Example usage for com.google.common.primitives Doubles max

Introduction

In this page you can find the example usage for com.google.common.primitives Doubles max.

Prototype

public static double max(double... array) 

Source Link

Document

Returns the greatest value present in array , using the same rules of comparison as Math#max(double,double) .

Usage

From source file:br.usp.lti.cdds.metaheuristics.RoulleteSelection.java

public static Solution select(ArrayList<Solution> solutionSet) {
    double[] prob = new double[solutionSet.size()];
    for (int i = 0; i < prob.length; i++) {
        prob[i] = solutionSet.get(i).getFitness();
    }//ww w  . j a  v  a 2  s . c o m
    double min = Doubles.min(prob);
    double max = Doubles.max(prob);
    double sum = 0;
    for (int i = 0; i < prob.length; i++) {
        prob[i] = (max - prob[i]) / (max - min);
        sum += prob[i];
    }

    for (int i = 0; i < prob.length; i++) {
        prob[i] = prob[i] / sum;
    }

    //prepare roullete
    int[] roullete = new int[100];
    int roulletepos = 0;

    for (int i = 0; i < prob.length; i++) {
        double roulletepart = Math.round(prob[i] * 100.0);
        for (int k = 0; k < roulletepart && roulletepos < roullete.length; k++) {
            roullete[roulletepos++] = i;
        }
    }

    int truepos;
    //do{
    Random rdn = new Random();
    int selected = rdn.nextInt(roullete.length);
    truepos = roullete[selected];
    //}while(truepos==previousSelected);

    return solutionSet.get(truepos);
}

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From source file:ummisco.gama.opengl.scene.FieldDrawer.java

@Override
protected void _draw(final FieldObject demObj) {
    try {/*from  w ww .  ja v  a 2s. c om*/
        gl.pushMatrix();
        if (demObj.getObject() == null) {
            drawFromImage(demObj);
            return;
        }
        final double altFactor = MoreObjects.firstNonNull(demObj.getAttributes().getHeight(), 1.0);
        final double maxZ = Doubles.max(demObj.getObject());
        if (demObj.getAttributes().grayScaled) {
            gl.disableTextures();
        }
        if (demObj.getAttributes().triangulated) {
            drawAsTriangles(demObj, altFactor, maxZ);
        } else {
            drawAsRectangles(demObj, altFactor, maxZ);
        }
        if (demObj.getAttributes().withText && demObj.getObject() != null) {
            drawLabels(demObj, altFactor);
        }
    } finally {
        gl.popMatrix();
    }
}

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From source file:inflor.core.transforms.LogrithmicTransform.java

public void optimize(double[] data) {
    min = Doubles.min(data);
    max = Doubles.max(data);
}

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From source file:com.github.rinde.rinsim.scenario.measure.LoadRequirement.java

@Override
public boolean apply(@Nullable Scenario scenario) {
    assert scenario != null;
    final List<Double> loads = newArrayList(
            relative ? Metrics.measureRelativeLoad(scenario) : Metrics.measureLoad(scenario));
    final int toAdd = desiredLoadList.size() - loads.size();
    for (int j = 0; j < toAdd; j++) {
        loads.add(0d);//from  w w  w.  j  a  v  a 2 s.  c  o  m
    }

    final double[] deviations = abs(subtract(Doubles.toArray(desiredLoadList), Doubles.toArray(loads)));
    final double mean = StatUtils.mean(deviations);
    final double max = Doubles.max(deviations);
    return max <= maxMax && mean <= maxMean;
}

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From source file:inflor.core.plots.CategoryResponseChart.java

public JFreeChart createChart(FCSFrame dataFrame) {

    CategoryXYZDataSet categoryData = new CategoryXYZDataSet();
    double zMin = Double.MAX_VALUE;
    double zMax = 1;
    if (dataFrame.getKeywords().containsKey(FCSUtilities.KEY_MERGE_MAP)) {
        String[] mergeMap = dataFrame.getKeywordValue(FCSUtilities.KEY_MERGE_MAP)
                .split(NodeUtilities.DELIMITER_REGEX);
        FCSDimension dim = dataFrame.getDimension(axisName);
        double[] transformedData = transform.transform(dim.getData());
        int perFileSize = dim.getData().length / mergeMap.length;
        for (int i = 0; i < mergeMap.length; i++) {

