Index

A B C D E F G H I J K L M N O P Q R S T U V W X Z

A

aat() - Method in class smile.math.matrix.BandMatrix
aat() - Method in class smile.math.matrix.DenseMatrix
aat() - Method in class smile.math.matrix.JMatrix
aat() - Method in class smile.math.matrix.Matrix: Returns A * A'
aat() - Method in class smile.math.matrix.SparseMatrix
abmm(DenseMatrix) - Method in class smile.math.matrix.JMatrix
abmm(B) - Method in interface smile.math.matrix.MatrixMultiplication: Returns the result of matrix multiplication A * B.
abmm(SparseMatrix) - Method in class smile.math.matrix.SparseMatrix: Returns the matrix multiplication C = A * B.
abs() - Method in class smile.math.Complex: Returns abs/modulus/magnitude.
abs(double) - Static method in class smile.math.Math: Returns the absolute value of a double value.
abs(float) - Static method in class smile.math.Math: Returns the absolute value of a float value.
abs(int) - Static method in class smile.math.Math: Returns the absolute value of an int value.
abs(long) - Static method in class smile.math.Math: Returns the absolute value of a long value.
AbstractDifferentiableMultivariateFunction - Class in smile.math: An abstract implementation that uses finite differences to calculate the partial derivatives instead of providing them analytically.
AbstractDifferentiableMultivariateFunction() - Constructor for class smile.math.AbstractDifferentiableMultivariateFunction
AbstractDistribution - Class in smile.stat.distribution: This is the base class of univariate distributions.
AbstractDistribution() - Constructor for class smile.stat.distribution.AbstractDistribution
AbstractMultivariateDistribution - Class in smile.stat.distribution: This is the base class of multivariate distributions.
AbstractMultivariateDistribution() - Constructor for class smile.stat.distribution.AbstractMultivariateDistribution
abtmm(DenseMatrix) - Method in class smile.math.matrix.JMatrix
abtmm(B) - Method in interface smile.math.matrix.MatrixMultiplication: Returns the result of matrix multiplication A * B'.
abtmm(SparseMatrix) - Method in class smile.math.matrix.SparseMatrix
acos(double) - Static method in class smile.math.Math: Returns the arc cosine of an angle, in the range of 0.0 through pi.
add(int, int, double) - Method in class smile.math.Array2D
add(Array2D) - Method in class smile.math.Array2D
add(double) - Method in class smile.math.Array2D
add(double) - Method in class smile.math.DoubleArrayList: Appends the specified value to the end of this list.
add(double[]) - Method in class smile.math.DoubleArrayList: Appends an array to the end of this list.
add(int) - Method in class smile.math.IntArrayList: Appends the specified value to the end of this list.
add(int[]) - Method in class smile.math.IntArrayList: Appends an array to the end of this list.
add(int, int, double) - Method in class smile.math.matrix.DenseMatrix: A[i][j] += x
add(DenseMatrix, DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: C = A + B
add(DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: In place addition A = A + B
add(double, DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: Element-wise addition C = A + x
add(double) - Method in class smile.math.matrix.DenseMatrix: In place element-wise addition A = A + x
add(int, int, double) - Method in class smile.math.matrix.JMatrix
add(DenseMatrix) - Method in class smile.math.matrix.JMatrix
add(JMatrix) - Method in class smile.math.matrix.JMatrix
add(DenseMatrix, DenseMatrix) - Method in class smile.math.matrix.JMatrix
add(JMatrix, JMatrix) - Method in class smile.math.matrix.JMatrix
add(double) - Method in class smile.math.matrix.JMatrix
add(double, DenseMatrix) - Method in class smile.math.matrix.JMatrix
add(double, JMatrix) - Method in class smile.math.matrix.JMatrix
add(double) - Method in class smile.sort.DoubleHeapSelect: Assimilate a new value from the stream.
add(float) - Method in class smile.sort.FloatHeapSelect: Assimilate a new value from the stream.
add(T) - Method in class smile.sort.HeapSelect: Assimilate a new value from the stream.
add(int) - Method in class smile.sort.IntHeapSelect: Assimilate a new value from the stream.
add(double) - Method in class smile.sort.IQAgent: Assimilate a new value from the stream.
all(boolean[]) - Static method in class smile.math.Math: Given a set of boolean values, are all of the values true?
any(boolean[]) - Static method in class smile.math.Math: Given a set of boolean values, is at least one of the values true?
append(int, double) - Method in class smile.math.SparseArray: Append an entry to the array, optimizing for the case where the index is greater than all existing indices in the array.
apply(int, int) - Method in class smile.math.Array2D: Returns A(i, j).
apply(int, int) - Method in class smile.math.matrix.Matrix: Returns the entry value at row i and column j.
array() - Method in class smile.math.matrix.DenseMatrix: Return the two-dimensional array of matrix.
Array2D - Class in smile.math: 2-dimensional array of doubles.
Array2D(double[][]) - Constructor for class smile.math.Array2D: Constructor.
Array2D(int, int) - Constructor for class smile.math.Array2D: Constructor of all-zero matrix.
Array2D(int, int, double) - Constructor for class smile.math.Array2D: Constructor.
Array2D(int, int, double[]) - Constructor for class smile.math.Array2D: Constructor.
asin(double) - Static method in class smile.math.Math: Returns the arc sine of an angle, in the range of -pi/2 through pi/2.
asolve(double[], double[]) - Method in interface smile.math.matrix.Preconditioner: Solve A_d * x = b for the preconditioner matrix A_d.
ata() - Method in class smile.math.matrix.BandMatrix
ata() - Method in class smile.math.matrix.DenseMatrix
ata() - Method in class smile.math.matrix.JMatrix
ata() - Method in class smile.math.matrix.Matrix: Returns A' * A
ata() - Method in class smile.math.matrix.SparseMatrix
atan(double) - Static method in class smile.math.Math: Returns the arc tangent of an angle, in the range of -pi/2 through pi/2.
atan2(double, double) - Static method in class smile.math.Math: Converts rectangular coordinates (x, y) to polar (r, theta).
atbmm(DenseMatrix) - Method in class smile.math.matrix.JMatrix
atbmm(B) - Method in interface smile.math.matrix.MatrixMultiplication: Returns the result of matrix multiplication A' * B.
atbmm(SparseMatrix) - Method in class smile.math.matrix.SparseMatrix
atx(double[], double[]) - Method in class smile.math.matrix.BandMatrix
atx(double[], double[]) - Method in class smile.math.matrix.JMatrix
atx(double[], double[]) - Method in class smile.math.matrix.Matrix: y = A' * x
atx(double[], double[]) - Method in class smile.math.matrix.SparseMatrix
atxpy(double[], double[]) - Method in class smile.math.matrix.BandMatrix
atxpy(double[], double[], double) - Method in class smile.math.matrix.BandMatrix
atxpy(double[], double[]) - Method in class smile.math.matrix.JMatrix
atxpy(double[], double[], double) - Method in class smile.math.matrix.JMatrix
atxpy(double[], double[]) - Method in class smile.math.matrix.Matrix: y = A' * x + y
atxpy(double[], double[], double) - Method in class smile.math.matrix.Matrix: y = A' * x + b * y
atxpy(double[], double[]) - Method in class smile.math.matrix.SparseMatrix
atxpy(double[], double[], double) - Method in class smile.math.matrix.SparseMatrix
ax(double[], double[]) - Method in class smile.math.matrix.BandMatrix
ax(double[], double[]) - Method in class smile.math.matrix.JMatrix
ax(double[], double[]) - Method in class smile.math.matrix.Matrix: y = A * x
ax(double[], double[]) - Method in class smile.math.matrix.SparseMatrix
axpy(double, double[], double[]) - Static method in class smile.math.Math: Update an array by adding a multiple of another array y = a * x + y.
axpy(double[], double[]) - Method in class smile.math.matrix.BandMatrix
axpy(double[], double[], double) - Method in class smile.math.matrix.BandMatrix
axpy(double[], double[]) - Method in class smile.math.matrix.JMatrix
axpy(double[], double[], double) - Method in class smile.math.matrix.JMatrix
axpy(double[], double[]) - Method in class smile.math.matrix.Matrix: y = A * x + y
axpy(double[], double[], double) - Method in class smile.math.matrix.Matrix: y = A * x + b * y
axpy(double[], double[]) - Method in class smile.math.matrix.SparseMatrix
axpy(double[], double[], double) - Method in class smile.math.matrix.SparseMatrix

B

BandMatrix - Class in smile.math.matrix: A band matrix is a sparse matrix, whose non-zero entries are confined to a diagonal band, comprising the main diagonal and zero or more diagonals on either side.
BandMatrix(int, int) - Constructor for class smile.math.matrix.BandMatrix: Constructor of an n-by-n band-diagonal matrix A with m subdiagonal rows and m superdiagonal rows.
BandMatrix(int, int, int) - Constructor for class smile.math.matrix.BandMatrix: Constructor of an n-by-n band-diagonal matrix A with m₁ subdiagonal rows and m₂ superdiagonal rows.
bandwidth() - Method in class smile.stat.distribution.KernelDensity: Returns the bandwidth of kernel.
BernoulliDistribution - Class in smile.stat.distribution: Bernoulli distribution is a discrete probability distribution, which takes value 1 with success probability p and value 0 with failure probability q = 1 - p.
BernoulliDistribution(double) - Constructor for class smile.stat.distribution.BernoulliDistribution: Constructor.
BernoulliDistribution(int[]) - Constructor for class smile.stat.distribution.BernoulliDistribution: Constructor.
BernoulliDistribution(boolean[]) - Constructor for class smile.stat.distribution.BernoulliDistribution: Construct an Bernoulli from the given samples.
Beta - Class in smile.math.special: The beta function, also called the Euler integral of the first kind.
beta(double, double) - Static method in class smile.math.special.Beta: Beta function, also called the Euler integral of the first kind.
BetaDistribution - Class in smile.stat.distribution: The beta distribution is defined on the interval [0, 1] parameterized by two positive shape parameters, typically denoted by α and β.
BetaDistribution(double, double) - Constructor for class smile.stat.distribution.BetaDistribution: Constructor.
BetaDistribution(double[]) - Constructor for class smile.stat.distribution.BetaDistribution: Construct an Beta from the given samples.
BIC - Class in smile.stat.distribution: Bayesian information criterion (BIC) or Schwarz Criterion is a criterion for model selection among a class of parametric models with different numbers of parameters.
BIC() - Constructor for class smile.stat.distribution.BIC
bic(double, int, int) - Static method in class smile.stat.distribution.BIC: Returns the BIC score of an estimated model.
bic(double[]) - Method in class smile.stat.distribution.DiscreteMixture: BIC score of the mixture for given data.
bic(double[]) - Method in class smile.stat.distribution.Mixture: BIC score of the mixture for given data.
bic(double[][]) - Method in class smile.stat.distribution.MultivariateMixture: BIC score of the mixture for given data.
BiconjugateGradient - Class in smile.math.matrix: The biconjugate gradient method is an algorithm to solve systems of linear equations.
BiconjugateGradient() - Constructor for class smile.math.matrix.BiconjugateGradient
BinarySparseGaussianKernel - Class in smile.math.kernel: The Gaussian Mercer Kernel.
BinarySparseGaussianKernel(double) - Constructor for class smile.math.kernel.BinarySparseGaussianKernel: Constructor.
BinarySparseHyperbolicTangentKernel - Class in smile.math.kernel: The hyperbolic tangent kernel.
BinarySparseHyperbolicTangentKernel() - Constructor for class smile.math.kernel.BinarySparseHyperbolicTangentKernel: Constructor with scale 1.0 and offset 0.0.
BinarySparseHyperbolicTangentKernel(double, double) - Constructor for class smile.math.kernel.BinarySparseHyperbolicTangentKernel: Constructor.
BinarySparseLaplacianKernel - Class in smile.math.kernel: The Laplacian Kernel.
BinarySparseLaplacianKernel(double) - Constructor for class smile.math.kernel.BinarySparseLaplacianKernel: Constructor.
BinarySparseLinearKernel - Class in smile.math.kernel: The linear dot product kernel on sparse binary arrays in int[], which are the indices of nonzero elements.
BinarySparseLinearKernel() - Constructor for class smile.math.kernel.BinarySparseLinearKernel: Constructor.
BinarySparsePolynomialKernel - Class in smile.math.kernel: The polynomial kernel.
BinarySparsePolynomialKernel(int) - Constructor for class smile.math.kernel.BinarySparsePolynomialKernel: Constructor with scale 1 and bias 0.
BinarySparsePolynomialKernel(int, double, double) - Constructor for class smile.math.kernel.BinarySparsePolynomialKernel: Constructor.
BinarySparseThinPlateSplineKernel - Class in smile.math.kernel: The Thin Plate Spline Kernel.
BinarySparseThinPlateSplineKernel(double) - Constructor for class smile.math.kernel.BinarySparseThinPlateSplineKernel: Constructor.
BinomialDistribution - Class in smile.stat.distribution: The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p.
BinomialDistribution(int, double) - Constructor for class smile.stat.distribution.BinomialDistribution: Constructor.
bins(double[], double) - Static method in class smile.math.Histogram: Returns the number of bins for a data based on a suggested bin width h.
bins(int) - Static method in class smile.math.Histogram: Returns the number of bins by square-root rule, which takes the square root of the number of data points in the sample (used by Excel histograms and many others).
breaks(double[], double) - Static method in class smile.math.Histogram: Returns the breakpoints between histogram cells for a dataset based on a suggested bin width h.
breaks(double, double, double) - Static method in class smile.math.Histogram: Returns the breakpoints between histogram cells for a given range based on a suggested bin width h.
breaks(double[], int) - Static method in class smile.math.Histogram: Returns the breakpoints between histogram cells for a dataset.
breaks(double, double, int) - Static method in class smile.math.Histogram: Returns the breakpoints between histogram cells for a given range.

C

c(int...) - Static method in class smile.math.Math: Combines the arguments to form a vector.
c(float...) - Static method in class smile.math.Math: Combines the arguments to form a vector.
c(double...) - Static method in class smile.math.Math: Combines the arguments to form a vector.
c(String...) - Static method in class smile.math.Math: Combines the arguments to form a vector.
c(int[]...) - Static method in class smile.math.Math: Merges multiple vectors into one.
c(float[]...) - Static method in class smile.math.Math: Merges multiple vectors into one.
c(double[]...) - Static method in class smile.math.Math: Merges multiple vectors into one.
c(String[]...) - Static method in class smile.math.Math: Merges multiple vectors into one.
cbind(int[]...) - Static method in class smile.math.Math: Take a sequence of vector arguments and combine by columns.
cbind(float[]...) - Static method in class smile.math.Math: Take a sequence of vector arguments and combine by columns.
cbind(double[]...) - Static method in class smile.math.Math: Take a sequence of vector arguments and combine by columns.
cbind(String[]...) - Static method in class smile.math.Math: Take a sequence of vector arguments and combine by columns.
cbrt(double) - Static method in class smile.math.Math: Returns the cube root of a double value.
cdf(double) - Method in class smile.stat.distribution.BernoulliDistribution
cdf(double) - Method in class smile.stat.distribution.BetaDistribution
cdf(double) - Method in class smile.stat.distribution.BinomialDistribution
cdf(double) - Method in class smile.stat.distribution.ChiSquareDistribution
cdf(double) - Method in class smile.stat.distribution.DiscreteMixture
cdf(double) - Method in interface smile.stat.distribution.Distribution: Cumulative distribution function.
cdf(double) - Method in class smile.stat.distribution.EmpiricalDistribution
cdf(double) - Method in class smile.stat.distribution.ExponentialDistribution
cdf(double) - Method in class smile.stat.distribution.FDistribution
cdf(double) - Method in class smile.stat.distribution.GammaDistribution
cdf(double) - Method in class smile.stat.distribution.GaussianDistribution
cdf(double) - Method in class smile.stat.distribution.GeometricDistribution
cdf(double) - Method in class smile.stat.distribution.HyperGeometricDistribution
cdf(double) - Method in class smile.stat.distribution.KernelDensity: Cumulative distribution function.
cdf(double) - Method in class smile.stat.distribution.LogisticDistribution
cdf(double) - Method in class smile.stat.distribution.LogNormalDistribution
cdf(double) - Method in class smile.stat.distribution.Mixture
cdf(double[]) - Method in interface smile.stat.distribution.MultivariateDistribution: Cumulative distribution function.
cdf(double[]) - Method in class smile.stat.distribution.MultivariateGaussianDistribution: Algorithm from Alan Genz (1992) Numerical Computation of Multivariate Normal Probabilities, Journal of Computational and Graphical Statistics, pp.
cdf(double[]) - Method in class smile.stat.distribution.MultivariateMixture
cdf(double) - Method in class smile.stat.distribution.NegativeBinomialDistribution
cdf(double) - Method in class smile.stat.distribution.PoissonDistribution
cdf(double) - Method in class smile.stat.distribution.ShiftedGeometricDistribution
cdf(double) - Method in class smile.stat.distribution.TDistribution
cdf(double) - Method in class smile.stat.distribution.WeibullDistribution
cdf2tiled(double) - Method in class smile.stat.distribution.TDistribution: Two-tailed cdf.
ceil(double) - Static method in class smile.math.Math: Returns the smallest (closest to negative infinity) double value that is greater than or equal to the argument and is equal to a mathematical integer.
change(int) - Method in class smile.sort.PriorityQueue: The priority of item k has changed.
ChebyshevDistance - Class in smile.math.distance: Chebyshev distance (or Tchebychev distance), or L_∞ metric is a metric defined on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension.
ChebyshevDistance() - Constructor for class smile.math.distance.ChebyshevDistance: Constructor.
chisq - Variable in class smile.stat.hypothesis.ChiSqTest: chi-square statistic
chisq(int[][]) - Static method in class smile.stat.hypothesis.CorTest: Given a two-dimensional contingency table in the form of an array of integers, returns Chi-square test for independence.
ChiSqTest - Class in smile.stat.hypothesis: Pearson's chi-square test, also known as the chi-square goodness-of-fit test or chi-square test for independence.
ChiSquareDistribution - Class in smile.stat.distribution: Chi-square (or chi-squared) distribution with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables.
ChiSquareDistribution(int) - Constructor for class smile.stat.distribution.ChiSquareDistribution: Constructor.
Cholesky - Class in smile.math.matrix: Cholesky decomposition is a decomposition of a symmetric, positive-definite matrix into a lower triangular matrix L and the transpose of the lower triangular matrix such that A = L*L'.
Cholesky(DenseMatrix) - Constructor for class smile.math.matrix.Cholesky: Constructor.
cholesky() - Method in class smile.math.matrix.DenseMatrix: Returns the Cholesky decomposition.
cholesky(boolean) - Method in class smile.math.matrix.DenseMatrix: Returns the Cholesky decomposition.
cholesky() - Method in class smile.math.matrix.JMatrix: Cholesky decomposition for symmetric and positive definite matrix.
CholeskyOfAtA() - Method in class smile.math.matrix.QR: Returns the Cholesky decomposition of A'A.
CholeskyOfAtA() - Method in class smile.math.matrix.SVD: Returns the Cholesky decomposition of A'A.
choose(int, int) - Static method in class smile.math.Math: n choose k.
clear() - Method in class smile.math.DoubleArrayList: Removes all of the values from this list.
clear() - Method in class smile.math.IntArrayList: Removes all of the value from this list.
clone(int[][]) - Static method in class smile.math.Math: Deep clone a two-dimensional array.
clone(float[][]) - Static method in class smile.math.Math: Deep clone a two-dimensional array.
clone(double[][]) - Static method in class smile.math.Math: Deep clone a two-dimensional array.
colMax(double[][]) - Static method in class smile.math.Math: Returns the column maximum for a matrix.
colMeans(double[][]) - Static method in class smile.math.Math: Returns the column means for a matrix.
colMeans() - Method in class smile.math.matrix.DenseMatrix: Returns the mean of each column for a matrix.
colMin(double[][]) - Static method in class smile.math.Math: Returns the column minimum for a matrix.
colSds(double[][]) - Static method in class smile.math.Math: Returns the column deviations for a matrix.
colSums(double[][]) - Static method in class smile.math.Math: Returns the column sums for a matrix.
colSums() - Method in class smile.math.matrix.DenseMatrix: Returns the sum of each column for a matrix.
Complex - Class in smile.math: Complex number.
Complex(double, double) - Constructor for class smile.math.Complex: Constructor.
Component() - Constructor for class smile.stat.distribution.DiscreteMixture.Component
Component() - Constructor for class smile.stat.distribution.Mixture.Component
Component(double, Distribution) - Constructor for class smile.stat.distribution.Mixture.Component
Component() - Constructor for class smile.stat.distribution.MultivariateMixture.Component
condition() - Method in class smile.math.matrix.SVD: Returns the L₂ norm condition number, which is max(S) / min(S).
conjugate() - Method in class smile.math.Complex: Returns the conjugate.
contains(double[][], double[]) - Static method in class smile.math.Math: Determines if the polygon contains the specified coordinates.
contains(double[][], double, double) - Static method in class smile.math.Math: Determines if the polygon contains the specified coordinates.
copy(int[], int[]) - Static method in class smile.math.Math: Copy x into y.
copy(float[], float[]) - Static method in class smile.math.Math: Copy x into y.
copy(double[], double[]) - Static method in class smile.math.Math: Copy x into y.
copy(int[][], int[][]) - Static method in class smile.math.Math: Copy x into y.
copy(float[][], float[][]) - Static method in class smile.math.Math: Copy x into y.
copy(double[][], double[][]) - Static method in class smile.math.Math: Copy x into y.
copy() - Method in class smile.math.matrix.DenseMatrix: Returns a copy of this matrix.
copy() - Method in class smile.math.matrix.JMatrix
copySign(double, double) - Static method in class smile.math.Math: Returns the first floating-point argument with the sign of the second floating-point argument.
copySign(float, float) - Static method in class smile.math.Math: Returns the first floating-point argument with the sign of the second floating-point argument.
cor(int[], int[]) - Static method in class smile.math.Math: Returns the correlation coefficient between two vectors.
cor(float[], float[]) - Static method in class smile.math.Math: Returns the correlation coefficient between two vectors.
cor(double[], double[]) - Static method in class smile.math.Math: Returns the correlation coefficient between two vectors.
cor(double[][]) - Static method in class smile.math.Math: Returns the sample correlation matrix.
cor(double[][], double[]) - Static method in class smile.math.Math: Returns the sample correlation matrix.
cor - Variable in class smile.stat.hypothesis.CorTest: Correlation coefficient
CorrelationDistance - Class in smile.math.distance: Correlation distance is defined as 1 - correlation coefficient.
CorrelationDistance() - Constructor for class smile.math.distance.CorrelationDistance: Constructor.
CorTest - Class in smile.stat.hypothesis: Correlation test.
cos() - Method in class smile.math.Complex: Returns the complex cosine.
cos(double) - Static method in class smile.math.Math: Returns the trigonometric cosine of an angle.
cosh(double) - Static method in class smile.math.Math: Returns the hyperbolic cosine of a double value.
cov(int[], int[]) - Static method in class smile.math.Math: Returns the covariance between two vectors.
cov(float[], float[]) - Static method in class smile.math.Math: Returns the covariance between two vectors.
cov(double[], double[]) - Static method in class smile.math.Math: Returns the covariance between two vectors.
cov(double[][]) - Static method in class smile.math.Math: Returns the sample covariance matrix.
cov(double[][], double[]) - Static method in class smile.math.Math: Returns the sample covariance matrix.
cov() - Method in interface smile.stat.distribution.MultivariateDistribution: The covariance matrix of distribution.
cov() - Method in class smile.stat.distribution.MultivariateGaussianDistribution
cov() - Method in class smile.stat.distribution.MultivariateMixture

