f32 - Rust (original) (raw)

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A 32-bit floating-point type (specifically, the “binary32” type defined in IEEE 754-2008).

This type can represent a wide range of decimal numbers, like 3.5, 27,-113.75, 0.0078125, 34359738368, 0, -1. So unlike integer types (such as i32), floating-point types can represent non-integer numbers, too.

However, being able to represent this wide range of numbers comes at the cost of precision: floats can only represent some of the real numbers and calculation with floats round to a nearby representable number. For example,5.0 and 1.0 can be exactly represented as f32, but 1.0 / 5.0 results in 0.20000000298023223876953125 since 0.2 cannot be exactly represented as f32. Note, however, that printing floats with println and friends will often discard insignificant digits: println!("{}", 1.0f32 / 5.0f32) will print 0.2.

Additionally, f32 can represent some special values:

• −0.0: IEEE 754 floating-point numbers have a bit that indicates their sign, so −0.0 is a possible value. For comparison −0.0 = +0.0, but floating-point operations can carry the sign bit through arithmetic operations. This means −0.0 × +0.0 produces −0.0 and a negative number rounded to a value smaller than a float can represent also produces −0.0.
• ∞ and−∞: these result from calculations like 1.0 / 0.0.
• NaN (not a number): this value results from calculations like (-1.0).sqrt(). NaN has some potentially unexpected behavior:
  • It is not equal to any float, including itself! This is the reason f32doesn’t implement the Eq trait.
  • It is also neither smaller nor greater than any float, making it impossible to sort by the default comparison operation, which is the reason f32 doesn’t implement the Ord trait.
  • It is also considered infectious as almost all calculations where one of the operands is NaN will also result in NaN. The explanations on this page only explicitly document behavior on NaN operands if this default is deviated from.
  • Lastly, there are multiple bit patterns that are considered NaN. Rust does not currently guarantee that the bit patterns of NaN are preserved over arithmetic operations, and they are not guaranteed to be portable or even fully deterministic! This means that there may be some surprising results upon inspecting the bit patterns, as the same calculations might produce NaNs with different bit patterns. This also affects the sign of the NaN: checking is_sign_positive or is_sign_negative on a NaN is the most common way to run into these surprising results. (Checking x >= 0.0 or x <= 0.0 avoids those surprises, but also how negative/positive zero are treated.) See the section below for what exactly is guaranteed about the bit pattern of a NaN.

When a primitive operation (addition, subtraction, multiplication, or division) is performed on this type, the result is rounded according to the roundTiesToEven direction defined in IEEE 754-2008. That means:

• The result is the representable value closest to the true value, if there is a unique closest representable value.
• If the true value is exactly half-way between two representable values, the result is the one with an even least-significant binary digit.
• If the true value’s magnitude is ≥ f32::MAX + 2(f32::MAX_EXP −f32::MANTISSA_DIGITS − 1), the result is ∞ or −∞ (preserving the true value’s sign).
• If the result of a sum exactly equals zero, the outcome is +0.0 unless both arguments were negative, then it is -0.0. Subtraction a - b is regarded as a sum a + (-b).

For more information on floating-point numbers, see Wikipedia.

See also the std::f32::consts module.

§NaN bit patterns

This section defines the possible NaN bit patterns returned by floating-point operations.

The bit pattern of a floating-point NaN value is defined by:

• a sign bit.
• a quiet/signaling bit. Rust assumes that the quiet/signaling bit being set to 1 indicates a quiet NaN (QNaN), and a value of 0 indicates a signaling NaN (SNaN). In the following we will hence just call it the “quiet bit”.
• a payload, which makes up the rest of the significand (i.e., the mantissa) except for the quiet bit.

The rules for NaN values differ between arithmetic and non-arithmetic (or “bitwise”) operations. The non-arithmetic operations are unary -, abs, copysign, signum,{to,from}_bits, {to,from}_{be,le,ne}_bytes and is_sign_{positive,negative}. These operations are guaranteed to exactly preserve the bit pattern of their input except for possibly changing the sign bit.

The following rules apply when a NaN value is returned from an arithmetic operation:

• The result has a non-deterministic sign.
• The quiet bit and payload are non-deterministically chosen from the following set of options:
  • Preferred NaN: The quiet bit is set and the payload is all-zero.
  • Quieting NaN propagation: The quiet bit is set and the payload is copied from any input operand that is a NaN. If the inputs and outputs do not have the same payload size (i.e., foras casts), then
    * If the output is smaller than the input, low-order bits of the payload get dropped.
    * If the output is larger than the input, the payload gets filled up with 0s in the low-order bits.
  • Unchanged NaN propagation: The quiet bit and payload are copied from any input operand that is a NaN. If the inputs and outputs do not have the same size (i.e., for as casts), the same rules as for “quieting NaN propagation” apply, with one caveat: if the output is smaller than the input, dropping the low-order bits may result in a payload of 0; a payload of 0 is not possible with a signaling NaN (the all-0 significand encodes an infinity) so unchanged NaN propagation cannot occur with some inputs.
  • Target-specific NaN: The quiet bit is set and the payload is picked from a target-specific set of “extra” possible NaN payloads. The set can depend on the input operand values. See the table below for the concrete NaNs this set contains on various targets.

