Example usage for java.lang Double toString

Introduction

In this page you can find the example usage for java.lang Double toString.

Prototype

public String toString() 

Source Link

Document

Returns a string representation of this Double object.

Usage

From source file:com.nridge.core.base.std.XMLUtl.java

public static void setAttrDoubleValue(Element anElement, String aName, double aValue) {
    Double doubleObject;

    if (StringUtils.isNotEmpty(aName)) {
        doubleObject = aValue;//  ww w.j av  a2s  .c  o  m
        anElement.setAttribute(aName, doubleObject.toString());
    }
}

---

From source file:aula1.Aula1.java

public static void calcula(double[] x, String entrada) {
    String[] y = new String[x.length];
    String[] lx = new String[x.length];
    int i = 0;/*from   w w  w  .ja v a 2  s  . c  o m*/
    for (Double c : x) {
        lx[i] = c.toString();
        i++;
    }
    for (i = 0; i < x.length; i++) {
        try {
            y[i] = conversor(entrada, lx[i]);
        } catch (Exception ex) {
            System.out.println("Erro: " + ex.getMessage());
        }
        System.out.println("F(" + x[i] + ") = " + y[i]);
    }
    double[] ly = new double[y.length];
    i = 0;
    for (String c : y) {
        ly[i] = Double.parseDouble(c);
        i++;
    }
    geraGrafico(x, ly);
}

---

From source file:org.dkpro.similarity.experiments.sts2013baseline.util.Evaluator.java

public static void runEvaluationMetric(Mode mode, EvaluationMetric metric, Dataset... datasets)
        throws IOException {
    StringBuilder sb = new StringBuilder();

    // Compute Pearson correlation for the specified datasets
    for (Dataset dataset : datasets) {
        computePearsonCorrelation(mode, dataset);
    }//from w  w  w .  ja  v  a  2  s.  c  o m

    if (metric == PearsonAll) {
        List<Double> concatExp = new ArrayList<Double>();
        List<Double> concatGS = new ArrayList<Double>();

        // Concat the scores
        for (Dataset dataset : datasets) {
            File expScoresFile = new File(
                    OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv");

            List<String> lines = FileUtils.readLines(expScoresFile);

            for (String line : lines) {
                concatExp.add(Double.parseDouble(line));
            }
        }

        // Concat the gold standard
        for (Dataset dataset : datasets) {
            String gsScoresFilePath = GOLDSTANDARD_DIR + "/" + mode.toString().toLowerCase() + "/" + "STS.gs."
                    + dataset.toString() + ".txt";

            PathMatchingResourcePatternResolver r = new PathMatchingResourcePatternResolver();
            Resource res = r.getResource(gsScoresFilePath);
            File gsScoresFile = res.getFile();

            List<String> lines = FileUtils.readLines(gsScoresFile);

            for (String line : lines) {
                concatGS.add(Double.parseDouble(line));
            }
        }

        double[] concatExpArray = ArrayUtils.toPrimitive(concatExp.toArray(new Double[concatExp.size()]));
        double[] concatGSArray = ArrayUtils.toPrimitive(concatGS.toArray(new Double[concatGS.size()]));

        PearsonsCorrelation pearson = new PearsonsCorrelation();
        Double correl = pearson.correlation(concatExpArray, concatGSArray);

        sb.append(correl.toString());
    } else if (metric == PearsonMean) {
        List<Double> scores = new ArrayList<Double>();

        for (Dataset dataset : datasets) {
            File resultFile = new File(
                    OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".txt");
            double score = Double.parseDouble(FileUtils.readFileToString(resultFile));

            scores.add(score);
        }

        double mean = 0.0;
        for (Double score : scores) {
            mean += score;
        }
        mean = mean / scores.size();

        sb.append(mean);
    }

    FileUtils.writeStringToFile(
            new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + metric.toString() + ".txt"),
            sb.toString());
}

---

From source file:org.dkpro.similarity.experiments.sts2013baseline.util.Evaluator.java

public static void runLinearRegressionCV(Mode mode, Dataset... datasets) throws Exception {
    for (Dataset dataset : datasets) {
        // Set parameters
        int folds = 10;
        Classifier baseClassifier = new LinearRegression();

