dk.alexandra.fresco.stat.DefaultStatistics

All Implemented Interfaces:: dk.alexandra.fresco.framework.builder.ComputationDirectory, Statistics

public class DefaultStatistics
extends Object
implements Statistics

Method Summary

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Details
- sampleMean
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> sampleMean(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)
  
  Description copied from interface: Statistics
  
  Compute the sample mean of the given data.
  
  Specified by:
  
  sampleMean in interface Statistics
  
  Parameters:
  
  data - A dataset.
  
  Returns:
  
  The sample mean.
- sampleMedian
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> sampleMedian(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)
  
  Description copied from interface: Statistics
  
  Compute the sample median of the sample set.
  
  Specified by:
  
  sampleMedian in interface Statistics
  
  Parameters:
  
  data - Samples.
  
  Returns:
  
  The median.
- samplePercentiles
  
  public dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> samplePercentiles(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data, double[] percentiles)
  
  Description copied from interface: Statistics
  
  Compute the sample percentiles of a sample set.
  
  Specified by:
  
  samplePercentiles in interface Statistics
  
  Parameters:
  
  data - Samples.
  
  Returns:
  
  The median.
- sampleVariance
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> sampleVariance(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data, dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> mean)
  
  Description copied from interface: Statistics
  
  Compute the sample variance of the given data, assuming the sample mean has already been calculated.
  
  Specified by:
  
  sampleVariance in interface Statistics
  
  Parameters:
  
  data - A dataset.
  
  mean - The sample mean for the given dataset.
  
  Returns:
  
  The sample variance.
- sampleVariance
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> sampleVariance(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)
  
  Description copied from interface: Statistics
  
  Compute the sample variance of the given data.
  
  Specified by:
  
  sampleVariance in interface Statistics
  
  Parameters:
  
  data - A dataset.
  
  Returns:
  
  The sample variance.
- sampleStandardDeviation
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> sampleStandardDeviation(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)
  
  Description copied from interface: Statistics
  
  Compute the standard deviation of the data.
  
  Specified by:
  
  sampleStandardDeviation in interface Statistics
  
  Parameters:
  
  data - A dataset.
  
  Returns:
  
  The sample standard deviation.
- sampleStandardDeviation
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> sampleStandardDeviation(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data, dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> mean)
  
  Description copied from interface: Statistics
  
  Compute the sample standard deviation of the data given that the sample mean has already been calculated.
  
  Specified by:
  
  sampleStandardDeviation in interface Statistics
  
  Parameters:
  
  data - A dataset.
  
  mean - The sample mean for the given dataset.
  
  Returns:
  
  The sample standard deviation.
- ttest
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> ttest(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data, dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> mu)
  
  Description copied from interface: Statistics
  
  Compute the test statistics for a Student's t-test for the hypothesis that the mean of the sample is equal to mu.
  
  Specified by:
  
  ttest in interface Statistics
  
  Parameters:
  
  data - A dataset.
  
  mu - The parameter for the t-test.
  
  Returns:
  
  The test statistics.
- ttest
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> ttest(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data1, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data2)
  
  Description copied from interface: Statistics
  
  Compute the test statistics for a two-sample Student's t-test for the hypothesis that the mean of the two samples are equal. It is assumed that the two samples have the same variance.
  
  Specified by:
  
  ttest in interface Statistics
  
  Parameters:
  
  data1 - A dataset.
  
  data2 - A dataset.
  
  Returns:
  
  The test statistics for the hypothesis that the two datasets have the same mean.
- chiSquare
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> chiSquare(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> observed, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> expected)
  
  Description copied from interface: Statistics
  
  Compute the test statistics for a Χ²-test.
  
  Specified by:
  
  chiSquare in interface Statistics
  
  Parameters:
  
  observed - The observed data.
  
  expected - The expected number of observations in each bucket.
  
  Returns:
  
  The test statistics that the observed data fits the distribution of the expected.
- chiSquare
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> chiSquare(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> observed, double[] expected)
  
  Description copied from interface: Statistics
  
  Compute the test statistics for a Χ²-test.
  
