Modifier and Type | Method and Description |
---|---|
static <Testing,PredictionValue> |
SVM.evaluate$default$2() |
static <Training> ParameterMap |
SVM.fit$default$2() |
static ParameterMap |
SVM.parameters() |
static <Testing,Prediction> |
SVM.predict$default$2() |
Modifier and Type | Method and Description |
---|---|
static <Testing,PredictionValue> |
SVM.evaluate(DataSet<Testing> testing,
ParameterMap evaluateParameters,
EvaluateDataSetOperation<Self,Testing,PredictionValue> evaluator) |
static <Training> void |
SVM.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation) |
static <Testing,Prediction> |
SVM.predict(DataSet<Testing> testing,
ParameterMap predictParameters,
PredictDataSetOperation<Self,Testing,Prediction> predictor) |
Modifier and Type | Method and Description |
---|---|
<T> ParameterMap |
ParameterMap.add(Parameter<T> parameter,
T value)
Adds a new parameter value to the ParameterMap.
|
ParameterMap |
ParameterMap$.apply() |
ParameterMap |
ParameterMap$.Empty() |
static ParameterMap |
ParameterMap.Empty() |
ParameterMap |
WithParameters.parameters() |
Modifier and Type | Method and Description |
---|---|
static <Testing,PredictionValue> |
KNN.evaluate$default$2() |
static <Training> ParameterMap |
KNN.fit$default$2() |
static ParameterMap |
KNN.parameters() |
static <Testing,Prediction> |
KNN.predict$default$2() |
Modifier and Type | Method and Description |
---|---|
static <Testing,PredictionValue> |
KNN.evaluate(DataSet<Testing> testing,
ParameterMap evaluateParameters,
EvaluateDataSetOperation<Self,Testing,PredictionValue> evaluator) |
static <Training> void |
KNN.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation) |
static <Testing,Prediction> |
KNN.predict(DataSet<Testing> testing,
ParameterMap predictParameters,
PredictDataSetOperation<Self,Testing,Prediction> predictor) |
Modifier and Type | Method and Description |
---|---|
static ParameterMap |
Solver.parameters() |
static ParameterMap |
GradientDescent.parameters() |
static ParameterMap |
IterativeSolver.parameters() |
Modifier and Type | Method and Description |
---|---|
static <Training> ParameterMap |
StochasticOutlierSelection.fit$default$2() |
static ParameterMap |
StochasticOutlierSelection.parameters() |
static <Input,Output> |
StochasticOutlierSelection.transform$default$2() |
Modifier and Type | Method and Description |
---|---|
static DataSet<StochasticOutlierSelection.BreezeLabeledVector> |
StochasticOutlierSelection.computeAffinity(DataSet<StochasticOutlierSelection.BreezeLabeledVector> dissimilarityVectors,
ParameterMap resultingParameters)
Approximate the affinity by fitting a Gaussian-like function
|
DataSet<StochasticOutlierSelection.BreezeLabeledVector> |
StochasticOutlierSelection$.computeAffinity(DataSet<StochasticOutlierSelection.BreezeLabeledVector> dissimilarityVectors,
ParameterMap resultingParameters)
Approximate the affinity by fitting a Gaussian-like function
|
static <Training> void |
StochasticOutlierSelection.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation) |
static <Input,Output> |
StochasticOutlierSelection.transform(DataSet<Input> input,
ParameterMap transformParameters,
TransformDataSetOperation<Self,Input,Output> transformOperation) |
Modifier and Type | Method and Description |
---|---|
static <Testing,PredictionValue> |
ChainedPredictor.evaluate$default$2() |
static <Training> ParameterMap |
ChainedTransformer.fit$default$2() |
static <Training> ParameterMap |
ChainedPredictor.fit$default$2() |
static ParameterMap |
ChainedTransformer.parameters() |
static ParameterMap |
ChainedPredictor.parameters() |
static <Testing,Prediction> |
ChainedPredictor.predict$default$2() |
static <Input,Output> |
ChainedTransformer.transform$default$2() |
Modifier and Type | Method and Description |
---|---|
static <Testing,PredictionValue> |
ChainedPredictor.evaluate(DataSet<Testing> testing,
ParameterMap evaluateParameters,
EvaluateDataSetOperation<Self,Testing,PredictionValue> evaluator) |
<Testing,PredictionValue> |
Predictor.evaluate(DataSet<Testing> testing,
ParameterMap evaluateParameters,
EvaluateDataSetOperation<Self,Testing,PredictionValue> evaluator)
Evaluates the testing data by computing the prediction value and returning a pair of true
label value and prediction value.
|
DataSet<scala.Tuple2<Prediction,Prediction>> |
EvaluateDataSetOperation.evaluateDataSet(Instance instance,
ParameterMap evaluateParameters,
DataSet<Testing> testing) |
<Training> void |
Estimator.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation)
Fits the estimator to the given input data.
