Math

java.lang.Object
- smile.math.Math

```
public class Math
extends java.lang.Object
```
A collection of useful mathematical functions. The following functions are included:
- scalar functions: Besides all methods in java.lang.Math are included for convenience, sqr, factorial, logFractorial, choose, logChoose, log2 are provided.
- vector functions: min, max, mean, sum, var, sd, cov, L₁ norm, L₂ norm, L_∞ norm, normalize, unitize, cor, Spearman correlation, Kendall correlation, distance, dot product, histogram, vector (element-wise) copy, equal, plus, minus, times, and divide.
- matrix functions: min, max, rowmean, sum, L₁ norm, L₂ norm, L_∞ norm, rank, det, trace, transpose, inverse, SVD and eigen decomposition, linear systems (dense or tridiagonal) and least square, matrix copy, equal, plus, minus and times.
- random functions: random, randomInt, and permutate.
- Find the root of a univariate function with or without derivative.

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`DIGITS` The number of digits (in radix base) in the mantissa.
`static double`	`E` The base of the natural logarithms.
`static double`	`EPSILON` 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.
`static int`	`MACHEP` The largest negative integer such that 1.0 + RADIX^MACHEP ≠ 1.0, except that machep is bounded below by -(DIGITS+3)
`static int`	`NEGEP` The largest negative integer such that 1.0 - RADIX^NEGEP ≠ 1.0, except that negeps is bounded below by -(DIGITS+3)
`static double`	`PI` The ratio of the circumference of a circle to its diameter.
`static int`	`RADIX` The base of the exponent of the double type.
`static int`	`ROUND_STYLE` Rounding style.