D

d(int[], int[]) - Static method in class smile.math.distance.ChebyshevDistance: Chebyshev distance between the two arrays of type integer.
d(float[], float[]) - Static method in class smile.math.distance.ChebyshevDistance: Chebyshev distance between the two arrays of type float.
d(double[], double[]) - Method in class smile.math.distance.ChebyshevDistance: Chebyshev distance between the two arrays of type double.
d(double[], double[]) - Method in class smile.math.distance.CorrelationDistance: Pearson correlation distance between the two arrays of type double.
d(T, T) - Method in interface smile.math.distance.Distance: Returns the distance measure between two objects.
d(T[], T[]) - Method in class smile.math.distance.DynamicTimeWarping
d(int[], int[]) - Static method in class smile.math.distance.DynamicTimeWarping: Dynamic time warping without path constraints.
d(int[], int[], int) - Static method in class smile.math.distance.DynamicTimeWarping: Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.
d(float[], float[]) - Static method in class smile.math.distance.DynamicTimeWarping: Dynamic time warping without path constraints.
d(float[], float[], int) - Static method in class smile.math.distance.DynamicTimeWarping: Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.
d(double[], double[]) - Static method in class smile.math.distance.DynamicTimeWarping: Dynamic time warping without path constraints.
d(double[], double[], int) - Static method in class smile.math.distance.DynamicTimeWarping: Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.
d(String, String) - Method in class smile.math.distance.EditDistance: Edit distance between two strings.
d(char[], char[]) - Method in class smile.math.distance.EditDistance: Edit distance between two strings.
d(int[], int[]) - Method in class smile.math.distance.EuclideanDistance: Euclidean distance between the two arrays of type integer.
d(float[], float[]) - Method in class smile.math.distance.EuclideanDistance: Euclidean distance between the two arrays of type float.
d(double[], double[]) - Method in class smile.math.distance.EuclideanDistance: Euclidean distance between the two arrays of type double.
d(T[], T[]) - Method in class smile.math.distance.HammingDistance: Returns Hamming distance between the two arrays.
d(byte, byte) - Static method in class smile.math.distance.HammingDistance: Returns Hamming distance between the two bytes.
d(short, short) - Static method in class smile.math.distance.HammingDistance: Returns Hamming distance between the two shorts.
d(int, int) - Static method in class smile.math.distance.HammingDistance: Returns Hamming distance between the two integers.
d(long, long) - Static method in class smile.math.distance.HammingDistance: Returns Hamming distance between the two long integers.
d(byte[], byte[]) - Static method in class smile.math.distance.HammingDistance: Returns Hamming distance between the two byte arrays.
d(short[], short[]) - Static method in class smile.math.distance.HammingDistance: Returns Hamming distance between the two short arrays.
d(int[], int[]) - Static method in class smile.math.distance.HammingDistance: Returns Hamming distance between the two integer arrays.
d(BitSet, BitSet) - Static method in class smile.math.distance.HammingDistance: Returns Hamming distance between the two BitSets.
d(T[], T[]) - Method in class smile.math.distance.JaccardDistance
d(Set<T>, Set<T>) - Static method in class smile.math.distance.JaccardDistance: Returns the Jaccard distance between sets.
d(double[], double[]) - Method in class smile.math.distance.JensenShannonDistance
d(int[], int[]) - Method in class smile.math.distance.LeeDistance
d(double[], double[]) - Method in class smile.math.distance.MahalanobisDistance
d(int[], int[]) - Method in class smile.math.distance.ManhattanDistance: Manhattan distance between two arrays of type integer.
d(float[], float[]) - Method in class smile.math.distance.ManhattanDistance: Manhattan distance between two arrays of type float.
d(double[], double[]) - Method in class smile.math.distance.ManhattanDistance: Manhattan distance between two arrays of type double.
d(int[], int[]) - Method in class smile.math.distance.MinkowskiDistance: Minkowski distance between the two arrays of type integer.
d(float[], float[]) - Method in class smile.math.distance.MinkowskiDistance: Minkowski distance between the two arrays of type float.
d(double[], double[]) - Method in class smile.math.distance.MinkowskiDistance: Minkowski distance between the two arrays of type double.
d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseChebyshevDistance
d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseEuclideanDistance
d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseManhattanDistance
d(SparseArray, SparseArray) - Method in class smile.math.distance.SparseMinkowskiDistance
d - Variable in class smile.stat.hypothesis.KSTest: Kolmogorov-Smirnov statistic
damerau(String, String) - Static method in class smile.math.distance.EditDistance: Damerau-Levenshtein distance between two strings allows insertion, deletion, substitution, or transposition of characters.
damerau(char[], char[]) - Static method in class smile.math.distance.EditDistance: Damerau-Levenshtein distance between two strings allows insertion, deletion, substitution, or transposition of characters.
data() - Method in class smile.math.matrix.DenseMatrix: Returns the array of storing the matrix.
data() - Method in class smile.math.matrix.JMatrix
decompose() - Method in class smile.math.matrix.BandMatrix: LU decomposition.
DenseMatrix - Class in smile.math.matrix: An abstract interface of dense matrix.
DenseMatrix() - Constructor for class smile.math.matrix.DenseMatrix
det() - Method in class smile.math.matrix.BandMatrix: Returns the matrix determinant.
det() - Method in class smile.math.matrix.Cholesky: Returns the matrix determinant
det() - Method in class smile.math.matrix.LU: Returns the matrix determinant
df(double) - Method in interface smile.math.DifferentiableFunction: Compute the value of the derivative function at x.
df - Variable in class smile.stat.hypothesis.ChiSqTest: The degree of freedom of chisq-statistic.
df - Variable in class smile.stat.hypothesis.CorTest: Degree of freedom
df - Variable in class smile.stat.hypothesis.TTest: The degree of freedom of t-statistic.
df1 - Variable in class smile.stat.hypothesis.FTest: The degree of freedom of f-statistic.
df2 - Variable in class smile.stat.hypothesis.FTest: The degree of freedom of f-statistic.
diag() - Method in class smile.math.matrix.BandMatrix
diag(double[]) - Static method in class smile.math.matrix.Matrix: Returns a square diagonal matrix with the elements of vector diag on the main diagonal.
diag() - Method in class smile.math.matrix.Matrix: Returns the diagonal elements.
diag() - Method in class smile.math.matrix.SparseMatrix
DifferentiableFunction - Interface in smile.math: A differentiable function is a function whose derivative exists at each point in its domain.
DifferentiableMultivariateFunction - Interface in smile.math: A differentiable function is a function whose derivative exists at each point in its domain.
digamma(double) - Static method in class smile.math.special.Gamma: The digamma function is defined as the logarithmic derivative of the gamma function.
DIGITS - Static variable in class smile.math.Math: The number of digits (in radix base) in the mantissa.
DiscreteDistribution - Class in smile.stat.distribution: This is the base class of univariate discrete distributions.
DiscreteDistribution() - Constructor for class smile.stat.distribution.DiscreteDistribution
DiscreteExponentialFamily - Interface in smile.stat.distribution: The purpose of this interface is mainly to define the method M that is the Maximization step in the EM algorithm.
DiscreteExponentialFamilyMixture - Class in smile.stat.distribution: The finite mixture of distributions from discrete exponential family.
DiscreteExponentialFamilyMixture(List<DiscreteMixture.Component>) - Constructor for class smile.stat.distribution.DiscreteExponentialFamilyMixture: Constructor.
DiscreteExponentialFamilyMixture(List<DiscreteMixture.Component>, int[]) - Constructor for class smile.stat.distribution.DiscreteExponentialFamilyMixture: Constructor.
DiscreteMixture - Class in smile.stat.distribution: The finite mixture of discrete distributions.
DiscreteMixture(List<DiscreteMixture.Component>) - Constructor for class smile.stat.distribution.DiscreteMixture: Constructor.
DiscreteMixture.Component - Class in smile.stat.distribution: A component in the mixture distribution is defined by a distribution and its weight in the mixture.
Distance<T> - Interface in smile.math.distance: An interface to calculate a distance measure between two objects.
distance(int[], int[]) - Static method in class smile.math.Math: The Euclidean distance.
distance(float[], float[]) - Static method in class smile.math.Math: The Euclidean distance.
distance(double[], double[]) - Static method in class smile.math.Math: The Euclidean distance.
distance(SparseArray, SparseArray) - Static method in class smile.math.Math: The Euclidean distance.
distribution - Variable in class smile.stat.distribution.DiscreteMixture.Component: The distribution of component.
Distribution - Interface in smile.stat.distribution: Probability distribution of univariate random variable.
distribution - Variable in class smile.stat.distribution.Mixture.Component: The distribution of component.
distribution - Variable in class smile.stat.distribution.MultivariateMixture.Component: The distribution of component.
div(int, int, double) - Method in class smile.math.Array2D
div(Array2D) - Method in class smile.math.Array2D
div(double) - Method in class smile.math.Array2D
div(Complex) - Method in class smile.math.Complex: Returns a / b.
div(int, int, double) - Method in class smile.math.matrix.DenseMatrix: A[i][j] /= x
div(DenseMatrix, DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: C = A / B
div(DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: In place element-wise division A = A / B A = A - B
div(double, DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: Element-wise addition C = A / x
div(double) - Method in class smile.math.matrix.DenseMatrix: In place element-wise division A = A / x
div(int, int, double) - Method in class smile.math.matrix.JMatrix
div(DenseMatrix) - Method in class smile.math.matrix.JMatrix
div(JMatrix) - Method in class smile.math.matrix.JMatrix
div(DenseMatrix, DenseMatrix) - Method in class smile.math.matrix.JMatrix
div(JMatrix, JMatrix) - Method in class smile.math.matrix.JMatrix
div(double) - Method in class smile.math.matrix.JMatrix
div(double, DenseMatrix) - Method in class smile.math.matrix.JMatrix
div(double, JMatrix) - Method in class smile.math.matrix.JMatrix
dot(int[], int[]) - Static method in class smile.math.Math: Returns the dot product between two vectors.
dot(float[], float[]) - Static method in class smile.math.Math: Returns the dot product between two vectors.
dot(double[], double[]) - Static method in class smile.math.Math: Returns the dot product between two vectors.
dot(SparseArray, SparseArray) - Static method in class smile.math.Math: Returns the dot product between two sparse arrays.
DoubleArrayList - Class in smile.math: A resizeable, array-backed list of double primitives.
DoubleArrayList() - Constructor for class smile.math.DoubleArrayList: Constructs an empty list.
DoubleArrayList(int) - Constructor for class smile.math.DoubleArrayList: Constructs an empty list with the specified initial capacity.
DoubleArrayList(double[]) - Constructor for class smile.math.DoubleArrayList: Constructs a list containing the values of the specified array.
DoubleHeapSelect - Class in smile.sort: This class tracks the smallest values seen thus far in a stream of values.
DoubleHeapSelect(int) - Constructor for class smile.sort.DoubleHeapSelect: Constructor.
DoubleHeapSelect(double[]) - Constructor for class smile.sort.DoubleHeapSelect: Constructor.
DynamicTimeWarping<T> - Class in smile.math.distance: Dynamic time warping is an algorithm for measuring similarity between two sequences which may vary in time or speed.
DynamicTimeWarping(Distance<T>) - Constructor for class smile.math.distance.DynamicTimeWarping: Constructor.
DynamicTimeWarping(Distance<T>, double) - Constructor for class smile.math.distance.DynamicTimeWarping: Dynamic time warping with Sakoe-Chiba band, which primarily to prevent unreasonable warping and also improve computational cost.

