ModelIdentity
the "command" used to instantiate VW.
either the FsInstance pointing to the VW model or the Base64 encoded VW model.
names of features (parallel to featureFunctions)
functions to extract values from the input value
indices of features that will be placed in the default VW namespace
mapping from namespace name to indices of features that will be placed in the namespace
A function that when given a VWLearner creates a function that can make a prediction, given string-based input.
A threshold dictating how many missing features to allow before making the prediction fail. See com.eharmony.aloha.models.reg.RegressionFeatures.numMissingThreshold in aloha-core.
Close the underlying VW model.
Close the underlying VW model.
indices of features that will be placed in the default VW namespace
functions to extract values from the input value
functions to extract values from the input value
names of features (parallel to featureFunctions)
names of features (parallel to featureFunctions)
A function that when given a VWLearner creates a function that can make a prediction, given string-based input.
ModelIdentity
ModelIdentity
either the FsInstance pointing to the VW model or the Base64 encoded VW model.
mapping from namespace name to indices of features that will be placed in the namespace
A threshold dictating how many missing features to allow before making the prediction fail.
A threshold dictating how many missing features to allow before making the prediction fail. See com.eharmony.aloha.models.reg.RegressionFeatures.numMissingThreshold in aloha-core.
the "command" used to instantiate VW.
Model that delegates to a VW JNI model.
model input type
model output type
ModelIdentity
the "command" used to instantiate VW.
either the FsInstance pointing to the VW model or the Base64 encoded VW model.
names of features (parallel to featureFunctions)
functions to extract values from the input value
indices of features that will be placed in the default VW namespace
mapping from namespace name to indices of features that will be placed in the namespace
A function that when given a VWLearner creates a function that can make a prediction, given string-based input.
A threshold dictating how many missing features to allow before making the prediction fail. See com.eharmony.aloha.models.reg.RegressionFeatures.numMissingThreshold in aloha-core.