Sqrt java lang math

Class Math

The class Math contains methods for performing basic numeric operations such as the elementary exponential, logarithm, square root, and trigonometric functions.

Unlike some of the numeric methods of class StrictMath , all implementations of the equivalent functions of class Math are not defined to return the bit-for-bit same results. This relaxation permits better-performing implementations where strict reproducibility is not required.

By default many of the Math methods simply call the equivalent method in StrictMath for their implementation. Code generators are encouraged to use platform-specific native libraries or microprocessor instructions, where available, to provide higher-performance implementations of Math methods. Such higher-performance implementations still must conform to the specification for Math .

The quality of implementation specifications concern two properties, accuracy of the returned result and monotonicity of the method. Accuracy of the floating-point Math methods is measured in terms of ulps, units in the last place. For a given floating-point format, an ulp of a specific real number value is the distance between the two floating-point values bracketing that numerical value. When discussing the accuracy of a method as a whole rather than at a specific argument, the number of ulps cited is for the worst-case error at any argument. If a method always has an error less than 0.5 ulps, the method always returns the floating-point number nearest the exact result; such a method is correctly rounded. A correctly rounded method is generally the best a floating-point approximation can be; however, it is impractical for many floating-point methods to be correctly rounded. Instead, for the Math class, a larger error bound of 1 or 2 ulps is allowed for certain methods. Informally, with a 1 ulp error bound, when the exact result is a representable number, the exact result should be returned as the computed result; otherwise, either of the two floating-point values which bracket the exact result may be returned. For exact results large in magnitude, one of the endpoints of the bracket may be infinite. Besides accuracy at individual arguments, maintaining proper relations between the method at different arguments is also important. Therefore, most methods with more than 0.5 ulp errors are required to be semi-monotonic: whenever the mathematical function is non-decreasing, so is the floating-point approximation, likewise, whenever the mathematical function is non-increasing, so is the floating-point approximation. Not all approximations that have 1 ulp accuracy will automatically meet the monotonicity requirements.

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The platform uses signed two’s complement integer arithmetic with int and long primitive types. The developer should choose the primitive type to ensure that arithmetic operations consistently produce correct results, which in some cases means the operations will not overflow the range of values of the computation. The best practice is to choose the primitive type and algorithm to avoid overflow. In cases where the size is int or long and overflow errors need to be detected, the methods whose names end with Exact throw an ArithmeticException when the results overflow.

The 2019 revision of the IEEE 754 floating-point standard includes a section of recommended operations and the semantics of those operations if they are included in a programming environment. The recommended operations present in this class include sin , cos , tan , asin , acos , atan , exp , expm1 , log , log10 , log1p , sinh , cosh , tanh , hypot , and pow . (The sqrt operation is a required part of IEEE 754 from a different section of the standard.) The special case behavior of the recommended operations generally follows the guidance of the IEEE 754 standard. However, the pow method defines different behavior for some arguments, as noted in its specification. The IEEE 754 standard defines its operations to be correctly rounded, which is a more stringent quality of implementation condition than required for most of the methods in question that are also included in this class.

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Class Math

The class Math contains methods for performing basic numeric operations such as the elementary exponential, logarithm, square root, and trigonometric functions.

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Unlike some of the numeric methods of class StrictMath , all implementations of the equivalent functions of class Math are not defined to return the bit-for-bit same results. This relaxation permits better-performing implementations where strict reproducibility is not required.

By default many of the Math methods simply call the equivalent method in StrictMath for their implementation. Code generators are encouraged to use platform-specific native libraries or microprocessor instructions, where available, to provide higher-performance implementations of Math methods. Such higher-performance implementations still must conform to the specification for Math .

