data library
All data packages in a single import.
Classes
- BernoulliDistribution
-
The Bernoulli distribution is a discrete probability distribution which
takes value 1 with probability
p
and value 0 with probabilityq = 1 − p
. - BigIntDataType
- BigIntEquality
- BigIntField
- BinomialDistribution
- The Binomial distribution is a discrete probability distribution which describes the number of successes in a series of independent yes/no experiments all with the same probability of success.
- BooleanDataType
- Complex
-
A complex number of the form
a + b*i
. - ComplexDataType
- ComplexEquality
- ComplexField
- ContinuousDistribution
- Abstract interface of all continuous distributions.
- CurveFit
- Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints.
- CurveFitResult
- Generic result of a curve fitting.
-
DataType<
T> -
Descriptor of a data type
T
, how it is efficiently represented and stored in memory, and strategy of how common operations work. - DegenerateDistribution
- The Degenerate distribution, a continuous probability distribution that is certain to take the value k.
- DiscreteDistribution
- Abstract interface of all continuous distributions.
-
Distribution<
T> - Abstract interface of all distributions.
-
Equality<
T> - Encapsulates equality between and the hash code of objects.
- ExponentialDistribution
- The exponential distribution.
-
Field<
T> - Encapsulates a mathematical field.
- Float32DataType
- Float64DataType
-
FloatDataType<
L extends List< double> > - FloatEquality
- FloatField
- Fraction
- A rational number.
- FractionDataType
- FractionEquality
- FractionField
- GammaDistribution
- The gamma distribution.
- Int16DataType
- Int32DataType
- Int64DataType
- Int8DataType
-
IntegerDataType<
L extends List< int> > - IntegerEquality
- IntegerField
- InverseGammaDistribution
- The inverse gamma distribution.
-
Jackknife<
T> - A deterministic resampling technique to estimate variance, bias, and confidence intervals.
- Layout
- Immutable object describing a multi-dimensional data layout in a flat list of values.
- LevenbergMarquardt
- The Levenberg–Marquardt algorithm, also known as the damped least-squares method, is used to solve non-linear least squares problems.
- LevenbergMarquardtResult
- LogNormalDistribution
- Log-normal distribution of a random variable whose logarithm is normally distributed.
-
Matrix<
T> - Abstract matrix type.
-
ModuloDataType<
T> -
ModuloEquality<
T> -
ModuloField<
T> -
NaturalEquality<
T> - The natural and canonical equality of objects.
- NegativeBinomialDistribution
- The Binomial distribution is a discrete probability distribution which models the number of successes in a sequence of independent and identically distributed Bernoulli trials before a specified (non-random) number of failures (denoted r) occurs.
- NormalDistribution
- Normal (or Gaussian) distribution described by the mean or expectation of the distribution and its standard deviation.
-
NullableDataType<
T> -
Some DataType instances do not support
null
values in the way they represent their data. This wrapper turns those types into nullable ones. -
NullableList<
T> -
A list with null values, where the null values are tracked in a separate
BitList
. For certain types of typed lists, this is the only way to tracknull
values. - NumericDataType
- NumericEquality
- NumericField
-
ObjectDataType<
T> -
ParametrizedUnaryFunction<
T> -
Abstract factory of parametrized unary functions of type
UnaryFunction<T>
. - PoissonDistribution
- The Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event.
-
Polynomial<
T> - Abstract polynomial type.
-
PolynomialDivision<
T> - Data holder for the result of a polynomial division.
- PolynomialRegression
-
Polynomial least-squares regression, in which the relationship between the
independent elements
xs
and the dependent elementsys
is modelled as a polynomial of a given degree. - PolynomialRegressionResult
- Quaternion
-
A quaternion number of the form
w + x*i + y*j + z*k
. - QuaternionDataType
- QuaternionEquality
- QuaternionField
- RademacherDistribution
- The Rademacher distribution is a discrete probability function which takes value 1 with probability 1/2 and value −1 with probability 1/2.
- StringDataType
- StringEquality
- StudentDistribution
- The Student's t-distribution.
-
Tensor<
T> - A multi-dimensional fixed-size container of items of a specific type.
-
TensorPrinter<
T> - Uint16DataType
- Uint32DataType
- Uint64DataType
- Uint8DataType
- UniformDiscreteDistribution
- A discrete uniform distribution between a and b, for details see https://en.wikipedia.org/wiki/Discrete_uniform_distribution.
