Development.Shake.Progress:message from shake-0.15.5

Percentage Accurate: 99.4% → 99.4%

Time: 4.6s

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: 99.4% 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]

Derivation

1. Initial program 99.7%

   \[\frac{x \cdot 100}{x + y} \]
2. Add Preprocessing

3. Final simplification 99.7%

   \[\leadsto \frac{x \cdot 100}{x + y} \]
4. Add Preprocessing

Alternative 2: 72.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+83}:\\ \;\;\;\;100\\ \mathbf{elif}\;x \leq -94 \lor \neg \left(x \leq -1.45 \cdot 10^{-42}\right) \land x \leq 1.05 \cdot 10^{+83}:\\ \;\;\;\;100 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -5e+83)
   100.0
   (if (or (<= x -94.0) (and (not (<= x -1.45e-42)) (<= x 1.05e+83)))
     (* 100.0 (/ x y))
     100.0)))

C

double code(double x, double y) {
	double tmp;
	if (x <= -5e+83) {
		tmp = 100.0;
	} else if ((x <= -94.0) || (!(x <= -1.45e-42) && (x <= 1.05e+83))) {
		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 (x <= (-5d+83)) then
        tmp = 100.0d0
    else if ((x <= (-94.0d0)) .or. (.not. (x <= (-1.45d-42))) .and. (x <= 1.05d+83)) 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 (x <= -5e+83) {
		tmp = 100.0;
	} else if ((x <= -94.0) || (!(x <= -1.45e-42) && (x <= 1.05e+83))) {
		tmp = 100.0 * (x / y);
	} else {
		tmp = 100.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -5e+83:
		tmp = 100.0
	elif (x <= -94.0) or (not (x <= -1.45e-42) and (x <= 1.05e+83)):
		tmp = 100.0 * (x / y)
	else:
		tmp = 100.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -5e+83)
		tmp = 100.0;
	elseif ((x <= -94.0) || (!(x <= -1.45e-42) && (x <= 1.05e+83)))
		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 (x <= -5e+83)
		tmp = 100.0;
	elseif ((x <= -94.0) || (~((x <= -1.45e-42)) && (x <= 1.05e+83)))
		tmp = 100.0 * (x / y);
	else
		tmp = 100.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -5e+83], 100.0, If[Or[LessEqual[x, -94.0], And[N[Not[LessEqual[x, -1.45e-42]], $MachinePrecision], LessEqual[x, 1.05e+83]]], N[(100.0 * N[(x / y), $MachinePrecision]), $MachinePrecision], 100.0]]

Derivation

1. Split input into 2 regimes

2. if x < -5.00000000000000029e83 or -94 < x < -1.4500000000000001e-42 or 1.05000000000000001e83 < x

   1. Initial program 99.8%

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

      1. *-commutative 99.8%

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

      2. associate-/l* 99.9%

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

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{100}{\frac{x + y}{x}}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 82.7%

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

   if -5.00000000000000029e83 < x < -94 or -1.4500000000000001e-42 < x < 1.05000000000000001e83

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. associate-/l* 99.5%

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

   3. Simplified 99.5%

      \[\leadsto \color{blue}{\frac{100}{\frac{x + y}{x}}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 77.3%

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

4. Final simplification 79.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+83}:\\ \;\;\;\;100\\ \mathbf{elif}\;x \leq -94 \lor \neg \left(x \leq -1.45 \cdot 10^{-42}\right) \land x \leq 1.05 \cdot 10^{+83}:\\ \;\;\;\;100 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 72.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+83}:\\ \;\;\;\;100\\ \mathbf{elif}\;x \leq -85 \lor \neg \left(x \leq -1.25 \cdot 10^{-42}\right) \land x \leq 6.8 \cdot 10^{+82}:\\ \;\;\;\;x \cdot \frac{100}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -5e+83)
   100.0
   (if (or (<= x -85.0) (and (not (<= x -1.25e-42)) (<= x 6.8e+82)))
     (* x (/ 100.0 y))
     100.0)))

