de.lmu.ifi.dbs.elki.utilities.documentation.Reference.<init>()方法的使用及代码示例

x33g5p2x  于2022-01-28 转载在 其他  
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本文整理了Java中de.lmu.ifi.dbs.elki.utilities.documentation.Reference.<init>方法的一些代码示例，展示了Reference.<init>的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Reference.<init>方法的具体详情如下：
包路径：de.lmu.ifi.dbs.elki.utilities.documentation.Reference
类名称：Reference
方法名：<init>

Reference.<init>介绍

暂无

代码示例

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

  1. /**
  2.  * Dummy method, just to attach a third reference.
  3.  */
  4. @Reference(authors = "L. R. Dice", title = "Measures of the Amount of Ecologic Association Between Species", booktitle = "Ecology 26 (3)")
  5. static void thirdReference() {
  6.  // Empty, just to attach a second reference
  7. };

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

  1. /**
  2.  * Get the canonical bandwidth for this kernel.
  3.  * 
  4.  * Note: R uses a different definition of "canonical bandwidth", and also uses
  5.  * differently scaled kernels.
  6.  * 
  7.  * @return Canonical bandwidth
  8.  */
  9. @Reference(authors = "J.S. Marron, D. Nolan", title = "Canonical kernels for density estimation", booktitle = "Statistics & Probability Letters, Volume 7, Issue 3", url = "http://dx.doi.org/10.1016/0167-7152(88)90050-8")
  10. public double canonicalBandwidth();

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

  1. /**
  2.  * Dummy method, just to attach a second reference.
  3.  */
  4. @Reference(authors = "T. Sørensen", title = "A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons", booktitle = "Kongelige Danske Videnskabernes Selskab 5 (4)")
  5. static void secondReference() {
  6.  // Empty, just to attach a second reference
  7. };

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

  1. /**
  2.  * Secondary reference.
  3.  */
  4. @Reference(authors = "Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek", title = "Outlier Detection in Arbitrarily Oriented Subspaces", booktitle = "Proc. IEEE International Conference on Data Mining (ICDM 2012)")
  5. public static final void secondReference() {
  6.  // Dummy, reference attachment point only.
  7. }

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

  1. /**
  2.  * Get the set matchings purity (first:second clustering) (normalized, 1 =
  3.  * equal)
  4.  * 
  5.  * @return purity
  6.  */
  7. @Reference(authors = "Zhao, Y. and Karypis, G.", //
  8. title = "Criterion functions for document clustering: Experiments and analysis", //
  9. booktitle = "University of Minnesota, Department of Computer Science, Technical Report 01-40, 2001", //
  10. url = "http://www-users.cs.umn.edu/~karypis/publications/Papers/PDF/vscluster.pdf")
  11. public double purity() {
  12.  return smPurity;
  13. }

代码示例来源：origin: elki-project/elki

  1. /**
  2.  * Get the canonical bandwidth for this kernel.
  3.  * <p>
  4.  * Note: R uses a different definition of "canonical bandwidth", and also uses
  5.  * differently scaled kernels.
  6.  * 
  7.  * @return Canonical bandwidth
  8.  */
  9. @Reference(authors = "J. S. Marron, D. Nolan", //
  10.   title = "Canonical kernels for density estimation", //
  11.   booktitle = "Statistics & Probability Letters, Volume 7, Issue 3", //
  12.   url = "https://doi.org/10.1016/0167-7152(88)90050-8", //
  13.   bibkey = "doi:10.1016/0167-71528890050-8")
  14. double canonicalBandwidth();

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki-core-math

  1. /**
  2.  * Get the canonical bandwidth for this kernel.
  3.  * <p>
  4.  * Note: R uses a different definition of "canonical bandwidth", and also uses
  5.  * differently scaled kernels.
  6.  * 
  7.  * @return Canonical bandwidth
  8.  */
  9. @Reference(authors = "J. S. Marron, D. Nolan", //
  10.   title = "Canonical kernels for density estimation", //
  11.   booktitle = "Statistics & Probability Letters, Volume 7, Issue 3", //
  12.   url = "https://doi.org/10.1016/0167-7152(88)90050-8", //
  13.   bibkey = "doi:10.1016/0167-71528890050-8")
  14. double canonicalBandwidth();