- UniformDistribution
- The continuous uniform distribution between the bounds a and b. The distribution describes an experiment where there is an arbitrary outcome that lies between certain bounds.
-
Vector<
T> - Abstract vector type.
- WeibullDistribution
- The Weibull distribution.
Enums
- ConvolutionMode
- Convolution mode, i.e. how the borders are handled.
- IntegrateWarning
- Integration warnings that can be triggered for badly behaving functions or ill defined parameters.
- MatrixFormat
- Formats of matrices.
- PolynomialFormat
- Formats of polynomials.
- VectorFormat
- Formats of vectors.
Mixins
-
CompareOperators<
T> -
A generic mixin that provides standard comparison operators like
<
,<=
,>=
and>
provided the class isComparable
.
Extensions
-
AddMatrixExtension
on Matrix<
T> -
AddPolynomialExtension
on Polynomial<
T> -
AddVectorExtension
on Vector<
T> -
ApplyMatrixExtension
on Matrix<
T> -
BinaryOperationMatrixExtension
on Matrix<
T> -
BinaryOperationVectorExtension
on Vector<
T> - BroadcastLayoutExtension on Layout
-
CastMatrixExtension
on Matrix<
T> -
CastVectorExtension
on Vector<
T> -
CholeskyDecompositionExtension
on Matrix<
T> - CollapseLayoutExtension on Layout
-
CollapseTensorExtension
on Tensor<
T> -
ColumnMatrixExtension
on Vector<
T> -
ColumnVectorExtension
on Matrix<
T> -
CompareMatrixExtension
on Matrix<
T> -
ComparePolynomialExtension
on Polynomial<
T> -
CompareVectorExtension
on Vector<
T> -
ComparisonTensorExtension
on Tensor<
T> -
CompoundComparator
on Comparator<
T> -
CompoundIterableComparator
on Iterable<
Comparator< T> > -
ContiguousTensorExtension
on Tensor<
T> -
ConvolutionMatrixExtension
on Matrix<
T> -
ConvolutionVectorExtension
on Vector<
T> -
CopyTensorExtension
on Tensor<
T> -
DiagonalMatrixExtension
on Vector<
T> -
DiagonalVectorExtension
on Matrix<
T> -
DifferentiatePolynomialExtension
on Polynomial<
T> -
DivPolynomialExtension
on Polynomial<
T> -
DivVectorExtension
on Vector<
T> -
DotVectorExtension
on Vector<
T> -
EigenvalueDecompositionExtension
on Matrix<
T> - ElementLayoutExtension on Layout
-
ElementTensorExtension
on Tensor<
T> - ExpandLayoutExtension on Layout
-
ExpandTensorExtension
on Tensor<
T> - FlipLayoutExtension on Layout
-
FlippedHorizontalMatrixExtension
on Matrix<
T> -
FlippedVerticalMatrixExtension
on Matrix<
T> -
FlipTensorExtension
on Tensor<
T> -
IndexMatrixExtension
on Matrix<
T> -
IndexVectorExtension
on Vector<
T> -
IntegratePolynomialExtension
on Polynomial<
T> -
IterableIntExtension
on Iterable<
int> -
IterableNumExtension
on Iterable<
num> -
IterableVectorExtension
on Iterable<
T> -
IteratorMatrixExtension
on Matrix<
T> -
LargestComparator
on Comparator<
T> -
LerpMatrixExtension
on Matrix<
T> -
LerpPolynomialExtension
on Polynomial<
T> -
LerpVectorExtension
on Vector<
T> -
LexicographicalComparator
on Comparator<
T> -
ListPolynomialExtension
on List<
T> -
ListVectorExtension
on List<
T> -
LogicalTensorExtension
on Tensor<
bool> -
LUDecompositionExtension
on Matrix<
T> -
MagnitudeVectorExtension
on Vector<
T> -
MathTensorExtension
on Tensor<
T> -
MatrixMatrixMultiplicationMatrixExtension
on Matrix<
T> -
MatrixVectorMultiplicationVectorExtension
on Matrix<
T> -
MinMaxComparator
on Comparator<
T> -
MulMatrixExtension
on Matrix<
T> -
MulPolynomialExtension
on Polynomial<
T> -
MulVectorExtension
on Vector<
T> -
NegMatrixExtension
on Matrix<
T> -
NegPolynomialExtension
on Polynomial<
T> -
NegVectorExtension
on Vector<
T> -
NormDoubleExtension
on Matrix<
double> -
NormIntegerExtension
on Matrix<
int> -
NormNumberExtension
on Matrix<
num> -
NullsFirstComparator
on Comparator<
T> -
NullsLastComparator
on Comparator<
T> -
OperationTensorExtension