Method Summary

All Methods Static Methods Concrete Methods
Modifier and Type	Method and Description
`static double[][]`	`aatmm(double[][] A)` Matrix multiplication A * A' according to the rules of linear algebra.
`static void`	`aatmm(double[][] A, double[][] C)` Matrix multiplication C = A * A' according to the rules of linear algebra.
`static double[][]`	`abmm(double[][] A, double[][] B)` Matrix multiplication A * B according to the rules of linear algebra.
`static void`	`abmm(double[][] A, double[][] B, double[][] C)` Matrix multiplication C = A * B according to the rules of linear algebra.
`static double`	`abs(double a)` Returns the absolute value of a double value.
`static float`	`abs(float a)` Returns the absolute value of a float value.
`static int`	`abs(int a)` Returns the absolute value of an int value.
`static long`	`abs(long a)` Returns the absolute value of a long value.
`static double[][]`	`abtmm(double[][] A, double[][] B)` Matrix multiplication A * B' according to the rules of linear algebra.
`static void`	`abtmm(double[][] A, double[][] B, double[][] C)` Matrix multiplication C = A * B' according to the rules of linear algebra.
`static double`	`acos(double a)` Returns the arc cosine of an angle, in the range of 0.0 through pi.
`static boolean`	`all(boolean[] x)` Given a set of boolean values, are all of the values true?
`static boolean`	`any(boolean[] x)` Given a set of boolean values, is at least one of the values true?
`static double`	`asin(double a)` Returns the arc sine of an angle, in the range of -pi/2 through pi/2.
`static double[][]`	`atamm(double[][] A)` Matrix multiplication A' * A according to the rules of linear algebra.
`static void`	`atamm(double[][] A, double[][] C)` Matrix multiplication C = A' * A according to the rules of linear algebra.
`static double`	`atan(double a)` Returns the arc tangent of an angle, in the range of -pi/2 through pi/2.
`static double`	`atan2(double y, double x)` Converts rectangular coordinates (x, y) to polar (r, theta).
`static double[][]`	`atbmm(double[][] A, double[][] B)` Matrix multiplication A' * B according to the rules of linear algebra.
`static void`	`atbmm(double[][] A, double[][] B, double[][] C)` Matrix multiplication C = A' * B according to the rules of linear algebra.
`static double[][]`	`atbtmm(double[][] A, double[][] B)` Matrix multiplication A' * B' according to the rules of linear algebra.
`static void`	`atbtmm(double[][] A, double[][] B, double[][] C)` Matrix multiplication C = A' * B' according to the rules of linear algebra.
`static double[]`	`atx(double[][] A, double[] x, double[] y)` Product of a matrix and a vector y = A^T * x according to the rules of linear algebra.
`static double[]`	`atxpy(double[][] A, double[] x, double[] y)` Product of a matrix and a vector y = A^T * x + y according to the rules of linear algebra.
`static double[]`	`atxpy(double[][] A, double[] x, double[] y, double b)` Product of a matrix and a vector y = A^T * x + b * y according to the rules of linear algebra.
`static double[]`	`ax(double[][] A, double[] x, double[] y)` Product of a matrix and a vector y = A * x according to the rules of linear algebra.
`static double[]`	`axpy(double[][] A, double[] x, double[] y)` Product of a matrix and a vector y = A * x + y according to the rules of linear algebra.
`static double[]`	`axpy(double[][] A, double[] x, double[] y, double b)` Product of a matrix and a vector y = A * x + b * y according to the rules of linear algebra.
`static double[]`	`axpy(double a, double[] x, double[] y)` Update an array by adding a multiple of another array y = a * x + y.
`static double`	`cbrt(double a)` Returns the cube root of a double value.
`static double`	`ceil(double a)` 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.
`static double`	`choose(int n, int k)` n choose k.
`static double[][]`	`clone(double[][] x)` Deep clone a two-dimensional array.
`static float[][]`	`clone(float[][] x)` Deep clone a two-dimensional array.
`static int[][]`	`clone(int[][] x)` Deep clone a two-dimensional array.
`static double[]`	`colMax(double[][] data)` Returns the column maximum for a matrix.
`static double[]`	`colMean(double[][] data)` Returns the column sums for a matrix.
`static double[]`	`colMin(double[][] data)` Returns the column minimum for a matrix.
`static double[]`	`colSd(double[][] data)` Returns the column deviations for a matrix.
`static double[]`	`colSum(double[][] data)` Returns the column sums for a matrix.
`static boolean`	`contains(double[][] polygon, double[] point)` Determines if the polygon contains the specified coordinates.
`static boolean`	`contains(double[][] polygon, double x, double y)` Determines if the polygon contains the specified coordinates.
`static void`	`copy(double[][] x, double[][] y)` Copy x into y.
`static void`	`copy(double[] x, double[] y)` Copy x into y.
`static void`	`copy(float[][] x, float[][] y)` Copy x into y.
`static void`	`copy(float[] x, float[] y)` Copy x into y.
`static void`	`copy(int[][] x, int[][] y)` Copy x into y.
`static void`	`copy(int[] x, int[] y)` Copy x into y.
`static double`	`copySign(double magnitude, double sign)` Returns the first floating-point argument with the sign of the second floating-point argument.
`static float`	`copySign(float magnitude, float sign)` Returns the first floating-point argument with the sign of the second floating-point argument.
`static double[][]`	`cor(double[][] data)` Returns the sample correlation matrix.
`static double[][]`	`cor(double[][] data, double[] mu)` Returns the sample correlation matrix.
`static double`	`cor(double[] x, double[] y)` Returns the correlation coefficient between two vectors.
`static double`	`cor(float[] x, float[] y)` Returns the correlation coefficient between two vectors.
`static double`	`cor(int[] x, int[] y)` Returns the correlation coefficient between two vectors.
`static double`	`cos(double a)` Returns the trigonometric cosine of an angle.
`static double`	`cosh(double x)` Returns the hyperbolic cosine of a double value.
`static double[][]`	`cov(double[][] data)` Returns the sample covariance matrix.
`static double[][]`	`cov(double[][] data, double[] mu)` Returns the sample covariance matrix.
`static double`	`cov(double[] x, double[] y)` Returns the covariance between two vectors.
`static double`	`cov(float[] x, float[] y)` Returns the covariance between two vectors.
`static double`	`cov(int[] x, int[] y)` Returns the covariance between two vectors.
`static double`	`det(double[][] A)` Returns the matrix determinant
`static double`	`distance(double[] x, double[] y)` The Euclidean distance.
`static double`	`distance(float[] x, float[] y)` The Euclidean distance.
`static double`	`distance(int[] x, int[] y)` The Euclidean distance.
`static double`	`distance(SparseArray x, SparseArray y)` The Euclidean distance.
`static double`	`dot(double[] x, double[] y)` Returns the dot product between two vectors.
`static double`	`dot(float[] x, float[] y)` Returns the dot product between two vectors.
`static double`	`dot(int[] x, int[] y)` Returns the dot product between two vectors.
`static double`	`dot(SparseArray x, SparseArray y)` Returns the dot product between two sparse arrays.
`static EigenValueDecomposition`	`eigen(double[][] A)` Returns the eigen value decomposition of a square matrix.
`static EigenValueDecomposition`	`eigen(double[][] A, boolean symmetric)` Returns the eigen value decomposition of a square matrix.
`static EigenValueDecomposition`	`eigen(double[][] A, boolean symmetric, boolean onlyValues)` Returns the eigen value decomposition of a square matrix.
`static double`	`eigen(double[][] A, double[] v)` 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 its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
`static double`	`eigen(double[][] A, double[] v, double tol)` 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 its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
`static EigenValueDecomposition`	`eigen(double[][] A, int k)` Find k largest approximate eigen pairs of a symmetric matrix by an iterative Lanczos algorithm.
`static double`	`eigen(Matrix A, double[] v)` 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 its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
`static double`	`eigen(Matrix A, double[] v, double tol)` 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 its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
`static EigenValueDecomposition`	`eigen(Matrix A, int k)` Find k largest approximate eigen pairs of a symmetric matrix by an iterative Lanczos algorithm.
`static boolean`	`equals(double[][] x, double[][] y)` Check if x element-wisely equals y with default epsilon 1E-10.
`static boolean`	`equals(double[][] x, double[][] y, double eps)` Check if x element-wisely equals y.
`static boolean`	`equals(double[] x, double[] y)` Check if x element-wisely equals y with default epsilon 1E-10.
`static boolean`	`equals(double[] x, double[] y, double eps)` Check if x element-wisely equals y.
`static boolean`	`equals(double a, double b)` Returns true if two double values equals to each other in the system precision.
`static boolean`	`equals(float[][] x, float[][] y)` Check if x element-wisely equals y with default epsilon 1E-7.
`static boolean`	`equals(float[][] x, float[][] y, float epsilon)` Check if x element-wisely equals y.
`static boolean`	`equals(float[] x, float[] y)` Check if x element-wisely equals y with default epsilon 1E-7.
`static boolean`	`equals(float[] x, float[] y, float epsilon)` Check if x element-wisely equals y.
`static boolean`	`equals(int[][] x, int[][] y)` Check if x element-wisely equals y.
`static boolean`	`equals(int[] x, int[] y)` Check if x element-wisely equals y.
`static <T extends java.lang.Comparable<? super T>> boolean`	`equals(T[][] x, T[][] y)` Check if x element-wisely equals y.
`static <T extends java.lang.Comparable<? super T>> boolean`	`equals(T[] x, T[] y)` Check if x element-wisely equals y.
`static double`	`exp(double a)` Returns Euler's number e raised to the power of a double value.
`static double`	`expm1(double x)` Returns e^x-1.
`static double[][]`	`eye(int n)` Returns a square identity matrix of size n.
`static double[][]`	`eye(int m, int n)` Returns an identity matrix of size m by n.
`static double`	`factorial(int n)` factorial of n
`static double`	`floor(double a)` 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.
`static int`	`getExponent(double d)` Returns the unbiased exponent used in the representation of a double.
`static int`	`getExponent(float f)` Returns the unbiased exponent used in the representation of a float.
`static double`	`GoodTuring(int[] r, int[] Nr, double[] p)` 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.
`static double`	`hypot(double x, double y)` Returns sqrt(x2 +y2) without intermediate overflow or underflow.
`static double`	`IEEEremainder(double f1, double f2)` Computes the remainder operation on two arguments as prescribed by the IEEE 754 standard.
`static DenseMatrix`	`inverse(double[][] A)` Returns the matrix inverse or pseudo inverse.
`static boolean`	`isPower2(int x)` Returns true if x is a power of 2.
`static boolean`	`isZero(double x)` Tests if a floating number is zero.
`static boolean`	`isZero(double x, double epsilon)` Tests if a floating number is zero with given epsilon.
`static boolean`	`isZero(float x)` Tests if a floating number is zero.
`static boolean`	`isZero(float x, float epsilon)` Tests if a floating number is zero with given epsilon.
`static double`	`JensenShannonDivergence(double[] x, double[] y)` Jensen-Shannon divergence JS(P\|\|Q) = (KL(P\|\|M) + KL(Q\|\|M)) / 2, where M = (P+Q)/2.
`static double`	`JensenShannonDivergence(double[] x, SparseArray y)` Jensen-Shannon divergence JS(P\|\|Q) = (KL(P\|\|M) + KL(Q\|\|M)) / 2, where M = (P+Q)/2.
`static double`	`JensenShannonDivergence(SparseArray x, double[] y)` Jensen-Shannon divergence JS(P\|\|Q) = (KL(P\|\|M) + KL(Q\|\|M)) / 2, where M = (P+Q)/2.
`static double`	`JensenShannonDivergence(SparseArray x, SparseArray y)` Jensen-Shannon divergence JS(P\|\|Q) = (KL(P\|\|M) + KL(Q\|\|M)) / 2, where M = (P+Q)/2.
`static double`	`kendall(double[] x, double[] y)` The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant.
`static double`	`kendall(float[] x, float[] y)` The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant.
`static double`	`kendall(int[] x, int[] y)` The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant.
`static double`	`KullbackLeiblerDivergence(double[] x, double[] y)` Kullback-Leibler divergence.
`static double`	`KullbackLeiblerDivergence(double[] x, SparseArray y)` Kullback-Leibler divergence.
`static double`	`KullbackLeiblerDivergence(SparseArray x, double[] y)` Kullback-Leibler divergence.
`static double`	`KullbackLeiblerDivergence(SparseArray x, SparseArray y)` Kullback-Leibler divergence.
`static double`	`log(double a)` Returns the natural logarithm (base e) of a double value.
`static double`	`log10(double a)` Returns the base 10 logarithm of a double value.
`static double`	`log1p(double x)` Returns the natural logarithm of the sum of the argument and 1.
`static double`	`log2(double x)` Log of base 2.
`static double`	`logChoose(int n, int k)` log of n choose k
`static double`	`logFactorial(int n)` log of factorial of n
`static double`	`logistic(double x)` Logistic sigmoid function.
`static double`	`mad(double[] x)` Returns the median absolute deviation (MAD).
`static double`	`mad(float[] x)` Returns the median absolute deviation (MAD).
`static double`	`mad(int[] x)` Returns the median absolute deviation (MAD).
`static double`	`max(double[] x)` Returns the maximum value of an array.
`static double`	`max(double[][] matrix)` Returns the maximum of a matrix.
`static double`	`max(double a, double b)` Returns the greater of two double values.
`static double`	`max(double a, double b, double c)` maximum of 3 doubles
`static float`	`max(float[] x)` Returns the maximum value of an array.
`static float`	`max(float a, float b)` Returns the greater of two float values.
`static float`	`max(float a, float b, float c)` maximum of 3 floats
`static int`	`max(int[] x)` Returns the maximum value of an array.
`static int`	`max(int[][] matrix)` Returns the maximum of a matrix.
`static int`	`max(int a, int b)` Returns the greater of two int values.
`static int`	`max(int a, int b, int c)` maximum of 3 integers
