Graphics.Rasterific.Svg.PathConverter:segmentToBezier from rasterific-svg-0.2.3.1, B

Percentage Accurate: 99.9% → 99.9%

Time: 9.3s

Alternatives: 10

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(x + \cos y\right) - z \cdot \sin y \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (+ x (cos y)) (* z (sin y))))

C

double code(double x, double y, double z) {
	return (x + cos(y)) - (z * sin(y));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x + cos(y)) - (z * sin(y))
end function

Java

public static double code(double x, double y, double z) {
	return (x + Math.cos(y)) - (z * Math.sin(y));
}

Python

def code(x, y, z):
	return (x + math.cos(y)) - (z * math.sin(y))

Julia

function code(x, y, z)
	return Float64(Float64(x + cos(y)) - Float64(z * sin(y)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x + cos(y)) - (z * sin(y));
end

Wolfram

code[x_, y_, z_] := N[(N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision] - N[(z * N[Sin[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x + \cos y\right) - z \cdot \sin y \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (+ x (cos y)) (* z (sin y))))

C

double code(double x, double y, double z) {
	return (x + cos(y)) - (z * sin(y));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x + cos(y)) - (z * sin(y))
end function

Java

public static double code(double x, double y, double z) {
	return (x + Math.cos(y)) - (z * Math.sin(y));
}

Python

def code(x, y, z):
	return (x + math.cos(y)) - (z * math.sin(y))

Julia

function code(x, y, z)
	return Float64(Float64(x + cos(y)) - Float64(z * sin(y)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x + cos(y)) - (z * sin(y));
end

Wolfram

code[x_, y_, z_] := N[(N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision] - N[(z * N[Sin[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x + \cos y\right) + \left(1 - \left(z \cdot \sin y + 1\right)\right) \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ (+ x (cos y)) (- 1.0 (+ (* z (sin y)) 1.0))))

C

double code(double x, double y, double z) {
	return (x + cos(y)) + (1.0 - ((z * sin(y)) + 1.0));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x + cos(y)) + (1.0d0 - ((z * sin(y)) + 1.0d0))
end function

Java

public static double code(double x, double y, double z) {
	return (x + Math.cos(y)) + (1.0 - ((z * Math.sin(y)) + 1.0));
}

Python

def code(x, y, z):
	return (x + math.cos(y)) + (1.0 - ((z * math.sin(y)) + 1.0))

Julia

function code(x, y, z)
	return Float64(Float64(x + cos(y)) + Float64(1.0 - Float64(Float64(z * sin(y)) + 1.0)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x + cos(y)) + (1.0 - ((z * sin(y)) + 1.0));
end

Wolfram

code[x_, y_, z_] := N[(N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision] + N[(1.0 - N[(N[(z * N[Sin[y], $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(x + \cos y\right) - z \cdot \sin y \]
2. Step-by-step derivation

   1. add-cbrt-cube 79.7%

      \[\leadsto \left(x + \cos y\right) - \color{blue}{\sqrt[3]{\left(\left(z \cdot \sin y\right) \cdot \left(z \cdot \sin y\right)\right) \cdot \left(z \cdot \sin y\right)}} \]

   2. pow3 79.7%

      \[\leadsto \left(x + \cos y\right) - \sqrt[3]{\color{blue}{{\left(z \cdot \sin y\right)}^{3}}} \]

3. Applied egg-rr 79.7%

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

   1. rem-cbrt-cube 99.9%

      \[\leadsto \left(x + \cos y\right) - \color{blue}{z \cdot \sin y} \]

   2. expm1-log1p-u 81.1%

      \[\leadsto \left(x + \cos y\right) - \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(z \cdot \sin y\right)\right)} \]

   3. expm1-def 81.1%

      \[\leadsto \left(x + \cos y\right) - \color{blue}{\left(e^{\mathsf{log1p}\left(z \cdot \sin y\right)} - 1\right)} \]

