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

Average Error: 0.0 → 0.0

Time: 967.0ms

Precision: binary64

Math

\[e^{\left(x \cdot y\right) \cdot y} \]

↓

\[e^{y \cdot \left(x \cdot y\right)} \]

FPCore

(FPCore (x y) :precision binary64 (exp (* (* x y) y)))

↓

(FPCore (x y) :precision binary64 (exp (* y (* x y))))

C

double code(double x, double y) {
	return exp(((x * y) * y));
}

↓

double code(double x, double y) {
	return exp((y * (x * y)));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = exp(((x * y) * y))
end function

↓

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = exp((y * (x * y)))
end function

Java

public static double code(double x, double y) {
	return Math.exp(((x * y) * y));
}

↓

public static double code(double x, double y) {
	return Math.exp((y * (x * y)));
}

Python

def code(x, y):
	return math.exp(((x * y) * y))

↓

def code(x, y):
	return math.exp((y * (x * y)))

Julia

function code(x, y)
	return exp(Float64(Float64(x * y) * y))
end

↓

function code(x, y)
	return exp(Float64(y * Float64(x * y)))
end

MATLAB

function tmp = code(x, y)
	tmp = exp(((x * y) * y));
end

↓

function tmp = code(x, y)
	tmp = exp((y * (x * y)));
end

Wolfram

code[x_, y_] := N[Exp[N[(N[(x * y), $MachinePrecision] * y), $MachinePrecision]], $MachinePrecision]

↓

code[x_, y_] := N[Exp[N[(y * N[(x * y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Error

Bits error versus x

Bits error versus y

Derivation

1. Initial program 0.0

   \[e^{\left(x \cdot y\right) \cdot y} \]

2. Final simplification 0.0

   \[\leadsto e^{y \cdot \left(x \cdot y\right)} \]

Reproduce

herbie shell --seed 2022150 
(FPCore (x y)
  :name "Data.Random.Distribution.Normal:normalF from random-fu-0.2.6.2"
  :precision binary64
  (exp (* (* x y) y)))