Numeric.SpecFunctions:invIncompleteGamma from math-functions-0.1.5.2, B

Percentage Accurate: 72.5% → 99.9%

Time: 10.8s

Alternatives: 10

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ 1 - \log \left(1 - \frac{x - y}{1 - y}\right) \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 72.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 1 - \log \left(1 - \frac{x - y}{1 - y}\right) \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{y - x}{-1 + y} \leq 0.995:\\ \;\;\;\;1 - \mathsf{log1p}\left(\frac{y - x}{1 - y}\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{-1 - \left(\frac{y - -1}{y \cdot y} - x\right)}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (/ (- y x) (+ -1.0 y)) 0.995)
   (- 1.0 (log1p (/ (- y x) (- 1.0 y))))
   (- 1.0 (log (/ (- -1.0 (- (/ (- y -1.0) (* y y)) x)) y)))))

C

double code(double x, double y) {
	double tmp;
	if (((y - x) / (-1.0 + y)) <= 0.995) {
		tmp = 1.0 - log1p(((y - x) / (1.0 - y)));
	} else {
		tmp = 1.0 - log(((-1.0 - (((y - -1.0) / (y * y)) - x)) / y));
	}
	return tmp;
}

Java

public static double code(double x, double y) {
	double tmp;
	if (((y - x) / (-1.0 + y)) <= 0.995) {
		tmp = 1.0 - Math.log1p(((y - x) / (1.0 - y)));
	} else {
		tmp = 1.0 - Math.log(((-1.0 - (((y - -1.0) / (y * y)) - x)) / y));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((y - x) / (-1.0 + y)) <= 0.995:
		tmp = 1.0 - math.log1p(((y - x) / (1.0 - y)))
	else:
		tmp = 1.0 - math.log(((-1.0 - (((y - -1.0) / (y * y)) - x)) / y))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(y - x) / Float64(-1.0 + y)) <= 0.995)
		tmp = Float64(1.0 - log1p(Float64(Float64(y - x) / Float64(1.0 - y))));
	else
		tmp = Float64(1.0 - log(Float64(Float64(-1.0 - Float64(Float64(Float64(y - -1.0) / Float64(y * y)) - x)) / y)));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(y - x), $MachinePrecision] / N[(-1.0 + y), $MachinePrecision]), $MachinePrecision], 0.995], N[(1.0 - N[Log[1 + N[(N[(y - x), $MachinePrecision] / N[(1.0 - y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(1.0 - N[Log[N[(N[(-1.0 - N[(N[(N[(y - -1.0), $MachinePrecision] / N[(y * y), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y)) < 0.994999999999999996

   1. Initial program 100.0%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-log.f64 N/A

         \[\leadsto 1 - \color{blue}{\log \left(1 - \frac{x - y}{1 - y}\right)} \]

      2. lift--.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(1 - \frac{x - y}{1 - y}\right)} \]

      3. sub-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(1 + \left(\mathsf{neg}\left(\frac{x - y}{1 - y}\right)\right)\right)} \]

      4. lower-log1p.f64 N/A

         \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(\mathsf{neg}\left(\frac{x - y}{1 - y}\right)\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\mathsf{neg}\left(\color{blue}{\frac{x - y}{1 - y}}\right)\right) \]

      6. distribute-neg-frac N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\frac{\mathsf{neg}\left(\left(x - y\right)\right)}{1 - y}}\right) \]

      7. lower-/.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\frac{\mathsf{neg}\left(\left(x - y\right)\right)}{1 - y}}\right) \]

      8. neg-sub0 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{0 - \left(x - y\right)}}{1 - y}\right) \]

      9. lift--.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(x - y\right)}}{1 - y}\right) \]

      10. sub-neg N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(x + \left(\mathsf{neg}\left(y\right)\right)\right)}}{1 - y}\right) \]

      11. +-commutative N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(\left(\mathsf{neg}\left(y\right)\right) + x\right)}}{1 - y}\right) \]

      12. associate--r+ N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{\left(0 - \left(\mathsf{neg}\left(y\right)\right)\right) - x}}{1 - y}\right) \]

      13. neg-sub0 N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(y\right)\right)\right)\right)} - x}{1 - y}\right) \]

      14. remove-double-neg N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{y} - x}{1 - y}\right) \]

      15. lower--.f64 100.0

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{y - x}}{1 - y}\right) \]

   4. Applied rewrites 100.0%

      \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(\frac{y - x}{1 - y}\right)} \]

   if 0.994999999999999996 < (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y))

   1. Initial program 8.2%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around -inf

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

   5. Applied rewrites 100.0%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{\left(x - \frac{\left(1 - \frac{x - 1}{y}\right) - x}{y}\right) - 1}{y}\right)} \]

   6. Taylor expanded in x around 0

      \[\leadsto 1 - \log \left(\frac{\left(x - \frac{1 + \frac{1}{y}}{y}\right) - 1}{y}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 100.0%

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

   9. Taylor expanded in y around 0

      \[\leadsto 1 - \log \left(\frac{\left(x - \frac{1 + y}{{y}^{2}}\right) - 1}{y}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 100.0%

       \[\leadsto 1 - \log \left(\frac{\left(x - \frac{y + 1}{y \cdot y}\right) - 1}{y}\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 100.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{y - x}{-1 + y} \leq 0.995:\\ \;\;\;\;1 - \mathsf{log1p}\left(\frac{y - x}{1 - y}\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{-1 - \left(\frac{y - -1}{y \cdot y} - x\right)}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 98.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{y - x}{-1 + y}\\ \mathbf{if}\;t\_0 \leq 0.8:\\ \;\;\;\;1 - \log \left(\frac{x - 1}{y}\right)\\ \mathbf{elif}\;t\_0 \leq 10:\\ \;\;\;\;1 - \left(\mathsf{log1p}\left(-x\right) + y\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{x}{-1 + y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ (- y x) (+ -1.0 y)))))
   (if (<= t_0 0.8)
     (- 1.0 (log (/ (- x 1.0) y)))
     (if (<= t_0 10.0)
       (- 1.0 (+ (log1p (- x)) y))
       (- 1.0 (log (/ x (+ -1.0 y))))))))

C

double code(double x, double y) {
	double t_0 = 1.0 - ((y - x) / (-1.0 + y));
	double tmp;
	if (t_0 <= 0.8) {
		tmp = 1.0 - log(((x - 1.0) / y));
	} else if (t_0 <= 10.0) {
		tmp = 1.0 - (log1p(-x) + y);
	} else {
		tmp = 1.0 - log((x / (-1.0 + y)));
	}
	return tmp;
}

