Linear.Quaternion:$cexp from linear-1.19.1.3

Percentage Accurate: 99.8% → 99.7%

Time: 9.0s

Alternatives: 7

Speedup: 1.0×

Specification

Math

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

FPCore

(FPCore (x y) :precision binary64 (* x (/ (sin y) y)))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.8% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (* x (/ (sin y) y)))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.7% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (/ x (/ y (sin y))))

C

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

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x / (y / sin(y))
end function

Java

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

Python

def code(x, y):
	return x / (y / math.sin(y))

Julia

function code(x, y)
	return Float64(x / Float64(y / sin(y)))
end

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.8%

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

   1. clear-num 99.7%

      \[\leadsto x \cdot \color{blue}{\frac{1}{\frac{y}{\sin y}}} \]

   2. un-div-inv 99.8%

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

3. Applied egg-rr 99.8%

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

4. Final simplification 99.8%

   \[\leadsto \frac{x}{\frac{y}{\sin y}} \]

Alternative 2: 99.8% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (* x (/ (sin y) y)))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]

2. Final simplification 99.8%

   \[\leadsto x \cdot \frac{\sin y}{y} \]

Alternative 3: 57.1% accurate, 6.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 2.1 \cdot 10^{-8}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1}{y}}{0.16666666666666666 \cdot \frac{y}{x} + \frac{1}{x \cdot y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y 2.1e-8)
   x
   (/ (/ 1.0 y) (+ (* 0.16666666666666666 (/ y x)) (/ 1.0 (* x y))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 2.1e-8) {
		tmp = x;
	} else {
		tmp = (1.0 / y) / ((0.16666666666666666 * (y / x)) + (1.0 / (x * 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 <= 2.1d-8) then
        tmp = x
    else
        tmp = (1.0d0 / y) / ((0.16666666666666666d0 * (y / x)) + (1.0d0 / (x * y)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 2.1e-8) {
		tmp = x;
	} else {
		tmp = (1.0 / y) / ((0.16666666666666666 * (y / x)) + (1.0 / (x * y)));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 2.1e-8:
		tmp = x
	else:
		tmp = (1.0 / y) / ((0.16666666666666666 * (y / x)) + (1.0 / (x * y)))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 2.1e-8)
		tmp = x;
	else
		tmp = Float64(Float64(1.0 / y) / Float64(Float64(0.16666666666666666 * Float64(y / x)) + Float64(1.0 / Float64(x * y))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 2.1e-8)
		tmp = x;
	else
		tmp = (1.0 / y) / ((0.16666666666666666 * (y / x)) + (1.0 / (x * y)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 2.1e-8], x, N[(N[(1.0 / y), $MachinePrecision] / N[(N[(0.16666666666666666 * N[(y / x), $MachinePrecision]), $MachinePrecision] + N[(1.0 / N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 2.09999999999999994e-8

   1. Initial program 99.8%

      \[x \cdot \frac{\sin y}{y} \]

   2. Taylor expanded in y around 0 66.7%

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

   if 2.09999999999999994e-8 < y

   1. Initial program 99.7%

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

      1. associate-*r/ 99.6%

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

      2. associate-*l/ 99.6%

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

      3. *-commutative 99.6%

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

      4. add-cbrt-cube 49.3%

         \[\leadsto \color{blue}{\sqrt[3]{\left(\left(\sin y \cdot \frac{x}{y}\right) \cdot \left(\sin y \cdot \frac{x}{y}\right)\right) \cdot \left(\sin y \cdot \frac{x}{y}\right)}} \]

      5. pow3 49.3%

         \[\leadsto \sqrt[3]{\color{blue}{{\left(\sin y \cdot \frac{x}{y}\right)}^{3}}} \]

   3. Applied egg-rr 49.3%

      \[\leadsto \color{blue}{\sqrt[3]{{\left(\sin y \cdot \frac{x}{y}\right)}^{3}}} \]
   4. Step-by-step derivation

      1. rem-cbrt-cube 99.6%

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

      2. associate-*r/ 99.6%

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

      3. clear-num 97.8%

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{\sin y \cdot x}}} \]

      4. div-inv 97.8%

         \[\leadsto \frac{1}{\color{blue}{y \cdot \frac{1}{\sin y \cdot x}}} \]

