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

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

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

/**
 * Get the canonical bandwidth for this kernel.
 * 
 * Note: R uses a different definition of "canonical bandwidth", and also uses
 * differently scaled kernels.
 * 
 * @return Canonical bandwidth
 */
@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")
public double canonicalBandwidth();

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

/**
 * Dummy method, just to attach a second reference.
 */
@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)")
static void secondReference() {
 // Empty, just to attach a second reference
};

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

/**
 * Secondary reference.
 */
@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)")
public static final void secondReference() {
 // Dummy, reference attachment point only.
}

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

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

代码示例来源：origin: elki-project/elki

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

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

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

代码示例来源：origin: elki-project/elki

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

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

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

代码示例来源：origin: elki-project/elki

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

代码示例来源：origin: elki-project/elki

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

代码示例来源：origin: elki-project/elki

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

代码示例来源：origin: elki-project/elki

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

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

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

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

/**
 * Return the quantile function for this distribution
 * 
 * Reference:
 * <p>
 * Algorithm AS 91: The percentage points of the $\chi$^2 distribution<br />
 * D.J. Best, D. E. Roberts<br />
 * Journal of the Royal Statistical Society. Series C (Applied Statistics)
 * </p>
 * 
 * @param x Quantile
 * @param dof Degrees of freedom
 * @return quantile position
 */
@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)")
public static double quantile(double x, double dof) {
 return GammaDistribution.quantile(x, .5 * dof, .5);
}

代码示例来源：origin: elki-project/elki

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

代码示例来源：origin: elki-project/elki

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

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

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

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

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

代码示例来源：origin: elki-project/elki

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