  Specified by:
  
  chiSquare in interface Statistics
  
  Parameters:
  
  observed - The observed data.
  
  expected - The expected number of observations in each bucket.
  
  Returns:
  
  The test statistics that the observed data fits the distribution of the expected.
- linearRegression
  
  public dk.alexandra.fresco.framework.DRes<LinearRegression.LinearRegressionResult> linearRegression(List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> x, ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> y)
  
  Description copied from interface: Statistics
  
  Compute estimates for the parameters b of a linear model such that b0 x0 + ... + bk xk = y.
  
  Specified by:
  
  linearRegression in interface Statistics
  
  Parameters:
  
  x - The dataset.
  
  y - The dependant values
  
  Returns:
  
  An estimation for the parameters of a linear model for the given data.
- simpleLinearRegression
  
  public dk.alexandra.fresco.framework.DRes<SimpleLinearRegression.SimpleLinearRegressionResult> simpleLinearRegression(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> x, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> y)
  
  Description copied from interface: Statistics
  
  Compute simple linear regression on two samples.
  
  Specified by:
  
  simpleLinearRegression in interface Statistics
  
  Parameters:
  
  x - The dataset.
  
  y - The dependant values.
  
  Returns:
  
  An estimation for the parameters of a linear model.
- correlation
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> correlation(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data1, dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> mean1, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data2, dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> mean2)
  
  Description copied from interface: Statistics
  
  Compute Pearson's correlation coefficient on the two samples. Here it's assumed that the sample means has already been calculated.
  
  Specified by:
  
  correlation in interface Statistics
  
  Returns:
- correlation
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> correlation(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data1, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data2)
  
  Description copied from interface: Statistics
  
  Compute Pearson's correlation coefficient on the two samples.
  
  Specified by:
  
  correlation in interface Statistics
  
  Returns:
- ffest
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> ffest(List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> observed)
  
  Description copied from interface: Statistics
  
  Compute the F-test statistics for the null hypothesis that the given datasets have the same mean.
  
  Specified by:
  
  ffest in interface Statistics
  
  Parameters:
  
  observed - A list of datasets.
  
  Returns:
  
  The test statistics.
- kruskallWallisTest
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> kruskallWallisTest(List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> observed)
  
  Description copied from interface: Statistics
  
  Compute the Kruskall-Wallis test statistics for the null hypothesis that the given samples are drawn from same the distribution.
  
  Specified by:
  
  kruskallWallisTest in interface Statistics
  
  Returns:
- leakyFrequencyTable
  
  public dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.util.Pair<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>,Integer>>> leakyFrequencyTable(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data)
  
  Description copied from interface: Statistics
  
  Compute a frequency table for the data. Note that the frequencies will be leaked but the corresponding values will not.
  
  Specified by:
  
  leakyFrequencyTable in interface Statistics
  
  Parameters:
  
  data - A dataset
  
  Returns:
  
  A frequency table.
- frequencyTable
  
  public dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.util.Pair<BigInteger,Integer>>> frequencyTable(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data)
  
  Description copied from interface: Statistics
  
  Compute a frequency table for the data.
  
  Specified by:
  
  frequencyTable in interface Statistics
  
  Parameters:
  
  data - A dataset
  
  Returns:
  
  A frequency table.
- coxRegressionDiscrete
  
  public dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> coxRegressionDiscrete(List<SurvivalInfoDiscrete> data, int iterations, double alpha, double[] beta)
  
  Description copied from interface: Statistics
  
  Estimate the parameters of a Cox model on the given data. Here it's assumed that each covariate only takes values in a (small) finite set, e.g. when they indicate group membership. If many different values are possible, use Statistics.coxRegressionContinuous(java.util.List<dk.alexandra.fresco.stat.survival.SurvivalInfoContinuous>, int, double, double[]) instead.
  
  Specified by:
  
  coxRegressionDiscrete in interface Statistics
  
  Parameters:
  
  data - The data set.
  
  iterations - The number of iterations.
  
  alpha - The learning rate.
  
  beta - The initial coefficient guess.
  