Java

public static double code(double x, double y) {
	double t_0 = 1.0 - ((y - x) / (-1.0 + y));
	double tmp;
	if (t_0 <= 0.8) {
		tmp = 1.0 - Math.log(((x - 1.0) / y));
	} else if (t_0 <= 10.0) {
		tmp = 1.0 - (Math.log1p(-x) + y);
	} else {
		tmp = 1.0 - Math.log((x / (-1.0 + y)));
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - ((y - x) / (-1.0 + y))
	tmp = 0
	if t_0 <= 0.8:
		tmp = 1.0 - math.log(((x - 1.0) / y))
	elif t_0 <= 10.0:
		tmp = 1.0 - (math.log1p(-x) + y)
	else:
		tmp = 1.0 - math.log((x / (-1.0 + y)))
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - Float64(Float64(y - x) / Float64(-1.0 + y)))
	tmp = 0.0
	if (t_0 <= 0.8)
		tmp = Float64(1.0 - log(Float64(Float64(x - 1.0) / y)));
	elseif (t_0 <= 10.0)
		tmp = Float64(1.0 - Float64(log1p(Float64(-x)) + y));
	else
		tmp = Float64(1.0 - log(Float64(x / Float64(-1.0 + y))));
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 - N[(N[(y - x), $MachinePrecision] / N[(-1.0 + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, 0.8], N[(1.0 - N[Log[N[(N[(x - 1.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 10.0], N[(1.0 - N[(N[Log[1 + (-x)], $MachinePrecision] + y), $MachinePrecision]), $MachinePrecision], N[(1.0 - N[Log[N[(x / N[(-1.0 + y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 #s(literal 1 binary64) (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y))) < 0.80000000000000004

   1. Initial program 10.8%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto 1 - \log \color{blue}{\left(-1 \cdot \frac{1 + -1 \cdot x}{y}\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\mathsf{neg}\left(\frac{1 + -1 \cdot x}{y}\right)\right)} \]

      2. distribute-frac-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{\mathsf{neg}\left(\left(1 + -1 \cdot x\right)\right)}{y}\right)} \]

      3. +-commutative N/A

         \[\leadsto 1 - \log \left(\frac{\mathsf{neg}\left(\color{blue}{\left(-1 \cdot x + 1\right)}\right)}{y}\right) \]

      4. distribute-neg-in N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{\left(\mathsf{neg}\left(-1 \cdot x\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}}{y}\right) \]

      5. mul-1-neg N/A

         \[\leadsto 1 - \log \left(\frac{\left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(x\right)\right)}\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      6. remove-double-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x} + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      7. sub-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

      9. lower--.f64 98.2

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

   5. Applied rewrites 98.2%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

   if 0.80000000000000004 < (-.f64 #s(literal 1 binary64) (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y))) < 10

   1. Initial program 100.0%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto 1 - \color{blue}{\left(\log \left(1 - x\right) + y \cdot \left(-1 \cdot \frac{x}{1 - x} + \frac{1}{1 - x}\right)\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. +-commutative N/A

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

      3. mul-1-neg N/A

         \[\leadsto 1 - \left(y \cdot \left(\frac{1}{1 - x} + \color{blue}{\left(\mathsf{neg}\left(\frac{x}{1 - x}\right)\right)}\right) + \log \left(1 - x\right)\right) \]

      4. sub-neg N/A

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

      5. sub-neg N/A

         \[\leadsto 1 - \left(y \cdot \left(\frac{1}{\color{blue}{1 + \left(\mathsf{neg}\left(x\right)\right)}} - \frac{x}{1 - x}\right) + \log \left(1 - x\right)\right) \]

      6. mul-1-neg N/A

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

      7. sub-neg N/A

         \[\leadsto 1 - \left(y \cdot \left(\frac{1}{1 + -1 \cdot x} - \frac{x}{\color{blue}{1 + \left(\mathsf{neg}\left(x\right)\right)}}\right) + \log \left(1 - x\right)\right) \]

      8. mul-1-neg N/A

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

      9. div-sub N/A

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

      10. sub-neg N/A

          \[\leadsto 1 - \left(y \cdot \frac{\color{blue}{1 + \left(\mathsf{neg}\left(x\right)\right)}}{1 + -1 \cdot x} + \log \left(1 - x\right)\right) \]

      11. mul-1-neg N/A

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

      12. *-inverses N/A

          \[\leadsto 1 - \left(y \cdot \color{blue}{1} + \log \left(1 - x\right)\right) \]

      13. *-rgt-identity N/A

          \[\leadsto 1 - \left(\color{blue}{y} + \log \left(1 - x\right)\right) \]

      14. lower-+.f64 N/A

          \[\leadsto 1 - \color{blue}{\left(y + \log \left(1 - x\right)\right)} \]

      15. sub-neg N/A

          \[\leadsto 1 - \left(y + \log \color{blue}{\left(1 + \left(\mathsf{neg}\left(x\right)\right)\right)}\right) \]

      16. mul-1-neg N/A

          \[\leadsto 1 - \left(y + \log \left(1 + \color{blue}{-1 \cdot x}\right)\right) \]

   5. Applied rewrites 98.8%

      \[\leadsto 1 - \color{blue}{\left(y + \mathsf{log1p}\left(-x\right)\right)} \]

   if 10 < (-.f64 #s(literal 1 binary64) (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y)))

   1. Initial program 100.0%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto 1 - \log \color{blue}{\left(-1 \cdot \frac{x}{1 - y}\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\mathsf{neg}\left(\frac{x}{1 - y}\right)\right)} \]

      2. distribute-neg-frac2 N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{x}{\mathsf{neg}\left(\left(1 - y\right)\right)}\right)} \]

      3. lower-/.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{x}{\mathsf{neg}\left(\left(1 - y\right)\right)}\right)} \]

      4. sub-neg N/A

         \[\leadsto 1 - \log \left(\frac{x}{\mathsf{neg}\left(\color{blue}{\left(1 + \left(\mathsf{neg}\left(y\right)\right)\right)}\right)}\right) \]

      5. neg-mul-1 N/A

         \[\leadsto 1 - \log \left(\frac{x}{\mathsf{neg}\left(\left(1 + \color{blue}{-1 \cdot y}\right)\right)}\right) \]

      6. distribute-neg-in N/A

         \[\leadsto 1 - \log \left(\frac{x}{\color{blue}{\left(\mathsf{neg}\left(1\right)\right) + \left(\mathsf{neg}\left(-1 \cdot y\right)\right)}}\right) \]

      7. metadata-eval N/A

         \[\leadsto 1 - \log \left(\frac{x}{\color{blue}{-1} + \left(\mathsf{neg}\left(-1 \cdot y\right)\right)}\right) \]

      8. neg-mul-1 N/A

         \[\leadsto 1 - \log \left(\frac{x}{-1 + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(y\right)\right)}\right)\right)}\right) \]

