Index

A B C D E F G H I K L M N O P Q R S T U V W
All Classes All Packages

A

Accuracy - Class in dk.alexandra.fresco.stat.mlp.evaluation

Return the number of correct predictions in the given data set.

Accuracy(MLP, List<ArrayList<DRes<SFixed>>>, ArrayList<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.mlp.evaluation.Accuracy

AccuracyBinary - Class in dk.alexandra.fresco.stat.mlp.evaluation

Return the number of correct predictions in the given data set.

AccuracyBinary(MLP, List<ArrayList<DRes<SFixed>>>, ArrayList<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.mlp.evaluation.AccuracyBinary

ActivationFunction - Enum in dk.alexandra.fresco.stat.mlp.activationfunction

This enum represents the available activation functions f: Rⁿ→ Rⁿ for use with neural networks.

add(List<DRes<SFixed>>, List<DRes<SFixed>>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Add two secret vectors.

AdvancedLinearAlgebra - Interface in dk.alexandra.fresco.stat

This computation directory contains variuous linear algebra functions.

AffineMap - Class in dk.alexandra.fresco.stat.linearalgebra

Apply an affine map to a vector

AffineMap(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.AffineMap

AffineMap(Layer, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.AffineMap

apply(int, int) - Method in interface dk.alexandra.fresco.stat.utils.MatrixUtils.MatrixPopulator

apply(A, B, ProtocolBuilderNumeric) - Method in interface dk.alexandra.fresco.stat.utils.VectorUtils.EntrywiseBinaryOp

apply(A, ProtocolBuilderNumeric) - Method in interface dk.alexandra.fresco.stat.utils.VectorUtils.EntrywiseUnaryOp

apply(ArrayList<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.mlp.MLP

Apply this neural network in an input vector.

B

BackPropagationOutput - Class in dk.alexandra.fresco.stat.mlp

This class represents the output of back propagation on a single layer.

BackPropagationOutput(DRes<ArrayList<DRes<SFixed>>>, DRes<ArrayList<DRes<SFixed>>>) - Constructor for class dk.alexandra.fresco.stat.mlp.BackPropagationOutput

backSubstitution(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

Use back substitution to compute a vector x such that ax = b where a is an upper triangular square matrix.

backSubstitution(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultLinearAlgebra

BackSubstitution - Class in dk.alexandra.fresco.stat.linearalgebra

Use backward substitution to compute a vector x such that ax = b, where a is upper triangular square matrix.

BackSubstitution(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.BackSubstitution

build(int, IntFunction<MultiDimensionalArray<S>>) - Static method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Create a new MultiDimensionalArray.

build(List<Integer>, Function<List<Integer>, S>) - Static method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Create a new multi-dimensional array with the given shape and with each entry being generated by the populator function.

build(List<S>) - Static method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Create a new one-dimensional array with the given entries.

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.anonymisation.LeakyKAnonymity

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.anonymisation.NoisyHistogram

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.anonymisation.NoisyStats

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.helpers.SP

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.helpers.SPD

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.helpers.SSD

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.helpers.SSE

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.helpers.USS

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.Histogram

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.LeakyBreakTies

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.LeakyFrequencyTable

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.MultiDimensionalHistogram

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.PearsonCorrelation

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.Ranks

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.SampleCovariance

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.SampleMean

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.SampleMedian

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.SampleQuantiles

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.SampleStandardDeviation

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.SampleVariance

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.sort.FindTiedGroups

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.descriptive.TwoDimensionalHistogram

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.filtered.helpers.SSDFiltered

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.filtered.helpers.SumFiltered

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.filtered.helpers.USSFiltered

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.filtered.HistogramFiltered

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.filtered.OneSampleTTestFiltered

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.filtered.SampleMeanFiltered

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.filtered.SampleVarianceFiltered

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.AffineMap

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.BackSubstitution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.Convolution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.ForwardSubstitution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.GramSchmidt

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.InvertLowerTriangularMatrix

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.InvertUpperTriangularMatrix

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.LinearInverseProblem

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.MoorePenrosePseudoInverse

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.NormalizeVector

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.Projection

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.QRAlgorithm

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.linearalgebra.QRDecomposition

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.mlp.activationfunction.Relu

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.mlp.activationfunction.ReluDerivative

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.mlp.activationfunction.Sigmoid

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.mlp.activationfunction.SigmoidDerivative

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.mlp.evaluation.Accuracy

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.mlp.evaluation.AccuracyBinary

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.mlp.Predict

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.outlier.MahalanobisDistance

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.regression.linear.LinearRegression

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.regression.linear.SimpleLinearRegression

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.regression.linear.SimpleLinearRegressionTTest

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.regression.logistic.LogisticRegression

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.regression.logistic.LogisticRegressionGD

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.regression.logistic.LogisticRegressionPrediction

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.sampling.SampleBernoulliDistribution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.sampling.SampleCategoricalDistribution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.sampling.SampleExponentialDistribution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.sampling.SampleIrwinHallDistribution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.sampling.SampleLaplaceDistribution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.sampling.SampleNormalDistribution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.sampling.SampleRademacherDistribution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.sampling.SampleUniformDistribution

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.survival.cox.CoxGradientContinuous

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.survival.cox.CoxGradientDiscrete

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.survival.SurvivalInfoSorter

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.tests.ChiSquareTest

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.tests.FTest

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.tests.KruskallWallisTest

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.tests.OneSampleTTest

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.tests.TwoSampleTTest

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.utils.DivideBySInt

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.utils.MaxList

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.utils.MaxPair

buildComputation(ProtocolBuilderNumeric) - Method in class dk.alexandra.fresco.stat.utils.TransposedMatrixAction

buildMatrix(int, int, MatrixUtils.MatrixPopulator<E>) - Static method in class dk.alexandra.fresco.stat.utils.MatrixUtils

Create a new matrix with the given height and width and populate it use the populator.

C

chiSquare(List<DRes<SInt>>, double[]) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

chiSquare(List<DRes<SInt>>, double[]) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the test statistics for a Χ²-test.

chiSquare(List<DRes<SInt>>, List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

chiSquare(List<DRes<SInt>>, List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the test statistics for a Χ²-test.

ChiSquareTest - Class in dk.alexandra.fresco.stat.tests

Compute the Χ²-test for goodness of fit of the given observatinos.

ChiSquareTest(List<DRes<SInt>>, double[]) - Constructor for class dk.alexandra.fresco.stat.tests.ChiSquareTest

ChiSquareTest(List<DRes<SInt>>, List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.tests.ChiSquareTest

Convolution - Class in dk.alexandra.fresco.stat.linearalgebra

Compute the discrete convolution of two vectors

Convolution(ArrayList<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.Convolution

correlation(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SFixed>>, DRes<SFixed>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

correlation(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SFixed>>, DRes<SFixed>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute Pearson's correlation coefficient on the two samples.

correlation(List<DRes<SFixed>>, List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

correlation(List<DRes<SFixed>>, List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute Pearson's correlation coefficient on the two samples.

