Example usage for org.apache.commons.math3.linear ArrayRealVector mapDivideToSelf

Introduction

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

Prototype

@Override
public RealVector mapDivideToSelf(double d) 

Source Link

Usage

From source file:info.rmarcus.birkhoffvonneumann.MatrixUtils.java

public static double[] normalize(double[] input) {
    ArrayRealVector arv = new ArrayRealVector(input);
    arv.mapDivideToSelf(arv.getNorm());
    return NullUtils.orThrow(arv.toArray(), () -> new BVNRuntimeException("Could not normalize array!"));
}

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From source file:edu.utexas.cs.tactex.subscriptionspredictors.LWRCustOldAppache.java

private ArrayRealVector createNormalizedXVector(Set<Double> xValues, double min, double max) {
    Double[] dummy1 = new Double[1]; // needed to determine the type of toArray?     
    ArrayRealVector xVector = new ArrayRealVector(xValues.toArray(dummy1));
    xVector.mapSubtractToSelf(min);//from   w w w .j  a va  2  s .  c  o m
    xVector.mapDivideToSelf(max - min);
    // translating [0,1]=>[0.1,0.9]
    xVector.mapMultiplyToSelf(SQUEEZE);
    xVector.mapAddToSelf(OFFSET);
    return xVector;
}

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From source file:edu.utexas.cs.tactex.servercustomers.factoredcustomer.DefaultCapacityBundle.java

/**
 * sum all originator's (scaled) energies (since I believe the broker sees the
 * sum of them only). /*from w w w  . j a va2  s  .  c  om*/
 * @param currentTimeslot 
 * @throws Exception 
 * @throws DimensionMismatchException 
 */
@Override
public ArrayRealVector getPredictedEnergy(TariffSubscription subscription, int recordLength,
        int currentTimeslot) throws DimensionMismatchException, Exception {
    ArrayRealVector result = new ArrayRealVector(recordLength);

    // sum all originator's energies 
    for (CapacityOriginator originator : capacityOriginators) {
        ArrayRealVector originatorPredictedEnergy = originator.getPredictedEnergy(subscription, recordLength,
                currentTimeslot);
        //log.info("originatorPredictedEnergy " + Arrays.toString(originatorPredictedEnergy.toArray()));
        result = result.add(originatorPredictedEnergy);
        //log.info("bundleresult " + Arrays.toString(result.toArray()));
    }

    // normalize to 1 population member
    result.mapDivideToSelf(customerInfo.getPopulation());
    //log.info("bundleresultnormalized " + Arrays.toString(result.toArray()));

    // all predictions are positive and are adjusted in the server in
    // DefaultUtilityOptimizer.usePower() therefore we adjust the sign here,
    // the last point before returning to our shifting predictor
    double usageSign = getPowerType().isConsumption() ? +1 : -1;
    result.mapMultiplyToSelf(usageSign);

    return result;
}

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From source file:automenta.vivisect.dimensionalize.HyperassociativeMap.java

public void align() {
    // refresh all nodes
    /*/*  ww  w.  jav a2s  .  c om*/
    if (!coordinates.keySet().equals(graph.vertexSet())) {
    final Map<N, ArrayRealVector> newCoordinates = new HashMap<N, ArrayRealVector>();
    for (final N node : graph.vertexSet()) {
        if (coordinates.containsKey(node)) {
            newCoordinates.put(node, coordinates.get(node));
        } else {
            newCoordinates.put(node, randomCoordinates(dimensions));
        }
    }
    coordinates = Collections.synchronizedMap(newCoordinates);
    }
    */

    totalMovement = DEFAULT_TOTAL_MOVEMENT;
    maxMovement = DEFAULT_MAX_MOVEMENT;
    ArrayRealVector center;
    //if (threadExecutor == null) {
    center = processLocally();
    /*} else {
    // align all nodes in parallel
    final List<Future<ArrayRealVector>> futures = submitFutureAligns();
            
     // wait for all nodes to finish aligning and calculate new sum of
    // all the points
    try {
        center = waitAndProcessFutures(futures);
    } catch (InterruptedException caught) {
        //LOGGER.warn("waitAndProcessFutures was unexpectedly interrupted", caught);
        throw new RuntimeException("Unexpected interruption. Get should block indefinitely", caught);
    }
    }*/

    //LOGGER.debug("maxMove: " + maxMovement + ", Average Move: " + getAverageMovement());

    //TODO use normalize max speed parameter, to control the speed it does this so it's not discontinuous and jumpy
    if (normalize()) {

        // divide each coordinate of the sum of all the points by the number of
        // nodes in order to calculate the average point, or center of all the
        // points
        center.mapDivideToSelf(vertices.length);

        recenterNodes(center);
    }
}

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