      5. associate-/r* 99.5%

         \[\leadsto \color{blue}{\frac{\frac{1}{y}}{\frac{1}{\sin y \cdot x}}} \]

      6. *-commutative 99.5%

         \[\leadsto \frac{\frac{1}{y}}{\frac{1}{\color{blue}{x \cdot \sin y}}} \]

      7. associate-/r* 99.5%

         \[\leadsto \frac{\frac{1}{y}}{\color{blue}{\frac{\frac{1}{x}}{\sin y}}} \]

   5. Applied egg-rr 99.5%

      \[\leadsto \color{blue}{\frac{\frac{1}{y}}{\frac{\frac{1}{x}}{\sin y}}} \]

   6. Taylor expanded in y around 0 24.2%

      \[\leadsto \frac{\frac{1}{y}}{\color{blue}{0.16666666666666666 \cdot \frac{y}{x} + \frac{1}{x \cdot y}}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 55.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 2.1 \cdot 10^{-8}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1}{y}}{0.16666666666666666 \cdot \frac{y}{x} + \frac{1}{x \cdot y}}\\ \end{array} \]

Alternative 4: 56.7% accurate, 11.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 7.2 \cdot 10^{-15}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{\frac{y}{x}}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 7.2e-15) x (/ 1.0 (/ (/ y x) y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 7.2e-15) {
		tmp = x;
	} else {
		tmp = 1.0 / ((y / x) / 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 <= 7.2d-15) then
        tmp = x
    else
        tmp = 1.0d0 / ((y / x) / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 7.2e-15) {
		tmp = x;
	} else {
		tmp = 1.0 / ((y / x) / y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 7.2e-15:
		tmp = x
	else:
		tmp = 1.0 / ((y / x) / y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 7.2e-15)
		tmp = x;
	else
		tmp = Float64(1.0 / Float64(Float64(y / x) / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 7.2e-15)
		tmp = x;
	else
		tmp = 1.0 / ((y / x) / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 7.2e-15], x, N[(1.0 / N[(N[(y / x), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 7.2000000000000002e-15

   1. Initial program 99.8%

      \[x \cdot \frac{\sin y}{y} \]

   2. Taylor expanded in y around 0 66.4%

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

   if 7.2000000000000002e-15 < y

   1. Initial program 99.7%

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

      1. associate-*r/ 99.6%

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

      2. clear-num 97.8%

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{x \cdot \sin y}}} \]

      3. *-commutative 97.8%

         \[\leadsto \frac{1}{\frac{y}{\color{blue}{\sin y \cdot x}}} \]

   3. Applied egg-rr 97.8%

      \[\leadsto \color{blue}{\frac{1}{\frac{y}{\sin y \cdot x}}} \]

   4. Taylor expanded in y around 0 7.6%

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

      1. *-commutative 7.6%

         \[\leadsto \frac{1}{\frac{y}{\color{blue}{y \cdot x}}} \]

   6. Simplified 7.6%

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

      1. *-un-lft-identity 7.6%

         \[\leadsto \frac{1}{\frac{\color{blue}{1 \cdot y}}{y \cdot x}} \]

      2. times-frac 24.7%

         \[\leadsto \frac{1}{\color{blue}{\frac{1}{y} \cdot \frac{y}{x}}} \]

   8. Applied egg-rr 24.7%

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

      1. associate-*l/ 24.7%

         \[\leadsto \frac{1}{\color{blue}{\frac{1 \cdot \frac{y}{x}}{y}}} \]

      2. *-un-lft-identity 24.7%

         \[\leadsto \frac{1}{\frac{\color{blue}{\frac{y}{x}}}{y}} \]

   10. Applied egg-rr 24.7%

       \[\leadsto \frac{1}{\color{blue}{\frac{\frac{y}{x}}{y}}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 55.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 7.2 \cdot 10^{-15}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{\frac{y}{x}}{y}}\\ \end{array} \]

Alternative 5: 56.4% accurate, 14.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 4 \cdot 10^{+18}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 4e+18) x (* y (/ x y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 4e+18) {
		tmp = x;
	} else {
		tmp = y * (x / 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 <= 4d+18) then
        tmp = x
    else
        tmp = y * (x / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 4e+18) {
		tmp = x;
	} else {
		tmp = y * (x / y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 4e+18:
		tmp = x
	else:
		tmp = y * (x / y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 4e+18)
		tmp = x;
	else
		tmp = Float64(y * Float64(x / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 4e+18)
		tmp = x;
	else
		tmp = y * (x / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 4e+18], x, N[(y * N[(x / y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 4e18

