with an 18W fast charger out of the box. Sensors on the Vivo U20 include an accelerometer, proximity sensor, magnetometer, gyroscope, ambient light sensor and fingerprint sensor at the back. On the inside, the Vivo U20 runs Qualcomm Snapdragon 675 processor.
Realme 5s comes with internal storage up to 128GB, expandable up to 256GB via a microSD card. The new Realme smartphone includes 4G VoLTE, Wi-Fi, Bluetooth 5.0, GPS/A-GPS, Beidou, Galileo, and Glonass. It is powered through a 5,000mAh battery and has sensors like -- a fingerprint reader, accelerometer, proximity, ambient light, and gyroscope. Under the hood, Realme 5s gets an octa-core Qualcomm Snapdragon, 665 SoC.
Camera
Vivo U20 comes with triple rear camera setup, featuring a 16MP Sony IMX499 sensor with an f/1.8 aperture. Other sensors include an additional 8MP wide-angle sensor with an f/2.2 aperture and a 2MP macro camera with an f/2.4 aperture.
On the imaging front, Realme 5s is equipped with quad rear camera setup featuring a 48MP Samsung ISOCELL GM1 sensor with an f/1.8 aperture. The other camera sensors in Realme 5s are an 8MP wide-angle lens with an f/2.25 aperture, a 2MP macro lens with an f/2.4 aperture, and a 2MP portrait camera with an f/2.4 aperture.
On the front, Vivo U20 sports a 16MP camera, while Realme 5s has a 13MP selfie snapper.
Price
Vivo U20's price in India for the 4GB + 64GB storage variant is Rs 10,990 and for 6GB + 64GB storage model is Rs 11,990. Vivo's latest smartphone comes in Black and Blue colour options. Vivo U20 will go on sale from November 28 via official Vivo e-shop and Amazon.in.
Realme 5s's price for the 4GB RAM + 64GB storage version is Rs 9,999, while the 4GB RAM + 128GB storage model costs Rs 10,999. The smartphone will be available in Blue, Purple, and Red colour options and will go on sale starting November 29 via the company's official website Realme.com and Flipkart.
Also Read: Realme X2 Pro, Realme 5s launched in India: Price starts at Rs 29,999 and Rs 9,999
Also Read: Vivo U20 with triple rear camera launched in India: Check out price, availability, specifications
Also Read: Samsung Galaxy S11 certified with 5G, 25W fast charging in China


Zika virus (ZIKV) is a little known flavivirus that caused a major outbreak in 2007, in the South-western Pacific Island of Yap. It causes dengue-like syndromes but with milder symptoms. In Africa, where it was first isolated, ZIKV is mainly transmitted by sylvatic Aedes mosquitoes. The virus has also been isolated from Ae. aegypti and it is considered to be the vector involved in the urban transmission of the virus. Transmission of the virus by an African strain of Ae. aegypti has also been demonstrated under laboratory conditions. The aim of the present study is to describe the oral susceptibility of a Singapore strain of Ae. aegypti to ZIKV, under conditions that simulate local climate.
To assess the receptivity of Singapore's Ae. aegypti to the virus, we orally exposed a local mosquito strain to a Ugandan strain of ZIKV. Upon exposure, fully engorged mosquitoes were maintained in an environmental chamber set at 29°C and 70–75% RH. Eight mosquitoes were then sampled daily from day 1 to day 7, and subsequently on days 10 and 14 post exposure (pe). The virus titer of the midgut and salivary glands of each mosquito were determined using a tissue culture infectious dose
This study showed that Singapore's urban Ae. aegypti are susceptible and are potentially capable of transmitting ZIKV. The virus could be established in Singapore should it be introduced. Nevertheless, Singapore's current dengue control strategy is applicable to control ZIKV.
Zika virus (ZIKV) is an emerging mosquito-borne zoonotic pathogen that causes dengue-like syndromes. Despite its high epidemic potential, little is known about the virus. Although the isolation of the virus was confined to the African continent, serological evidences have shown the widespread distribution of ZIKV, particularly in Asia. In 2007, it caused a major outbreak on the Pacific Island of Yap, infecting more than 70% of the island’s inhabitants. The propensity of the virus to spread outside its known geographical range was again demonstrated when it was detected in the US from travellers coming back from endemic countries. Several species of Aedes spp. mosquitoes have been incriminated as vectors of ZIKV, including Ae. aegypti. The current study showed that local Ae. aegypti are highly susceptible to ZIKV and by day 5 post-infectious blood meal, more than 50% of mosquitoes were potentially infective. Singapore being a tourist and a business hub, coupled with the presence of susceptible vector and a population that is immunologically naive and vulnerable, the local transmission of the ZIKV is plausible. Nevertheless, Singapore's current dengue control strategy is applicable to control ZIKV.