  Returns:
- coxRegressionContinuous
  
  public dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> coxRegressionContinuous(List<SurvivalInfoContinuous> data, int iterations, double alpha, double[] beta)
  
  Description copied from interface: Statistics
  
  Estimate the parameters of a Cox model on the given data.
  
  Specified by:
  
  coxRegressionContinuous in interface Statistics
  
  Parameters:
  
  data - The data set.
  
  iterations - The number of iterations.
  
  alpha - The learning rate.
  
  beta - The initial coefficient guess.
  
  Returns:
- histogramDiscrete
  
  public dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> histogramDiscrete(int[] buckets, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data)
  
  Description copied from interface: Statistics
  
  Compute the histogram for the given sample.
  
  Specified by:
  
  histogramDiscrete in interface Statistics
  
  Parameters:
  
  buckets - Upper bound for the buckets to use in the histogram.
  
  data - The sample data.
  
  Returns:
- histogramDiscrete
  
  public dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> histogramDiscrete(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> buckets, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data)
  
  Description copied from interface: Statistics
  
  Compute the histogram for the given sample.
  
  Specified by:
  
  histogramDiscrete in interface Statistics
  
  Parameters:
  
  buckets - Upper bound for the buckets to use in the histogram.
  
  data - The sample data.
  
  Returns:
- histogramContinuous
  
  public dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> histogramContinuous(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> buckets, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)
  
  Description copied from interface: Statistics
  
  Compute the histogram for the given sample.
  
  Specified by:
  
  histogramContinuous in interface Statistics
  
  Parameters:
  
  buckets - Upper bound for the buckets to use in the histogram.
  
  data - The sample data.
  
  Returns:
- histogramContinuous
  
  public dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> histogramContinuous(double[] buckets, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)
  
  Description copied from interface: Statistics
  
  Compute the histogram for the given sample.
  
  Specified by:
  
  histogramContinuous in interface Statistics
  
  Parameters:
  
  buckets - Upper bound for the buckets to use in the histogram.
  
  data - The sample data.
  
  Returns:
- twoDimensionalHistogramDiscrete
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.common.collections.Matrix<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> twoDimensionalHistogramDiscrete(dk.alexandra.fresco.framework.util.Pair<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>,List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> buckets, List<dk.alexandra.fresco.framework.util.Pair<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>,dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> data)
  
  Description copied from interface: Statistics
  
  Compute the histogram for the given two-dimensional sample.
  
  Specified by:
  
  twoDimensionalHistogramDiscrete in interface Statistics
  
  Parameters:
  
  buckets - Upper bounds for the buckets to use in the histogram.
  
  data - The sample data.
  
  Returns:
- twoDimensionalHistogramContinuous
  
  public dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.common.collections.Matrix<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> twoDimensionalHistogramContinuous(dk.alexandra.fresco.framework.util.Pair<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>,List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> buckets, List<dk.alexandra.fresco.framework.util.Pair<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>,dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> data)
  
  Description copied from interface: Statistics
  
  Compute the histogram for the given two-dimensional sample.
  
  Specified by:
  
  twoDimensionalHistogramContinuous in interface Statistics
  
  Parameters:
  
  buckets - Upper bounds for the buckets to use in the histogram.
  
  data - The sample data.
  
  Returns:
- multiDimensionalHistogramDiscrete
  
  public dk.alexandra.fresco.framework.DRes<MultiDimensionalArray<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> multiDimensionalHistogramDiscrete(List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> buckets, dk.alexandra.fresco.lib.common.collections.Matrix<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data)
  
  Description copied from interface: Statistics
  
  Compute the histogram for the given multi-dimensional sample.
  
  Specified by:
  
  multiDimensionalHistogramDiscrete in interface Statistics
  
  Parameters:
  
  buckets - Upper bounds for the buckets to use in the histogram.
  
  data - The sample data.
  