        // Set up the random number generator
        long seed = new Date().getTime();
        Random random = new Random(seed);

        // Add IDs to the instances
        AddID.main(new String[] { "-i",
                MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".arff", "-o",
                MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString()
                        + "-plusIDs.arff" });

        String location = MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString()
                + "-plusIDs.arff";

        Instances data = DataSource.read(location);

        if (data == null) {
            throw new IOException("Could not load data from: " + location);
        }//from  w  w w  .  j  av a2s  .  c om

        data.setClassIndex(data.numAttributes() - 1);

        // Instantiate the Remove filter
        Remove removeIDFilter = new Remove();
        removeIDFilter.setAttributeIndices("first");

        // Randomize the data
        data.randomize(random);

        // Perform cross-validation
        Instances predictedData = null;
        Evaluation eval = new Evaluation(data);

        for (int n = 0; n < folds; n++) {
            Instances train = data.trainCV(folds, n, random);
            Instances test = data.testCV(folds, n);

            // Apply log filter
            Filter logFilter = new LogFilter();
            logFilter.setInputFormat(train);
            train = Filter.useFilter(train, logFilter);
            logFilter.setInputFormat(test);
            test = Filter.useFilter(test, logFilter);

            // Copy the classifier
            Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);

            // Instantiate the FilteredClassifier
            FilteredClassifier filteredClassifier = new FilteredClassifier();
            filteredClassifier.setFilter(removeIDFilter);
            filteredClassifier.setClassifier(classifier);

            // Build the classifier
            filteredClassifier.buildClassifier(train);

            // Evaluate
            eval.evaluateModel(classifier, test);

            // Add predictions
            AddClassification filter = new AddClassification();
            filter.setClassifier(classifier);
            filter.setOutputClassification(true);
            filter.setOutputDistribution(false);
            filter.setOutputErrorFlag(true);
            filter.setInputFormat(train);
            Filter.useFilter(train, filter); // trains the classifier

            Instances pred = Filter.useFilter(test, filter); // performs predictions on test set
            if (predictedData == null) {
                predictedData = new Instances(pred, 0);
            }
            for (int j = 0; j < pred.numInstances(); j++) {
                predictedData.add(pred.instance(j));
            }
        }

        // Prepare output scores
        double[] scores = new double[predictedData.numInstances()];

        for (Instance predInst : predictedData) {
            int id = new Double(predInst.value(predInst.attribute(0))).intValue() - 1;

            int valueIdx = predictedData.numAttributes() - 2;

            double value = predInst.value(predInst.attribute(valueIdx));

            scores[id] = value;

            // Limit to interval [0;5]
            if (scores[id] > 5.0) {
                scores[id] = 5.0;
            }
            if (scores[id] < 0.0) {
                scores[id] = 0.0;
            }
        }

        // Output
        StringBuilder sb = new StringBuilder();
        for (Double score : scores) {
            sb.append(score.toString() + LF);
        }

        FileUtils.writeStringToFile(
                new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv"),
                sb.toString());
    }
}

---

From source file:com.nridge.core.base.std.XMLUtl.java

public static void makeElemDoubleValue(Document aDocument, Element anElement, String aName, double aValue) {
    Element subElement;/* w  ww.j a  v  a 2 s .  c  o  m*/
    Double doubleObject;

    if (StringUtils.isNotEmpty(aName)) {
        doubleObject = aValue;
        subElement = aDocument.createElement(aName);
        subElement.appendChild(aDocument.createTextNode(doubleObject.toString()));
        anElement.appendChild(subElement);
    }
}