            double[] unMergedData = new double[perFileSize];
            for (int j = 0; j < unMergedData.length; j++) {
                unMergedData[j] = transformedData[i * unMergedData.length + j];
            }/*from   w w  w  .j a  v  a  2 s .com*/
            double tMin = transform.getMinTranformedValue();
            double tMax = transform.getMaxTransformedValue();
            Histogram1D hist = new Histogram1D(unMergedData, tMin, tMax, ChartingDefaults.BIN_COUNT);
            double[] x = hist.getNonZeroX();
            double[] y = new double[x.length];
            for (int j = 0; j < y.length; j++) {
                y[j] = i;
            }
            double[] z = hist.getNonZeroY();
            double currentZMin = Doubles.min(z);
            double currentZMax = Doubles.max(z);
            if (currentZMin < zMin) {
                zMin = currentZMin;
            } else if (currentZMax > zMax) {
                zMax = currentZMax;
            }

            categoryData.addCategoricalSeries(mergeMap[i], x, z);
        }
    } else {
        FCSDimension dim = dataFrame.getDimension(axisName);
        double[] transformedData = transform.transform(dim.getData());
        Histogram1D hist = new Histogram1D(transformedData, transform.getMinTranformedValue(),
                transform.getMaxTransformedValue(), ChartingDefaults.BIN_COUNT);
        double[] x = hist.getNonZeroX();
        double[] y = new double[x.length];
        for (int j = 0; j < y.length; j++) {
            y[j] = 0;
        }
        double[] z = hist.getNonZeroY();
        double currentZMin = Doubles.min(z);
        double currentZMax = Doubles.max(z);
        if (currentZMin < zMin) {
            zMin = currentZMin;
        } else if (currentZMax > zMax) {
            zMax = currentZMax;
        }

        categoryData.addCategoricalSeries(dataFrame.getDisplayName(), x, z);
    }

    ValueAxis domainAxis = PlotUtils.createAxis(axisName, transform);
    NumberAxis rangeAxis = new CategoricalNumberAxis("", categoryData.getLabelMap());
    // Renderer configuration
    XYBlockRenderer renderer = new XYBlockRenderer();
    double xWidth = (transform.getMaxTransformedValue() - transform.getMinTranformedValue())
            / ChartingDefaults.BIN_COUNT;
    renderer.setBlockWidth(xWidth);
    renderer.setBlockHeight(0.5);
    renderer.setBlockAnchor(RectangleAnchor.LEFT);

    PaintScale paintScale = PlotUtils.createPaintScale(zMax, ChartingDefaults.DEFAULT_COLOR_SCHEME);
    renderer.setPaintScale(paintScale);

    // Add to panel.
    XYPlot responsePlot = new XYPlot(categoryData, domainAxis, rangeAxis, renderer);
    JFreeChart chart = new JFreeChart(responsePlot);
    chart.removeLegend();
    return chart;
}

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From source file:com.davidbracewell.math.linear.DenseVector.java

@Override
public double max() {
    return Doubles.max(map);
}

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From source file:com.davidbracewell.math.linear.SparseVector.java

@Override
public double max() {
    return Doubles.max(map.values().elements());
}

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From source file:controller.ClassificationController.java

public static void classify(String classifier, FeatureModel featureModel) {

    //scaling of features
    float[][] features_scaled = Util.deepcopy(featureModel.getFeatures());
    double[][] scaling = (double[][]) Util.load(classifier.replace("[model]", "[scaling]"));

    double[] mean = scaling[0];
    double[] std = scaling[1];
    for (float[] feature : features_scaled) {
        for (int j = 0; j < feature.length; j++) {
            feature[j] = (float) ((feature[j] - mean[j]) / std[j]);
        }// w ww.j  a v  a 2s .  co  m
    }

    //load classifier 1 for arousals
    ClassificationController svmArousal = new ClassificationController();
    svmArousal.svmLoadModel(classifier.replace("[model]", "[arousal]"));

    //load classifier 2 for sleep stages
    ClassificationController svm = new ClassificationController();
    svm.svmLoadModel(classifier);

    //load indices indicating the features of the feature vector of classifier 2
    String dir = System.getProperty("user.dir");
    String filename = "sparse_features.jo";
    String fullPath = dir + File.separator + "Classifiers" + File.separator + filename;
    double[] fidx = (double[]) Util.load(fullPath);

    float[] kcPercentage = featureModel.getKcPercentage();

    double[] output;

    int N1flag = 0;
    int Wflag = 0;

    for (int i = 0; i < featureModel.getNumberOfEpochs(); i++) {
        //get unscaled features
        float[] features_unscaled = featureModel.getFeatureVector(i);

