Data.Random.Dice:roll from dice-0.1

Average Error: 0.0 → 0.0

Time: 1.2s

Precision: binary64

Cost: 320

Math

\[x \cdot x - 1 \]

↓

\[x \cdot x - 1 \]

FPCore

(FPCore (x) :precision binary64 (- (* x x) 1.0))

↓

(FPCore (x) :precision binary64 (- (* x x) 1.0))

C

double code(double x) {
	return (x * x) - 1.0;
}

↓

double code(double x) {
	return (x * x) - 1.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * x) - 1.0d0
end function

↓

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * x) - 1.0d0
end function

Java

public static double code(double x) {
	return (x * x) - 1.0;
}

↓

public static double code(double x) {
	return (x * x) - 1.0;
}

Python

def code(x):
	return (x * x) - 1.0

↓

def code(x):
	return (x * x) - 1.0

Julia

function code(x)
	return Float64(Float64(x * x) - 1.0)
end

↓

function code(x)
	return Float64(Float64(x * x) - 1.0)
end

MATLAB

function tmp = code(x)
	tmp = (x * x) - 1.0;
end

↓

function tmp = code(x)
	tmp = (x * x) - 1.0;
end

Wolfram

code[x_] := N[(N[(x * x), $MachinePrecision] - 1.0), $MachinePrecision]

↓

code[x_] := N[(N[(x * x), $MachinePrecision] - 1.0), $MachinePrecision]

Derivation

1. Initial program 0.0

   \[x \cdot x - 1 \]

Reproduce

herbie shell --seed 2023010 
(FPCore (x)
  :name "Data.Random.Dice:roll from dice-0.1"
  :precision binary64
  (- (* x x) 1.0))