      9. remove-double-neg N/A

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

      10. lower-+.f64 99.7

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

   5. Applied rewrites 99.7%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{x}{-1 + y}\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 98.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;1 - \frac{y - x}{-1 + y} \leq 0.8:\\ \;\;\;\;1 - \log \left(\frac{x - 1}{y}\right)\\ \mathbf{elif}\;1 - \frac{y - x}{-1 + y} \leq 10:\\ \;\;\;\;1 - \left(\mathsf{log1p}\left(-x\right) + y\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{x}{-1 + y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 89.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \frac{y - x}{-1 + y}\\ \mathbf{if}\;t\_0 \leq 5 \cdot 10^{-12}:\\ \;\;\;\;1 - \log \left(\frac{-1}{y}\right)\\ \mathbf{elif}\;t\_0 \leq 2:\\ \;\;\;\;1 - \mathsf{log1p}\left(\frac{y}{1 - y}\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{x}{-1 + y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (/ (- y x) (+ -1.0 y)))))
   (if (<= t_0 5e-12)
     (- 1.0 (log (/ -1.0 y)))
     (if (<= t_0 2.0)
       (- 1.0 (log1p (/ y (- 1.0 y))))
       (- 1.0 (log (/ x (+ -1.0 y))))))))

C

double code(double x, double y) {
	double t_0 = 1.0 - ((y - x) / (-1.0 + y));
	double tmp;
	if (t_0 <= 5e-12) {
		tmp = 1.0 - log((-1.0 / y));
	} else if (t_0 <= 2.0) {
		tmp = 1.0 - log1p((y / (1.0 - y)));
	} else {
		tmp = 1.0 - log((x / (-1.0 + y)));
	}
	return tmp;
}

Java

public static double code(double x, double y) {
	double t_0 = 1.0 - ((y - x) / (-1.0 + y));
	double tmp;
	if (t_0 <= 5e-12) {
		tmp = 1.0 - Math.log((-1.0 / y));
	} else if (t_0 <= 2.0) {
		tmp = 1.0 - Math.log1p((y / (1.0 - y)));
	} else {
		tmp = 1.0 - Math.log((x / (-1.0 + y)));
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - ((y - x) / (-1.0 + y))
	tmp = 0
	if t_0 <= 5e-12:
		tmp = 1.0 - math.log((-1.0 / y))
	elif t_0 <= 2.0:
		tmp = 1.0 - math.log1p((y / (1.0 - y)))
	else:
		tmp = 1.0 - math.log((x / (-1.0 + y)))
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - Float64(Float64(y - x) / Float64(-1.0 + y)))
	tmp = 0.0
	if (t_0 <= 5e-12)
		tmp = Float64(1.0 - log(Float64(-1.0 / y)));
	elseif (t_0 <= 2.0)
		tmp = Float64(1.0 - log1p(Float64(y / Float64(1.0 - y))));
	else
		tmp = Float64(1.0 - log(Float64(x / Float64(-1.0 + y))));
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 - N[(N[(y - x), $MachinePrecision] / N[(-1.0 + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, 5e-12], N[(1.0 - N[Log[N[(-1.0 / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 2.0], N[(1.0 - N[Log[1 + N[(y / N[(1.0 - y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(1.0 - N[Log[N[(x / N[(-1.0 + y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 #s(literal 1 binary64) (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y))) < 4.9999999999999997e-12

   1. Initial program 4.8%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto 1 - \log \color{blue}{\left(-1 \cdot \frac{1 + -1 \cdot x}{y}\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\mathsf{neg}\left(\frac{1 + -1 \cdot x}{y}\right)\right)} \]

      2. distribute-frac-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{\mathsf{neg}\left(\left(1 + -1 \cdot x\right)\right)}{y}\right)} \]

      3. +-commutative N/A

         \[\leadsto 1 - \log \left(\frac{\mathsf{neg}\left(\color{blue}{\left(-1 \cdot x + 1\right)}\right)}{y}\right) \]

      4. distribute-neg-in N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{\left(\mathsf{neg}\left(-1 \cdot x\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}}{y}\right) \]

      5. mul-1-neg N/A

         \[\leadsto 1 - \log \left(\frac{\left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(x\right)\right)}\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      6. remove-double-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x} + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      7. sub-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

      9. lower--.f64 99.8

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

   5. Applied rewrites 99.8%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

   6. Taylor expanded in x around 0

      \[\leadsto 1 - \log \left(\frac{-1}{y}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 65.4%

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

   if 4.9999999999999997e-12 < (-.f64 #s(literal 1 binary64) (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y))) < 2

   1. Initial program 99.4%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto 1 - \color{blue}{\log \left(1 + \frac{y}{1 - y}\right)} \]
   4. Step-by-step derivation

      1. lower-log1p.f64 N/A

         \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(\frac{y}{1 - y}\right)} \]

      2. lower-/.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\frac{y}{1 - y}}\right) \]

      3. lower--.f64 96.3

         \[\leadsto 1 - \mathsf{log1p}\left(\frac{y}{\color{blue}{1 - y}}\right) \]

   5. Applied rewrites 96.3%

      \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(\frac{y}{1 - y}\right)} \]

   if 2 < (-.f64 #s(literal 1 binary64) (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y)))

   1. Initial program 100.0%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto 1 - \log \color{blue}{\left(-1 \cdot \frac{x}{1 - y}\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\mathsf{neg}\left(\frac{x}{1 - y}\right)\right)} \]

      2. distribute-neg-frac2 N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{x}{\mathsf{neg}\left(\left(1 - y\right)\right)}\right)} \]

      3. lower-/.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{x}{\mathsf{neg}\left(\left(1 - y\right)\right)}\right)} \]

      4. sub-neg N/A

         \[\leadsto 1 - \log \left(\frac{x}{\mathsf{neg}\left(\color{blue}{\left(1 + \left(\mathsf{neg}\left(y\right)\right)\right)}\right)}\right) \]

      5. neg-mul-1 N/A

         \[\leadsto 1 - \log \left(\frac{x}{\mathsf{neg}\left(\left(1 + \color{blue}{-1 \cdot y}\right)\right)}\right) \]

      6. distribute-neg-in N/A

         \[\leadsto 1 - \log \left(\frac{x}{\color{blue}{\left(\mathsf{neg}\left(1\right)\right) + \left(\mathsf{neg}\left(-1 \cdot y\right)\right)}}\right) \]

      7. metadata-eval N/A

         \[\leadsto 1 - \log \left(\frac{x}{\color{blue}{-1} + \left(\mathsf{neg}\left(-1 \cdot y\right)\right)}\right) \]

      8. neg-mul-1 N/A

         \[\leadsto 1 - \log \left(\frac{x}{-1 + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(y\right)\right)}\right)\right)}\right) \]