In particular, if all input NaNs are quiet (or if there are no input NaNs), then the output NaN is definitely quiet. Signaling NaN outputs can only occur if they are provided as an input value. Similarly, if all input NaNs are preferred (or if there are no input NaNs) and the target does not have any “extra” NaN payloads, then the output NaN is guaranteed to be preferred.

The non-deterministic choice happens when the operation is executed; i.e., the result of a NaN-producing floating-point operation is a stable bit pattern (looking at these bits multiple times will yield consistent results), but running the same operation twice with the same inputs can produce different results.

These guarantees are neither stronger nor weaker than those of IEEE 754: IEEE 754 guarantees that an operation never returns a signaling NaN, whereas it is possible for operations likeSNAN * 1.0 to return a signaling NaN in Rust. Conversely, IEEE 754 makes no statement at all about which quiet NaN is returned, whereas Rust restricts the set of possible results to the ones listed above.

Unless noted otherwise, the same rules also apply to NaNs returned by other library functions (e.g. min, minimum, max, maximum); other aspects of their semantics and which IEEE 754 operation they correspond to are documented with the respective functions.

When an arithmetic floating-point operation is executed in const context, the same rules apply: no guarantee is made about which of the NaN bit patterns described above will be returned. The result does not have to match what happens when executing the same code at runtime, and the result can vary depending on factors such as compiler version and flags.

For targets not in this table, all payloads are possible.

Source§

1.0.0 · Source

Returns the largest integer less than or equal to self.

This function always returns the precise result.

§Examples

let f = 3.7_f32;
let g = 3.0_f32;
let h = -3.7_f32;

assert_eq!(f.floor(), 3.0);
assert_eq!(g.floor(), 3.0);
assert_eq!(h.floor(), -4.0);

1.0.0 · Source

Returns the smallest integer greater than or equal to self.

This function always returns the precise result.

§Examples

let f = 3.01_f32;
let g = 4.0_f32;

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

1.0.0 · Source

Returns the nearest integer to self. If a value is half-way between two integers, round away from 0.0.

This function always returns the precise result.

§Examples

let f = 3.3_f32;
let g = -3.3_f32;
let h = -3.7_f32;
let i = 3.5_f32;
let j = 4.5_f32;

assert_eq!(f.round(), 3.0);
assert_eq!(g.round(), -3.0);
assert_eq!(h.round(), -4.0);
assert_eq!(i.round(), 4.0);
assert_eq!(j.round(), 5.0);

1.77.0 · Source

Returns the nearest integer to a number. Rounds half-way cases to the number with an even least significant digit.

This function always returns the precise result.

§Examples

let f = 3.3_f32;
let g = -3.3_f32;
let h = 3.5_f32;
let i = 4.5_f32;

assert_eq!(f.round_ties_even(), 3.0);
assert_eq!(g.round_ties_even(), -3.0);
assert_eq!(h.round_ties_even(), 4.0);
assert_eq!(i.round_ties_even(), 4.0);

1.0.0 · Source

Returns the integer part of self. This means that non-integer numbers are always truncated towards zero.

This function always returns the precise result.

§Examples

let f = 3.7_f32;
let g = 3.0_f32;
let h = -3.7_f32;

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

1.0.0 · Source

Returns the fractional part of self.

This function always returns the precise result.

§Examples

let x = 3.6_f32;
let y = -3.6_f32;
let abs_difference_x = (x.fract() - 0.6).abs();
let abs_difference_y = (y.fract() - (-0.6)).abs();

assert!(abs_difference_x <= f32::EPSILON);
assert!(abs_difference_y <= f32::EPSILON);

1.0.0 · Source

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 may be more performant than an unfused multiply-add if the target architecture has a dedicated fma CPU instruction. However, this is not always true, and will be heavily dependant on designing algorithms with specific target hardware in mind.

§Precision

The result of this operation is guaranteed to be the rounded infinite-precision result. It is specified by IEEE 754 asfusedMultiplyAdd and guaranteed not to change.

§Examples

let m = 10.0_f32;
let x = 4.0_f32;
let b = 60.0_f32;

assert_eq!(m.mul_add(x, b), 100.0);
assert_eq!(m * x + b, 100.0);

let one_plus_eps = 1.0_f32 + f32::EPSILON;
let one_minus_eps = 1.0_f32 - f32::EPSILON;
let minus_one = -1.0_f32;

// The exact result (1 + eps) * (1 - eps) = 1 - eps * eps.
assert_eq!(one_plus_eps.mul_add(one_minus_eps, minus_one), -f32::EPSILON * f32::EPSILON);
// Different rounding with the non-fused multiply and add.
assert_eq!(one_plus_eps * one_minus_eps + minus_one, 0.0);

1.38.0 · Source

Calculates Euclidean division, the matching method for rem_euclid.

This computes the integer n such thatself = n * rhs + self.rem_euclid(rhs). In other words, the result is self / rhs rounded to the integer nsuch that self >= n * rhs.

§Precision

The result of this operation is guaranteed to be the rounded infinite-precision result.

§Examples

let a: f32 = 7.0;
let b = 4.0;
assert_eq!(a.div_euclid(b), 1.0); // 7.0 > 4.0 * 1.0
assert_eq!((-a).div_euclid(b), -2.0); // -7.0 >= 4.0 * -2.0
assert_eq!(a.div_euclid(-b), -1.0); // 7.0 >= -4.0 * -1.0
assert_eq!((-a).div_euclid(-b), 2.0); // -7.0 >= -4.0 * 2.0

1.38.0 · Source

Calculates the least nonnegative remainder of self (mod rhs).

