Development.Shake.Progress:message from shake-0.15.5

Percentage Accurate: 99.4% → 99.7%

Time: 4.5s

Alternatives: 6

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x \cdot 100}{x + y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (* x 100.0) (+ x y)))

C

double code(double x, double y) {
	return (x * 100.0) / (x + y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 100.0d0) / (x + y)
end function

Java

public static double code(double x, double y) {
	return (x * 100.0) / (x + y);
}

Python

def code(x, y):
	return (x * 100.0) / (x + y)

Julia

function code(x, y)
	return Float64(Float64(x * 100.0) / Float64(x + y))
end

MATLAB

function tmp = code(x, y)
	tmp = (x * 100.0) / (x + y);
end

Wolfram

code[x_, y_] := N[(N[(x * 100.0), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x \cdot 100}{x + y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (* x 100.0) (+ x y)))

C

double code(double x, double y) {
	return (x * 100.0) / (x + y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * 100.0d0) / (x + y)
end function

Java

public static double code(double x, double y) {
	return (x * 100.0) / (x + y);
}

Python

def code(x, y):
	return (x * 100.0) / (x + y)

Julia

function code(x, y)
	return Float64(Float64(x * 100.0) / Float64(x + y))
end

MATLAB

function tmp = code(x, y)
	tmp = (x * 100.0) / (x + y);
end

Wolfram

code[x_, y_] := N[(N[(x * 100.0), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \frac{100}{x + y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (/ 100.0 (+ x y))))

C

double code(double x, double y) {
	return x * (100.0 / (x + y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (100.0d0 / (x + y))
end function

Java

public static double code(double x, double y) {
	return x * (100.0 / (x + y));
}

Python

def code(x, y):
	return x * (100.0 / (x + y))

Julia

function code(x, y)
	return Float64(x * Float64(100.0 / Float64(x + y)))
end

MATLAB

function tmp = code(x, y)
	tmp = x * (100.0 / (x + y));
end

Wolfram

code[x_, y_] := N[(x * N[(100.0 / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\frac{x \cdot 100}{x + y} \]
2. Step-by-step derivation

   1. associate-*r/ 99.8%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

4. Final simplification 99.8%

   \[\leadsto x \cdot \frac{100}{x + y} \]

Alternative 2: 73.2% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7 \cdot 10^{+46} \lor \neg \left(y \leq -4.8 \cdot 10^{+28} \lor \neg \left(y \leq -3.9 \cdot 10^{-70}\right) \land \left(y \leq 5.2 \lor \neg \left(y \leq 3 \cdot 10^{+29}\right) \land y \leq 1.25 \cdot 10^{+79}\right)\right):\\ \;\;\;\;100 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -7e+46)
         (not
          (or (<= y -4.8e+28)
              (and (not (<= y -3.9e-70))
                   (or (<= y 5.2) (and (not (<= y 3e+29)) (<= y 1.25e+79)))))))
   (* 100.0 (/ x y))
   100.0))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -7e+46) || !((y <= -4.8e+28) || (!(y <= -3.9e-70) && ((y <= 5.2) || (!(y <= 3e+29) && (y <= 1.25e+79)))))) {
		tmp = 100.0 * (x / y);
	} else {
		tmp = 100.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-7d+46)) .or. (.not. (y <= (-4.8d+28)) .or. (.not. (y <= (-3.9d-70))) .and. (y <= 5.2d0) .or. (.not. (y <= 3d+29)) .and. (y <= 1.25d+79))) then
        tmp = 100.0d0 * (x / y)
    else
        tmp = 100.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -7e+46) || !((y <= -4.8e+28) || (!(y <= -3.9e-70) && ((y <= 5.2) || (!(y <= 3e+29) && (y <= 1.25e+79)))))) {
		tmp = 100.0 * (x / y);
	} else {
		tmp = 100.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -7e+46) or not ((y <= -4.8e+28) or (not (y <= -3.9e-70) and ((y <= 5.2) or (not (y <= 3e+29) and (y <= 1.25e+79))))):
		tmp = 100.0 * (x / y)
	else:
		tmp = 100.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -7e+46) || !((y <= -4.8e+28) || (!(y <= -3.9e-70) && ((y <= 5.2) || (!(y <= 3e+29) && (y <= 1.25e+79))))))
		tmp = Float64(100.0 * Float64(x / y));
	else
		tmp = 100.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -7e+46) || ~(((y <= -4.8e+28) || (~((y <= -3.9e-70)) && ((y <= 5.2) || (~((y <= 3e+29)) && (y <= 1.25e+79)))))))
		tmp = 100.0 * (x / y);
	else
		tmp = 100.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -7e+46], N[Not[Or[LessEqual[y, -4.8e+28], And[N[Not[LessEqual[y, -3.9e-70]], $MachinePrecision], Or[LessEqual[y, 5.2], And[N[Not[LessEqual[y, 3e+29]], $MachinePrecision], LessEqual[y, 1.25e+79]]]]]], $MachinePrecision]], N[(100.0 * N[(x / y), $MachinePrecision]), $MachinePrecision], 100.0]