      9. remove-double-neg N/A

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

      10. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{x}{-1 + y}\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 89.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;1 - \frac{y - x}{-1 + y} \leq 5 \cdot 10^{-12}:\\ \;\;\;\;1 - \log \left(\frac{-1}{y}\right)\\ \mathbf{elif}\;1 - \frac{y - x}{-1 + y} \leq 2:\\ \;\;\;\;1 - \mathsf{log1p}\left(\frac{y}{1 - y}\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{x}{-1 + y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{y - x}{-1 + y} \leq 0.995:\\ \;\;\;\;1 - \mathsf{log1p}\left(\frac{y - x}{1 - y}\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{\left(x - \frac{1 - x}{y}\right) - 1}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (/ (- y x) (+ -1.0 y)) 0.995)
   (- 1.0 (log1p (/ (- y x) (- 1.0 y))))
   (- 1.0 (log (/ (- (- x (/ (- 1.0 x) y)) 1.0) y)))))

C

double code(double x, double y) {
	double tmp;
	if (((y - x) / (-1.0 + y)) <= 0.995) {
		tmp = 1.0 - log1p(((y - x) / (1.0 - y)));
	} else {
		tmp = 1.0 - log((((x - ((1.0 - x) / y)) - 1.0) / y));
	}
	return tmp;
}

Java

public static double code(double x, double y) {
	double tmp;
	if (((y - x) / (-1.0 + y)) <= 0.995) {
		tmp = 1.0 - Math.log1p(((y - x) / (1.0 - y)));
	} else {
		tmp = 1.0 - Math.log((((x - ((1.0 - x) / y)) - 1.0) / y));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((y - x) / (-1.0 + y)) <= 0.995:
		tmp = 1.0 - math.log1p(((y - x) / (1.0 - y)))
	else:
		tmp = 1.0 - math.log((((x - ((1.0 - x) / y)) - 1.0) / y))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(y - x) / Float64(-1.0 + y)) <= 0.995)
		tmp = Float64(1.0 - log1p(Float64(Float64(y - x) / Float64(1.0 - y))));
	else
		tmp = Float64(1.0 - log(Float64(Float64(Float64(x - Float64(Float64(1.0 - x) / y)) - 1.0) / y)));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(y - x), $MachinePrecision] / N[(-1.0 + y), $MachinePrecision]), $MachinePrecision], 0.995], N[(1.0 - N[Log[1 + N[(N[(y - x), $MachinePrecision] / N[(1.0 - y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(1.0 - N[Log[N[(N[(N[(x - N[(N[(1.0 - x), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y)) < 0.994999999999999996

   1. Initial program 100.0%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-log.f64 N/A

         \[\leadsto 1 - \color{blue}{\log \left(1 - \frac{x - y}{1 - y}\right)} \]

      2. lift--.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(1 - \frac{x - y}{1 - y}\right)} \]

      3. sub-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(1 + \left(\mathsf{neg}\left(\frac{x - y}{1 - y}\right)\right)\right)} \]

      4. lower-log1p.f64 N/A

         \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(\mathsf{neg}\left(\frac{x - y}{1 - y}\right)\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\mathsf{neg}\left(\color{blue}{\frac{x - y}{1 - y}}\right)\right) \]

      6. distribute-neg-frac N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\frac{\mathsf{neg}\left(\left(x - y\right)\right)}{1 - y}}\right) \]

      7. lower-/.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\frac{\mathsf{neg}\left(\left(x - y\right)\right)}{1 - y}}\right) \]

      8. neg-sub0 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{0 - \left(x - y\right)}}{1 - y}\right) \]

      9. lift--.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(x - y\right)}}{1 - y}\right) \]

      10. sub-neg N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(x + \left(\mathsf{neg}\left(y\right)\right)\right)}}{1 - y}\right) \]

      11. +-commutative N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(\left(\mathsf{neg}\left(y\right)\right) + x\right)}}{1 - y}\right) \]

      12. associate--r+ N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{\left(0 - \left(\mathsf{neg}\left(y\right)\right)\right) - x}}{1 - y}\right) \]

      13. neg-sub0 N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(y\right)\right)\right)\right)} - x}{1 - y}\right) \]

      14. remove-double-neg N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{y} - x}{1 - y}\right) \]

      15. lower--.f64 100.0

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{y - x}}{1 - y}\right) \]

   4. Applied rewrites 100.0%

      \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(\frac{y - x}{1 - y}\right)} \]

   if 0.994999999999999996 < (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y))

   1. Initial program 8.2%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around -inf

      \[\leadsto 1 - \log \color{blue}{\left(-1 \cdot \frac{\left(1 + -1 \cdot \frac{x - 1}{y}\right) - x}{y}\right)} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

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

      2. mul-1-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{\mathsf{neg}\left(\left(\left(1 + -1 \cdot \frac{x - 1}{y}\right) - x\right)\right)}}{y}\right) \]

   5. Applied rewrites 99.8%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{\left(x - \frac{1 - x}{y}\right) - 1}{y}\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{y - x}{-1 + y} \leq 0.995:\\ \;\;\;\;1 - \mathsf{log1p}\left(\frac{y - x}{1 - y}\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{\left(x - \frac{1 - x}{y}\right) - 1}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 99.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{y - x}{-1 + y} \leq 0.995:\\ \;\;\;\;1 - \mathsf{log1p}\left(\frac{y - x}{1 - y}\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{-1 - \left(\frac{1}{y} - x\right)}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (/ (- y x) (+ -1.0 y)) 0.995)
   (- 1.0 (log1p (/ (- y x) (- 1.0 y))))
   (- 1.0 (log (/ (- -1.0 (- (/ 1.0 y) x)) y)))))

C

double code(double x, double y) {
	double tmp;
	if (((y - x) / (-1.0 + y)) <= 0.995) {
		tmp = 1.0 - log1p(((y - x) / (1.0 - y)));
	} else {
		tmp = 1.0 - log(((-1.0 - ((1.0 / y) - x)) / y));
	}
	return tmp;
}

Java

public static double code(double x, double y) {
	double tmp;
	if (((y - x) / (-1.0 + y)) <= 0.995) {
		tmp = 1.0 - Math.log1p(((y - x) / (1.0 - y)));
	} else {
		tmp = 1.0 - Math.log(((-1.0 - ((1.0 / y) - x)) / y));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((y - x) / (-1.0 + y)) <= 0.995:
		tmp = 1.0 - math.log1p(((y - x) / (1.0 - y)))
	else:
		tmp = 1.0 - math.log(((-1.0 - ((1.0 / y) - x)) / y))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(y - x) / Float64(-1.0 + y)) <= 0.995)
		tmp = Float64(1.0 - log1p(Float64(Float64(y - x) / Float64(1.0 - y))));
	else
		tmp = Float64(1.0 - log(Float64(Float64(-1.0 - Float64(Float64(1.0 / y) - x)) / y)));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(y - x), $MachinePrecision] / N[(-1.0 + y), $MachinePrecision]), $MachinePrecision], 0.995], N[(1.0 - N[Log[1 + N[(N[(y - x), $MachinePrecision] / N[(1.0 - y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(1.0 - N[Log[N[(N[(-1.0 - N[(N[(1.0 / y), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y)) < 0.994999999999999996