E

E - Static variable in class smile.math.Math: The base of the natural logarithms.
EditDistance - Class in smile.math.distance: The Edit distance between two strings is a metric for measuring the amount of difference between two sequences.
EditDistance(double[][]) - Constructor for class smile.math.distance.EditDistance: Constructor.
EditDistance(double[][], double) - Constructor for class smile.math.distance.EditDistance: Constructor.
EditDistance(int) - Constructor for class smile.math.distance.EditDistance: Constructor.
EditDistance(int, boolean) - Constructor for class smile.math.distance.EditDistance: Constructor.
eig() - Method in class smile.math.matrix.DenseMatrix: Returns the eigen values in an array of size 2N.
eig(boolean) - Method in class smile.math.matrix.DenseMatrix: Returns the eigen values in an array of size 2N.
eig() - Method in class smile.math.matrix.JMatrix
eigen() - Method in class smile.math.matrix.DenseMatrix: Returns the eigen value decomposition.
eigen(boolean) - Method in class smile.math.matrix.DenseMatrix: Returns the eigen value decomposition.
eigen() - Method in class smile.math.matrix.JMatrix
eigen(Matrix, int) - Static method in class smile.math.matrix.Lanczos: Find k largest approximate eigen pairs of a symmetric matrix by the Lanczos algorithm.
eigen(Matrix, int, double, int) - Static method in class smile.math.matrix.Lanczos: Find k largest approximate eigen pairs of a symmetric matrix by the Lanczos algorithm.
eigen(int) - Method in class smile.math.matrix.Matrix: Find k largest approximate eigen pairs of a symmetric matrix by the Lanczos algorithm.
eigen(int, double, int) - Method in class smile.math.matrix.Matrix: Find k largest approximate eigen pairs of a symmetric matrix by the Lanczos algorithm.
eigen(Matrix, double[]) - Static method in class smile.math.matrix.PowerIteration: Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
eigen(Matrix, double[], double) - Static method in class smile.math.matrix.PowerIteration: Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
eigen(Matrix, double[], double, int) - Static method in class smile.math.matrix.PowerIteration: Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
eigen(Matrix, double[], double, double) - Static method in class smile.math.matrix.PowerIteration: Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
eigen(Matrix, double[], double, double, int) - Static method in class smile.math.matrix.PowerIteration: Returns the largest eigen pair of matrix with the power iteration under the assumptions A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
EmpiricalDistribution - Class in smile.stat.distribution: An empirical distribution function or empirical cdf, is a cumulative probability distribution function that concentrates probability 1/n at each of the n numbers in a sample.
EmpiricalDistribution(double[]) - Constructor for class smile.stat.distribution.EmpiricalDistribution: Constructor.
EmpiricalDistribution(int[]) - Constructor for class smile.stat.distribution.EmpiricalDistribution: Constructor.
empty() - Method in class smile.sort.PriorityQueue: Returns true if the queue is empty.
ensureCapacity(int) - Method in class smile.math.DoubleArrayList: Increases the capacity, if necessary, to ensure that it can hold at least the number of values specified by the minimum capacity argument.
ensureCapacity(int) - Method in class smile.math.IntArrayList: Increases the capacity, if necessary, to ensure that it can hold at least the number of values specified by the minimum capacity argument.
entropy() - Method in class smile.stat.distribution.BernoulliDistribution
entropy() - Method in class smile.stat.distribution.BetaDistribution
entropy() - Method in class smile.stat.distribution.BinomialDistribution
entropy() - Method in class smile.stat.distribution.ChiSquareDistribution
entropy() - Method in class smile.stat.distribution.DiscreteMixture: Shannon entropy.
entropy() - Method in interface smile.stat.distribution.Distribution: Shannon entropy of the distribution.
entropy() - Method in class smile.stat.distribution.EmpiricalDistribution
entropy() - Method in class smile.stat.distribution.ExponentialDistribution
entropy() - Method in class smile.stat.distribution.FDistribution: Shannon entropy.
entropy() - Method in class smile.stat.distribution.GammaDistribution
entropy() - Method in class smile.stat.distribution.GaussianDistribution
entropy() - Method in class smile.stat.distribution.GeometricDistribution: Shannon entropy.
entropy() - Method in class smile.stat.distribution.HyperGeometricDistribution
entropy() - Method in class smile.stat.distribution.KernelDensity: Shannon entropy.
entropy() - Method in class smile.stat.distribution.LogisticDistribution
entropy() - Method in class smile.stat.distribution.LogNormalDistribution
entropy() - Method in class smile.stat.distribution.Mixture: Shannon entropy.
entropy() - Method in interface smile.stat.distribution.MultivariateDistribution: Shannon entropy of the distribution.
entropy() - Method in class smile.stat.distribution.MultivariateGaussianDistribution
entropy() - Method in class smile.stat.distribution.MultivariateMixture: Shannon entropy.
entropy() - Method in class smile.stat.distribution.NegativeBinomialDistribution: Shannon entropy.
entropy() - Method in class smile.stat.distribution.PoissonDistribution
entropy() - Method in class smile.stat.distribution.ShiftedGeometricDistribution
entropy() - Method in class smile.stat.distribution.TDistribution
entropy() - Method in class smile.stat.distribution.WeibullDistribution
Entry(int, double) - Constructor for class smile.math.SparseArray.Entry: Constructor.
EPSILON - Static variable in class smile.math.Math: The machine precision for the double type, which is the difference between 1 and the smallest value greater than 1 that is representable for the double type.
equals(Object) - Method in class smile.math.Complex
equals(double, double) - Static method in class smile.math.Math: Returns true if two double values equals to each other in the system precision.
equals(int[], int[]) - Static method in class smile.math.Math: Check if x element-wisely equals y.
equals(float[], float[]) - Static method in class smile.math.Math: Check if x element-wisely equals y with default epsilon 1E-7.
equals(float[], float[], float) - Static method in class smile.math.Math: Check if x element-wisely equals y.
equals(double[], double[]) - Static method in class smile.math.Math: Check if x element-wisely equals y with default epsilon 1E-10.
equals(double[], double[], double) - Static method in class smile.math.Math: Check if x element-wisely equals y.
equals(T[], T[]) - Static method in class smile.math.Math: Check if x element-wisely equals y.
equals(int[][], int[][]) - Static method in class smile.math.Math: Check if x element-wisely equals y.
equals(float[][], float[][]) - Static method in class smile.math.Math: Check if x element-wisely equals y with default epsilon 1E-7.
equals(float[][], float[][], float) - Static method in class smile.math.Math: Check if x element-wisely equals y.
equals(double[][], double[][]) - Static method in class smile.math.Math: Check if x element-wisely equals y with default epsilon 1E-10.
equals(double[][], double[][], double) - Static method in class smile.math.Math: Check if x element-wisely equals y.
equals(T[][], T[][]) - Static method in class smile.math.Math: Check if x element-wisely equals y.
Erf - Class in smile.math.special: The error function (also called the Gauss error function) is a special function of sigmoid shape which occurs in probability, statistics, materials science, and partial differential equations.
erf(double) - Static method in class smile.math.special.Erf: The Gauss error function.
erfc(double) - Static method in class smile.math.special.Erf: The complementary error function.
erfcc(double) - Static method in class smile.math.special.Erf: The complementary error function with fractional error everywhere less than 1.2 × 10^-7.
EuclideanDistance - Class in smile.math.distance: Euclidean distance.
EuclideanDistance() - Constructor for class smile.math.distance.EuclideanDistance: Constructor.
EuclideanDistance(double[]) - Constructor for class smile.math.distance.EuclideanDistance: Constructor with a given weight vector.
EVD - Class in smile.math.matrix: Eigen decomposition of a real matrix.
EVD(DenseMatrix, double[]) - Constructor for class smile.math.matrix.EVD: Private constructor.
EVD(DenseMatrix, double[], double[]) - Constructor for class smile.math.matrix.EVD: Private constructor.
exp() - Method in class smile.math.Complex: Returns the complex exponential.
exp(double) - Static method in class smile.math.Math: Returns Euler's number e raised to the power of a double value.
expm1(double) - Static method in class smile.math.Math: Returns e^x-1.
ExponentialDistribution - Class in smile.stat.distribution: An exponential distribution describes the times between events in a Poisson process, in which events occur continuously and independently at a constant average rate.
ExponentialDistribution(double) - Constructor for class smile.stat.distribution.ExponentialDistribution: Constructor.
ExponentialDistribution(double[]) - Constructor for class smile.stat.distribution.ExponentialDistribution: Constructor.
ExponentialFamily - Interface in smile.stat.distribution: The exponential family is a class of probability distributions sharing a certain form.
ExponentialFamilyMixture - Class in smile.stat.distribution: The finite mixture of distributions from exponential family.
ExponentialFamilyMixture(List<Mixture.Component>) - Constructor for class smile.stat.distribution.ExponentialFamilyMixture: Constructor.
ExponentialFamilyMixture(List<Mixture.Component>, double[]) - Constructor for class smile.stat.distribution.ExponentialFamilyMixture: Constructor.
eye(int) - Static method in class smile.math.matrix.Matrix: Returns an n-by-n identity matrix.
eye(int, int) - Static method in class smile.math.matrix.Matrix: Returns an m-by-n identity matrix.

F

f(double[], double[]) - Method in class smile.math.AbstractDifferentiableMultivariateFunction
f(double[], double[]) - Method in interface smile.math.DifferentiableMultivariateFunction: Compute the value and gradient of the function at x.
f(double) - Method in interface smile.math.Function: Compute the value of the function at x.
f(double[]) - Method in interface smile.math.MultivariateFunction: Compute the value of the function at x.
f(double) - Method in class smile.math.rbf.GaussianRadialBasis
f(double) - Method in class smile.math.rbf.InverseMultiquadricRadialBasis
f(double) - Method in class smile.math.rbf.MultiquadricRadialBasis
f(double) - Method in class smile.math.rbf.ThinPlateRadialBasis
f - Variable in class smile.stat.hypothesis.FTest: f-statistic
factorial(int) - Static method in class smile.math.Math: factorial of n
FDistribution - Class in smile.stat.distribution: F-distribution arises in the testing of whether two observed samples have the same variance.
FDistribution(int, int) - Constructor for class smile.stat.distribution.FDistribution: Constructor.
FloatHeapSelect - Class in smile.sort: This class tracks the smallest values seen thus far in a stream of values.
FloatHeapSelect(int) - Constructor for class smile.sort.FloatHeapSelect: Constructor.
FloatHeapSelect(float[]) - Constructor for class smile.sort.FloatHeapSelect: Constructor.
floor(double) - Static method in class smile.math.Math: Returns the largest (closest to positive infinity) double value that is less than or equal to the argument and is equal to a mathematical integer.
fmix(long) - Static method in class smile.hash.MurmurHash
FTest - Class in smile.stat.hypothesis: F test of the hypothesis that two independent samples come from normal distributions with the same variance, against the alternative that they come from normal distributions with different variances.
full - Variable in class smile.math.matrix.SVD: Is this a full decomposition?
Function - Interface in smile.math: An interface representing a univariate real function.

G

Gamma - Class in smile.math.special: The gamma, digamma, and incomplete gamma functions.
gamma(double) - Static method in class smile.math.special.Gamma: Gamma function.
GammaDistribution - Class in smile.stat.distribution: The Gamma distribution is a continuous probability distributions with a scale parameter θ and a shape parameter k.
GammaDistribution(double, double) - Constructor for class smile.stat.distribution.GammaDistribution: Constructor.
GammaDistribution(double[]) - Constructor for class smile.stat.distribution.GammaDistribution: Constructor.
GaussianDistribution - Class in smile.stat.distribution: The normal distribution or Gaussian distribution is a continuous probability distribution that describes data that clusters around a mean.
GaussianDistribution(double, double) - Constructor for class smile.stat.distribution.GaussianDistribution: Constructor
GaussianDistribution(double[]) - Constructor for class smile.stat.distribution.GaussianDistribution: Constructor.
GaussianKernel - Class in smile.math.kernel: The Gaussian Mercer Kernel.
GaussianKernel(double) - Constructor for class smile.math.kernel.GaussianKernel: Constructor.
GaussianMixture - Class in smile.stat.distribution: Finite univariate Gaussian mixture.
GaussianMixture(List<Mixture.Component>) - Constructor for class smile.stat.distribution.GaussianMixture: Constructor.
GaussianMixture(double[], int) - Constructor for class smile.stat.distribution.GaussianMixture: Constructor.
GaussianMixture(double[]) - Constructor for class smile.stat.distribution.GaussianMixture: Constructor.
GaussianRadialBasis - Class in smile.math.rbf: Gaussian RBF.
GaussianRadialBasis() - Constructor for class smile.math.rbf.GaussianRadialBasis: Constructor.
GaussianRadialBasis(double) - Constructor for class smile.math.rbf.GaussianRadialBasis: Constructor.
GeometricDistribution - Class in smile.stat.distribution: The geometric distribution is a discrete probability distribution of the number X of Bernoulli trials needed to get one success, supported on the set {1, 2, 3, …}.
GeometricDistribution(double) - Constructor for class smile.stat.distribution.GeometricDistribution: Constructor.
GeometricDistribution(int[]) - Constructor for class smile.stat.distribution.GeometricDistribution: Constructor.
get(int, int) - Method in class smile.math.Array2D: Returns A(i, j).
get(int) - Method in class smile.math.DoubleArrayList: Returns the value at the specified position in this list.
get(int) - Method in class smile.math.IntArrayList: Returns the value at the specified position in this list.
get(int, int) - Method in class smile.math.matrix.BandMatrix
get(int, int) - Method in class smile.math.matrix.JMatrix
get(int, int) - Method in class smile.math.matrix.Matrix: Returns the entry value at row i and column j.
get(int, int) - Method in class smile.math.matrix.SparseMatrix
get(int) - Method in class smile.math.SparseArray: Returns the value of i-th entry.
get(int) - Method in class smile.sort.DoubleHeapSelect: Returns the i-th smallest value seen so far.
get(int) - Method in class smile.sort.FloatHeapSelect: Returns the i-th smallest value seen so far.
get(int) - Method in class smile.sort.HeapSelect: Returns the i-th smallest value seen so far.
get(int) - Method in class smile.sort.IntHeapSelect: Returns the i-th smallest value seen so far.
getAlpha() - Method in class smile.stat.distribution.BetaDistribution: Returns the shape parameter alpha.
getBeta() - Method in class smile.stat.distribution.BetaDistribution: Returns the shape parameter beta.
getblock(ByteBuffer, int, int) - Static method in class smile.hash.MurmurHash
getComponents() - Method in class smile.stat.distribution.DiscreteMixture: Returns the list of components in the mixture.
getComponents() - Method in class smile.stat.distribution.Mixture: Returns the list of components in the mixture.
getComponents() - Method in class smile.stat.distribution.MultivariateMixture: Returns the list of components in the mixture.
getD() - Method in class smile.math.matrix.EVD: Returns the block diagonal eigenvalue matrix whose diagonal are the real part of eigenvalues, lower subdiagonal are positive imaginary parts, and upper subdiagonal are negative imaginary parts.
getEigenValues() - Method in class smile.math.matrix.EVD: Returns the eigenvalues, ordered from largest to smallest.
getEigenVectors() - Method in class smile.math.matrix.EVD: Returns the eigenvector matrix, ordered by eigen values from largest to smallest.
getExponent(double) - Static method in class smile.math.Math: Returns the unbiased exponent used in the representation of a double.
getExponent(float) - Static method in class smile.math.Math: Returns the unbiased exponent used in the representation of a float.
getImagEigenValues() - Method in class smile.math.matrix.EVD: Returns the imaginary parts of the eigenvalues, ordered in real part from largest to smallest.
getInstance() - Static method in class smile.stat.distribution.GaussianDistribution
getL() - Method in class smile.math.matrix.Cholesky: Returns lower triangular factor.
getLambda() - Method in class smile.stat.distribution.ExponentialDistribution: Returns the rate parameter.
getLambda() - Method in class smile.stat.distribution.PoissonDistribution: Returns the rate parameter, the expected number of occurrences in a time unit.
getMu() - Method in class smile.stat.distribution.LogNormalDistribution: Returns the parameter mu, which is the mean of normal distribution log(X).
getN() - Method in class smile.stat.distribution.BinomialDistribution: Returns the parameter n, the number of experiments.
getNu() - Method in class smile.stat.distribution.ChiSquareDistribution: Returns the parameter nu, the degrees of freedom.
getNu1() - Method in class smile.stat.distribution.FDistribution: Returns the parameter nu1, the degrees of freedom of chi-square distribution in numerator.
getNu2() - Method in class smile.stat.distribution.FDistribution: Returns the parameter nu2, the degrees of freedom chi-square distribution in denominator.
getOmega() - Method in class smile.math.kernel.PearsonKernel: Get the omega parameter.
getProb() - Method in class smile.stat.distribution.BernoulliDistribution: Returns the probability of success.
getProb() - Method in class smile.stat.distribution.BinomialDistribution: Returns the probability of success.
getProb() - Method in class smile.stat.distribution.GeometricDistribution: Returns the probability of success.
getProb() - Method in class smile.stat.distribution.ShiftedGeometricDistribution: Returns the probability of success.
getQ() - Method in class smile.math.matrix.QR: Returns the orthogonal factor.
getR() - Method in class smile.math.matrix.QR: Returns the upper triangular factor.
getRealEigenValues() - Method in class smile.math.matrix.EVD: Returns the real parts of the eigenvalues, ordered in real part from largest to smallest.
getS() - Method in class smile.math.matrix.SVD: Returns the diagonal matrix of singular values
getScale() - Method in class smile.stat.distribution.GammaDistribution: Returns the scale parameter.
getShape() - Method in class smile.stat.distribution.GammaDistribution: Returns the shape parameter.
getSigma() - Method in class smile.math.kernel.PearsonKernel: Get the sigma parameter.
getSigma() - Method in class smile.stat.distribution.LogNormalDistribution: Returns the parameter sigma, which is the standard deviation of normal distribution log(X).
getSingularValues() - Method in class smile.math.matrix.SVD: Returns the one-dimensional array of singular values, ordered by from largest to smallest.
getU() - Method in class smile.math.matrix.SVD: Returns the left singular vectors
getV() - Method in class smile.math.matrix.SVD: Returns the right singular vectors
GoodTuring(int[], int[], double[]) - Static method in class smile.math.Math: Takes a set of (frequency, frequency-of-frequency) pairs, and applies the "Simple Good-Turing" technique for estimating the probabilities corresponding to the observed frequencies, and P₀, the joint probability of all unobserved species.

H

HammingDistance<T> - Class in smile.math.distance: In information theory, the Hamming distance between two strings of equal length is the number of positions for which the corresponding symbols are different.
hash2_64(ByteBuffer, int, int, long) - Static method in class smile.hash.MurmurHash
hash32(ByteBuffer, int, int, int) - Static method in class smile.hash.MurmurHash
hash3_x64_128(ByteBuffer, int, int, long, long[]) - Static method in class smile.hash.MurmurHash
hashCode() - Method in class smile.math.Complex
heapify() - Method in class smile.sort.HeapSelect: In case of avoiding creating new objects frequently, one may check and update the peek object directly and call this method to sort the internal array in heap order.
HeapSelect<T extends java.lang.Comparable<? super T>> - Class in smile.sort: This class tracks the smallest values seen thus far in a stream of values.
HeapSelect(T[]) - Constructor for class smile.sort.HeapSelect: Constructor.
HeapSort - Class in smile.sort: Heapsort is a comparison-based sorting algorithm, and is part of the selection sort family.
HellingerKernel - Class in smile.math.kernel: The Hellinger Mercer Kernel.
HellingerKernel() - Constructor for class smile.math.kernel.HellingerKernel: Constructor.
Histogram - Class in smile.math: Histogram utilities.
histogram(int[]) - Static method in class smile.math.Histogram: Generate the histogram of given data.
histogram(float[]) - Static method in class smile.math.Histogram: Generate the histogram of given data.
histogram(double[]) - Static method in class smile.math.Histogram: Generate the histogram of given data.
histogram(int[], int) - Static method in class smile.math.Histogram: Generate the histogram of k bins.
histogram(int[], double[]) - Static method in class smile.math.Histogram: Generate the histogram of n bins.
histogram(float[], int) - Static method in class smile.math.Histogram: Generate the histogram of n bins.
histogram(float[], float[]) - Static method in class smile.math.Histogram: Generate the histogram of n bins.
histogram(double[], int) - Static method in class smile.math.Histogram: Generate the histogram of n bins.
histogram(double[], double[]) - Static method in class smile.math.Histogram: Generate the histogram of n bins.
HyperbolicTangentKernel - Class in smile.math.kernel: The hyperbolic tangent kernel.
HyperbolicTangentKernel() - Constructor for class smile.math.kernel.HyperbolicTangentKernel: Constructor.
HyperbolicTangentKernel(double, double) - Constructor for class smile.math.kernel.HyperbolicTangentKernel: Constructor.
HyperGeometricDistribution - Class in smile.stat.distribution: The hypergeometric distribution is a discrete probability distribution that describes the number of successes in a sequence of n draws from a finite population without replacement, just as the binomial distribution describes the number of successes for draws with replacement.
HyperGeometricDistribution(int, int, int) - Constructor for class smile.stat.distribution.HyperGeometricDistribution: Constructor.
hypot(double, double) - Static method in class smile.math.Math: Returns sqrt(x2 +y2) without intermediate overflow or underflow.