Zika virus (ZIKV) is an emerging mosquito-borne pathogen belonging to the genus Flavivirus of the Family Flaviviridae [1] . It is a positive single stranded RNA virus with a 10,794 nucleotide genome that is closely related to the Spondweni virus ( Flavivirus, Family Flaviviridae) [2] , [3] . The virus was first isolated in 1947 from a febrile rhesus monkey in the Zika forest of Uganda [4] . Non-human primates were implicated as the reservoir host of ZIKV in Africa and Asia [5] .
In humans, ZIKV causes a mild infection manifested by a rash, fever, joint and muscle pain, headache and peri-orbital pain, which are characteristic signs and symptoms of flavivirus infections [6] , [7] . The first human ZIKV infection was reported in Uganda in 1964 [6] . Although the isolation of ZIKV has so far been confined to the African continent [8] , [9] , serological evidence has shown widespread distribution of the virus even in Asian countries such as Malaysia, India, Philippines, Thailand, Vietnam, Indonesia, and Pakistan [10] , [11] , [12] , [13] , [14] , [15] . The first major outbreak of human ZIKV infection was reported in the Pacific island of Yap and its adjoining islands in the Federated State of Micronesia in 2007 [3] , [7] , [16] , [17] . The outbreak lasted four months infecting approximately 73% of the islands' population [7] . In 2011, ZIKV was first reported in the western hemisphere in travellers returning from Senegal [18] . Most recently, ZIKV was isolated from a 3-year old boy in Cambodia in 2010 [19] .
ZIKV is transmitted to humans by Aedes spp. mosquitoes. The earliest evidence of ZIKV in a pool of Ae. africanus from Uganda in 1948 coincides with its first isolation from a rhesus monkey in the same location [4] . Subsequent documents reported further isolation of the virus from Ae. africanus and Ae. apicoargenteus caught in the Zika forest [20] , [21] , [22] ; from Ae. luteocephalus in Nigeria in 1969 [23] ; and from Ae. vitattus, Ae. furcifer, and Ae. aegypti in Ivory Coast in 1999 [24] . High prevalence of ZIKV antibodies in the urban population of Nigeria has led Fagbami [23] to suspect that Ae. aegypti may play an important role in the urban transmission of ZIKV. Further evidence came from Asia, when ZIKV was isolated from a pool of Ae. aegypti caught in Bentong, Peninsular Malaysia [25] . This finding provided evidence of ZIKV transmission outside Africa. In Indonesia, the peak of human ZIKV infections coincides with peak Ae. aegypti population which is by the end of rainy season [14] . Apart from field surveillance data, early experimental studies conducted by Boorman and Porterfield [26] and Cornet et al. [27] have also demonstrated the competency of Ae. aegypti to transmit ZIKV.
Considering the geographic spread and the possible impact on susceptible human populations, mosquito-borne diseases are currently considered as a major threat to global health in both developing and developed world [28] , [29] . According to Gushulak et al. [30] , the threat of emerging infectious diseases is mainly influenced by the migration and mobility of the human populations. The dengue, chikungunya and malaria situations in Singapore clearly demonstrate the role of importation in shaping the epidemiology of these diseases [31] , [32] , [33] . Introduction of ZIKV into Singapore, a travel and trading hub, is plausible. Coupled with the local presence of Ae. aegypti, local transmission of the virus is likely. Furthermore, as ZIKV has never been reported in Singapore, the local population is presumed to be immunologically naive and vulnerable to the infection.
Although experimental studies conducted in the past have shown that Ae. aegypti is a competent vector for ZIKV, these studies used African strains of Ae. aegypti that were caught in Nigeria [26] and Senegal [27] and had been maintained in the laboratory for years. Furthermore, experimental methods used in these studies differed from those of the current study. Although Boorman and Porterfield [26] infected the mosquitoes using the oral route, the average incubation temperature was 24°C, which is low in the tropical context and resulted in an extrinsic incubation period that suggested low vectorial capacity. While Cornett et al. [27] incubated their infected mosquitoes between 27 to 28°C, the method of infection was by intrathoracic route which can artificially lead to shorter extrinsic incubation period and higher number of mosquitoes infected. In addition, the geographical variations in terms of oral susceptibility of mosquitoes to different viruses are also well documented [34] , [35] , [36] , [37] , [38] , [39] . The present study describes the oral susceptibility of a Singapore field strain Ae. aegypti to ZIKV, under condition that simulate local climate.