   1. Initial program 99.8%

      \[x \cdot \frac{\sin y}{y} \]

   2. Taylor expanded in y around 0 65.6%

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

   if 4e18 < y

   1. Initial program 99.7%

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

      1. associate-*r/ 99.7%

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

      2. clear-num 97.6%

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{x \cdot \sin y}}} \]

      3. *-commutative 97.6%

         \[\leadsto \frac{1}{\frac{y}{\color{blue}{\sin y \cdot x}}} \]

   3. Applied egg-rr 97.6%

      \[\leadsto \color{blue}{\frac{1}{\frac{y}{\sin y \cdot x}}} \]

   4. Taylor expanded in y around 0 3.8%

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

      1. clear-num 3.8%

         \[\leadsto \color{blue}{\frac{x}{1}} \]

      2. *-inverses 3.8%

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

      3. associate-/l* 3.6%

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

      4. *-commutative 3.6%

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

      5. *-un-lft-identity 3.6%

         \[\leadsto \frac{y \cdot x}{\color{blue}{1 \cdot y}} \]

      6. times-frac 21.5%

         \[\leadsto \color{blue}{\frac{y}{1} \cdot \frac{x}{y}} \]

      7. /-rgt-identity 21.5%

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

   6. Applied egg-rr 21.5%

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

4. Final simplification 54.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 4 \cdot 10^{+18}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x}{y}\\ \end{array} \]

Alternative 6: 56.7% accurate, 14.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 0.00098:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{y}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 0.00098) x (/ y (/ y x))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 0.00098) {
		tmp = x;
	} else {
		tmp = y / (y / x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 0.00098d0) then
        tmp = x
    else
        tmp = y / (y / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 0.00098) {
		tmp = x;
	} else {
		tmp = y / (y / x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 0.00098:
		tmp = x
	else:
		tmp = y / (y / x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 0.00098)
		tmp = x;
	else
		tmp = Float64(y / Float64(y / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 0.00098)
		tmp = x;
	else
		tmp = y / (y / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 0.00098], x, N[(y / N[(y / x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 9.7999999999999997e-4

   1. Initial program 99.8%

      \[x \cdot \frac{\sin y}{y} \]

   2. Taylor expanded in y around 0 66.7%

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

   if 9.7999999999999997e-4 < y

   1. Initial program 99.7%

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

      1. associate-*r/ 99.6%

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

      2. clear-num 97.8%

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{x \cdot \sin y}}} \]

      3. *-commutative 97.8%

         \[\leadsto \frac{1}{\frac{y}{\color{blue}{\sin y \cdot x}}} \]

   3. Applied egg-rr 97.8%

      \[\leadsto \color{blue}{\frac{1}{\frac{y}{\sin y \cdot x}}} \]

   4. Taylor expanded in y around 0 5.1%

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

      1. clear-num 5.1%

         \[\leadsto \color{blue}{\frac{x}{1}} \]

      2. *-inverses 5.1%

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

      3. associate-/l* 4.9%

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

      4. *-commutative 4.9%

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

      5. *-un-lft-identity 4.9%

         \[\leadsto \frac{y \cdot x}{\color{blue}{1 \cdot y}} \]

      6. times-frac 21.5%

         \[\leadsto \color{blue}{\frac{y}{1} \cdot \frac{x}{y}} \]

      7. /-rgt-identity 21.5%

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

   6. Applied egg-rr 21.5%

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

      1. clear-num 22.5%

         \[\leadsto y \cdot \color{blue}{\frac{1}{\frac{y}{x}}} \]

      2. div-inv 22.5%

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

   8. Applied egg-rr 22.5%

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

4. Final simplification 55.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 0.00098:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{y}{x}}\\ \end{array} \]

Alternative 7: 50.9% accurate, 105.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 x)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return x

Julia

function code(x, y)
	return x
end

MATLAB

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

Wolfram

code[x_, y_] := x

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]

2. Taylor expanded in y around 0 50.4%

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

3. Final simplification 50.4%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023301 
(FPCore (x y)
  :name "Linear.Quaternion:$cexp from linear-1.19.1.3"
  :precision binary64
  (* x (/ (sin y) y)))