   4. log1p-udef 81.1%

      \[\leadsto \left(x + \cos y\right) - \left(e^{\color{blue}{\log \left(1 + z \cdot \sin y\right)}} - 1\right) \]

   5. add-exp-log 99.9%

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

   6. +-commutative 99.9%

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

5. Applied egg-rr 99.9%

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

6. Final simplification 99.9%

   \[\leadsto \left(x + \cos y\right) + \left(1 - \left(z \cdot \sin y + 1\right)\right) \]

Alternative 2: 97.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z \cdot \sin y\\ \mathbf{if}\;x \leq -6.2 \cdot 10^{+21}:\\ \;\;\;\;x - t_0\\ \mathbf{elif}\;x \leq 7.2 \cdot 10^{-12}:\\ \;\;\;\;\cos y - t_0\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-z, \sin y, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* z (sin y))))
   (if (<= x -6.2e+21)
     (- x t_0)
     (if (<= x 7.2e-12) (- (cos y) t_0) (fma (- z) (sin y) x)))))

C

double code(double x, double y, double z) {
	double t_0 = z * sin(y);
	double tmp;
	if (x <= -6.2e+21) {
		tmp = x - t_0;
	} else if (x <= 7.2e-12) {
		tmp = cos(y) - t_0;
	} else {
		tmp = fma(-z, sin(y), x);
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(z * sin(y))
	tmp = 0.0
	if (x <= -6.2e+21)
		tmp = Float64(x - t_0);
	elseif (x <= 7.2e-12)
		tmp = Float64(cos(y) - t_0);
	else
		tmp = fma(Float64(-z), sin(y), x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z * N[Sin[y], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -6.2e+21], N[(x - t$95$0), $MachinePrecision], If[LessEqual[x, 7.2e-12], N[(N[Cos[y], $MachinePrecision] - t$95$0), $MachinePrecision], N[((-z) * N[Sin[y], $MachinePrecision] + x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -6.2e21

   1. Initial program 100.0%

      \[\left(x + \cos y\right) - z \cdot \sin y \]
   2. Step-by-step derivation

      1. add-cube-cbrt 98.1%

         \[\leadsto \color{blue}{\left(\sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y} \cdot \sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}\right) \cdot \sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}} \]

      2. pow3 98.0%

         \[\leadsto \color{blue}{{\left(\sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}\right)}^{3}} \]

      3. associate--l+ 98.0%

         \[\leadsto {\left(\sqrt[3]{\color{blue}{x + \left(\cos y - z \cdot \sin y\right)}}\right)}^{3} \]

   3. Applied egg-rr 98.0%

      \[\leadsto \color{blue}{{\left(\sqrt[3]{x + \left(\cos y - z \cdot \sin y\right)}\right)}^{3}} \]

   4. Taylor expanded in z around inf 98.0%

      \[\leadsto {\left(\sqrt[3]{x + \color{blue}{-1 \cdot \left(z \cdot \sin y\right)}}\right)}^{3} \]
   5. Step-by-step derivation

      1. associate-*r* 98.0%

         \[\leadsto {\left(\sqrt[3]{x + \color{blue}{\left(-1 \cdot z\right) \cdot \sin y}}\right)}^{3} \]

      2. neg-mul-1 98.0%

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

      3. *-commutative 98.0%

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

   6. Simplified 98.0%

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

   7. Taylor expanded in y around inf 100.0%

      \[\leadsto \color{blue}{{1}^{0.3333333333333333} \cdot \left(-1 \cdot \left(z \cdot \sin y\right) + x\right)} \]
   8. Step-by-step derivation

      1. pow-base-1 100.0%

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

      2. *-lft-identity 100.0%

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

      3. neg-mul-1 100.0%

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

      4. +-commutative 100.0%

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

      5. sub-neg 100.0%

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

   9. Simplified 100.0%

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

   if -6.2e21 < x < 7.2e-12

   1. Initial program 99.8%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in x around 0 99.3%

      \[\leadsto \color{blue}{\cos y - z \cdot \sin y} \]

   if 7.2e-12 < x

   1. Initial program 100.0%

      \[\left(x + \cos y\right) - z \cdot \sin y \]
   2. Step-by-step derivation

      1. add-cube-cbrt 98.0%

         \[\leadsto \color{blue}{\left(\sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y} \cdot \sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}\right) \cdot \sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}} \]