CoxGradientContinuous - Class in dk.alexandra.fresco.stat.survival.cox

CoxGradientContinuous(List<SurvivalInfoContinuous>, List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.survival.cox.CoxGradientContinuous

Compute the gradient of the score function for a Cox model on the given data with coefficients beta and learning rate alpha.

CoxGradientDiscrete - Class in dk.alexandra.fresco.stat.survival.cox

CoxGradientDiscrete(List<SurvivalInfoDiscrete>, List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.survival.cox.CoxGradientDiscrete

Compute the gradient of the score function for a Cox model on the given data with coefficients beta and learning rate alpha.

coxRegressionContinuous(List<SurvivalInfoContinuous>, int, double, double[]) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

coxRegressionContinuous(List<SurvivalInfoContinuous>, int, double, double[]) - Method in interface dk.alexandra.fresco.stat.Statistics

Estimate the parameters of a Cox model on the given data.

CoxRegressionContinuous - Class in dk.alexandra.fresco.stat.survival.cox

Estimate the coefficients of a Cox model on the given data using gradient descent.

CoxRegressionContinuous(List<SurvivalInfoContinuous>, int, double, double[]) - Constructor for class dk.alexandra.fresco.stat.survival.cox.CoxRegressionContinuous

Estimate the coefficients of a Cox model on the given data using gradient descent.

coxRegressionDiscrete(List<SurvivalInfoDiscrete>, int, double, double[]) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

coxRegressionDiscrete(List<SurvivalInfoDiscrete>, int, double, double[]) - Method in interface dk.alexandra.fresco.stat.Statistics

Estimate the parameters of a Cox model on the given data.

CoxRegressionDiscrete - Class in dk.alexandra.fresco.stat.survival.cox

Estimate the coefficients of a Cox model on the given data using gradient descent.

CoxRegressionDiscrete(List<SurvivalInfoDiscrete>, int, double, double[]) - Constructor for class dk.alexandra.fresco.stat.survival.cox.CoxRegressionDiscrete

Estimate the coefficients of a Cox model on the given data using gradient descent.

D

data - Variable in class dk.alexandra.fresco.stat.survival.SurvivalInfoSorter

DefaultFilteredStatistics - Class in dk.alexandra.fresco.stat

DefaultLinearAlgebra - Class in dk.alexandra.fresco.stat

DefaultMachineLearning - Class in dk.alexandra.fresco.stat

DefaultSampler - Class in dk.alexandra.fresco.stat

DefaultStatistics - Class in dk.alexandra.fresco.stat

div(List<DRes<SFixed>>, DRes<SFixed>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Divide all values in the given vector by the scalar.

DivideBySInt - Class in dk.alexandra.fresco.stat.utils

DivideBySInt(DRes<SFixed>, DRes<SInt>) - Constructor for class dk.alexandra.fresco.stat.utils.DivideBySInt

dk.alexandra.fresco.stat - package dk.alexandra.fresco.stat

dk.alexandra.fresco.stat.anonymisation - package dk.alexandra.fresco.stat.anonymisation

dk.alexandra.fresco.stat.descriptive - package dk.alexandra.fresco.stat.descriptive

dk.alexandra.fresco.stat.descriptive.helpers - package dk.alexandra.fresco.stat.descriptive.helpers

dk.alexandra.fresco.stat.descriptive.sort - package dk.alexandra.fresco.stat.descriptive.sort

dk.alexandra.fresco.stat.filtered - package dk.alexandra.fresco.stat.filtered

dk.alexandra.fresco.stat.filtered.helpers - package dk.alexandra.fresco.stat.filtered.helpers

dk.alexandra.fresco.stat.linearalgebra - package dk.alexandra.fresco.stat.linearalgebra

dk.alexandra.fresco.stat.mlp - package dk.alexandra.fresco.stat.mlp

dk.alexandra.fresco.stat.mlp.activationfunction - package dk.alexandra.fresco.stat.mlp.activationfunction

dk.alexandra.fresco.stat.mlp.evaluation - package dk.alexandra.fresco.stat.mlp.evaluation

dk.alexandra.fresco.stat.outlier - package dk.alexandra.fresco.stat.outlier

dk.alexandra.fresco.stat.regression.linear - package dk.alexandra.fresco.stat.regression.linear

dk.alexandra.fresco.stat.regression.logistic - package dk.alexandra.fresco.stat.regression.logistic

dk.alexandra.fresco.stat.sampling - package dk.alexandra.fresco.stat.sampling

dk.alexandra.fresco.stat.survival - package dk.alexandra.fresco.stat.survival

dk.alexandra.fresco.stat.survival.cox - package dk.alexandra.fresco.stat.survival.cox

dk.alexandra.fresco.stat.tests - package dk.alexandra.fresco.stat.tests

dk.alexandra.fresco.stat.utils - package dk.alexandra.fresco.stat.utils

E

entrywiseBinaryOp(List<A>, List<B>, VectorUtils.EntrywiseBinaryOp<A, B, C>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

entrywiseUnaryOp(List<A>, VectorUtils.EntrywiseUnaryOp<A, C>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

F

ffest(List<List<DRes<SFixed>>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

ffest(List<List<DRes<SFixed>>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the F-test statistics for the null hypothesis that the given datasets have the same mean.

FilteredStatistics - Interface in dk.alexandra.fresco.stat

Various analysis methods for filtered data, eg a data set and a bit vector of the same length with entry i indicating whether the i'th data point should be considered.

FilteredTTestResult(DRes<SFixed>, DRes<SInt>) - Constructor for class dk.alexandra.fresco.stat.filtered.OneSampleTTestFiltered.FilteredTTestResult

FindTiedGroups - Class in dk.alexandra.fresco.stat.descriptive.sort

Find sets of mututally equal in a list of secret shared integers: If the i'th and j'th elements are the same in the result, it indicates that the i'th and j'th elements are equal in the input data.

FindTiedGroups(List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.sort.FindTiedGroups

fit(MLP, List<ArrayList<DRes<SFixed>>>, List<ArrayList<DRes<SFixed>>>, int, double) - Method in class dk.alexandra.fresco.stat.DefaultMachineLearning

fit(MLP, List<ArrayList<DRes<SFixed>>>, List<ArrayList<DRes<SFixed>>>, int, double) - Method in interface dk.alexandra.fresco.stat.MachineLearning

Fit the given multilayer perceptron to a dataset using back propagation.

fit(List<ArrayList<DRes<SFixed>>>, List<ArrayList<DRes<SFixed>>>, int, double) - Method in class dk.alexandra.fresco.stat.mlp.MLP

Train this neural network using single step training (batch size = 1) and return a new neural network with the updated weights.

forEachWithIndices(BiConsumer<T, List<Integer>>) - Method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Perform an operation on all elements

ForwardPropagationOutput - Class in dk.alexandra.fresco.stat.mlp

This class represents the output of forward propagation on a single layer.

forwardSubstitution(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

Use forward substitution to compute a vector x such that ax = b where a is a lower triangular square matrix.

forwardSubstitution(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultLinearAlgebra

ForwardSubstitution - Class in dk.alexandra.fresco.stat.linearalgebra

Use forward substitution to compute a vector x such that ax = b, where a is lower triangular square matrix.