In particular, the return value r satisfies 0.0 <= r < rhs.abs() in most cases. However, due to a floating point round-off error it can result in r == rhs.abs(), violating the mathematical definition, ifself is much smaller than rhs.abs() in magnitude and self < 0.0. This result is not an element of the function’s codomain, but it is the closest floating point number in the real numbers and thus fulfills the property self == self.div_euclid(rhs) * rhs + self.rem_euclid(rhs)approximately.

§Precision

The result of this operation is guaranteed to be the rounded infinite-precision result.

§Examples

let a: f32 = 7.0;
let b = 4.0;
assert_eq!(a.rem_euclid(b), 3.0);
assert_eq!((-a).rem_euclid(b), 1.0);
assert_eq!(a.rem_euclid(-b), 3.0);
assert_eq!((-a).rem_euclid(-b), 1.0);
// limitation due to round-off error
assert!((-f32::EPSILON).rem_euclid(3.0) != 0.0);

1.0.0 · Source

Raises a number to an integer power.

Using this function is generally faster than using powf. It might have a different sequence of rounding operations than powf, so the results are not guaranteed to agree.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let x = 2.0_f32;
let abs_difference = (x.powi(2) - (x * x)).abs();
assert!(abs_difference <= f32::EPSILON);

assert_eq!(f32::powi(f32::NAN, 0), 1.0);

1.0.0 · Source

Raises a number to a floating point power.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let x = 2.0_f32;
let abs_difference = (x.powf(2.0) - (x * x)).abs();
assert!(abs_difference <= f32::EPSILON);

assert_eq!(f32::powf(1.0, f32::NAN), 1.0);
assert_eq!(f32::powf(f32::NAN, 0.0), 1.0);

1.0.0 · Source

Returns the square root of a number.

Returns NaN if self is a negative number other than -0.0.

§Precision

The result of this operation is guaranteed to be the rounded infinite-precision result. It is specified by IEEE 754 as squareRootand guaranteed not to change.

§Examples

let positive = 4.0_f32;
let negative = -4.0_f32;
let negative_zero = -0.0_f32;

assert_eq!(positive.sqrt(), 2.0);
assert!(negative.sqrt().is_nan());
assert!(negative_zero.sqrt() == negative_zero);

1.0.0 · Source

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

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let one = 1.0f32;
// e^1
let e = one.exp();

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

Returns 2^(self).

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let f = 2.0f32;

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

Returns the natural logarithm of the number.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let one = 1.0f32;
// e^1
let e = one.exp();

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

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

The result might not be correctly rounded owing to implementation details;self.log2() can produce more accurate results for base 2, andself.log10() can produce more accurate results for base 10.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let five = 5.0f32;

// log5(5) - 1 == 0
let abs_difference = (five.log(5.0) - 1.0).abs();

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

Returns the base 2 logarithm of the number.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let two = 2.0f32;

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

Returns the base 10 logarithm of the number.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let ten = 10.0f32;

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

👎Deprecated since 1.10.0: you probably meant (self - other).abs(): this operation is (self - other).max(0.0) except that abs_sub also propagates NaNs (also known as fdimf in C). If you truly need the positive difference, consider using that expression or the C function fdimf, depending on how you wish to handle NaN (please consider filing an issue describing your use-case too).

The positive difference of two numbers.

• If self <= other: 0.0
• Else: self - other

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the fdimf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let x = 3.0f32;
let y = -3.0f32;

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 <= f32::EPSILON);
assert!(abs_difference_y <= f32::EPSILON);

1.0.0 · Source

Returns the cube root of a number.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the cbrtf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let x = 8.0f32;

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

Compute the distance between the origin and a point (x, y) on the Euclidean plane. Equivalently, compute the length of the hypotenuse of a right-angle triangle with other sides having length x.abs() andy.abs().

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the hypotf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let x = 2.0f32;
let y = 3.0f32;

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

Computes the sine of a number (in radians).

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let x = std::f32::consts::FRAC_PI_2;

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

Computes the cosine of a number (in radians).

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let x = 2.0 * std::f32::consts::PI;

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

Computes the tangent of a number (in radians).

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the tanf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let x = std::f32::consts::FRAC_PI_4;
let abs_difference = (x.tan() - 1.0).abs();

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

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].

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the asinf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let f = std::f32::consts::FRAC_PI_2;

// asin(sin(pi/2))
let abs_difference = (f.sin().asin() - std::f32::consts::FRAC_PI_2).abs();

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

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].

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the acosf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let f = std::f32::consts::FRAC_PI_4;

// acos(cos(pi/4))
let abs_difference = (f.cos().acos() - std::f32::consts::FRAC_PI_4).abs();

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

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

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the atanf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let f = 1.0f32;

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

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

• 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)

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the atan2f from libc on Unix and Windows. Note that this might change in the future.