---

From source file:com.splicemachine.mrio.api.hive.SMSerDe.java

/**
 * Replace with Lazy eventually/*  www .  j  a va2  s .  com*/
 * 
 */
private static Object hiveTypeToObject(String hiveType, DataValueDescriptor dvd) throws SerDeException {
    final String lctype = trim(hiveType.toLowerCase());

    try {
        switch (lctype) {
        case "string":
        case "varchar":
            HiveVarchar hiveVarchar = null;
            String s = dvd.getString();
            if (s != null) {
                hiveVarchar = new HiveVarchar();
                hiveVarchar.setValue(s);
            }
            return hiveVarchar;
        case "char":
            HiveChar hiveChar = null;
            s = dvd.getString();
            if (s != null) {
                hiveChar = new HiveChar();
                hiveChar.setValue(s);
            }
            return hiveChar;
        case "float":
            return dvd.getFloat();
        case "double":
            return dvd.getDouble();
        case "decimal":
            Double d = dvd.getDouble();
            HiveDecimal hiveDecimal = HiveDecimal.create(d.toString());
            return hiveDecimal;
        case "boolean":
            return dvd.getBoolean();
        case "tinyint":
            return dvd.getByte();
        case "int":
            return dvd.getInt();
        case "smallint":
            return dvd.getShort();
        case "bigint":
            return dvd.getLong();
        case "timestamp":
            return dvd.getTimestamp(null);
        case "date":
            return dvd.getDate(null);
        case "binary":
            return dvd.getBytes();
        default:
            throw new SerDeException("Unrecognized column type: " + hiveType);
        }
    } catch (StandardException se) {
        throw new SerDeException(se);
    }
}

---

From source file:org.dkpro.similarity.experiments.sts2013.util.Evaluator.java

public static void runLinearRegressionCV(Mode mode, Dataset... datasets) throws Exception {
    for (Dataset dataset : datasets) {
        // Set parameters
        int folds = 10;
        Classifier baseClassifier = new LinearRegression();

        // Set up the random number generator
        long seed = new Date().getTime();
        Random random = new Random(seed);

        // Add IDs to the instances
        AddID.main(new String[] { "-i",
                MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".arff", "-o",
                MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString()
                        + "-plusIDs.arff" });
        Instances data = DataSource.read(
                MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + "-plusIDs.arff");
        data.setClassIndex(data.numAttributes() - 1);

        // Instantiate the Remove filter
        Remove removeIDFilter = new Remove();
        removeIDFilter.setAttributeIndices("first");

        // Randomize the data
        data.randomize(random);/*from w ww.  ja  va 2  s. c  o m*/

        // Perform cross-validation
        Instances predictedData = null;
        Evaluation eval = new Evaluation(data);

        for (int n = 0; n < folds; n++) {
            Instances train = data.trainCV(folds, n, random);
            Instances test = data.testCV(folds, n);

            // Apply log filter
            Filter logFilter = new LogFilter();
            logFilter.setInputFormat(train);
            train = Filter.useFilter(train, logFilter);
            logFilter.setInputFormat(test);
            test = Filter.useFilter(test, logFilter);

            // Copy the classifier
            Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);

            // Instantiate the FilteredClassifier
            FilteredClassifier filteredClassifier = new FilteredClassifier();
            filteredClassifier.setFilter(removeIDFilter);
            filteredClassifier.setClassifier(classifier);

            // Build the classifier
            filteredClassifier.buildClassifier(train);

            // Evaluate
            eval.evaluateModel(classifier, test);

            // Add predictions
            AddClassification filter = new AddClassification();
            filter.setClassifier(classifier);
            filter.setOutputClassification(true);
            filter.setOutputDistribution(false);
            filter.setOutputErrorFlag(true);
            filter.setInputFormat(train);
            Filter.useFilter(train, filter); // trains the classifier

            Instances pred = Filter.useFilter(test, filter); // performs predictions on test set
            if (predictedData == null) {
                predictedData = new Instances(pred, 0);
            }
            for (int j = 0; j < pred.numInstances(); j++) {
                predictedData.add(pred.instance(j));
            }
        }

        // Prepare output scores
        double[] scores = new double[predictedData.numInstances()];

        for (Instance predInst : predictedData) {
            int id = new Double(predInst.value(predInst.attribute(0))).intValue() - 1;

            int valueIdx = predictedData.numAttributes() - 2;

            double value = predInst.value(predInst.attribute(valueIdx));

            scores[id] = value;

            // Limit to interval [0;5]
            if (scores[id] > 5.0) {
                scores[id] = 5.0;
            }
            if (scores[id] < 0.0) {
                scores[id] = 0.0;
            }
        }