   1. Initial program 100.0%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-log.f64 N/A

         \[\leadsto 1 - \color{blue}{\log \left(1 - \frac{x - y}{1 - y}\right)} \]

      2. lift--.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(1 - \frac{x - y}{1 - y}\right)} \]

      3. sub-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(1 + \left(\mathsf{neg}\left(\frac{x - y}{1 - y}\right)\right)\right)} \]

      4. lower-log1p.f64 N/A

         \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(\mathsf{neg}\left(\frac{x - y}{1 - y}\right)\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\mathsf{neg}\left(\color{blue}{\frac{x - y}{1 - y}}\right)\right) \]

      6. distribute-neg-frac N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\frac{\mathsf{neg}\left(\left(x - y\right)\right)}{1 - y}}\right) \]

      7. lower-/.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\frac{\mathsf{neg}\left(\left(x - y\right)\right)}{1 - y}}\right) \]

      8. neg-sub0 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{0 - \left(x - y\right)}}{1 - y}\right) \]

      9. lift--.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(x - y\right)}}{1 - y}\right) \]

      10. sub-neg N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(x + \left(\mathsf{neg}\left(y\right)\right)\right)}}{1 - y}\right) \]

      11. +-commutative N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(\left(\mathsf{neg}\left(y\right)\right) + x\right)}}{1 - y}\right) \]

      12. associate--r+ N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{\left(0 - \left(\mathsf{neg}\left(y\right)\right)\right) - x}}{1 - y}\right) \]

      13. neg-sub0 N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(y\right)\right)\right)\right)} - x}{1 - y}\right) \]

      14. remove-double-neg N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{y} - x}{1 - y}\right) \]

      15. lower--.f64 100.0

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{y - x}}{1 - y}\right) \]

   4. Applied rewrites 100.0%

      \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(\frac{y - x}{1 - y}\right)} \]

   if 0.994999999999999996 < (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y))

   1. Initial program 8.2%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around -inf

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

   5. Applied rewrites 100.0%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{\left(x - \frac{\left(1 - \frac{x - 1}{y}\right) - x}{y}\right) - 1}{y}\right)} \]

   6. Taylor expanded in x around 0

      \[\leadsto 1 - \log \left(\frac{\left(x - \frac{1 + \frac{1}{y}}{y}\right) - 1}{y}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 100.0%

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

   9. Taylor expanded in y around inf

      \[\leadsto 1 - \log \left(\frac{\left(x - \frac{1}{y}\right) - 1}{y}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 99.8%

       \[\leadsto 1 - \log \left(\frac{\left(x - \frac{1}{y}\right) - 1}{y}\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{y - x}{-1 + y} \leq 0.995:\\ \;\;\;\;1 - \mathsf{log1p}\left(\frac{y - x}{1 - y}\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{-1 - \left(\frac{1}{y} - x\right)}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 99.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;1 - \frac{y - x}{-1 + y} \leq 0.0002:\\ \;\;\;\;1 - \log \left(\frac{x - 1}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \mathsf{log1p}\left(\frac{y - x}{1 - y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (- 1.0 (/ (- y x) (+ -1.0 y))) 0.0002)
   (- 1.0 (log (/ (- x 1.0) y)))
   (- 1.0 (log1p (/ (- y x) (- 1.0 y))))))

C

double code(double x, double y) {
	double tmp;
	if ((1.0 - ((y - x) / (-1.0 + y))) <= 0.0002) {
		tmp = 1.0 - log(((x - 1.0) / y));
	} else {
		tmp = 1.0 - log1p(((y - x) / (1.0 - y)));
	}
	return tmp;
}

Java

public static double code(double x, double y) {
	double tmp;
	if ((1.0 - ((y - x) / (-1.0 + y))) <= 0.0002) {
		tmp = 1.0 - Math.log(((x - 1.0) / y));
	} else {
		tmp = 1.0 - Math.log1p(((y - x) / (1.0 - y)));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (1.0 - ((y - x) / (-1.0 + y))) <= 0.0002:
		tmp = 1.0 - math.log(((x - 1.0) / y))
	else:
		tmp = 1.0 - math.log1p(((y - x) / (1.0 - y)))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(1.0 - Float64(Float64(y - x) / Float64(-1.0 + y))) <= 0.0002)
		tmp = Float64(1.0 - log(Float64(Float64(x - 1.0) / y)));
	else
		tmp = Float64(1.0 - log1p(Float64(Float64(y - x) / Float64(1.0 - y))));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(1.0 - N[(N[(y - x), $MachinePrecision] / N[(-1.0 + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0002], N[(1.0 - N[Log[N[(N[(x - 1.0), $MachinePrecision] / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(1.0 - N[Log[1 + N[(N[(y - x), $MachinePrecision] / N[(1.0 - y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 #s(literal 1 binary64) (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y))) < 2.0000000000000001e-4

   1. Initial program 8.2%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto 1 - \log \color{blue}{\left(-1 \cdot \frac{1 + -1 \cdot x}{y}\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\mathsf{neg}\left(\frac{1 + -1 \cdot x}{y}\right)\right)} \]

      2. distribute-frac-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{\mathsf{neg}\left(\left(1 + -1 \cdot x\right)\right)}{y}\right)} \]

      3. +-commutative N/A

         \[\leadsto 1 - \log \left(\frac{\mathsf{neg}\left(\color{blue}{\left(-1 \cdot x + 1\right)}\right)}{y}\right) \]

      4. distribute-neg-in N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{\left(\mathsf{neg}\left(-1 \cdot x\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}}{y}\right) \]

      5. mul-1-neg N/A

         \[\leadsto 1 - \log \left(\frac{\left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(x\right)\right)}\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      6. remove-double-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x} + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      7. sub-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

      9. lower--.f64 99.1

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

   5. Applied rewrites 99.1%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

   if 2.0000000000000001e-4 < (-.f64 #s(literal 1 binary64) (/.f64 (-.f64 x y) (-.f64 #s(literal 1 binary64) y)))

   1. Initial program 100.0%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-log.f64 N/A

         \[\leadsto 1 - \color{blue}{\log \left(1 - \frac{x - y}{1 - y}\right)} \]

      2. lift--.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(1 - \frac{x - y}{1 - y}\right)} \]

      3. sub-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(1 + \left(\mathsf{neg}\left(\frac{x - y}{1 - y}\right)\right)\right)} \]

      4. lower-log1p.f64 N/A

         \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(\mathsf{neg}\left(\frac{x - y}{1 - y}\right)\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\mathsf{neg}\left(\color{blue}{\frac{x - y}{1 - y}}\right)\right) \]

      6. distribute-neg-frac N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\frac{\mathsf{neg}\left(\left(x - y\right)\right)}{1 - y}}\right) \]

      7. lower-/.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\frac{\mathsf{neg}\left(\left(x - y\right)\right)}{1 - y}}\right) \]