`static long`	`max(long a, long b)` Returns the greater of two long values.
`static double`	`mean(double[] x)` Returns the mean of an array.
`static double`	`mean(float[] x)` Returns the mean of an array.
`static double`	`mean(int[] x)` Returns the mean of an array.
`static double`	`median(double[] a)` Find the median of an array of type double.
`static float`	`median(float[] a)` Find the median of an array of type float.
`static int`	`median(int[] a)` Find the median of an array of type int.
`static <T extends java.lang.Comparable<? super T>> T`	`median(T[] a)` Find the median of an array of type double.
`static double`	`min(DifferentiableMultivariateFunction func, double[] x, double gtol)` This method solves the unconstrained minimization problem
`static double`	`min(DifferentiableMultivariateFunction func, double[] x, double gtol, int maxIter)` This method solves the unconstrained minimization problem
`static double`	`min(DifferentiableMultivariateFunction func, int m, double[] x, double gtol)` This method solves the unconstrained minimization problem
`static double`	`min(DifferentiableMultivariateFunction func, int m, double[] x, double gtol, int maxIter)` This method solves the unconstrained minimization problem
`static double`	`min(double[] x)` Returns the minimum value of an array.
`static double`	`min(double[][] matrix)` Returns the minimum of a matrix.
`static double`	`min(double a, double b)` Returns the smaller of two double values.
`static double`	`min(double a, double b, double c)` minimum of 3 doubles
`static float`	`min(float[] x)` Returns the minimum value of an array.
`static float`	`min(float a, float b)` Returns the smaller of two float values.
`static double`	`min(float a, float b, float c)` minimum of 3 floats
`static int`	`min(int[] x)` Returns the minimum value of an array.
`static int`	`min(int[][] matrix)` Returns the minimum of a matrix.
`static int`	`min(int a, int b)` Returns the smaller of two int values.
`static int`	`min(int a, int b, int c)` minimum of 3 integers
`static long`	`min(long a, long b)` Returns the smaller of two long values.
`static void`	`minus(double[][] y, double[][] x)` Element-wise subtraction of two matrices y = y - x.
`static void`	`minus(double[] y, double[] x)` Element-wise subtraction of two arrays y = y - x.
`static double`	`nextAfter(double start, double direction)` Returns the floating-point number adjacent to the first argument in the direction of the second argument.
`static float`	`nextAfter(float start, double direction)` Returns the floating-point number adjacent to the first argument in the direction of the second argument.
`static double`	`nextUp(double d)` Returns the floating-point value adjacent to d in the direction of positive infinity.
`static float`	`nextUp(float f)` Returns the floating-point value adjacent to f in the direction of positive infinity.
`static double`	`norm(double[] x)` L2 vector norm.
`static double`	`norm(double[][] x)` L2 matrix norm.
`static double`	`norm1(double[] x)` L1 vector norm.
`static double`	`norm1(double[][] x)` L1 matrix norm.
`static double`	`norm2(double[] x)` L2 vector norm.
`static double`	`norm2(double[][] x)` L2 matrix norm.
`static java.util.function.Function<double[],double[]>`	`normalize(double[][] x, boolean centerizing)` Unitizes each column of a matrix to unit length (L_2 norm).
`static double`	`normFro(double[][] x)` Frobenius matrix norm.
`static double`	`normInf(double[] x)` L-infinity vector norm.
`static double`	`normInf(double[][] x)` Infinity matrix norm.
`static void`	`permutate(double[] x)` Generates a permutation of given array.
`static void`	`permutate(float[] x)` Generates a permutation of given array.
`static int[]`	`permutate(int n)` Generates a permutation of 0, 1, 2, ..., n-1, which is useful for sampling without replacement.
`static void`	`permutate(int[] x)` Generates a permutation of given array.
`static void`	`permutate(java.lang.Object[] x)` Generates a permutation of given array.
`static void`	`plus(double[][] y, double[][] x)` Element-wise sum of two matrices y = x + y.
`static void`	`plus(double[] y, double[] x)` Element-wise sum of two arrays y = x + y.
`static double[][]`	`pow(double[][] x, double n)` Raise each element of a matrix to a scalar power.
`static double[]`	`pow(double[] x, double n)` Raise each element of an array to a scalar power.
`static double`	`pow(double a, double b)` Returns the value of the first argument raised to the power of the second argument.
`static double`	`q1(double[] a)` Find the first quantile (p = 1/4) of an array of type double.
`static float`	`q1(float[] a)` Find the first quantile (p = 1/4) of an array of type float.
`static int`	`q1(int[] a)` Find the first quantile (p = 1/4) of an array of type int.
`static <T extends java.lang.Comparable<? super T>> T`	`q1(T[] a)` Find the first quantile (p = 1/4) of an array of type double.
`static double`	`q3(double[] a)` Find the third quantile (p = 3/4) of an array of type double.
`static float`	`q3(float[] a)` Find the third quantile (p = 3/4) of an array of type float.
`static int`	`q3(int[] a)` Find the third quantile (p = 3/4) of an array of type int.
`static <T extends java.lang.Comparable<? super T>> T`	`q3(T[] a)` Find the third quantile (p = 3/4) of an array of type double.
`static double`	`random()` Generate a random number in [0, 1).
`static int`	`random(double[] prob)` Given a set of n probabilities, generate a random number in [0, n).
`static int[]`	`random(double[] prob, int n)` Given a set of m probabilities, draw with replacement a set of n random number in [0, m).
`static double`	`random(double lo, double hi)` Generate a uniform random number in the range [lo, hi).
`static double[]`	`random(double lo, double hi, int n)` Generate n uniform random numbers in the range [lo, hi).
`static double[]`	`random(int n)` Generate n random numbers in [0, 1).
`static int`	`randomInt(int n)` Returns a random integer in [0, n).
`static int`	`randomInt(int lo, int hi)` Returns a random integer in [lo, hi).
`static int`	`rank(double[][] A)` Returns the matrix rank.
`static java.util.function.Function<double[],double[]>`	`rescale(double[][] x)` Rescales each column of a matrix to range [0, 1].
`static java.util.function.Function<double[],double[]>`	`rescale(double[][] x, double lo, double hi)` Rescales each column of a matrix to range [lo, hi].
`static void`	`reverse(double[] a)` Reverses the order of the elements in the specified array.
`static void`	`reverse(float[] a)` Reverses the order of the elements in the specified array.
`static void`	`reverse(int[] a)` Reverses the order of the elements in the specified array.
`static <T> void`	`reverse(T[] a)` Reverses the order of the elements in the specified array.
`static double`	`rint(double a)` Returns the double value that is closest in value to the argument and is equal to a mathematical integer.
`static double`	`root(DifferentiableFunction func, double x1, double x2, double tol)` Returns the root of a function whose derivative is available known to lie between x1 and x2 by Newton-Raphson method.
`static double`	`root(DifferentiableFunction func, double x1, double x2, double tol, int maxIter)` Returns the root of a function whose derivative is available known to lie between x1 and x2 by Newton-Raphson method.
`static double`	`root(Function func, double x1, double x2, double tol)` Returns the root of a function known to lie between x1 and x2 by Brent's method.
`static double`	`root(Function func, double x1, double x2, double tol, int maxIter)` Returns the root of a function known to lie between x1 and x2 by Brent's method.
`static long`	`round(double a)` Returns the closest long to the argument.
`static double`	`round(double x, int decimal)` Round a double vale to given digits such as 10^n, where n is a positive or negative integer.
`static int`	`round(float a)` Returns the closest int to the argument.
`static double[]`	`rowMax(double[][] data)` Returns the row maximum for a matrix.
`static double[]`	`rowMean(double[][] data)` Returns the row means for a matrix.
`static double[]`	`rowMin(double[][] data)` Returns the row minimum for a matrix.
`static double[]`	`rowSd(double[][] data)` Returns the row standard deviations for a matrix.
`static double[]`	`rowSum(double[][] data)` Returns the row sums for a matrix.
`static double`	`scalb(double d, int scaleFactor)` 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.
`static float`	`scalb(float f, int scaleFactor)` 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.
`static void`	`scale(double a, double[] x)` Scale each element of an array by a constant x = a * x.
`static void`	`scale(double a, double[] x, double[] y)` Scale each element of an array by a constant y = a * x.
`static double`	`sd(double[] x)` Returns the standard deviation of an array.
`static double`	`sd(float[] x)` Returns the standard deviation of an array.
`static double`	`sd(int[] x)` Returns the standard deviation of an array.
`static void`	`setSeed(long seed)` Initialize the random generator with a seed.
`static double`	`signum(double d)` 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.
`static float`	`signum(float f)` 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.
`static double`	`sin(double a)` Returns the trigonometric sine of an angle.
`static double`	`sinh(double x)` Returns the hyperbolic sine of a double value.
`static double[]`	`slice(double[] data, int[] index)` Returns a slice of data for given indices.
`static <E> E[]`	`slice(E[] data, int[] index)` Returns a slice of data for given indices.
`static float[]`	`slice(float[] data, int[] index)` Returns a slice of data for given indices.
`static int[]`	`slice(int[] data, int[] index)` Returns a slice of data for given indices.
`static double[]`	`solve(double[][] A, double[] b)` Solve A*x = b (exact solution if A is square, least squares solution otherwise), which means the LU or QR decomposition will take place in A and the results will be stored in b.
`static DenseMatrix`	`solve(double[][] A, double[][] B)` Solve A*X = B (exact solution if A is square, least squares solution otherwise), which means the LU or QR decomposition will take place in A and the results will be stored in B.
`static double[]`	`solve(double[] a, double[] b, double[] c, double[] r)` Solve the tridiagonal linear set which is of diagonal dominance \|b_i\| > \|a_i\| + \|c_i\|.
`static int[][]`	`sort(double[][] x)` Sorts each variable and returns the index of values in ascending order.
`static double`	`spearman(double[] x, double[] y)` The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie.
`static double`	`spearman(float[] x, float[] y)` The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie.
`static double`	`spearman(int[] x, int[] y)` The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie.
`static double`	`sqr(double x)` Returns x * x.
`static double`	`sqrt(double a)` Returns the correctly rounded positive square root of a double value.
`static double`	`squaredDistance(double[] x, double[] y)` The squared Euclidean distance.
`static double`	`squaredDistance(float[] x, float[] y)` The squared Euclidean distance.
`static double`	`squaredDistance(int[] x, int[] y)` The squared Euclidean distance.
`static double`	`squaredDistance(SparseArray x, SparseArray y)` The Euclidean distance.
`static void`	`standardize(double[] x)` Standardizes an array to mean 0 and variance 1.
`static java.util.function.Function<double[],double[]>`	`standardize(double[][] x)` Standardizes each column of a matrix to 0 mean and unit variance.
`static double`	`sum(double[] x)` Returns the sum of an array.
`static double`	`sum(float[] x)` Returns the sum of an array.
`static int`	`sum(int[] x)` Returns the sum of an array.
`static SingularValueDecomposition`	`svd(double[][] A)` Returns the singular value decomposition.
`static void`	`swap(double[] x, double[] y)` Swap two arrays.
`static void`	`swap(double[] x, int i, int j)` Swap two elements of an array.
`static <E> void`	`swap(E[] x, E[] y)` Swap two arrays.
`static void`	`swap(float[] x, float[] y)` Swap two arrays.
`static void`	`swap(float[] x, int i, int j)` Swap two elements of an array.
`static void`	`swap(int[] x, int[] y)` Swap two arrays.
`static void`	`swap(int[] x, int i, int j)` Swap two elements of an array.
`static void`	`swap(java.lang.Object[] x, int i, int j)` Swap two elements of an array.
`static double`	`tan(double a)` Returns the trigonometric tangent of an angle.
`static double`	`tanh(double x)` Returns the hyperbolic tangent of a double value.
`static double`	`toDegrees(double angrad)` Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
`static double`	`toRadians(double angdeg)` Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
`static double`	`trace(double[][] A)` Returns the matrix trace.
`static double[][]`	`transpose(double[][] A)` Returns the matrix transpose.
`static double`	`ulp(double d)` Returns the size of an ulp of the argument.
`static float`	`ulp(float f)` * Returns the size of an ulp of the argument.
`static int[]`	`unique(int[] x)` Find unique elements of vector.
`static java.lang.String[]`	`unique(java.lang.String[] x)` Find unique elements of vector.
`static void`	`unitize(double[] x)` Unitize an array so that L2 norm of x = 1.
`static void`	`unitize1(double[] x)` Unitize an array so that L1 norm of x is 1.
`static void`	`unitize2(double[] x)` Unitize an array so that L2 norm of x = 1.
`static double`	`var(double[] x)` Returns the variance of an array.
`static double`	`var(float[] x)` Returns the variance of an array.
`static double`	`var(int[] x)` Returns the variance of an array.
`static int`	`whichMax(double[] x)` Returns the index of maximum value of an array.
`static int`	`whichMax(float[] x)` Returns the index of maximum value of an array.
`static int`	`whichMax(int[] x)` Returns the index of maximum value of an array.
`static int`	`whichMin(double[] x)` Returns the index of minimum value of an array.
`static int`	`whichMin(float[] x)` Returns the index of minimum value of an array.
`static int`	`whichMin(int[] x)` Returns the index of minimum value of an array.
`static double`	`xax(double[][] A, double[] x)` Returns x' * A * x.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - E
```
public static final double E
```
    The base of the natural logarithms.
    