The quality of implementation specifications concern two properties, accuracy of the returned result and monotonicity of the method. Accuracy of the floating-point Math methods is measured in terms of ulps, units in the last place. For a given floating-point format, an ulp of a specific real number value is the distance between the two floating-point values bracketing that numerical value. When discussing the accuracy of a method as a whole rather than at a specific argument, the number of ulps cited is for the worst-case error at any argument. If a method always has an error less than 0.5 ulps, the method always returns the floating-point number nearest the exact result; such a method is correctly rounded. A correctly rounded method is generally the best a floating-point approximation can be; however, it is impractical for many floating-point methods to be correctly rounded. Instead, for the Math class, a larger error bound of 1 or 2 ulps is allowed for certain methods. Informally, with a 1 ulp error bound, when the exact result is a representable number, the exact result should be returned as the computed result; otherwise, either of the two floating-point values which bracket the exact result may be returned. For exact results large in magnitude, one of the endpoints of the bracket may be infinite. Besides accuracy at individual arguments, maintaining proper relations between the method at different arguments is also important. Therefore, most methods with more than 0.5 ulp errors are required to be semi-monotonic: whenever the mathematical function is non-decreasing, so is the floating-point approximation, likewise, whenever the mathematical function is non-increasing, so is the floating-point approximation. Not all approximations that have 1 ulp accuracy will automatically meet the monotonicity requirements.

The platform uses signed two’s complement integer arithmetic with int and long primitive types. The developer should choose the primitive type to ensure that arithmetic operations consistently produce correct results, which in some cases means the operations will not overflow the range of values of the computation. The best practice is to choose the primitive type and algorithm to avoid overflow. In cases where the size is int or long and overflow errors need to be detected, the methods addExact , subtractExact , multiplyExact , toIntExact , incrementExact , decrementExact and negateExact throw an ArithmeticException when the results overflow. For the arithmetic operations divide and absolute value, overflow occurs only with a specific minimum or maximum value and should be checked against the minimum or maximum as appropriate.

The 2019 revision of the IEEE 754 floating-point standard includes a section of recommended operations and the semantics of those operations if they are included in a programming environment. The recommended operations present in this class include sin , cos , tan , asin , acos , atan , exp , expm1 , log , log10 , log1p , sinh , cosh , tanh , hypot , and pow . (The sqrt operation is a required part of IEEE 754 from a different section of the standard.) The special case behavior of the recommended operations generally follows the guidance of the IEEE 754 standard. However, the pow method defines different behavior for some arguments, as noted in its specification. The IEEE 754 standard defines its operations to be correctly rounded, which is a more stringent quality of implementation condition than required for most of the methods in question that are also included in this class.

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Java Math класс и его методы

Java-университет

Класс Java Math и его методы - 1

В данной статье мы проведем краткий обзор класса Math в Java. Поговорим о методах данного класса и о том, как их использовать. Класс Math располагается в пакете java.lang и предоставляет набор статических методов для осуществления ряда различных математических вычислений. Ниже приведены примеры вычислений, для которых класс Math может оказаться полезным:

  • Вычисление абсолютных значений (значений по модулю)
  • Вычисление значений тригонометрических функций (синусов, косинусов и т.д.)
  • Возведение в различные степени
  • Извлечение корней различных степеней
  • Генерация случайных чисел
  • Округления
  • И пр.

Ниже мы попробуем рассмотреть как класс Java Math помогает решать задачи, перечисленные выше.Начнем разбор класса с методов, которые позволяют вычислить значение по модулю. За это отвечает метод abs. Данный метод перегружен и в классе Math имеются следующие его различия:

  • static double abs(double a)
  • static float abs(float a)
  • static int abs(int a)
  • static long abs(long a)

Пример использования:

 public static void main(String[] args) < System.out.println(Math.abs(-1)); // 1 System.out.println(Math.abs(-21.8d)); // 21.8 System.out.println(Math.abs(4532L)); // 4532 System.out.println(Math.abs(5.341f)); // 5.341 >

Вычисление значений тригонометрических функций

  • static double sin(double a)
  • static double cos(double a)
  • static double tan(double a)
  • static double asin(double a)
  • static double acos(double a)
  • static double atan(double a)
  • static double toDegrees(double angrad)
  • static double toRadians(double angdeg)
 public static void main(String[] args)
 0.0 0.49999999999999994 1.0 1.0 0.8660254037844387 6.123233995736766E-17 

Что не совсем соответствует таблицам синусов и косинусов, отчасти благодаря погрешностям при переводе из градусов в радианы.

