Required Methods

fn nan() -> Self

Returns the NaN value.

use num_traits::Float;

let nan: f32 = Float::nan();

assert!(nan.is_nan());

fn infinity() -> Self

Returns the infinite value.

use num_traits::Float;
use std::f32;

let infinity: f32 = Float::infinity();

assert!(infinity.is_infinite());
assert!(!infinity.is_finite());
assert!(infinity > f32::MAX);

fn neg_infinity() -> Self

Returns the negative infinite value.

use num_traits::Float;
use std::f32;

let neg_infinity: f32 = Float::neg_infinity();

assert!(neg_infinity.is_infinite());
assert!(!neg_infinity.is_finite());
assert!(neg_infinity < f32::MIN);

fn neg_zero() -> Self

Returns -0.0.

use num_traits::{Zero, Float};

let inf: f32 = Float::infinity();
let zero: f32 = Zero::zero();
let neg_zero: f32 = Float::neg_zero();

assert_eq!(zero, neg_zero);
assert_eq!(7.0f32/inf, zero);
assert_eq!(zero * 10.0, zero);

fn min_value() -> Self

Returns the smallest finite value that this type can represent.

use num_traits::Float;
use std::f64;

let x: f64 = Float::min_value();

assert_eq!(x, f64::MIN);

fn min_positive_value() -> Self

Returns the smallest positive, normalized value that this type can represent.

use num_traits::Float;
use std::f64;

let x: f64 = Float::min_positive_value();

assert_eq!(x, f64::MIN_POSITIVE);

fn max_value() -> Self

Returns the largest finite value that this type can represent.

use num_traits::Float;
use std::f64;

let x: f64 = Float::max_value();
assert_eq!(x, f64::MAX);

fn is_nan(self: Self) -> bool

Returns true if this value is NaN and false otherwise.

use num_traits::Float;
use std::f64;

let nan = f64::NAN;
let f = 7.0;

assert!(nan.is_nan());
assert!(!f.is_nan());

fn is_infinite(self: Self) -> bool

Returns true if this value is positive infinity or negative infinity and false otherwise.

use num_traits::Float;
use std::f32;

let f = 7.0f32;
let inf: f32 = Float::infinity();
let neg_inf: f32 = Float::neg_infinity();
let nan: f32 = f32::NAN;

assert!(!f.is_infinite());
assert!(!nan.is_infinite());

assert!(inf.is_infinite());
assert!(neg_inf.is_infinite());

fn is_finite(self: Self) -> bool

Returns true if this number is neither infinite nor NaN.

use num_traits::Float;
use std::f32;

let f = 7.0f32;
let inf: f32 = Float::infinity();
let neg_inf: f32 = Float::neg_infinity();
let nan: f32 = f32::NAN;

assert!(f.is_finite());

assert!(!nan.is_finite());
assert!(!inf.is_finite());
assert!(!neg_inf.is_finite());

fn is_normal(self: Self) -> bool

Returns true if the number is neither zero, infinite, subnormal, or NaN.

use num_traits::Float;
use std::f32;

let min = f32::MIN_POSITIVE; // 1.17549435e-38f32
let max = f32::MAX;
let lower_than_min = 1.0e-40_f32;
let zero = 0.0f32;

assert!(min.is_normal());
assert!(max.is_normal());

assert!(!zero.is_normal());
assert!(!f32::NAN.is_normal());
assert!(!f32::INFINITY.is_normal());
// Values between `0` and `min` are Subnormal.
assert!(!lower_than_min.is_normal());

fn classify(self: Self) -> FpCategory

Returns the floating point category of the number. If only one property is going to be tested, it is generally faster to use the specific predicate instead.

use num_traits::Float;
use std::num::FpCategory;
use std::f32;

let num = 12.4f32;
let inf = f32::INFINITY;

assert_eq!(num.classify(), FpCategory::Normal);
assert_eq!(inf.classify(), FpCategory::Infinite);

fn floor(self: Self) -> Self

Returns the largest integer less than or equal to a number.

use num_traits::Float;

let f = 3.99;
let g = 3.0;

assert_eq!(f.floor(), 3.0);
assert_eq!(g.floor(), 3.0);

fn ceil(self: Self) -> Self

Returns the smallest integer greater than or equal to a number.

use num_traits::Float;

let f = 3.01;
let g = 4.0;

assert_eq!(f.ceil(), 4.0);
assert_eq!(g.ceil(), 4.0);

fn round(self: Self) -> Self

Returns the nearest integer to a number. Round half-way cases away from 0.0.

use num_traits::Float;

let f = 3.3;
let g = -3.3;

assert_eq!(f.round(), 3.0);
assert_eq!(g.round(), -3.0);

fn trunc(self: Self) -> Self

Return the integer part of a number.