§Examples

// Positive angles measured counter-clockwise
// from positive x axis
// -pi/4 radians (45 deg clockwise)
let x1 = 3.0f32;
let y1 = -3.0f32;

// 3pi/4 radians (135 deg counter-clockwise)
let x2 = -3.0f32;
let y2 = 3.0f32;

let abs_difference_1 = (y1.atan2(x1) - (-std::f32::consts::FRAC_PI_4)).abs();
let abs_difference_2 = (y2.atan2(x2) - (3.0 * std::f32::consts::FRAC_PI_4)).abs();

assert!(abs_difference_1 <= f32::EPSILON);
assert!(abs_difference_2 <= f32::EPSILON);

1.0.0 · Source

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

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the (f32::sin(x), f32::cos(x)). Note that this might change in the future.

§Examples

let x = std::f32::consts::FRAC_PI_4;
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 <= f32::EPSILON);
assert!(abs_difference_1 <= f32::EPSILON);

1.0.0 · Source

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

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the expm1f from libc on Unix and Windows. Note that this might change in the future.

§Examples

let x = 1e-8_f32;

// for very small x, e^x is approximately 1 + x + x^2 / 2
let approx = x + x * x / 2.0;
let abs_difference = (x.exp_m1() - approx).abs();

assert!(abs_difference < 1e-10);

1.0.0 · Source

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

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the log1pf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let x = 1e-8_f32;

// for very small x, ln(1 + x) is approximately x - x^2 / 2
let approx = x - x * x / 2.0;
let abs_difference = (x.ln_1p() - approx).abs();

assert!(abs_difference < 1e-10);

1.0.0 · Source

Hyperbolic sine function.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the sinhf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let e = std::f32::consts::E;
let x = 1.0f32;

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 <= f32::EPSILON);

1.0.0 · Source

Hyperbolic cosine function.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the coshf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let e = std::f32::consts::E;
let x = 1.0f32;
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 <= f32::EPSILON);

1.0.0 · Source

Hyperbolic tangent function.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the tanhf from libc on Unix and Windows. Note that this might change in the future.

§Examples

let e = std::f32::consts::E;
let x = 1.0f32;

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 <= f32::EPSILON);

1.0.0 · Source

Inverse hyperbolic sine function.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let x = 1.0f32;
let f = x.sinh().asinh();

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

Inverse hyperbolic cosine function.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let x = 1.0f32;
let f = x.cosh().acosh();

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

assert!(abs_difference <= f32::EPSILON);

1.0.0 · Source

Inverse hyperbolic tangent function.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

§Examples

let e = std::f32::consts::E;
let f = e.tanh().atanh();

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

assert!(abs_difference <= 1e-5);

Source

🔬This is a nightly-only experimental API. (float_gamma #99842)

Gamma function.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the tgammaf from libc on Unix and Windows. Note that this might change in the future.

§Examples

#![feature(float_gamma)]
let x = 5.0f32;

let abs_difference = (x.gamma() - 24.0).abs();

assert!(abs_difference <= f32::EPSILON);

Source

🔬This is a nightly-only experimental API. (float_gamma #99842)

Natural logarithm of the absolute value of the gamma function

The integer part of the tuple indicates the sign of the gamma function.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next. This function currently corresponds to the lgamma_r from libc on Unix and Windows. Note that this might change in the future.

§Examples

#![feature(float_gamma)]
let x = 2.0f32;

let abs_difference = (x.ln_gamma().0 - 0.0).abs();

assert!(abs_difference <= f32::EPSILON);

Source

🔬This is a nightly-only experimental API. (float_erf #136321)

Error function.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

This function currently corresponds to the erff from libc on Unix and Windows. Note that this might change in the future.

§Examples

#![feature(float_erf)]
/// The error function relates what percent of a normal distribution lies
/// within `x` standard deviations (scaled by `1/sqrt(2)`).
fn within_standard_deviations(x: f32) -> f32 {
    (x * std::f32::consts::FRAC_1_SQRT_2).erf() * 100.0
}

// 68% of a normal distribution is within one standard deviation
assert!((within_standard_deviations(1.0) - 68.269).abs() < 0.01);
// 95% of a normal distribution is within two standard deviations
assert!((within_standard_deviations(2.0) - 95.450).abs() < 0.01);
// 99.7% of a normal distribution is within three standard deviations
assert!((within_standard_deviations(3.0) - 99.730).abs() < 0.01);

Source

🔬This is a nightly-only experimental API. (float_erf #136321)

Complementary error function.

§Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

This function currently corresponds to the erfcf from libc on Unix and Windows. Note that this might change in the future.

§Examples

#![feature(float_erf)]
let x: f32 = 0.123;

let one = x.erf() + x.erfc();
let abs_difference = (one - 1.0).abs();

assert!(abs_difference <= f32::EPSILON);

Source§

1.43.0 · Source

The radix or base of the internal representation of f32.

1.43.0 · Source

Number of significant digits in base 2.

1.43.0 · Source

Approximate number of significant digits in base 10.

This is the maximum x such that any decimal number with _x_significant digits can be converted to f32 and back without loss.

Equal to floor(log10 2MANTISSA_DIGITS − 1).

1.43.0 · Source

1.43.0 · Source

Smallest finite f32 value.

Equal to −MAX.

1.43.0 · Source

Smallest positive normal f32 value.

Equal to 2MIN_EXP − 1.

1.43.0 · Source

1.43.0 · Source

One greater than the minimum possible normal power of 2 exponent.

If x = MIN_EXP, then normal numbers ≥ 0.5 × 2_x_.

1.43.0 · Source

Maximum possible power of 2 exponent.

If x = MAX_EXP, then normal numbers < 1 × 2_x_.