Возведение в степень

Для возведения числа в степень класс Math предоставляет метод pow, который имеет следующую сигнатуру:

 static double pow(double a, double b) 
 public static void main(String[] args) < System.out.println(Math.pow(1,2)); // 1.0 System.out.println(Math.pow(2,2)); // 4.0 System.out.println(Math.pow(3,2)); // 9.0 System.out.println(Math.pow(4,2)); // 16.0 System.out.println(Math.pow(5,2)); // 25.0 System.out.println(Math.pow(1,3)); // 1.0 System.out.println(Math.pow(2,3)); // 8.0 System.out.println(Math.pow(3,3)); // 27.0 System.out.println(Math.pow(4,3)); // 64.0 System.out.println(Math.pow(5,3)); // 125.0 >

Извлечение корней

 public static void main(String[] args) < System.out.println(Math.sqrt(4)); // 2.0 System.out.println(Math.sqrt(9)); // 3.0 System.out.println(Math.sqrt(16)); // 4.0 System.out.println(Math.cbrt(8)); // 2.0 System.out.println(Math.cbrt(27)); // 3.0 System.out.println(Math.cbrt(125)); // 5.0 >

Генерация случайных чисел

Для генерации случайных чисел класс Math предоставляет метод random. Данный метод генерирует случайное позитивное вещественное (double) число в промежутке от 0.0 до 1.0. Сигнатура метода имеет следующий вид:

 public static double random() 
 public static void main(String[] args) < for (int i = 0; i < 5; i++) < System.out.println(Math.random()); >> 
 0.37057465028778513 0.2516253742011597 0.9315649439611121 0.6346725713527239 0.7442959932755443 

С помощью небольших манипуляций, можно использовать метод random класса Math для получения целочисленных случайных чисел лежащих в определенном диапазоне. Приведем пример функции которая принимает два аргумента min и max и возвращает случайное целое число, которое лежит в промежутке от min (включительно) до max (включительно):

 static int randomInARange(int min, int max)
 public class MathExample < public static void main(String[] args) < // Карта, в которой мы будем хранить количество выпадений какого-то числа Mapmap = new TreeMap<>(); // За 10000 операций for (int i = 0; i < 10000; i++) < // Сгенерируем рандомное число от -10 включительно до 10 включительно final Integer randomNumber = randomInARange(-10, 10); if (!map.containsKey(randomNumber)) < // Если карта еще не содержит "выпавшего случайного числа" // Положим его в карту с кол-вом выпадений = 1 map.put(randomNumber, 1); >else < // Иначе, увеличим количество выпадений данного числа на 1 map.put(randomNumber, map.get(randomNumber) + 1); >> // Выведем на экран содержимое карты в формате ключ=[значение] for (Map.Entry entry : map.entrySet()) < System.out.println(String.format("%d=[%d]", entry.getKey(), entry.getValue())); >> static int randomInARange(int min, int max) < return (int) (Math.random() * ((max - min) + 1)) + min; >> 
 -10=[482] -9=[495] -8=[472] -7=[514] -6=[457] -5=[465] -4=[486] -3=[500] -2=[490] -1=[466] 0=[458] 1=[488] 2=[461] 3=[470] 4=[464] 5=[463] 6=[484] 7=[479] 8=[459] 9=[503] 10=[444] Process finished with exit code 0 

Округление

  • static long round(double a)
  • static int round(float a)
  • static double floor(double a)
  • static double ceil(double a)
 public static void main(String[] args) < System.out.println(Math.round(1.3)); // 1 System.out.println(Math.round(1.4)); // 1 System.out.println(Math.round(1.5)); // 2 System.out.println(Math.round(1.6)); // 2 System.out.println(Math.floor(1.3)); // 1.0 System.out.println(Math.floor(1.4)); // 1.0 System.out.println(Math.floor(1.5)); // 1.0 System.out.println(Math.floor(1.6)); // 1.0 System.out.println(Math.ceil(1.3)); // 2.0 System.out.println(Math.ceil(1.4)); // 2.0 System.out.println(Math.ceil(1.5)); // 2.0 System.out.println(Math.ceil(1.6)); // 2.0 >

Заключение

  • Вычислять значения по модулю;
  • Вычислять значения тригонометрических функций;
  • Возводить числа в степень;
  • Извлекать квадратный и кубический корни;
  • Генерировать случайные числа;
  • Округлять числа.

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