        // Output
        StringBuilder sb = new StringBuilder();
        for (Double score : scores) {
            sb.append(score.toString() + LF);
        }

        FileUtils.writeStringToFile(
                new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv"),
                sb.toString());
    }
}

---

From source file:cn.afterturn.easypoi.util.PoiPublicUtil.java

/**
 * double to String /*w  w w  . j a va  2s.  co m*/
 * @param value
 * @return
 */
public static String doubleToString(Double value) {
    String temp = value.toString();
    if (temp.contains("E")) {
        BigDecimal bigDecimal = new BigDecimal(temp);
        temp = bigDecimal.toPlainString();
    }
    return temp;
}

---

From source file:org.dkpro.similarity.experiments.sts2013.util.Evaluator.java

@SuppressWarnings("unchecked")
public static void runEvaluationMetric(Mode mode, EvaluationMetric metric, Dataset... datasets)
        throws IOException {
    StringBuilder sb = new StringBuilder();

    // Compute Pearson correlation for the specified datasets
    for (Dataset dataset : datasets) {
        computePearsonCorrelation(mode, dataset);
    }/*w w w .  j  av  a 2s. com*/

    if (metric == PearsonAll) {
        List<Double> concatExp = new ArrayList<Double>();
        List<Double> concatGS = new ArrayList<Double>();

        // Concat the scores
        for (Dataset dataset : datasets) {
            File expScoresFile = new File(
                    OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv");

            List<String> lines = FileUtils.readLines(expScoresFile);

            for (String line : lines) {
                concatExp.add(Double.parseDouble(line));
            }
        }

        // Concat the gold standard
        for (Dataset dataset : datasets) {
            String gsScoresFilePath = GOLDSTANDARD_DIR + "/" + mode.toString().toLowerCase() + "/" + "STS.gs."
                    + dataset.toString() + ".txt";

            PathMatchingResourcePatternResolver r = new PathMatchingResourcePatternResolver();
            Resource res = r.getResource(gsScoresFilePath);
            File gsScoresFile = res.getFile();

            List<String> lines = FileUtils.readLines(gsScoresFile);

            for (String line : lines) {
                concatGS.add(Double.parseDouble(line));
            }
        }

        double[] concatExpArray = ArrayUtils.toPrimitive(concatExp.toArray(new Double[concatExp.size()]));
        double[] concatGSArray = ArrayUtils.toPrimitive(concatGS.toArray(new Double[concatGS.size()]));

        PearsonsCorrelation pearson = new PearsonsCorrelation();
        Double correl = pearson.correlation(concatExpArray, concatGSArray);

        sb.append(correl.toString());
    } else if (metric == PearsonMean) {
        List<Double> scores = new ArrayList<Double>();

        for (Dataset dataset : datasets) {
            File resultFile = new File(
                    OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".txt");
            double score = Double.parseDouble(FileUtils.readFileToString(resultFile));

            scores.add(score);
        }

        double mean = 0.0;
        for (Double score : scores) {
            mean += score;
        }
        mean = mean / scores.size();

        sb.append(mean);
    } else if (metric == PearsonWeightedMean) {
        List<Double> scores = new ArrayList<Double>();
        List<Integer> weights = new ArrayList<Integer>();

        for (Dataset dataset : datasets) {
            File resultFile = new File(
                    OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".txt");
            double score = Double.parseDouble(FileUtils.readFileToString(resultFile));

            File scoresFile = new File(
                    OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv");
            int weight = FileUtils.readLines(scoresFile).size();

            scores.add(score);
            weights.add(weight);
        }

        double mean = 0.0;
        int weightsum = 0;
        for (int i = 0; i < scores.size(); i++) {
            Double score = scores.get(i);
            Integer weight = weights.get(i);

            mean += weight * score;
            weightsum += weight;
        }
        mean = mean / weightsum;

        sb.append(mean);
    }

    FileUtils.writeStringToFile(
            new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + metric.toString() + ".txt"),
            sb.toString());
}

---

From source file:fxts.stations.util.UserPreferences.java

public static String getStringValue(Double aValue) {
    return aValue.toString();
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• 21
• »