|
static <Training> void |
ChainedTransformer.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation) |
static <Training> void |
ChainedPredictor.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation) |
void |
FitOperation.fit(Self instance,
ParameterMap fitParameters,
DataSet<Training> input) |
DataSet<Model> |
TransformOperation.getModel(Instance instance,
ParameterMap transformParemters)
Retrieves the model of the
Transformer for which this operation has been defined. |
DataSet<Model> |
PredictOperation.getModel(Instance instance,
ParameterMap predictParameters)
Defines how to retrieve the model of the type for which this operation was defined
|
static <Testing,Prediction> |
ChainedPredictor.predict(DataSet<Testing> testing,
ParameterMap predictParameters,
PredictDataSetOperation<Self,Testing,Prediction> predictor) |
<Testing,Prediction> |
Predictor.predict(DataSet<Testing> testing,
ParameterMap predictParameters,
PredictDataSetOperation<Self,Testing,Prediction> predictor)
Predict testing data according the learned model.
|
DataSet<Prediction> |
PredictDataSetOperation.predictDataSet(Self instance,
ParameterMap predictParameters,
DataSet<Testing> input)
Calculates the predictions for all elements in the
DataSet input |
static <Input,Output> |
ChainedTransformer.transform(DataSet<Input> input,
ParameterMap transformParameters,
TransformDataSetOperation<Self,Input,Output> transformOperation) |
<Input,Output> |
Transformer.transform(DataSet<Input> input,
ParameterMap transformParameters,
TransformDataSetOperation<Self,Input,Output> transformOperation)
Transform operation which transforms an input
DataSet of type I into an ouptut DataSet
of type O. |
DataSet<Output> |
TransformDataSetOperation.transformDataSet(Instance instance,
ParameterMap transformParameters,
DataSet<Input> input) |
Modifier and Type | Method and Description |
---|---|
static <Training> ParameterMap |
PolynomialFeatures.fit$default$2() |
static <Training> ParameterMap |
MinMaxScaler.fit$default$2() |
static <Training> ParameterMap |
StandardScaler.fit$default$2() |
static ParameterMap |
PolynomialFeatures.parameters() |
static ParameterMap |
MinMaxScaler.parameters() |
static ParameterMap |
StandardScaler.parameters() |
static <Input,Output> |
PolynomialFeatures.transform$default$2() |
static <Input,Output> |
MinMaxScaler.transform$default$2() |
static <Input,Output> |
StandardScaler.transform$default$2() |
Modifier and Type | Method and Description |
---|---|
static <Training> void |
PolynomialFeatures.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation) |
static <Training> void |
MinMaxScaler.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation) |
static <Training> void |
StandardScaler.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation) |
DataSet<scala.Tuple2<breeze.linalg.Vector<Object>,breeze.linalg.Vector<Object>>> |
StandardScaler.StandardScalerTransformOperation.getModel(StandardScaler instance,
ParameterMap transformParameters) |
static <Input,Output> |
PolynomialFeatures.transform(DataSet<Input> input,
ParameterMap transformParameters,
TransformDataSetOperation<Self,Input,Output> transformOperation) |
static <Input,Output> |
MinMaxScaler.transform(DataSet<Input> input,
ParameterMap transformParameters,
TransformDataSetOperation<Self,Input,Output> transformOperation) |
static <Input,Output> |
StandardScaler.transform(DataSet<Input> input,
ParameterMap transformParameters,
TransformDataSetOperation<Self,Input,Output> transformOperation) |
Modifier and Type | Method and Description |
---|---|
static <Testing,PredictionValue> |
ALS.evaluate$default$2() |
static <Training> ParameterMap |
ALS.fit$default$2() |
static ParameterMap |
ALS.parameters() |
static <Testing,Prediction> |
ALS.predict$default$2() |
Modifier and Type | Method and Description |
---|---|
DataSet<Object> |
ALS.empiricalRisk(DataSet<scala.Tuple3<Object,Object,Object>> labeledData,
ParameterMap riskParameters)
Empirical risk of the trained model (matrix factorization).
|
static <Testing,PredictionValue> |
ALS.evaluate(DataSet<Testing> testing,
ParameterMap evaluateParameters,
EvaluateDataSetOperation<Self,Testing,PredictionValue> evaluator) |
static <Training> void |
ALS.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation) |
static <Testing,Prediction> |
ALS.predict(DataSet<Testing> testing,
ParameterMap predictParameters,
PredictDataSetOperation<Self,Testing,Prediction> predictor) |
Modifier and Type | Method and Description |
---|---|
static <Testing,PredictionValue> |
MultipleLinearRegression.evaluate$default$2() |
static <Training> ParameterMap |
MultipleLinearRegression.fit$default$2() |
static ParameterMap |
MultipleLinearRegression.parameters() |
static <Testing,Prediction> |
MultipleLinearRegression.predict$default$2() |
Modifier and Type | Method and Description |
---|---|
static <Testing,PredictionValue> |
MultipleLinearRegression.evaluate(DataSet<Testing> testing,
ParameterMap evaluateParameters,
EvaluateDataSetOperation<Self,Testing,PredictionValue> evaluator) |
static <Training> void |
MultipleLinearRegression.fit(DataSet<Training> training,
ParameterMap fitParameters,
FitOperation<Self,Training> fitOperation) |
static <Testing,Prediction> |
MultipleLinearRegression.predict(DataSet<Testing> testing,
ParameterMap predictParameters,
PredictDataSetOperation<Self,Testing,Prediction> predictor) |
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