use num_traits::Float;

let f = 3.3;
let g = -3.7;

assert_eq!(f.trunc(), 3.0);
assert_eq!(g.trunc(), -3.0);

fn fract(self: Self) -> Self

Returns the fractional part of a number.

use num_traits::Float;

let x = 3.5;
let y = -3.5;
let abs_difference_x = (x.fract() - 0.5).abs();
let abs_difference_y = (y.fract() - (-0.5)).abs();

assert!(abs_difference_x < 1e-10);
assert!(abs_difference_y < 1e-10);

fn abs(self: Self) -> Self

Computes the absolute value of self. Returns Float::nan() if the number is Float::nan().

use num_traits::Float;
use std::f64;

let x = 3.5;
let y = -3.5;

let abs_difference_x = (x.abs() - x).abs();
let abs_difference_y = (y.abs() - (-y)).abs();

assert!(abs_difference_x < 1e-10);
assert!(abs_difference_y < 1e-10);

assert!(f64::NAN.abs().is_nan());

fn signum(self: Self) -> Self

Returns a number that represents the sign of self.

• 1.0 if the number is positive, +0.0 or Float::infinity()
• -1.0 if the number is negative, -0.0 or Float::neg_infinity()
• Float::nan() if the number is Float::nan()

use num_traits::Float;
use std::f64;

let f = 3.5;

assert_eq!(f.signum(), 1.0);
assert_eq!(f64::NEG_INFINITY.signum(), -1.0);

assert!(f64::NAN.signum().is_nan());

fn is_sign_positive(self: Self) -> bool

Returns true if self is positive, including +0.0, Float::infinity(), and Float::nan().

use num_traits::Float;
use std::f64;

let nan: f64 = f64::NAN;
let neg_nan: f64 = -f64::NAN;

let f = 7.0;
let g = -7.0;

assert!(f.is_sign_positive());
assert!(!g.is_sign_positive());
assert!(nan.is_sign_positive());
assert!(!neg_nan.is_sign_positive());

fn is_sign_negative(self: Self) -> bool

Returns true if self is negative, including -0.0, Float::neg_infinity(), and -Float::nan().

use num_traits::Float;
use std::f64;

let nan: f64 = f64::NAN;
let neg_nan: f64 = -f64::NAN;

let f = 7.0;
let g = -7.0;

assert!(!f.is_sign_negative());
assert!(g.is_sign_negative());
assert!(!nan.is_sign_negative());
assert!(neg_nan.is_sign_negative());

fn mul_add(self: Self, a: Self, b: Self) -> Self

Fused multiply-add. Computes (self * a) + b with only one rounding error, yielding a more accurate result than an unfused multiply-add.

Using mul_add can be more performant than an unfused multiply-add if the target architecture has a dedicated fma CPU instruction.

use num_traits::Float;

let m = 10.0;
let x = 4.0;
let b = 60.0;

// 100.0
let abs_difference = (m.mul_add(x, b) - (m*x + b)).abs();

assert!(abs_difference < 1e-10);

fn recip(self: Self) -> Self

Take the reciprocal (inverse) of a number, 1/x.

use num_traits::Float;

let x = 2.0;
let abs_difference = (x.recip() - (1.0/x)).abs();

assert!(abs_difference < 1e-10);

fn powi(self: Self, n: i32) -> Self

Raise a number to an integer power.

Using this function is generally faster than using powf

use num_traits::Float;

let x = 2.0;
let abs_difference = (x.powi(2) - x*x).abs();

assert!(abs_difference < 1e-10);

fn powf(self: Self, n: Self) -> Self

Raise a number to a floating point power.

use num_traits::Float;

let x = 2.0;
let abs_difference = (x.powf(2.0) - x*x).abs();

assert!(abs_difference < 1e-10);

fn sqrt(self: Self) -> Self

Take the square root of a number.

Returns NaN if self is a negative number.

use num_traits::Float;

let positive = 4.0;
let negative = -4.0;

let abs_difference = (positive.sqrt() - 2.0).abs();

assert!(abs_difference < 1e-10);
assert!(negative.sqrt().is_nan());

fn exp(self: Self) -> Self

Returns e^(self), (the exponential function).

use num_traits::Float;

let one = 1.0;
// e^1
let e = one.exp();

// ln(e) - 1 == 0
let abs_difference = (e.ln() - 1.0).abs();

assert!(abs_difference < 1e-10);

fn exp2(self: Self) -> Self

Returns 2^(self).

use num_traits::Float;

let f = 2.0;

// 2^2 - 4 == 0
let abs_difference = (f.exp2() - 4.0).abs();

assert!(abs_difference < 1e-10);

fn ln(self: Self) -> Self

Returns the natural logarithm of the number.

use num_traits::Float;

let one = 1.0;
// e^1
let e = one.exp();

