Penelope v0.2.2 Penelope.NIF
NIF wrapper module
for blas, see http://www.netlib.org/blas/ for libsvm, see https://github.com/cjlin1/libsvm
Link to this section Summary
Functions
z = ax + y
y = ax
compiles crf model parameters into a model resource
extracts crf model parameters from a model resource
predicts a sequence from a sequence of features
trains a crf model using crfsuite
module initialization callback
compiles linear model parameters into a model resource
extracts linear model parameters from a model resource
predicts a class from a feature vector
predicts an ordered list of class probabilities from a feature vector
trains a inear model using liblinear
compiles svm model parameters into a model resource
extracts svm model parameters from a model resource
predicts a class from a feature vector
predicts an ordered list of class probabilities from a feature vector
trains an svm model using libsvm
Link to this section Functions
blas_saxpy(a :: float(), x :: Penelope.ML.Vector.t(), y :: Penelope.ML.Vector.t()) :: Penelope.ML.Vector.t()
z = ax + y
blas_sscal(a :: float(), x :: Penelope.ML.Vector.t()) :: Penelope.ML.Vector.t()
y = ax
compiles crf model parameters into a model resource
extracts crf model parameters from a model resource
predicts a sequence from a sequence of features
trains a crf model using crfsuite
module initialization callback
compiles linear model parameters into a model resource
extracts linear model parameters from a model resource
lin_predict_class(model :: reference(), x :: Penelope.ML.Vector.t()) :: integer()
predicts a class from a feature vector
lin_predict_probability(model :: reference(), x :: Penelope.ML.Vector.t()) :: [{integer(), float()}]
predicts an ordered list of class probabilities from a feature vector
lin_train(x :: [Penelope.ML.Vector.t()], y :: [integer()], params :: map()) :: reference()
trains a inear model using liblinear
compiles svm model parameters into a model resource
extracts svm model parameters from a model resource
svm_predict_class(model :: reference(), x :: Penelope.ML.Vector.t()) :: integer()
predicts a class from a feature vector
svm_predict_probability(model :: reference(), x :: Penelope.ML.Vector.t()) :: [{integer(), float()}]
predicts an ordered list of class probabilities from a feature vector
svm_train(x :: [Penelope.ML.Vector.t()], y :: [integer()], params :: map()) :: reference()
trains an svm model using libsvm