Ae. aegypti, used for the experimental infection, were derived from eggs collected in the Western part of Singapore during a weekly ovitrap surveillance study to determine mosquito population density. Ovitraps were placed in public areas, mostly along the common corridors of public housing. The surveillance study was conducted by colleagues from the Environmental Health Institute. F
Ugandan MR766 ZIKV strain obtained from the American Type Culture Collection (Manassas, VA, USA) was used to expose the mosquitoes to ZIKV. This virus was originally isolated from the blood of an experimental sentinel rhesus monkey in 1947 [4] and passaged in suckling mouse brains. The stock virus used in the current study has been passaged thrice in Vero cells prior to the infectious feed.
Five- to 7-day-old female mosquitoes (n = 120) were transferred to 0.5 L containers and starved for 24 hours prior to the infectious blood meal. The blood meal consisted of 1∶1 100% swine-packed RBC (Innovative Research, USA) and fresh virus suspension at a final concentration of 7.0 Log
To determine the ZIKV infection and dissemination rates in Ae. aegypti, eight mosquitoes were sampled daily from day 1 to day 7, and subsequently on days 10 and 14 post exposure (pe). To prevent cross-contamination of virus between midgut and salivary glands of each mosquito, these organs were carefully dissected using different dissecting needles and the organs were rinsed in Medium 199 (M199) (Gibco, USA) supplemented with amphotericin B (Sigma Aldrich, USA). The midguts and salivary glands from each mosquito were individually transferred to 2 mL microtubes containing 250 µL of M199. These organs were then homogenized using five mm stainless steel grinding balls (Retsch, Germany) in a MM301 mixer mill (Retsch, Germany) set at frequency of 12/sec for 1 min. The supernatant of the homogenate was applied in the viral titer assay. All dissecting needles were dipped in 80% ethanol and cleaned before being re-used. All experiments were conducted inside an ACL-2 facility.
Viral titers in this study were determined with a tissue culture infectious dose
Proportion infected was calculated by dividing the number of infected midguts (or salivary glands) by the total number of miguts (or salivary glands) sampled. To compare viral titers at different time points, raw data was subjected to a normality test using SPSS Ver 18 (IBM, USA). Data that passed the normality test were analyzed by analysis of variance using the above mentioned software.
Presence or absence of blood in the midgut was verified during dissection under a Stereoscope (Olympus, USA). By Day 3, when blood had been completely digested, seven (87.5%) of the analyzed mosquitoes were positive for ZIKV ( Figure 1). From day 6 pe onwards, all midguts were positive for ZIKV except for one of the mosquitoes that was negative for the virus at day 7-pe.
Eight mosquitoes were sampled per day.
The presence of viable ZIKV in the salivary glands (n = 1) was first observed on day 4 pe ( Figure 1) and 62% of mosquitoes sampled on day 5-pe showed detectable virus in the salivary glands. ZIKV was observed in salivary glands of all infected mosquitoes sampled at days 10 and 14 pe.
Figure 2 presents ZIKV midgut titers at different days pe. Although remaining blood meal in midgut was not removed, an eclipse phase typically associated with low virus midgut titer can be seen on day 1 pe, with only one of the midgut showing detectable ZIKV. Virus titers in day 2 pe were higher than that observed for day 1 pe, mirroring the results obtained on percentage of midguts infected ( Figure 1). These suggest that midgut ZIKV titer observed during day 2 pe was most probably due to virus replication in the midgut rather than to the remaining amount of blood observed in some of the mosquitoes. A significant increase (P<0.026) in mean viral titers was observed between days 3 pe (3.9 Log
The bar indicates median viral titers and limit of detection is represented by broken lines. A significant increase(*) (P = 0.026) in mean viral titer was observed between days 3 and 5 pe.
ZIKV titers in the salivary glands increased from day 4 pe onwards ( Figure 3). Although the difference in mean viral titers from day 5 pe (2.7 Log
The bar indicates median viral titers and limit of detection is represented by broken lines. A significant increase(*) (P<0.001) in mean viral titer was observed between days 7 and 10 pe.