      8. neg-sub0 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{0 - \left(x - y\right)}}{1 - y}\right) \]

      9. lift--.f64 N/A

         \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(x - y\right)}}{1 - y}\right) \]

      10. sub-neg N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(x + \left(\mathsf{neg}\left(y\right)\right)\right)}}{1 - y}\right) \]

      11. +-commutative N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{0 - \color{blue}{\left(\left(\mathsf{neg}\left(y\right)\right) + x\right)}}{1 - y}\right) \]

      12. associate--r+ N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{\left(0 - \left(\mathsf{neg}\left(y\right)\right)\right) - x}}{1 - y}\right) \]

      13. neg-sub0 N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(y\right)\right)\right)\right)} - x}{1 - y}\right) \]

      14. remove-double-neg N/A

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{y} - x}{1 - y}\right) \]

      15. lower--.f64 100.0

          \[\leadsto 1 - \mathsf{log1p}\left(\frac{\color{blue}{y - x}}{1 - y}\right) \]

   4. Applied rewrites 100.0%

      \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(\frac{y - x}{1 - y}\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;1 - \frac{y - x}{-1 + y} \leq 0.0002:\\ \;\;\;\;1 - \log \left(\frac{x - 1}{y}\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \mathsf{log1p}\left(\frac{y - x}{1 - y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 89.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -17.5:\\ \;\;\;\;1 - \log \left(\frac{-1}{y}\right)\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;1 - \left(\mathsf{log1p}\left(-x\right) + y\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{x}{y}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -17.5)
   (- 1.0 (log (/ -1.0 y)))
   (if (<= y 1.0) (- 1.0 (+ (log1p (- x)) y)) (- 1.0 (log (/ x y))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -17.5) {
		tmp = 1.0 - log((-1.0 / y));
	} else if (y <= 1.0) {
		tmp = 1.0 - (log1p(-x) + y);
	} else {
		tmp = 1.0 - log((x / y));
	}
	return tmp;
}

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -17.5) {
		tmp = 1.0 - Math.log((-1.0 / y));
	} else if (y <= 1.0) {
		tmp = 1.0 - (Math.log1p(-x) + y);
	} else {
		tmp = 1.0 - Math.log((x / y));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -17.5:
		tmp = 1.0 - math.log((-1.0 / y))
	elif y <= 1.0:
		tmp = 1.0 - (math.log1p(-x) + y)
	else:
		tmp = 1.0 - math.log((x / y))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -17.5)
		tmp = Float64(1.0 - log(Float64(-1.0 / y)));
	elseif (y <= 1.0)
		tmp = Float64(1.0 - Float64(log1p(Float64(-x)) + y));
	else
		tmp = Float64(1.0 - log(Float64(x / y)));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, -17.5], N[(1.0 - N[Log[N[(-1.0 / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.0], N[(1.0 - N[(N[Log[1 + (-x)], $MachinePrecision] + y), $MachinePrecision]), $MachinePrecision], N[(1.0 - N[Log[N[(x / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -17.5

   1. Initial program 31.0%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto 1 - \log \color{blue}{\left(-1 \cdot \frac{1 + -1 \cdot x}{y}\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\mathsf{neg}\left(\frac{1 + -1 \cdot x}{y}\right)\right)} \]

      2. distribute-frac-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{\mathsf{neg}\left(\left(1 + -1 \cdot x\right)\right)}{y}\right)} \]

      3. +-commutative N/A

         \[\leadsto 1 - \log \left(\frac{\mathsf{neg}\left(\color{blue}{\left(-1 \cdot x + 1\right)}\right)}{y}\right) \]

      4. distribute-neg-in N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{\left(\mathsf{neg}\left(-1 \cdot x\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}}{y}\right) \]

      5. mul-1-neg N/A

         \[\leadsto 1 - \log \left(\frac{\left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(x\right)\right)}\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      6. remove-double-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x} + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      7. sub-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

      9. lower--.f64 97.6

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

   5. Applied rewrites 97.6%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

   6. Taylor expanded in x around 0

      \[\leadsto 1 - \log \left(\frac{-1}{y}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 61.1%

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

   if -17.5 < y < 1

   1. Initial program 100.0%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto 1 - \color{blue}{\left(\log \left(1 - x\right) + y \cdot \left(-1 \cdot \frac{x}{1 - x} + \frac{1}{1 - x}\right)\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. +-commutative N/A

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

      3. mul-1-neg N/A

         \[\leadsto 1 - \left(y \cdot \left(\frac{1}{1 - x} + \color{blue}{\left(\mathsf{neg}\left(\frac{x}{1 - x}\right)\right)}\right) + \log \left(1 - x\right)\right) \]

      4. sub-neg N/A

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

      5. sub-neg N/A

         \[\leadsto 1 - \left(y \cdot \left(\frac{1}{\color{blue}{1 + \left(\mathsf{neg}\left(x\right)\right)}} - \frac{x}{1 - x}\right) + \log \left(1 - x\right)\right) \]

      6. mul-1-neg N/A

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

      7. sub-neg N/A

         \[\leadsto 1 - \left(y \cdot \left(\frac{1}{1 + -1 \cdot x} - \frac{x}{\color{blue}{1 + \left(\mathsf{neg}\left(x\right)\right)}}\right) + \log \left(1 - x\right)\right) \]

      8. mul-1-neg N/A

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

      9. div-sub N/A

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

      10. sub-neg N/A

          \[\leadsto 1 - \left(y \cdot \frac{\color{blue}{1 + \left(\mathsf{neg}\left(x\right)\right)}}{1 + -1 \cdot x} + \log \left(1 - x\right)\right) \]

      11. mul-1-neg N/A

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

      12. *-inverses N/A

          \[\leadsto 1 - \left(y \cdot \color{blue}{1} + \log \left(1 - x\right)\right) \]

      13. *-rgt-identity N/A

          \[\leadsto 1 - \left(\color{blue}{y} + \log \left(1 - x\right)\right) \]

      14. lower-+.f64 N/A

          \[\leadsto 1 - \color{blue}{\left(y + \log \left(1 - x\right)\right)} \]

      15. sub-neg N/A

          \[\leadsto 1 - \left(y + \log \color{blue}{\left(1 + \left(\mathsf{neg}\left(x\right)\right)\right)}\right) \]

      16. mul-1-neg N/A

          \[\leadsto 1 - \left(y + \log \left(1 + \color{blue}{-1 \cdot x}\right)\right) \]

   5. Applied rewrites 99.2%

      \[\leadsto 1 - \color{blue}{\left(y + \mathsf{log1p}\left(-x\right)\right)} \]

   if 1 < y

   1. Initial program 57.3%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto 1 - \log \color{blue}{\left(-1 \cdot \frac{1 + -1 \cdot x}{y}\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\mathsf{neg}\left(\frac{1 + -1 \cdot x}{y}\right)\right)} \]