  Returns:
- kAnonymize
  
  public dk.alexandra.fresco.framework.DRes<MultiDimensionalArray<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>>> kAnonymize(dk.alexandra.fresco.lib.common.collections.Matrix<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> sensitiveAttributes, List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> buckets, int k)
  
  Description copied from interface: Statistics
  
  Compute a k-anonymized version of the given datset.
  Each row in the data set are the quasi-identifiers of an individual with a corresponding entry in the list of values of the sensitive attribute. The buckets indicates the desired generalization of the quasi-identifiers as in a histogram. K is the smallest allowed number of individuals in each bucket.
  The output is a histogram on the given buckets with the value in the histogram being a list of size data.getHeight() with a non-zero entry x at index i indicating that the data point at row i is in this bucket and that the corresponding sensitive attribute was x.
  
  Specified by:
  
  kAnonymize in interface Statistics
  
  Parameters:
  
  data - The quasi identifiers for each individual.
  
  sensitiveAttributes - The corresponding sensitive attributes. Must be non-zero
  
  buckets - The buckets defining the desired generalization.
  
  k - The smallest allowed number of individuals in each bucket.
  
  Returns:
  
  A k-anonymous data set with all buckets with fewer than k elements suppressed.
- kAnonymizeAndOpen
  
  public dk.alexandra.fresco.framework.DRes<MultiDimensionalArray<List<BigInteger>>> kAnonymizeAndOpen(dk.alexandra.fresco.lib.common.collections.Matrix<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> sensitiveAttributes, List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> buckets, int k)
  
  Description copied from interface: Statistics
  
  Compute a k-anonymized version of the given dataset and open it to all parties.
  Each row in the data set are the quasi-identifiers of an individual with a corresponding entry in the list of values of the sensitive attribute. The buckets indicates the desired generalization of the quasi-identifiers as in a histogram. K is the smallest allowed number of individuals in each bucket.
  The output is a histogram on the given buckets with the value corresponding to a bucket is a list of the sensitive attributes from the original dataset which ended up in this bucket.
  
  Specified by:
  
  kAnonymizeAndOpen in interface Statistics
  
  Parameters:
  
  data - The quasi identifiers for each individual.
  
  sensitiveAttributes - The corresponding sensitive attributes. Must be non-zero.
  
  buckets - The buckets defining the desired generalization.
  
  k - The smallest allowed number of individuals in each bucket.
  
  Returns:
  
  A k-anonymous data set with all buckets with fewer than k elements suppressed.
- mahalanobisDistance
  
  public dk.alexandra.fresco.framework.DRes<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> mahalanobisDistance(List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> X)
  
  Description copied from interface: Statistics
  
  Compute the Mahalanobis Distance of all samples in a data set. This may be used to detect outliers in the data set which may be filtered out obliviously (see FilteredStatistics before using the data set for analysis.
  
  Specified by:
  
  mahalanobisDistance in interface Statistics
  
  Parameters:
  