Recent unprecedented spread of chikungunya virus (CHIKV) in many parts of the world, with millions of people affected, exemplifies how arboviruses can adapt and affect human health on a global scale [31] . Singapore's vulnerability to emerging and re-emerging arboviruses is accentuated by the country's location as a popular tourist and business hub, high dependency on migrant workers, tropical climate, dense population, and the presence of potential mosquito vectors. An outbreak of chikungunya in Singapore during the 2008–09 period attests the country's vulnerability to mosquito-borne diseases [31] , [41] . The outbreaks of ZIKV on Yap Island and the worldwide spread of CHIKV have shown the propensity of arboviruses to spread outside their known geographical range and their potential to cause large-scale epidemics.
Unlike CHIKV which has received much scientific attention, ZIKV is a little-known flavivirus despite its outbreak potential [42] . Most studies on ZIKV were conducted more than two decades ago and there is a dearth of information on mosquito-ZIKV interactions that are salient to a better understand virus transmission. In 1956, Boorman and Porterfield [26] successfully transmitted the virus to both mice and monkeys using ZIKV-infected laboratory strains of Ae. aegypti. Cornet et al. [27] further demonstrated that a high percentage (88%) of intrathoracically infected Ae. aegypti can transmit ZIKV to experimental mice within 7 days and transmission rates increased up to 95% on day 21 pe. The current study, using a field strain of mosquitoes, showed that Singapore's Ae. aegypti are highly susceptible to ZIKV, with high midgut infection and salivary gland dissemination rates. By day 5 pe, 62% of the mosquitoes had detectable ZIKV in their salivary glands and by day 10 pe all mosquitoes were potentially infective. Based on the studies of Cornet et al. [27] , nearly all mosquitoes with ZIKV in their salivary glands are assumed to be able to transmit the virus. This is supported by previous studies that have shown oral transmission of dengue (DENV) [43] , [44] and West Nile (WNV) [45] viruses were correlated with the proportion of mosquitoes with infected salivary glands.
The decrease in midgut viral titer at day 14 pe observed in our study was consistent with other published DENV and WNV studies [46] , [47] , [48] and were probably due to virus clearance by the mosquito immune system [47] , [49] , [50] . Despite a decrease in midgut viral titer, ZIKV infection in salivary glands was found to be higher than that observed in midgut. This suggests that the proliferation of ZIKV in Ae. aegypti salivary glands is not attributed to direct dissemination from the midgut, but rather a result of viral dissemination and amplification within the glands or other organs or tissues such as hemocytes, ganglion, fat bodies etc [49] , [51] , [52] . Salivary gland dissemination rates obtained from our current study is similar to that observed for a local highly epidemic DENV-2 in the same strain of Ae. aegypti (Tan et al., unpublished data).
A phylogenetic analysis, based on the NS5 region, of ZIKV revealed three branches: West African (Nigeria), East African (Uganda) and those from Yap island (ZIKV 2007 EC), with the latter virus being the most distally related [17] . The strain used in our current study, MR766, is the Ugandan prototype strain and the only strain available to our laboratory. It would be very interesting to study and compare the recent epidemic ZIKV 2007 EC strain in Ae. aegypti, especially in the light of a four amino acid motif found in the viral envelope genes of the ZIKV 2007 EC strain that are absent in the MR766 strain [17] . Unfortunately, no ZIKV 2007 EC was isolated during the outbreak in Yap Island. The four amino acid motif found in the ZIKV 2007 EC strain correspond to an envelope protein 154 glycosylation motif and the loss of this motif in the ZIKV prototype strain is thought to have been due to extensive passage in mice [17] . Studies have showed that loss of glycosylation motif due to mutation has been found to affect the replication rates of tick-borne encephalitis virus. DENV, and WNV in both vector hosts and insect cell lines and the dissemination rate of WNV in different Culex spp. mosquitoes [53] , [54] , [55] , [56] . Despite the absence of this aa 154 glycan, the present study has shown that ZIKV MR766 has a high dissemination rate in Singapore's Ae. aegypti. This could probably be due to the high midgut pH found in Ae. aegypti [57] , a characteristic shared by Cx. tarsalis, which rendered it susceptible to WNV virus lacking the aa 154 glycan [53] . Future studies with other strains will take these observations into consideration.
