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

Percentage Accurate: 99.9% → 99.9%

Time: 10.0s

Alternatives: 11

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return (x + sin(y)) + (z * cos(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 + sin(y)) + (z * cos(y))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return (x + sin(y)) + (z * cos(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 + sin(y)) + (z * cos(y))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return (x + sin(y)) + (z * cos(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 + sin(y)) + (z * cos(y))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

   \[\left(x + \sin y\right) + z \cdot \cos y \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 82.4% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z \cdot \cos y\\ \mathbf{if}\;z \leq -1.46 \cdot 10^{+161}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq -2.4 \cdot 10^{+25}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;z \leq -4.8 \cdot 10^{-21} \lor \neg \left(z \leq 6.2 \cdot 10^{+16}\right):\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;x + \sin y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* z (cos y))))
   (if (<= z -1.46e+161)
     t_0
     (if (<= z -2.4e+25)
       (+ x z)
       (if (or (<= z -4.8e-21) (not (<= z 6.2e+16))) t_0 (+ x (sin y)))))))

C

double code(double x, double y, double z) {
	double t_0 = z * cos(y);
	double tmp;
	if (z <= -1.46e+161) {
		tmp = t_0;
	} else if (z <= -2.4e+25) {
		tmp = x + z;
	} else if ((z <= -4.8e-21) || !(z <= 6.2e+16)) {
		tmp = t_0;
	} else {
		tmp = x + 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) :: t_0
    real(8) :: tmp
    t_0 = z * cos(y)
    if (z <= (-1.46d+161)) then
        tmp = t_0
    else if (z <= (-2.4d+25)) then
        tmp = x + z
    else if ((z <= (-4.8d-21)) .or. (.not. (z <= 6.2d+16))) then
        tmp = t_0
    else
        tmp = x + sin(y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = z * Math.cos(y);
	double tmp;
	if (z <= -1.46e+161) {
		tmp = t_0;
	} else if (z <= -2.4e+25) {
		tmp = x + z;
	} else if ((z <= -4.8e-21) || !(z <= 6.2e+16)) {
		tmp = t_0;
	} else {
		tmp = x + Math.sin(y);
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = z * math.cos(y)
	tmp = 0
	if z <= -1.46e+161:
		tmp = t_0
	elif z <= -2.4e+25:
		tmp = x + z
	elif (z <= -4.8e-21) or not (z <= 6.2e+16):
		tmp = t_0
	else:
		tmp = x + math.sin(y)
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(z * cos(y))
	tmp = 0.0
	if (z <= -1.46e+161)
		tmp = t_0;
	elseif (z <= -2.4e+25)
		tmp = Float64(x + z);
	elseif ((z <= -4.8e-21) || !(z <= 6.2e+16))
		tmp = t_0;
	else
		tmp = Float64(x + sin(y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = z * cos(y);
	tmp = 0.0;
	if (z <= -1.46e+161)
		tmp = t_0;
	elseif (z <= -2.4e+25)
		tmp = x + z;
	elseif ((z <= -4.8e-21) || ~((z <= 6.2e+16)))
		tmp = t_0;
	else
		tmp = x + sin(y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z * N[Cos[y], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.46e+161], t$95$0, If[LessEqual[z, -2.4e+25], N[(x + z), $MachinePrecision], If[Or[LessEqual[z, -4.8e-21], N[Not[LessEqual[z, 6.2e+16]], $MachinePrecision]], t$95$0, N[(x + N[Sin[y], $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.46000000000000008e161 or -2.39999999999999996e25 < z < -4.7999999999999999e-21 or 6.2e16 < z