I

i - Variable in class smile.math.SparseArray.Entry: The index of entry.
IEEEremainder(double, double) - Static method in class smile.math.Math: Computes the remainder operation on two arguments as prescribed by the IEEE 754 standard.
im() - Method in class smile.math.Complex: Returns the imaginary part.
improve(double[], double[]) - Method in class smile.math.matrix.BandMatrix: Iteratively improve a solution to linear equations.
insert(int) - Method in class smile.sort.PriorityQueue: Insert a new item into queue.
IntArrayList - Class in smile.math: A resizeable, array-backed list of integer primitives.
IntArrayList() - Constructor for class smile.math.IntArrayList: Constructs an empty list.
IntArrayList(int) - Constructor for class smile.math.IntArrayList: Constructs an empty list with the specified initial capacity.
IntArrayList(int[]) - Constructor for class smile.math.IntArrayList: Constructs a list containing the values of the specified array.
IntHeapSelect - Class in smile.sort: This class tracks the smallest values seen thus far in a stream of values.
IntHeapSelect(int) - Constructor for class smile.sort.IntHeapSelect: Constructor.
IntHeapSelect(int[]) - Constructor for class smile.sort.IntHeapSelect: Constructor.
inverf(double) - Static method in class smile.math.special.Erf: The inverse error function.
inverfc(double) - Static method in class smile.math.special.Erf: The inverse complementary error function.
inverse() - Method in class smile.math.matrix.Cholesky: Returns the matrix inverse.
inverse() - Method in class smile.math.matrix.DenseMatrix: Returns the inverse matrix.
inverse(boolean) - Method in class smile.math.matrix.DenseMatrix: Returns the inverse matrix.
inverse() - Method in class smile.math.matrix.LU: Returns the matrix inverse.
InverseMultiquadricRadialBasis - Class in smile.math.rbf: Inverse multiquadric RBF.
InverseMultiquadricRadialBasis() - Constructor for class smile.math.rbf.InverseMultiquadricRadialBasis
InverseMultiquadricRadialBasis(double) - Constructor for class smile.math.rbf.InverseMultiquadricRadialBasis
inverseRegularizedIncompleteBetaFunction(double, double, double) - Static method in class smile.math.special.Beta: Inverse of regularized incomplete beta function.
inverseRegularizedIncompleteGamma(double, double) - Static method in class smile.math.special.Gamma: The inverse of regularized incomplete gamma function.
inverseTransformSampling() - Method in class smile.stat.distribution.AbstractDistribution: Use inverse transform sampling (also known as the inverse probability integral transform or inverse transformation method or Smirnov transform) to draw a sample from the given distribution.
IQAgent - Class in smile.sort: This class provide a robust and extremely fast algorithm to estimate arbitary quantile values from a continuing stream of data values.
IQAgent() - Constructor for class smile.sort.IQAgent: Constructor.
IQAgent(int) - Constructor for class smile.sort.IQAgent: Constructor.
isDiagonal() - Method in class smile.stat.distribution.MultivariateGaussianDistribution: Returns true if the covariance matrix is diagonal.
isEmpty() - Method in class smile.math.DoubleArrayList: Returns true if this list contains no values.
isEmpty() - Method in class smile.math.IntArrayList: Returns true if this list contains no values.
isEmpty() - Method in class smile.math.SparseArray: Returns true if the array is empty.
isPower2(int) - Static method in class smile.math.Math: Returns true if x is a power of 2.
isSingular() - Method in class smile.math.matrix.LU: Returns true if the matrix is singular or false otherwise.
isSingular() - Method in class smile.math.matrix.QR: Returns true if the matrix is singular (not full column rank).
isSymmetric() - Method in class smile.math.matrix.Matrix: Returns true if the matrix is symmetric.
isZero(float) - Static method in class smile.math.Math: Tests if a floating number is zero.
isZero(float, float) - Static method in class smile.math.Math: Tests if a floating number is zero with given epsilon.
isZero(double) - Static method in class smile.math.Math: Tests if a floating number is zero.
isZero(double, double) - Static method in class smile.math.Math: Tests if a floating number is zero with given epsilon.
iterator() - Method in class smile.math.SparseArray: Returns an iterator of nonzero entries.

J

JaccardDistance<T> - Class in smile.math.distance: The Jaccard index, also known as the Jaccard similarity coefficient is a statistic used for comparing the similarity and diversity of sample sets.
JaccardDistance() - Constructor for class smile.math.distance.JaccardDistance: Constructor.
JensenShannonDistance - Class in smile.math.distance: The Jensen-Shannon divergence is a popular method of measuring the similarity between two probability distributions.
JensenShannonDistance() - Constructor for class smile.math.distance.JensenShannonDistance: Constructor.
JensenShannonDivergence(double[], double[]) - Static method in class smile.math.Math: Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2.
JensenShannonDivergence(SparseArray, SparseArray) - Static method in class smile.math.Math: Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2.
JensenShannonDivergence(double[], SparseArray) - Static method in class smile.math.Math: Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2.
JensenShannonDivergence(SparseArray, double[]) - Static method in class smile.math.Math: Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2.
JMatrix - Class in smile.math.matrix: A pure Java implementation of DenseMatrix whose data is stored in a single 1D array of doubles in column major order.
JMatrix(double[][]) - Constructor for class smile.math.matrix.JMatrix: Constructor.
JMatrix(double[]) - Constructor for class smile.math.matrix.JMatrix: Constructor of a column vector/matrix with given array as the internal storage.
JMatrix(int, int) - Constructor for class smile.math.matrix.JMatrix: Constructor of all-zero matrix.
JMatrix(int, int, double) - Constructor for class smile.math.matrix.JMatrix: Constructor.
JMatrix(int, int, double[]) - Constructor for class smile.math.matrix.JMatrix: Constructor.

K

k(int[], int[]) - Method in class smile.math.kernel.BinarySparseGaussianKernel
k(int[], int[]) - Method in class smile.math.kernel.BinarySparseHyperbolicTangentKernel
k(int[], int[]) - Method in class smile.math.kernel.BinarySparseLaplacianKernel
k(int[], int[]) - Method in class smile.math.kernel.BinarySparseLinearKernel
k(int[], int[]) - Method in class smile.math.kernel.BinarySparsePolynomialKernel
k(int[], int[]) - Method in class smile.math.kernel.BinarySparseThinPlateSplineKernel
k(double[], double[]) - Method in class smile.math.kernel.GaussianKernel
k(double[], double[]) - Method in class smile.math.kernel.HellingerKernel
k(double[], double[]) - Method in class smile.math.kernel.HyperbolicTangentKernel
k(int, int) - Method in interface smile.math.kernel.KernelMatrix: Returns the element k(x_i, x_j) of kernel matrix for a given dataset.
k(double[], double[]) - Method in class smile.math.kernel.LaplacianKernel
k(double[], double[]) - Method in class smile.math.kernel.LinearKernel
k(T, T) - Method in interface smile.math.kernel.MercerKernel: Kernel function.
k(double[], double[]) - Method in class smile.math.kernel.PearsonKernel
k(double[], double[]) - Method in class smile.math.kernel.PolynomialKernel
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseGaussianKernel
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseHyperbolicTangentKernel
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseLaplacianKernel
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseLinearKernel
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparsePolynomialKernel
k(SparseArray, SparseArray) - Method in class smile.math.kernel.SparseThinPlateSplineKernel
k(double[], double[]) - Method in class smile.math.kernel.ThinPlateSplineKernel
kendall(int[], int[]) - Static method in class smile.math.distance.CorrelationDistance: Kendall rank correlation distance between the two arrays of type int.
kendall(float[], float[]) - Static method in class smile.math.distance.CorrelationDistance: Kendall rank correlation distance between the two arrays of type float.
kendall(double[], double[]) - Static method in class smile.math.distance.CorrelationDistance: Kendall rank correlation distance between the two arrays of type double.
kendall(int[], int[]) - Static method in class smile.math.Math: The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant.
kendall(float[], float[]) - Static method in class smile.math.Math: The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant.
kendall(double[], double[]) - Static method in class smile.math.Math: The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant.
kendall(double[], double[]) - Static method in class smile.stat.hypothesis.CorTest: Kendall rank correlation test.
KernelDensity - Class in smile.stat.distribution: Kernel density estimation is a non-parametric way of estimating the probability density function of a random variable.
KernelDensity(double[]) - Constructor for class smile.stat.distribution.KernelDensity: Constructor.
KernelDensity(double[], double) - Constructor for class smile.stat.distribution.KernelDensity: Constructor.
KernelMatrix - Interface in smile.math.kernel: A kernel matrix of dataset is the array of k(x_i, x_j).
KSTest - Class in smile.stat.hypothesis: The Kolmogorov-Smirnov test (K-S test) is a form of minimum distance estimation used as a non-parametric test of equality of one-dimensional probability distributions.
KullbackLeiblerDivergence(double[], double[]) - Static method in class smile.math.Math: Kullback-Leibler divergence.
KullbackLeiblerDivergence(SparseArray, SparseArray) - Static method in class smile.math.Math: Kullback-Leibler divergence.
KullbackLeiblerDivergence(double[], SparseArray) - Static method in class smile.math.Math: Kullback-Leibler divergence.
KullbackLeiblerDivergence(SparseArray, double[]) - Static method in class smile.math.Math: Kullback-Leibler divergence.

L

L - Variable in class smile.math.matrix.Cholesky: Array for internal storage of decomposition.
Lanczos - Class in smile.math.matrix: The Lanczos algorithm is a direct algorithm devised by Cornelius Lanczos that is an adaptation of power methods to find the most useful eigenvalues and eigenvectors of an n^th order linear system with a limited number of operations, m, where m is much smaller than n.
Lanczos() - Constructor for class smile.math.matrix.Lanczos
LaplacianKernel - Class in smile.math.kernel: The Laplacian Kernel.
LaplacianKernel(double) - Constructor for class smile.math.kernel.LaplacianKernel: Constructor.
ld() - Method in class smile.math.matrix.DenseMatrix: The LDA (and LDB, LDC, etc.) parameter in BLAS is effectively the stride of the matrix as it is laid out in linear memory.
ld() - Method in class smile.math.matrix.JMatrix
LeeDistance - Class in smile.math.distance: In coding theory, the Lee distance is a distance between two strings x₁x₂...x_n and y₁y₂...y_n of equal length n over the q-ary alphabet {0,1,...,q-1} of size q ≥ 2, defined as
LeeDistance(int) - Constructor for class smile.math.distance.LeeDistance: Constructor with a given size q of alphabet.
levenshtein(String, String) - Static method in class smile.math.distance.EditDistance: Levenshtein distance between two strings allows insertion, deletion, or substitution of characters.
levenshtein(char[], char[]) - Static method in class smile.math.distance.EditDistance: Levenshtein distance between two strings allows insertion, deletion, or substitution of characters.
lgamma(double) - Static method in class smile.math.special.Gamma: log of the Gamma function.
likelihood(double[]) - Method in class smile.stat.distribution.AbstractDistribution: The likelihood given a sample set following the distribution.
likelihood(double[][]) - Method in class smile.stat.distribution.AbstractMultivariateDistribution: The likelihood given a sample set following the distribution.
likelihood(int[]) - Method in class smile.stat.distribution.DiscreteDistribution: The likelihood given a sample set following the distribution.
likelihood(double[]) - Method in interface smile.stat.distribution.Distribution: The likelihood of the sample set following this distribution.
likelihood(double[]) - Method in class smile.stat.distribution.KernelDensity: The likelihood of the samples.
likelihood(double[][]) - Method in interface smile.stat.distribution.MultivariateDistribution: The likelihood of the sample set following this distribution.
LinearKernel - Class in smile.math.kernel: The linear dot product kernel.
LinearKernel() - Constructor for class smile.math.kernel.LinearKernel: Constructor.
LinearSolver - Interface in smile.math.matrix: The interface of the solver of linear system.
log(double) - Static method in class smile.math.Math: Returns the natural logarithm (base e) of a double value.
log10(double) - Static method in class smile.math.Math: Returns the base 10 logarithm of a double value.
log1p(double) - Static method in class smile.math.Math: Returns the natural logarithm of the sum of the argument and 1.
log2(double) - Static method in class smile.math.Math: Log of base 2.
logChoose(int, int) - Static method in class smile.math.Math: log of n choose k
logFactorial(int) - Static method in class smile.math.Math: log of factorial of n
logistic(double) - Static method in class smile.math.Math: Logistic sigmoid function.
LogisticDistribution - Class in smile.stat.distribution: The logistic distribution is a continuous probability distribution whose cumulative distribution function is the logistic function, which appears in logistic regression and feedforward neural networks.
LogisticDistribution(double, double) - Constructor for class smile.stat.distribution.LogisticDistribution: Constructor.
logLikelihood(double[]) - Method in class smile.stat.distribution.AbstractDistribution: The likelihood given a sample set following the distribution.
logLikelihood(double[][]) - Method in class smile.stat.distribution.AbstractMultivariateDistribution: The likelihood given a sample set following the distribution.
logLikelihood(int[]) - Method in class smile.stat.distribution.DiscreteDistribution: The likelihood given a sample set following the distribution.
logLikelihood(double[]) - Method in interface smile.stat.distribution.Distribution: The log likelihood of the sample set following this distribution.
logLikelihood(double[]) - Method in class smile.stat.distribution.KernelDensity: The log likelihood of the samples.
logLikelihood(double[][]) - Method in interface smile.stat.distribution.MultivariateDistribution: The log likelihood of the sample set following this distribution.
LogNormalDistribution - Class in smile.stat.distribution: A log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed.
LogNormalDistribution(double, double) - Constructor for class smile.stat.distribution.LogNormalDistribution: Constructor.
LogNormalDistribution(double[]) - Constructor for class smile.stat.distribution.LogNormalDistribution: Constructor.
logp(int) - Method in class smile.stat.distribution.BernoulliDistribution
logp(double) - Method in class smile.stat.distribution.BetaDistribution
logp(int) - Method in class smile.stat.distribution.BinomialDistribution
logp(double) - Method in class smile.stat.distribution.ChiSquareDistribution
logp(int) - Method in class smile.stat.distribution.DiscreteDistribution: The probability mass function in log scale.
logp(double) - Method in class smile.stat.distribution.DiscreteDistribution
logp(int) - Method in class smile.stat.distribution.DiscreteMixture
logp(double) - Method in interface smile.stat.distribution.Distribution: The density at x in log scale, which may prevents the underflow problem.
logp(int) - Method in class smile.stat.distribution.EmpiricalDistribution
logp(double) - Method in class smile.stat.distribution.ExponentialDistribution
logp(double) - Method in class smile.stat.distribution.FDistribution
logp(double) - Method in class smile.stat.distribution.GammaDistribution
logp(double) - Method in class smile.stat.distribution.GaussianDistribution
logp(int) - Method in class smile.stat.distribution.GeometricDistribution
logp(int) - Method in class smile.stat.distribution.HyperGeometricDistribution
logp(double) - Method in class smile.stat.distribution.KernelDensity
logp(double) - Method in class smile.stat.distribution.LogisticDistribution
logp(double) - Method in class smile.stat.distribution.LogNormalDistribution
logp(double) - Method in class smile.stat.distribution.Mixture
logp(double[]) - Method in interface smile.stat.distribution.MultivariateDistribution: The density at x in log scale, which may prevents the underflow problem.
logp(double[]) - Method in class smile.stat.distribution.MultivariateGaussianDistribution
logp(double[]) - Method in class smile.stat.distribution.MultivariateMixture
logp(int) - Method in class smile.stat.distribution.NegativeBinomialDistribution
logp(int) - Method in class smile.stat.distribution.PoissonDistribution
logp(int) - Method in class smile.stat.distribution.ShiftedGeometricDistribution
logp(double) - Method in class smile.stat.distribution.TDistribution
logp(double) - Method in class smile.stat.distribution.WeibullDistribution
lower(int) - Method in class smile.sort.PriorityQueue: The value of item k is lower (higher priority) now.
lu() - Method in class smile.math.matrix.DenseMatrix: Returns the LU decomposition.
lu(boolean) - Method in class smile.math.matrix.DenseMatrix: Returns the LU decomposition.
lu() - Method in class smile.math.matrix.JMatrix: LU decomposition is computed by a "left-looking", dot-product, Crout/Doolittle algorithm.
LU - Class in smile.math.matrix: For an m-by-n matrix A with m ≥ n, the LU decomposition is an m-by-n unit lower triangular matrix L, an n-by-n upper triangular matrix U, and a permutation vector piv of length m so that A(piv,:) = L*U.
LU(DenseMatrix, int[], int, boolean) - Constructor for class smile.math.matrix.LU: Constructor.
LU(DenseMatrix, int[], boolean) - Constructor for class smile.math.matrix.LU: Constructor.
lu - Variable in class smile.math.matrix.LU: Array for internal storage of decomposition.