1.43.0 · Source

Minimum x for which 10_x_ is normal.

Equal to ceil(log10 MIN_POSITIVE).

1.43.0 · Source

Maximum x for which 10_x_ is normal.

Equal to floor(log10 MAX).

1.43.0 · Source

Not a Number (NaN).

Note that IEEE 754 doesn’t define just a single NaN value; a plethora of bit patterns are considered to be NaN. Furthermore, the standard makes a difference between a “signaling” and a “quiet” NaN, and allows inspecting its “payload” (the unspecified bits in the bit pattern). This constant isn’t guaranteed to equal to any specific NaN bitpattern, and the stability of its representation over Rust versions and target platforms isn’t guaranteed.

1.43.0 · Source

Infinity (∞).

1.43.0 · Source

Negative infinity (−∞).

1.0.0 (const: 1.83.0) · Source

Returns true if this value is NaN.

let nan = f32::NAN;
let f = 7.0_f32;

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

1.0.0 (const: 1.83.0) · Source

Returns true if this value is positive infinity or negative infinity, andfalse otherwise.

let f = 7.0f32;
let inf = f32::INFINITY;
let neg_inf = f32::NEG_INFINITY;
let nan = f32::NAN;

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

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

1.0.0 (const: 1.83.0) · Source

Returns true if this number is neither infinite nor NaN.

let f = 7.0f32;
let inf = f32::INFINITY;
let neg_inf = f32::NEG_INFINITY;
let nan = f32::NAN;

assert!(f.is_finite());

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

1.53.0 (const: 1.83.0) · Source

Returns true if the number is subnormal.

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

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

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

1.0.0 (const: 1.83.0) · Source

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

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

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());

1.0.0 (const: 1.83.0) · Source

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 std::num::FpCategory;

let num = 12.4_f32;
let inf = f32::INFINITY;

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

1.0.0 (const: 1.83.0) · Source

Returns true if self has a positive sign, including +0.0, NaNs with positive sign bit and positive infinity.

Note that IEEE 754 doesn’t assign any meaning to the sign bit in case of a NaN, and as Rust doesn’t guarantee that the bit pattern of NaNs are conserved over arithmetic operations, the result of is_sign_positive on a NaN might produce an unexpected or non-portable result. See the specification of NaN bit patterns for more info. Use self.signum() == 1.0if you need fully portable behavior (will return false for all NaNs).

let f = 7.0_f32;
let g = -7.0_f32;

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

1.0.0 (const: 1.83.0) · Source

Returns true if self has a negative sign, including -0.0, NaNs with negative sign bit and negative infinity.

Note that IEEE 754 doesn’t assign any meaning to the sign bit in case of a NaN, and as Rust doesn’t guarantee that the bit pattern of NaNs are conserved over arithmetic operations, the result of is_sign_negative on a NaN might produce an unexpected or non-portable result. See the specification of NaN bit patterns for more info. Use self.signum() == -1.0if you need fully portable behavior (will return false for all NaNs).

let f = 7.0f32;
let g = -7.0f32;

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

1.86.0 (const: 1.86.0) · Source

Returns the least number greater than self.

Let TINY be the smallest representable positive f32. Then,

• if self.is_nan(), this returns self;
• if self is NEG_INFINITY, this returns MIN;
• if self is -TINY, this returns -0.0;
• if self is -0.0 or +0.0, this returns TINY;
• if self is MAX or INFINITY, this returns INFINITY;
• otherwise the unique least value greater than self is returned.

The identity x.next_up() == -(-x).next_down() holds for all non-NaN x. When xis finite x == x.next_up().next_down() also holds.

// f32::EPSILON is the difference between 1.0 and the next number up.
assert_eq!(1.0f32.next_up(), 1.0 + f32::EPSILON);
// But not for most numbers.
assert!(0.1f32.next_up() < 0.1 + f32::EPSILON);
assert_eq!(16777216f32.next_up(), 16777218.0);

This operation corresponds to IEEE-754 nextUp.

1.86.0 (const: 1.86.0) · Source

Returns the greatest number less than self.

Let TINY be the smallest representable positive f32. Then,

• if self.is_nan(), this returns self;
• if self is INFINITY, this returns MAX;
• if self is TINY, this returns 0.0;
• if self is -0.0 or +0.0, this returns -TINY;
• if self is MIN or NEG_INFINITY, this returns NEG_INFINITY;
• otherwise the unique greatest value less than self is returned.

The identity x.next_down() == -(-x).next_up() holds for all non-NaN x. When xis finite x == x.next_down().next_up() also holds.

let x = 1.0f32;
// Clamp value into range [0, 1).
let clamped = x.clamp(0.0, 1.0f32.next_down());
assert!(clamped < 1.0);
assert_eq!(clamped.next_up(), 1.0);

This operation corresponds to IEEE-754 nextDown.

1.0.0 (const: 1.85.0) · Source

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

let x = 2.0_f32;
let abs_difference = (x.recip() - (1.0 / x)).abs();

assert!(abs_difference <= f32::EPSILON);

1.7.0 (const: 1.85.0) · Source

Converts radians to degrees.

let angle = std::f32::consts::PI;

let abs_difference = (angle.to_degrees() - 180.0).abs();
assert!(abs_difference <= f32::EPSILON);

1.7.0 (const: 1.85.0) · Source

Converts degrees to radians.

let angle = 180.0f32;

let abs_difference = (angle.to_radians() - std::f32::consts::PI).abs();

assert!(abs_difference <= f32::EPSILON);

1.0.0 (const: 1.85.0) · Source

Returns the maximum of the two numbers, ignoring NaN.

