Uses of Class
com.indeed.vw.wrapper.api.VowpalWabbit.Builder

Packages that use VowpalWabbit.Builder

Package	Description
com.indeed.vw.wrapper.api	This package contains OOP api for wowpal wabbit jni wrapper.

Uses of VowpalWabbit.Builder in com.indeed.vw.wrapper.api

Methods in com.indeed.vw.wrapper.api that return VowpalWabbit.Builder

Modifier and Type	Method and Description
VowpalWabbit.Builder	VowpalWabbit.Builder.active() enable active learning
VowpalWabbit.Builder	VowpalWabbit.Builder.activeCover() enable active learning with cover
VowpalWabbit.Builder	VowpalWabbit.Builder.adaptive() use adaptive, individual learning rates.
static VowpalWabbit.Builder	VowpalWabbit.advancedBuilder() Create advanced builder for Vowpal Wabbit learner
VowpalWabbit.Builder	VowpalWabbit.Builder.affix(String arg) generate prefixes/suffixes of features; argument '+2a,-3b,+1'
VowpalWabbit.Builder	VowpalWabbit.Builder.auditRegressor(Path regressor) stores feature names and their regressor values.
VowpalWabbit.Builder	VowpalWabbit.Builder.autolink(int d) create link function with polynomial d
VowpalWabbit.Builder	VowpalWabbit.Builder.bfgs() use bfgs optimization
VowpalWabbit.Builder	VowpalWabbit.Builder.binary() report loss as binary classification on -1,1
VowpalWabbit.Builder	VowpalWabbit.Builder.bitPrecision(int bitsNum) number of bits in the feature table.
VowpalWabbit.Builder	VowpalWabbit.Builder.boosting(int n) Online boosting with <N> weak learners
VowpalWabbit.Builder	VowpalWabbit.Builder.bootstrap(int k) k-way bootstrap by online importance resampling
VowpalWabbit.Builder	VowpalWabbit.Builder.cache() Use a cache.
VowpalWabbit.Builder	VowpalWabbit.Builder.cacheFile(Path cacheFile) The location(s) of cacheFile.
VowpalWabbit.Builder	VowpalWabbit.Builder.cb(int k) Use contextual bandit learning with <k> costs
VowpalWabbit.Builder	VowpalWabbit.Builder.cbAdf() Do Contextual Bandit learning with multiline action dependent features.
VowpalWabbit.Builder	VowpalWabbit.Builder.cbExplore(int k) Online explore-exploit for a <k> action contextual bandit problem
VowpalWabbit.Builder	VowpalWabbit.Builder.cbExploreAdf() Online explore-exploit for a contextual bandit problem with multiline action dependent features
VowpalWabbit.Builder	VowpalWabbit.Builder.cbify(int k) Convert multiclass on <k> classes into a contextual bandit problem
VowpalWabbit.Builder	VowpalWabbit.Builder.compressed() use gzip format whenever possible.
VowpalWabbit.Builder	VowpalWabbit.Builder.confidence() Get confidence for binary predictions
VowpalWabbit.Builder	VowpalWabbit.Builder.confidenceAfterTraining() Confidence after training
VowpalWabbit.Builder	VowpalWabbit.Builder.conjugateGradient() use conjugate gradient based optimization
VowpalWabbit.Builder	VowpalWabbit.Builder.constant(double initialValue) Set initial value of constant
VowpalWabbit.Builder	VowpalWabbit.Builder.csoaa(int k) One-against-all multiclass with <k> costs
VowpalWabbit.Builder	VowpalWabbit.Builder.csoaaLdf(VowpalWabbit.LDF ldf) Use one-against-all multiclass learning with label dependent features.
VowpalWabbit.Builder	VowpalWabbit.Builder.cubic(String firstNameSpace, String secondNamespace, String thirdNamespace) Create and use cubic features
VowpalWabbit.Builder	VowpalWabbit.Builder.decayLearningRate(double decay) Set Decay factor for learning_rate between passes
VowpalWabbit.Builder	VowpalWabbit.Builder.dictionary(Path file) read a dictionary for additional features (arg either 'x:file' or just 'file')
VowpalWabbit.Builder	VowpalWabbit.Builder.dictionaryPath(Path dir) look in this directory for dictionaries; defaults to current directory or env{PATH}
VowpalWabbit.Builder	VowpalWabbit.Builder.earlyTerminate(int passes) Specify the number of passes tolerated when holdout loss doesn't decrease before early termination, default is 3
VowpalWabbit.Builder	VowpalWabbit.Builder.ect(int k) Error correcting tournament with <k> labels
VowpalWabbit.Builder	VowpalWabbit.Builder.examples(int examples) number of examples to parse
VowpalWabbit.Builder	VowpalWabbit.Builder.featureLimit(int n) limit to N features.
