com.jstarcraft.ai.data.DataInstance#setQuantityMark

Source File: ItemClusterModel.java From jstarcraft-rns with Apache License 2.0

6 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float value = 0F;
    for (int topicIndex = 0; topicIndex < factorSize; topicIndex++) {
        float topicProbability = itemTopicProbabilities.getValue(itemIndex, topicIndex); // probability
        float topicValue = 0F;
        for (Entry<Float, Integer> entry : scoreIndexes.entrySet()) {
            float score = entry.getKey();
            float probability = topicScoreMatrix.getValue(topicIndex, entry.getValue());
            topicValue += score * probability;
        }
        value += topicProbability * topicValue;
    }
    instance.setQuantityMark(value);
}

Source File: BHFreeRankingModel.java From jstarcraft-rns with Apache License 2.0

6 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float value = 0F;
    for (Entry<Float, Integer> entry : scoreIndexes.entrySet()) {
        float score = entry.getKey();
        float probability = 0F;
        for (int userTopic = 0; userTopic < userTopicSize; userTopic++) {
            for (int itemTopic = 0; itemTopic < itemTopicSize; itemTopic++) {
                probability += user2TopicProbabilities.getValue(userIndex, userTopic) * userTopic2ItemTopicProbabilities.getValue(userTopic, itemTopic) * userTopic2ItemTopicItemSums[userTopic][itemTopic][itemIndex] * userTopic2ItemTopicScoreProbabilities[userTopic][itemTopic][entry.getValue()];
            }
        }
        value += score * probability;
    }
    instance.setQuantityMark(value);
}

Source File: AspectModelRatingModel.java From jstarcraft-rns with Apache License 2.0

6 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float value = 0F;
    float denominator = 0F;
    for (int topicIndex = 0; topicIndex < factorSize; topicIndex++) {
        float weight = topicProbabilities.getValue(topicIndex) * userProbabilities.getValue(topicIndex, userIndex) * itemProbabilities.getValue(topicIndex, itemIndex);
        denominator += weight;
        value += weight * meanProbabilities.getValue(topicIndex);
    }
    value = value / denominator;
    if (Float.isNaN(value)) {
        value = meanScore;
    }
    instance.setQuantityMark(value);
}

Source File: SVDPlusPlusModel.java From jstarcraft-rns with Apache License 2.0

6 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    SparseVector userVector = scoreMatrix.getRowVector(userIndex);
    // TODO 此处需要重构,取消DenseVector.
    DenseVector factorVector = DenseVector.valueOf(factorSize);
    // sum of implicit feedback factors of userIdx with weight Math.sqrt(1.0
    // / userItemsList.get(userIdx).size())
    for (VectorScalar term : userVector) {
        factorVector.addVector(factorMatrix.getRowVector(term.getIndex()));
    }
    float scale = (float) Math.sqrt(userVector.getElementSize());
    if (scale > 0F) {
        factorVector.scaleValues(1F / scale);
    }
    instance.setQuantityMark(predict(userIndex, itemIndex, factorVector));
}

Source File: DeepFMModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float value = outputData.getFloat(userIndex, itemIndex);
    instance.setQuantityMark(value);
}

Source File: MovieDataConfigurer.java From jstarcraft-example with Apache License 2.0

5 votes

/**
 * 装配数据模型
 * 
 * @param movieDataSpace
 * @return
 */
@Bean("movieDataModule")
DataModule getMovieDataModule(DataSpace movieDataSpace, List<MovieUser> movieUsers, List<MovieItem> movieItems) throws Exception {
    TreeMap<Integer, String> configuration = new TreeMap<>();
    configuration.put(1, "user");
    configuration.put(2, "item");
    configuration.put(3, "score");
    configuration.put(4, "instant");
    DataModule dataModule = movieDataSpace.makeDenseModule("score", configuration, 1000000);