ForwardSubstitution(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.ForwardSubstitution

frequencyTable(List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

frequencyTable(List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute a frequency table for the data.

fromSFixed(List<List<DRes<SFixed>>>) - Static method in class dk.alexandra.fresco.stat.tests.KruskallWallisTest

If the test is to be applied on fixed point numbers (SFixed's), this method should be used to transform the data,

FTest - Class in dk.alexandra.fresco.stat.tests

Compute the F-test for equal mean (one-way-anova) for the given data sets.

FTest(List<List<DRes<SFixed>>>) - Constructor for class dk.alexandra.fresco.stat.tests.FTest

G

get(int[]) - Method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Get the element in this array with the given index vector.

get(Integer...) - Method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Get the element in this array with the given index vector.

get(ArrayList<DRes<SFixed>>, ActivationFunction) - Static method in enum dk.alexandra.fresco.stat.mlp.activationfunction.ActivationFunction

get(List<Integer>) - Method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Get the element in this array with the given index vector.

getAdjustedRSquared() - Method in class dk.alexandra.fresco.stat.regression.linear.LinearRegression.LinearRegressionResult

The adjusted coefficient of determination (R²_adj)

getAfterActivation() - Method in class dk.alexandra.fresco.stat.mlp.ForwardPropagationOutput

getAlpha() - Method in class dk.alexandra.fresco.stat.regression.linear.SimpleLinearRegression.SimpleLinearRegressionResult

getBeforeActivation() - Method in class dk.alexandra.fresco.stat.mlp.ForwardPropagationOutput

getBeta() - Method in class dk.alexandra.fresco.stat.regression.linear.LinearRegression.LinearRegressionResult

Estimates for the coefficients

getBeta() - Method in class dk.alexandra.fresco.stat.regression.linear.SimpleLinearRegression.SimpleLinearRegressionResult

getBias() - Method in class dk.alexandra.fresco.stat.mlp.Layer

getCensored() - Method in class dk.alexandra.fresco.stat.survival.SurvivalInfoContinuous

getCensored() - Method in class dk.alexandra.fresco.stat.survival.SurvivalInfoDiscrete

getCovariates() - Method in class dk.alexandra.fresco.stat.survival.SurvivalInfoContinuous

getCovariates() - Method in class dk.alexandra.fresco.stat.survival.SurvivalInfoDiscrete

getDelta() - Method in class dk.alexandra.fresco.stat.mlp.BackPropagationOutput

getDerivative(ArrayList<DRes<SFixed>>, ArrayList<DRes<SFixed>>, ActivationFunction) - Static method in enum dk.alexandra.fresco.stat.mlp.activationfunction.ActivationFunction

Compute f'(x) for the given activation function f: Rⁿ → Rⁿ.

getDimension() - Method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Get the dimension of this array.

getError() - Method in class dk.alexandra.fresco.stat.mlp.BackPropagationOutput

getErrorAlphaSquared() - Method in class dk.alexandra.fresco.stat.regression.linear.SimpleLinearRegression.SimpleLinearRegressionResult

getErrorBetaSquared() - Method in class dk.alexandra.fresco.stat.regression.linear.SimpleLinearRegression.SimpleLinearRegressionResult

getErrorVariance() - Method in class dk.alexandra.fresco.stat.regression.linear.LinearRegression.LinearRegressionResult

The regression error variance (s²) which is equal to the regression standard error squared

getFirst() - Method in class dk.alexandra.fresco.stat.utils.Triple

getFTestStatistics() - Method in class dk.alexandra.fresco.stat.regression.linear.LinearRegression.LinearRegressionResult

The F test statistics for null hypothesis that all coefficients are simultaneously zero.

getLayer(int) - Method in class dk.alexandra.fresco.stat.mlp.MLP

getN() - Method in class dk.alexandra.fresco.stat.filtered.OneSampleTTestFiltered.FilteredTTestResult

The sample size

getResult() - Method in class dk.alexandra.fresco.stat.filtered.OneSampleTTestFiltered.FilteredTTestResult

The test statistics

getRSquared() - Method in class dk.alexandra.fresco.stat.regression.linear.LinearRegression.LinearRegressionResult

The coefficient of determination (R²)

getRSquared() - Method in class dk.alexandra.fresco.stat.regression.linear.SimpleLinearRegression.SimpleLinearRegressionResult

getSecond() - Method in class dk.alexandra.fresco.stat.utils.Triple

getShape() - Method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Get the shape of this array.

getStdErrors() - Method in class dk.alexandra.fresco.stat.regression.linear.LinearRegression.LinearRegressionResult

Standard errors for each coefficient estimate

getThird() - Method in class dk.alexandra.fresco.stat.utils.Triple

getTime() - Method in class dk.alexandra.fresco.stat.survival.SurvivalInfoContinuous

getTime() - Method in class dk.alexandra.fresco.stat.survival.SurvivalInfoDiscrete

getTTestStatistics() - Method in class dk.alexandra.fresco.stat.regression.linear.LinearRegression.LinearRegressionResult

The t-test statistics for the null hypothesis that each coefficient are zero.

getWeights() - Method in class dk.alexandra.fresco.stat.mlp.Layer

gramSchmidt(List<ArrayList<DRes<SFixed>>>) - Method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

Return a list of mutually orthogonal vectors spanning the same space as the given vectors.

gramSchmidt(List<ArrayList<DRes<SFixed>>>) - Method in class dk.alexandra.fresco.stat.DefaultLinearAlgebra

GramSchmidt - Class in dk.alexandra.fresco.stat.linearalgebra

Perform the Gram-Schmidt process on a list of linearly independent vectors.

GramSchmidt(List<ArrayList<DRes<SFixed>>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.GramSchmidt

H

histogram(List<DRes<SInt>>, List<DRes<SInt>>, List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultFilteredStatistics

histogram(List<DRes<SInt>>, List<DRes<SInt>>, List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.FilteredStatistics

Compute a histogram on a filtered data set.

Histogram - Class in dk.alexandra.fresco.stat.descriptive

Compute a 1-dimensional histogram for a data set.

Histogram(List<DRes<SInt>>, List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.Histogram

Given a list of upper bounds for buckets and a list of samples, this computation computes the histogram for the given buckets.

histogramContinuous(double[], List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

histogramContinuous(double[], List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the histogram for the given sample.

histogramContinuous(List<DRes<SFixed>>, List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

histogramContinuous(List<DRes<SFixed>>, List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the histogram for the given sample.

histogramDiscrete(int[], List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

histogramDiscrete(int[], List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the histogram for the given sample.

histogramDiscrete(List<DRes<SInt>>, List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

histogramDiscrete(List<DRes<SInt>>, List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the histogram for the given sample.

HistogramFiltered - Class in dk.alexandra.fresco.stat.filtered

Compute a 1-dimensional histogram for a data set.

HistogramFiltered(List<DRes<SInt>>, List<DRes<SInt>>, List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.filtered.HistogramFiltered

Given a list of upper bounds for buckets and a list of samples, this computation computes the histogram for the given buckets.