   1. Initial program 99.8%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 99.2%

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

   4. Taylor expanded in x around inf 74.5%

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

      1. associate-/l* 74.3%

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

   6. Simplified 74.3%

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

   7. Taylor expanded in x around 0 80.4%

      \[\leadsto \color{blue}{z \cdot \cos y} \]
   8. Step-by-step derivation

      1. *-commutative 80.4%

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

   9. Simplified 80.4%

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

   if -1.46000000000000008e161 < z < -2.39999999999999996e25

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 99.9%

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

   4. Taylor expanded in y around 0 83.2%

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

      1. +-commutative 83.2%

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

   6. Simplified 83.2%

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

   if -4.7999999999999999e-21 < z < 6.2e16

   1. Initial program 100.0%

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

      1. expm1-log1p-u 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in z around 0 93.6%

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

4. Final simplification 87.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.46 \cdot 10^{+161}:\\ \;\;\;\;z \cdot \cos y\\ \mathbf{elif}\;z \leq -2.4 \cdot 10^{+25}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;z \leq -4.8 \cdot 10^{-21} \lor \neg \left(z \leq 6.2 \cdot 10^{+16}\right):\\ \;\;\;\;z \cdot \cos y\\ \mathbf{else}:\\ \;\;\;\;x + \sin y\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 72.5% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z \cdot \cos y\\ \mathbf{if}\;z \leq -1.45 \cdot 10^{+161}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq -4.5 \cdot 10^{-237}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;z \leq 1.85 \cdot 10^{-136}:\\ \;\;\;\;y + \left(x + z\right)\\ \mathbf{elif}\;z \leq 5.9 \cdot 10^{+196}:\\ \;\;\;\;x + z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* z (cos y))))
   (if (<= z -1.45e+161)
     t_0
     (if (<= z -4.5e-237)
       (+ x z)
       (if (<= z 1.85e-136) (+ y (+ x z)) (if (<= z 5.9e+196) (+ x z) t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = z * cos(y);
	double tmp;
	if (z <= -1.45e+161) {
		tmp = t_0;
	} else if (z <= -4.5e-237) {
		tmp = x + z;
	} else if (z <= 1.85e-136) {
		tmp = y + (x + z);
	} else if (z <= 5.9e+196) {
		tmp = x + z;
	} else {
		tmp = t_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) :: t_0
    real(8) :: tmp
    t_0 = z * cos(y)
    if (z <= (-1.45d+161)) then
        tmp = t_0
    else if (z <= (-4.5d-237)) then
        tmp = x + z
    else if (z <= 1.85d-136) then
        tmp = y + (x + z)
    else if (z <= 5.9d+196) then
        tmp = x + z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = z * Math.cos(y);
	double tmp;
	if (z <= -1.45e+161) {
		tmp = t_0;
	} else if (z <= -4.5e-237) {
		tmp = x + z;
	} else if (z <= 1.85e-136) {
		tmp = y + (x + z);
	} else if (z <= 5.9e+196) {
		tmp = x + z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = z * math.cos(y)
	tmp = 0
	if z <= -1.45e+161:
		tmp = t_0
	elif z <= -4.5e-237:
		tmp = x + z
	elif z <= 1.85e-136:
		tmp = y + (x + z)
	elif z <= 5.9e+196:
		tmp = x + z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(z * cos(y))
	tmp = 0.0
	if (z <= -1.45e+161)
		tmp = t_0;
	elseif (z <= -4.5e-237)
		tmp = Float64(x + z);
	elseif (z <= 1.85e-136)
		tmp = Float64(y + Float64(x + z));
	elseif (z <= 5.9e+196)
		tmp = Float64(x + z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = z * cos(y);
	tmp = 0.0;
	if (z <= -1.45e+161)
		tmp = t_0;
	elseif (z <= -4.5e-237)
		tmp = x + z;
	elseif (z <= 1.85e-136)
		tmp = y + (x + z);
	elseif (z <= 5.9e+196)
		tmp = x + z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z * N[Cos[y], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.45e+161], t$95$0, If[LessEqual[z, -4.5e-237], N[(x + z), $MachinePrecision], If[LessEqual[z, 1.85e-136], N[(y + N[(x + z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 5.9e+196], N[(x + z), $MachinePrecision], t$95$0]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.45000000000000008e161 or 5.8999999999999999e196 < z