on Tensor<
T> -
OrderedComparator
on Comparator<
T> -
OverlayMatrixExtension
on Matrix<
T> -
OverlayVectorExtension
on Vector<
T> -
PolynomialListExtension
on Polynomial<
T> -
PredicateComparator
on Comparator<
T> -
QRDecompositionExtension
on Matrix<
T> - RangeLayoutExtension on Layout
-
RangeMatrixExtension
on Matrix<
T> -
RangeTensorExtension
on Tensor<
T> -
RangeVectorExtension
on Vector<
T> -
ReshapeTensorExtension
on Tensor<
T> -
ResultOfComparator
on Comparator<
R> -
ReversedComparator
on Comparator<
T> -
ReversedVectorExtension
on Vector<
T> -
RootsPolynomialExtension
on Polynomial<
T> -
RotatedMatrixExtension
on Matrix<
T> -
RowMatrixExtension
on Vector<
T> -
RowVectorExtension
on Matrix<
T> -
SearchComparator
on Comparator<
T> -
ShiftPolynomialExtension
on Polynomial<
T> -
SingularValueDecompositionExtension
on Matrix<
T> -
SmallestComparator
on Comparator<
T> -
SolverExtension
on Matrix<
T> -
SortComparator
on Comparator<
T> -
SubMatrixExtension
on Matrix<
T> -
SubPolynomialExtension
on Polynomial<
T> -
SubVectorExtension
on Vector<
T> -
SumVectorExtension
on Vector<
T> -
TestingMatrixExtension
on Matrix<
T> -
ToObjectTensorExtension
on Tensor<
T> -
ToTensorIterableExtension
on Iterable<
T> -
TransformedMatrixExtension
on Matrix<
T> -
TransformedVectorExtension
on Vector<
T> -
TransposedMatrixExtension
on Matrix<
T> - TransposeLayoutExtension on Layout
-
TransposeTensorExtension
on Tensor<
T> -
UnaryOperationMatrixExtension
on Matrix<
T> -
UnaryOperationVectorExtension
on Vector<
T> -
UnmodifiableMatrixExtension
on Matrix<
T> -
UnmodifiablePolynomialExtension
on Polynomial<
T> -
UnmodifiableVectorExtension
on Vector<
T> -
VectorListExtension
on Vector<
T>
Functions
-
beta(
num x, num y) → double - Beta function based on the gamma function.
-
betacf_(
num x, num a, num b) → double - Evaluates the continued fraction for incomplete beta function by modified Lentz's method.
-
betaLn(
num x, num y) → double - Logarithm of the beta function based on the gammaLn function.
-
combination(
num n, num k) → double - Returns the combinations based on the gamma function.
-
combinationLn(
num n, num k) → double - Returns the logarithm of the combinations based on the gammaLn function.
-
derivative(
UnaryFunction< double> function, double x, {int derivative = 1, int accuracy = 2, double epsilon = 1e-5}) → double -
Returns the numerical derivative of the provided function
function
atx
. -
editDistance(
String a, String b) → int -
Computes the Levenshtein edit distance between two strings
a
andb
: https://en.wikipedia.org/wiki/Levenshtein_distance -
erf(
num x) → double - Returns an approximation of the error function, for details see https://en.wikipedia.org/wiki/Error_function.
-
erfc(
num x) → double - Returns the complementary error function.
-
erfcInv(
num x) → double - Returns the inverse complementary error function.
-
erfInv(
num x) → double - Returns the inverse error function.
-
explicitComparator<
T> (Iterable< T> iterable) → Comparator<T> -
Returns an explicit Comparator based on an
iterable
of elements. -
factorial(
num n) → double - Returns the factorial based on the gamma function.
-
factorialLn(
num n) → double - Returns the logarithm of the factorial based on the gammaLn function.
-
fft(
List< Complex> values, {bool inverse = false}) → List<Complex> -
Performs an in-place Discrete Fast Fourier transformation on the provided
values
. If necessary, extends the size the provided list to a power of two. Returns the modified collection of transformed values. -
gamma(
num x) → double - Returns an approximation of the gamma function, for details see https://en.wikipedia.org/wiki/Gamma_function.
