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    Namespace Microsoft.ML.Probabilistic.Math

    Classes

    AllZeroException

    Exception type thrown when probability vector = (0,0,0,...,0).

    ApproximateSparseVector

    A one-dimensional vector of double values, optimised for the case where many of the elements share a common value (which need not be zero) within some tolerance.

    BFGS

    This implementation of BFGS is based on Algorithm 6.1 from Nocedal and Wright (Second edition, 2006).

    BranchAndBound

    ContinuedFraction

    A class for evaluating continued fractions

    DenseVector

    1-dimensional dense container of double precision data that supports vector operations.

    DerivativeChecker

    Class used to check analytic derivatives using finite difference approximation

    ExtendedDouble

    Represents a number as Mantissa * exp(Exponent).

    LBFGS

    Implements the LBFGS compact Quasi-Newton solver.

    LBFGSArray

    Implements the LBFGS compact Quasi-Newton solver on an array of Vectors (which may be sparse)

    LineSearch

    This line search algorithm is algorithm 3.5/3.6 from Nocedal and Wright (Second edition, 2006). It provides a step length that satisfies the strong Wolfe conditions.

    LowerTriangularMatrix

    Class for lower triangular matrices

    LuDecomposition

    Class for calculating and doing operations with an LU decomposition

    Matrix

    Two-dimensional container of doubles.

    MatrixMeanVarianceAccumulator

    Class for accumulating weighted noisy matrix observations, and computing sample count, mean matrix, and covariance matrix

    MatrixSingularException

    Exception thrown when a singular matrix is encountered.

    MeanAccumulator

    Class for accumulating weighted scalar observations and computing sample count and mean

    MeanVarianceAccumulator

    Class for accumulating weighted noisy scalar observations, and computing sample count, mean, and variance

    MeanVarianceAccumulator2

    Class for accumulating weighted noisy scalar observations, and computing sample count, mean, and variance

    MeanVarianceAccumulatorSkipNaNs

    Decorator of MeanVarianceAccumulator that does not add if any input is NaN.

    MMath

    This class provides mathematical constants and special functions, analogous to System.Math. It cannot be instantiated and consists of static members only.

    OptimiserIterationEventArgs

    Optimiser iteration event

    PiecewiseVector

    A one-dimensional vector of double values, optimised for the case where many contiguous ranges of elements have the same value.

    PositiveDefiniteMatrix

    A subclass of Matrix with extra methods appropriate to positive-definite matrices.

    PositiveDefiniteMatrixException

    Exception thrown when a matrix is not positive definite.

    Quadrature

    Quadrature nodes and weights

    Rand

    This class provides a source of non-uniform random numbers. It cannot be instantiated and consists of only static functions.

    Region

    Represents a hyper-rectangle in arbitrary dimensions.

    SparseVector

    A one-dimensional vector of double values, optimised for the case where many of the elements share a common value (which need not be zero).

    Sparsity

    Defines sparsity settings for vectors. The sparsity handling has been designed to deal with large dimensional distributions such as Discrete and Dirichlet distributions.

    UpperTriangularMatrix

    Upper triangular matrix class

    Vector

    Base class for vectors. DenseVector, SparseVector, and ApproximateSparseVector all inherit from this base class.

    VectorMeanVarianceAccumulator

    Class for accumulating weighted noisy vector observations, and computing sample count, mean vector, and covariance matrix

    Structs

    ConstantVector

    A vector which has a constant value between its start and end indices.

    Interfaces

    CanSetAllElementsTo<T>

    Supports setting all elements to duplicates of the same value

    Diffable

    Supports calculating the maximum difference between this instance and another object (not necessarily of the same type)

    SettableTo<T>

    Supports setting an instance to a value

    SettableToPower<T>

    Supports setting an instance to a value raised to a power

    SettableToProduct<T>

    Supports setting an instance to the product of two values of the same type

    SettableToProduct<T, U>

    Supports setting an instance to the product of two values of different types

    SettableToRatio<T>

    Supports setting an instance to the ratio of two values of the same type

    SettableToRatio<T, U>

    Supports setting an instance to the ratio of two values of different types

    SettableToWeightedSum<T>

    Supports setting an instance to the weighted sum of two values of the same type

    SettableToWeightedSumExact<T>

    Indicates that a distribution can represent weighted sum of distributions of type T exactly.

    Enums

    BFGS.ConvergenceCriteria

    Convergence criteria:

    • Gradient: |grad F|/sqrt(dimensions) <= eps
    • Objective: |F(k+1)-F(k)|<=eps*max{|F(k)|,|F(k+1)|,1}

    StorageType

    The type of storage used in a vector, which is specified as part of the Sparsity class.

    Delegates

    FunctionEval

    Delegate type for function evaluation

    IterationEventHandler

    Event delegate for handling iteration event

    LBFGSArray.FunctionEvalArray

    Delegate type for function evaluation

    LineSearchEval

    Delegate type for function evaluation

    PiecewiseVector.RangeFunc

    An action that takes a range and two values.

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