If one of the arguments is NaN, then the other argument is returned. This follows the IEEE 754-2008 semantics for maxNum, except for handling of signaling NaNs; this function handles all NaNs the same way and avoids maxNum’s problems with associativity. This also matches the behavior of libm’s fmax. In particular, if the inputs compare equal (such as for the case of +0.0 and -0.0), either input may be returned non-deterministically.

let x = 1.0f32;
let y = 2.0f32;

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

1.0.0 (const: 1.85.0) · Source

Returns the minimum of the two numbers, ignoring NaN.

If one of the arguments is NaN, then the other argument is returned. This follows the IEEE 754-2008 semantics for minNum, except for handling of signaling NaNs; this function handles all NaNs the same way and avoids minNum’s problems with associativity. This also matches the behavior of libm’s fmin. In particular, if the inputs compare equal (such as for the case of +0.0 and -0.0), either input may be returned non-deterministically.

let x = 1.0f32;
let y = 2.0f32;

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

Source

🔬This is a nightly-only experimental API. (float_minimum_maximum #91079)

Returns the maximum of the two numbers, propagating NaN.

This returns NaN when either argument is NaN, as opposed tof32::max which only returns NaN when both arguments are NaN.

#![feature(float_minimum_maximum)]
let x = 1.0f32;
let y = 2.0f32;

assert_eq!(x.maximum(y), y);
assert!(x.maximum(f32::NAN).is_nan());

If one of the arguments is NaN, then NaN is returned. Otherwise this returns the greater of the two numbers. For this operation, -0.0 is considered to be less than +0.0. Note that this follows the semantics specified in IEEE 754-2019.

Also note that “propagation” of NaNs here doesn’t necessarily mean that the bitpattern of a NaN operand is conserved; see the specification of NaN bit patterns for more info.

Source

🔬This is a nightly-only experimental API. (float_minimum_maximum #91079)

Returns the minimum of the two numbers, propagating NaN.

This returns NaN when either argument is NaN, as opposed tof32::min which only returns NaN when both arguments are NaN.

#![feature(float_minimum_maximum)]
let x = 1.0f32;
let y = 2.0f32;

assert_eq!(x.minimum(y), x);
assert!(x.minimum(f32::NAN).is_nan());

If one of the arguments is NaN, then NaN is returned. Otherwise this returns the lesser of the two numbers. For this operation, -0.0 is considered to be less than +0.0. Note that this follows the semantics specified in IEEE 754-2019.

Also note that “propagation” of NaNs here doesn’t necessarily mean that the bitpattern of a NaN operand is conserved; see the specification of NaN bit patterns for more info.

1.85.0 (const: 1.85.0) · Source

Calculates the middle point of self and rhs.

This returns NaN when either argument is NaN or if a combination of +inf and -inf is provided as arguments.

§Examples

assert_eq!(1f32.midpoint(4.0), 2.5);
assert_eq!((-5.5f32).midpoint(8.0), 1.25);

1.44.0 · Source

Rounds toward zero and converts to any primitive integer type, assuming that the value is finite and fits in that type.

let value = 4.6_f32;
let rounded = unsafe { value.to_int_unchecked::<u16>() };
assert_eq!(rounded, 4);

let value = -128.9_f32;
let rounded = unsafe { value.to_int_unchecked::<i8>() };
assert_eq!(rounded, i8::MIN);

§Safety

The value must:

• Not be NaN
• Not be infinite
• Be representable in the return type Int, after truncating off its fractional part

1.20.0 (const: 1.83.0) · Source

Raw transmutation to u32.

This is currently identical to transmute::<f32, u32>(self) on all platforms.

See from_bits for some discussion of the portability of this operation (there are almost no issues).

Note that this function is distinct from as casting, which attempts to preserve the numeric value, and not the bitwise value.

§Examples

assert_ne!((1f32).to_bits(), 1f32 as u32); // to_bits() is not casting!
assert_eq!((12.5f32).to_bits(), 0x41480000);

1.20.0 (const: 1.83.0) · Source

Raw transmutation from u32.

This is currently identical to transmute::<u32, f32>(v) on all platforms. It turns out this is incredibly portable, for two reasons:

• Floats and Ints have the same endianness on all supported platforms.
• IEEE 754 very precisely specifies the bit layout of floats.

However there is one caveat: prior to the 2008 version of IEEE 754, how to interpret the NaN signaling bit wasn’t actually specified. Most platforms (notably x86 and ARM) picked the interpretation that was ultimately standardized in 2008, but some didn’t (notably MIPS). As a result, all signaling NaNs on MIPS are quiet NaNs on x86, and vice-versa.

Rather than trying to preserve signaling-ness cross-platform, this implementation favors preserving the exact bits. This means that any payloads encoded in NaNs will be preserved even if the result of this method is sent over the network from an x86 machine to a MIPS one.

If the results of this method are only manipulated by the same architecture that produced them, then there is no portability concern.

If the input isn’t NaN, then there is no portability concern.

If you don’t care about signalingness (very likely), then there is no portability concern.