M

m - Variable in class smile.math.matrix.SVD: The number of rows.
M(double[], double[]) - Method in class smile.stat.distribution.BetaDistribution
M(double[], double[]) - Method in class smile.stat.distribution.ChiSquareDistribution
M(int[], double[]) - Method in interface smile.stat.distribution.DiscreteExponentialFamily: The M step in the EM algorithm, which depends the specific distribution.
M(double[], double[]) - Method in class smile.stat.distribution.ExponentialDistribution
M(double[], double[]) - Method in interface smile.stat.distribution.ExponentialFamily: The M step in the EM algorithm, which depends the specific distribution.
M(double[], double[]) - Method in class smile.stat.distribution.GammaDistribution
M(double[], double[]) - Method in class smile.stat.distribution.GaussianDistribution
M(int[], double[]) - Method in class smile.stat.distribution.GeometricDistribution
M(double[][], double[]) - Method in interface smile.stat.distribution.MultivariateExponentialFamily: The M step in the EM algorithm, which depends the specific distribution.
M(double[][], double[]) - Method in class smile.stat.distribution.MultivariateGaussianDistribution
M(int[], double[]) - Method in class smile.stat.distribution.PoissonDistribution
M(int[], double[]) - Method in class smile.stat.distribution.ShiftedGeometricDistribution
MACHEP - Static variable in class smile.math.Math: The largest negative integer such that 1.0 + RADIX^MACHEP ≠ 1.0, except that machep is bounded below by -(DIGITS+3)
mad(int[]) - Static method in class smile.math.Math: Returns the median absolute deviation (MAD).
mad(float[]) - Static method in class smile.math.Math: Returns the median absolute deviation (MAD).
mad(double[]) - Static method in class smile.math.Math: Returns the median absolute deviation (MAD).
MahalanobisDistance - Class in smile.math.distance: In statistics, Mahalanobis distance is based on correlations between variables by which different patterns can be identified and analyzed.
MahalanobisDistance(double[][]) - Constructor for class smile.math.distance.MahalanobisDistance: Constructor with given covariance matrix.
ManhattanDistance - Class in smile.math.distance: Manhattan distance, also known as L₁ distance or L₁ norm, is the sum of the (absolute) differences of their coordinates.
ManhattanDistance() - Constructor for class smile.math.distance.ManhattanDistance: Constructor.
ManhattanDistance(double[]) - Constructor for class smile.math.distance.ManhattanDistance: Constructor.
Math - Class in smile.math: A collection of useful mathematical functions.
Matrix - Class in smile.math.matrix: An abstract interface of matrix.
Matrix() - Constructor for class smile.math.matrix.Matrix
MatrixMultiplication<A,B> - Interface in smile.math.matrix: Matrix multiplication interface.
max(double, double) - Static method in class smile.math.Math: Returns the greater of two double values.
max(float, float) - Static method in class smile.math.Math: Returns the greater of two float values.
max(int, int) - Static method in class smile.math.Math: Returns the greater of two int values.
max(long, long) - Static method in class smile.math.Math: Returns the greater of two long values.
max(int, int, int) - Static method in class smile.math.Math: maximum of 3 integers
max(float, float, float) - Static method in class smile.math.Math: maximum of 3 floats
max(double, double, double) - Static method in class smile.math.Math: maximum of 3 doubles
max(int[]) - Static method in class smile.math.Math: Returns the maximum value of an array.
max(float[]) - Static method in class smile.math.Math: Returns the maximum value of an array.
max(double[]) - Static method in class smile.math.Math: Returns the maximum value of an array.
max(int[][]) - Static method in class smile.math.Math: Returns the maximum of a matrix.
max(double[][]) - Static method in class smile.math.Math: Returns the maximum of a matrix.
mean(int[]) - Static method in class smile.math.Math: Returns the mean of an array.
mean(float[]) - Static method in class smile.math.Math: Returns the mean of an array.
mean(double[]) - Static method in class smile.math.Math: Returns the mean of an array.
mean() - Method in class smile.stat.distribution.BernoulliDistribution
mean() - Method in class smile.stat.distribution.BetaDistribution
mean() - Method in class smile.stat.distribution.BinomialDistribution
mean() - Method in class smile.stat.distribution.ChiSquareDistribution
mean() - Method in class smile.stat.distribution.DiscreteMixture
mean() - Method in interface smile.stat.distribution.Distribution: The mean of distribution.
mean() - Method in class smile.stat.distribution.EmpiricalDistribution
mean() - Method in class smile.stat.distribution.ExponentialDistribution
mean() - Method in class smile.stat.distribution.FDistribution
mean() - Method in class smile.stat.distribution.GammaDistribution
mean() - Method in class smile.stat.distribution.GaussianDistribution
mean() - Method in class smile.stat.distribution.GeometricDistribution
mean() - Method in class smile.stat.distribution.HyperGeometricDistribution
mean() - Method in class smile.stat.distribution.KernelDensity
mean() - Method in class smile.stat.distribution.LogisticDistribution
mean() - Method in class smile.stat.distribution.LogNormalDistribution
mean() - Method in class smile.stat.distribution.Mixture
mean() - Method in interface smile.stat.distribution.MultivariateDistribution: The mean vector of distribution.
mean() - Method in class smile.stat.distribution.MultivariateGaussianDistribution
mean() - Method in class smile.stat.distribution.MultivariateMixture
mean() - Method in class smile.stat.distribution.NegativeBinomialDistribution
mean() - Method in class smile.stat.distribution.PoissonDistribution
mean() - Method in class smile.stat.distribution.ShiftedGeometricDistribution
mean() - Method in class smile.stat.distribution.TDistribution
mean() - Method in class smile.stat.distribution.WeibullDistribution
median(int[]) - Static method in class smile.math.Math: Find the median of an array of type int.
median(float[]) - Static method in class smile.math.Math: Find the median of an array of type float.
median(double[]) - Static method in class smile.math.Math: Find the median of an array of type double.
median(T[]) - Static method in class smile.math.Math: Find the median of an array of type double.
median(int[]) - Static method in class smile.sort.QuickSelect: Find the median of an array of type integer.
median(float[]) - Static method in class smile.sort.QuickSelect: Find the median of an array of type float.
median(double[]) - Static method in class smile.sort.QuickSelect: Find the median of an array of type double.
median(T[]) - Static method in class smile.sort.QuickSelect: Find the median of an array of type double.
MercerKernel<T> - Interface in smile.math.kernel: A Mercer Kernel is a kernel that is positive semi-definite.
MersenneTwister - Class in smile.math.random
MersenneTwister() - Constructor for class smile.math.random.MersenneTwister: Constructor.
MersenneTwister(int) - Constructor for class smile.math.random.MersenneTwister: Constructor.
MersenneTwister(long) - Constructor for class smile.math.random.MersenneTwister: Constructor.
MersenneTwister64 - Class in smile.math.random: Mersenne Twister 64-bit.
MersenneTwister64() - Constructor for class smile.math.random.MersenneTwister64: Constructor.
MersenneTwister64(long) - Constructor for class smile.math.random.MersenneTwister64: Constructor.
Metric<T> - Interface in smile.math.distance: A metric function defines a distance between elements of a set.
min(double, double) - Static method in class smile.math.Math: Returns the smaller of two double values.
min(float, float) - Static method in class smile.math.Math: Returns the smaller of two float values.
min(int, int) - Static method in class smile.math.Math: Returns the smaller of two int values.
min(long, long) - Static method in class smile.math.Math: Returns the smaller of two long values.
min(int, int, int) - Static method in class smile.math.Math: minimum of 3 integers
min(float, float, float) - Static method in class smile.math.Math: minimum of 3 floats
min(double, double, double) - Static method in class smile.math.Math: minimum of 3 doubles
min(int[]) - Static method in class smile.math.Math: Returns the minimum value of an array.
min(float[]) - Static method in class smile.math.Math: Returns the minimum value of an array.
min(double[]) - Static method in class smile.math.Math: Returns the minimum value of an array.
min(int[][]) - Static method in class smile.math.Math: Returns the minimum of a matrix.
min(double[][]) - Static method in class smile.math.Math: Returns the minimum of a matrix.
min(DifferentiableMultivariateFunction, int, double[], double) - Static method in class smile.math.Math: This method solves the unconstrained minimization problem
min(DifferentiableMultivariateFunction, int, double[], double, int) - Static method in class smile.math.Math: This method solves the unconstrained minimization problem
min(DifferentiableMultivariateFunction, double[], double) - Static method in class smile.math.Math: This method solves the unconstrained minimization problem
min(DifferentiableMultivariateFunction, double[], double, int) - Static method in class smile.math.Math: This method solves the unconstrained minimization problem
MinkowskiDistance - Class in smile.math.distance: Minkowski distance of order p or L_p-norm, is a generalization of Euclidean distance that is actually L₂-norm.
MinkowskiDistance(int) - Constructor for class smile.math.distance.MinkowskiDistance: Constructor.
MinkowskiDistance(int, double[]) - Constructor for class smile.math.distance.MinkowskiDistance: Constructor.
minus(Complex) - Method in class smile.math.Complex: Returns this - b.
minus(double[], double[]) - Static method in class smile.math.Math: Element-wise subtraction of two arrays y = y - x.
Mixture - Class in smile.stat.distribution: A finite mixture model is a probabilistic model for density estimation using a mixture distribution.
Mixture(List<Mixture.Component>) - Constructor for class smile.stat.distribution.Mixture: Constructor.
Mixture.Component - Class in smile.stat.distribution: A component in the mixture distribution is defined by a distribution and its weight in the mixture.
mul(int, int, double) - Method in class smile.math.Array2D
mul(Array2D) - Method in class smile.math.Array2D
mul(double) - Method in class smile.math.Array2D
mul(int, int, double) - Method in class smile.math.matrix.DenseMatrix: A[i][j] *= x
mul(DenseMatrix, DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: C = A * B
mul(DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: In place element-wise multiplication A = A * B
mul(double, DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: Element-wise addition C = A * x
mul(double) - Method in class smile.math.matrix.DenseMatrix: In place element-wise multiplication A = A * x
mul(int, int, double) - Method in class smile.math.matrix.JMatrix
mul(DenseMatrix) - Method in class smile.math.matrix.JMatrix
mul(JMatrix) - Method in class smile.math.matrix.JMatrix
mul(DenseMatrix, DenseMatrix) - Method in class smile.math.matrix.JMatrix
mul(JMatrix, JMatrix) - Method in class smile.math.matrix.JMatrix
mul(double) - Method in class smile.math.matrix.JMatrix
mul(double, DenseMatrix) - Method in class smile.math.matrix.JMatrix
mul(double, JMatrix) - Method in class smile.math.matrix.JMatrix
MultiquadricRadialBasis - Class in smile.math.rbf: Multiquadric RBF.
MultiquadricRadialBasis() - Constructor for class smile.math.rbf.MultiquadricRadialBasis: Constructor.
MultiquadricRadialBasis(double) - Constructor for class smile.math.rbf.MultiquadricRadialBasis: Constructor.
MultivariateDistribution - Interface in smile.stat.distribution: Probability distribution of multivariate random variable.
MultivariateExponentialFamily - Interface in smile.stat.distribution: The purpose of this interface is mainly to define the method M that is the Maximization step in the EM algorithm.
MultivariateExponentialFamilyMixture - Class in smile.stat.distribution: The finite mixture of distributions from multivariate exponential family.
MultivariateExponentialFamilyMixture(List<MultivariateMixture.Component>) - Constructor for class smile.stat.distribution.MultivariateExponentialFamilyMixture: Constructor.
MultivariateExponentialFamilyMixture(List<MultivariateMixture.Component>, double[][]) - Constructor for class smile.stat.distribution.MultivariateExponentialFamilyMixture: Constructor.
MultivariateFunction - Interface in smile.math: An interface representing a multivariate real function.
MultivariateGaussianDistribution - Class in smile.stat.distribution: Multivariate Gaussian distribution.
MultivariateGaussianDistribution(double[], double) - Constructor for class smile.stat.distribution.MultivariateGaussianDistribution: Constructor.
MultivariateGaussianDistribution(double[], double[]) - Constructor for class smile.stat.distribution.MultivariateGaussianDistribution: Constructor.
MultivariateGaussianDistribution(double[], double[][]) - Constructor for class smile.stat.distribution.MultivariateGaussianDistribution: Constructor.
MultivariateGaussianDistribution(double[][]) - Constructor for class smile.stat.distribution.MultivariateGaussianDistribution: Constructor.
MultivariateGaussianDistribution(double[][], boolean) - Constructor for class smile.stat.distribution.MultivariateGaussianDistribution: Constructor.
MultivariateGaussianMixture - Class in smile.stat.distribution: Finite multivariate Gaussian mixture.
MultivariateGaussianMixture(List<MultivariateMixture.Component>) - Constructor for class smile.stat.distribution.MultivariateGaussianMixture: Constructor.
MultivariateGaussianMixture(double[][], int) - Constructor for class smile.stat.distribution.MultivariateGaussianMixture: Constructor.
MultivariateGaussianMixture(double[][], int, boolean) - Constructor for class smile.stat.distribution.MultivariateGaussianMixture: Constructor.
MultivariateGaussianMixture(double[][]) - Constructor for class smile.stat.distribution.MultivariateGaussianMixture: Constructor.
MultivariateGaussianMixture(double[][], boolean) - Constructor for class smile.stat.distribution.MultivariateGaussianMixture: Constructor.
MultivariateMixture - Class in smile.stat.distribution: The finite mixture of multivariate distributions.
MultivariateMixture(List<MultivariateMixture.Component>) - Constructor for class smile.stat.distribution.MultivariateMixture: Constructor.
MultivariateMixture.Component - Class in smile.stat.distribution: A component in the mixture distribution is defined by a distribution and its weight in the mixture.
MurmurHash - Class in smile.hash: This is a very fast, non-cryptographic hash suitable for general hash-based lookup.
MurmurHash() - Constructor for class smile.hash.MurmurHash

N

n - Variable in class smile.math.matrix.SVD: The number of columns.
ncols() - Method in class smile.math.Array2D
ncols() - Method in class smile.math.matrix.BandMatrix
ncols() - Method in class smile.math.matrix.JMatrix
ncols() - Method in class smile.math.matrix.Matrix: Returns the number of columns.
ncols() - Method in class smile.math.matrix.SparseMatrix
NegativeBinomialDistribution - Class in smile.stat.distribution: Negative binomial distribution arises as the probability distribution of the number of successes in a series of independent and identically distributed Bernoulli trials needed to get a specified (non-random) number r of failures.
NegativeBinomialDistribution(double, double) - Constructor for class smile.stat.distribution.NegativeBinomialDistribution: Constructor.
NEGEP - Static variable in class smile.math.Math: The largest negative integer such that 1.0 - RADIX^NEGEP ≠ 1.0, except that negeps is bounded below by -(DIGITS+3)
newInstance(double[][]) - Static method in class smile.math.matrix.Matrix: Returns an matrix initialized by given two-dimensional array.
newInstance(double[]) - Static method in class smile.math.matrix.Matrix: Returns a column vector/matrix initialized by given one-dimensional array.
newInstance(int, int, double) - Static method in class smile.math.matrix.Matrix: Creates a matrix filled with given value.
next(int) - Method in class smile.math.random.MersenneTwister
next(int) - Method in class smile.math.random.MersenneTwister64
next(int) - Method in interface smile.math.random.RandomNumberGenerator: Returns up to 32 random bits.
next(int) - Method in class smile.math.random.UniversalGenerator
nextAfter(double, double) - Static method in class smile.math.Math: Returns the floating-point number adjacent to the first argument in the direction of the second argument.
nextAfter(float, double) - Static method in class smile.math.Math: Returns the floating-point number adjacent to the first argument in the direction of the second argument.
nextDouble() - Method in class smile.math.random.MersenneTwister
nextDouble() - Method in class smile.math.random.MersenneTwister64
nextDouble() - Method in class smile.math.Random: Generator a random number uniformly distributed in [0, 1).
nextDouble(double, double) - Method in class smile.math.Random: Generate a uniform random number in the range [lo, hi)
nextDouble() - Method in interface smile.math.random.RandomNumberGenerator: Returns the next pseudorandom, uniformly distributed double value between 0.0 and 1.0 from this random number generator's sequence.
nextDouble() - Method in class smile.math.random.UniversalGenerator
nextDoubles(double[]) - Method in class smile.math.random.MersenneTwister
nextDoubles(double[]) - Method in class smile.math.random.MersenneTwister64
nextDoubles(double[]) - Method in class smile.math.Random: Generate n uniform random numbers in the range [0, 1)
nextDoubles(double[], double, double) - Method in class smile.math.Random: Generate n uniform random numbers in the range [lo, hi)
nextDoubles(double[]) - Method in interface smile.math.random.RandomNumberGenerator: Returns a vector of pseudorandom, uniformly distributed double values between 0.0 and 1.0 from this random number generator's sequence.
nextDoubles(double[]) - Method in class smile.math.random.UniversalGenerator
nextInt() - Method in class smile.math.random.MersenneTwister
nextInt(int) - Method in class smile.math.random.MersenneTwister
nextInt() - Method in class smile.math.random.MersenneTwister64
nextInt(int) - Method in class smile.math.random.MersenneTwister64
nextInt() - Method in class smile.math.Random: Returns a random integer.
nextInt(int) - Method in class smile.math.Random: Returns a random integer in [0, n).
nextInt() - Method in interface smile.math.random.RandomNumberGenerator: Returns the next pseudorandom, uniformly distributed int value from this random number generator's sequence.
nextInt(int) - Method in interface smile.math.random.RandomNumberGenerator: Returns a pseudorandom, uniformly distributed int value between 0 (inclusive) and the specified value (exclusive), drawn from this random number generator's sequence.
nextInt() - Method in class smile.math.random.UniversalGenerator
nextInt(int) - Method in class smile.math.random.UniversalGenerator
nextLong() - Method in class smile.math.random.MersenneTwister
nextLong() - Method in class smile.math.random.MersenneTwister64
nextLong() - Method in class smile.math.Random
nextLong() - Method in interface smile.math.random.RandomNumberGenerator: Returns the next pseudorandom, uniformly distributed long value from this random number generator's sequence.
nextLong() - Method in class smile.math.random.UniversalGenerator
nextUp(double) - Static method in class smile.math.Math: Returns the floating-point value adjacent to d in the direction of positive infinity.
nextUp(float) - Static method in class smile.math.Math: Returns the floating-point value adjacent to f in the direction of positive infinity.
norm(double[]) - Static method in class smile.math.Math: L2 vector norm.
norm() - Method in class smile.math.matrix.DenseMatrix: L2 matrix norm.
norm() - Method in class smile.math.matrix.SVD: Returns the L2 matrix norm.
norm1(double[]) - Static method in class smile.math.Math: L1 vector norm.
norm1() - Method in class smile.math.matrix.DenseMatrix: L1 matrix norm.
norm2(double[]) - Static method in class smile.math.Math: L2 vector norm.
norm2() - Method in class smile.math.matrix.DenseMatrix: L2 matrix norm.
normalize(double[][]) - Static method in class smile.math.Math: Unitizes each column of a matrix to unit length (L_2 norm).
normalize(double[][], boolean) - Static method in class smile.math.Math: Unitizes each column of a matrix to unit length (L_2 norm).
normFro() - Method in class smile.math.matrix.DenseMatrix: Frobenius matrix norm.
normInf(double[]) - Static method in class smile.math.Math: L-infinity vector norm.
normInf() - Method in class smile.math.matrix.DenseMatrix: Infinity matrix norm.
npara() - Method in class smile.stat.distribution.BernoulliDistribution
npara() - Method in class smile.stat.distribution.BetaDistribution
npara() - Method in class smile.stat.distribution.BinomialDistribution
npara() - Method in class smile.stat.distribution.ChiSquareDistribution
npara() - Method in class smile.stat.distribution.DiscreteMixture
npara() - Method in interface smile.stat.distribution.Distribution: The number of parameters of the distribution.
npara() - Method in class smile.stat.distribution.EmpiricalDistribution
npara() - Method in class smile.stat.distribution.ExponentialDistribution
npara() - Method in class smile.stat.distribution.FDistribution
npara() - Method in class smile.stat.distribution.GammaDistribution
npara() - Method in class smile.stat.distribution.GaussianDistribution
npara() - Method in class smile.stat.distribution.GeometricDistribution
npara() - Method in class smile.stat.distribution.HyperGeometricDistribution
npara() - Method in class smile.stat.distribution.KernelDensity
npara() - Method in class smile.stat.distribution.LogisticDistribution
npara() - Method in class smile.stat.distribution.LogNormalDistribution
npara() - Method in class smile.stat.distribution.Mixture
npara() - Method in interface smile.stat.distribution.MultivariateDistribution: The number of parameters of the distribution.
npara() - Method in class smile.stat.distribution.MultivariateGaussianDistribution
npara() - Method in class smile.stat.distribution.MultivariateMixture
npara() - Method in class smile.stat.distribution.NegativeBinomialDistribution
npara() - Method in class smile.stat.distribution.PoissonDistribution
npara() - Method in class smile.stat.distribution.ShiftedGeometricDistribution
npara() - Method in class smile.stat.distribution.TDistribution
npara() - Method in class smile.stat.distribution.WeibullDistribution
nrows() - Method in class smile.math.Array2D
nrows() - Method in class smile.math.matrix.BandMatrix
nrows() - Method in class smile.math.matrix.JMatrix
nrows() - Method in class smile.math.matrix.Matrix: Returns the number of rows.
nrows() - Method in class smile.math.matrix.SparseMatrix
nullity() - Method in class smile.math.matrix.SVD: Returns the dimension of null space.
nullspace() - Method in class smile.math.matrix.SVD: Returns a matrix of which columns give an orthonormal basis for the null space.

O

ones(int, int) - Static method in class smile.math.matrix.Matrix: Return an all-one matrix.

