Example usage for org.apache.commons.math3.linear RealVector getL1Norm

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear RealVector getL1Norm.

Prototype

public double getL1Norm() 

Source Link

Document

Returns the L₁ norm of the vector.

Usage

From source file:org.lenskit.mf.funksvd.FunkSVDModelProvider.java

@Override
public FunkSVDModel get() {
    int userCount = snapshot.getUserIds().size();
    RealMatrix userFeatures = MatrixUtils.createRealMatrix(userCount, featureCount);

    int itemCount = snapshot.getItemIds().size();
    RealMatrix itemFeatures = MatrixUtils.createRealMatrix(itemCount, featureCount);

    logger.debug("Learning rate is {}", rule.getLearningRate());
    logger.debug("Regularization term is {}", rule.getTrainingRegularization());

    logger.info("Building SVD with {} features for {} ratings", featureCount, snapshot.getRatings().size());

    TrainingEstimator estimates = rule.makeEstimator(snapshot);

    List<FeatureInfo> featureInfo = new ArrayList<>(featureCount);

    // Use scratch vectors for each feature for better cache locality
    // Per-feature vectors are strided in the output matrices
    RealVector uvec = MatrixUtils.createRealVector(new double[userCount]);
    RealVector ivec = MatrixUtils.createRealVector(new double[itemCount]);

    for (int f = 0; f < featureCount; f++) {
        logger.debug("Training feature {}", f);
        StopWatch timer = new StopWatch();
        timer.start();//from  ww  w  .  j ava2s .c  o m

        uvec.set(initialValue);
        ivec.set(initialValue);

        FeatureInfo.Builder fib = new FeatureInfo.Builder(f);
        double rmse = trainFeature(f, estimates, uvec, ivec, fib);
        summarizeFeature(uvec, ivec, fib);
        featureInfo.add(fib.build());

        // Update each rating's cached value to accommodate the feature values.
        estimates.update(uvec, ivec);

        // And store the data into the matrix
        userFeatures.setColumnVector(f, uvec);
        assert Math.abs(userFeatures.getColumnVector(f).getL1Norm()
                - uvec.getL1Norm()) < 1.0e-4 : "user column sum matches";
        itemFeatures.setColumnVector(f, ivec);
        assert Math.abs(itemFeatures.getColumnVector(f).getL1Norm()
                - ivec.getL1Norm()) < 1.0e-4 : "item column sum matches";

        timer.stop();
        logger.info("Finished feature {} in {} (RMSE={})", f, timer, rmse);
    }

    // Wrap the user/item matrices because we won't use or modify them again
    return new FunkSVDModel(userFeatures, itemFeatures, snapshot.userIndex(), snapshot.itemIndex(),
            featureInfo);
}

---

From source file:trustframework.utils.Similarity.java

/**
 * Build a real vector from frequencies//from   w ww .  ja v a2s.  c o m
 * @param <T> Type of hask keys
 * @param freq hash with frequencies
 * @param keys set of all keys available
 * @return 
 */
private static <T> RealVector buildRealVector(Map<T, Integer> freq, Set<T> keys) {
    RealVector vector = new ArrayRealVector(keys.size());
    int i = 0;
    for (T term : keys) {
        int value = freq.containsKey(term) ? freq.get(term) : 0;
        vector.setEntry(i++, value);
    }
    vector = vector.mapDivide(vector.getL1Norm());
    return vector;
}

---

• «
• 1
• 2
• »