    See Also:
    
    Constant Field Values
  - PI
```
public static final double PI
```
    The ratio of the circumference of a circle to its diameter.
    
    See Also:
    
    Constant Field Values
  - EPSILON
```
public static double EPSILON
```
    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.
  - RADIX
```
public static int RADIX
```
    The base of the exponent of the double type.
  - DIGITS
```
public static int DIGITS
```
    The number of digits (in radix base) in the mantissa.
  - ROUND_STYLE
```
public static int ROUND_STYLE
```
    Rounding style.
    - 0 if floating-point addition chops
    - 1 if floating-point addition rounds, but not in the ieee style
    - 2 if floating-point addition rounds in the ieee style
    - 3 if floating-point addition chops, and there is partial underflow
    - 4 if floating-point addition rounds, but not in the ieee style, and there is partial underflow
    - 5 if floating-point addition rounds in the IEEEE style, and there is partial underflow
  - MACHEP
```
public static int MACHEP
```
    The largest negative integer such that 1.0 + RADIX^MACHEP ≠ 1.0, except that machep is bounded below by -(DIGITS+3)
  - NEGEP
```
public static int NEGEP
```
    The largest negative integer such that 1.0 - RADIX^NEGEP ≠ 1.0, except that negeps is bounded below by -(DIGITS+3)
- Method Detail
  - abs
```
public static double abs(double a)
```
    Returns the absolute value of a double value.
  - abs
```
public static float abs(float a)
```
    Returns the absolute value of a float value.
  - abs
```
public static int abs(int a)
```
    Returns the absolute value of an int value.
  - abs
```
public static long abs(long a)
```
    Returns the absolute value of a long value.
  - acos
```
public static double acos(double a)
```
    Returns the arc cosine of an angle, in the range of 0.0 through pi.
  - asin
```
public static double asin(double a)
```
    Returns the arc sine of an angle, in the range of -pi/2 through pi/2.
  - atan
```
public static double atan(double a)
```
    Returns the arc tangent of an angle, in the range of -pi/2 through pi/2.
  - atan2
```
public static double atan2(double y,
                           double x)
```
    Converts rectangular coordinates (x, y) to polar (r, theta).
  - cbrt
```
public static double cbrt(double a)
```
    Returns the cube root of a double value.
  - ceil
```
public static double ceil(double a)
```
    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.
  - copySign
```
public static double copySign(double magnitude,
                              double sign)
```
    Returns the first floating-point argument with the sign of the second floating-point argument.
  - copySign
```
public static float copySign(float magnitude,
                             float sign)
```
    Returns the first floating-point argument with the sign of the second floating-point argument.
  - cos
```
public static double cos(double a)
```
    Returns the trigonometric cosine of an angle.
  - cosh
```
public static double cosh(double x)
```
    Returns the hyperbolic cosine of a double value.
  - exp
```
public static double exp(double a)
```
    Returns Euler's number e raised to the power of a double value.
  - expm1
```
public static double expm1(double x)
```
    Returns e^x-1.
  - floor
```
public static double floor(double a)
```
    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.
  - getExponent
```
public static int getExponent(double d)
```
    Returns the unbiased exponent used in the representation of a double.
  - getExponent
```
public static int getExponent(float f)
```
    Returns the unbiased exponent used in the representation of a float.
  - hypot
```
public static double hypot(double x,
                           double y)
```
    Returns sqrt(x2 +y2) without intermediate overflow or underflow.
  - IEEEremainder
```
public static double IEEEremainder(double f1,
                                   double f2)
```
    Computes the remainder operation on two arguments as prescribed by the IEEE 754 standard.
  - log
```
public static double log(double a)
```
    Returns the natural logarithm (base e) of a double value.
  - log10
```
public static double log10(double a)
```
    Returns the base 10 logarithm of a double value.
  - log1p
```
public static double log1p(double x)
```
    Returns the natural logarithm of the sum of the argument and 1.
  - max
```
public static double max(double a,
                         double b)
```
    Returns the greater of two double values.
  - max
```
public static float max(float a,
                        float b)
```
    Returns the greater of two float values.
  - max
```
public static int max(int a,
                      int b)
```
    Returns the greater of two int values.
  - max
```
public static long max(long a,
                       long b)
```
    Returns the greater of two long values.
  - min
```
public static double min(double a,
                         double b)
```
    Returns the smaller of two double values.
  - min
```
public static float min(float a,
                        float b)
```
    Returns the smaller of two float values.
  - min
```
public static int min(int a,
                      int b)
```
    Returns the smaller of two int values.
  - min
```
public static long min(long a,
                       long b)
```
    Returns the smaller of two long values.
  - nextAfter
```
public static double nextAfter(double start,
                               double direction)
```
    Returns the floating-point number adjacent to the first argument in the direction of the second argument.
  - nextAfter
```
public static float nextAfter(float start,
                              double direction)
```
    Returns the floating-point number adjacent to the first argument in the direction of the second argument.
  - nextUp
```
public static double nextUp(double d)
```
    Returns the floating-point value adjacent to d in the direction of positive infinity.
  - nextUp
```
public static float nextUp(float f)
```
    Returns the floating-point value adjacent to f in the direction of positive infinity.
  - pow
```
public static double pow(double a,
                         double b)
```
    Returns the value of the first argument raised to the power of the second argument.
  - rint
```
public static double rint(double a)
```
    Returns the double value that is closest in value to the argument and is equal to a mathematical integer.
  - round
```
public static long round(double a)
```
    Returns the closest long to the argument.
  - round
```
public static int round(float a)
```
    Returns the closest int to the argument.
  - scalb
```
public static double scalb(double d,
                           int scaleFactor)
```
    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
```
public static float scalb(float f,
                          int scaleFactor)
```
    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.
  - signum
```
public static double signum(double d)
```
    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
```
public static float signum(float f)
```
    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
```
public static double sin(double a)
```
    Returns the trigonometric sine of an angle.
  - sinh
```
public static double sinh(double x)
```
    Returns the hyperbolic sine of a double value.
  - sqrt
```
public static double sqrt(double a)
```
    Returns the correctly rounded positive square root of a double value.
  - tan
```
public static double tan(double a)
```
    Returns the trigonometric tangent of an angle.
  - tanh
```
public static double tanh(double x)
```
    Returns the hyperbolic tangent of a double value.
  - toDegrees
```
public static double toDegrees(double angrad)
```
    Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
  - toRadians
```
public static double toRadians(double angdeg)
```
    Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
  - ulp
```
public static double ulp(double d)
```
    Returns the size of an ulp of the argument.
  - ulp
```
public static float ulp(float f)
```
    * Returns the size of an ulp of the argument.
  - log2
```
public static double log2(double x)
```
    Log of base 2.
  - equals
```
public static boolean equals(double a,
                             double b)
```
    Returns true if two double values equals to each other in the system precision.
    
    Parameters:
    
    a - a double value.
    
    b - a double value.
    
    Returns:
    
    true if two double values equals to each other in the system precision
  - logistic
```
public static double logistic(double x)
```
    Logistic sigmoid function.
  - sqr
```
public static double sqr(double x)
```
    Returns x * x.
  - isPower2
```
public static boolean isPower2(int x)
```
    Returns true if x is a power of 2.
  - round
```
public static double round(double x,
                           int decimal)
```
    Round a double vale to given digits such as 10^n, where n is a positive or negative integer.
  - factorial
```
public static double factorial(int n)
```
    factorial of n
    
    Returns:
    
    factorial returned as double but is, numerically, an integer. Numerical rounding may makes this an approximation after n = 21
  - logFactorial
```
public static double logFactorial(int n)
```
    log of factorial of n
  - choose
```
public static double choose(int n,
                            int k)
```
    n choose k. Returns 0 if n is less than k.
  - logChoose
```
public static double logChoose(int n,
                               int k)
```
    log of n choose k
  - setSeed
```
public static void setSeed(long seed)
```
    Initialize the random generator with a seed.
  - random
```
public static int random(double[] prob)
```
    Given a set of n probabilities, generate a random number in [0, n).
    
    Parameters:
    
    prob - probabilities of size n. The prob argument can be used to give a vector of weights for obtaining the elements of the vector being sampled. They need not sum to one, but they should be nonnegative and not all zero.
    
    Returns:
    
    a random integer in [0, n).
  - random
```
public static int[] random(double[] prob,
                           int n)
```
    Given a set of m probabilities, draw with replacement a set of n random number in [0, m).
    
    Parameters:
    
    prob - probabilities of size n. The prob argument can be used to give a vector of weights for obtaining the elements of the vector being sampled. They need not sum to one, but they should be nonnegative and not all zero.
    
    Returns:
    
    an random array of length n in range of [0, m).
  - random
```
public static double random()
```
    Generate a random number in [0, 1).
  - random
```
public static double[] random(int n)
```
    Generate n random numbers in [0, 1).
  - random
```
public static double random(double lo,
                            double hi)
```
    Generate a uniform random number in the range [lo, hi).
    
    Parameters:
    
    lo - lower limit of range
    
    hi - upper limit of range
    
    Returns:
    
    a uniform random real in the range [lo, hi)
  - random
```
public static double[] random(double lo,
                              double hi,
                              int n)
```
    Generate n uniform random numbers in the range [lo, hi).
    
    Parameters:
    
    n - size of the array
    
    lo - lower limit of range
    
    hi - upper limit of range
    
    Returns:
    
    a uniform random real in the range [lo, hi)
  - randomInt
```
public static int randomInt(int n)
```
    Returns a random integer in [0, n).
  - randomInt
```
public static int randomInt(int lo,
                            int hi)
```
    Returns a random integer in [lo, hi).
  - permutate
```
public static int[] permutate(int n)
```
    Generates a permutation of 0, 1, 2, ..., n-1, which is useful for sampling without replacement.
  - permutate
```
public static void permutate(int[] x)
```
    Generates a permutation of given array.
  - permutate
```
public static void permutate(float[] x)
```
    Generates a permutation of given array.
  - permutate
```
public static void permutate(double[] x)
```
    Generates a permutation of given array.
  - permutate
```
public static void permutate(java.lang.Object[] x)
```
    Generates a permutation of given array.
  - slice
```
public static <E> E[] slice(E[] data,
                            int[] index)
```
    Returns a slice of data for given indices.
  - slice
```
public static int[] slice(int[] data,
                          int[] index)
```
    Returns a slice of data for given indices.
  - slice
```
public static float[] slice(float[] data,
                            int[] index)
```
    Returns a slice of data for given indices.
  - slice
```
public static double[] slice(double[] data,
                             int[] index)
```
    Returns a slice of data for given indices.
  - contains
```
public static boolean contains(double[][] polygon,
                               double[] point)
```
    Determines if the polygon contains the specified coordinates.
    
    Parameters:
    
    point - the coordinates of specified point to be tested.
    
    Returns:
    
    true if the Polygon contains the specified coordinates; false otherwise.
  - contains
```
public static boolean contains(double[][] polygon,
                               double x,
                               double y)
```
    Determines if the polygon contains the specified coordinates.
    
    Parameters:
    
    x - the specified x coordinate.
    
    y - the specified y coordinate.
    
    Returns:
    
    true if the Polygon contains the specified coordinates; false otherwise.
  - reverse
```
public static void reverse(int[] a)
```
    Reverses the order of the elements in the specified array.
    
    Parameters:
    
    a - an array to reverse.
  - reverse
```
public static void reverse(float[] a)
```
    Reverses the order of the elements in the specified array.
    
    Parameters:
    
    a - an array to reverse.
  - reverse
```
public static void reverse(double[] a)
```
    Reverses the order of the elements in the specified array.
    
    Parameters:
    
    a - an array to reverse.
  - reverse
```
public static <T> void reverse(T[] a)
```
    Reverses the order of the elements in the specified array.
    
