Data.Random.Distribution.Normal:doubleStdNormalZ from random-fu-0.2.6.2

Average Error: 0 → 0

Time: 1.0s

Precision: binary64

Cost: 320

Math

\[\left(x + x\right) - 1 \]

↓

\[\left(x + x\right) + -1 \]

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

   \[\left(x + x\right) - 1 \]

2. Final simplification 0

   \[\leadsto \left(x + x\right) + -1 \]

Reproduce

herbie shell --seed 2022291 
(FPCore (x)
  :name "Data.Random.Distribution.Normal:doubleStdNormalZ from random-fu-0.2.6.2"
  :precision binary64
  (- (+ x x) 1.0))