I

innerProductWithBitvector(List<DRes<SInt>>, List<DRes<SFixed>>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Compute the inner product of a secret vector with a secret bit vector.

innerProductWithBitvectorPublic(List<DRes<SInt>>, List<Double>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Compute the inner product of a public vector with a secret bit vector.

input(List<BigInteger>, int, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

invertLowerTriangularMatrix(Matrix<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

Compute the inverse of a lower triangular matrix.

invertLowerTriangularMatrix(Matrix<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultLinearAlgebra

InvertLowerTriangularMatrix - Class in dk.alexandra.fresco.stat.linearalgebra

Invert lower triangular matrix.

InvertLowerTriangularMatrix(Matrix<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.InvertLowerTriangularMatrix

InvertUpperTriangularMatrix - Class in dk.alexandra.fresco.stat.linearalgebra

Invert upper triangular matrix.

InvertUpperTriangularMatrix(Matrix<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.InvertUpperTriangularMatrix

iterativeEigenvalues(Matrix<DRes<SFixed>>, int) - Method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

Approximate the eigenvalues of a matrix using the QR-algorithm.

iterativeEigenvalues(Matrix<DRes<SFixed>>, int) - Method in class dk.alexandra.fresco.stat.DefaultLinearAlgebra

K

kAnonymize(Matrix<DRes<SInt>>, List<DRes<SInt>>, List<List<DRes<SInt>>>, int) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

kAnonymize(Matrix<DRes<SInt>>, List<DRes<SInt>>, List<List<DRes<SInt>>>, int) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute a k-anonymized version of the given datset.

kAnonymize(Matrix<DRes<SInt>>, List<DRes<SInt>>, List<List<DRes<SInt>>>, int, List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultFilteredStatistics

kAnonymize(Matrix<DRes<SInt>>, List<DRes<SInt>>, List<List<DRes<SInt>>>, int, List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.FilteredStatistics

Compute a k-anonymized version of the given filtered datset.

kAnonymizeAndOpen(Matrix<DRes<SInt>>, List<DRes<SInt>>, List<List<DRes<SInt>>>, int) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

kAnonymizeAndOpen(Matrix<DRes<SInt>>, List<DRes<SInt>>, List<List<DRes<SInt>>>, int) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute a k-anonymized version of the given dataset and open it to all parties.

kAnonymizeAndOpen(Matrix<DRes<SInt>>, List<DRes<SInt>>, List<List<DRes<SInt>>>, int, List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultFilteredStatistics

kAnonymizeAndOpen(Matrix<DRes<SInt>>, List<DRes<SInt>>, List<List<DRes<SInt>>>, int, List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.FilteredStatistics

Compute a k-anonymized version of the given filtered datset.

kruskallWallisTest(List<List<DRes<SFixed>>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

kruskallWallisTest(List<List<DRes<SFixed>>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the Kruskall-Wallis test statistics for the null hypothesis that the given samples are drawn from same the distribution.

KruskallWallisTest - Class in dk.alexandra.fresco.stat.tests

Compute the Kruskall-Wallis test statistic on k groups, also known as one-way ANOVA on ranks.

KruskallWallisTest(List<List<DRes<SInt>>>) - Constructor for class dk.alexandra.fresco.stat.tests.KruskallWallisTest

L

Layer - Class in dk.alexandra.fresco.stat.mlp

Instances of this class represents fully connected layers in a neural network.

Layer(double[][], double[], ProtocolBuilderNumeric) - Constructor for class dk.alexandra.fresco.stat.mlp.Layer

Create a new fully connected layer with the given weights and biases and using a sigmoid activation function.

Layer(double[][], double[], ProtocolBuilderNumeric, ActivationFunction) - Constructor for class dk.alexandra.fresco.stat.mlp.Layer

Create a new fully connected layer with the given open weights, biases and activation function.

Layer(int, int, Random, ProtocolBuilderNumeric) - Constructor for class dk.alexandra.fresco.stat.mlp.Layer

Layer(int, int, Random, ProtocolBuilderNumeric, ActivationFunction) - Constructor for class dk.alexandra.fresco.stat.mlp.Layer

Create a new layer with random weights (bias is zero and weights are distributed as a standard Gaussian distribution divided by the number of neurons.

Layer(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>, ActivationFunction) - Constructor for class dk.alexandra.fresco.stat.mlp.Layer

Create a new fully connected layer with the given secret weights, biases and activation function.

lazy(E, F, G) - Static method in class dk.alexandra.fresco.stat.utils.Triple

LeakyBreakTies - Class in dk.alexandra.fresco.stat.descriptive

Assuming that the input data is sorted, this computation outputs the ranks of the elements using the given strategy.

LeakyBreakTies(List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.LeakyBreakTies

leakyFrequencyTable(List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

leakyFrequencyTable(List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute a frequency table for the data.

LeakyFrequencyTable - Class in dk.alexandra.fresco.stat.descriptive

Compute the frequencies of entries in the given data.

LeakyFrequencyTable(List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.LeakyFrequencyTable

LeakyKAnonymity - Class in dk.alexandra.fresco.stat.anonymisation

Compute a k-anonymous version of a dataset.

LeakyKAnonymity(Matrix<DRes<SInt>>, List<DRes<SInt>>, List<List<DRes<SInt>>>, int) - Constructor for class dk.alexandra.fresco.stat.anonymisation.LeakyKAnonymity

Each row in the data set contains the quasi-identifiers of an individual with a corresponding entry in the list of values of the sensitive attribute.

linearInverseProblem(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

Solve a linear inverse problem, eg.

linearInverseProblem(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultLinearAlgebra

LinearInverseProblem - Class in dk.alexandra.fresco.stat.linearalgebra

Solve a linear inverse problem, eg.

LinearInverseProblem(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.LinearInverseProblem

LinearInverseProblem(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>, Pair<Matrix<DRes<SFixed>>, Matrix<DRes<SFixed>>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.LinearInverseProblem

linearRegression(List<ArrayList<DRes<SFixed>>>, ArrayList<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

linearRegression(List<ArrayList<DRes<SFixed>>>, ArrayList<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute estimates for the parameters b of a linear model such that b0 x0 + ...

LinearRegression - Class in dk.alexandra.fresco.stat.regression.linear

Fit a linear model to the given dataset and output estimates for the coefficients and some model diagnostics (see LinearRegression.LinearRegressionResult).

LinearRegression(List<ArrayList<DRes<SFixed>>>, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.regression.linear.LinearRegression

LinearRegression.LinearRegressionResult - Class in dk.alexandra.fresco.stat.regression.linear

LinearRegressionResult(LinearRegression.State) - Constructor for class dk.alexandra.fresco.stat.regression.linear.LinearRegression.LinearRegressionResult

listBuilder(int, IntFunction<T>) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Build a list of the given size using a generator.

logisticRegression(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>, double[], IntToDoubleFunction, int) - Method in class dk.alexandra.fresco.stat.DefaultMachineLearning

logisticRegression(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>, double[], IntToDoubleFunction, int) - Method in interface dk.alexandra.fresco.stat.MachineLearning

Estimate the parameters of a logistic model using gradient descent.