      2. pow3 98.0%

         \[\leadsto \color{blue}{{\left(\sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}\right)}^{3}} \]

      3. associate--l+ 98.0%

         \[\leadsto {\left(\sqrt[3]{\color{blue}{x + \left(\cos y - z \cdot \sin y\right)}}\right)}^{3} \]

   3. Applied egg-rr 98.0%

      \[\leadsto \color{blue}{{\left(\sqrt[3]{x + \left(\cos y - z \cdot \sin y\right)}\right)}^{3}} \]

   4. Taylor expanded in z around inf 96.7%

      \[\leadsto {\left(\sqrt[3]{x + \color{blue}{-1 \cdot \left(z \cdot \sin y\right)}}\right)}^{3} \]
   5. Step-by-step derivation

      1. associate-*r* 96.7%

         \[\leadsto {\left(\sqrt[3]{x + \color{blue}{\left(-1 \cdot z\right) \cdot \sin y}}\right)}^{3} \]

      2. neg-mul-1 96.7%

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

      3. *-commutative 96.7%

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

   6. Simplified 96.7%

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

   7. Taylor expanded in y around inf 98.6%

      \[\leadsto \color{blue}{{1}^{0.3333333333333333} \cdot \left(-1 \cdot \left(z \cdot \sin y\right) + x\right)} \]
   8. Step-by-step derivation

      1. pow-base-1 98.6%

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

      2. *-lft-identity 98.6%

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

      3. associate-*r* 98.6%

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

      4. fma-def 98.6%

         \[\leadsto \color{blue}{\mathsf{fma}\left(-1 \cdot z, \sin y, x\right)} \]

      5. neg-mul-1 98.6%

         \[\leadsto \mathsf{fma}\left(\color{blue}{-z}, \sin y, x\right) \]

   9. Simplified 98.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-z, \sin y, x\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -6.2 \cdot 10^{+21}:\\ \;\;\;\;x - z \cdot \sin y\\ \mathbf{elif}\;x \leq 7.2 \cdot 10^{-12}:\\ \;\;\;\;\cos y - z \cdot \sin y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-z, \sin y, x\right)\\ \end{array} \]

Alternative 3: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x + \cos y\right) - z \cdot \sin y \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (+ x (cos y)) (* z (sin y))))

C

double code(double x, double y, double z) {
	return (x + cos(y)) - (z * sin(y));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x + cos(y)) - (z * sin(y))
end function

Java

public static double code(double x, double y, double z) {
	return (x + Math.cos(y)) - (z * Math.sin(y));
}

Python

def code(x, y, z):
	return (x + math.cos(y)) - (z * math.sin(y))

Julia

function code(x, y, z)
	return Float64(Float64(x + cos(y)) - Float64(z * sin(y)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x + cos(y)) - (z * sin(y));
end

Wolfram

code[x_, y_, z_] := N[(N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision] - N[(z * N[Sin[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(x + \cos y\right) - z \cdot \sin y \]

2. Final simplification 99.9%

   \[\leadsto \left(x + \cos y\right) - z \cdot \sin y \]

