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

Introduction

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

Prototype

public abstract int getDimension();

Source Link

Document

Returns the size of the vector.

Usage

From source file:org.grouplens.samantha.modeler.knn.KnnModelFeatureTrigger.java

private void getNeighbors(Object2DoubleMap<String> item2score, FeatureKnnModel knnModel, String key,
        double weight) {
    if (knnModel.hasKey(key)) {
        RealVector sims = knnModel.getKeyVector(key);
        for (int i = 0; i < sims.getDimension(); i += 2) {
            int idx = (int) sims.getEntry(i);
            double sim = sims.getEntry(i + 1);
            String recItem = knnModel.getKeyByIndex(idx);
            double oldVal = item2score.getOrDefault(recItem, 0.0);
            item2score.put(recItem, weight * sim + oldVal);
        }/*from w w w  .  java2  s. co m*/
    }
}

From source file:org.grouplens.samantha.modeler.knn.KnnModelFeatureTrigger.java

private void getNeighbors(ObjectSet<String> items, FeatureKnnModel knnModel, String key) {
    if (knnModel.hasKey(key)) {
        RealVector sims = knnModel.getKeyVector(key);
        for (int i = 0; i < sims.getDimension(); i += 2) {
            int idx = (int) sims.getEntry(i);
            String recItem = knnModel.getKeyByIndex(idx);
            items.add(recItem);//from  www . ja  va  2  s  .c o  m
        }
    }
}

From source file:org.grouplens.samantha.modeler.reinforce.LinearUCB.java

public double predict(LearningInstance instance) {
    RealMatrix A = variableSpace.getMatrixVarByName(LinearUCBKey.A.get());
    RealVector B = variableSpace.getScalarVarByName(LinearUCBKey.B.get());
    RealMatrix invA = new LUDecomposition(A).getSolver().getInverse();
    RealVector theta = invA.operate(B);
    RealVector x = extractDenseVector(theta.getDimension(), instance);
    double bound = Math.sqrt(x.dotProduct(invA.operate(x)));
    double mean = x.dotProduct(theta);
    double pred = mean + alpha * bound;
    if (Double.isNaN(pred)) {
        logger.error("Prediction is NaN, model parameter A probably goes wrong.");
        pred = 0.0;//from w  w w.  j  ava2s.c o m
    }
    return pred;
}

From source file:org.grouplens.samantha.modeler.solver.RandomInitializer.java

public void randInitVector(RealVector vec, boolean normalize) {
    int len = vec.getDimension();
    double sum = 0.0;
    for (int i = 0; i < len; i++) {
        double val;
        if (normalize) {
            val = rand.nextDouble();
        } else {//w  w w  .  j  a va2 s. co  m
            val = (rand.nextDouble() - subtract) * multi;
        }
        vec.setEntry(i, val);
        if (normalize) {
            sum += val;
        }
    }
    if (normalize) {
        vec.mapDivideToSelf(sum);
    }
}

From source file:org.grouplens.samantha.modeler.svdfeature.SVDFeature.java

private String realVectorToString(RealVector vec) {
    String[] arr = new String[vec.getDimension()];
    for (int i = 0; i < vec.getDimension(); i++) {
        arr[i] = Double.valueOf(vec.getEntry(i)).toString();
    }//from   w  ww. j a  v  a 2s .  com
    return StringUtils.join(arr, "\t");
}

From source file:org.grouplens.samantha.modeler.svdfeature.SVDFeature.java

public void dump(File modelFile) {
    try {//from   w  ww .j  av a 2 s  . co m
        BufferedWriter writer = new BufferedWriter(new FileWriter(modelFile));
        RealVector biases = variableSpace.getScalarVarByName(SVDFeatureKey.BIASES.get());
        writer.write(Double.valueOf(biases.getEntry(0)).toString() + "\n");
        String biasLine = realVectorToString(biases.getSubVector(1, biases.getDimension() - 1));
        writer.write(biasLine + "\n");
        List<RealVector> factors = variableSpace.getVectorVarByName(SVDFeatureKey.FACTORS.get());
        for (int i = 0; i < factors.size(); i++) {
            String factLine = realVectorToString(factors.get(i));
            writer.write(factLine + "\n");
        }
        writer.close();
    } catch (IOException e) {
        logger.error(e.getMessage());
        throw new BadRequestException(e);
    }
}

From source file:org.grouplens.samantha.server.space.RedisVariableSpace.java

public void setScalarVarByName(String name, RealVector vars) {
    for (int i = 0; i < vars.getDimension(); i++) {
        setScalarVarByNameIndex(name, i, vars.getEntry(i));
    }/*from  ww w. jav a2 s .  c o m*/
}

From source file:org.grouplens.samantha.server.space.RedisVariableSpace.java

public void setVectorVarByNameIndex(String name, int index, RealVector var) {
    String varIdxName = "IDX_V_" + name + "_" + Integer.valueOf(index).toString();
    ArrayNode values = Json.newArray();/*from  w ww .j ava  2  s  .c o m*/
    values.add(index);
    for (int i = 0; i < var.getDimension(); i++) {
        values.add(var.getEntry(i));
    }
    redisService.setValue(spaceIdentifier, varIdxName, values);
}

From source file:org.knime.al.util.noveltydetection.knfst.MatrixFunctions.java

public static RealVector sqrt(final RealVector vector) {
    final RealVector result = vector.copy();
    for (int e = 0; e < result.getDimension(); e++) {
        result.setEntry(e, Math.sqrt(result.getEntry(e)));
    }//from  www . j  a  v  a2  s.co m
    return result;
}

From source file:org.knime.knip.core.algorithm.convolvers.KernelTools.java

/**
 * Creates a numDims dimensions image where all dimensions d<numDims are of size 1 and the last dimensions contains
 * the vector./* w ww.  j av a  2 s .c  om*/
 * 
 * @param <R>
 * @param ar
 * @param type
 * @param numDims number of dimensions
 * @return
 */
public static <R extends RealType<R>> Img<R> vectorToImage(final RealVector ar, final R type, final int numDims,
        final ImgFactory<R> fac) {
    long[] dims = new long[numDims];

    for (int i = 0; i < dims.length - 1; i++) {
        dims[i] = 1;
    }
    dims[dims.length - 1] = ar.getDimension();
    Img<R> res = fac.create(dims, type);
    Cursor<R> c = res.cursor();
    while (c.hasNext()) {
        c.fwd();
        c.get().setReal(ar.getEntry(c.getIntPosition(numDims - 1)));
    }

    return res;
}