@ModelData(value={"isRanking","fmals","W","V","W0","k"}) public class FMALSRecommender extends FactorizationMachineRecommender
k, LOG, n, numFactors, numIterations, p, regF, regW, regW0, testTensor, trainTensor, V, validTensor, W, w0
conf, context, decay, earlyStop, globalMean, isBoldDriver, isRanking, itemMappingData, lastLoss, loss, maxRate, minRate, numItems, numRates, numUsers, ratingScale, recommendedList, testMatrix, topN, trainMatrix, userMappingData, validMatrix, verbose
Constructor and Description |
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FMALSRecommender() |
Modifier and Type | Method and Description |
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protected double |
predict(int userIdx,
int itemIdx)
Deprecated.
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protected void |
setup()
setup
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protected void |
trainModel()
train Model
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predict, predict, recommendRating, tenserKeysToFeatureVector
cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, saveModel, setContext
protected void setup() throws LibrecException
FactorizationMachineRecommender
setup
in class FactorizationMachineRecommender
LibrecException
- if error occursprotected void trainModel() throws LibrecException
AbstractRecommender
trainModel
in class AbstractRecommender
LibrecException
- if error occurs during training model@Deprecated protected double predict(int userIdx, int itemIdx) throws LibrecException
predict
in class AbstractRecommender
userIdx
- user indexitemIdx
- item indexLibrecException
- if error occurs during predictingCopyright © 2017. All Rights Reserved.