Note that this function is distinct from as casting, which attempts to preserve the numeric value, and not the bitwise value.

§Examples

let v = f32::from_bits(0x41480000);
assert_eq!(v, 12.5);

1.40.0 (const: 1.83.0) · Source

Returns the memory representation of this floating point number as a byte array in big-endian (network) byte order.

See from_bits for some discussion of the portability of this operation (there are almost no issues).

§Examples

let bytes = 12.5f32.to_be_bytes();
assert_eq!(bytes, [0x41, 0x48, 0x00, 0x00]);

1.40.0 (const: 1.83.0) · Source

Returns the memory representation of this floating point number as a byte array in little-endian byte order.

See from_bits for some discussion of the portability of this operation (there are almost no issues).

§Examples

let bytes = 12.5f32.to_le_bytes();
assert_eq!(bytes, [0x00, 0x00, 0x48, 0x41]);

1.40.0 (const: 1.83.0) · Source

Returns the memory representation of this floating point number as a byte array in native byte order.

As the target platform’s native endianness is used, portable code should use to_be_bytes or to_le_bytes, as appropriate, instead.

See from_bits for some discussion of the portability of this operation (there are almost no issues).

§Examples

let bytes = 12.5f32.to_ne_bytes();
assert_eq!(
    bytes,
    if cfg!(target_endian = "big") {
        [0x41, 0x48, 0x00, 0x00]
    } else {
        [0x00, 0x00, 0x48, 0x41]
    }
);

1.40.0 (const: 1.83.0) · Source

Creates a floating point value from its representation as a byte array in big endian.

See from_bits for some discussion of the portability of this operation (there are almost no issues).

§Examples

let value = f32::from_be_bytes([0x41, 0x48, 0x00, 0x00]);
assert_eq!(value, 12.5);

1.40.0 (const: 1.83.0) · Source

Creates a floating point value from its representation as a byte array in little endian.

See from_bits for some discussion of the portability of this operation (there are almost no issues).

§Examples

let value = f32::from_le_bytes([0x00, 0x00, 0x48, 0x41]);
assert_eq!(value, 12.5);

1.40.0 (const: 1.83.0) · Source

Creates a floating point value from its representation as a byte array in native endian.

As the target platform’s native endianness is used, portable code likely wants to use from_be_bytes or from_le_bytes, as appropriate instead.

See from_bits for some discussion of the portability of this operation (there are almost no issues).

§Examples

let value = f32::from_ne_bytes(if cfg!(target_endian = "big") {
    [0x41, 0x48, 0x00, 0x00]
} else {
    [0x00, 0x00, 0x48, 0x41]
});
assert_eq!(value, 12.5);

1.62.0 · Source

Returns the ordering between self and other.

Unlike the standard partial comparison between floating point numbers, this comparison always produces an ordering in accordance to the totalOrder predicate as defined in the IEEE 754 (2008 revision) floating point standard. The values are ordered in the following sequence:

• negative quiet NaN
• negative signaling NaN
• negative infinity
• negative numbers
• negative subnormal numbers
• negative zero
• positive zero
• positive subnormal numbers
• positive numbers
• positive infinity
• positive signaling NaN
• positive quiet NaN.

The ordering established by this function does not always agree with thePartialOrd and PartialEq implementations of f32. For example, they consider negative and positive zero equal, while total_cmpdoesn’t.

The interpretation of the signaling NaN bit follows the definition in the IEEE 754 standard, which may not match the interpretation by some of the older, non-conformant (e.g. MIPS) hardware implementations.

§Example

struct GoodBoy {
    name: String,
    weight: f32,
}

let mut bois = vec![
    GoodBoy { name: "Pucci".to_owned(), weight: 0.1 },
    GoodBoy { name: "Woofer".to_owned(), weight: 99.0 },
    GoodBoy { name: "Yapper".to_owned(), weight: 10.0 },
    GoodBoy { name: "Chonk".to_owned(), weight: f32::INFINITY },
    GoodBoy { name: "Abs. Unit".to_owned(), weight: f32::NAN },
    GoodBoy { name: "Floaty".to_owned(), weight: -5.0 },
];

bois.sort_by(|a, b| a.weight.total_cmp(&b.weight));

// `f32::NAN` could be positive or negative, which will affect the sort order.
if f32::NAN.is_sign_negative() {
    assert!(bois.into_iter().map(|b| b.weight)
        .zip([f32::NAN, -5.0, 0.1, 10.0, 99.0, f32::INFINITY].iter())
        .all(|(a, b)| a.to_bits() == b.to_bits()))
} else {
    assert!(bois.into_iter().map(|b| b.weight)
        .zip([-5.0, 0.1, 10.0, 99.0, f32::INFINITY, f32::NAN].iter())
        .all(|(a, b)| a.to_bits() == b.to_bits()))
}

1.50.0 (const: 1.85.0) · Source

Restrict a value to a certain interval unless it is NaN.

Returns max if self is greater than max, and min if self is less than min. Otherwise this returns self.

Note that this function returns NaN if the initial value was NaN as well.

§Panics

Panics if min > max, min is NaN, or max is NaN.

§Examples

assert!((-3.0f32).clamp(-2.0, 1.0) == -2.0);
assert!((0.0f32).clamp(-2.0, 1.0) == 0.0);
assert!((2.0f32).clamp(-2.0, 1.0) == 1.0);
assert!((f32::NAN).clamp(-2.0, 1.0).is_nan());

1.0.0 (const: 1.85.0) · Source

Computes the absolute value of self.