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 99.9%

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

   4. Taylor expanded in x around inf 68.2%

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

      1. associate-/l* 68.1%

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

   6. Simplified 68.1%

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

   7. Taylor expanded in x around 0 87.8%

      \[\leadsto \color{blue}{z \cdot \cos y} \]
   8. Step-by-step derivation

      1. *-commutative 87.8%

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

   9. Simplified 87.8%

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

   if -1.45000000000000008e161 < z < -4.50000000000000009e-237 or 1.85e-136 < z < 5.8999999999999999e196

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 87.6%

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

   4. Taylor expanded in y around 0 76.1%

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

      1. +-commutative 76.1%

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

   6. Simplified 76.1%

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

   if -4.50000000000000009e-237 < z < 1.85e-136

   1. Initial program 100.0%

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

      1. expm1-log1p-u 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in y around 0 76.1%

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

      1. +-commutative 76.1%

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

      2. associate-+l+ 76.1%

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

   7. Simplified 76.1%

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

4. Final simplification 78.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.45 \cdot 10^{+161}:\\ \;\;\;\;z \cdot \cos y\\ \mathbf{elif}\;z \leq -4.5 \cdot 10^{-237}:\\ \;\;\;\;x + z\\ \mathbf{elif}\;z \leq 1.85 \cdot 10^{-136}:\\ \;\;\;\;y + \left(x + z\right)\\ \mathbf{elif}\;z \leq 5.9 \cdot 10^{+196}:\\ \;\;\;\;x + z\\ \mathbf{else}:\\ \;\;\;\;z \cdot \cos y\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -0.86 \lor \neg \left(z \leq 1.6\right):\\ \;\;\;\;x + z \cdot \cos y\\ \mathbf{else}:\\ \;\;\;\;\left(x + \sin y\right) + z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -0.86) (not (<= z 1.6)))
   (+ x (* z (cos y)))
   (+ (+ x (sin y)) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.86) || !(z <= 1.6)) {
		tmp = x + (z * cos(y));
	} else {
		tmp = (x + sin(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 ((z <= (-0.86d0)) .or. (.not. (z <= 1.6d0))) then
        tmp = x + (z * cos(y))
    else
        tmp = (x + sin(y)) + z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -0.86) || !(z <= 1.6)) {
		tmp = x + (z * Math.cos(y));
	} else {
		tmp = (x + Math.sin(y)) + z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -0.86) or not (z <= 1.6):
		tmp = x + (z * math.cos(y))
	else:
		tmp = (x + math.sin(y)) + z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -0.86) || !(z <= 1.6))
		tmp = Float64(x + Float64(z * cos(y)));
	else
		tmp = Float64(Float64(x + sin(y)) + z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -0.86) || ~((z <= 1.6)))
		tmp = x + (z * cos(y));
	else
		tmp = (x + sin(y)) + z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -0.86], N[Not[LessEqual[z, 1.6]], $MachinePrecision]], N[(x + N[(z * N[Cos[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[Sin[y], $MachinePrecision]), $MachinePrecision] + z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -0.859999999999999987 or 1.6000000000000001 < z