LogisticRegression - Class in dk.alexandra.fresco.stat.regression.logistic

A naive implementation of logistic regression, not optimized for secure computation.

LogisticRegression(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>, double[], IntToDoubleFunction, int) - Constructor for class dk.alexandra.fresco.stat.regression.logistic.LogisticRegression

LogisticRegressionGD - Class in dk.alexandra.fresco.stat.regression.logistic

A gradient descent algorithm to fit a logistic model to a dataset.

LogisticRegressionGD(Matrix<DRes<SFixed>>, List<DRes<SFixed>>, double, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.regression.logistic.LogisticRegressionGD

LogisticRegressionPrediction - Class in dk.alexandra.fresco.stat.regression.logistic

LogisticRegressionPrediction(List<DRes<SFixed>>, List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.regression.logistic.LogisticRegressionPrediction

M

MachineLearning - Interface in dk.alexandra.fresco.stat

This computation library contains various functions for machine learning.

mahalanobisDistance(List<List<DRes<SFixed>>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

mahalanobisDistance(List<List<DRes<SFixed>>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the Mahalanobis Distance of all samples in a data set.

MahalanobisDistance - Class in dk.alexandra.fresco.stat.outlier

Compute the Mahalanobis Distance of all samples in a data set.

MahalanobisDistance(List<List<DRes<SFixed>>>) - Constructor for class dk.alexandra.fresco.stat.outlier.MahalanobisDistance

MahalanobisDistance(List<List<DRes<SFixed>>>, List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.outlier.MahalanobisDistance

map(Matrix<F>, Function<F, E>) - Static method in class dk.alexandra.fresco.stat.utils.MatrixUtils

Map a matrix to another matrix of the same size using the given function

map(Function<T, S>) - Method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Return a new MultiDimensionalArray of the same size as this, using the given function to map from the elements of this array to the corresponding entry in the new array

MatrixUtils - Class in dk.alexandra.fresco.stat.utils

MatrixUtils() - Constructor for class dk.alexandra.fresco.stat.utils.MatrixUtils

MatrixUtils.MatrixPopulator<E> - Interface in dk.alexandra.fresco.stat.utils

MaxList - Class in dk.alexandra.fresco.stat.utils

Given a list X = [x₀, ..., x_n-1] with n a power of 2, this method returns a pair [max(X), i : x_i = max(X)].

MaxList(List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.utils.MaxList

MaxPair - Class in dk.alexandra.fresco.stat.utils

Returns [max(x, y), 1 if y = max(x, y) and 0 otherwise]

MaxPair(DRes<SInt>, DRes<SInt>) - Constructor for class dk.alexandra.fresco.stat.utils.MaxPair

MLP - Class in dk.alexandra.fresco.stat.mlp

This class represents layered neural networks.

MLP(List<Layer>) - Constructor for class dk.alexandra.fresco.stat.mlp.MLP

moorePenrosePseudoInverse(Matrix<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

Compute the Moore-Penrose pseudo-inverse of an m×n-matrix with full column rank.

moorePenrosePseudoInverse(Matrix<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultLinearAlgebra

MoorePenrosePseudoInverse - Class in dk.alexandra.fresco.stat.linearalgebra

Compute the Moore-Penrose pseudo inverse of a matrix A.

MoorePenrosePseudoInverse(Matrix<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.MoorePenrosePseudoInverse

mult(List<DRes<SInt>>, List<DRes<SInt>>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Subtract two secret vectors.

MultiDimensionalArray<T> - Class in dk.alexandra.fresco.stat.utils

A multi-dimensional array is a data collection where entries are indexed by a fixed length vector (the length equals the dimension of the array).

MultiDimensionalArray() - Constructor for class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

MultiDimensionalHistogram - Class in dk.alexandra.fresco.stat.descriptive

Compute a multi-dimensiona histogram on a dataset

MultiDimensionalHistogram(List<List<DRes<SInt>>>, Matrix<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.MultiDimensionalHistogram

multiDimensionalHistogramDiscrete(List<List<DRes<SInt>>>, Matrix<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

multiDimensionalHistogramDiscrete(List<List<DRes<SInt>>>, Matrix<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the histogram for the given multi-dimensional sample.

N

negate(List<DRes<SInt>>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Compute the entry-wise binary negation of a secret vector

NoisyHistogram - Class in dk.alexandra.fresco.stat.anonymisation

Compute a differentially private histogram for a dataset.

NoisyHistogram(List<DRes<SInt>>, List<DRes<SInt>>, double) - Constructor for class dk.alexandra.fresco.stat.anonymisation.NoisyHistogram

Given a list of upper bounds for buckets and a list of samples, this computation computes a differentially private histogram for the given buckets.

NoisyStats - Class in dk.alexandra.fresco.stat.anonymisation

Compute differentially private estimates for the coefficients of a linear model fitted on a dataset.

NoisyStats(List<DRes<SFixed>>, List<DRes<SFixed>>, double) - Constructor for class dk.alexandra.fresco.stat.anonymisation.NoisyStats

normalizeVector(ArrayList<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

Normalize a non-zero vector.

normalizeVector(ArrayList<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultLinearAlgebra

NormalizeVector - Class in dk.alexandra.fresco.stat.linearalgebra

Normalize a vector.

NormalizeVector(ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.NormalizeVector

O

of(E, F, G) - Static method in class dk.alexandra.fresco.stat.utils.Triple

OneSampleTTest - Class in dk.alexandra.fresco.stat.tests

Compute a t-test statistics on a sample for the null hypothesis the mean of the sample is equal to mu.

OneSampleTTest(List<DRes<SFixed>>, DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.tests.OneSampleTTest

OneSampleTTestFiltered - Class in dk.alexandra.fresco.stat.filtered

Compute a t-test statistics on a sample for the null hypothesis the mean of the sample is equal to mu.

OneSampleTTestFiltered(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.filtered.OneSampleTTestFiltered

OneSampleTTestFiltered.FilteredTTestResult - Class in dk.alexandra.fresco.stat.filtered

open(List<DRes<SInt>>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

out() - Method in class dk.alexandra.fresco.stat.filtered.OneSampleTTestFiltered.FilteredTTestResult

out() - Method in class dk.alexandra.fresco.stat.mlp.BackPropagationOutput

out() - Method in class dk.alexandra.fresco.stat.mlp.ForwardPropagationOutput

out() - Method in class dk.alexandra.fresco.stat.mlp.Layer

out() - Method in class dk.alexandra.fresco.stat.regression.linear.LinearRegression.LinearRegressionResult

P

PearsonCorrelation - Class in dk.alexandra.fresco.stat.descriptive

Compute the correlation between two samples.

PearsonCorrelation(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SFixed>>, DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.descriptive.PearsonCorrelation

predict(MLP, ArrayList<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultMachineLearning

predict(MLP, ArrayList<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.MachineLearning

Assuming that the given MLP has n output neurons, this function applies the network to the given input and finds the index of the output i with 0 ≤ i < n containing the largest number.