VowpalWabbit.Builder	VowpalWabbit.Builder.featureMask(Path featureMask) Use existing regressor to determine which parameters may be updated.
VowpalWabbit.Builder	VowpalWabbit.Builder.finalRegressor(Path regressor) Final regressor
VowpalWabbit.Builder	VowpalWabbit.Builder.ftrl() FTRL: Follow the Proximal Regularized Leader
VowpalWabbit.Builder	VowpalWabbit.Builder.hash(VowpalWabbit.Hash hash) how to hash the features.
VowpalWabbit.Builder	VowpalWabbit.Builder.holdoutAfter(int n) holdout after n training examples, default off (disables holdoutPeriod)
VowpalWabbit.Builder	VowpalWabbit.Builder.holdoutPeriod(int holdout) holdout period for test only, default 10
VowpalWabbit.Builder	VowpalWabbit.Builder.id(String id) User supplied ID embedded into the final regressor
VowpalWabbit.Builder	VowpalWabbit.Builder.ignore(String namespace) ignore namespace <arg>
VowpalWabbit.Builder	VowpalWabbit.Builder.initialPassLength(int examples) initial number of examples per pass
VowpalWabbit.Builder	VowpalWabbit.Builder.initialRegressor(Path initialRegressor) Initial regressor(s)
VowpalWabbit.Builder	VowpalWabbit.Builder.initialT(double initialT) initial t value
VowpalWabbit.Builder	VowpalWabbit.Builder.initialWeight(double weight) Set all weights to an initial value of arg.
VowpalWabbit.Builder	VowpalWabbit.Builder.inputFeatureRegularizer(Path regularizationPath) Per feature regularization input file
VowpalWabbit.Builder	VowpalWabbit.Builder.interact(String arg) Put weights on feature products from namespaces <n1> and <n2>
VowpalWabbit.Builder	VowpalWabbit.Builder.interactions(String... namespaces) Create feature interactions of any level between namespaces.
VowpalWabbit.Builder	VowpalWabbit.Builder.invariant() use safe/importance aware updates.
VowpalWabbit.Builder	VowpalWabbit.Builder.keep(String namespace) keep namespace <arg>
VowpalWabbit.Builder	VowpalWabbit.Builder.killCache() do not reuse existing cache: create a new one always
VowpalWabbit.Builder	VowpalWabbit.Builder.ksvm() kernel svm
VowpalWabbit.Builder	VowpalWabbit.Builder.l1(double l1) l_1 lambda
VowpalWabbit.Builder	VowpalWabbit.Builder.l2(double l2) l_2 lambda
VowpalWabbit.Builder	VowpalWabbit.Builder.lda(int topics) Run lda with <int> topics
VowpalWabbit.Builder	VowpalWabbit.Builder.learningRate(double learningRate) Set learning rate
VowpalWabbit.Builder	VowpalWabbit.Builder.leaveDuplicateInteractions() Don't remove interactions with duplicate combinations of namespaces.
VowpalWabbit.Builder	VowpalWabbit.Builder.link(Link link) Specify the link function: identity, logistic, glf1 or poisson (=identity)
VowpalWabbit.Builder	VowpalWabbit.Builder.logMulti(int k) Use online tree for multiclass
VowpalWabbit.Builder	VowpalWabbit.Builder.lossFunction(Loss loss) Specify the loss function to be used, uses squared by default.
VowpalWabbit.Builder	VowpalWabbit.Builder.lrq(String firstNamespace, String secondNamespace, int k) use low rank quadratic features
VowpalWabbit.Builder	VowpalWabbit.Builder.lrqdropout() use dropout training for low rank quadratic features
VowpalWabbit.Builder	VowpalWabbit.Builder.lrqfa(String firstNamespace, String secondNamespace, int k) use low rank quadratic features with field aware weights
VowpalWabbit.Builder	VowpalWabbit.Builder.maxPrediction(double max) Largest prediction to output
VowpalWabbit.Builder	VowpalWabbit.Builder.minPrediction(double min) Smallest prediction to output
VowpalWabbit.Builder	VowpalWabbit.Builder.multilabelOaa(int k) One-against-all multilabel with <k> labels
VowpalWabbit.Builder	VowpalWabbit.Builder.multiworldTest(String arg) Evaluate features as a policies
VowpalWabbit.Builder	VowpalWabbit.Builder.namedLabels(String... labels) use names for labels (multiclass, etc.) rather than integers, argument specified all possible labels, comma-sep,
VowpalWabbit.Builder	VowpalWabbit.Builder.newMf(int rank) rank for reduction-based matrix factorization
VowpalWabbit.Builder	VowpalWabbit.Builder.ngram(String namespace, int n) Generate N grams.
VowpalWabbit.Builder	VowpalWabbit.Builder.nn(int units) Sigmoidal feedforward network with <k> hidden units