Timely detection of the causative agents and implementation of effective control strategies during an epidemic or outbreak are always challenging. A fully-integrated vector control program incorporating advances in laboratory techniques and surveillance programs designed to address all components of the virus life cycle is considered the best approach in detecting and controlling any vector-borne disease as they emerge [42] . Such was the case of the successful control of the CHIKV outbreak in Singapore in 2008 [41] . The use of rapid and sensitive diagnostic and effective field surveillance tools and good coordination between field and laboratory personnel coupled with an understanding of mosquito-virus relationship assisted in the situation assessment and operational decision-making in controlling the outbreak.
The present study revealed the potential role of local Ae. aegypti as a vector of ZIKV. Given the presence of the virus in the region, the Environmental Health Institute screened febrile cases not attributable to DENV and CHIKV for ZIKV and other arboviruses. Among the 690 cases screened between 2009 and 2010, none was found positive for flaviviruses other than DENV. While there is currently no evidence of its circulation in Singapore, regular screening will be performed to monitor the situation. Based on the information gathered from this study (e.g. viral dissemination rate), the threat of ZIKV can be addressed by the existing dengue control programme. However, there is also a need to determine the susceptibility of other common mosquito species, in order to design a comprehensive vector control strategy for Zika infection.
We thank colleagues from EHI, Dr. Chanditha Hapuarachchi for his critical reading of this article, Dr. Chong Chee Seng for his assistance in the use of SPSS, and Ms. Sharon Siok Yin Tan, Mr. Md. Alif Abdul Razak, Ms. Suzanna Chiang, Mr. Mohamed Amir Hamzah Bin Samat for their assistance in the collection of field mosquitoes and for the colonization of mosquitoes.


I woke up this Sat and decided it was time to try 2CI for real. Although I had 1 week earlier procured 1g from an online source I had only dabbled with about 5mg up until now. A friend was coming over for some beer, pot and Family Guy at 6pm, so decided I would clean the house.
Ingested 12mg of 2CI at 11am and began cleaning. After about 45 mins I felt a huge rush of energy and I was off like a tornado; blitzing the kitchen, lounge, hall, bathroom and my bedroom by about 1:30pm.
Felt really happy looking at the sparkling bathroom sink and realised that the 2CI was ramping it up. At this point I was starting to get the 'acid tingles' so I thought that some music would be a good idea; Something chilled but with a strong beat. I went for Snoop Dogg and Dr Dre and happily spent two hours in front of the PC smoking a joint or two and really, really getting into the music.
3:30pm and things began to get difficult. I was having trouble rolling as my hands appeared strange to me and I had a case of the shakes. In hindsight this was due to me forgetting to put the heating on lol but under the 2CI I didnt recognise the sensation of cold at first. Rolling a joint, it was like I was using someone else hand, hard to explain but wierd!
As I was running out of marlboros I decided to walk round to the corner shop, even though the thought of having to interact with people in my state was rather daunting. I left the house and walked round the corner to the shop. It was below 0 and the cold air felt really nice. Everything was covered in frost which leant a nice 'christmassy'
feel to the area. However something about the fresh air started me tripping quite hard.
As I stood in the queue in the shop (4pmish) I heard a loud vomiting noise. For a second I worried I had thrown up but a quick look down confirmed I hadnt. Wasn't sure though as trippy feeling was getting more intense. The world seemed much more intense and I now understand what people mean about the 'cartoony, visuals on 2CI. Definate clean lined, cartoony feel to the world.
Got home with my smokes and watched telly, began to get rather anxious as trip felt like it could go either way. Realised my friend would be round in a couple of hours and that chilled me out somewhat. Spend rest of afternoon watching Joey and Friends which was good as it was nice familiar, gentle comedy. By 6pm I was very chilled, more stoned than 2CI but definately still some 2CI in there. Chatted with friend, had a couple of beers and another joint, by 8pm I felt myself coming back to baseline, everything began to look normal and began to just feel stoned.
Went to bed at 1:30am feeling tired and aching all over but happy. Slept till 11am. Woke up feeling good, very relaxed and refreshed.
Overall I am really glad I didn't go straight to 20mg as that would have been way to much for me. 12 was just right and I think it would be excellent for a night out clubbing as it was a very energetic and trippy high and although I didnt get the loved up feeling some report I reckon I might've had I been out at a club. Despite the short bad patch I really enjoyed this experience and am looking forward to experimenting 2CI further, perhaps on a night out next time.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.