C

double code(double x, double y) {
	double tmp;
	if (x <= -5e+83) {
		tmp = 100.0;
	} else if ((x <= -85.0) || (!(x <= -1.25e-42) && (x <= 6.8e+82))) {
		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 (x <= (-5d+83)) then
        tmp = 100.0d0
    else if ((x <= (-85.0d0)) .or. (.not. (x <= (-1.25d-42))) .and. (x <= 6.8d+82)) 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 (x <= -5e+83) {
		tmp = 100.0;
	} else if ((x <= -85.0) || (!(x <= -1.25e-42) && (x <= 6.8e+82))) {
		tmp = x * (100.0 / y);
	} else {
		tmp = 100.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -5e+83:
		tmp = 100.0
	elif (x <= -85.0) or (not (x <= -1.25e-42) and (x <= 6.8e+82)):
		tmp = x * (100.0 / y)
	else:
		tmp = 100.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -5e+83)
		tmp = 100.0;
	elseif ((x <= -85.0) || (!(x <= -1.25e-42) && (x <= 6.8e+82)))
		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 (x <= -5e+83)
		tmp = 100.0;
	elseif ((x <= -85.0) || (~((x <= -1.25e-42)) && (x <= 6.8e+82)))
		tmp = x * (100.0 / y);
	else
		tmp = 100.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -5e+83], 100.0, If[Or[LessEqual[x, -85.0], And[N[Not[LessEqual[x, -1.25e-42]], $MachinePrecision], LessEqual[x, 6.8e+82]]], N[(x * N[(100.0 / y), $MachinePrecision]), $MachinePrecision], 100.0]]

Derivation

1. Split input into 2 regimes

2. if x < -5.00000000000000029e83 or -85 < x < -1.25000000000000001e-42 or 6.79999999999999989e82 < x

   1. Initial program 99.8%

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

      1. *-commutative 99.8%

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

      2. associate-/l* 99.9%

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

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{100}{\frac{x + y}{x}}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 82.7%

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

   if -5.00000000000000029e83 < x < -85 or -1.25000000000000001e-42 < x < 6.79999999999999989e82

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. associate-/l* 99.5%

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

   3. Simplified 99.5%

      \[\leadsto \color{blue}{\frac{100}{\frac{x + y}{x}}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 77.3%

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

      1. associate-*r/ 77.3%

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

      2. associate-/l* 77.1%

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

      3. associate-/r/ 77.3%

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

   7. Simplified 77.3%

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

4. Final simplification 79.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+83}:\\ \;\;\;\;100\\ \mathbf{elif}\;x \leq -85 \lor \neg \left(x \leq -1.25 \cdot 10^{-42}\right) \land x \leq 6.8 \cdot 10^{+82}:\\ \;\;\;\;x \cdot \frac{100}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 72.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+83}:\\ \;\;\;\;100\\ \mathbf{elif}\;x \leq -175:\\ \;\;\;\;x \cdot \frac{100}{y}\\ \mathbf{elif}\;x \leq -5.5 \cdot 10^{-43}:\\ \;\;\;\;100\\ \mathbf{elif}\;x \leq 1.15 \cdot 10^{+83}:\\ \;\;\;\;\frac{x \cdot 100}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -5e+83)
   100.0
   (if (<= x -175.0)
     (* x (/ 100.0 y))
     (if (<= x -5.5e-43) 100.0 (if (<= x 1.15e+83) (/ (* x 100.0) y) 100.0)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -5e+83) {
		tmp = 100.0;
	} else if (x <= -175.0) {
		tmp = x * (100.0 / y);
	} else if (x <= -5.5e-43) {
		tmp = 100.0;
	} else if (x <= 1.15e+83) {
		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 (x <= (-5d+83)) then
        tmp = 100.0d0
    else if (x <= (-175.0d0)) then
        tmp = x * (100.0d0 / y)
    else if (x <= (-5.5d-43)) then
        tmp = 100.0d0
    else if (x <= 1.15d+83) 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 (x <= -5e+83) {
		tmp = 100.0;
	} else if (x <= -175.0) {
		tmp = x * (100.0 / y);
	} else if (x <= -5.5e-43) {
		tmp = 100.0;
	} else if (x <= 1.15e+83) {
		tmp = (x * 100.0) / y;
	} else {
		tmp = 100.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -5e+83:
		tmp = 100.0
	elif x <= -175.0:
		tmp = x * (100.0 / y)
	elif x <= -5.5e-43:
		tmp = 100.0
	elif x <= 1.15e+83:
		tmp = (x * 100.0) / y
	else:
		tmp = 100.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -5e+83)
		tmp = 100.0;
	elseif (x <= -175.0)
		tmp = Float64(x * Float64(100.0 / y));
	elseif (x <= -5.5e-43)
		tmp = 100.0;
	elseif (x <= 1.15e+83)
		tmp = Float64(Float64(x * 100.0) / y);
	else
		tmp = 100.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -5e+83)
		tmp = 100.0;
	elseif (x <= -175.0)
		tmp = x * (100.0 / y);
	elseif (x <= -5.5e-43)
		tmp = 100.0;
	elseif (x <= 1.15e+83)
		tmp = (x * 100.0) / y;
	else
		tmp = 100.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -5e+83], 100.0, If[LessEqual[x, -175.0], N[(x * N[(100.0 / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -5.5e-43], 100.0, If[LessEqual[x, 1.15e+83], N[(N[(x * 100.0), $MachinePrecision] / y), $MachinePrecision], 100.0]]]]