Alternative 4: 93.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.6 \cdot 10^{+19}:\\ \;\;\;\;\mathsf{fma}\left(-z, \sin y, x\right)\\ \mathbf{elif}\;z \leq 132000000000:\\ \;\;\;\;x + \cos y\\ \mathbf{else}:\\ \;\;\;\;x - z \cdot \sin y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -2.6e+19)
   (fma (- z) (sin y) x)
   (if (<= z 132000000000.0) (+ x (cos y)) (- x (* z (sin y))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.6e+19) {
		tmp = fma(-z, sin(y), x);
	} else if (z <= 132000000000.0) {
		tmp = x + cos(y);
	} else {
		tmp = x - (z * sin(y));
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -2.6e+19)
		tmp = fma(Float64(-z), sin(y), x);
	elseif (z <= 132000000000.0)
		tmp = Float64(x + cos(y));
	else
		tmp = Float64(x - Float64(z * sin(y)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -2.6e+19], N[((-z) * N[Sin[y], $MachinePrecision] + x), $MachinePrecision], If[LessEqual[z, 132000000000.0], N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision], N[(x - N[(z * N[Sin[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.6e19

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]
   2. Step-by-step derivation

      1. add-cube-cbrt 98.0%

         \[\leadsto \color{blue}{\left(\sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y} \cdot \sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}\right) \cdot \sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}} \]

      2. pow3 98.0%

         \[\leadsto \color{blue}{{\left(\sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}\right)}^{3}} \]

      3. associate--l+ 98.0%

         \[\leadsto {\left(\sqrt[3]{\color{blue}{x + \left(\cos y - z \cdot \sin y\right)}}\right)}^{3} \]

   3. Applied egg-rr 98.0%

      \[\leadsto \color{blue}{{\left(\sqrt[3]{x + \left(\cos y - z \cdot \sin y\right)}\right)}^{3}} \]

   4. Taylor expanded in z around inf 89.9%

      \[\leadsto {\left(\sqrt[3]{x + \color{blue}{-1 \cdot \left(z \cdot \sin y\right)}}\right)}^{3} \]
   5. Step-by-step derivation

      1. associate-*r* 89.9%

         \[\leadsto {\left(\sqrt[3]{x + \color{blue}{\left(-1 \cdot z\right) \cdot \sin y}}\right)}^{3} \]

      2. neg-mul-1 89.9%

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

      3. *-commutative 89.9%

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

   6. Simplified 89.9%

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

   7. Taylor expanded in y around inf 91.6%

      \[\leadsto \color{blue}{{1}^{0.3333333333333333} \cdot \left(-1 \cdot \left(z \cdot \sin y\right) + x\right)} \]
   8. Step-by-step derivation

      1. pow-base-1 91.6%

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

      2. *-lft-identity 91.6%

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

      3. associate-*r* 91.6%

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

      4. fma-def 91.7%

         \[\leadsto \color{blue}{\mathsf{fma}\left(-1 \cdot z, \sin y, x\right)} \]

      5. neg-mul-1 91.7%

         \[\leadsto \mathsf{fma}\left(\color{blue}{-z}, \sin y, x\right) \]

   9. Simplified 91.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-z, \sin y, x\right)} \]

   if -2.6e19 < z < 1.32e11

   1. Initial program 100.0%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in z around 0 98.8%

      \[\leadsto \color{blue}{\cos y + x} \]

   if 1.32e11 < z

   1. Initial program 99.7%

      \[\left(x + \cos y\right) - z \cdot \sin y \]
   2. Step-by-step derivation

      1. add-cube-cbrt 98.0%

         \[\leadsto \color{blue}{\left(\sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y} \cdot \sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}\right) \cdot \sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}} \]

      2. pow3 98.0%

         \[\leadsto \color{blue}{{\left(\sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}\right)}^{3}} \]

      3. associate--l+ 98.0%

         \[\leadsto {\left(\sqrt[3]{\color{blue}{x + \left(\cos y - z \cdot \sin y\right)}}\right)}^{3} \]

   3. Applied egg-rr 98.0%

      \[\leadsto \color{blue}{{\left(\sqrt[3]{x + \left(\cos y - z \cdot \sin y\right)}\right)}^{3}} \]

   4. Taylor expanded in z around inf 90.3%

      \[\leadsto {\left(\sqrt[3]{x + \color{blue}{-1 \cdot \left(z \cdot \sin y\right)}}\right)}^{3} \]
   5. Step-by-step derivation

