neg log

Average Error: 0.0 → 0.0

Time: 1.1s

Precision: binary64

Math

\[-\log \left(\frac{1}{x} - 1\right) \]

↓

\[-\log \left(\frac{1}{x} + -1\right) \]

FPCore

(FPCore (x) :precision binary64 (- (log (- (/ 1.0 x) 1.0))))

↓

(FPCore (x) :precision binary64 (- (log (+ (/ 1.0 x) -1.0))))

C

double code(double x) {
	return -log(((1.0 / x) - 1.0));
}

↓

double code(double x) {
	return -log(((1.0 / x) + -1.0));
}

Fortran

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

↓

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

Java

public static double code(double x) {
	return -Math.log(((1.0 / x) - 1.0));
}

↓

public static double code(double x) {
	return -Math.log(((1.0 / x) + -1.0));
}

Python

def code(x):
	return -math.log(((1.0 / x) - 1.0))

↓

def code(x):
	return -math.log(((1.0 / x) + -1.0))

Julia

function code(x)
	return Float64(-log(Float64(Float64(1.0 / x) - 1.0)))
end

↓

function code(x)
	return Float64(-log(Float64(Float64(1.0 / x) + -1.0)))
end

MATLAB

function tmp = code(x)
	tmp = -log(((1.0 / x) - 1.0));
end

↓

function tmp = code(x)
	tmp = -log(((1.0 / x) + -1.0));
end

Wolfram

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

↓

code[x_] := (-N[Log[N[(N[(1.0 / x), $MachinePrecision] + -1.0), $MachinePrecision]], $MachinePrecision])

Error

Bits error versus x

Derivation

1. Initial program 0.0

   \[-\log \left(\frac{1}{x} - 1\right) \]

2. Final simplification 0.0

   \[\leadsto -\log \left(\frac{1}{x} + -1\right) \]

Reproduce

herbie shell --seed 2022156 
(FPCore (x)
  :name "neg log"
  :precision binary64
  (- (log (- (/ 1.0 x) 1.0))))