    File file = new File("data/ml-100k/u.data");
    CSVFormat format = CSVFormat.DEFAULT.withDelimiter('\t');
    DataConverter<InputStream> convertor = new CsvConverter(format, movieDataSpace.getQualityAttributes(), movieDataSpace.getQuantityAttributes());
    try (InputStream stream = new FileInputStream(file)) {
        convertor.convert(dataModule, stream);
    }

    int userDimension = dataModule.getQualityInner("user");
    int itemDimension = dataModule.getQualityInner("item");
    int scoreDimension = dataModule.getQuantityInner("score");
    for (DataInstance instance : dataModule) {
        int userIndex = instance.getQualityFeature(userDimension);
        int itemIndex = instance.getQualityFeature(itemDimension);
        instance.setQuantityMark(instance.getQuantityFeature(scoreDimension));
        movieUsers.get(userIndex).click(itemIndex);
    }

    return dataModule;
}

Source File: LDAModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    DefaultScalar scalar = DefaultScalar.getInstance();
    DenseVector userVector = userTopicProbabilities.getRowVector(userIndex);
    DenseVector itemVector = topicItemProbabilities.getColumnVector(itemIndex);
    instance.setQuantityMark(scalar.dotProduct(userVector, itemVector).getValue());
}

Source File: DeepCrossModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float value = outputData.getValue(userIndex, itemIndex);
    instance.setQuantityMark(value);
}

Source File: RankVFCDModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    DefaultScalar scalar = DefaultScalar.getInstance();
    float score = 0F;
    if (scoreMatrix.getColumnVector(itemIndex).getElementSize() == 0) {
        score = scalar.dotProduct(userFactors.getRowVector(userIndex), factorMatrix.getColumnVector(itemIndex)).getValue();
    } else {
        score = scalar.dotProduct(userFactors.getRowVector(userIndex), explicitItemFactors.getRowVector(itemIndex)).getValue();
    }
    instance.setQuantityMark(score);
}

Source File: SLIMModel.java From jstarcraft-rns with Apache License 2.0

5 votes

/**
 * predict a specific ranking score for user userIdx on item itemIdx.
 *
 * @param userIndex user index
 * @param itemIndex item index
 * @return predictive ranking score for user userIdx on item itemIdx
 * @throws ModelException if error occurs
 */
@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    int[] neighbors = itemNeighbors[itemIndex];
    if (neighbors == null) {
        instance.setQuantityMark(0F);
        return;
    }
    instance.setQuantityMark(predict(userIndex, itemIndex, neighbors, -1));
}

Source File: HMMModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float score = 0F;
    for (int state = 0; state < numberOfStates; state++) {
        score += Math.exp(norms.getValue(userIndex, state) - viewProbabilities.getValue(state, itemIndex));
    }
    score = 1F - score;
    instance.setQuantityMark(score);
}

Source File: AspectModelRankingModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float value = 0F;
    for (int topicIndex = 0; topicIndex < factorSize; topicIndex++) {
        value += userProbabilities.getValue(topicIndex, userIndex) * itemProbabilities.getValue(topicIndex, itemIndex) * topicProbabilities.getValue(topicIndex);
    }
    instance.setQuantityMark(value);
}

Source File: TopicMFMTModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(predict(userIndex, itemIndex));
}

Source File: ZeroRuleRegressor.java From jstarcraft-ai with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance, DataInstance... contexts) {
    instance.setQuantityMark(quantity);
}

Source File: WBPRModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(predict(userIndex, itemIndex));
}

Source File: ASVDPlusPlusModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(predict(userIndex, itemIndex));
}

Source File: UserAverageModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    instance.setQuantityMark(userMeans[userIndex]);
}

Source File: CDAEModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(outputData.getValue(userIndex, itemIndex));
}

Source File: VBPRModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(predict(userIndex, itemIndex, featureVector.getValue(itemIndex), factorMatrix.getColumnVector(itemIndex)));
}

Source File: TopicMFATModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(predict(userIndex, itemIndex));
}

Java Code Examples for com.jstarcraft.ai.data.DataInstance#setQuantityMark()