      1. associate-*r* 90.3%

         \[\leadsto {\left(\sqrt[3]{x + \color{blue}{\left(-1 \cdot z\right) \cdot \sin y}}\right)}^{3} \]

      2. neg-mul-1 90.3%

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

      3. *-commutative 90.3%

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

   6. Simplified 90.3%

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

   7. Taylor expanded in y around inf 92.0%

      \[\leadsto \color{blue}{{1}^{0.3333333333333333} \cdot \left(-1 \cdot \left(z \cdot \sin y\right) + x\right)} \]
   8. Step-by-step derivation

      1. pow-base-1 92.0%

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

      2. *-lft-identity 92.0%

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

      3. neg-mul-1 92.0%

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

      4. +-commutative 92.0%

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

      5. sub-neg 92.0%

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

   9. Simplified 92.0%

      \[\leadsto \color{blue}{x - z \cdot \sin y} \]
3. Recombined 3 regimes into one program.

4. Final simplification 95.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.6 \cdot 10^{+19}:\\ \;\;\;\;\mathsf{fma}\left(-z, \sin y, x\right)\\ \mathbf{elif}\;z \leq 132000000000:\\ \;\;\;\;x + \cos y\\ \mathbf{else}:\\ \;\;\;\;x - z \cdot \sin y\\ \end{array} \]

Alternative 5: 76.3% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq 4.4 \cdot 10^{+20} \lor \neg \left(z \leq 9 \cdot 10^{+56}\right) \land z \leq 9.2 \cdot 10^{+85}:\\ \;\;\;\;x + \cos y\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-\sin y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z 4.4e+20) (and (not (<= z 9e+56)) (<= z 9.2e+85)))
   (+ x (cos y))
   (* z (- (sin y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= 4.4e+20) || (!(z <= 9e+56) && (z <= 9.2e+85))) {
		tmp = x + cos(y);
	} else {
		tmp = z * -sin(y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z <= 4.4d+20) .or. (.not. (z <= 9d+56)) .and. (z <= 9.2d+85)) then
        tmp = x + cos(y)
    else
        tmp = z * -sin(y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= 4.4e+20) || (!(z <= 9e+56) && (z <= 9.2e+85))) {
		tmp = x + Math.cos(y);
	} else {
		tmp = z * -Math.sin(y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= 4.4e+20) or (not (z <= 9e+56) and (z <= 9.2e+85)):
		tmp = x + math.cos(y)
	else:
		tmp = z * -math.sin(y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= 4.4e+20) || (!(z <= 9e+56) && (z <= 9.2e+85)))
		tmp = Float64(x + cos(y));
	else
		tmp = Float64(z * Float64(-sin(y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= 4.4e+20) || (~((z <= 9e+56)) && (z <= 9.2e+85)))
		tmp = x + cos(y);
	else
		tmp = z * -sin(y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, 4.4e+20], And[N[Not[LessEqual[z, 9e+56]], $MachinePrecision], LessEqual[z, 9.2e+85]]], N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision], N[(z * (-N[Sin[y], $MachinePrecision])), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 4.4e20 or 9.0000000000000006e56 < z < 9.1999999999999996e85

   1. Initial program 99.9%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in z around 0 81.3%

      \[\leadsto \color{blue}{\cos y + x} \]

   if 4.4e20 < z < 9.0000000000000006e56 or 9.1999999999999996e85 < z

   1. Initial program 99.6%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in z around inf 73.0%

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \sin y\right)} \]
   3. Step-by-step derivation

      1. associate-*r* 73.0%

         \[\leadsto \color{blue}{\left(-1 \cdot z\right) \cdot \sin y} \]

      2. neg-mul-1 73.0%

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \sin y \]

      3. *-commutative 73.0%

         \[\leadsto \color{blue}{\sin y \cdot \left(-z\right)} \]