VowpalWabbit.Builder	VowpalWabbit.Builder.noconstant() Don't add a constant feature
VowpalWabbit.Builder	VowpalWabbit.Builder.noop() do no learning
VowpalWabbit.Builder	VowpalWabbit.Builder.normalized() use per feature normalized updates
VowpalWabbit.Builder	VowpalWabbit.Builder.oaa(int k) One-against-all multiclass with <k> labels
VowpalWabbit.Builder	VowpalWabbit.Builder.ojaNewton() Online Newton with Oja's Sketch
VowpalWabbit.Builder	VowpalWabbit.Builder.outputFeatureRegularizerBinary(Path regularizationFile) Per feature regularization output file
VowpalWabbit.Builder	VowpalWabbit.Builder.outputFeatureRegularizerText(Path regularizationFile) Per feature regularization output file, in text
VowpalWabbit.Builder	VowpalWabbit.Builder.parameter(String argumentLine) Add vowpal wabit argument
VowpalWabbit.Builder	VowpalWabbit.Builder.passes(int passes) Number of Training Passes
VowpalWabbit.Builder	VowpalWabbit.Builder.permutations() Use permutations instead of combinations for feature interactions of same namespace.
VowpalWabbit.Builder	VowpalWabbit.Builder.pistol() FTRL: Parameter-free Stochastic Learning
VowpalWabbit.Builder	VowpalWabbit.Builder.powerT(double powerT) t power value
VowpalWabbit.Builder	VowpalWabbit.Builder.quadratic(String firstNameSpace, String secondNamespace) Create and use quadratic features
VowpalWabbit.Builder	VowpalWabbit.Builder.quantileTau(double tau) Parameter \tau associated with Quantile loss.
VowpalWabbit.Builder	VowpalWabbit.Builder.randomSeed(int seed) seed random number generator
VowpalWabbit.Builder	VowpalWabbit.Builder.randomWeights(double arg) make initial weights random
VowpalWabbit.Builder	VowpalWabbit.Builder.rank(int rank) rank for matrix factorization.
VowpalWabbit.Builder	VowpalWabbit.Builder.readableModel(Path model) Output human-readable final regressor with numeric features
VowpalWabbit.Builder	VowpalWabbit.Builder.recallTree(int k) Use online tree for multiclass
VowpalWabbit.Builder	VowpalWabbit.Builder.redefine(String newNamespace, String... namespaces) redefine namespaces beginning with characters of string S as namespace N.
VowpalWabbit.Builder	VowpalWabbit.Builder.replayB(String arg) use experience replay at a specified level
VowpalWabbit.Builder	VowpalWabbit.Builder.replayC(String arg) use experience replay at a specified level
VowpalWabbit.Builder	VowpalWabbit.Builder.replayM(String arg) use experience replay at a specified level
VowpalWabbit.Builder	VowpalWabbit.Builder.ringSize(int ringSize) size of example ring buffer
VowpalWabbit.Builder	VowpalWabbit.Builder.savePerPass() Save the model after every pass over data
VowpalWabbit.Builder	VowpalWabbit.Builder.saveResume() save extra state so learning can be resumed later with new data
VowpalWabbit.Builder	VowpalWabbit.Builder.search(int maxActionID) Use learning to search, argument=maximum action id or 0 for LDF
VowpalWabbit.Builder	VowpalWabbit.Builder.sgd() use regular stochastic gradient descent update.
VowpalWabbit.Builder	VowpalWabbit.Builder.skips(String namespace, int n) Generate skips in N grams.
VowpalWabbit.Builder	VowpalWabbit.Builder.sortFeatures() turn this on to disregard order in which features have been defined.
VowpalWabbit.Builder	VowpalWabbit.Builder.sparseL2(double l2) use per feature normalized updates (=0)
VowpalWabbit.Builder	VowpalWabbit.Builder.spelling(String namespace) compute spelling features for a give namespace (use '_' for default namespace)
VowpalWabbit.Builder	VowpalWabbit.Builder.stagePoly() use stagewise polynomial feature learning
VowpalWabbit.Builder	VowpalWabbit.Builder.svrg() Streaming Stochastic Variance Reduced Gradient
VowpalWabbit.Builder	VowpalWabbit.Builder.testonly() Ignore label information and just test
VowpalWabbit.Builder	VowpalWabbit.Builder.top(int k) top k recommendation
VowpalWabbit.Builder	VowpalWabbit.Builder.verbose() Make vowpal wabbit writing debug and performance information to stderr
VowpalWabbit.Builder	VowpalWabbit.Builder.wapLdf(VowpalWabbit.LDF ldf) Use weighted all-pairs multiclass learning with label dependent features.