Predict - Class in dk.alexandra.fresco.stat.mlp

Assuming that the given neural network has n dimensional output, this function applies the network to the given input and finds the index of the output i with 0 ≤ i < n containing the largest number.

Predict(MLP, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.mlp.Predict

product(List<DRes<SFixed>>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.RealUtils

Compute the product of all elements in the list.

project(Function<List<T>, T>) - Method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Project this array into an array of dimension d-1 using the given projection function.

projection(ArrayList<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

Compute the projection of a vector a onto another vector u.

projection(ArrayList<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultLinearAlgebra

Projection - Class in dk.alexandra.fresco.stat.linearalgebra

Compute the projection of a vector onto another.

Projection(ArrayList<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.Projection

Q

QRAlgorithm - Class in dk.alexandra.fresco.stat.linearalgebra

Compute eigenvalues of a matrix using the iterative QR algorithm.

QRAlgorithm(Matrix<DRes<SFixed>>, int) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.QRAlgorithm

qrDecomposition(Matrix<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

Compute the QR-decomposition of an mxn-matrix a with m ≥ n and full column rank.

qrDecomposition(Matrix<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultLinearAlgebra

QRDecomposition - Class in dk.alexandra.fresco.stat.linearalgebra

Compute the QR-decomposition of an mxn-matrix A with m ≥ n and full column rank.

QRDecomposition(Matrix<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.linearalgebra.QRDecomposition

R

Ranks - Class in dk.alexandra.fresco.stat.descriptive

Output ranks with averaged ties and correction term for Kruskall-Wallis.

Ranks(List<List<DRes<SInt>>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.Ranks

RealUtils - Class in dk.alexandra.fresco.stat.utils

RealUtils() - Constructor for class dk.alexandra.fresco.stat.utils.RealUtils

Relu - Class in dk.alexandra.fresco.stat.mlp.activationfunction

Compute the rectified linear function f(x) = x if x > 0 and f(x) = 0 otherwise.

Relu(DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.mlp.activationfunction.Relu

RELU - dk.alexandra.fresco.stat.mlp.activationfunction.ActivationFunction

ReluDerivative - Class in dk.alexandra.fresco.stat.mlp.activationfunction

Compute the derivative of the Relu function, eg.

ReluDerivative(DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.mlp.activationfunction.ReluDerivative

S

sampleBernoulliDistribution(double) - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleBernoulliDistribution(double) - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from a Bernoulli distribution with parameter p with 0 ≤ p ≤ 1.

sampleBernoulliDistribution(DRes<SFixed>) - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleBernoulliDistribution(DRes<SFixed>) - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from a Bernoulli distribution with parameter p with 0 ≤ p ≤ 1.

SampleBernoulliDistribution - Class in dk.alexandra.fresco.stat.sampling

Sample a number from a Bernoulli distribution which is 0 with probability p and 1 with probability 1-p.

SampleBernoulliDistribution(double) - Constructor for class dk.alexandra.fresco.stat.sampling.SampleBernoulliDistribution

SampleBernoulliDistribution(DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.sampling.SampleBernoulliDistribution

sampleCategoricalDistribution(double[]) - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleCategoricalDistribution(double[]) - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from the set {0, ..., probabilities.length - 1} with probabilities[i] indicating the probability of drawing i.

sampleCategoricalDistribution(List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleCategoricalDistribution(List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from the set {0, ..., probabilities.size() - 1} with probabilities.get(i) indicating the probability of drawing i.

sampleCategoricalDistribution(List<DRes<SFixed>>, boolean) - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleCategoricalDistribution(List<DRes<SFixed>>, boolean) - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from the set {0, ..., probabilities.size() - 1} with probabilities.get(i) indicating the probability of drawing i.

SampleCategoricalDistribution - Class in dk.alexandra.fresco.stat.sampling

Sample an element from a categorical distribution.

SampleCategoricalDistribution(double[]) - Constructor for class dk.alexandra.fresco.stat.sampling.SampleCategoricalDistribution

SampleCategoricalDistribution(List<DRes<SFixed>>, boolean) - Constructor for class dk.alexandra.fresco.stat.sampling.SampleCategoricalDistribution

SampleCovariance - Class in dk.alexandra.fresco.stat.descriptive

Compute the unbiased covariance matrix for the given observations

SampleCovariance(List<List<DRes<SFixed>>>, List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.SampleCovariance

Create a new computation with a given computed sample mean.

sampleExponentialDistribution(double) - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleExponentialDistribution(double) - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from an exponential distribution with parameter λ = 1 / b with b gt; 0.

sampleExponentialDistribution(DRes<SFixed>) - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleExponentialDistribution(DRes<SFixed>) - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from an exponential distribution with parameter λ = 1 / b with b gt; 0.

SampleExponentialDistribution - Class in dk.alexandra.fresco.stat.sampling

This computation samples from an exponential distribution with rate 1/lambda and location 0.

SampleExponentialDistribution(double) - Constructor for class dk.alexandra.fresco.stat.sampling.SampleExponentialDistribution

SampleExponentialDistribution(DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.sampling.SampleExponentialDistribution

SampleExponentialDistribution(BigDecimal) - Constructor for class dk.alexandra.fresco.stat.sampling.SampleExponentialDistribution

SampleIrwinHallDistribution - Class in dk.alexandra.fresco.stat.sampling

Sample a number from an Irwin-Hall distribution which is the sum of n iid U(0,1) distributions.

SampleIrwinHallDistribution(int) - Constructor for class dk.alexandra.fresco.stat.sampling.SampleIrwinHallDistribution

sampleLaplaceDistribution(double) - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleLaplaceDistribution(double) - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from a Laplace distribution with location 0 and scale b > 0.

sampleLaplaceDistribution(DRes<SFixed>) - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleLaplaceDistribution(DRes<SFixed>) - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from a Laplace distribution with location 0 and scale b gt; 0.

SampleLaplaceDistribution - Class in dk.alexandra.fresco.stat.sampling

This computation samples from a Laplace distribution with scale b and location 0.

SampleLaplaceDistribution(double) - Constructor for class dk.alexandra.fresco.stat.sampling.SampleLaplaceDistribution

SampleLaplaceDistribution(DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.sampling.SampleLaplaceDistribution

sampleMean(List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

sampleMean(List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the sample mean of the given data.

sampleMean(List<DRes<SFixed>>, List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultFilteredStatistics

sampleMean(List<DRes<SFixed>>, List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.FilteredStatistics

Compute the sample mean of filtered data set.

SampleMean - Class in dk.alexandra.fresco.stat.descriptive

Compute the mean of a list of observations.

SampleMean(List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.SampleMean

SampleMeanFiltered - Class in dk.alexandra.fresco.stat.filtered

Compute the sample mean of a set of samples.

SampleMeanFiltered(List<DRes<SFixed>>, List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.filtered.SampleMeanFiltered

sampleMedian(List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

sampleMedian(List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the sample median of the sample set.

SampleMedian - Class in dk.alexandra.fresco.stat.descriptive

Compute the sample median of a sample

SampleMedian(List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.SampleMedian

sampleNormalDistribution() - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleNormalDistribution() - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from a normal distribution with mean 0 and variance 1.