Derivation

1. Split input into 2 regimes

2. if y < -6.9999999999999997e46 or -4.79999999999999962e28 < y < -3.90000000000000019e-70 or 5.20000000000000018 < y < 2.9999999999999999e29 or 1.25e79 < y

   1. Initial program 99.7%

      \[\frac{x \cdot 100}{x + y} \]
   2. Step-by-step derivation

      1. associate-*r/ 99.8%

         \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   4. Taylor expanded in x around 0 85.0%

      \[\leadsto \color{blue}{100 \cdot \frac{x}{y}} \]

   if -6.9999999999999997e46 < y < -4.79999999999999962e28 or -3.90000000000000019e-70 < y < 5.20000000000000018 or 2.9999999999999999e29 < y < 1.25e79

   1. Initial program 99.7%

      \[\frac{x \cdot 100}{x + y} \]
   2. Step-by-step derivation

      1. associate-*r/ 99.7%

         \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   4. Taylor expanded in x around inf 80.3%

      \[\leadsto \color{blue}{100} \]
3. Recombined 2 regimes into one program.

4. Final simplification 82.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -7 \cdot 10^{+46} \lor \neg \left(y \leq -4.8 \cdot 10^{+28} \lor \neg \left(y \leq -3.9 \cdot 10^{-70}\right) \land \left(y \leq 5.2 \lor \neg \left(y \leq 3 \cdot 10^{+29}\right) \land y \leq 1.25 \cdot 10^{+79}\right)\right):\\ \;\;\;\;100 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \]

Alternative 3: 73.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.6 \cdot 10^{+47} \lor \neg \left(y \leq -1 \cdot 10^{+30} \lor \neg \left(y \leq -3.8 \cdot 10^{-70}\right) \land \left(y \leq 0.74 \lor \neg \left(y \leq 9.6 \cdot 10^{+28}\right) \land y \leq 3 \cdot 10^{+73}\right)\right):\\ \;\;\;\;x \cdot \frac{100}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -1.6e+47)
         (not
          (or (<= y -1e+30)
              (and (not (<= y -3.8e-70))
                   (or (<= y 0.74) (and (not (<= y 9.6e+28)) (<= y 3e+73)))))))
   (* x (/ 100.0 y))
   100.0))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -1.6e+47) || !((y <= -1e+30) || (!(y <= -3.8e-70) && ((y <= 0.74) || (!(y <= 9.6e+28) && (y <= 3e+73)))))) {
		tmp = x * (100.0 / y);
	} else {
		tmp = 100.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-1.6d+47)) .or. (.not. (y <= (-1d+30)) .or. (.not. (y <= (-3.8d-70))) .and. (y <= 0.74d0) .or. (.not. (y <= 9.6d+28)) .and. (y <= 3d+73))) then
        tmp = x * (100.0d0 / y)
    else
        tmp = 100.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -1.6e+47) || !((y <= -1e+30) || (!(y <= -3.8e-70) && ((y <= 0.74) || (!(y <= 9.6e+28) && (y <= 3e+73)))))) {
		tmp = x * (100.0 / y);
	} else {
		tmp = 100.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -1.6e+47) or not ((y <= -1e+30) or (not (y <= -3.8e-70) and ((y <= 0.74) or (not (y <= 9.6e+28) and (y <= 3e+73))))):
		tmp = x * (100.0 / y)
	else:
		tmp = 100.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -1.6e+47) || !((y <= -1e+30) || (!(y <= -3.8e-70) && ((y <= 0.74) || (!(y <= 9.6e+28) && (y <= 3e+73))))))
		tmp = Float64(x * Float64(100.0 / y));
	else
		tmp = 100.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -1.6e+47) || ~(((y <= -1e+30) || (~((y <= -3.8e-70)) && ((y <= 0.74) || (~((y <= 9.6e+28)) && (y <= 3e+73)))))))
		tmp = x * (100.0 / y);
	else
		tmp = 100.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -1.6e+47], N[Not[Or[LessEqual[y, -1e+30], And[N[Not[LessEqual[y, -3.8e-70]], $MachinePrecision], Or[LessEqual[y, 0.74], And[N[Not[LessEqual[y, 9.6e+28]], $MachinePrecision], LessEqual[y, 3e+73]]]]]], $MachinePrecision]], N[(x * N[(100.0 / y), $MachinePrecision]), $MachinePrecision], 100.0]