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 99.3%

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

   if -0.859999999999999987 < z < 1.6000000000000001

   1. Initial program 100.0%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 99.4%

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

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -0.86 \lor \neg \left(z \leq 1.6\right):\\ \;\;\;\;x + z \cdot \cos y\\ \mathbf{else}:\\ \;\;\;\;\left(x + \sin y\right) + z\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 95.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.5 \cdot 10^{-24} \lor \neg \left(z \leq 0.017\right):\\ \;\;\;\;x + z \cdot \cos y\\ \mathbf{else}:\\ \;\;\;\;x + \sin y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -6.5e-24) (not (<= z 0.017)))
   (+ x (* z (cos y)))
   (+ x (sin y))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -6.5e-24) || !(z <= 0.017)) {
		tmp = x + (z * cos(y));
	} else {
		tmp = x + 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 <= (-6.5d-24)) .or. (.not. (z <= 0.017d0))) then
        tmp = x + (z * cos(y))
    else
        tmp = x + sin(y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -6.5e-24) || !(z <= 0.017)) {
		tmp = x + (z * Math.cos(y));
	} else {
		tmp = x + Math.sin(y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -6.5e-24) or not (z <= 0.017):
		tmp = x + (z * math.cos(y))
	else:
		tmp = x + math.sin(y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -6.5e-24) || !(z <= 0.017))
		tmp = Float64(x + Float64(z * cos(y)));
	else
		tmp = Float64(x + sin(y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -6.5e-24) || ~((z <= 0.017)))
		tmp = x + (z * cos(y));
	else
		tmp = x + sin(y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -6.5e-24], N[Not[LessEqual[z, 0.017]], $MachinePrecision]], N[(x + N[(z * N[Cos[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[Sin[y], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -6.5e-24 or 0.017000000000000001 < z

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 99.4%

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

   if -6.5e-24 < z < 0.017000000000000001

   1. Initial program 100.0%

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

      1. expm1-log1p-u 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in z around 0 93.4%

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

4. Final simplification 96.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -6.5 \cdot 10^{-24} \lor \neg \left(z \leq 0.017\right):\\ \;\;\;\;x + z \cdot \cos y\\ \mathbf{else}:\\ \;\;\;\;x + \sin y\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 69.9% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.05 \cdot 10^{+233}:\\ \;\;\;\;\sin y\\ \mathbf{elif}\;y \leq -16 \lor \neg \left(y \leq 8.2 \cdot 10^{-14}\right):\\ \;\;\;\;x + z\\ \mathbf{else}:\\ \;\;\;\;y + \left(x + z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.05e+233)
   (sin y)
   (if (or (<= y -16.0) (not (<= y 8.2e-14))) (+ x z) (+ y (+ x z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.05e+233) {
		tmp = sin(y);
	} else if ((y <= -16.0) || !(y <= 8.2e-14)) {
		tmp = x + z;
	} else {
		tmp = y + (x + 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 <= (-1.05d+233)) then
        tmp = sin(y)
    else if ((y <= (-16.0d0)) .or. (.not. (y <= 8.2d-14))) then
        tmp = x + z
    else
        tmp = y + (x + z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.05e+233) {
		tmp = Math.sin(y);
	} else if ((y <= -16.0) || !(y <= 8.2e-14)) {
		tmp = x + z;
	} else {
		tmp = y + (x + z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.05e+233:
		tmp = math.sin(y)
	elif (y <= -16.0) or not (y <= 8.2e-14):
		tmp = x + z
	else:
		tmp = y + (x + z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.05e+233)
		tmp = sin(y);
	elseif ((y <= -16.0) || !(y <= 8.2e-14))
		tmp = Float64(x + z);
	else
		tmp = Float64(y + Float64(x + z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.05e+233)
		tmp = sin(y);
	elseif ((y <= -16.0) || ~((y <= 8.2e-14)))
		tmp = x + z;
	else
		tmp = y + (x + z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.05e+233], N[Sin[y], $MachinePrecision], If[Or[LessEqual[y, -16.0], N[Not[LessEqual[y, 8.2e-14]], $MachinePrecision]], N[(x + z), $MachinePrecision], N[(y + N[(x + z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.04999999999999998e233

   1. Initial program 99.8%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 93.3%

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

   4. Taylor expanded in z around 0 41.8%

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

   if -1.04999999999999998e233 < y < -16 or 8.2000000000000004e-14 < y

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 81.5%

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

   4. Taylor expanded in y around 0 50.9%

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

      1. +-commutative 50.9%

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

   6. Simplified 50.9%

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

   if -16 < y < 8.2000000000000004e-14

   1. Initial program 100.0%

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

      1. expm1-log1p-u 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in y around 0 99.9%

