@Override public void reset() { sum = new DoubleAdder(); } }
if (arg1 instanceof Double) { Adder<Double> adder = new DoubleAdder(); }
/** * Instantiates a new Local double accumulator. * * @param value the initial value of the accumulator * @param name the name of the accumulator */ public LocalMDoubleAccumulator(double value, String name) { super(name); this.value = new DoubleAdder(); this.value.add(value); }
DoubleAdder adder = new DoubleAdder(); stream.forEach(e -> adder.add(e.getKey().getExitValue() * e.getValue())); System.out.println(adder.sum());
/** * Will calculate and return the dot product of this 1D-structure and another input 1D-vector. * * @param vector Another 1D-structure * @return The dot product */ default double dot(final Access1D<?> vector) { final DoubleAdder retVal = new DoubleAdder(); Structure1D.loopMatching(this, vector, i -> retVal.add(this.doubleValue(i) * vector.doubleValue(i))); return retVal.doubleValue(); }
/** * Will calculate and return the dot product of this 1D-structure and another input 1D-vector. * * @param vector Another 1D-structure * @return The dot product */ default double dot(final Access1D<?> vector) { final DoubleAdder retVal = new DoubleAdder(); Structure1D.loopMatching(this, vector, i -> retVal.add(this.doubleValue(i) * vector.doubleValue(i))); return retVal.doubleValue(); }
Object2DoubleMap<F> auxMap = new Object2DoubleOpenHashMap<>(); auxMap.defaultReturnValue(0.0); DoubleAdder norm1 = new DoubleAdder(); DoubleAdder prod = new DoubleAdder(); DoubleAdder norm2 = new DoubleAdder(); features2.forEach(fv -> { prod.add(fv.v2 * auxMap.getDouble(fv.v1));
/** * Returns a score for the recommendation list. * * @param recommendation recommendation list * @return score of the metric to the recommendation */ @Override public double evaluate(Recommendation<U, I> recommendation) { RelevanceModel.UserRelevanceModel<U, I> userRelModel = relModel.getModel(recommendation.getUser()); UserIntentModel<U, I, F> uim = intentModel.getModel(recommendation.getUser()); DoubleAdder erria = new DoubleAdder(); Object2DoubleMap<F> pNoPrevRel = new Object2DoubleOpenHashMap<>(); pNoPrevRel.defaultReturnValue(0.0); uim.getIntents().forEach(f -> pNoPrevRel.put(f, 1.0)); AtomicInteger rank = new AtomicInteger(); recommendation.getItems().stream().limit(cutoff).forEach(iv -> { if (userRelModel.isRelevant(iv.v1)) { double gain = userRelModel.gain(iv.v1); uim.getItemIntents(iv.v1).forEach(f -> { double red = pNoPrevRel.getDouble(f); erria.add(uim.pf_u(f) * gain * red / (1.0 + rank.intValue())); pNoPrevRel.put(f, red * (1 - gain)); }); } rank.incrementAndGet(); }); return erria.doubleValue(); }