SampleNormalDistribution - Class in dk.alexandra.fresco.stat.sampling

Sample a number from an approximately standard normal distribution.

SampleNormalDistribution() - Constructor for class dk.alexandra.fresco.stat.sampling.SampleNormalDistribution

samplePercentiles(List<DRes<SFixed>>, double[]) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

samplePercentiles(List<DRes<SFixed>>, double[]) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the sample percentiles of a sample set.

SampleQuantiles - Class in dk.alexandra.fresco.stat.descriptive

Compute some quantiles for a sample

SampleQuantiles(List<DRes<SFixed>>, double[]) - Constructor for class dk.alexandra.fresco.stat.descriptive.SampleQuantiles

Sampler - Interface in dk.alexandra.fresco.stat

This computation library contains functions which samples random values from various distributions.

sampleRademacherDistribution() - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleRademacherDistribution() - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample from a Rademacher distribution.

SampleRademacherDistribution - Class in dk.alexandra.fresco.stat.sampling

This computation samples from a Rademacher distribution which can be -1 or +1 each with probability 1/2.

SampleRademacherDistribution() - Constructor for class dk.alexandra.fresco.stat.sampling.SampleRademacherDistribution

sampleStandardDeviation(List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

sampleStandardDeviation(List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the standard deviation of the data.

sampleStandardDeviation(List<DRes<SFixed>>, DRes<SFixed>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

sampleStandardDeviation(List<DRes<SFixed>>, DRes<SFixed>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the sample standard deviation of the data given that the sample mean has already been calculated.

SampleStandardDeviation - Class in dk.alexandra.fresco.stat.descriptive

Compute the standard deviation of a list of observations.

SampleStandardDeviation(List<DRes<SFixed>>, DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.descriptive.SampleStandardDeviation

sampleUniformDistribution() - Method in class dk.alexandra.fresco.stat.DefaultSampler

sampleUniformDistribution() - Method in interface dk.alexandra.fresco.stat.Sampler

Draw a sample form a uniform distribution on [0, 1).

SampleUniformDistribution - Class in dk.alexandra.fresco.stat.sampling

Sample a number uniformly in the interval [0,1).

SampleUniformDistribution() - Constructor for class dk.alexandra.fresco.stat.sampling.SampleUniformDistribution

sampleVariance(List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

sampleVariance(List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the sample variance of the given data.

sampleVariance(List<DRes<SFixed>>, DRes<SFixed>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

sampleVariance(List<DRes<SFixed>>, DRes<SFixed>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the sample variance of the given data, assuming the sample mean has already been calculated.

sampleVariance(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultFilteredStatistics

sampleVariance(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.FilteredStatistics

Compute the sample variance of a filtered data set.

sampleVariance(List<DRes<SFixed>>, List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultFilteredStatistics

sampleVariance(List<DRes<SFixed>>, List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.FilteredStatistics

Compute the sample variance of a filtered data set.

SampleVariance - Class in dk.alexandra.fresco.stat.descriptive

Compute the sample variance for a list of observations.

SampleVariance(List<DRes<SFixed>>, DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.descriptive.SampleVariance

Create a new computation with a given computed sample mean.

SampleVarianceFiltered - Class in dk.alexandra.fresco.stat.filtered

Compute the sample variance of a set of samples.

SampleVarianceFiltered(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.filtered.SampleVarianceFiltered

SampleVarianceFiltered(List<DRes<SFixed>>, List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.filtered.SampleVarianceFiltered

scale(List<DRes<SFixed>>, double, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Scale all values in the given vector by the scalar.

scale(List<DRes<SFixed>>, DRes<SFixed>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Scale all values in the given vector by the scalar.

scaleInt(List<DRes<SInt>>, DRes<SInt>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Scale all values in the given vector by the scalar.

set(int[], T) - Method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Set a new value for the given index vector.

set(List<Integer>, T) - Method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

Set a new value for the given index vector.

Sigmoid - Class in dk.alexandra.fresco.stat.mlp.activationfunction

Compute the sigmoid (logistic) function f(x) = 1 / (1 + e^-x).

Sigmoid(DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.mlp.activationfunction.Sigmoid

SIGMOID - dk.alexandra.fresco.stat.mlp.activationfunction.ActivationFunction

SigmoidDerivative - Class in dk.alexandra.fresco.stat.mlp.activationfunction

Compute the derivative of the Sigmoid function f'(x)given the function value y = f(x)in the point.

SigmoidDerivative(DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.mlp.activationfunction.SigmoidDerivative

simpleLinearRegression(List<DRes<SFixed>>, List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

simpleLinearRegression(List<DRes<SFixed>>, List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute simple linear regression on two samples.

SimpleLinearRegression - Class in dk.alexandra.fresco.stat.regression.linear

This computation returns coefficients a and b based on a simple linear regression of the observed x and y values.

SimpleLinearRegression(List<DRes<SFixed>>, List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.regression.linear.SimpleLinearRegression

SimpleLinearRegression(List<DRes<SFixed>>, List<DRes<SFixed>>, boolean, boolean) - Constructor for class dk.alexandra.fresco.stat.regression.linear.SimpleLinearRegression

SimpleLinearRegression.SimpleLinearRegressionResult - Class in dk.alexandra.fresco.stat.regression.linear

SimpleLinearRegressionTTest - Class in dk.alexandra.fresco.stat.regression.linear

Test for the null hypothesis H₀: β = β₀ where β is a coefficient in a linear model.

SimpleLinearRegressionTTest(DRes<SFixed>, DRes<SFixed>, DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.regression.linear.SimpleLinearRegressionTTest

SP - Class in dk.alexandra.fresco.stat.descriptive.helpers

Calculate the sum of products (aka the dot product) of two samples

SP(List<DRes<SFixed>>, List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.helpers.SP

sparse(List<Integer>) - Static method in class dk.alexandra.fresco.stat.utils.MultiDimensionalArray

SPD - Class in dk.alexandra.fresco.stat.descriptive.helpers

Compute the sum of products of deviations of two samples.

SPD(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SFixed>>, DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.descriptive.helpers.SPD

SSD - Class in dk.alexandra.fresco.stat.descriptive.helpers

Compute the sum of squared deviations

SSD(List<DRes<SFixed>>, DRes<SFixed>) - Constructor for class dk.alexandra.fresco.stat.descriptive.helpers.SSD

SSDFiltered - Class in dk.alexandra.fresco.stat.filtered.helpers

SSDFiltered(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.filtered.helpers.SSDFiltered

SSDFiltered(List<DRes<SFixed>>, List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.filtered.helpers.SSDFiltered

SSE - Class in dk.alexandra.fresco.stat.descriptive.helpers

Compute the sum of squared estimate of errors (aka the residual sum of squares)

SSE(List<DRes<SFixed>>, List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.helpers.SSE

Statistics - Interface in dk.alexandra.fresco.stat

This computation library contains various statistical functions.

sub(List<DRes<SFixed>>, List<DRes<SFixed>>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Subtract two secret vectors.

subMatrix(Matrix<E>, int, int, int, int) - Static method in class dk.alexandra.fresco.stat.utils.MatrixUtils

Create a new matrix from the given one with rows and columns taken from certain intervals.

sum(List<List<DRes<SFixed>>>, ProtocolBuilderNumeric) - Static method in class dk.alexandra.fresco.stat.utils.VectorUtils

Add a list of secret vectors.