代码示例来源：origin: elki-project/elki

  1. /**
  2.  * Get the Van Rijsbergen’s F measure (asymmetric) for first clustering
  3.  * <p>
  4.  * E. Amigó, J. Gonzalo, J. Artiles, and F. Verdejo<br>
  5.  * A comparison of extrinsic clustering evaluation metrics based on formal
  6.  * constraints<br>
  7.  * Information Retrieval 12(5)
  8.  *
  9.  * @return Set Matching F-Measure of first clustering
  10.  */
  11. @Reference(authors = "E. Amigó, J. Gonzalo, J. Artiles, F. Verdejo", //
  12.   title = "A comparison of extrinsic clustering evaluation metrics based on formal constraints", //
  13.   booktitle = "Information Retrieval 12(5)", //
  14.   url = "https://doi.org/10.1007/s10791-009-9106-z", //
  15.   bibkey = "DBLP:journals/ir/AmigoGAV09a")
  16. public double fMeasureFirst() {
  17.  return smFFirst;
  18. }

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

  1. /**
  2.   * Remove bias from the Anderson-Darling statistic if the mean and standard
  3.   * deviation were estimated from the data, and a normal distribution was
  4.   * assumed.
  5.   * 
  6.   * @param A2 A2 statistic
  7.   * @param n Sample size
  8.   * @return Unbiased test statistic
  9.   */
  10.  @Reference(authors = "M. A. Stephens",//
  11.  title = "EDF Statistics for Goodness of Fit and Some Comparisons",//
  12.  booktitle = "Journal of the American Statistical Association, Volume 69, Issue 347", //
  13.  url = "http://dx.doi.org/10.1080/01621459.1974.10480196")
  14.  public static double removeBiasNormalDistribution(double A2, int n) {
  15.   return A2 * (1 + 4. / n - 25. / (n * n));
  16.  }
  17. }

代码示例来源：origin: elki-project/elki

  1. /**
  2.  * Computes the Rand index (RI).
  3.  * <p>
  4.  * W. M. Rand<br>
  5.  * Objective Criteria for the Evaluation of Clustering Methods<br>
  6.  * Journal of the American Statistical Association, Vol. 66 Issue 336
  7.  *
  8.  * @return The Rand index (RI).
  9.  */
  10. @Reference(authors = "W. M. Rand", //
  11.   title = "Objective Criteria for the Evaluation of Clustering Methods", //
  12.   booktitle = "Journal of the American Statistical Association, Vol. 66 Issue 336", //
  13.   url = "https://doi.org/10.2307/2284239", //
  14.   bibkey = "doi:10.2307/2284239")
  15. public double randIndex() {
  16.  final double sum = pairconfuse[0] + pairconfuse[1] + pairconfuse[2] + pairconfuse[3];
  17.  return (pairconfuse[0] + pairconfuse[3]) / sum;
  18. }

代码示例来源：origin: elki-project/elki

  1. /**
  2.  * Get the set matchings purity (first:second clustering)
  3.  * (normalized, 1 = equal)
  4.  * <p>
  5.  * Y. Zhao, G. Karypis<br>
  6.  * Criterion functions for document clustering: Experiments and analysis<br>
  7.  * University of Minnesota, Dep. Computer Science, Technical Report 01-40
  8.  *
  9.  * @return purity
  10.  */
  11. @Reference(authors = "Y. Zhao, G. Karypis", //
  12.   title = "Criterion functions for document clustering: Experiments and analysis", //
  13.   booktitle = "University of Minnesota, Dep. Computer Science, Technical Report 01-40", //
  14.   url = "http://www-users.cs.umn.edu/~karypis/publications/Papers/PDF/vscluster.pdf", //
  15.   bibkey = "tr/umn/ZhaoK01")
  16. public double purity() {
  17.  return smPurity;
  18. }

代码示例来源：origin: elki-project/elki

  1. /**
  2.   * Remove bias from the Anderson-Darling statistic if the mean and standard
  3.   * deviation were estimated from the data, and a normal distribution was
  4.   * assumed.
  5.   * 
  6.   * @param A2 A2 statistic
  7.   * @param n Sample size
  8.   * @return Unbiased test statistic
  9.   */
  10.  @Reference(authors = "M. A. Stephens", //
  11.    title = "EDF Statistics for Goodness of Fit and Some Comparisons", //
  12.    booktitle = "Journal of the American Statistical Association, Volume 69, Issue 347", //
  13.    url = "https://doi.org/10.1080/01621459.1974.10480196", //
  14.    bibkey = "doi:10.1080/01621459.1974.10480196")
  15.  public static double removeBiasNormalDistribution(double A2, int n) {
  16.   return A2 * (1 + 4. / n - 25. / (n * n));
  17.  }
  18. }