      2. distribute-frac-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{\mathsf{neg}\left(\left(1 + -1 \cdot x\right)\right)}{y}\right)} \]

      3. +-commutative N/A

         \[\leadsto 1 - \log \left(\frac{\mathsf{neg}\left(\color{blue}{\left(-1 \cdot x + 1\right)}\right)}{y}\right) \]

      4. distribute-neg-in N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{\left(\mathsf{neg}\left(-1 \cdot x\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}}{y}\right) \]

      5. mul-1-neg N/A

         \[\leadsto 1 - \log \left(\frac{\left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(x\right)\right)}\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      6. remove-double-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x} + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      7. sub-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

      9. lower--.f64 96.4

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

   5. Applied rewrites 96.4%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

   6. Taylor expanded in x around inf

      \[\leadsto 1 - \log \left(\frac{x}{\color{blue}{y}}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 93.5%

      \[\leadsto 1 - \log \left(\frac{x}{\color{blue}{y}}\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 87.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -17.5:\\ \;\;\;\;1 - \log \left(\frac{-1}{y}\right)\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;1 - \left(\mathsf{log1p}\left(-x\right) + y\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{x}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 79.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \log \left(\frac{x}{y}\right)\\ \mathbf{if}\;y \leq -165:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;1 - \left(\mathsf{log1p}\left(-x\right) + y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (log (/ x y)))))
   (if (<= y -165.0) t_0 (if (<= y 1.0) (- 1.0 (+ (log1p (- x)) y)) t_0))))

C

double code(double x, double y) {
	double t_0 = 1.0 - log((x / y));
	double tmp;
	if (y <= -165.0) {
		tmp = t_0;
	} else if (y <= 1.0) {
		tmp = 1.0 - (log1p(-x) + y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Java

public static double code(double x, double y) {
	double t_0 = 1.0 - Math.log((x / y));
	double tmp;
	if (y <= -165.0) {
		tmp = t_0;
	} else if (y <= 1.0) {
		tmp = 1.0 - (Math.log1p(-x) + y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - math.log((x / y))
	tmp = 0
	if y <= -165.0:
		tmp = t_0
	elif y <= 1.0:
		tmp = 1.0 - (math.log1p(-x) + y)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - log(Float64(x / y)))
	tmp = 0.0
	if (y <= -165.0)
		tmp = t_0;
	elseif (y <= 1.0)
		tmp = Float64(1.0 - Float64(log1p(Float64(-x)) + y));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 - N[Log[N[(x / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -165.0], t$95$0, If[LessEqual[y, 1.0], N[(1.0 - N[(N[Log[1 + (-x)], $MachinePrecision] + y), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -165 or 1 < y

   1. Initial program 38.2%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto 1 - \log \color{blue}{\left(-1 \cdot \frac{1 + -1 \cdot x}{y}\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\mathsf{neg}\left(\frac{1 + -1 \cdot x}{y}\right)\right)} \]

      2. distribute-frac-neg N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{\mathsf{neg}\left(\left(1 + -1 \cdot x\right)\right)}{y}\right)} \]

      3. +-commutative N/A

         \[\leadsto 1 - \log \left(\frac{\mathsf{neg}\left(\color{blue}{\left(-1 \cdot x + 1\right)}\right)}{y}\right) \]

      4. distribute-neg-in N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{\left(\mathsf{neg}\left(-1 \cdot x\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}}{y}\right) \]

      5. mul-1-neg N/A

         \[\leadsto 1 - \log \left(\frac{\left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(x\right)\right)}\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      6. remove-double-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x} + \left(\mathsf{neg}\left(1\right)\right)}{y}\right) \]

      7. sub-neg N/A

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

      9. lower--.f64 97.9

         \[\leadsto 1 - \log \left(\frac{\color{blue}{x - 1}}{y}\right) \]

   5. Applied rewrites 97.9%

      \[\leadsto 1 - \log \color{blue}{\left(\frac{x - 1}{y}\right)} \]

   6. Taylor expanded in x around inf

      \[\leadsto 1 - \log \left(\frac{x}{\color{blue}{y}}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 57.4%

      \[\leadsto 1 - \log \left(\frac{x}{\color{blue}{y}}\right) \]

   if -165 < y < 1

   1. Initial program 100.0%

      \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto 1 - \color{blue}{\left(\log \left(1 - x\right) + y \cdot \left(-1 \cdot \frac{x}{1 - x} + \frac{1}{1 - x}\right)\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. +-commutative N/A

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

      3. mul-1-neg N/A

         \[\leadsto 1 - \left(y \cdot \left(\frac{1}{1 - x} + \color{blue}{\left(\mathsf{neg}\left(\frac{x}{1 - x}\right)\right)}\right) + \log \left(1 - x\right)\right) \]

      4. sub-neg N/A

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

      5. sub-neg N/A

         \[\leadsto 1 - \left(y \cdot \left(\frac{1}{\color{blue}{1 + \left(\mathsf{neg}\left(x\right)\right)}} - \frac{x}{1 - x}\right) + \log \left(1 - x\right)\right) \]

      6. mul-1-neg N/A

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

      7. sub-neg N/A

         \[\leadsto 1 - \left(y \cdot \left(\frac{1}{1 + -1 \cdot x} - \frac{x}{\color{blue}{1 + \left(\mathsf{neg}\left(x\right)\right)}}\right) + \log \left(1 - x\right)\right) \]

      8. mul-1-neg N/A

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

      9. div-sub N/A

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

      10. sub-neg N/A

          \[\leadsto 1 - \left(y \cdot \frac{\color{blue}{1 + \left(\mathsf{neg}\left(x\right)\right)}}{1 + -1 \cdot x} + \log \left(1 - x\right)\right) \]

      11. mul-1-neg N/A

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

      12. *-inverses N/A

          \[\leadsto 1 - \left(y \cdot \color{blue}{1} + \log \left(1 - x\right)\right) \]

      13. *-rgt-identity N/A

          \[\leadsto 1 - \left(\color{blue}{y} + \log \left(1 - x\right)\right) \]

      14. lower-+.f64 N/A

          \[\leadsto 1 - \color{blue}{\left(y + \log \left(1 - x\right)\right)} \]

      15. sub-neg N/A

          \[\leadsto 1 - \left(y + \log \color{blue}{\left(1 + \left(\mathsf{neg}\left(x\right)\right)\right)}\right) \]

      16. mul-1-neg N/A

          \[\leadsto 1 - \left(y + \log \left(1 + \color{blue}{-1 \cdot x}\right)\right) \]

   5. Applied rewrites 98.6%

      \[\leadsto 1 - \color{blue}{\left(y + \mathsf{log1p}\left(-x\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 82.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -165:\\ \;\;\;\;1 - \log \left(\frac{x}{y}\right)\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;1 - \left(\mathsf{log1p}\left(-x\right) + y\right)\\ \mathbf{else}:\\ \;\;\;\;1 - \log \left(\frac{x}{y}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 62.5% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ 1 - \mathsf{log1p}\left(-x\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- 1.0 (log1p (- x))))

