use of com.amazon.randomcutforest.parkservices.state.statistics.DeviationMapper in project random-cut-forest-by-aws by aws.
the class PreprocessorMapper method toModel.
@Override
public Preprocessor toModel(PreprocessorState state, long seed) {
DeviationMapper deviationMapper = new DeviationMapper();
Deviation timeStampDeviation = deviationMapper.toModel(state.getTimeStampDeviationState());
Deviation dataQuality = deviationMapper.toModel(state.getDataQualityState());
Deviation[] deviations = null;
if (state.getDeviationStates() != null) {
deviations = new Deviation[state.getDeviationStates().length];
for (int i = 0; i < state.getDeviationStates().length; i++) {
deviations[i] = deviationMapper.toModel(state.getDeviationStates()[i]);
}
}
Preprocessor.Builder<?> preprocessorBuilder = new Preprocessor.Builder<>().forestMode(ForestMode.valueOf(state.getForestMode())).shingleSize(state.getShingleSize()).dimensions(state.getDimensions()).normalizeTime(state.isNormalizeTime()).imputationMethod(ImputationMethod.valueOf(state.getImputationMethod())).fillValues(state.getDefaultFill()).inputLength(state.getInputLength()).weights(state.getWeights()).transformMethod(TransformMethod.valueOf(state.getTransformMethod())).startNormalization(state.getStartNormalization()).useImputedFraction(state.getUseImputedFraction()).timeDeviation(timeStampDeviation).dataQuality(dataQuality);
if (deviations != null) {
preprocessorBuilder.deviations(deviations);
}
Preprocessor preprocessor = preprocessorBuilder.build();
preprocessor.setInitialValues(state.getInitialValues());
preprocessor.setInitialTimeStamps(state.getInitialTimeStamps());
preprocessor.setClipFactor(state.getClipFactor());
preprocessor.setValuesSeen(state.getValuesSeen());
preprocessor.setInternalTimeStamp(state.getInternalTimeStamp());
preprocessor.setLastShingledInput(state.getLastShingledInput());
preprocessor.setLastShingledPoint(state.getLastShingledPoint());
preprocessor.setPreviousTimeStamps(state.getPreviousTimeStamps());
preprocessor.setNormalizeTime(state.isNormalizeTime());
return preprocessor;
}
use of com.amazon.randomcutforest.parkservices.state.statistics.DeviationMapper in project random-cut-forest-by-aws by aws.
the class BasicThresholderMapper method toModel.
@Override
public BasicThresholder toModel(BasicThresholderState state, long seed) {
DeviationMapper deviationMapper = new DeviationMapper();
Deviation primaryDeviation = deviationMapper.toModel(state.getPrimaryDeviationState());
Deviation secondaryDeviation = deviationMapper.toModel(state.getSecondaryDeviationState());
Deviation thresholdDeviation = deviationMapper.toModel(state.getThresholdDeviationState());
BasicThresholder thresholder = new BasicThresholder(primaryDeviation, secondaryDeviation, thresholdDeviation);
thresholder.setAbsoluteThreshold(state.getAbsoluteThreshold());
thresholder.setLowerThreshold(state.getLowerThreshold(), state.isAutoThreshold());
thresholder.setUpperThreshold(state.getUpperThreshold());
thresholder.setInitialThreshold(state.getInitialThreshold());
thresholder.setElasticity(state.getElasticity());
thresholder.setInPotentialAnomaly(state.isInAnomaly());
thresholder.setHorizon(state.getHorizon());
thresholder.setCount(state.getCount());
thresholder.setMinimumScores(state.getMinimumScores());
thresholder.setAbsoluteScoreFraction(state.getAbsoluteScoreFraction());
thresholder.setUpperZfactor(state.getUpperZfactor());
thresholder.setZfactor(state.getZFactor());
return thresholder;
}
use of com.amazon.randomcutforest.parkservices.state.statistics.DeviationMapper in project random-cut-forest-by-aws by aws.
the class PreprocessorMapper method toState.
@Override
public PreprocessorState toState(Preprocessor model) {
PreprocessorState state = new PreprocessorState();
state.setShingleSize(model.getShingleSize());
state.setDimensions(model.getDimension());
state.setInputLength(model.getInputLength());
state.setClipFactor(model.getClipFactor());
state.setDefaultFill(model.getDefaultFill());
state.setImputationMethod(model.getImputationMethod().name());
state.setTransformMethod(model.getTransformMethod().name());
state.setWeights(model.getWeights());
state.setForestMode(model.getMode().name());
state.setInitialTimeStamps(model.getInitialTimeStamps());
state.setInitialValues(model.getInitialValues());
state.setUseImputedFraction(model.getUseImputedFraction());
state.setNormalizeTime(model.isNormalizeTime());
state.setStartNormalization(model.getStartNormalization());
state.setStopNormalization(model.getStopNormalization());
state.setPreviousTimeStamps(model.getPreviousTimeStamps());
state.setLastShingledInput(model.getLastShingledInput());
state.setLastShingledPoint(model.getLastShingledPoint());
state.setValuesSeen(model.getValuesSeen());
state.setInternalTimeStamp(model.getInternalTimeStamp());
DeviationMapper deviationMapper = new DeviationMapper();
state.setTimeStampDeviationState(deviationMapper.toState(model.getTimeStampDeviation()));
state.setDataQualityState(deviationMapper.toState(model.getDataQuality()));
DeviationState[] deviationStates = null;
if (model.getDeviationList() != null) {
Deviation[] list = model.getDeviationList();
deviationStates = new DeviationState[list.length];
for (int i = 0; i < list.length; i++) {
deviationStates[i] = deviationMapper.toState(list[i]);
}
}
state.setDeviationStates(deviationStates);
return state;
}
use of com.amazon.randomcutforest.parkservices.state.statistics.DeviationMapper in project random-cut-forest-by-aws by aws.
the class BasicThresholderMapper method toState.
@Override
public BasicThresholderState toState(BasicThresholder model) {
BasicThresholderState state = new BasicThresholderState();
DeviationMapper deviationMapper = new DeviationMapper();
state.setZFactor(model.getZFactor());
state.setUpperZfactor(model.getUpperZfactor());
state.setUpperThreshold(model.getUpperThreshold());
state.setLowerThreshold(model.getLowerThreshold());
state.setAbsoluteThreshold(model.getAbsoluteThreshold());
state.setInitialThreshold(model.getInitialThreshold());
state.setAbsoluteScoreFraction(model.getAbsoluteScoreFraction());
state.setElasticity(model.getElasticity());
state.setCount(model.getCount());
state.setInAnomaly(model.isInPotentialAnomaly());
state.setAutoThreshold(model.isAutoThreshold());
state.setMinimumScores(model.getMinimumScores());
state.setPrimaryDeviationState(deviationMapper.toState(model.getPrimaryDeviation()));
state.setSecondaryDeviationState(deviationMapper.toState(model.getSecondaryDeviation()));
state.setThresholdDeviationState(deviationMapper.toState(model.getThresholdDeviation()));
state.setHorizon(model.getHorizon());
return state;
}
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