        //use only subset of feature vector as stored in sparse_features.jo array
        float[] f1 = new float[fidx.length];
        for (int j = 0; j < f1.length; j++) {
            //use scaled features here
            f1[j] = features_scaled[i][(int) (fidx[j] - 1)];
        }

        output = svm.svmPredict(Util.floatToDouble(f1));
        int classLabel = Doubles.indexOf(output, Doubles.max(output));

        double W = features_unscaled[18];
        double N2 = features_unscaled[1]; //(skewness)
        double N3 = features_unscaled[49]; //cepstrum
        double MA1 = features_unscaled[3]; //max value of high passed epoch
        double MA2 = features_unscaled[9]; //energy in HF wavelet band
        double KC = kcPercentage[i];

        //hand selected features for arousals
        boolean MA3 = MA1 > 40 & MA2 > 0.4;

        double[] MAtmp = svmArousal.svmPredict(Util.floatToDouble(features_scaled[i]));
        boolean MA = (MAtmp[1] > MAtmp[0]) | MA3;

        //convert SVM labels to SleepPilot labels (4 is REM)
        if (classLabel == 4) {
            classLabel = 5;
        }

        if (classLabel == 5 & MA) {
            classLabel = 1;
        }

        if (classLabel == 2 & KC == 0 & MA) {
            classLabel = 1;
        }

        //adjust classifier output to be consistent with SleepPilots K-complex detector
        if (classLabel == 3 & KC <= 20) {
            classLabel = 2;
        }
        if (classLabel == 2 & KC > 20 & !MA) {
            classLabel = 3;
        }

        //set artefacts and arousals to zero (standard)
        featureModel.setArtefact(i, 0);
        featureModel.setArousal(i, 0);

        if (classLabel != 0 & MA) {
            featureModel.setArtefact(i, 1);
            featureModel.setArousal(i, 1);
        }
        if (classLabel != 0 & MA1 > 80) {
            featureModel.setArtefact(i, 1);
        }

        //REM is always after N3, at least in non-pathological cases
        if (classLabel == 3) {
            N1flag += 1;
        }

        if (classLabel == 5 & N1flag < 2) {
            classLabel = 1;
        }

        featureModel.setPredictProbabilities(i, output.clone());
        featureModel.setLabel(i, classLabel);
        System.out.println("Predicted Class Label: " + classLabel);

    }

    featureModel.setClassificationDone(true);
}

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From source file:edu.cmu.lti.oaqa.baseqa.answer.collective_score.scorers.EditDistanceCollectiveAnswerScorer.java

@Override
public Map<String, Double> score(JCas jcas, Answer answer) {
    Map<Answer, Double> neighbor2distance = distances.row(answer);
    ImmutableMap.Builder<String, Double> builder = ImmutableMap.builder();
    for (int topLimit : topLimits) {
        double[] distances = answers.subList(0, Math.min(answers.size(), topLimit)).stream()
                .mapToDouble(neighbor -> neighbor2distance.getOrDefault(neighbor, 0.0)).toArray();
        builder.put("edit-distance-min-" + topLimit, Doubles.min(distances));
        builder.put("edit-distance-max-" + topLimit, Doubles.max(distances));
        builder.put("edit-distance-avg-" + topLimit, DoubleStream.of(distances).average().orElse(0.0));
    }/*from  w ww. ja  va2 s  .co m*/
    return builder.build();
}

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From source file:com.mgmtp.perfload.perfalyzer.binning.PerfMonBinningStrategy.java

private void writeAggregatedLine(final WritableByteChannel destChannel) throws IOException {
    double[] allValues = binManager.flatValuesStream().toArray();

    StrBuilder sb = new StrBuilder();

    String min = intNumberFormat.format(Doubles.min(allValues));
    String max = intNumberFormat.format(Doubles.max(allValues));

    switch (typeConfig) {
    case CPU://w ww  .  j  ava2 s .c  o  m
    case IO:
    case JAVA:
        String mean = intNumberFormat.format(StatUtils.mean(allValues));
        appendEscapedAndQuoted(sb, DELIMITER, min, mean, max);
        break;
    case MEM:
    case SWAP:
        Percentile percentile = new Percentile();
        percentile.setData(allValues);
        String q10 = intNumberFormat.format(percentile.evaluate(10d));
        String q50 = intNumberFormat.format(percentile.evaluate(50d));
        String q90 = intNumberFormat.format(percentile.evaluate(90d));
        appendEscapedAndQuoted(sb, DELIMITER, min, q10, q50, q90, max);
        break;
    default:
        throw new IllegalStateException("Invalid perfMon data type");
    }

    writeLineToChannel(destChannel, sb.toString(), Charsets.UTF_8);
}

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