Derivation

1. Split input into 2 regimes

2. if y < -1.6e47 or -1e30 < y < -3.7999999999999998e-70 or 0.73999999999999999 < y < 9.59999999999999925e28 or 3.00000000000000011e73 < y

   1. Initial program 99.7%

      \[\frac{x \cdot 100}{x + y} \]
   2. Step-by-step derivation

      1. associate-*r/ 99.8%

         \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   4. Taylor expanded in x around 0 85.5%

      \[\leadsto x \cdot \color{blue}{\frac{100}{y}} \]

   if -1.6e47 < y < -1e30 or -3.7999999999999998e-70 < y < 0.73999999999999999 or 9.59999999999999925e28 < y < 3.00000000000000011e73

   1. Initial program 99.7%

      \[\frac{x \cdot 100}{x + y} \]
   2. Step-by-step derivation

      1. associate-*r/ 99.7%

         \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   4. Taylor expanded in x around inf 80.3%

      \[\leadsto \color{blue}{100} \]
3. Recombined 2 regimes into one program.

4. Final simplification 82.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.6 \cdot 10^{+47} \lor \neg \left(y \leq -1 \cdot 10^{+30} \lor \neg \left(y \leq -3.8 \cdot 10^{-70}\right) \land \left(y \leq 0.74 \lor \neg \left(y \leq 9.6 \cdot 10^{+28}\right) \land y \leq 3 \cdot 10^{+73}\right)\right):\\ \;\;\;\;x \cdot \frac{100}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \]