    Parameters:
    
    a - an array to reverse.
  - min
```
public static int min(int a,
                      int b,
                      int c)
```
    minimum of 3 integers
  - min
```
public static double min(float a,
                         float b,
                         float c)
```
    minimum of 3 floats
  - min
```
public static double min(double a,
                         double b,
                         double c)
```
    minimum of 3 doubles
  - max
```
public static int max(int a,
                      int b,
                      int c)
```
    maximum of 3 integers
  - max
```
public static float max(float a,
                        float b,
                        float c)
```
    maximum of 3 floats
  - max
```
public static double max(double a,
                         double b,
                         double c)
```
    maximum of 3 doubles
  - min
```
public static int min(int[] x)
```
    Returns the minimum value of an array.
  - min
```
public static float min(float[] x)
```
    Returns the minimum value of an array.
  - min
```
public static double min(double[] x)
```
    Returns the minimum value of an array.
  - whichMin
```
public static int whichMin(int[] x)
```
    Returns the index of minimum value of an array.
  - whichMin
```
public static int whichMin(float[] x)
```
    Returns the index of minimum value of an array.
  - whichMin
```
public static int whichMin(double[] x)
```
    Returns the index of minimum value of an array.
  - max
```
public static int max(int[] x)
```
    Returns the maximum value of an array.
  - max
```
public static float max(float[] x)
```
    Returns the maximum value of an array.
  - max
```
public static double max(double[] x)
```
    Returns the maximum value of an array.
  - whichMax
```
public static int whichMax(int[] x)
```
    Returns the index of maximum value of an array.
  - whichMax
```
public static int whichMax(float[] x)
```
    Returns the index of maximum value of an array.
  - whichMax
```
public static int whichMax(double[] x)
```
    Returns the index of maximum value of an array.
  - min
```
public static int min(int[][] matrix)
```
    Returns the minimum of a matrix.
  - min
```
public static double min(double[][] matrix)
```
    Returns the minimum of a matrix.
  - max
```
public static int max(int[][] matrix)
```
    Returns the maximum of a matrix.
  - max
```
public static double max(double[][] matrix)
```
    Returns the maximum of a matrix.
  - rowMin
```
public static double[] rowMin(double[][] data)
```
    Returns the row minimum for a matrix.
  - rowMax
```
public static double[] rowMax(double[][] data)
```
    Returns the row maximum for a matrix.
  - rowSum
```
public static double[] rowSum(double[][] data)
```
    Returns the row sums for a matrix.
  - rowMean
```
public static double[] rowMean(double[][] data)
```
    Returns the row means for a matrix.
  - rowSd
```
public static double[] rowSd(double[][] data)
```
    Returns the row standard deviations for a matrix.
  - colMin
```
public static double[] colMin(double[][] data)
```
    Returns the column minimum for a matrix.
  - colMax
```
public static double[] colMax(double[][] data)
```
    Returns the column maximum for a matrix.
  - colSum
```
public static double[] colSum(double[][] data)
```
    Returns the column sums for a matrix.
  - colMean
```
public static double[] colMean(double[][] data)
```
    Returns the column sums for a matrix.
  - colSd
```
public static double[] colSd(double[][] data)
```
    Returns the column deviations for a matrix.
  - sum
```
public static int sum(int[] x)
```
    Returns the sum of an array.
  - sum
```
public static double sum(float[] x)
```
    Returns the sum of an array.
  - sum
```
public static double sum(double[] x)
```
    Returns the sum of an array.
  - median
```
public static int median(int[] a)
```
    Find the median of an array of type int. The input array will be rearranged.
  - median
```
public static float median(float[] a)
```
    Find the median of an array of type float. The input array will be rearranged.
  - median
```
public static double median(double[] a)
```
    Find the median of an array of type double. The input array will be rearranged.
  - median
```
public static <T extends java.lang.Comparable<? super T>> T median(T[] a)
```
    Find the median of an array of type double. The input array will be rearranged.
  - q1
```
public static int q1(int[] a)
```
    Find the first quantile (p = 1/4) of an array of type int. The input array will be rearranged.
  - q1
```
public static float q1(float[] a)
```
    Find the first quantile (p = 1/4) of an array of type float. The input array will be rearranged.
  - q1
```
public static double q1(double[] a)
```
    Find the first quantile (p = 1/4) of an array of type double. The input array will be rearranged.
  - q1
```
public static <T extends java.lang.Comparable<? super T>> T q1(T[] a)
```
    Find the first quantile (p = 1/4) of an array of type double. The input array will be rearranged.
  - q3
```
public static int q3(int[] a)
```
    Find the third quantile (p = 3/4) of an array of type int. The input array will be rearranged.
  - q3
```
public static float q3(float[] a)
```
    Find the third quantile (p = 3/4) of an array of type float. The input array will be rearranged.
  - q3
```
public static double q3(double[] a)
```
    Find the third quantile (p = 3/4) of an array of type double. The input array will be rearranged.
  - q3
```
public static <T extends java.lang.Comparable<? super T>> T q3(T[] a)
```
    Find the third quantile (p = 3/4) of an array of type double. The input array will be rearranged.
  - mean
```
public static double mean(int[] x)
```
    Returns the mean of an array.
  - mean
```
public static double mean(float[] x)
```
    Returns the mean of an array.
  - mean
```
public static double mean(double[] x)
```
    Returns the mean of an array.
  - var
```
public static double var(int[] x)
```
    Returns the variance of an array.
  - var
```
public static double var(float[] x)
```
    Returns the variance of an array.
  - var
```
public static double var(double[] x)
```
    Returns the variance of an array.
  - sd
```
public static double sd(int[] x)
```
    Returns the standard deviation of an array.
  - sd
```
public static double sd(float[] x)
```
    Returns the standard deviation of an array.
  - sd
```
public static double sd(double[] x)
```
    Returns the standard deviation of an array.
  - mad
```
public static double mad(int[] x)
```
    Returns the median absolute deviation (MAD). Note that input array will be altered after the computation. MAD is a robust measure of the variability of a univariate sample of quantitative data. For a univariate data set X₁, X₂, ..., X_n, the MAD is defined as the median of the absolute deviations from the data's median:
    MAD(X) = median(|X_i - median(X_i)|)
    that is, starting with the residuals (deviations) from the data's median, the MAD is the median of their absolute values.
    MAD is a more robust estimator of scale than the sample variance or standard deviation. For instance, MAD is more resilient to outliers in a data set than the standard deviation. It thus behaves better with distributions without a mean or variance, such as the Cauchy distribution.
    In order to use the MAD as a consistent estimator for the estimation of the standard deviation σ, one takes σ = K * MAD, where K is a constant scale factor, which depends on the distribution. For normally distributed data K is taken to be 1.4826. Other distributions behave differently: for example for large samples from a uniform continuous distribution, this factor is about 1.1547.
  - mad
```
public static double mad(float[] x)
```
    Returns the median absolute deviation (MAD). Note that input array will be altered after the computation. MAD is a robust measure of the variability of a univariate sample of quantitative data. For a univariate data set X₁, X₂, ..., X_n, the MAD is defined as the median of the absolute deviations from the data's median:
    MAD(X) = median(|X_i - median(X_i)|)
    that is, starting with the residuals (deviations) from the data's median, the MAD is the median of their absolute values.
    MAD is a more robust estimator of scale than the sample variance or standard deviation. For instance, MAD is more resilient to outliers in a data set than the standard deviation. It thus behaves better with distributions without a mean or variance, such as the Cauchy distribution.
    In order to use the MAD as a consistent estimator for the estimation of the standard deviation σ, one takes σ = K * MAD, where K is a constant scale factor, which depends on the distribution. For normally distributed data K is taken to be 1.4826. Other distributions behave differently: for example for large samples from a uniform continuous distribution, this factor is about 1.1547.
  - mad
```
public static double mad(double[] x)
```
    Returns the median absolute deviation (MAD). Note that input array will be altered after the computation. MAD is a robust measure of the variability of a univariate sample of quantitative data. For a univariate data set X₁, X₂, ..., X_n, the MAD is defined as the median of the absolute deviations from the data's median:
    MAD(X) = median(|X_i - median(X_i)|)
    that is, starting with the residuals (deviations) from the data's median, the MAD is the median of their absolute values.
    MAD is a more robust estimator of scale than the sample variance or standard deviation. For instance, MAD is more resilient to outliers in a data set than the standard deviation. It thus behaves better with distributions without a mean or variance, such as the Cauchy distribution.
    In order to use the MAD as a consistent estimator for the estimation of the standard deviation σ, one takes σ = K * MAD, where K is a constant scale factor, which depends on the distribution. For normally distributed data K is taken to be 1.4826. Other distributions behave differently: for example for large samples from a uniform continuous distribution, this factor is about 1.1547.
  - all
```
public static boolean all(boolean[] x)
```
    Given a set of boolean values, are all of the values true?
  - any
```
public static boolean any(boolean[] x)
```
    Given a set of boolean values, is at least one of the values true?
  - distance
```
public static double distance(int[] x,
                              int[] y)
```
    The Euclidean distance.
  - distance
```
public static double distance(float[] x,
                              float[] y)
```
    The Euclidean distance.
  - distance
```
public static double distance(double[] x,
                              double[] y)
```
    The Euclidean distance.
  - distance
```
public static double distance(SparseArray x,
                              SparseArray y)
```
    The Euclidean distance.
  - squaredDistance
```
public static double squaredDistance(int[] x,
                                     int[] y)
```
    The squared Euclidean distance.
  - squaredDistance
```
public static double squaredDistance(float[] x,
                                     float[] y)
```
    The squared Euclidean distance.
  - squaredDistance
```
public static double squaredDistance(double[] x,
                                     double[] y)
```
    The squared Euclidean distance.
  - squaredDistance
```
public static double squaredDistance(SparseArray x,
                                     SparseArray y)
```
    The Euclidean distance.
  - KullbackLeiblerDivergence
```
public static double KullbackLeiblerDivergence(double[] x,
                                               double[] y)
```
    Kullback-Leibler divergence. The Kullback-Leibler divergence (also information divergence, information gain, relative entropy, or KLIC) is a non-symmetric measure of the difference between two probability distributions P and Q. KL measures the expected number of extra bits required to code samples from P when using a code based on Q, rather than using a code based on P. Typically P represents the "true" distribution of data, observations, or a precise calculated theoretical distribution. The measure Q typically represents a theory, model, description, or approximation of P.
    Although it is often intuited as a distance metric, the KL divergence is not a true metric - for example, the KL from P to Q is not necessarily the same as the KL from Q to P.
  - KullbackLeiblerDivergence
```
public static double KullbackLeiblerDivergence(SparseArray x,
                                               SparseArray y)
```
    Kullback-Leibler divergence. The Kullback-Leibler divergence (also information divergence, information gain, relative entropy, or KLIC) is a non-symmetric measure of the difference between two probability distributions P and Q. KL measures the expected number of extra bits required to code samples from P when using a code based on Q, rather than using a code based on P. Typically P represents the "true" distribution of data, observations, or a precise calculated theoretical distribution. The measure Q typically represents a theory, model, description, or approximation of P.
    Although it is often intuited as a distance metric, the KL divergence is not a true metric - for example, the KL from P to Q is not necessarily the same as the KL from Q to P.
  - KullbackLeiblerDivergence
```
public static double KullbackLeiblerDivergence(double[] x,
                                               SparseArray y)
```
    Kullback-Leibler divergence. The Kullback-Leibler divergence (also information divergence, information gain, relative entropy, or KLIC) is a non-symmetric measure of the difference between two probability distributions P and Q. KL measures the expected number of extra bits required to code samples from P when using a code based on Q, rather than using a code based on P. Typically P represents the "true" distribution of data, observations, or a precise calculated theoretical distribution. The measure Q typically represents a theory, model, description, or approximation of P.
    Although it is often intuited as a distance metric, the KL divergence is not a true metric - for example, the KL from P to Q is not necessarily the same as the KL from Q to P.
  - KullbackLeiblerDivergence
```
public static double KullbackLeiblerDivergence(SparseArray x,
                                               double[] y)
```
    Kullback-Leibler divergence. The Kullback-Leibler divergence (also information divergence, information gain, relative entropy, or KLIC) is a non-symmetric measure of the difference between two probability distributions P and Q. KL measures the expected number of extra bits required to code samples from P when using a code based on Q, rather than using a code based on P. Typically P represents the "true" distribution of data, observations, or a precise calculated theoretical distribution. The measure Q typically represents a theory, model, description, or approximation of P.
    Although it is often intuited as a distance metric, the KL divergence is not a true metric - for example, the KL from P to Q is not necessarily the same as the KL from Q to P.
  - JensenShannonDivergence
```
public static double JensenShannonDivergence(double[] x,
                                             double[] y)
```
    Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2. The Jensen-Shannon divergence is a popular method of measuring the similarity between two probability distributions. It is also known as information radius or total divergence to the average. It is based on the Kullback-Leibler divergence, with the difference that it is always a finite value. The square root of the Jensen-Shannon divergence is a metric.
  - JensenShannonDivergence
```
public static double JensenShannonDivergence(SparseArray x,
                                             SparseArray y)
```
    Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2. The Jensen-Shannon divergence is a popular method of measuring the similarity between two probability distributions. It is also known as information radius or total divergence to the average. It is based on the Kullback-Leibler divergence, with the difference that it is always a finite value. The square root of the Jensen-Shannon divergence is a metric.
  - JensenShannonDivergence
```
public static double JensenShannonDivergence(double[] x,
                                             SparseArray y)
```
    Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2. The Jensen-Shannon divergence is a popular method of measuring the similarity between two probability distributions. It is also known as information radius or total divergence to the average. It is based on the Kullback-Leibler divergence, with the difference that it is always a finite value. The square root of the Jensen-Shannon divergence is a metric.
  - JensenShannonDivergence
```
public static double JensenShannonDivergence(SparseArray x,
                                             double[] y)
```
    Jensen-Shannon divergence JS(P||Q) = (KL(P||M) + KL(Q||M)) / 2, where M = (P+Q)/2. The Jensen-Shannon divergence is a popular method of measuring the similarity between two probability distributions. It is also known as information radius or total divergence to the average. It is based on the Kullback-Leibler divergence, with the difference that it is always a finite value. The square root of the Jensen-Shannon divergence is a metric.
  - dot
```
public static double dot(int[] x,
                         int[] y)
```
    Returns the dot product between two vectors.
  - dot
```
public static double dot(float[] x,
                         float[] y)
```
    Returns the dot product between two vectors.
  - dot
```
public static double dot(double[] x,
                         double[] y)
```
    Returns the dot product between two vectors.
  - dot
```
public static double dot(SparseArray x,
                         SparseArray y)
```
    Returns the dot product between two sparse arrays.
  - cov
```
public static double cov(int[] x,
                         int[] y)
```
    Returns the covariance between two vectors.
  - cov
```
public static double cov(float[] x,
                         float[] y)
```
    Returns the covariance between two vectors.
  - cov
```
public static double cov(double[] x,
                         double[] y)
```
    Returns the covariance between two vectors.
  - cov
```
public static double[][] cov(double[][] data)
```
    Returns the sample covariance matrix.
  - cov
```
public static double[][] cov(double[][] data,
                             double[] mu)
```
    Returns the sample covariance matrix.
    