This function always returns the precise result.

§Examples

let x = 3.5_f32;
let y = -3.5_f32;

assert_eq!(x.abs(), x);
assert_eq!(y.abs(), -y);

assert!(f32::NAN.abs().is_nan());

1.0.0 (const: 1.85.0) · Source

Returns a number that represents the sign of self.

• 1.0 if the number is positive, +0.0 or INFINITY
• -1.0 if the number is negative, -0.0 or NEG_INFINITY
• NaN if the number is NaN

§Examples

let f = 3.5_f32;

assert_eq!(f.signum(), 1.0);
assert_eq!(f32::NEG_INFINITY.signum(), -1.0);

assert!(f32::NAN.signum().is_nan());

1.35.0 (const: 1.85.0) · Source

Returns a number composed of the magnitude of self and the sign ofsign.

Equal to self if the sign of self and sign are the same, otherwise equal to -self. If self is a NaN, then a NaN with the same payload as self and the sign bit of sign is returned.

If sign is a NaN, then this operation will still carry over its sign into the result. Note that IEEE 754 doesn’t assign any meaning to the sign bit in case of a NaN, and as Rust doesn’t guarantee that the bit pattern of NaNs are conserved over arithmetic operations, the result of copysign with sign being a NaN might produce an unexpected or non-portable result. See the specification of NaN bit patterns for more info.

§Examples

let f = 3.5_f32;

assert_eq!(f.copysign(0.42), 3.5_f32);
assert_eq!(f.copysign(-0.42), -3.5_f32);
assert_eq!((-f).copysign(0.42), 3.5_f32);
assert_eq!((-f).copysign(-0.42), -3.5_f32);

assert!(f32::NAN.copysign(1.0).is_nan());

1.0.0 · Source§

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The resulting type after applying the + operator.

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The resulting type after applying the + operator.

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The resulting type after applying the + operator.

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The resulting type after applying the + operator.

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Returns the default value of 0.0

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The resulting type after applying the / operator.

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1.0.0 · Source§

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The resulting type after applying the / operator.

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1.0.0 · Source§

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The resulting type after applying the / operator.

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1.0.0 · Source§

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The resulting type after applying the / operator.

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Converts a bool to f32 losslessly. The resulting value is positive 0.0 for false and 1.0 for true values.

§Examples

let x: f32 = false.into();
assert_eq!(x, 0.0);
assert!(x.is_sign_positive());

let y: f32 = true.into();
assert_eq!(y, 1.0);

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Converts a string in base 10 to a float. Accepts an optional decimal exponent.

This function accepts strings such as

• ‘3.14’
• ‘-3.14’
• ‘2.5E10’, or equivalently, ‘2.5e10’
• ‘2.5E-10’
• ‘5.’
• ‘.5’, or, equivalently, ‘0.5’
• ‘inf’, ‘-inf’, ‘+infinity’, ‘NaN’

Note that alphabetical characters are not case-sensitive.

Leading and trailing whitespace represent an error.

§Grammar

All strings that adhere to the following EBNF grammar when lowercased will result in an Ok being returned:

Float  ::= Sign? ( 'inf' | 'infinity' | 'nan' | Number )
Number ::= ( Digit+ |
             Digit+ '.' Digit* |
             Digit* '.' Digit+ ) Exp?
Exp    ::= 'e' Sign? Digit+
Sign   ::= [+-]
Digit  ::= [0-9]

§Arguments

• src - A string

§Return value

Err(ParseFloatError) if the string did not represent a valid number. Otherwise, Ok(n) where n is the closest representable floating-point number to the number represented by src (following the same rules for rounding as for the results of primitive operations).

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The associated error which can be returned from parsing.

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The resulting type after applying the * operator.

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The resulting type after applying the * operator.

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The resulting type after applying the * operator.

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The resulting type after applying the * operator.

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Tests for self and other values to be equal, and is used by ==.

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Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

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This method returns an ordering between self and other values if one exists. Read more

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Tests less than (for self and other) and is used by the < operator. Read more

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Tests less than or equal to (for self and other) and is used by the<= operator. Read more

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Tests greater than or equal to (for self and other) and is used by the >= operator. Read more

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Tests greater than (for self and other) and is used by the >operator. Read more

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Takes an iterator and generates Self from the elements by multiplying the items.

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Takes an iterator and generates Self from the elements by multiplying the items.

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The resulting type after applying the % operator.

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The resulting type after applying the % operator.

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The resulting type after applying the % operator.

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The remainder from the division of two floats.

The remainder has the same sign as the dividend and is computed as:x - (x / y).trunc() * y.

§Examples

let x: f32 = 50.50;
let y: f32 = 8.125;
let remainder = x - (x / y).trunc() * y;

// The answer to both operations is 1.75
assert_eq!(x % y, remainder);

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The resulting type after applying the % operator.

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🔬This is a nightly-only experimental API. (portable_simd #86656)

The mask element type corresponding to this element type.

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The resulting type after applying the - operator.

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The resulting type after applying the - operator.

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The resulting type after applying the - operator.

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The resulting type after applying the - operator.

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Takes an iterator and generates Self from the elements by “summing up” the items.

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Takes an iterator and generates Self from the elements by “summing up” the items.

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