Alternative 4: 73.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.8 \cdot 10^{+46}:\\ \;\;\;\;\frac{x}{y \cdot 0.01}\\ \mathbf{elif}\;y \leq -2.9 \cdot 10^{+31}:\\ \;\;\;\;100\\ \mathbf{elif}\;\neg \left(y \leq -3.8 \cdot 10^{-70}\right) \land \left(y \leq 450 \lor \neg \left(y \leq 3.1 \cdot 10^{+29}\right) \land y \leq 10^{+78}\right):\\ \;\;\;\;100\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{100}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -7.8e+46)
   (/ x (* y 0.01))
   (if (<= y -2.9e+31)
     100.0
     (if (and (not (<= y -3.8e-70))
              (or (<= y 450.0) (and (not (<= y 3.1e+29)) (<= y 1e+78))))
       100.0
       (* x (/ 100.0 y))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -7.8e+46) {
		tmp = x / (y * 0.01);
	} else if (y <= -2.9e+31) {
		tmp = 100.0;
	} else if (!(y <= -3.8e-70) && ((y <= 450.0) || (!(y <= 3.1e+29) && (y <= 1e+78)))) {
		tmp = 100.0;
	} else {
		tmp = x * (100.0 / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-7.8d+46)) then
        tmp = x / (y * 0.01d0)
    else if (y <= (-2.9d+31)) then
        tmp = 100.0d0
    else if ((.not. (y <= (-3.8d-70))) .and. (y <= 450.0d0) .or. (.not. (y <= 3.1d+29)) .and. (y <= 1d+78)) then
        tmp = 100.0d0
    else
        tmp = x * (100.0d0 / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -7.8e+46) {
		tmp = x / (y * 0.01);
	} else if (y <= -2.9e+31) {
		tmp = 100.0;
	} else if (!(y <= -3.8e-70) && ((y <= 450.0) || (!(y <= 3.1e+29) && (y <= 1e+78)))) {
		tmp = 100.0;
	} else {
		tmp = x * (100.0 / y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -7.8e+46:
		tmp = x / (y * 0.01)
	elif y <= -2.9e+31:
		tmp = 100.0
	elif not (y <= -3.8e-70) and ((y <= 450.0) or (not (y <= 3.1e+29) and (y <= 1e+78))):
		tmp = 100.0
	else:
		tmp = x * (100.0 / y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -7.8e+46)
		tmp = Float64(x / Float64(y * 0.01));
	elseif (y <= -2.9e+31)
		tmp = 100.0;
	elseif (!(y <= -3.8e-70) && ((y <= 450.0) || (!(y <= 3.1e+29) && (y <= 1e+78))))
		tmp = 100.0;
	else
		tmp = Float64(x * Float64(100.0 / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -7.8e+46)
		tmp = x / (y * 0.01);
	elseif (y <= -2.9e+31)
		tmp = 100.0;
	elseif (~((y <= -3.8e-70)) && ((y <= 450.0) || (~((y <= 3.1e+29)) && (y <= 1e+78))))
		tmp = 100.0;
	else
		tmp = x * (100.0 / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -7.8e+46], N[(x / N[(y * 0.01), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -2.9e+31], 100.0, If[And[N[Not[LessEqual[y, -3.8e-70]], $MachinePrecision], Or[LessEqual[y, 450.0], And[N[Not[LessEqual[y, 3.1e+29]], $MachinePrecision], LessEqual[y, 1e+78]]]], 100.0, N[(x * N[(100.0 / y), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -7.7999999999999999e46

   1. Initial program 99.8%

      \[\frac{x \cdot 100}{x + y} \]
   2. Step-by-step derivation

      1. associate-/l* 99.6%

         \[\leadsto \color{blue}{\frac{x}{\frac{x + y}{100}}} \]

   3. Simplified 99.6%

      \[\leadsto \color{blue}{\frac{x}{\frac{x + y}{100}}} \]

   4. Taylor expanded in x around 0 81.9%

      \[\leadsto \frac{x}{\color{blue}{0.01 \cdot y}} \]
   5. Step-by-step derivation

      1. *-commutative 81.9%

         \[\leadsto \frac{x}{\color{blue}{y \cdot 0.01}} \]

   6. Simplified 81.9%

      \[\leadsto \frac{x}{\color{blue}{y \cdot 0.01}} \]

   if -7.7999999999999999e46 < y < -2.9e31 or -3.7999999999999998e-70 < y < 450 or 3.0999999999999999e29 < y < 1.00000000000000001e78

   1. Initial program 99.7%

      \[\frac{x \cdot 100}{x + y} \]
   2. Step-by-step derivation

      1. associate-*r/ 99.7%

         \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   4. Taylor expanded in x around inf 80.3%

      \[\leadsto \color{blue}{100} \]

   if -2.9e31 < y < -3.7999999999999998e-70 or 450 < y < 3.0999999999999999e29 or 1.00000000000000001e78 < y