    Parameters:
    
    mu - the known mean of data.
  - cor
```
public static double cor(int[] x,
                         int[] y)
```
    Returns the correlation coefficient between two vectors.
  - cor
```
public static double cor(float[] x,
                         float[] y)
```
    Returns the correlation coefficient between two vectors.
  - cor
```
public static double cor(double[] x,
                         double[] y)
```
    Returns the correlation coefficient between two vectors.
  - cor
```
public static double[][] cor(double[][] data)
```
    Returns the sample correlation matrix.
  - cor
```
public static double[][] cor(double[][] data,
                             double[] mu)
```
    Returns the sample correlation matrix.
    
    Parameters:
    
    mu - the known mean of data.
  - spearman
```
public static double spearman(int[] x,
                              int[] y)
```
    The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie. when variables are ordinal). It can be used when there is non-parametric data and hence Pearson cannot be used.
  - spearman
```
public static double spearman(float[] x,
                              float[] y)
```
    The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie. when variables are ordinal). It can be used when there is non-parametric data and hence Pearson cannot be used.
  - spearman
```
public static double spearman(double[] x,
                              double[] y)
```
    The Spearman Rank Correlation Coefficient is a form of the Pearson coefficient with the data converted to rankings (ie. when variables are ordinal). It can be used when there is non-parametric data and hence Pearson cannot be used.
  - kendall
```
public static double kendall(int[] x,
                             int[] y)
```
    The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant. It is used with non-parametric data.
  - kendall
```
public static double kendall(float[] x,
                             float[] y)
```
    The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant. It is used with non-parametric data.
  - kendall
```
public static double kendall(double[] x,
                             double[] y)
```
    The Kendall Tau Rank Correlation Coefficient is used to measure the degree of correspondence between sets of rankings where the measures are not equidistant. It is used with non-parametric data.
  - norm1
```
public static double norm1(double[] x)
```
    L1 vector norm.
  - norm2
```
public static double norm2(double[] x)
```
    L2 vector norm.
  - normInf
```
public static double normInf(double[] x)
```
    L-infinity vector norm. Maximum absolute value.
  - norm
```
public static double norm(double[] x)
```
    L2 vector norm.
  - norm1
```
public static double norm1(double[][] x)
```
    L1 matrix norm. Maximum column sum.
  - norm2
```
public static double norm2(double[][] x)
```
    L2 matrix norm. Maximum singular value.
  - norm
```
public static double norm(double[][] x)
```
    L2 matrix norm. Maximum singular value.
  - normInf
```
public static double normInf(double[][] x)
```
    Infinity matrix norm. Maximum row sum.
  - normFro
```
public static double normFro(double[][] x)
```
    Frobenius matrix norm. Sqrt of sum of squares of all elements.
  - standardize
```
public static void standardize(double[] x)
```
    Standardizes an array to mean 0 and variance 1.
  - rescale
```
public static java.util.function.Function<double[],double[]> rescale(double[][] x)
```
    Rescales each column of a matrix to range [0, 1].
  - rescale
```
public static java.util.function.Function<double[],double[]> rescale(double[][] x,
                                                                     double lo,
                                                                     double hi)
```
    Rescales each column of a matrix to range [lo, hi].
    
    Parameters:
    
    lo - lower limit of range
    
    hi - upper limit of range
  - standardize
```
public static java.util.function.Function<double[],double[]> standardize(double[][] x)
```
    Standardizes each column of a matrix to 0 mean and unit variance.
  - normalize
```
public static java.util.function.Function<double[],double[]> normalize(double[][] x,
                                                                       boolean centerizing)
```
    Unitizes each column of a matrix to unit length (L_2 norm).
    
    Parameters:
    
    centerizing - If true, centerize each column to 0 mean.
  - unitize
```
public static void unitize(double[] x)
```
    Unitize an array so that L2 norm of x = 1.
    
    Parameters:
    
    x - the array of double
  - unitize1
```
public static void unitize1(double[] x)
```
    Unitize an array so that L1 norm of x is 1.
    
    Parameters:
    
    x - an array of nonnegative double
  - unitize2
```
public static void unitize2(double[] x)
```
    Unitize an array so that L2 norm of x = 1.
    
    Parameters:
    
    x - the array of double
  - GoodTuring
```
public static double GoodTuring(int[] r,
                                int[] Nr,
                                double[] p)
```
    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.
    
    Parameters:
    
    r - the frequency in ascending order.
    
    Nr - the frequency of frequencies.
    
    p - on output, it is the estimated probabilities.
    