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

      1. +-commutative 99.9%

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

      2. associate-+l+ 99.9%

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

   7. Simplified 99.9%

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

4. Final simplification 75.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.05 \cdot 10^{+233}:\\ \;\;\;\;\sin y\\ \mathbf{elif}\;y \leq -16 \lor \neg \left(y \leq 8.2 \cdot 10^{-14}\right):\\ \;\;\;\;x + z\\ \mathbf{else}:\\ \;\;\;\;y + \left(x + z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 70.8% accurate, 13.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -60 \lor \neg \left(y \leq 8.2 \cdot 10^{-14}\right):\\ \;\;\;\;x + z\\ \mathbf{else}:\\ \;\;\;\;y + \left(x + z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -60.0) (not (<= y 8.2e-14))) (+ x z) (+ y (+ x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -60.0) || !(y <= 8.2e-14)) {
		tmp = x + z;
	} else {
		tmp = y + (x + 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 <= (-60.0d0)) .or. (.not. (y <= 8.2d-14))) then
        tmp = x + z
    else
        tmp = y + (x + z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -60.0) || !(y <= 8.2e-14)) {
		tmp = x + z;
	} else {
		tmp = y + (x + z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -60.0) or not (y <= 8.2e-14):
		tmp = x + z
	else:
		tmp = y + (x + z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -60.0) || !(y <= 8.2e-14))
		tmp = Float64(x + z);
	else
		tmp = Float64(y + Float64(x + z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -60.0) || ~((y <= 8.2e-14)))
		tmp = x + z;
	else
		tmp = y + (x + z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -60.0], N[Not[LessEqual[y, 8.2e-14]], $MachinePrecision]], N[(x + z), $MachinePrecision], N[(y + N[(x + z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -60 or 8.2000000000000004e-14 < y

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 79.2%

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

   4. Taylor expanded in y around 0 46.2%

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

      1. +-commutative 46.2%

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

   6. Simplified 46.2%

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

   if -60 < y < 8.2000000000000004e-14

   1. Initial program 100.0%

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

      1. expm1-log1p-u 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in y around 0 99.9%

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

      1. +-commutative 99.9%

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

      2. associate-+l+ 99.9%

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

   7. Simplified 99.9%

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

4. Final simplification 73.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -60 \lor \neg \left(y \leq 8.2 \cdot 10^{-14}\right):\\ \;\;\;\;x + z\\ \mathbf{else}:\\ \;\;\;\;y + \left(x + z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 68.6% accurate, 15.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.3 \cdot 10^{-141} \lor \neg \left(x \leq 4.5 \cdot 10^{-93}\right):\\ \;\;\;\;x + z\\ \mathbf{else}:\\ \;\;\;\;y + z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -3.3e-141) (not (<= x 4.5e-93))) (+ x z) (+ y z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -3.3e-141) || !(x <= 4.5e-93)) {
		tmp = x + z;
	} else {
		tmp = 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 ((x <= (-3.3d-141)) .or. (.not. (x <= 4.5d-93))) then
        tmp = x + z
    else
        tmp = y + z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -3.3e-141) || !(x <= 4.5e-93)) {
		tmp = x + z;
	} else {
		tmp = y + z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -3.3e-141) or not (x <= 4.5e-93):
		tmp = x + z
	else:
		tmp = y + z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -3.3e-141) || !(x <= 4.5e-93))
		tmp = Float64(x + z);
	else
		tmp = Float64(y + z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -3.3e-141) || ~((x <= 4.5e-93)))
		tmp = x + z;
	else
		tmp = y + z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -3.3e-141], N[Not[LessEqual[x, 4.5e-93]], $MachinePrecision]], N[(x + z), $MachinePrecision], N[(y + z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -3.29999999999999999e-141 or 4.5000000000000002e-93 < x