SumFiltered - Class in dk.alexandra.fresco.stat.filtered.helpers

SumFiltered(List<DRes<SFixed>>, List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.filtered.helpers.SumFiltered

SurvivalInfoContinuous - Class in dk.alexandra.fresco.stat.survival

Represents a data point in data for survival analysis with continuous covariates.

SurvivalInfoContinuous(List<DRes<SFixed>>, DRes<SInt>, DRes<SInt>) - Constructor for class dk.alexandra.fresco.stat.survival.SurvivalInfoContinuous

SurvivalInfoDiscrete - Class in dk.alexandra.fresco.stat.survival

Represents a data point in data for survival analysis with only discrete covariates on finite sets.

SurvivalInfoDiscrete(List<List<DRes<SInt>>>, DRes<SInt>, DRes<SInt>) - Constructor for class dk.alexandra.fresco.stat.survival.SurvivalInfoDiscrete

SurvivalInfoSorter<T> - Class in dk.alexandra.fresco.stat.survival

Convert an instance of type T into a Pair of a DRes<SInt> and a List<DRes<SInt>> and back for use with Collections.sort(List) to sort on the time of event parameter.

SurvivalInfoSorter(List<T>) - Constructor for class dk.alexandra.fresco.stat.survival.SurvivalInfoSorter

SurvivalInfoSorterContinuous - Class in dk.alexandra.fresco.stat.survival

SurvivalInfoSorterContinuous(List<SurvivalInfoContinuous>) - Constructor for class dk.alexandra.fresco.stat.survival.SurvivalInfoSorterContinuous

SurvivalInfoSorterDiscrete - Class in dk.alexandra.fresco.stat.survival

SurvivalInfoSorterDiscrete(List<SurvivalInfoDiscrete>) - Constructor for class dk.alexandra.fresco.stat.survival.SurvivalInfoSorterDiscrete

T

transpose(Matrix<E>) - Static method in class dk.alexandra.fresco.stat.utils.MatrixUtils

Return a new matrix equal to the transpose of the given matrix

TransposedMatrixAction - Class in dk.alexandra.fresco.stat.utils

This computation multiplies the transpose of the given matrix to a vector without explicitly representing the matrix in its transposed form.

TransposedMatrixAction(Matrix<DRes<SFixed>>, ArrayList<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.utils.TransposedMatrixAction

Triple<A,B,C> - Class in dk.alexandra.fresco.stat.utils

Instances of this class holds three values of arbitrary type.

Triple(A, B, C) - Constructor for class dk.alexandra.fresco.stat.utils.Triple

ttest(List<DRes<SFixed>>, DRes<SFixed>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

ttest(List<DRes<SFixed>>, DRes<SFixed>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the test statistics for a Student's t-test for the hypothesis that the mean of the sample is equal to mu.

ttest(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SInt>>) - Method in class dk.alexandra.fresco.stat.DefaultFilteredStatistics

ttest(List<DRes<SFixed>>, DRes<SFixed>, List<DRes<SInt>>) - Method in interface dk.alexandra.fresco.stat.FilteredStatistics

Compute the test statistics for a student t-test on the filtered data set.

ttest(List<DRes<SFixed>>, List<DRes<SFixed>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

ttest(List<DRes<SFixed>>, List<DRes<SFixed>>) - Method in interface dk.alexandra.fresco.stat.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.

TwoDimensionalHistogram - Class in dk.alexandra.fresco.stat.descriptive

Compute a two-dimensional histogram for a given two dimensional data set.

TwoDimensionalHistogram(Pair<List<DRes<SInt>>, List<DRes<SInt>>>, List<Pair<DRes<SInt>, DRes<SInt>>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.TwoDimensionalHistogram

twoDimensionalHistogramContinuous(Pair<List<DRes<SFixed>>, List<DRes<SFixed>>>, List<Pair<DRes<SFixed>, DRes<SFixed>>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

twoDimensionalHistogramContinuous(Pair<List<DRes<SFixed>>, List<DRes<SFixed>>>, List<Pair<DRes<SFixed>, DRes<SFixed>>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the histogram for the given two-dimensional sample.

twoDimensionalHistogramDiscrete(Pair<List<DRes<SInt>>, List<DRes<SInt>>>, List<Pair<DRes<SInt>, DRes<SInt>>>) - Method in class dk.alexandra.fresco.stat.DefaultStatistics

twoDimensionalHistogramDiscrete(Pair<List<DRes<SInt>>, List<DRes<SInt>>>, List<Pair<DRes<SInt>, DRes<SInt>>>) - Method in interface dk.alexandra.fresco.stat.Statistics

Compute the histogram for the given two-dimensional sample.

TwoSampleTTest - Class in dk.alexandra.fresco.stat.tests

This implements the calculation of a t-test statistics for two samples where it can be assumed that the variances are equal.

TwoSampleTTest(List<DRes<SFixed>>, List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.tests.TwoSampleTTest

U

using(ProtocolBuilderNumeric) - Static method in interface dk.alexandra.fresco.stat.AdvancedLinearAlgebra

using(ProtocolBuilderNumeric) - Static method in interface dk.alexandra.fresco.stat.FilteredStatistics

using(ProtocolBuilderNumeric) - Static method in interface dk.alexandra.fresco.stat.MachineLearning

using(ProtocolBuilderNumeric) - Static method in interface dk.alexandra.fresco.stat.Sampler

using(ProtocolBuilderNumeric) - Static method in interface dk.alexandra.fresco.stat.Statistics

USS - Class in dk.alexandra.fresco.stat.descriptive.helpers

Compute the uncorrected sum of squares

USS(List<DRes<SFixed>>) - Constructor for class dk.alexandra.fresco.stat.descriptive.helpers.USS

USSFiltered - Class in dk.alexandra.fresco.stat.filtered.helpers

USSFiltered(List<DRes<SFixed>>, List<DRes<SInt>>) - Constructor for class dk.alexandra.fresco.stat.filtered.helpers.USSFiltered

V

valueOf(String) - Static method in enum dk.alexandra.fresco.stat.mlp.activationfunction.ActivationFunction

Returns the enum constant of this type with the specified name.

values() - Static method in enum dk.alexandra.fresco.stat.mlp.activationfunction.ActivationFunction

Returns an array containing the constants of this enum type, in the order they are declared.

VectorUtils - Class in dk.alexandra.fresco.stat.utils

VectorUtils() - Constructor for class dk.alexandra.fresco.stat.utils.VectorUtils

VectorUtils.EntrywiseBinaryOp<A,B,C> - Interface in dk.alexandra.fresco.stat.utils

VectorUtils.EntrywiseUnaryOp<A,C> - Interface in dk.alexandra.fresco.stat.utils

W

withSFixed(List<DRes<SFixed>>) - Static method in class dk.alexandra.fresco.stat.utils.MaxList

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