-
gammaLn(
num x) → double - Returns the natural logarithm of the gamma function.
-
gammap(
num a, num x) → double -
gammapInv(
num p, num a) → double -
geometricSpaced(
double start, double stop, {int count = 10, bool includeEndpoint = true, DataType< double> ? dataType, VectorFormat? format}) → Vector<double> -
Generates a Vector with a sequence of
count
evenly spaced values on a log scale (a geometric progression) on the interval betweenstart
andstop
. -
ibeta(
num x, num a, num b) → double - Incomplete beta function.
-
ibetaInv(
num p, num a, num b) → double - Inverse of the incomplete beta function.
-
integrate(
UnaryFunction< double> function, double a, double b, {int depth = 6, double epsilon = 1e-6, Iterable<double> poles = const [], void onWarning(IntegrateWarning type, double x)?}) → double -
Returns the numerical integration of the provided
function
froma
tob
, that is the result of int(f(x), dx=a..b). -
lagrangeInterpolation<
T> (DataType< T> dataType, {required Vector<T> xs, required Vector<T> ys}) → UnaryFunction<T> -
A function providing a Lagrange polynomial interpolation through the unique
sample points
xs
andys
. Related to Polynomial.lagrange. -
linearInterpolation<
T> (DataType< T> dataType, {required Vector<T> xs, required Vector<T> ys, T? left, T? right}) → UnaryFunction<T> -
A function providing linear interpolation of a discrete monotonically
increasing set of sample points
xs
andys
. Returnsleft
orright
, if the point is outside the data range, by default extrapolate linearly. -
linearSpaced(
double start, double stop, {int count = 10, bool includeEndpoint = true, DataType< double> ? dataType, VectorFormat? format}) → Vector<double> -
Generates a Vector with a sequence of
count
evenly spaced values over an interval betweenstart
andstop
. -
logarithmicSpaced(
double start, double stop, {int count = 10, double base = 10.0, bool includeEndpoint = true, DataType< double> ? dataType, VectorFormat? format}) → Vector<double> -
Generates a Vector with a sequence of
count
evenly spaced values on a log scale (a geometric progression) on the interval betweenbase ^ start
andbase ^ stop
. -
lowRegGamma(
num a, num x) → double -
naturalComparable<
T extends Comparable< (T> >T a, T b) → int - Natural static Comparator function using Comparable arguments.
-
naturalCompare(
Object? a, Object? b) → int - Natural dynamic Comparator function.
-
nearestInterpolation(
{required Vector< double> xs, required Vector<double> ys, bool preferLower = true}) → UnaryFunction<double> -
A function providing the nearest value of a discrete monotonically
increasing set of sample points
xs
andys
. -
nextInterpolation(
{required Vector< double> xs, required Vector<double> ys, double right = double.nan}) → UnaryFunction<double> -
A function providing the next value of a discrete monotonically
increasing set of sample points
xs
andys
. Returnsright
if there is no next sample point. -
permutation(
num n, num m) → double - Returns the permutations based on the gamma function.
-
permutationLn(
num n, num m) → double - Returns the logarithm of the permutations based on the gammaLn function.
-
previousInterpolation(
{required Vector< double> xs, required Vector<double> ys, double left = double.nan}) → UnaryFunction<double> -
A function providing the previous value of a discrete monotonically
increasing set of sample points
xs
andys
. Returnsleft
if there is no previous sample point. -
reverseComparable<
T extends Comparable< (T> >T a, T b) → int - Reversed static Comparator function using Comparable arguments.
-
reverseCompare(
Object? a, Object? b) → int - Reversed dynamic Comparator function.
-
solve(
UnaryFunction< double> function, double a, double b, {double bracketEpsilon = 1e-10, double solutionEpsilon = 1e-50, int maxIterations = 50}) → double -
Returns the root of the provided
function
bracketed betweena
andb
, that is f(x) = 0 is solved for x in the range ofa, b
.
Typedefs
-
UnaryFunction<
T> = T Function(T x) -
A function with a single argument and an identical return type. Typically
used for numerical functions like f(x) where x ∈
T
and f(x) ∈T
.
Exceptions / Errors
- IntegrateError
- Integration error that is thrown when warnings are not handled explicitly.
- InvalidProbability
- Error of an invalid probability outside the range of 0 to 1.
- LayoutError
- Error indicating an unexpected Layout state.