代码示例来源：origin: elki-project/elki

  1. /**
  2.   * Get the Van Rijsbergen’s F measure (asymmetric) for second clustering
  3.   * <p>
  4.   * E. Amigó, J. Gonzalo, J. Artiles, and F. Verdejo<br>
  5.   * A comparison of extrinsic clustering evaluation metrics based on formal
  6.   * constraints<br>
  7.   * Information Retrieval 12(5)
  8.   *
  9.   * @return Set Matching F-Measure of second clustering
  10.   */
  11.  @Reference(authors = "E. Amigó, J. Gonzalo, J. Artiles, F. Verdejo", //
  12.    title = "A comparison of extrinsic clustering evaluation metrics based on formal constraints", //
  13.    booktitle = "Information Retrieval 12(5)", //
  14.    url = "https://doi.org/10.1007/s10791-009-9106-z", //
  15.    bibkey = "DBLP:journals/ir/AmigoGAV09a")
  16.  public double fMeasureSecond() {
  17.   return smFSecond;
  18.  }
  19. }

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

  1. /**
  2.  * Compute the Tau correlation measure
  3.  *
  4.  * @param c Concordant pairs
  5.  * @param d Discordant pairs
  6.  * @param m Total number of pairs
  7.  * @param wd Number of within distances
  8.  * @param bd Number of between distances
  9.  * @return Gamma plus statistic
  10.  */
  11. @Reference(authors = "F. J. Rohlf", title = "Methods of comparing classifications", //
  12. booktitle = "Annual Review of Ecology and Systematics", //
  13. url = "http://dx.doi.org/10.1146/annurev.es.05.110174.000533")
  14. public double computeTau(long c, long d, double m, long wd, long bd) {
  15.  double tie = (wd * (wd - 1) + bd * (bd - 1)) >>> 1;
  16.  return (c - d) / Math.sqrt((m - tie) * m);
  17.  // return (4. * c - m) / m;
  18. }

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki

  1. /**
  2.  * Return the quantile function for this distribution
  3.  * 
  4.  * Reference:
  5.  * <p>
  6.  * Algorithm AS 91: The percentage points of the $\chi$^2 distribution<br />
  7.  * D.J. Best, D. E. Roberts<br />
  8.  * Journal of the Royal Statistical Society. Series C (Applied Statistics)
  9.  * </p>
  10.  * 
  11.  * @param x Quantile
  12.  * @param dof Degrees of freedom
  13.  * @return quantile position
  14.  */
  15. @Reference(title = "Algorithm AS 91: The percentage points of the $\\chi^2$ distribution", authors = "D.J. Best, D. E. Roberts", booktitle = "Journal of the Royal Statistical Society. Series C (Applied Statistics)")
  16. public static double quantile(double x, double dof) {
  17.  return GammaDistribution.quantile(x, .5 * dof, .5);
  18. }

代码示例来源：origin: elki-project/elki

  1. /**
  2.  * Get the set matching F1-Measure
  3.  * <p>
  4.  * M. Steinbach, G. Karypis, V. Kumar<br>
  5.  * A Comparison of Document Clustering Techniques<br>
  6.  * KDD workshop on text mining. Vol. 400. No. 1
  7.  *
  8.  * @return Set Matching F1-Measure
  9.  */
  10. @Reference(authors = "M. Steinbach, G. Karypis, V. Kumar", //
  11.   title = "A Comparison of Document Clustering Techniques", //
  12.   booktitle = "KDD workshop on text mining. Vol. 400. No. 1", //
  13.   url = "http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf", //
  14.   bibkey = "conf/kdd/SteinbachKK00")
  15. public double f1Measure() {
  16.  return Util.f1Measure(purity(), inversePurity());
  17. }