P

p(int) - Method in class smile.stat.distribution.BernoulliDistribution
p(double) - Method in class smile.stat.distribution.BetaDistribution
p(int) - Method in class smile.stat.distribution.BinomialDistribution
p(double) - Method in class smile.stat.distribution.ChiSquareDistribution
p(int) - Method in class smile.stat.distribution.DiscreteDistribution: The probability mass function.
p(double) - Method in class smile.stat.distribution.DiscreteDistribution
p(int) - Method in class smile.stat.distribution.DiscreteMixture
p(double) - Method in interface smile.stat.distribution.Distribution: The probability density function for continuous distribution or probability mass function for discrete distribution at x.
p(int) - Method in class smile.stat.distribution.EmpiricalDistribution
p(double) - Method in class smile.stat.distribution.ExponentialDistribution
p(double) - Method in class smile.stat.distribution.FDistribution
p(double) - Method in class smile.stat.distribution.GammaDistribution
p(double) - Method in class smile.stat.distribution.GaussianDistribution
p(int) - Method in class smile.stat.distribution.GeometricDistribution
p(int) - Method in class smile.stat.distribution.HyperGeometricDistribution
p(double) - Method in class smile.stat.distribution.KernelDensity
p(double) - Method in class smile.stat.distribution.LogisticDistribution
p(double) - Method in class smile.stat.distribution.LogNormalDistribution
p(double) - Method in class smile.stat.distribution.Mixture
p(double[]) - Method in interface smile.stat.distribution.MultivariateDistribution: The probability density function for continuous distribution or probability mass function for discrete distribution at x.
p(double[]) - Method in class smile.stat.distribution.MultivariateGaussianDistribution
p(double[]) - Method in class smile.stat.distribution.MultivariateMixture
p(int) - Method in class smile.stat.distribution.NegativeBinomialDistribution
p(int) - Method in class smile.stat.distribution.PoissonDistribution
p(int) - Method in class smile.stat.distribution.ShiftedGeometricDistribution
p(double) - Method in class smile.stat.distribution.TDistribution
p(double) - Method in class smile.stat.distribution.WeibullDistribution
PageRank - Class in smile.math.matrix: PageRank is a link analysis algorithm and it assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of "measuring" its relative importance within the set.
PageRank() - Constructor for class smile.math.matrix.PageRank
pagerank(Matrix) - Static method in class smile.math.matrix.PageRank: Calculate the page rank vector.
pagerank(Matrix, double[]) - Static method in class smile.math.matrix.PageRank: Calculate the page rank vector.
pagerank(Matrix, double[], double, double, int) - Static method in class smile.math.matrix.PageRank: Calculate the page rank vector.
pairedTest(double[], double[]) - Static method in class smile.stat.hypothesis.TTest: Given the paired arrays x and y, test if they have significantly different means.
pdist(double[][]) - Static method in class smile.math.Math: Pairwise distance between pairs of objects.
pdist(double[][], double[][], boolean, boolean) - Static method in class smile.math.Math: Pairwise distance between pairs of objects.
pearson(int[], int[]) - Static method in class smile.math.distance.CorrelationDistance: Pearson correlation distance between the two arrays of type int.
pearson(float[], float[]) - Static method in class smile.math.distance.CorrelationDistance: Pearson correlation distance between the two arrays of type float.
pearson(double[], double[]) - Static method in class smile.math.distance.CorrelationDistance: Pearson correlation distance between the two arrays of type double.
pearson(double[], double[]) - Static method in class smile.stat.hypothesis.CorTest: Pearson correlation coefficient test.
PearsonKernel - Class in smile.math.kernel: The Pearson Mercer Kernel.
PearsonKernel() - Constructor for class smile.math.kernel.PearsonKernel: Constructor.
PearsonKernel(double, double) - Constructor for class smile.math.kernel.PearsonKernel: Constructor.
peek() - Method in class smile.sort.DoubleHeapSelect: Returns the k-th smallest value seen so far.
peek() - Method in class smile.sort.FloatHeapSelect: Returns the k-th smallest value seen so far.
peek() - Method in class smile.sort.HeapSelect: Returns the k-th smallest value seen so far.
peek() - Method in class smile.sort.IntHeapSelect: Returns the k-th smallest value seen so far.
permutate(int) - Static method in class smile.math.Math: Generates a permutation of 0, 1, 2, ..., n-1, which is useful for sampling without replacement.
permutate(int[]) - Static method in class smile.math.Math: Generates a permutation of given array.
permutate(float[]) - Static method in class smile.math.Math: Generates a permutation of given array.
permutate(double[]) - Static method in class smile.math.Math: Generates a permutation of given array.
permutate(Object[]) - Static method in class smile.math.Math: Generates a permutation of given array.
permutate(int) - Method in class smile.math.Random: Generates a permutation of 0, 1, 2, ..., n-1, which is useful for sampling without replacement.
permutate(int[]) - Method in class smile.math.Random: Generates a permutation of given array.
permutate(float[]) - Method in class smile.math.Random: Generates a permutation of given array.
permutate(double[]) - Method in class smile.math.Random: Generates a permutation of given array.
permutate(Object[]) - Method in class smile.math.Random: Generates a permutation of given array.
phase() - Method in class smile.math.Complex: Returns angle/phase/argument between -pi and pi.
PI - Static variable in class smile.math.Math: The ratio of the circumference of a circle to its diameter.
piv - Variable in class smile.math.matrix.LU: Internal storage of pivot vector.
pivsign - Variable in class smile.math.matrix.LU: pivot sign.
plus(Complex) - Method in class smile.math.Complex: Returns this + b.
plus(double[], double[]) - Static method in class smile.math.Math: Element-wise sum of two arrays y = x + y.
PoissonDistribution - Class in smile.stat.distribution: Poisson distribution expresses the probability of a number of events occurring in a fixed period of time if these events occur with a known average rate and independently of the time since the last event.
PoissonDistribution(double) - Constructor for class smile.stat.distribution.PoissonDistribution: Constructor.
PoissonDistribution(int[]) - Constructor for class smile.stat.distribution.PoissonDistribution: Constructor.
poll() - Method in class smile.sort.PriorityQueue: Removes and returns the index of item with minimum value (highest priority).
PolynomialKernel - Class in smile.math.kernel: The polynomial kernel.
PolynomialKernel(int) - Constructor for class smile.math.kernel.PolynomialKernel: Constructor with scale 1 and bias 0.
PolynomialKernel(int, double, double) - Constructor for class smile.math.kernel.PolynomialKernel: Constructor.
pow(double, double) - Static method in class smile.math.Math: Returns the value of the first argument raised to the power of the second argument.
pow(double[], double) - Static method in class smile.math.Math: Raise each element of an array to a scalar power.
PowerIteration - Class in smile.math.matrix: The power iteration (also known as power method) is an eigenvalue algorithm that will produce the greatest (in absolute value) eigenvalue and a nonzero vector the corresponding eigenvector.
PowerIteration() - Constructor for class smile.math.matrix.PowerIteration
Preconditioner - Interface in smile.math.matrix: The preconditioner matrix in the biconjugate gradient method.
priori - Variable in class smile.stat.distribution.DiscreteMixture.Component: The priori probability of component.
priori - Variable in class smile.stat.distribution.Mixture.Component: The priori probability of component.
priori - Variable in class smile.stat.distribution.MultivariateMixture.Component: The priori probability of component.
PriorityQueue - Class in smile.sort: Priority Queue for index items.
PriorityQueue(double[]) - Constructor for class smile.sort.PriorityQueue: Constructor.
PriorityQueue(int, double[]) - Constructor for class smile.sort.PriorityQueue: Constructor.
pvalue - Variable in class smile.stat.hypothesis.ChiSqTest: p-value
pvalue - Variable in class smile.stat.hypothesis.CorTest: (two-sided) p-value of test
pvalue - Variable in class smile.stat.hypothesis.FTest: p-value
pvalue - Variable in class smile.stat.hypothesis.KSTest: P-value
pvalue - Variable in class smile.stat.hypothesis.TTest: p-value

Q

q1(int[]) - Static method in class smile.math.Math: Find the first quantile (p = 1/4) of an array of type int.
q1(float[]) - Static method in class smile.math.Math: Find the first quantile (p = 1/4) of an array of type float.
q1(double[]) - Static method in class smile.math.Math: Find the first quantile (p = 1/4) of an array of type double.
q1(T[]) - Static method in class smile.math.Math: Find the first quantile (p = 1/4) of an array of type double.
q1(int[]) - Static method in class smile.sort.QuickSelect: Find the first quantile (p = 1/4) of an array of type integer.
q1(float[]) - Static method in class smile.sort.QuickSelect: Find the first quantile (p = 1/4) of an array of type float.
q1(double[]) - Static method in class smile.sort.QuickSelect: Find the first quantile (p = 1/4) of an array of type double.
q1(T[]) - Static method in class smile.sort.QuickSelect: Find the first quantile (p = 1/4) of an array of type double.
q3(int[]) - Static method in class smile.math.Math: Find the third quantile (p = 3/4) of an array of type int.
q3(float[]) - Static method in class smile.math.Math: Find the third quantile (p = 3/4) of an array of type float.
q3(double[]) - Static method in class smile.math.Math: Find the third quantile (p = 3/4) of an array of type double.
q3(T[]) - Static method in class smile.math.Math: Find the third quantile (p = 3/4) of an array of type double.
q3(int[]) - Static method in class smile.sort.QuickSelect: Find the third quantile (p = 3/4) of an array of type integer.
q3(float[]) - Static method in class smile.sort.QuickSelect: Find the third quantile (p = 3/4) of an array of type float.
q3(double[]) - Static method in class smile.sort.QuickSelect: Find the third quantile (p = 3/4) of an array of type double.
q3(T[]) - Static method in class smile.sort.QuickSelect: Find the third quantile (p = 3/4) of an array of type double.
qr() - Method in class smile.math.matrix.DenseMatrix: Returns the QR decomposition.
qr(boolean) - Method in class smile.math.matrix.DenseMatrix: Returns the QR decomposition.
qr() - Method in class smile.math.matrix.JMatrix: QR Decomposition is computed by Householder reflections.
QR - Class in smile.math.matrix: For an m-by-n matrix A with m ≥ n, the QR decomposition is an m-by-n orthogonal matrix Q and an n-by-n upper triangular matrix R such that A = Q*R.
QR(DenseMatrix, double[], boolean) - Constructor for class smile.math.matrix.QR: Constructor.
qr - Variable in class smile.math.matrix.QR: Array for internal storage of decomposition.
quantile(double) - Method in class smile.sort.IQAgent: Returns the estimated p-quantile for the data seen so far.
quantile(double, double, double, double) - Method in class smile.stat.distribution.AbstractDistribution: Inversion of CDF by bisection numeric root finding of "cdf(x) = p" for continuous distribution.
quantile(double, double, double) - Method in class smile.stat.distribution.AbstractDistribution: Inversion of CDF by bisection numeric root finding of "cdf(x) = p" for continuous distribution.
quantile(double) - Method in class smile.stat.distribution.BernoulliDistribution
quantile(double) - Method in class smile.stat.distribution.BetaDistribution
quantile(double) - Method in class smile.stat.distribution.BinomialDistribution
quantile(double) - Method in class smile.stat.distribution.ChiSquareDistribution
quantile(double, int, int) - Method in class smile.stat.distribution.DiscreteDistribution: Invertion of cdf by bisection numeric root finding of "cdf(x) = p" for discrete distribution.* Returns integer n such that P(
quantile(double) - Method in class smile.stat.distribution.DiscreteMixture
quantile(double) - Method in interface smile.stat.distribution.Distribution: The quantile, the probability to the left of quantile is p.
quantile(double) - Method in class smile.stat.distribution.EmpiricalDistribution
quantile(double) - Method in class smile.stat.distribution.ExponentialDistribution
quantile(double) - Method in class smile.stat.distribution.FDistribution
quantile(double) - Method in class smile.stat.distribution.GammaDistribution
quantile(double) - Method in class smile.stat.distribution.GaussianDistribution: The quantile, the probability to the left of quantile(p) is p.
quantile(double) - Method in class smile.stat.distribution.GeometricDistribution
quantile(double) - Method in class smile.stat.distribution.HyperGeometricDistribution
quantile(double) - Method in class smile.stat.distribution.KernelDensity: Inverse of CDF.
quantile(double) - Method in class smile.stat.distribution.LogisticDistribution
quantile(double) - Method in class smile.stat.distribution.LogNormalDistribution
quantile(double) - Method in class smile.stat.distribution.Mixture
quantile(double) - Method in class smile.stat.distribution.NegativeBinomialDistribution
quantile(double) - Method in class smile.stat.distribution.PoissonDistribution
quantile(double) - Method in class smile.stat.distribution.ShiftedGeometricDistribution
quantile(double) - Method in class smile.stat.distribution.TDistribution
quantile(double) - Method in class smile.stat.distribution.WeibullDistribution
quantile2tiled(double) - Method in class smile.stat.distribution.TDistribution: Two-tailed quantile.
QuickSelect - Class in smile.sort: Selection is asking for the k th smallest element out of n elements.
QuickSort - Class in smile.sort: Quicksort is a well-known sorting algorithm that, on average, makes O(n log n) comparisons to sort n items.

R

RadialBasisFunction - Interface in smile.math.rbf: A radial basis function (RBF) is a real-valued function whose value depends only on the distance from the origin, so that φ(x)=φ(||x||); or alternatively on the distance from some other point c, called a center, so that φ(x,c)=φ(||x-c||).
RADIX - Static variable in class smile.math.Math: The base of the exponent of the double type.
rand() - Method in class smile.stat.distribution.BernoulliDistribution
rand() - Method in class smile.stat.distribution.BetaDistribution
rand() - Method in class smile.stat.distribution.BinomialDistribution: This function generates a random variate with the binomial distribution.
rand() - Method in class smile.stat.distribution.ChiSquareDistribution
rand() - Method in class smile.stat.distribution.DiscreteMixture
rand() - Method in interface smile.stat.distribution.Distribution: Generates a random number following this distribution.
rand() - Method in class smile.stat.distribution.EmpiricalDistribution
rand(int) - Method in class smile.stat.distribution.EmpiricalDistribution
rand() - Method in class smile.stat.distribution.ExponentialDistribution
rand() - Method in class smile.stat.distribution.FDistribution
rand() - Method in class smile.stat.distribution.GammaDistribution: Only support shape parameter k of integer.
rand() - Method in class smile.stat.distribution.GaussianDistribution: Uses the Box-Muller algorithm to transform Random.random()'s into Gaussian deviates.
rand() - Method in class smile.stat.distribution.GeometricDistribution
rand() - Method in class smile.stat.distribution.HyperGeometricDistribution: Uses inversion by chop-down search from the mode when the mean < 20 and the patchwork-rejection method when the mean > 20.
rand() - Method in class smile.stat.distribution.KernelDensity: Random number generator.
rand() - Method in class smile.stat.distribution.LogisticDistribution
rand() - Method in class smile.stat.distribution.LogNormalDistribution
rand() - Method in class smile.stat.distribution.Mixture
rand() - Method in class smile.stat.distribution.MultivariateGaussianDistribution: Generate a random multivariate Gaussian sample.
rand() - Method in class smile.stat.distribution.NegativeBinomialDistribution
rand() - Method in class smile.stat.distribution.PoissonDistribution: This function generates a random variate with the poisson distribution.
rand() - Method in class smile.stat.distribution.ShiftedGeometricDistribution
rand() - Method in class smile.stat.distribution.TDistribution
rand() - Method in class smile.stat.distribution.WeibullDistribution
randInverseCDF() - Method in class smile.stat.distribution.GaussianDistribution: Uses Inverse CDF method to generate a Gaussian deviate.
randn(int, int) - Static method in class smile.math.matrix.Matrix: Returns a random matrix of standard normal distributed values with given mean and standard dev.
randn(int, int, double, double) - Static method in class smile.math.matrix.Matrix: Returns a random matrix of normal distributed values with given mean and standard dev.
random(double[]) - Static method in class smile.math.Math: Given a set of n probabilities, generate a random number in [0, n).
random(double[], int) - Static method in class smile.math.Math: Given a set of m probabilities, draw with replacement a set of n random number in [0, m).
random() - Static method in class smile.math.Math: Generate a random number in [0, 1).
random(int) - Static method in class smile.math.Math: Generate n random numbers in [0, 1).
random(double, double) - Static method in class smile.math.Math: Generate a uniform random number in the range [lo, hi).
random(double, double, int) - Static method in class smile.math.Math: Generate n uniform random numbers in the range [lo, hi).
Random - Class in smile.math: This is a high quality random number generator as a replacement of the standard Random class of Java system.
Random() - Constructor for class smile.math.Random: Initialize with default random number generator engine.
Random(long) - Constructor for class smile.math.Random: Initialize with given seed for default random number generator engine.
randomInt(int) - Static method in class smile.math.Math: Returns a random integer in [0, n).
randomInt(int, int) - Static method in class smile.math.Math: Returns a random integer in [lo, hi).
RandomNumberGenerator - Interface in smile.math.random: Random number generator interface.
range() - Method in class smile.math.matrix.SVD: Returns a matrix of which columns give an orthonormal basis for the range space.
rank() - Method in class smile.math.matrix.SVD: Returns the effective numerical matrix rank.
rbind(int[]...) - Static method in class smile.math.Math: Take a sequence of vector arguments and combine by rows.
rbind(float[]...) - Static method in class smile.math.Math: Take a sequence of vector arguments and combine by rows.
rbind(double[]...) - Static method in class smile.math.Math: Take a sequence of vector arguments and combine by rows.
rbind(String[]...) - Static method in class smile.math.Math: Take a sequence of vector arguments and combine by rows.
re() - Method in class smile.math.Complex: Returns the real part.
reciprocal() - Method in class smile.math.Complex: Returns the reciprocal.
regularizedIncompleteBetaFunction(double, double, double) - Static method in class smile.math.special.Beta: Regularized Incomplete Beta function.
regularizedIncompleteGamma(double, double) - Static method in class smile.math.special.Gamma: Regularized Incomplete Gamma Function P(s,x) = ∫₀^x e^-t t^(s-1) dt
regularizedUpperIncompleteGamma(double, double) - Static method in class smile.math.special.Gamma: Regularized Upper/Complementary Incomplete Gamma Function Q(s,x) = 1 - P(s,x) = 1 - ∫₀^x e^-t t^(s-1) dt
rejection(double, double, double) - Method in class smile.stat.distribution.AbstractDistribution: Use the rejection technique to draw a sample from the given distribution.
remove(int) - Method in class smile.math.DoubleArrayList: Removes the value at specified index from the list.
remove(int) - Method in class smile.math.IntArrayList: Removes the value at specified index from the list.
remove(int) - Method in class smile.math.SparseArray: Removes an entry.
replaceNaN(double) - Method in class smile.math.Array2D
replaceNaN(double) - Method in class smile.math.matrix.DenseMatrix: Replaces NaN's with given value.
replaceNaN(double) - Method in class smile.math.matrix.JMatrix
reverse(int[]) - Static method in class smile.math.Math: Reverses the order of the elements in the specified array.
reverse(float[]) - Static method in class smile.math.Math: Reverses the order of the elements in the specified array.
reverse(double[]) - Static method in class smile.math.Math: Reverses the order of the elements in the specified array.
reverse(T[]) - Static method in class smile.math.Math: Reverses the order of the elements in the specified array.
rint(double) - Static method in class smile.math.Math: Returns the double value that is closest in value to the argument and is equal to a mathematical integer.
root(Function, double, double, double) - Static method in class smile.math.Math: Returns the root of a function known to lie between x1 and x2 by Brent's method.
root(Function, double, double, double, int) - Static method in class smile.math.Math: Returns the root of a function known to lie between x1 and x2 by Brent's method.
root(DifferentiableFunction, double, double, double) - Static method in class smile.math.Math: Returns the root of a function whose derivative is available known to lie between x1 and x2 by Newton-Raphson method.
root(DifferentiableFunction, double, double, double, int) - Static method in class smile.math.Math: Returns the root of a function whose derivative is available known to lie between x1 and x2 by Newton-Raphson method.
rotl64(long, int) - Static method in class smile.hash.MurmurHash
round(double) - Static method in class smile.math.Math: Returns the closest long to the argument.
round(float) - Static method in class smile.math.Math: Returns the closest int to the argument.
round(double, int) - Static method in class smile.math.Math: Round a double vale to given digits such as 10^n, where n is a positive or negative integer.
ROUND_STYLE - Static variable in class smile.math.Math: Rounding style.
rowMax(double[][]) - Static method in class smile.math.Math: Returns the row maximum for a matrix.
rowMeans(double[][]) - Static method in class smile.math.Math: Returns the row means for a matrix.
rowMeans() - Method in class smile.math.matrix.DenseMatrix: Returns the mean of each row for a matrix.
rowMin(double[][]) - Static method in class smile.math.Math: Returns the row minimum for a matrix.
rowSds(double[][]) - Static method in class smile.math.Math: Returns the row standard deviations for a matrix.
rowSums(double[][]) - Static method in class smile.math.Math: Returns the row sums for a matrix.
rowSums() - Method in class smile.math.matrix.DenseMatrix: Returns the sum of each row for a matrix.