   4. Simplified 73.0%

      \[\leadsto \color{blue}{\sin y \cdot \left(-z\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 80.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq 4.4 \cdot 10^{+20} \lor \neg \left(z \leq 9 \cdot 10^{+56}\right) \land z \leq 9.2 \cdot 10^{+85}:\\ \;\;\;\;x + \cos y\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(-\sin y\right)\\ \end{array} \]

Alternative 6: 93.2% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.15 \cdot 10^{+20} \lor \neg \left(z \leq 2550000000\right):\\ \;\;\;\;x - z \cdot \sin y\\ \mathbf{else}:\\ \;\;\;\;x + \cos y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -1.15e+20) (not (<= z 2550000000.0)))
   (- x (* z (sin y)))
   (+ x (cos y))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -1.15e+20) || !(z <= 2550000000.0)) {
		tmp = x - (z * sin(y));
	} else {
		tmp = x + cos(y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z <= (-1.15d+20)) .or. (.not. (z <= 2550000000.0d0))) then
        tmp = x - (z * sin(y))
    else
        tmp = x + cos(y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -1.15e+20) || !(z <= 2550000000.0)) {
		tmp = x - (z * Math.sin(y));
	} else {
		tmp = x + Math.cos(y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -1.15e+20) or not (z <= 2550000000.0):
		tmp = x - (z * math.sin(y))
	else:
		tmp = x + math.cos(y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -1.15e+20) || !(z <= 2550000000.0))
		tmp = Float64(x - Float64(z * sin(y)));
	else
		tmp = Float64(x + cos(y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -1.15e+20) || ~((z <= 2550000000.0)))
		tmp = x - (z * sin(y));
	else
		tmp = x + cos(y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -1.15e+20], N[Not[LessEqual[z, 2550000000.0]], $MachinePrecision]], N[(x - N[(z * N[Sin[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -1.15e20 or 2.55e9 < z

   1. Initial program 99.8%

      \[\left(x + \cos y\right) - z \cdot \sin y \]
   2. Step-by-step derivation

      1. add-cube-cbrt 98.0%

         \[\leadsto \color{blue}{\left(\sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y} \cdot \sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}\right) \cdot \sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}} \]

      2. pow3 98.0%

         \[\leadsto \color{blue}{{\left(\sqrt[3]{\left(x + \cos y\right) - z \cdot \sin y}\right)}^{3}} \]

      3. associate--l+ 98.0%

         \[\leadsto {\left(\sqrt[3]{\color{blue}{x + \left(\cos y - z \cdot \sin y\right)}}\right)}^{3} \]

   3. Applied egg-rr 98.0%

      \[\leadsto \color{blue}{{\left(\sqrt[3]{x + \left(\cos y - z \cdot \sin y\right)}\right)}^{3}} \]

   4. Taylor expanded in z around inf 90.1%

      \[\leadsto {\left(\sqrt[3]{x + \color{blue}{-1 \cdot \left(z \cdot \sin y\right)}}\right)}^{3} \]
   5. Step-by-step derivation

      1. associate-*r* 90.1%

         \[\leadsto {\left(\sqrt[3]{x + \color{blue}{\left(-1 \cdot z\right) \cdot \sin y}}\right)}^{3} \]

      2. neg-mul-1 90.1%

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

      3. *-commutative 90.1%

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

   6. Simplified 90.1%

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

   7. Taylor expanded in y around inf 91.8%

      \[\leadsto \color{blue}{{1}^{0.3333333333333333} \cdot \left(-1 \cdot \left(z \cdot \sin y\right) + x\right)} \]
   8. Step-by-step derivation

      1. pow-base-1 91.8%

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

      2. *-lft-identity 91.8%

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

      3. neg-mul-1 91.8%

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

      4. +-commutative 91.8%

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

      5. sub-neg 91.8%

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

   9. Simplified 91.8%

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

   if -1.15e20 < z < 2.55e9

   1. Initial program 100.0%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in z around 0 98.8%

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

4. Final simplification 95.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.15 \cdot 10^{+20} \lor \neg \left(z \leq 2550000000\right):\\ \;\;\;\;x - z \cdot \sin y\\ \mathbf{else}:\\ \;\;\;\;x + \cos y\\ \end{array} \]