    Returns:
    
    P₀ for all unobserved species.
  - equals
```
public static boolean equals(int[] x,
                             int[] y)
```
    Check if x element-wisely equals y.
  - equals
```
public static boolean equals(float[] x,
                             float[] y)
```
    Check if x element-wisely equals y with default epsilon 1E-7.
  - equals
```
public static boolean equals(float[] x,
                             float[] y,
                             float epsilon)
```
    Check if x element-wisely equals y.
  - equals
```
public static boolean equals(double[] x,
                             double[] y)
```
    Check if x element-wisely equals y with default epsilon 1E-10.
  - equals
```
public static boolean equals(double[] x,
                             double[] y,
                             double eps)
```
    Check if x element-wisely equals y.
  - equals
```
public static <T extends java.lang.Comparable<? super T>> boolean equals(T[] x,
                                                                         T[] y)
```
    Check if x element-wisely equals y.
  - equals
```
public static boolean equals(int[][] x,
                             int[][] y)
```
    Check if x element-wisely equals y.
  - equals
```
public static boolean equals(float[][] x,
                             float[][] y)
```
    Check if x element-wisely equals y with default epsilon 1E-7.
  - equals
```
public static boolean equals(float[][] x,
                             float[][] y,
                             float epsilon)
```
    Check if x element-wisely equals y.
  - equals
```
public static boolean equals(double[][] x,
                             double[][] y)
```
    Check if x element-wisely equals y with default epsilon 1E-10.
  - equals
```
public static boolean equals(double[][] x,
                             double[][] y,
                             double eps)
```
    Check if x element-wisely equals y.
  - isZero
```
public static boolean isZero(float x)
```
    Tests if a floating number is zero.
  - isZero
```
public static boolean isZero(float x,
                             float epsilon)
```
    Tests if a floating number is zero with given epsilon.
  - isZero
```
public static boolean isZero(double x)
```
    Tests if a floating number is zero.
  - isZero
```
public static boolean isZero(double x,
                             double epsilon)
```
    Tests if a floating number is zero with given epsilon.
  - equals
```
public static <T extends java.lang.Comparable<? super T>> boolean equals(T[][] x,
                                                                         T[][] y)
```
    Check if x element-wisely equals y.
  - clone
```
public static int[][] clone(int[][] x)
```
    Deep clone a two-dimensional array.
  - clone
```
public static float[][] clone(float[][] x)
```
    Deep clone a two-dimensional array.
  - clone
```
public static double[][] clone(double[][] x)
```
    Deep clone a two-dimensional array.
  - swap
```
public static void swap(int[] x,
                        int i,
                        int j)
```
    Swap two elements of an array.
  - swap
```
public static void swap(float[] x,
                        int i,
                        int j)
```
    Swap two elements of an array.
  - swap
```
public static void swap(double[] x,
                        int i,
                        int j)
```
    Swap two elements of an array.
  - swap
```
public static void swap(java.lang.Object[] x,
                        int i,
                        int j)
```
    Swap two elements of an array.
  - swap
```
public static void swap(int[] x,
                        int[] y)
```
    Swap two arrays.
  - swap
```
public static void swap(float[] x,
                        float[] y)
```
    Swap two arrays.
  - swap
```
public static void swap(double[] x,
                        double[] y)
```
    Swap two arrays.
  - swap
```
public static <E> void swap(E[] x,
                            E[] y)
```
    Swap two arrays.
  - copy
```
public static void copy(int[] x,
                        int[] y)
```
    Copy x into y.
  - copy
```
public static void copy(float[] x,
                        float[] y)
```
    Copy x into y.
  - copy
```
public static void copy(double[] x,
                        double[] y)
```
    Copy x into y.
  - copy
```
public static void copy(int[][] x,
                        int[][] y)
```
    Copy x into y.
  - copy
```
public static void copy(float[][] x,
                        float[][] y)
```
    Copy x into y.
  - copy
```
public static void copy(double[][] x,
                        double[][] y)
```
    Copy x into y.
  - plus
```
public static void plus(double[] y,
                        double[] x)
```
    Element-wise sum of two arrays y = x + y.
  - plus
```
public static void plus(double[][] y,
                        double[][] x)
```
    Element-wise sum of two matrices y = x + y.
  - minus
```
public static void minus(double[] y,
                         double[] x)
```
    Element-wise subtraction of two arrays y = y - x.
    
    Parameters:
    
    y - minuend matrix
    
    x - subtrahend matrix
  - minus
```
public static void minus(double[][] y,
                         double[][] x)
```
    Element-wise subtraction of two matrices y = y - x.
    
    Parameters:
    
    y - minuend matrix
    
    x - subtrahend matrix
  - scale
```
public static void scale(double a,
                         double[] x)
```
    Scale each element of an array by a constant x = a * x.
  - scale
```
public static void scale(double a,
                         double[] x,
                         double[] y)
```
    Scale each element of an array by a constant y = a * x.
  - axpy
```
public static double[] axpy(double a,
                            double[] x,
                            double[] y)
```
    Update an array by adding a multiple of another array y = a * x + y.
  - ax
```
public static double[] ax(double[][] A,
                          double[] x,
                          double[] y)
```
    Product of a matrix and a vector y = A * x according to the rules of linear algebra. The left upper submatrix of A is used in the computation based on the size of x and y.
  - axpy
```
public static double[] axpy(double[][] A,
                            double[] x,
                            double[] y)
```
    Product of a matrix and a vector y = A * x + y according to the rules of linear algebra. The left upper submatrix of A is used in the computation based on the size of x and y.
  - axpy
```
public static double[] axpy(double[][] A,
                            double[] x,
                            double[] y,
                            double b)
```
    Product of a matrix and a vector y = A * x + b * y according to the rules of linear algebra. The left upper submatrix of A is used in the computation based on the size of x and y.
  - atx
```
public static double[] atx(double[][] A,
                           double[] x,
                           double[] y)
```
    Product of a matrix and a vector y = A^T * x according to the rules of linear algebra. The left upper submatrix of A is used in the computation based on the size of x and y.
  - atxpy
```
public static double[] atxpy(double[][] A,
                             double[] x,
                             double[] y)
```
    Product of a matrix and a vector y = A^T * x + y according to the rules of linear algebra. The left upper submatrix of A is used in the computation based on the size of x and y.
  - atxpy
```
public static double[] atxpy(double[][] A,
                             double[] x,
                             double[] y,
                             double b)
```
    Product of a matrix and a vector y = A^T * x + b * y according to the rules of linear algebra. The left upper submatrix of A is used in the computation based on the size of x and y.
  - xax
```
public static double xax(double[][] A,
                         double[] x)
```
    Returns x' * A * x. The left upper submatrix of A is used in the computation based on the size of x.
  - aatmm
```
public static double[][] aatmm(double[][] A)
```
    Matrix multiplication A * A' according to the rules of linear algebra.
  - aatmm
```
public static void aatmm(double[][] A,
                         double[][] C)
```
    Matrix multiplication C = A * A' according to the rules of linear algebra.
  - atamm
```
public static double[][] atamm(double[][] A)
```
    Matrix multiplication A' * A according to the rules of linear algebra.
  - atamm
```
public static void atamm(double[][] A,
                         double[][] C)
```
    Matrix multiplication C = A' * A according to the rules of linear algebra.
  - abmm
```
public static double[][] abmm(double[][] A,
                              double[][] B)
```
    Matrix multiplication A * B according to the rules of linear algebra.
  - abmm
```
public static void abmm(double[][] A,
                        double[][] B,
                        double[][] C)
```
    Matrix multiplication C = A * B according to the rules of linear algebra.
  - atbmm
```
public static double[][] atbmm(double[][] A,
                               double[][] B)
```
    Matrix multiplication A' * B according to the rules of linear algebra.
  - atbmm
```
public static void atbmm(double[][] A,
                         double[][] B,
                         double[][] C)
```
    Matrix multiplication C = A' * B according to the rules of linear algebra.
  - abtmm
```
public static double[][] abtmm(double[][] A,
                               double[][] B)
```
    Matrix multiplication A * B' according to the rules of linear algebra.
  - abtmm
```
public static void abtmm(double[][] A,
                         double[][] B,
                         double[][] C)
```
    Matrix multiplication C = A * B' according to the rules of linear algebra.
  - atbtmm
```
public static double[][] atbtmm(double[][] A,
                                double[][] B)
```
    Matrix multiplication A' * B' according to the rules of linear algebra.
  - atbtmm
```
public static void atbtmm(double[][] A,
                          double[][] B,
                          double[][] C)
```
    Matrix multiplication C = A' * B' according to the rules of linear algebra.
  - pow
```
public static double[] pow(double[] x,
                           double n)
```
    Raise each element of an array to a scalar power.
    
    Parameters:
    
    x - array
    
    n - scalar exponent
    
    Returns:
    
    xⁿ
  - pow
```
public static double[][] pow(double[][] x,
                             double n)
```
    Raise each element of a matrix to a scalar power.
    
    Parameters:
    
    x - matrix
    
    n - exponent
    
    Returns:
    
    xⁿ
  - unique
```
public static int[] unique(int[] x)
```
    Find unique elements of vector.
    
    Parameters:
    
    x - an integer array.
    
    Returns:
    
    the same values as in x but with no repetitions.
  - unique
```
public static java.lang.String[] unique(java.lang.String[] x)
```
    Find unique elements of vector.
    
    Parameters:
    
    x - an array of strings.
    
    Returns:
    
    the same values as in x but with no repetitions.
  - sort
```
public static int[][] sort(double[][] x)
```
    Sorts each variable and returns the index of values in ascending order. Note that the order of original array is NOT altered.
    
    Parameters:
    
    x - a set of variables to be sorted. Each row is an instance. Each column is a variable.
    
    Returns:
    
    the index of values in ascending order
  - eye
```
public static double[][] eye(int n)
```
    Returns a square identity matrix of size n.
    
    Returns:
    
    An n-by-n matrix with ones on the diagonal and zeros elsewhere.
  - eye
```
public static double[][] eye(int m,
                             int n)
```
    Returns an identity matrix of size m by n.
    
    Parameters:
    
    m - the number of rows.
    
    n - the number of columns.
    
    Returns:
    
    an m-by-n matrix with ones on the diagonal and zeros elsewhere.
  - trace
```
public static double trace(double[][] A)
```
    Returns the matrix trace. The sum of the diagonal elements.
  - transpose
```
public static double[][] transpose(double[][] A)
```
    Returns the matrix transpose.
  - inverse
```
public static DenseMatrix inverse(double[][] A)
```
    Returns the matrix inverse or pseudo inverse.
    
    Returns:
    
    matrix inverse if A is square, pseudo inverse otherwise.
  - det
```
public static double det(double[][] A)
```
    Returns the matrix determinant
  - rank
```
public static int rank(double[][] A)
```
    Returns the matrix rank. Note that the input matrix will be altered.
    
    Returns:
    
    Effective numerical rank.
  - eigen
```
public static double eigen(double[][] A,
                           double[] v)
```
    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 its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
    
    Parameters:
    
    v - on input, it is the non-zero initial guess of the eigen vector. On output, it is the eigen vector corresponding largest eigen value.
    
    Returns:
    
    the largest eigen value.
  - eigen
```
public static double eigen(double[][] A,
                           double[] v,
                           double tol)
```
    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 its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
    
    Parameters:
    
    v - on input, it is the non-zero initial guess of the eigen vector. On output, it is the eigen vector corresponding largest eigen value.
    
    tol - the desired convergence tolerance.
    