  X - The data set

Modifier and Type	Method	Description
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`chiSquare(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> observed, double[] expected)`	Compute the test statistics for a Χ²-test.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`chiSquare(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> observed, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> expected)`	Compute the test statistics for a Χ²-test.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`correlation(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data1, dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> mean1, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data2, dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> mean2)`	Compute Pearson's correlation coefficient on the two samples.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`correlation(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data1, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data2)`	Compute Pearson's correlation coefficient on the two samples.
`dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>>`	`coxRegressionContinuous(List<SurvivalInfoContinuous> data, int iterations, double alpha, double[] beta)`	Estimate the parameters of a Cox model on the given data.
`dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>>`	`coxRegressionDiscrete(List<SurvivalInfoDiscrete> data, int iterations, double alpha, double[] beta)`	Estimate the parameters of a Cox model on the given data.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`ffest(List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> observed)`	Compute the F-test statistics for the null hypothesis that the given datasets have the same mean.
`dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.util.Pair<BigInteger,Integer>>>`	`frequencyTable(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data)`	Compute a frequency table for the data.
`dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>>`	`histogramContinuous(double[] buckets, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)`	Compute the histogram for the given sample.
`dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>>`	`histogramContinuous(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> buckets, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)`	Compute the histogram for the given sample.
`dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>>`	`histogramDiscrete(int[] buckets, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data)`	Compute the histogram for the given sample.
`dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>>`	`histogramDiscrete(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> buckets, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data)`	Compute the histogram for the given sample.
`dk.alexandra.fresco.framework.DRes<MultiDimensionalArray<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>>>`	`kAnonymize(dk.alexandra.fresco.lib.common.collections.Matrix<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> sensitiveAttributes, List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> buckets, int k)`	Compute a k-anonymized version of the given datset.
`dk.alexandra.fresco.framework.DRes<MultiDimensionalArray<List<BigInteger>>>`	`kAnonymizeAndOpen(dk.alexandra.fresco.lib.common.collections.Matrix<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> sensitiveAttributes, List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> buckets, int k)`	Compute a k-anonymized version of the given dataset and open it to all parties.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`kruskallWallisTest(List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> observed)`	Compute the Kruskall-Wallis test statistics for the null hypothesis that the given samples are drawn from same the distribution.
`dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.util.Pair<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>,Integer>>>`	`leakyFrequencyTable(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data)`	Compute a frequency table for the data.
`dk.alexandra.fresco.framework.DRes<LinearRegression.LinearRegressionResult>`	`linearRegression(List<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> x, ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> y)`	Compute estimates for the parameters b of a linear model such that b0 x0 + ...
`dk.alexandra.fresco.framework.DRes<ArrayList<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>>`	`mahalanobisDistance(List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> X)`	Compute the Mahalanobis Distance of all samples in a data set.
`dk.alexandra.fresco.framework.DRes<MultiDimensionalArray<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>>`	`multiDimensionalHistogramDiscrete(List<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> buckets, dk.alexandra.fresco.lib.common.collections.Matrix<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>> data)`	Compute the histogram for the given multi-dimensional sample.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`sampleMean(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)`	Compute the sample mean of the given data.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`sampleMedian(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)`	Compute the sample median of the sample set.
`dk.alexandra.fresco.framework.DRes<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>>`	`samplePercentiles(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data, double[] percentiles)`	Compute the sample percentiles of a sample set.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`sampleStandardDeviation(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)`	Compute the standard deviation of the data.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`sampleStandardDeviation(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data, dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> mean)`	Compute the sample standard deviation of the data given that the sample mean has already been calculated.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`sampleVariance(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data)`	Compute the sample variance of the given data.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`sampleVariance(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data, dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> mean)`	Compute the sample variance of the given data, assuming the sample mean has already been calculated.
`dk.alexandra.fresco.framework.DRes<SimpleLinearRegression.SimpleLinearRegressionResult>`	`simpleLinearRegression(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> x, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> y)`	Compute simple linear regression on two samples.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`ttest(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data, dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed> mu)`	Compute the test statistics for a Student's t-test for the hypothesis that the mean of the sample is equal to `mu`.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>`	`ttest(List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data1, List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>> data2)`	Compute the test statistics for a two-sample Student's t-test for the hypothesis that the mean of the two samples are equal.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.common.collections.Matrix<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>>`	`twoDimensionalHistogramContinuous(dk.alexandra.fresco.framework.util.Pair<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>,List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> buckets, List<dk.alexandra.fresco.framework.util.Pair<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>,dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.fixed.SFixed>>> data)`	Compute the histogram for the given two-dimensional sample.
`dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.lib.common.collections.Matrix<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>>`	`twoDimensionalHistogramDiscrete(dk.alexandra.fresco.framework.util.Pair<List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>,List<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> buckets, List<dk.alexandra.fresco.framework.util.Pair<dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>,dk.alexandra.fresco.framework.DRes<dk.alexandra.fresco.framework.value.SInt>>> data)`	Compute the histogram for the given two-dimensional sample.

Class DefaultStatistics

Method Summary

Methods inherited from class java.lang.Object

Method Details