public class PersonalityDiagnosisRecommender extends AbstractRecommender
conf, context, decay, earlyStop, globalMean, isBoldDriver, isRanking, itemMappingData, lastLoss, LOG, loss, maxRate, minRate, numItems, numRates, numUsers, ratingScale, recommendedList, testMatrix, topN, trainMatrix, userMappingData, validMatrix, verbose
Constructor and Description |
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PersonalityDiagnosisRecommender() |
Modifier and Type | Method and Description |
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protected double |
gaussian(double x,
double mu,
double sigma) |
protected double |
predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
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protected void |
setup()
initialization
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protected void |
trainModel()
train model
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cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext
protected void setup() throws LibrecException
setup
in class AbstractRecommender
LibrecException
- if error occursprotected void trainModel() throws LibrecException
trainModel
in class AbstractRecommender
LibrecException
- if error occursprotected double predict(int userIdx, int itemIdx) throws LibrecException
predict
in class AbstractRecommender
userIdx
- user indexitemIdx
- item indexLibrecException
- if error occursprotected double gaussian(double x, double mu, double sigma)
x
- input valuemu
- mean of normal distributionsigma
- standard deviation of normation distributionmu
and standard deviation sigma
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