代码示例来源：origin: elki-project/elki

  1. /**
  2.  * Computes the pair-counting Fowlkes-mallows (flat only, non-hierarchical!)
  3.  * <p>
  4.  * E. B. Fowlkes, C. L. Mallows<br>
  5.  * A method for comparing two hierarchical clusterings<br>
  6.  * In: Journal of the American Statistical Association, Vol. 78 Issue 383
  7.  *
  8.  * @return pair-counting Fowlkes-mallows
  9.  */
  10. @Reference(authors = "E. B. Fowlkes, C. L. Mallows", //
  11.   title = "A method for comparing two hierarchical clusterings", //
  12.   booktitle = "Journal of the American Statistical Association, Vol. 78 Issue 383", //
  13.   url = "https://doi.org/10.2307/2288117", //
  14.   bibkey = "doi:10.2307/2288117")
  15. public double fowlkesMallows() {
  16.  return FastMath.sqrt(precision() * recall());
  17. }

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki-clustering

  1. /**
  2.  * Computes the pair-counting Fowlkes-mallows (flat only, non-hierarchical!)
  3.  * <p>
  4.  * E. B. Fowlkes, C. L. Mallows<br>
  5.  * A method for comparing two hierarchical clusterings<br>
  6.  * In: Journal of the American Statistical Association, Vol. 78 Issue 383
  7.  *
  8.  * @return pair-counting Fowlkes-mallows
  9.  */
  10. @Reference(authors = "E. B. Fowlkes, C. L. Mallows", //
  11.   title = "A method for comparing two hierarchical clusterings", //
  12.   booktitle = "Journal of the American Statistical Association, Vol. 78 Issue 383", //
  13.   url = "https://doi.org/10.2307/2288117", //
  14.   bibkey = "doi:10.2307/2288117")
  15. public double fowlkesMallows() {
  16.  return FastMath.sqrt(precision() * recall());
  17. }

代码示例来源：origin: de.lmu.ifi.dbs.elki/elki-batik-visualization

  1. @Reference(authors = "D. W. Scott", title = "Multivariate density estimation: Theory, Practice, and Visualization", //
  2. booktitle = "Multivariate Density Estimation: Theory, Practice, and Visualization", //
  3. url = "http://dx.doi.org/10.1002/9780470316849")
  4. private double[] initializeBandwidth(double[][] data) {
  5.  MeanVariance mv0 = new MeanVariance();
  6.  MeanVariance mv1 = new MeanVariance();
  7.  // For Kernel bandwidth.
  8.  for(double[] projected : data) {
  9.   mv0.put(projected[0]);
  10.   mv1.put(projected[1]);
  11.  }
  12.  // Set bandwidths according to Scott's rule:
  13.  // Note: in projected space, d=2.
  14.  double[] bandwidth = new double[2];
  15.  bandwidth[0] = MathUtil.SQRT5 * mv0.getSampleStddev() * Math.pow(rel.size(), -1 / 6.);
  16.  bandwidth[1] = MathUtil.SQRT5 * mv1.getSampleStddev() * Math.pow(rel.size(), -1 / 6.);
  17.  return bandwidth;
  18. }

代码示例来源：origin: elki-project/elki

  1. @Reference(authors = "D. W. Scott", title = "Multivariate density estimation: Theory, Practice, and Visualization", //
  2.   booktitle = "Multivariate Density Estimation: Theory, Practice, and Visualization", //
  3.   url = "https://doi.org/10.1002/9780470316849", //
  4.   bibkey = "doi:10.1002/9780470316849")
  5. private double[] initializeBandwidth(double[][] data) {
  6.  MeanVariance mv0 = new MeanVariance();
  7.  MeanVariance mv1 = new MeanVariance();
  8.  // For Kernel bandwidth.
  9.  for(double[] projected : data) {
  10.   mv0.put(projected[0]);
  11.   mv1.put(projected[1]);
  12.  }
  13.  // Set bandwidths according to Scott's rule:
  14.  // Note: in projected space, d=2.
  15.  double[] bandwidth = new double[2];
  16.  bandwidth[0] = MathUtil.SQRT5 * mv0.getSampleStddev() * FastMath.pow(rel.size(), -1 / 6.);
  17.  bandwidth[1] = MathUtil.SQRT5 * mv1.getSampleStddev() * FastMath.pow(rel.size(), -1 / 6.);
  18.  return bandwidth;
  19. }

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