Alternative 7: 79.9% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.42 \cdot 10^{-6} \lor \neg \left(y \leq 15800\right):\\ \;\;\;\;x + \cos y\\ \mathbf{else}:\\ \;\;\;\;1 + \left(x - y \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -1.42e-6) (not (<= y 15800.0)))
   (+ x (cos y))
   (+ 1.0 (- x (* y z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.42e-6) || !(y <= 15800.0)) {
		tmp = x + cos(y);
	} else {
		tmp = 1.0 + (x - (y * z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((y <= (-1.42d-6)) .or. (.not. (y <= 15800.0d0))) then
        tmp = x + cos(y)
    else
        tmp = 1.0d0 + (x - (y * z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.42e-6) || !(y <= 15800.0)) {
		tmp = x + Math.cos(y);
	} else {
		tmp = 1.0 + (x - (y * z));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -1.42e-6) or not (y <= 15800.0):
		tmp = x + math.cos(y)
	else:
		tmp = 1.0 + (x - (y * z))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -1.42e-6) || !(y <= 15800.0))
		tmp = Float64(x + cos(y));
	else
		tmp = Float64(1.0 + Float64(x - Float64(y * z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -1.42e-6) || ~((y <= 15800.0)))
		tmp = x + cos(y);
	else
		tmp = 1.0 + (x - (y * z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -1.42e-6], N[Not[LessEqual[y, 15800.0]], $MachinePrecision]], N[(x + N[Cos[y], $MachinePrecision]), $MachinePrecision], N[(1.0 + N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.42e-6 or 15800 < y

   1. Initial program 99.8%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in z around 0 61.4%

      \[\leadsto \color{blue}{\cos y + x} \]

   if -1.42e-6 < y < 15800

   1. Initial program 100.0%

      \[\left(x + \cos y\right) - z \cdot \sin y \]
   2. Step-by-step derivation

      1. sub-neg 100.0%

         \[\leadsto \color{blue}{\left(x + \cos y\right) + \left(-z \cdot \sin y\right)} \]

      2. +-commutative 100.0%

         \[\leadsto \color{blue}{\left(-z \cdot \sin y\right) + \left(x + \cos y\right)} \]

      3. *-commutative 100.0%

         \[\leadsto \left(-\color{blue}{\sin y \cdot z}\right) + \left(x + \cos y\right) \]

      4. distribute-rgt-neg-in 100.0%

         \[\leadsto \color{blue}{\sin y \cdot \left(-z\right)} + \left(x + \cos y\right) \]

      5. fma-def 100.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\sin y, -z, x + \cos y\right)} \]

   3. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\sin y, -z, x + \cos y\right)} \]

   4. Taylor expanded in y around 0 97.4%

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

      1. +-commutative 97.4%

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

      2. mul-1-neg 97.4%

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

      3. unsub-neg 97.4%

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

   6. Simplified 97.4%

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

4. Final simplification 77.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.42 \cdot 10^{-6} \lor \neg \left(y \leq 15800\right):\\ \;\;\;\;x + \cos y\\ \mathbf{else}:\\ \;\;\;\;1 + \left(x - y \cdot z\right)\\ \end{array} \]