// ln(e) - 1 == 0
let abs_difference = (e.ln() - 1.0).abs();

assert!(abs_difference < 1e-10);

fn log(self: Self, base: Self) -> Self

Returns the logarithm of the number with respect to an arbitrary base.

use num_traits::Float;

let ten = 10.0;
let two = 2.0;

// log10(10) - 1 == 0
let abs_difference_10 = (ten.log(10.0) - 1.0).abs();

// log2(2) - 1 == 0
let abs_difference_2 = (two.log(2.0) - 1.0).abs();

assert!(abs_difference_10 < 1e-10);
assert!(abs_difference_2 < 1e-10);

fn log2(self: Self) -> Self

Returns the base 2 logarithm of the number.

use num_traits::Float;

let two = 2.0;

// log2(2) - 1 == 0
let abs_difference = (two.log2() - 1.0).abs();

assert!(abs_difference < 1e-10);

fn log10(self: Self) -> Self

Returns the base 10 logarithm of the number.

use num_traits::Float;

let ten = 10.0;

// log10(10) - 1 == 0
let abs_difference = (ten.log10() - 1.0).abs();

assert!(abs_difference < 1e-10);

fn max(self: Self, other: Self) -> Self

Returns the maximum of the two numbers.

use num_traits::Float;

let x = 1.0;
let y = 2.0;

assert_eq!(x.max(y), y);

fn min(self: Self, other: Self) -> Self

Returns the minimum of the two numbers.

use num_traits::Float;

let x = 1.0;
let y = 2.0;

assert_eq!(x.min(y), x);

fn abs_sub(self: Self, other: Self) -> Self

The positive difference of two numbers.

• If self <= other: 0:0
• Else: self - other

use num_traits::Float;

let x = 3.0;
let y = -3.0;

let abs_difference_x = (x.abs_sub(1.0) - 2.0).abs();
let abs_difference_y = (y.abs_sub(1.0) - 0.0).abs();

assert!(abs_difference_x < 1e-10);
assert!(abs_difference_y < 1e-10);

fn cbrt(self: Self) -> Self

Take the cubic root of a number.

use num_traits::Float;

let x = 8.0;

// x^(1/3) - 2 == 0
let abs_difference = (x.cbrt() - 2.0).abs();

assert!(abs_difference < 1e-10);

fn hypot(self: Self, other: Self) -> Self

Calculate the length of the hypotenuse of a right-angle triangle given legs of length x and y.

use num_traits::Float;

let x = 2.0;
let y = 3.0;

// sqrt(x^2 + y^2)
let abs_difference = (x.hypot(y) - (x.powi(2) + y.powi(2)).sqrt()).abs();

assert!(abs_difference < 1e-10);

fn sin(self: Self) -> Self

Computes the sine of a number (in radians).

use num_traits::Float;
use std::f64;

let x = f64::consts::PI/2.0;

let abs_difference = (x.sin() - 1.0).abs();

assert!(abs_difference < 1e-10);

fn cos(self: Self) -> Self

Computes the cosine of a number (in radians).

use num_traits::Float;
use std::f64;

let x = 2.0*f64::consts::PI;

let abs_difference = (x.cos() - 1.0).abs();

assert!(abs_difference < 1e-10);

fn tan(self: Self) -> Self

Computes the tangent of a number (in radians).

use num_traits::Float;
use std::f64;

let x = f64::consts::PI/4.0;
let abs_difference = (x.tan() - 1.0).abs();

assert!(abs_difference < 1e-14);

fn asin(self: Self) -> Self

Computes the arcsine of a number. Return value is in radians in the range [-pi/2, pi/2] or NaN if the number is outside the range [-1, 1].

use num_traits::Float;
use std::f64;

let f = f64::consts::PI / 2.0;

// asin(sin(pi/2))
let abs_difference = (f.sin().asin() - f64::consts::PI / 2.0).abs();

assert!(abs_difference < 1e-10);

fn acos(self: Self) -> Self

Computes the arccosine of a number. Return value is in radians in the range [0, pi] or NaN if the number is outside the range [-1, 1].

use num_traits::Float;
use std::f64;

let f = f64::consts::PI / 4.0;

// acos(cos(pi/4))
let abs_difference = (f.cos().acos() - f64::consts::PI / 4.0).abs();

assert!(abs_difference < 1e-10);

fn atan(self: Self) -> Self

Computes the arctangent of a number. Return value is in radians in the range [-pi/2, pi/2];

use num_traits::Float;

let f = 1.0;

// atan(tan(1))
let abs_difference = (f.tan().atan() - 1.0).abs();

assert!(abs_difference < 1e-10);

fn atan2(self: Self, other: Self) -> Self

Computes the four quadrant arctangent of self (y) and other (x).