    Returns:
    
    the largest eigen value.
  - eigen
```
public static double eigen(Matrix A,
                           double[] v)
```
    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 its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
    
    Parameters:
    
    v - on input, it is the non-zero initial guess of the eigen vector. On output, it is the eigen vector corresponding largest eigen value.
    
    Returns:
    
    the largest eigen value.
  - eigen
```
public static double eigen(Matrix A,
                           double[] v,
                           double tol)
```
    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 its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue.
    
    Parameters:
    
    v - on input, it is the non-zero initial guess of the eigen vector. On output, it is the eigen vector corresponding largest eigen value.
    
    tol - the desired convergence tolerance.
    
    Returns:
    
    the largest eigen value.
  - eigen
```
public static EigenValueDecomposition eigen(double[][] A,
                                            int k)
```
    Find k largest approximate eigen pairs of a symmetric matrix by an iterative Lanczos algorithm.
  - eigen
```
public static EigenValueDecomposition eigen(Matrix A,
                                            int k)
```
    Find k largest approximate eigen pairs of a symmetric matrix by an iterative Lanczos algorithm.
  - eigen
```
public static EigenValueDecomposition eigen(double[][] A)
```
    Returns the eigen value decomposition of a square matrix. Note that the input matrix will be altered during decomposition.
    
    Parameters:
    
    A - square matrix which will be altered after decomposition.
  - eigen
```
public static EigenValueDecomposition eigen(double[][] A,
                                            boolean symmetric)
```
    Returns the eigen value decomposition of a square matrix. Note that the input matrix will be altered during decomposition.
    
    Parameters:
    
    A - square matrix which will be altered after decomposition.
    
    symmetric - if the matrix A is symmetric.
  - eigen
```
public static EigenValueDecomposition eigen(double[][] A,
                                            boolean symmetric,
                                            boolean onlyValues)
```
    Returns the eigen value decomposition of a square matrix. Note that the input matrix will be altered during decomposition.
    
    Parameters:
    
    A - square matrix which will be altered after decomposition.
    
    symmetric - if the matrix A is symmetric.
    
    onlyValues - true if only compute eigenvalues; the default is to compute eigenvectors also.
  - svd
```
public static SingularValueDecomposition svd(double[][] A)
```
    Returns the singular value decomposition. Note that the input matrix will be altered after decomposition.
  - solve
```
public static double[] solve(double[][] A,
                             double[] b)
```
    Solve A*x = b (exact solution if A is square, least squares solution otherwise), which means the LU or QR decomposition will take place in A and the results will be stored in b.
    
    Returns:
    
    the solution, which is actually the vector b in case of exact solution.
  - solve
```
public static DenseMatrix solve(double[][] A,
                                double[][] B)
```
    Solve A*X = B (exact solution if A is square, least squares solution otherwise), which means the LU or QR decomposition will take place in A and the results will be stored in B.
    
    Returns:
    
    the solution.
  - solve
```
public static double[] solve(double[] a,
                             double[] b,
                             double[] c,
                             double[] r)
```
    Solve the tridiagonal linear set which is of diagonal dominance |b_i| > |a_i| + |c_i|.
```
 | b0 c0  0  0  0 ...                        |
 | a1 b1 c1  0  0 ...                        |
 |  0 a2 b2 c2  0 ...                        |
 |                ...                        |
 |                ... a(n-2)  b(n-2)  c(n-2) |
 |                ... 0       a(n-1)  b(n-1) |
 
```
    Parameters:
    
    a - the lower part of tridiagonal matrix. a[0] is undefined and not referenced by the method.
    
    b - the diagonal of tridiagonal matrix.
    
    c - the upper of tridiagonal matrix. c[n-1] is undefined and not referenced by the method.
    
    r - the right-hand side of linear equations.
    
    Returns:
    
    the solution.
  - root
```
public static double root(Function func,
                          double x1,
                          double x2,
                          double tol)
```
    Returns the root of a function known to lie between x1 and x2 by Brent's method. The root will be refined until its accuracy is tol. The method is guaranteed to converge as long as the function can be evaluated within the initial interval known to contain a root.
    
    Parameters:
    
    func - the function to be evaluated.
    
    x1 - the left end of search interval.
    
    x2 - the right end of search interval.
    
    tol - the accuracy tolerance.
    
    Returns:
    
    the root.
  - root
```
public static double root(Function func,
                          double x1,
                          double x2,
                          double tol,
                          int maxIter)
```
    Returns the root of a function known to lie between x1 and x2 by Brent's method. The root will be refined until its accuracy is tol. The method is guaranteed to converge as long as the function can be evaluated within the initial interval known to contain a root.
    
    Parameters:
    
    func - the function to be evaluated.
    
    x1 - the left end of search interval.
    
    x2 - the right end of search interval.
    
    tol - the accuracy tolerance.
    
    maxIter - the maximum number of allowed iterations.
    
    Returns:
    
    the root.
  - root
```
public static double root(DifferentiableFunction func,
                          double x1,
                          double x2,
                          double tol)
```
    Returns the root of a function whose derivative is available known to lie between x1 and x2 by Newton-Raphson method. The root will be refined until its accuracy is within xacc.
    
    Parameters:
    
    func - the function to be evaluated.
    
    x1 - the left end of search interval.
    
    x2 - the right end of search interval.
    
    tol - the accuracy tolerance.
    
    Returns:
    
    the root.
  - root
```
public static double root(DifferentiableFunction func,
                          double x1,
                          double x2,
                          double tol,
                          int maxIter)
```
    Returns the root of a function whose derivative is available known to lie between x1 and x2 by Newton-Raphson method. The root will be refined until its accuracy is within xacc.
    
    Parameters:
    
    func - the function to be evaluated.
    
    x1 - the left end of search interval.
    
    x2 - the right end of search interval.
    
    tol - the accuracy tolerance.
    
    maxIter - the maximum number of allowed iterations.
    
    Returns:
    
    the root.
  - min
```
public static double min(DifferentiableMultivariateFunction func,
                         int m,
                         double[] x,
                         double gtol)
```
    This method solves the unconstrained minimization problem
```
     min f(x),    x = (x1,x2,...,x_n),
 
```
    using the limited-memory BFGS method. The method is especially effective on problems involving a large number of variables. In a typical iteration of this method an approximation Hk to the inverse of the Hessian is obtained by applying m BFGS updates to a diagonal matrix Hk0, using information from the previous M steps. The user specifies the number m, which determines the amount of storage required by the routine. The algorithm is described in "On the limited memory BFGS method for large scale optimization", by D. Liu and J. Nocedal, Mathematical Programming B 45 (1989) 503-528.
    Parameters:
    
    func - the function to be minimized.
    
    m - the number of corrections used in the L-BFGS update. Values of m less than 3 are not recommended; large values of m will result in excessive computing time. 3 <= m <= 7 is recommended.
    
    x - on initial entry this must be set by the user to the values of the initial estimate of the solution vector. On exit with iflag = 0, it contains the values of the variables at the best point found (usually a solution).
    
    gtol - the convergence requirement on zeroing the gradient.
    
    Returns:
    
    the minimum value of the function.
  - min
```
public static double min(DifferentiableMultivariateFunction func,
                         int m,
                         double[] x,
                         double gtol,
                         int maxIter)
```
    This method solves the unconstrained minimization problem
```
     min f(x),    x = (x1,x2,...,x_n),
 
```
    using the limited-memory BFGS method. The method is especially effective on problems involving a large number of variables. In a typical iteration of this method an approximation Hk to the inverse of the Hessian is obtained by applying m BFGS updates to a diagonal matrix Hk0, using information from the previous M steps. The user specifies the number m, which determines the amount of storage required by the routine. The algorithm is described in "On the limited memory BFGS method for large scale optimization", by D. Liu and J. Nocedal, Mathematical Programming B 45 (1989) 503-528.
    Parameters:
    
    func - the function to be minimized.
    
    m - the number of corrections used in the L-BFGS update. Values of m less than 3 are not recommended; large values of m will result in excessive computing time. 3 <= m <= 7 is recommended. A common choice for m is m = 5.
    
    x - on initial entry this must be set by the user to the values of the initial estimate of the solution vector. On exit with iflag = 0, it contains the values of the variables at the best point found (usually a solution).
    
    gtol - the convergence requirement on zeroing the gradient.
    
    maxIter - the maximum allowed number of iterations.
    
    Returns:
    
    the minimum value of the function.
  - min
```
public static double min(DifferentiableMultivariateFunction func,
                         double[] x,
                         double gtol)
```
    This method solves the unconstrained minimization problem
```
     min f(x),    x = (x1,x2,...,x_n),
 
```
    using the BFGS method.
    Parameters:
    
    func - the function to be minimized.
    
    x - on initial entry this must be set by the user to the values of the initial estimate of the solution vector. On exit, it contains the values of the variables at the best point found (usually a solution).
    
    gtol - the convergence requirement on zeroing the gradient.
    
    Returns:
    
    the minimum value of the function.
  - min
```
public static double min(DifferentiableMultivariateFunction func,
                         double[] x,
                         double gtol,
                         int maxIter)
```
    This method solves the unconstrained minimization problem
```
     min f(x),    x = (x1,x2,...,x_n),
 
```
    using the BFGS method.
    Parameters:
    
    func - the function to be minimized.
    
    x - on initial entry this must be set by the user to the values of the initial estimate of the solution vector. On exit, it contains the values of the variables at the best point found (usually a solution).
    
    gtol - the convergence requirement on zeroing the gradient.
    
    maxIter - the maximum allowed number of iterations.
    
    Returns:
    
    the minimum value of the function.

Class Math

Field Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

E

PI

EPSILON

RADIX

DIGITS

ROUND_STYLE

MACHEP

NEGEP

Method Detail

abs

abs

abs

abs

acos

asin

atan

atan2

cbrt

ceil

copySign

copySign

cos

cosh

exp

expm1

floor

getExponent

getExponent

hypot

IEEEremainder

log

log10

log1p

max

max

max

max

min

min

min

min

nextAfter

nextAfter

nextUp

nextUp

pow

rint

round

round

scalb

scalb

signum

signum

sin

sinh

sqrt

tan

tanh

toDegrees

toRadians

ulp

ulp

log2

equals

logistic

sqr

isPower2

round

factorial

logFactorial

choose

logChoose

setSeed

random

random