All Classes
| Class | Description |
|---|---|
| Accuracy |
Return the number of correct predictions in the given data set.
|
| AccuracyBinary |
Return the number of correct predictions in the given data set.
|
| ActivationFunction |
This enum represents the available activation functions f: Rn→
Rn for use with neural networks.
|
| AdvancedLinearAlgebra |
This computation directory contains variuous linear algebra functions.
|
| AffineMap |
Apply an affine map to a vector
|
| BackPropagationOutput |
This class represents the output of back propagation on a single layer.
|
| BackSubstitution |
Use backward substitution to compute a vector x such that ax = b, where a is upper triangular
square matrix.
|
| ChiSquareTest |
Compute the Χ2-test for goodness of fit of the given observatinos.
|
| Convolution |
Compute the discrete convolution of two vectors
|
| CoxGradientContinuous | |
| CoxGradientDiscrete | |
| CoxRegressionContinuous |
Estimate the coefficients of a Cox model on the given data using gradient descent.
|
| CoxRegressionDiscrete |
Estimate the coefficients of a Cox model on the given data using gradient descent.
|
| DefaultFilteredStatistics | |
| DefaultLinearAlgebra | |
| DefaultMachineLearning | |
| DefaultSampler | |
| DefaultStatistics | |
| DivideBySInt | |
| FilteredStatistics |
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.
|
| FindTiedGroups |
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.
|
| ForwardPropagationOutput |
This class represents the output of forward propagation on a single layer.
|
| ForwardSubstitution |
Use forward substitution to compute a vector x such that ax = b, where a is lower triangular
square matrix.
|
| FTest |
Compute the F-test for equal mean (one-way-anova) for the given data sets.
|
| GramSchmidt |
Perform the Gram-Schmidt process on a list of linearly independent vectors.
|
| Histogram |
Compute a 1-dimensional histogram for a data set.
|
| HistogramFiltered |
Compute a 1-dimensional histogram for a data set.
|
| InvertLowerTriangularMatrix |
Invert lower triangular matrix.
|
| InvertUpperTriangularMatrix |
Invert upper triangular matrix.
|
| KruskallWallisTest |
Compute the Kruskall-Wallis test statistic on k groups, also known as one-way ANOVA on
ranks.
|
| Layer |
Instances of this class represents fully connected layers in a neural network.
|
| LeakyBreakTies |
Assuming that the input data is sorted, this computation outputs the ranks of the elements using
the given strategy.
|
| LeakyFrequencyTable |
Compute the frequencies of entries in the given data.
|
| LeakyKAnonymity |
Compute a k-anonymous version of
a dataset.
|
| LinearInverseProblem |
Solve a linear inverse problem, eg.
|
| LinearRegression |
Fit a linear model to the given dataset and output estimates for the coefficients and some model
diagnostics (see
LinearRegression.LinearRegressionResult). |
| LinearRegression.LinearRegressionResult | |
| LogisticRegression |
A naive implementation of logistic regression, not optimized for secure computation.
|
| LogisticRegressionGD |
A gradient descent algorithm to fit a logistic model to a dataset.
|
| LogisticRegressionPrediction | |
| MachineLearning |
This computation library contains various functions for machine learning.
|
| MahalanobisDistance |
Compute the Mahalanobis Distance
of all samples in a data set.
|
| MatrixUtils | |
| MatrixUtils.MatrixPopulator<E> | |
| MaxList |
Given a list X = [x0, ..., xn-1] with n a power of 2,
this method returns a pair [max(X), i : xi = max(X)].
|
| MaxPair |
Returns [max(x, y), 1 if y = max(x, y) and 0 otherwise]
|
| MLP |
This class represents layered neural networks.
|
| MoorePenrosePseudoInverse |
Compute the Moore-Penrose pseudo inverse of a matrix A.
|
| MultiDimensionalArray<T> |
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).
|
| MultiDimensionalHistogram |
Compute a multi-dimensiona histogram on a dataset
|
| NoisyHistogram |
Compute a differentially private
histogram for a dataset.
|
| NoisyStats |
Compute differentially private estimates for the coefficients of a linear model fitted on a
dataset.
|
| NormalizeVector |
Normalize a vector.
|
| OneSampleTTest |
Compute a t-test statistics on a sample for the null hypothesis the mean of the sample is equal to mu.