• x = 0, y = 0: 0
• x >= 0: arctan(y/x) -> [-pi/2, pi/2]
• y >= 0: arctan(y/x) + pi -> (pi/2, pi]
• y < 0: arctan(y/x) - pi -> (-pi, -pi/2)

use num_traits::Float;
use std::f64;

let pi = f64::consts::PI;
// All angles from horizontal right (+x)
// 45 deg counter-clockwise
let x1 = 3.0;
let y1 = -3.0;

// 135 deg clockwise
let x2 = -3.0;
let y2 = 3.0;

let abs_difference_1 = (y1.atan2(x1) - (-pi/4.0)).abs();
let abs_difference_2 = (y2.atan2(x2) - 3.0*pi/4.0).abs();

assert!(abs_difference_1 < 1e-10);
assert!(abs_difference_2 < 1e-10);

fn sin_cos(self: Self) -> (Self, Self)

Simultaneously computes the sine and cosine of the number, x. Returns (sin(x), cos(x)).

use num_traits::Float;
use std::f64;

let x = f64::consts::PI/4.0;
let f = x.sin_cos();

let abs_difference_0 = (f.0 - x.sin()).abs();
let abs_difference_1 = (f.1 - x.cos()).abs();

assert!(abs_difference_0 < 1e-10);
assert!(abs_difference_0 < 1e-10);

fn exp_m1(self: Self) -> Self

Returns e^(self) - 1 in a way that is accurate even if the number is close to zero.

use num_traits::Float;

let x = 7.0;

// e^(ln(7)) - 1
let abs_difference = (x.ln().exp_m1() - 6.0).abs();

assert!(abs_difference < 1e-10);

fn ln_1p(self: Self) -> Self

Returns ln(1+n) (natural logarithm) more accurately than if the operations were performed separately.

use num_traits::Float;
use std::f64;

let x = f64::consts::E - 1.0;

// ln(1 + (e - 1)) == ln(e) == 1
let abs_difference = (x.ln_1p() - 1.0).abs();

assert!(abs_difference < 1e-10);

fn sinh(self: Self) -> Self

Hyperbolic sine function.

use num_traits::Float;
use std::f64;

let e = f64::consts::E;
let x = 1.0;

let f = x.sinh();
// Solving sinh() at 1 gives `(e^2-1)/(2e)`
let g = (e*e - 1.0)/(2.0*e);
let abs_difference = (f - g).abs();

assert!(abs_difference < 1e-10);

fn cosh(self: Self) -> Self

Hyperbolic cosine function.

use num_traits::Float;
use std::f64;

let e = f64::consts::E;
let x = 1.0;
let f = x.cosh();
// Solving cosh() at 1 gives this result
let g = (e*e + 1.0)/(2.0*e);
let abs_difference = (f - g).abs();

// Same result
assert!(abs_difference < 1.0e-10);

fn tanh(self: Self) -> Self

Hyperbolic tangent function.

use num_traits::Float;
use std::f64;

let e = f64::consts::E;
let x = 1.0;

let f = x.tanh();
// Solving tanh() at 1 gives `(1 - e^(-2))/(1 + e^(-2))`
let g = (1.0 - e.powi(-2))/(1.0 + e.powi(-2));
let abs_difference = (f - g).abs();

assert!(abs_difference < 1.0e-10);

fn asinh(self: Self) -> Self

Inverse hyperbolic sine function.

use num_traits::Float;

let x = 1.0;
let f = x.sinh().asinh();

let abs_difference = (f - x).abs();

assert!(abs_difference < 1.0e-10);

fn acosh(self: Self) -> Self

Inverse hyperbolic cosine function.

use num_traits::Float;

let x = 1.0;
let f = x.cosh().acosh();

let abs_difference = (f - x).abs();

assert!(abs_difference < 1.0e-10);

fn atanh(self: Self) -> Self

Inverse hyperbolic tangent function.

use num_traits::Float;
use std::f64;

let e = f64::consts::E;
let f = e.tanh().atanh();

let abs_difference = (f - e).abs();

assert!(abs_difference < 1.0e-10);

fn integer_decode(self: Self) -> (u64, i16, i8)

Returns the mantissa, base 2 exponent, and sign as integers, respectively. The original number can be recovered by sign * mantissa * 2 ^ exponent.

use num_traits::Float;

let num = 2.0f32;

// (8388608, -22, 1)
let (mantissa, exponent, sign) = Float::integer_decode(num);
let sign_f = sign as f32;
let mantissa_f = mantissa as f32;
let exponent_f = num.powf(exponent as f32);

// 1 * 8388608 * 2^(-22) == 2
let abs_difference = (sign_f * mantissa_f * exponent_f - num).abs();

assert!(abs_difference < 1e-10);