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VFML File List

Here is a list of all documented files with brief descriptions:
AttributeTracker.h [code]Keep a record of which attributes are active
batchtestPerforms cross validation of a collection of learners on a collection of datasets
BeliefNet.h [code]A Belief Net Structure with CPT local models
beliefnetcorruptMakes some random changes to a BeliefNet
beliefnetdataCreates a data set by sampling from a Bayesian Network
beliefnetscoreTests a BeliefNet in several ways
bindataConverts continuous attributes into discrete ones
bitfield.h [code]Compactly represent a bit field
bnlearnLearns the structure of a BeliefNet from a data set. Designed to be easily modified
bnlearn-engine.h [code]Learn the structure of a BeliefNet from data
C45interface.h [code]Calls the C4.5 decision tree learning system and returns the learned tree
c45wrapperCalls C4.5 and tests the learned tree
c50wrapperCalls C5.0 and tests the learned tree
cleandataCleans up a data set in several ways
clusterdataCreates a synthetic data set from randomly generated clusters
combinedataCombines a series of data sets into a single large one
cvfdtLearns a DecisionTree from a high-speed time-changing data stream (or very large data set)
Debug.h [code]A set of functions that help your programs produce debugging output in a consistent way
decisionstumpLearns a decision stump (a DecisionTree with only one split)
DecisionTree.h [code]A Decision Tree Structure
doxygen.h [code]Used to hold doxygen documentation. Ignore this file
Example.h [code]ADT for training (and testing, etc.) data
ExampleGenerator.h [code]Generate a random (but reproducible) data set
ExampleGroupStats.h [code]Sufficient statistics for Entropy and Gini
ExampleSpec.h [code]Schema for training data
folddataRandomly splits a data set into a collection of train/test pairs
HashTable.h [code]A hash table
kmeansPerforms k-means clustering
lists.h [code]Generic list functions
memory.h [code]Tracks the size of allocations made
mostcommonclassPredicts the most common class in the training data
naivebayesA Naive Bayes learner
random.h [code]Generates random numbers in a number of ways, and has support for saving and restoring the state of the random number generator
REPrune.h [code]Peforms reduced error pruning on a decision tree
sampledataDraws a sample from a data set
shuffledataRandomizes the order of a data set and rewrites it
stats.h [code]Some statistical functions
treedataCreates a synthetic data set by sampling from a randomly generated DecisionTree
uRunnerDistribute a collection of jobs across a cluster of computers
vfbn1Learns the structure of a BeliefNet from a very large data set using sampling
vfbn2Learns the structure of a BeliefNet from a very large data set using sampling and a new search proceedure
vfdtLearns a decision tree from a high-speed data stream or very large data set
vfdt-engine.h [code]An API which lets your program learn a DecisionTree from a high-speed data stream
vfemPerforms EM clustering
vfkmPerforms k-means clustering accelerated with sampling
xvalidatePerforms cross validation of a learner on a data set

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