S

s - Variable in class smile.math.matrix.SVD: Array for internal storage of singular values.
scalb(double, int) - Static method in class smile.math.Math: Returns d x 2^scaleFactor rounded as if performed by a single correctly rounded floating-point multiply to a member of the double value set.
scalb(float, int) - Static method in class smile.math.Math: Returns f x 2^scaleFactor rounded as if performed by a single correctly rounded floating-point multiply to a member of the float value set.
scale(double[][]) - Static method in class smile.math.Math: Scales each column of a matrix to range [0, 1].
scale(double[][], double, double) - Static method in class smile.math.Math: Scales each column of a matrix to range [lo, hi].
scale(double, double[]) - Static method in class smile.math.Math: Scale each element of an array by a constant x = a * x.
scale(double, double[], double[]) - Static method in class smile.math.Math: Scale each element of an array by a constant y = a * x.
scatter() - Method in class smile.stat.distribution.MultivariateGaussianDistribution: Returns the scatter of distribution, which is defined as |Σ|.
scott(double[]) - Static method in class smile.math.Histogram: Returns the number of bins by Scott's rule h = 3.5 * σ / (n^1/3).
sd(int[]) - Static method in class smile.math.Math: Returns the standard deviation of an array.
sd(float[]) - Static method in class smile.math.Math: Returns the standard deviation of an array.
sd(double[]) - Static method in class smile.math.Math: Returns the standard deviation of an array.
sd() - Method in class smile.stat.distribution.BernoulliDistribution
sd() - Method in class smile.stat.distribution.BetaDistribution
sd() - Method in class smile.stat.distribution.BinomialDistribution
sd() - Method in class smile.stat.distribution.ChiSquareDistribution
sd() - Method in class smile.stat.distribution.DiscreteMixture
sd() - Method in interface smile.stat.distribution.Distribution: The standard deviation of distribution.
sd() - Method in class smile.stat.distribution.EmpiricalDistribution
sd() - Method in class smile.stat.distribution.ExponentialDistribution
sd() - Method in class smile.stat.distribution.FDistribution
sd() - Method in class smile.stat.distribution.GammaDistribution
sd() - Method in class smile.stat.distribution.GaussianDistribution
sd() - Method in class smile.stat.distribution.GeometricDistribution
sd() - Method in class smile.stat.distribution.HyperGeometricDistribution
sd() - Method in class smile.stat.distribution.KernelDensity
sd() - Method in class smile.stat.distribution.LogisticDistribution
sd() - Method in class smile.stat.distribution.LogNormalDistribution
sd() - Method in class smile.stat.distribution.Mixture
sd() - Method in class smile.stat.distribution.NegativeBinomialDistribution
sd() - Method in class smile.stat.distribution.PoissonDistribution
sd() - Method in class smile.stat.distribution.ShiftedGeometricDistribution
sd() - Method in class smile.stat.distribution.TDistribution
sd() - Method in class smile.stat.distribution.WeibullDistribution
select(int[], int) - Static method in class smile.sort.QuickSelect: Given k in [0, n-1], returns an array value from arr such that k array values are less than or equal to the one returned.
select(float[], int) - Static method in class smile.sort.QuickSelect: Given k in [0, n-1], returns an array value from arr such that k array values are less than or equal to the one returned.
select(double[], int) - Static method in class smile.sort.QuickSelect: Given k in [0, n-1], returns an array value from arr such that k array values are less than or equal to the one returned.
select(T[], int) - Static method in class smile.sort.QuickSelect: Given k in [0, n-1], returns an array value from arr such that k array values are less than or equal to the one returned.
set(int, int, double) - Method in class smile.math.Array2D: Sets A(i, j).
set(int, double) - Method in class smile.math.DoubleArrayList: Replaces the value at the specified position in this list with the specified value.
set(int, int) - Method in class smile.math.IntArrayList: Replaces the value at the specified position in this list with the specified value.
set(int, int, double) - Method in class smile.math.matrix.BandMatrix
set(int, int, double) - Method in class smile.math.matrix.DenseMatrix: Set the entry value at row i and column j.
set(int, int, double) - Method in class smile.math.matrix.JMatrix
set(int, double) - Method in class smile.math.SparseArray: Sets or add an entry.
setOmega(double) - Method in class smile.math.kernel.PearsonKernel: Set the omega parameter.
setSeed(long) - Static method in class smile.math.Math: Initialize the random generator with a seed.
setSeed(long) - Method in class smile.math.random.MersenneTwister
setSeed(int) - Method in class smile.math.random.MersenneTwister
setSeed(long) - Method in class smile.math.random.MersenneTwister64
setSeed(long) - Method in interface smile.math.random.RandomNumberGenerator: Initialize the random generator with a seed.
setSeed(long) - Method in class smile.math.Random: Initialize the random generator with a seed.
setSeed(long) - Method in class smile.math.random.UniversalGenerator
setSigma(double) - Method in class smile.math.kernel.PearsonKernel: Set the sigma parameter.
setSymmetric(boolean) - Method in class smile.math.matrix.Matrix: Sets if the matrix is symmetric.
ShellSort - Class in smile.sort: Shell sort is a sorting algorithm that is a generalization of insertion sort, with two observations: insertion sort is efficient if the input is "almost sorted", and insertion sort is typically inefficient because it moves values just one position at a time.
ShiftedGeometricDistribution - Class in smile.stat.distribution: The "shifted" geometric distribution is a discrete probability distribution of the number of failures before the first success, supported on the set {0, 1, 2, 3, …}.
ShiftedGeometricDistribution(double) - Constructor for class smile.stat.distribution.ShiftedGeometricDistribution: Constructor.
ShiftedGeometricDistribution(int[]) - Constructor for class smile.stat.distribution.ShiftedGeometricDistribution: Constructor.
siftDown(int[], int, int) - Static method in class smile.sort.SortUtils: To restore the max-heap condition when a node's priority is decreased.
siftDown(float[], int, int) - Static method in class smile.sort.SortUtils: To restore the max-heap condition when a node's priority is decreased.
siftDown(double[], int, int) - Static method in class smile.sort.SortUtils: To restore the max-heap condition when a node's priority is decreased.
siftDown(T[], int, int) - Static method in class smile.sort.SortUtils: To restore the max-heap condition when a node's priority is decreased.
siftUp(int[], int) - Static method in class smile.sort.SortUtils: To restore the max-heap condition when a node's priority is increased.
siftUp(float[], int) - Static method in class smile.sort.SortUtils: To restore the max-heap condition when a node's priority is increased.
siftUp(double[], int) - Static method in class smile.sort.SortUtils: To restore the max-heap condition when a node's priority is increased.
siftUp(T[], int) - Static method in class smile.sort.SortUtils: To restore the max-heap condition when a node's priority is increased.
signum(double) - Static method in class smile.math.Math: Returns the signum of the argument; zero if the argument is zero, 1.0 if the argument is greater than zero, -1.0 if the argument is less than zero.
signum(float) - Static method in class smile.math.Math: Returns the signum function of the argument; zero if the argument is zero, 1.0f if the argument is greater than zero, -1.0f if the argument is less than zero.
sin() - Method in class smile.math.Complex: Returns the complex sine.
sin(double) - Static method in class smile.math.Math: Returns the trigonometric sine of an angle.
singular - Variable in class smile.math.matrix.LU: True if the matrix is singular.
singular - Variable in class smile.math.matrix.QR: Indicate if the matrix is singular.
sinh(double) - Static method in class smile.math.Math: Returns the hyperbolic sine of a double value.
size() - Method in class smile.math.DoubleArrayList: Returns the number of values in the list.
size() - Method in class smile.math.IntArrayList: Returns the number of values in the list.
size() - Method in class smile.math.matrix.SparseMatrix: Returns the number of nonzero values.
size() - Method in class smile.math.SparseArray: Returns the number of nonzero entries.
size() - Method in class smile.stat.distribution.DiscreteMixture: Returns the number of components in the mixture.
size() - Method in class smile.stat.distribution.Mixture: Returns the number of components in the mixture.
size() - Method in class smile.stat.distribution.MultivariateMixture: Returns the number of components in the mixture.
slice(E[], int[]) - Static method in class smile.math.Math: Returns a slice of data for given indices.
slice(int[], int[]) - Static method in class smile.math.Math: Returns a slice of data for given indices.
slice(float[], int[]) - Static method in class smile.math.Math: Returns a slice of data for given indices.
slice(double[], int[]) - Static method in class smile.math.Math: Returns a slice of data for given indices.
smile.hash - package smile.hash: Hashing functions.
smile.math - package smile.math: Basic mathematical functions, complex, differentiable function interfaces, random number generators, unconstrained optimization, and raw data type (int and double) array lists, etc.
smile.math.distance - package smile.math.distance: Distance and metric measures.
smile.math.kernel - package smile.math.kernel: Mercer kernels.
smile.math.matrix - package smile.math.matrix: Matrix interface, dense and sparse (band or irregular) matrix encapsulation classes, LU, QR, Cholesky, SVD and eigen decompositions, etc.
smile.math.random - package smile.math.random: High quality random number generators as a replacement of the standard Random class of Java system.
smile.math.rbf - package smile.math.rbf: Radial basis functions.
smile.math.special - package smile.math.special: Special mathematical functions including beta, erf, and gamma.
smile.sort - package smile.sort: Sorting algorithms.
smile.stat.distribution - package smile.stat.distribution: Probability distributions.
smile.stat.hypothesis - package smile.stat.hypothesis: Statistical hypothesis tests.
solve(double[], double[], double[], double[]) - Static method in class smile.math.Math: Solve the tridiagonal linear set which is of diagonal dominance |b_i| > |a_i| + |c_i|.
solve(double[], double[]) - Method in class smile.math.matrix.BandMatrix: Solve A*x = b.
solve(Matrix, double[], double[]) - Static method in class smile.math.matrix.BiconjugateGradient: Solves A * x = b by iterative biconjugate gradient method.
solve(Matrix, Preconditioner, double[], double[]) - Static method in class smile.math.matrix.BiconjugateGradient: Solves A * x = b by iterative biconjugate gradient method.
solve(Matrix, double[], double[], double) - Static method in class smile.math.matrix.BiconjugateGradient: Solves A * x = b by iterative biconjugate gradient method.
solve(Matrix, Preconditioner, double[], double[], double) - Static method in class smile.math.matrix.BiconjugateGradient: Solves A * x = b by iterative biconjugate gradient method.
solve(Matrix, double[], double[], double, int) - Static method in class smile.math.matrix.BiconjugateGradient: Solves A * x = b by iterative biconjugate gradient method.
solve(Matrix, Preconditioner, double[], double[], double, int) - Static method in class smile.math.matrix.BiconjugateGradient: Solves A * x = b by iterative biconjugate gradient method.
solve(Matrix, double[], double[], double, int, int) - Static method in class smile.math.matrix.BiconjugateGradient: Solves A * x = b by iterative biconjugate gradient method.
solve(Matrix, Preconditioner, double[], double[], double, int, int) - Static method in class smile.math.matrix.BiconjugateGradient: Solves A * x = b by iterative biconjugate gradient method.
solve(double[]) - Method in class smile.math.matrix.Cholesky: Solve the linear system A * x = b.
solve(DenseMatrix) - Method in class smile.math.matrix.Cholesky: Solve the linear system A * X = B.
solve(double[], double[]) - Method in interface smile.math.matrix.LinearSolver: Solve A*x = b.
solve(double[]) - Method in class smile.math.matrix.LU: Solve A * x = b.
solve(DenseMatrix) - Method in class smile.math.matrix.LU: Solve A * X = B.
solve(double[], double[]) - Method in class smile.math.matrix.QR: Solve the least squares A*x = b.
solve(DenseMatrix) - Method in class smile.math.matrix.QR: Solve the least squares A * X = B.
solve(double[], double[]) - Method in class smile.math.matrix.SVD: Solve the least squares A*x = b.
solve(DenseMatrix) - Method in class smile.math.matrix.SVD: Solve the least squares A * X = B.
sort(double[][]) - Static method in class smile.math.Math: Sorts each variable and returns the index of values in ascending order.
sort(double[], double[]) - Static method in class smile.math.matrix.JMatrix: Sort eigenvalues.
sort(double[], double[], DenseMatrix) - Static method in class smile.math.matrix.JMatrix: Sort eigenvalues and eigenvectors.
sort() - Method in class smile.sort.DoubleHeapSelect: Sort the smallest values.
sort() - Method in class smile.sort.FloatHeapSelect: Sort the smallest values.
sort() - Method in class smile.sort.HeapSelect: Sort the smallest values.
sort(int[]) - Static method in class smile.sort.HeapSort: Sorts the specified array into ascending numerical order.
sort(float[]) - Static method in class smile.sort.HeapSort: Sorts the specified array into ascending numerical order.
sort(double[]) - Static method in class smile.sort.HeapSort: Sorts the specified array into ascending numerical order.
sort(T[]) - Static method in class smile.sort.HeapSort: Sorts the specified array into ascending order.
sort() - Method in class smile.sort.IntHeapSelect: Sort the smallest values.
sort(int[]) - Static method in class smile.sort.QuickSort: Sorts the specified array into ascending numerical order.
sort(int[], int[]) - Static method in class smile.sort.QuickSort: Besides sorting the array arr, the array brr will be also rearranged as the same order of arr.
sort(int[], int[], int) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(int[], Object[]) - Static method in class smile.sort.QuickSort: Besides sorting the array arr, the array brr will be also rearranged as the same order of arr.
sort(int[], Object[], int) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(float[]) - Static method in class smile.sort.QuickSort: Sorts the specified array into ascending numerical order.
sort(float[], int[]) - Static method in class smile.sort.QuickSort: Besides sorting the array arr, the array brr will be also rearranged as the same order of arr.
sort(float[], int[], int) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(float[], float[]) - Static method in class smile.sort.QuickSort: Besides sorting the array arr, the array brr will be also rearranged as the same order of arr.
sort(float[], float[], int) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(float[], Object[]) - Static method in class smile.sort.QuickSort: Besides sorting the array arr, the array brr will be also rearranged as the same order of arr.
sort(float[], Object[], int) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(double[]) - Static method in class smile.sort.QuickSort: Sorts the specified array into ascending numerical order.
sort(double[], int[]) - Static method in class smile.sort.QuickSort: Besides sorting the array arr, the array brr will be also rearranged as the same order of arr.
sort(double[], int[], int) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(double[], double[]) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(double[], double[], int) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(double[], Object[]) - Static method in class smile.sort.QuickSort: Besides sorting the array arr, the array brr will be also rearranged as the same order of arr.
sort(double[], Object[], int) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(T[]) - Static method in class smile.sort.QuickSort: Sorts the specified array into ascending order.
sort(T[], int[]) - Static method in class smile.sort.QuickSort: Besides sorting the array arr, the array brr will be also rearranged as the same order of arr.
sort(T[], int[], int) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(T[], int[], int, Comparator<T>) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(T[], Object[]) - Static method in class smile.sort.QuickSort: Besides sorting the array arr, the array brr will be also rearranged as the same order of arr.
sort(T[], Object[], int) - Static method in class smile.sort.QuickSort: This is an effecient implementation Quick Sort algorithm without recursive.
sort(int[]) - Static method in class smile.sort.ShellSort: Sorts the specified array into ascending numerical order.
sort(float[]) - Static method in class smile.sort.ShellSort: Sorts the specified array into ascending numerical order.
sort(double[]) - Static method in class smile.sort.ShellSort: Sorts the specified array into ascending numerical order.
sort(T[]) - Static method in class smile.sort.ShellSort: Sorts the specified array into ascending order.
SortUtils - Class in smile.sort: Some useful functions such as swap and swif-down used in many sorting algorithms.
SparseArray - Class in smile.math: Sparse array of double values.
SparseArray() - Constructor for class smile.math.SparseArray: Constructor.
SparseArray.Entry - Class in smile.math: The entry in a sparse array of double values.
SparseChebyshevDistance - Class in smile.math.distance: Chebyshev distance (or Tchebychev distance), or L_∞ metric is a metric defined on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension.
SparseChebyshevDistance() - Constructor for class smile.math.distance.SparseChebyshevDistance: Constructor.
SparseEuclideanDistance - Class in smile.math.distance: Euclidean distance.
SparseEuclideanDistance() - Constructor for class smile.math.distance.SparseEuclideanDistance: Constructor.
SparseEuclideanDistance(double[]) - Constructor for class smile.math.distance.SparseEuclideanDistance: Constructor with a given weight vector.
SparseGaussianKernel - Class in smile.math.kernel: The Gaussian Mercer Kernel.
SparseGaussianKernel(double) - Constructor for class smile.math.kernel.SparseGaussianKernel: Constructor.
SparseHyperbolicTangentKernel - Class in smile.math.kernel: The hyperbolic tangent kernel.
SparseHyperbolicTangentKernel() - Constructor for class smile.math.kernel.SparseHyperbolicTangentKernel: Constructor with scale 1.0 and offset 0.0.
SparseHyperbolicTangentKernel(double, double) - Constructor for class smile.math.kernel.SparseHyperbolicTangentKernel: Constructor.
SparseLaplacianKernel - Class in smile.math.kernel: The Laplacian Kernel.
SparseLaplacianKernel(double) - Constructor for class smile.math.kernel.SparseLaplacianKernel: Constructor.
SparseLinearKernel - Class in smile.math.kernel: The linear dot product kernel on sparse arrays.
SparseLinearKernel() - Constructor for class smile.math.kernel.SparseLinearKernel: Constructor.
SparseManhattanDistance - Class in smile.math.distance: Manhattan distance, also known as L₁ distance or L₁ norm, is the sum of the (absolute) differences of their coordinates.
SparseManhattanDistance() - Constructor for class smile.math.distance.SparseManhattanDistance: Constructor.
SparseManhattanDistance(double[]) - Constructor for class smile.math.distance.SparseManhattanDistance: Constructor.
SparseMatrix - Class in smile.math.matrix: A sparse matrix is a matrix populated primarily with zeros.
SparseMatrix(int, int, double[], int[], int[]) - Constructor for class smile.math.matrix.SparseMatrix: Constructor.
SparseMatrix(double[][]) - Constructor for class smile.math.matrix.SparseMatrix: Constructor.
SparseMatrix(double[][], double) - Constructor for class smile.math.matrix.SparseMatrix: Constructor.
SparseMinkowskiDistance - Class in smile.math.distance: Minkowski distance of order p or L_p-norm, is a generalization of Euclidean distance that is actually L₂-norm.
SparseMinkowskiDistance(int) - Constructor for class smile.math.distance.SparseMinkowskiDistance: Constructor.
SparseMinkowskiDistance(int, double[]) - Constructor for class smile.math.distance.SparseMinkowskiDistance: Constructor.
SparsePolynomialKernel - Class in smile.math.kernel: The polynomial kernel.
SparsePolynomialKernel(int) - Constructor for class smile.math.kernel.SparsePolynomialKernel: Constructor with scale 1 and bias 0.
SparsePolynomialKernel(int, double, double) - Constructor for class smile.math.kernel.SparsePolynomialKernel: Constructor.
SparseThinPlateSplineKernel - Class in smile.math.kernel: The Thin Plate Spline Kernel.
SparseThinPlateSplineKernel(double) - Constructor for class smile.math.kernel.SparseThinPlateSplineKernel: Constructor.
spearman(int[], int[]) - Static method in class smile.math.distance.CorrelationDistance: Spearman correlation distance between the two arrays of type int.
spearman(float[], float[]) - Static method in class smile.math.distance.CorrelationDistance: Spearman correlation distance between the two arrays of type float.
spearman(double[], double[]) - Static method in class smile.math.distance.CorrelationDistance: Spearman correlation distance between the two arrays of type double.
spearman(int[], int[]) - Static method in class smile.math.Math: The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie.
spearman(float[], float[]) - Static method in class smile.math.Math: The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie.
spearman(double[], double[]) - Static method in class smile.math.Math: The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie.
spearman(double[], double[]) - Static method in class smile.stat.hypothesis.CorTest: Spearman rank correlation coefficient test.
sqr(double) - Static method in class smile.math.Math: Returns x * x.
sqrt(double) - Static method in class smile.math.Math: Returns the correctly rounded positive square root of a double value.
squaredDistance(int[], int[]) - Static method in class smile.math.Math: The squared Euclidean distance.
squaredDistance(float[], float[]) - Static method in class smile.math.Math: The squared Euclidean distance.
squaredDistance(double[], double[]) - Static method in class smile.math.Math: The squared Euclidean distance.
squaredDistance(SparseArray, SparseArray) - Static method in class smile.math.Math: The Euclidean distance.
standardize(double[]) - Static method in class smile.math.Math: Standardizes an array to mean 0 and variance 1.
standardize(double[][]) - Static method in class smile.math.Math: Standardizes each column of a matrix to 0 mean and unit variance.
sturges(int) - Static method in class smile.math.Histogram: Returns the number of bins by Sturges' rule k = ceil(log2(n) + 1).
sub(int, int, double) - Method in class smile.math.Array2D
sub(Array2D) - Method in class smile.math.Array2D
sub(double) - Method in class smile.math.Array2D
sub(int, int, double) - Method in class smile.math.matrix.DenseMatrix: A[i][j] -= x
sub(DenseMatrix, DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: C = A - B
sub(DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: In place subtraction A = A - B
sub(double, DenseMatrix) - Method in class smile.math.matrix.DenseMatrix: Element-wise addition C = A - x
sub(double) - Method in class smile.math.matrix.DenseMatrix: In place element-wise subtraction A = A - x
sub(int, int, double) - Method in class smile.math.matrix.JMatrix
sub(DenseMatrix) - Method in class smile.math.matrix.JMatrix
sub(JMatrix) - Method in class smile.math.matrix.JMatrix
sub(DenseMatrix, DenseMatrix) - Method in class smile.math.matrix.JMatrix
sub(JMatrix, JMatrix) - Method in class smile.math.matrix.JMatrix
sub(double) - Method in class smile.math.matrix.JMatrix
sub(double, DenseMatrix) - Method in class smile.math.matrix.JMatrix
sub(double, JMatrix) - Method in class smile.math.matrix.JMatrix
sum() - Method in class smile.math.Array2D
sum(int[]) - Static method in class smile.math.Math: Returns the sum of an array.
sum(float[]) - Static method in class smile.math.Math: Returns the sum of an array.
sum(double[]) - Static method in class smile.math.Math: Returns the sum of an array.
sum() - Method in class smile.math.matrix.DenseMatrix: Returns the sum of all elements in the matrix.
sum() - Method in class smile.math.matrix.JMatrix
svd() - Method in class smile.math.matrix.DenseMatrix: Returns the singular value decomposition.
svd(boolean) - Method in class smile.math.matrix.DenseMatrix: Returns the singular value decomposition.
svd() - Method in class smile.math.matrix.JMatrix
svd(int) - Method in class smile.math.matrix.Matrix: Find k largest approximate singular triples of a matrix by the Lanczos algorithm.
svd(int, double, int) - Method in class smile.math.matrix.Matrix: Find k largest approximate singular triples of a matrix by the Lanczos algorithm.
SVD - Class in smile.math.matrix: Singular Value Decomposition.
SVD(DenseMatrix, DenseMatrix, double[]) - Constructor for class smile.math.matrix.SVD: Constructor.
swap(int[], int, int) - Static method in class smile.math.Math: Swap two elements of an array.
swap(float[], int, int) - Static method in class smile.math.Math: Swap two elements of an array.
swap(double[], int, int) - Static method in class smile.math.Math: Swap two elements of an array.
swap(Object[], int, int) - Static method in class smile.math.Math: Swap two elements of an array.
swap(int[], int[]) - Static method in class smile.math.Math: Swap two arrays.
swap(float[], float[]) - Static method in class smile.math.Math: Swap two arrays.
swap(double[], double[]) - Static method in class smile.math.Math: Swap two arrays.
swap(E[], E[]) - Static method in class smile.math.Math: Swap two arrays.
swap(int[], int, int) - Static method in class smile.sort.SortUtils: Swap two positions.
swap(float[], int, int) - Static method in class smile.sort.SortUtils: Swap two positions.
swap(double[], int, int) - Static method in class smile.sort.SortUtils: Swap two positions.
swap(Object[], int, int) - Static method in class smile.sort.SortUtils: Swap two positions.