Alternative 8: 69.0% accurate, 18.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.2 \cdot 10^{+66}:\\ \;\;\;\;x + 1\\ \mathbf{elif}\;y \leq 68000:\\ \;\;\;\;1 + \left(x - y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x + 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -2.2e+66)
   (+ x 1.0)
   (if (<= y 68000.0) (+ 1.0 (- x (* y z))) (+ x 1.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -2.2e+66) {
		tmp = x + 1.0;
	} else if (y <= 68000.0) {
		tmp = 1.0 + (x - (y * z));
	} else {
		tmp = x + 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-2.2d+66)) then
        tmp = x + 1.0d0
    else if (y <= 68000.0d0) then
        tmp = 1.0d0 + (x - (y * z))
    else
        tmp = x + 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -2.2e+66) {
		tmp = x + 1.0;
	} else if (y <= 68000.0) {
		tmp = 1.0 + (x - (y * z));
	} else {
		tmp = x + 1.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -2.2e+66:
		tmp = x + 1.0
	elif y <= 68000.0:
		tmp = 1.0 + (x - (y * z))
	else:
		tmp = x + 1.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -2.2e+66)
		tmp = Float64(x + 1.0);
	elseif (y <= 68000.0)
		tmp = Float64(1.0 + Float64(x - Float64(y * z)));
	else
		tmp = Float64(x + 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -2.2e+66)
		tmp = x + 1.0;
	elseif (y <= 68000.0)
		tmp = 1.0 + (x - (y * z));
	else
		tmp = x + 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -2.2e+66], N[(x + 1.0), $MachinePrecision], If[LessEqual[y, 68000.0], N[(1.0 + N[(x - N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + 1.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -2.1999999999999998e66 or 68000 < y

   1. Initial program 99.8%

      \[\left(x + \cos y\right) - z \cdot \sin y \]

   2. Taylor expanded in y around 0 36.9%

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

      1. +-commutative 36.9%

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

   4. Simplified 36.9%

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

   if -2.1999999999999998e66 < y < 68000

   1. Initial program 100.0%

      \[\left(x + \cos y\right) - z \cdot \sin y \]
   2. Step-by-step derivation

      1. sub-neg 100.0%

         \[\leadsto \color{blue}{\left(x + \cos y\right) + \left(-z \cdot \sin y\right)} \]

      2. +-commutative 100.0%

         \[\leadsto \color{blue}{\left(-z \cdot \sin y\right) + \left(x + \cos y\right)} \]

      3. *-commutative 100.0%

         \[\leadsto \left(-\color{blue}{\sin y \cdot z}\right) + \left(x + \cos y\right) \]

      4. distribute-rgt-neg-in 100.0%

         \[\leadsto \color{blue}{\sin y \cdot \left(-z\right)} + \left(x + \cos y\right) \]

      5. fma-def 100.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\sin y, -z, x + \cos y\right)} \]

   3. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\sin y, -z, x + \cos y\right)} \]

   4. Taylor expanded in y around 0 91.1%

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

      1. +-commutative 91.1%

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

      2. mul-1-neg 91.1%

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

      3. unsub-neg 91.1%

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

   6. Simplified 91.1%

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

4. Final simplification 64.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.2 \cdot 10^{+66}:\\ \;\;\;\;x + 1\\ \mathbf{elif}\;y \leq 68000:\\ \;\;\;\;1 + \left(x - y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x + 1\\ \end{array} \]

Alternative 9: 61.1% accurate, 69.0× speedup

Math

\[\begin{array}{l} \\ x + 1 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ x 1.0))

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return x + 1.0

Julia

function code(x, y, z)
	return Float64(x + 1.0)
end

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

   \[\left(x + \cos y\right) - z \cdot \sin y \]

2. Taylor expanded in y around 0 59.6%

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

   1. +-commutative 59.6%

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

4. Simplified 59.6%

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

5. Final simplification 59.6%

   \[\leadsto x + 1 \]

Alternative 10: 42.2% accurate, 207.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 x)

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return x

Julia

function code(x, y, z)
	return x
end

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 99.9%

   \[\left(x + \cos y\right) - z \cdot \sin y \]

2. Taylor expanded in x around inf 41.1%

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

3. Final simplification 41.1%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023192 
(FPCore (x y z)
  :name "Graphics.Rasterific.Svg.PathConverter:segmentToBezier from rasterific-svg-0.2.3.1, B"
  :precision binary64
  (- (+ x (cos y)) (* z (sin y))))