   1. Initial program 99.7%

      \[\frac{x \cdot 100}{x + y} \]
   2. Step-by-step derivation

      1. associate-*r/ 99.8%

         \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   4. Taylor expanded in x around 0 87.9%

      \[\leadsto x \cdot \color{blue}{\frac{100}{y}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 82.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -7.8 \cdot 10^{+46}:\\ \;\;\;\;\frac{x}{y \cdot 0.01}\\ \mathbf{elif}\;y \leq -2.9 \cdot 10^{+31}:\\ \;\;\;\;100\\ \mathbf{elif}\;\neg \left(y \leq -3.8 \cdot 10^{-70}\right) \land \left(y \leq 450 \lor \neg \left(y \leq 3.1 \cdot 10^{+29}\right) \land y \leq 10^{+78}\right):\\ \;\;\;\;100\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{100}{y}\\ \end{array} \]

Alternative 5: 73.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.5 \cdot 10^{+47}:\\ \;\;\;\;\frac{x \cdot 100}{y}\\ \mathbf{elif}\;y \leq -2.1 \cdot 10^{+31}:\\ \;\;\;\;100\\ \mathbf{elif}\;\neg \left(y \leq -3.9 \cdot 10^{-70}\right) \land \left(y \leq 48 \lor \neg \left(y \leq 2.3 \cdot 10^{+28}\right) \land y \leq 2.2 \cdot 10^{+80}\right):\\ \;\;\;\;100\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{100}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.5e+47)
   (/ (* x 100.0) y)
   (if (<= y -2.1e+31)
     100.0
     (if (and (not (<= y -3.9e-70))
              (or (<= y 48.0) (and (not (<= y 2.3e+28)) (<= y 2.2e+80))))
       100.0
       (* x (/ 100.0 y))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.5e+47) {
		tmp = (x * 100.0) / y;
	} else if (y <= -2.1e+31) {
		tmp = 100.0;
	} else if (!(y <= -3.9e-70) && ((y <= 48.0) || (!(y <= 2.3e+28) && (y <= 2.2e+80)))) {
		tmp = 100.0;
	} else {
		tmp = x * (100.0 / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-1.5d+47)) then
        tmp = (x * 100.0d0) / y
    else if (y <= (-2.1d+31)) then
        tmp = 100.0d0
    else if ((.not. (y <= (-3.9d-70))) .and. (y <= 48.0d0) .or. (.not. (y <= 2.3d+28)) .and. (y <= 2.2d+80)) then
        tmp = 100.0d0
    else
        tmp = x * (100.0d0 / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -1.5e+47) {
		tmp = (x * 100.0) / y;
	} else if (y <= -2.1e+31) {
		tmp = 100.0;
	} else if (!(y <= -3.9e-70) && ((y <= 48.0) || (!(y <= 2.3e+28) && (y <= 2.2e+80)))) {
		tmp = 100.0;
	} else {
		tmp = x * (100.0 / y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.5e+47:
		tmp = (x * 100.0) / y
	elif y <= -2.1e+31:
		tmp = 100.0
	elif not (y <= -3.9e-70) and ((y <= 48.0) or (not (y <= 2.3e+28) and (y <= 2.2e+80))):
		tmp = 100.0
	else:
		tmp = x * (100.0 / y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.5e+47)
		tmp = Float64(Float64(x * 100.0) / y);
	elseif (y <= -2.1e+31)
		tmp = 100.0;
	elseif (!(y <= -3.9e-70) && ((y <= 48.0) || (!(y <= 2.3e+28) && (y <= 2.2e+80))))
		tmp = 100.0;
	else
		tmp = Float64(x * Float64(100.0 / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1.5e+47)
		tmp = (x * 100.0) / y;
	elseif (y <= -2.1e+31)
		tmp = 100.0;
	elseif (~((y <= -3.9e-70)) && ((y <= 48.0) || (~((y <= 2.3e+28)) && (y <= 2.2e+80))))
		tmp = 100.0;
	else
		tmp = x * (100.0 / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.5e+47], N[(N[(x * 100.0), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[y, -2.1e+31], 100.0, If[And[N[Not[LessEqual[y, -3.9e-70]], $MachinePrecision], Or[LessEqual[y, 48.0], And[N[Not[LessEqual[y, 2.3e+28]], $MachinePrecision], LessEqual[y, 2.2e+80]]]], 100.0, N[(x * N[(100.0 / y), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.5000000000000001e47