C

double code(double x, double y) {
	return 1.0 - log1p(-x);
}

Java

public static double code(double x, double y) {
	return 1.0 - Math.log1p(-x);
}

Python

def code(x, y):
	return 1.0 - math.log1p(-x)

Julia

function code(x, y)
	return Float64(1.0 - log1p(Float64(-x)))
end

Wolfram

code[x_, y_] := N[(1.0 - N[Log[1 + (-x)], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 75.6%

   \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto 1 - \color{blue}{\log \left(1 - x\right)} \]
4. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto 1 - \log \color{blue}{\left(1 + \left(\mathsf{neg}\left(x\right)\right)\right)} \]

   2. mul-1-neg N/A

      \[\leadsto 1 - \log \left(1 + \color{blue}{-1 \cdot x}\right) \]

   3. lower-log1p.f64 N/A

      \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(-1 \cdot x\right)} \]

   4. mul-1-neg N/A

      \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\mathsf{neg}\left(x\right)}\right) \]

   5. lower-neg.f64 62.4

      \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{-x}\right) \]

5. Applied rewrites 62.4%

   \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(-x\right)} \]
6. Add Preprocessing

Alternative 10: 43.1% accurate, 20.7× speedup

Math

\[\begin{array}{l} \\ 1 - \left(-x\right) \end{array} \]

FPCore

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

C

double code(double x, double y) {
	return 1.0 - -x;
}

Fortran

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

Java

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

Python

def code(x, y):
	return 1.0 - -x

Julia

function code(x, y)
	return Float64(1.0 - Float64(-x))
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 - -x;
end

Wolfram

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

Derivation

1. Initial program 75.6%

   \[1 - \log \left(1 - \frac{x - y}{1 - y}\right) \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto 1 - \color{blue}{\log \left(1 - x\right)} \]
4. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto 1 - \log \color{blue}{\left(1 + \left(\mathsf{neg}\left(x\right)\right)\right)} \]

   2. mul-1-neg N/A

      \[\leadsto 1 - \log \left(1 + \color{blue}{-1 \cdot x}\right) \]

   3. lower-log1p.f64 N/A

      \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(-1 \cdot x\right)} \]

   4. mul-1-neg N/A

      \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{\mathsf{neg}\left(x\right)}\right) \]

   5. lower-neg.f64 62.4

      \[\leadsto 1 - \mathsf{log1p}\left(\color{blue}{-x}\right) \]

5. Applied rewrites 62.4%

   \[\leadsto 1 - \color{blue}{\mathsf{log1p}\left(-x\right)} \]

6. Taylor expanded in x around 0

   \[\leadsto 1 - -1 \cdot \color{blue}{x} \]
7. Step-by-step derivation

8. Applied rewrites 43.1%

   \[\leadsto 1 - \left(-x\right) \]
9. Add Preprocessing

Developer Target 1: 99.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \log \left(\frac{x}{y \cdot y} - \left(\frac{1}{y} - \frac{x}{y}\right)\right)\\ \mathbf{if}\;y < -81284752.61947241:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y < 3.0094271212461764 \cdot 10^{+25}:\\ \;\;\;\;\log \left(\frac{e^{1}}{1 - \frac{x - y}{1 - y}}\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (log (- (/ x (* y y)) (- (/ 1.0 y) (/ x y)))))))
   (if (< y -81284752.61947241)
     t_0
     (if (< y 3.0094271212461764e+25)
       (log (/ (exp 1.0) (- 1.0 (/ (- x y) (- 1.0 y)))))
       t_0))))

C

double code(double x, double y) {
	double t_0 = 1.0 - log(((x / (y * y)) - ((1.0 / y) - (x / y))));
	double tmp;
	if (y < -81284752.61947241) {
		tmp = t_0;
	} else if (y < 3.0094271212461764e+25) {
		tmp = log((exp(1.0) / (1.0 - ((x - y) / (1.0 - y)))));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 - log(((x / (y * y)) - ((1.0d0 / y) - (x / y))))
    if (y < (-81284752.61947241d0)) then
        tmp = t_0
    else if (y < 3.0094271212461764d+25) then
        tmp = log((exp(1.0d0) / (1.0d0 - ((x - y) / (1.0d0 - y)))))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 1.0 - Math.log(((x / (y * y)) - ((1.0 / y) - (x / y))));
	double tmp;
	if (y < -81284752.61947241) {
		tmp = t_0;
	} else if (y < 3.0094271212461764e+25) {
		tmp = Math.log((Math.exp(1.0) / (1.0 - ((x - y) / (1.0 - y)))));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - math.log(((x / (y * y)) - ((1.0 / y) - (x / y))))
	tmp = 0
	if y < -81284752.61947241:
		tmp = t_0
	elif y < 3.0094271212461764e+25:
		tmp = math.log((math.exp(1.0) / (1.0 - ((x - y) / (1.0 - y)))))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - log(Float64(Float64(x / Float64(y * y)) - Float64(Float64(1.0 / y) - Float64(x / y)))))
	tmp = 0.0
	if (y < -81284752.61947241)
		tmp = t_0;
	elseif (y < 3.0094271212461764e+25)
		tmp = log(Float64(exp(1.0) / Float64(1.0 - Float64(Float64(x - y) / Float64(1.0 - y)))));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 1.0 - log(((x / (y * y)) - ((1.0 / y) - (x / y))));
	tmp = 0.0;
	if (y < -81284752.61947241)
		tmp = t_0;
	elseif (y < 3.0094271212461764e+25)
		tmp = log((exp(1.0) / (1.0 - ((x - y) / (1.0 - y)))));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 - N[Log[N[(N[(x / N[(y * y), $MachinePrecision]), $MachinePrecision] - N[(N[(1.0 / y), $MachinePrecision] - N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[Less[y, -81284752.61947241], t$95$0, If[Less[y, 3.0094271212461764e+25], N[Log[N[(N[Exp[1.0], $MachinePrecision] / N[(1.0 - N[(N[(x - y), $MachinePrecision] / N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], t$95$0]]]

Reproduce

herbie shell --seed 2024268 
(FPCore (x y)
  :name "Numeric.SpecFunctions:invIncompleteGamma from math-functions-0.1.5.2, B"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< y -8128475261947241/100000000) (- 1 (log (- (/ x (* y y)) (- (/ 1 y) (/ x y))))) (if (< y 30094271212461764000000000) (log (/ (exp 1) (- 1 (/ (- x y) (- 1 y))))) (- 1 (log (- (/ x (* y y)) (- (/ 1 y) (/ x y))))))))

  (- 1.0 (log (- 1.0 (/ (- x y) (- 1.0 y))))))