Derivation

1. Split input into 3 regimes

2. if x < -5.00000000000000029e83 or -175 < x < -5.50000000000000013e-43 or 1.14999999999999997e83 < x

   1. Initial program 99.8%

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

      1. *-commutative 99.8%

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

      2. associate-/l* 99.9%

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

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\frac{100}{\frac{x + y}{x}}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 82.7%

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

   if -5.00000000000000029e83 < x < -175

   1. Initial program 99.3%

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

      1. *-commutative 99.3%

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

      2. associate-/l* 99.6%

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

   3. Simplified 99.6%

      \[\leadsto \color{blue}{\frac{100}{\frac{x + y}{x}}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 92.7%

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

      1. associate-*r/ 92.5%

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

      2. associate-/l* 92.7%

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

      3. associate-/r/ 92.8%

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

   7. Simplified 92.8%

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

   if -5.50000000000000013e-43 < x < 1.14999999999999997e83

   1. Initial program 99.7%

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

      1. *-commutative 99.7%

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

      2. associate-/l* 99.5%

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

   3. Simplified 99.5%

      \[\leadsto \color{blue}{\frac{100}{\frac{x + y}{x}}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 75.5%

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

      1. *-commutative 75.5%

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

      2. associate-*l/ 75.6%

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

   7. Applied egg-rr 75.6%

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

4. Final simplification 79.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+83}:\\ \;\;\;\;100\\ \mathbf{elif}\;x \leq -175:\\ \;\;\;\;x \cdot \frac{100}{y}\\ \mathbf{elif}\;x \leq -5.5 \cdot 10^{-43}:\\ \;\;\;\;100\\ \mathbf{elif}\;x \leq 1.15 \cdot 10^{+83}:\\ \;\;\;\;\frac{x \cdot 100}{y}\\ \mathbf{else}:\\ \;\;\;\;100\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 99.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.7%

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

   1. *-commutative 99.7%

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

   2. associate-/l* 99.7%

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

3. Simplified 99.7%

   \[\leadsto \color{blue}{\frac{100}{\frac{x + y}{x}}} \]
4. Add Preprocessing

5. Final simplification 99.7%

   \[\leadsto \frac{100}{\frac{x + y}{x}} \]
6. Add Preprocessing

Alternative 6: 50.9% 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. *-commutative 99.7%

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

   2. associate-/l* 99.7%

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

3. Simplified 99.7%

   \[\leadsto \color{blue}{\frac{100}{\frac{x + y}{x}}} \]
4. Add Preprocessing

5. Taylor expanded in x around inf 46.3%

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

6. Final simplification 46.3%

   \[\leadsto 100 \]
7. Add Preprocessing

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 2024031 
(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)))