T

t - Variable in class smile.stat.hypothesis.CorTest: test statistic
t - Variable in class smile.stat.hypothesis.TTest: t-statistic
tan() - Method in class smile.math.Complex: Returns the complex tangent.
tan(double) - Static method in class smile.math.Math: Returns the trigonometric tangent of an angle.
tanh(double) - Static method in class smile.math.Math: Returns the hyperbolic tangent of a double value.
tau - Variable in class smile.math.matrix.QR: The diagonal of R for this implementation.
TDistribution - Class in smile.stat.distribution: Student's t-distribution (or simply the t-distribution) is a probability distribution that arises in the problem of estimating the mean of a normally distributed population when the sample size is small.
TDistribution(int) - Constructor for class smile.stat.distribution.TDistribution: Constructor.
test(int[], double[]) - Static method in class smile.stat.hypothesis.ChiSqTest: One-sample chisq test.
test(int[], double[], int) - Static method in class smile.stat.hypothesis.ChiSqTest: One-sample chisq test.
test(int[], int[]) - Static method in class smile.stat.hypothesis.ChiSqTest: Two-sample chisq test.
test(int[], int[], int) - Static method in class smile.stat.hypothesis.ChiSqTest: Two-sample chisq test.
test(double[], double[]) - Static method in class smile.stat.hypothesis.FTest: Test if the arrays x and y have significantly different variances.
test(double[], Distribution) - Static method in class smile.stat.hypothesis.KSTest: The one-sample KS test for the null hypothesis that the data set x is drawn from the given distribution.
test(double[], double[]) - Static method in class smile.stat.hypothesis.KSTest: The two-sample KS test for the null hypothesis that the data sets are drawn from the same distribution.
test(double[], double) - Static method in class smile.stat.hypothesis.TTest: Independent one-sample t-test whether the mean of a normally distributed population has a value specified in a null hypothesis.
test(double[], double[]) - Static method in class smile.stat.hypothesis.TTest: Test if the arrays x and y have significantly different means.
test(double[], double[], boolean) - Static method in class smile.stat.hypothesis.TTest: Test if the arrays x and y have significantly different means.
test(double, int) - Static method in class smile.stat.hypothesis.TTest: Test whether the Pearson correlation coefficient, the slope of a regression line, differs significantly from 0.
ThinPlateRadialBasis - Class in smile.math.rbf: Thin plate RBF.
ThinPlateRadialBasis() - Constructor for class smile.math.rbf.ThinPlateRadialBasis: Constructor.
ThinPlateRadialBasis(double) - Constructor for class smile.math.rbf.ThinPlateRadialBasis: Constructor.
ThinPlateSplineKernel - Class in smile.math.kernel: The Thin Plate Spline Kernel.
ThinPlateSplineKernel(double) - Constructor for class smile.math.kernel.ThinPlateSplineKernel: Constructor.
times(Complex) - Method in class smile.math.Complex: Returns this * b.
times(double) - Method in class smile.math.Complex: Scalar multiplication.* Returns this * b.
toArray() - Method in class smile.math.DoubleArrayList: Returns an array containing all of the values in this list in proper sequence (from first to last value).
toArray(double[]) - Method in class smile.math.DoubleArrayList: Returns an array containing all of the values in this list in proper sequence (from first to last value).
toArray() - Method in class smile.math.IntArrayList: Returns an array containing all of the values in this list in proper sequence (from first to last value).
toArray(int[]) - Method in class smile.math.IntArrayList: Returns an array containing all of the values in this list in proper sequence (from first to last value).
toDegrees(double) - Static method in class smile.math.Math: Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
tol - Variable in class smile.math.matrix.SVD: Threshold of estimated roundoff.
toRadians(double) - Static method in class smile.math.Math: Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
toString() - Method in class smile.math.Complex
toString() - Method in class smile.math.distance.ChebyshevDistance
toString() - Method in class smile.math.distance.CorrelationDistance
toString() - Method in class smile.math.distance.DynamicTimeWarping
toString() - Method in class smile.math.distance.EditDistance
toString() - Method in class smile.math.distance.EuclideanDistance
toString() - Method in class smile.math.distance.HammingDistance
toString() - Method in class smile.math.distance.JaccardDistance
toString() - Method in class smile.math.distance.JensenShannonDistance
toString() - Method in class smile.math.distance.LeeDistance
toString() - Method in class smile.math.distance.MahalanobisDistance
toString() - Method in class smile.math.distance.ManhattanDistance
toString() - Method in class smile.math.distance.MinkowskiDistance
toString() - Method in class smile.math.distance.SparseChebyshevDistance
toString() - Method in class smile.math.distance.SparseEuclideanDistance
toString() - Method in class smile.math.distance.SparseManhattanDistance
toString() - Method in class smile.math.distance.SparseMinkowskiDistance
toString() - Method in class smile.math.kernel.BinarySparseGaussianKernel
toString() - Method in class smile.math.kernel.BinarySparseHyperbolicTangentKernel
toString() - Method in class smile.math.kernel.BinarySparseLaplacianKernel
toString() - Method in class smile.math.kernel.BinarySparseLinearKernel
toString() - Method in class smile.math.kernel.BinarySparsePolynomialKernel
toString() - Method in class smile.math.kernel.BinarySparseThinPlateSplineKernel
toString() - Method in class smile.math.kernel.GaussianKernel
toString() - Method in class smile.math.kernel.HellingerKernel
toString() - Method in class smile.math.kernel.HyperbolicTangentKernel
toString() - Method in class smile.math.kernel.LaplacianKernel
toString() - Method in class smile.math.kernel.LinearKernel
toString() - Method in class smile.math.kernel.PearsonKernel
toString() - Method in class smile.math.kernel.PolynomialKernel
toString() - Method in class smile.math.kernel.SparseGaussianKernel
toString() - Method in class smile.math.kernel.SparseHyperbolicTangentKernel
toString() - Method in class smile.math.kernel.SparseLaplacianKernel
toString() - Method in class smile.math.kernel.SparseLinearKernel
toString() - Method in class smile.math.kernel.SparsePolynomialKernel
toString() - Method in class smile.math.kernel.SparseThinPlateSplineKernel
toString() - Method in class smile.math.kernel.ThinPlateSplineKernel
toString() - Method in class smile.math.matrix.Matrix
toString(boolean) - Method in class smile.math.matrix.Matrix: Returns the string representation of matrix.
toString() - Method in class smile.math.rbf.GaussianRadialBasis
toString() - Method in class smile.math.rbf.InverseMultiquadricRadialBasis
toString() - Method in class smile.math.rbf.MultiquadricRadialBasis
toString() - Method in class smile.math.rbf.ThinPlateRadialBasis
toString() - Method in class smile.stat.distribution.BernoulliDistribution
toString() - Method in class smile.stat.distribution.BetaDistribution
toString() - Method in class smile.stat.distribution.BinomialDistribution
toString() - Method in class smile.stat.distribution.ChiSquareDistribution
toString() - Method in class smile.stat.distribution.DiscreteMixture
toString() - Method in class smile.stat.distribution.EmpiricalDistribution
toString() - Method in class smile.stat.distribution.ExponentialDistribution
toString() - Method in class smile.stat.distribution.FDistribution
toString() - Method in class smile.stat.distribution.GammaDistribution
toString() - Method in class smile.stat.distribution.GaussianDistribution
toString() - Method in class smile.stat.distribution.GeometricDistribution
toString() - Method in class smile.stat.distribution.HyperGeometricDistribution
toString() - Method in class smile.stat.distribution.LogisticDistribution
toString() - Method in class smile.stat.distribution.LogNormalDistribution
toString() - Method in class smile.stat.distribution.Mixture
toString() - Method in class smile.stat.distribution.MultivariateGaussianDistribution
toString() - Method in class smile.stat.distribution.MultivariateMixture
toString() - Method in class smile.stat.distribution.NegativeBinomialDistribution
toString() - Method in class smile.stat.distribution.PoissonDistribution
toString() - Method in class smile.stat.distribution.ShiftedGeometricDistribution
toString() - Method in class smile.stat.distribution.TDistribution
toString() - Method in class smile.stat.distribution.WeibullDistribution
trace() - Method in class smile.math.matrix.Matrix: Returns the matrix trace.
transpose(double[][]) - Static method in class smile.math.Math: Returns the matrix transpose.
transpose() - Method in class smile.math.matrix.BandMatrix: Returns the matrix transpose.
transpose() - Method in class smile.math.matrix.DenseMatrix: Returns the matrix transpose.
transpose() - Method in class smile.math.matrix.JMatrix
transpose() - Method in class smile.math.matrix.Matrix: Returns the matrix transpose.
transpose() - Method in class smile.math.matrix.SparseMatrix
trimToSize() - Method in class smile.math.DoubleArrayList: Trims the capacity to be the list's current size.
trimToSize() - Method in class smile.math.IntArrayList: Trims the capacity to be the list's current size.
TTest - Class in smile.stat.hypothesis: Student's t test.

U

U - Variable in class smile.math.matrix.SVD: Arrays for internal storage of left singular vectors U.
ulp(double) - Static method in class smile.math.Math: Returns the size of an ulp of the argument.
ulp(float) - Static method in class smile.math.Math: * Returns the size of an ulp of the argument.
unique(int[]) - Static method in class smile.math.Math: Find unique elements of vector.
unique(String[]) - Static method in class smile.math.Math: Find unique elements of vector.
unitize(double[]) - Static method in class smile.math.Math: Unitize an array so that L2 norm of x = 1.
unitize1(double[]) - Static method in class smile.math.Math: Unitize an array so that L1 norm of x is 1.
unitize2(double[]) - Static method in class smile.math.Math: Unitize an array so that L2 norm of x = 1.
UniversalGenerator - Class in smile.math.random: The so called "Universal Generator" based on multiplicative congruential method, which originally appeared in "Toward a Universal Random Number Generator" by Marsaglia, Zaman and Tsang.
UniversalGenerator() - Constructor for class smile.math.random.UniversalGenerator: Initialize Random with default seed.
UniversalGenerator(int) - Constructor for class smile.math.random.UniversalGenerator: Initialize Random with a specified integer seed
UniversalGenerator(long) - Constructor for class smile.math.random.UniversalGenerator: Initialize Random with a specified long seed
update(int, int, double) - Method in class smile.math.matrix.DenseMatrix: Set the entry value at row i and column j.

V

V - Variable in class smile.math.matrix.SVD: Arrays for internal storage of right singular vectors V.
values() - Method in class smile.math.matrix.SparseMatrix: Returns all nonzero values.
var(int[]) - Static method in class smile.math.Math: Returns the variance of an array.
var(float[]) - Static method in class smile.math.Math: Returns the variance of an array.
var(double[]) - Static method in class smile.math.Math: Returns the variance of an array.
var() - Method in class smile.stat.distribution.BernoulliDistribution
var() - Method in class smile.stat.distribution.BetaDistribution
var() - Method in class smile.stat.distribution.BinomialDistribution
var() - Method in class smile.stat.distribution.ChiSquareDistribution
var() - Method in class smile.stat.distribution.DiscreteMixture
var() - Method in interface smile.stat.distribution.Distribution: The variance of distribution.
var() - Method in class smile.stat.distribution.EmpiricalDistribution
var() - Method in class smile.stat.distribution.ExponentialDistribution
var() - Method in class smile.stat.distribution.FDistribution
var() - Method in class smile.stat.distribution.GammaDistribution
var() - Method in class smile.stat.distribution.GaussianDistribution
var() - Method in class smile.stat.distribution.GeometricDistribution
var() - Method in class smile.stat.distribution.HyperGeometricDistribution
var() - Method in class smile.stat.distribution.KernelDensity
var() - Method in class smile.stat.distribution.LogisticDistribution
var() - Method in class smile.stat.distribution.LogNormalDistribution
var() - Method in class smile.stat.distribution.Mixture
var() - Method in class smile.stat.distribution.NegativeBinomialDistribution
var() - Method in class smile.stat.distribution.PoissonDistribution
var() - Method in class smile.stat.distribution.ShiftedGeometricDistribution
var() - Method in class smile.stat.distribution.TDistribution
var() - Method in class smile.stat.distribution.WeibullDistribution

W

WeibullDistribution - Class in smile.stat.distribution: The Weibull distribution is one of the most widely used lifetime distributions in reliability engineering.
WeibullDistribution(double) - Constructor for class smile.stat.distribution.WeibullDistribution: Constructor.
WeibullDistribution(double, double) - Constructor for class smile.stat.distribution.WeibullDistribution: Constructor.
whichMax(int[]) - Static method in class smile.math.Math: Returns the index of maximum value of an array.
whichMax(float[]) - Static method in class smile.math.Math: Returns the index of maximum value of an array.
whichMax(double[]) - Static method in class smile.math.Math: Returns the index of maximum value of an array.
whichMin(int[]) - Static method in class smile.math.Math: Returns the index of minimum value of an array.
whichMin(float[]) - Static method in class smile.math.Math: Returns the index of minimum value of an array.
whichMin(double[]) - Static method in class smile.math.Math: Returns the index of minimum value of an array.

X

x - Variable in class smile.math.SparseArray.Entry: The value of entry.
xax(double[]) - Method in class smile.math.matrix.DenseMatrix: Returns x' * A * x.

Z

zeros(int, int) - Static method in class smile.math.matrix.Matrix: Returns all-zero matrix.

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