|
| OneSampleTTestFiltered |
Compute a t-test statistics on a sample for the null hypothesis the mean of the sample is
equal to mu.
|
| OneSampleTTestFiltered.FilteredTTestResult | |
| PearsonCorrelation |
Compute the correlation between two samples.
|
| Predict |
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.
|
| Projection |
Compute the projection of a vector onto another.
|
| QRAlgorithm |
Compute eigenvalues of a matrix using the iterative QR algorithm.
|
| QRDecomposition |
Compute the QR-decomposition of an mxn-matrix A with m ≥ n and full column rank.
|
| Ranks |
Output ranks with averaged ties and correction term for Kruskall-Wallis.
|
| RealUtils | |
| Relu |
Compute the rectified linear function f(x) = x if x > 0 and f(x) = 0
otherwise.
|
| ReluDerivative |
Compute the derivative of the
Relu function, eg. |
| SampleBernoulliDistribution |
Sample a number from a Bernoulli distribution which is 0 with probability p and 1 with
probability 1-p.
|
| SampleCategoricalDistribution |
Sample an element from a categorical distribution.
|
| SampleCovariance |
Compute the unbiased covariance matrix for the given observations
|
| SampleExponentialDistribution |
This computation samples from an exponential distribution with rate 1/lambda and location
0.
|
| SampleIrwinHallDistribution |
Sample a number from an Irwin-Hall distribution which is the sum of n iid U(0,1)
distributions.
|
| SampleLaplaceDistribution |
This computation samples from a Laplace distribution with scale b and location 0.
|
| SampleMean |
Compute the mean of a list of observations.
|
| SampleMeanFiltered |
Compute the sample mean of a set of samples.
|
| SampleMedian |
Compute the sample median of a sample
|
| SampleNormalDistribution |
Sample a number from an approximately standard normal distribution.
|
| SampleQuantiles |
Compute some quantiles for a sample
|
| Sampler |
This computation library contains functions which samples random values from various distributions.
|
| SampleRademacherDistribution |
This computation samples from a Rademacher distribution which can be -1 or +1 each with
probability 1/2.
|
| SampleStandardDeviation |
Compute the standard deviation of a list of observations.
|
| SampleUniformDistribution |
Sample a number uniformly in the interval [0,1).
|
| SampleVariance |
Compute the sample variance for a list of observations.
|
| SampleVarianceFiltered |
Compute the sample variance of a set of samples.
|
| Sigmoid |
Compute the sigmoid (logistic) function f(x) = 1 / (1 + e-x).
|
| SigmoidDerivative |
Compute the derivative of the
Sigmoid function f'(x)given the function value
y = f(x)in the point. |
| SimpleLinearRegression |
This computation returns coefficients a and b based on a simple linear regression of the observed
x and y values.
|
| SimpleLinearRegression.SimpleLinearRegressionResult | |
| SimpleLinearRegressionTTest |
Test for the null hypothesis H0: β = β0 where β
is a coefficient in a linear model.
|
| SP |
Calculate the sum of products (aka the dot product) of two samples
|
| SPD |
Compute the sum of products of deviations of two samples.
|
| SSD |
Compute the sum of squared deviations
|
| SSDFiltered | |
| SSE |
Compute the sum of squared estimate of errors (aka the residual sum of squares)
|
| Statistics |
This computation library contains various statistical functions.
|
| SumFiltered | |
| SurvivalInfoContinuous |
Represents a data point in data for survival analysis with continuous covariates.
|
| SurvivalInfoDiscrete |
Represents a data point in data for survival analysis with only discrete covariates on finite
sets.
|
| SurvivalInfoSorter<T> |
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. |
| SurvivalInfoSorterContinuous | |
| SurvivalInfoSorterDiscrete | |
| TransposedMatrixAction |
This computation multiplies the transpose of the given matrix to a vector without explicitly
representing the matrix in its transposed form.
|
| Triple<A,B,C> |
Instances of this class holds three values of arbitrary type.
|
| TwoDimensionalHistogram |
Compute a two-dimensional histogram for a given two dimensional data set.
|
| TwoSampleTTest |
This implements the calculation of a t-test statistics for two samples where it can be assumed
that the variances are equal.
|
| USS |
Compute the uncorrected sum of squares
|
| USSFiltered | |
| VectorUtils | |
| VectorUtils.EntrywiseBinaryOp<A,B,C> | |
| VectorUtils.EntrywiseUnaryOp<A,C> |