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 93.6%

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

   4. Taylor expanded in y around 0 79.2%

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

      1. +-commutative 79.2%

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

   6. Simplified 79.2%

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

   if -3.29999999999999999e-141 < x < 4.5000000000000002e-93

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 99.3%

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

   4. Taylor expanded in y around 0 57.0%

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

4. Final simplification 72.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.3 \cdot 10^{-141} \lor \neg \left(x \leq 4.5 \cdot 10^{-93}\right):\\ \;\;\;\;x + z\\ \mathbf{else}:\\ \;\;\;\;y + z\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 58.7% accurate, 15.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1 \cdot 10^{-51}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 0.72:\\ \;\;\;\;y + z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1e-51) x (if (<= x 0.72) (+ y z) x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1e-51) {
		tmp = x;
	} else if (x <= 0.72) {
		tmp = y + z;
	} else {
		tmp = x;
	}
	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 (x <= (-1d-51)) then
        tmp = x
    else if (x <= 0.72d0) then
        tmp = y + z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -1e-51) {
		tmp = x;
	} else if (x <= 0.72) {
		tmp = y + z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -1e-51:
		tmp = x
	elif x <= 0.72:
		tmp = y + z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1e-51)
		tmp = x;
	elseif (x <= 0.72)
		tmp = Float64(y + z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -1e-51)
		tmp = x;
	elseif (x <= 0.72)
		tmp = y + z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1e-51], x, If[LessEqual[x, 0.72], N[(y + z), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if x < -1e-51 or 0.71999999999999997 < x

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 97.1%

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

   4. Taylor expanded in x around inf 73.2%

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

   if -1e-51 < x < 0.71999999999999997

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 92.6%

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

   4. Taylor expanded in y around 0 50.2%

      \[\leadsto \color{blue}{y + z} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 55.9% accurate, 18.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.5 \cdot 10^{-52}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 0.68:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -4.5e-52) x (if (<= x 0.68) z x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -4.5e-52) {
		tmp = x;
	} else if (x <= 0.68) {
		tmp = z;
	} else {
		tmp = x;
	}
	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 (x <= (-4.5d-52)) then
        tmp = x
    else if (x <= 0.68d0) then
        tmp = z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -4.5e-52) {
		tmp = x;
	} else if (x <= 0.68) {
		tmp = z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -4.5e-52:
		tmp = x
	elif x <= 0.68:
		tmp = z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -4.5e-52)
		tmp = x;
	elseif (x <= 0.68)
		tmp = z;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -4.5e-52)
		tmp = x;
	elseif (x <= 0.68)
		tmp = z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -4.5e-52], x, If[LessEqual[x, 0.68], z, x]]

Derivation

1. Split input into 2 regimes

2. if x < -4.5e-52 or 0.680000000000000049 < x

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 97.1%

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

   4. Taylor expanded in x around inf 73.2%

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

   if -4.5e-52 < x < 0.680000000000000049

   1. Initial program 99.9%

      \[\left(x + \sin y\right) + z \cdot \cos y \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 69.9%

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

   4. Taylor expanded in x around inf 44.2%

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

      1. associate-/l* 44.2%

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

   6. Simplified 44.2%

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

   7. Taylor expanded in x around 0 63.4%

      \[\leadsto \color{blue}{z \cdot \cos y} \]
   8. Step-by-step derivation

      1. *-commutative 63.4%

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

   9. Simplified 63.4%

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

   10. Taylor expanded in y around 0 41.9%

       \[\leadsto \color{blue}{1} \cdot z \]
3. Recombined 2 regimes into one program.

4. Final simplification 59.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.5 \cdot 10^{-52}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 0.68:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 43.2% 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%

   \[\left(x + \sin y\right) + z \cdot \cos y \]
2. Add Preprocessing

3. Taylor expanded in x around inf 85.2%

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

4. Taylor expanded in x around inf 45.4%

   \[\leadsto \color{blue}{x} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024110 
(FPCore (x y z)
  :name "Graphics.Rasterific.Svg.PathConverter:segmentToBezier from rasterific-svg-0.2.3.1, C"
  :precision binary64
  (+ (+ x (sin y)) (* z (cos y))))