   1. Initial program 99.8%

      \[\frac{x \cdot 100}{x + y} \]
   2. Step-by-step derivation

      1. associate-*r/ 99.7%

         \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   4. Taylor expanded in x around 0 81.2%

      \[\leadsto \color{blue}{100 \cdot \frac{x}{y}} \]
   5. Step-by-step derivation

      1. *-commutative 81.2%

         \[\leadsto \color{blue}{\frac{x}{y} \cdot 100} \]

      2. associate-*l/ 81.9%

         \[\leadsto \color{blue}{\frac{x \cdot 100}{y}} \]

   6. Applied egg-rr 81.9%

      \[\leadsto \color{blue}{\frac{x \cdot 100}{y}} \]

   if -1.5000000000000001e47 < y < -2.09999999999999979e31 or -3.90000000000000019e-70 < y < 48 or 2.29999999999999984e28 < y < 2.20000000000000003e80

   1. Initial program 99.7%

      \[\frac{x \cdot 100}{x + y} \]
   2. Step-by-step derivation

      1. associate-*r/ 99.7%

         \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   4. Taylor expanded in x around inf 80.3%

      \[\leadsto \color{blue}{100} \]

   if -2.09999999999999979e31 < y < -3.90000000000000019e-70 or 48 < y < 2.29999999999999984e28 or 2.20000000000000003e80 < y

   1. Initial program 99.7%

      \[\frac{x \cdot 100}{x + y} \]
   2. Step-by-step derivation

      1. associate-*r/ 99.8%

         \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

   4. Taylor expanded in x around 0 87.9%

      \[\leadsto x \cdot \color{blue}{\frac{100}{y}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 82.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.5 \cdot 10^{+47}:\\ \;\;\;\;\frac{x \cdot 100}{y}\\ \mathbf{elif}\;y \leq -2.1 \cdot 10^{+31}:\\ \;\;\;\;100\\ \mathbf{elif}\;\neg \left(y \leq -3.9 \cdot 10^{-70}\right) \land \left(y \leq 48 \lor \neg \left(y \leq 2.3 \cdot 10^{+28}\right) \land y \leq 2.2 \cdot 10^{+80}\right):\\ \;\;\;\;100\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{100}{y}\\ \end{array} \]

Alternative 6: 51.1% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ 100 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 100.0)

C

double code(double x, double y) {
	return 100.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 100.0d0
end function

Java

public static double code(double x, double y) {
	return 100.0;
}

Python

def code(x, y):
	return 100.0

Julia

function code(x, y)
	return 100.0
end

MATLAB

function tmp = code(x, y)
	tmp = 100.0;
end

Wolfram

code[x_, y_] := 100.0

Derivation

1. Initial program 99.7%

   \[\frac{x \cdot 100}{x + y} \]
2. Step-by-step derivation

   1. associate-*r/ 99.8%

      \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

3. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \frac{100}{x + y}} \]

4. Taylor expanded in x around inf 49.1%

   \[\leadsto \color{blue}{100} \]

5. Final simplification 49.1%

   \[\leadsto 100 \]

Developer target: 99.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \frac{x}{1} \cdot \frac{100}{x + y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (/ x 1.0) (/ 100.0 (+ x y))))

C

double code(double x, double y) {
	return (x / 1.0) * (100.0 / (x + y));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x / 1.0d0) * (100.0d0 / (x + y))
end function

Java

public static double code(double x, double y) {
	return (x / 1.0) * (100.0 / (x + y));
}

Python

def code(x, y):
	return (x / 1.0) * (100.0 / (x + y))

Julia

function code(x, y)
	return Float64(Float64(x / 1.0) * Float64(100.0 / Float64(x + y)))
end

MATLAB

function tmp = code(x, y)
	tmp = (x / 1.0) * (100.0 / (x + y));
end

Wolfram

code[x_, y_] := N[(N[(x / 1.0), $MachinePrecision] * N[(100.0 / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023297 
(FPCore (x y)
  :name "Development.Shake.Progress:message from shake-0.15.5"
  :precision binary64

  :herbie-target
  (* (/ x 1.0) (/ 100.0 (+ x y)))

  (/ (* x 100.0) (+ x y)))