Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendOutside from plot-0.2.3.4, C

Percentage Accurate: 99.9% → 100.0%

Time: 4.6s

Alternatives: 6

Speedup: 1.0×

• 21.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x \cdot \left(y + z\right) + z \cdot 5 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* x (+ y z)) (* z 5.0)))

C

double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

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 * (y + z)) + (z * 5.0d0)
end function

Java

public static double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

Python

def code(x, y, z):
	return (x * (y + z)) + (z * 5.0)

Julia

function code(x, y, z)
	return Float64(Float64(x * Float64(y + z)) + Float64(z * 5.0))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * (y + z)) + (z * 5.0);
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision] + N[(z * 5.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(y + z\right) + z \cdot 5 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* x (+ y z)) (* z 5.0)))

C

double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

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 * (y + z)) + (z * 5.0d0)
end function

Java

public static double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

Python

def code(x, y, z):
	return (x * (y + z)) + (z * 5.0)

Julia

function code(x, y, z)
	return Float64(Float64(x * Float64(y + z)) + Float64(z * 5.0))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * (y + z)) + (z * 5.0);
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision] + N[(z * 5.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(z, 5, x \cdot \left(z + y\right)\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fma z 5.0 (* x (+ z y))))

C

double code(double x, double y, double z) {
	return fma(z, 5.0, (x * (z + y)));
}

Julia

function code(x, y, z)
	return fma(z, 5.0, Float64(x * Float64(z + y)))
end

Wolfram

code[x_, y_, z_] := N[(z * 5.0 + N[(x * N[(z + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x \cdot \left(y + z\right) + z \cdot 5 \]
2. Step-by-step derivation

   1. +-commutative 99.9%

      \[\leadsto \color{blue}{z \cdot 5 + x \cdot \left(y + z\right)} \]

   2. fma-def 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, 5, x \cdot \left(y + z\right)\right)} \]

3. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(z, 5, x \cdot \left(y + z\right)\right)} \]

4. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(z, 5, x \cdot \left(z + y\right)\right) \]

Alternative 2: 60.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.2 \cdot 10^{+168}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq -2.2 \cdot 10^{+142}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq -4.8 \cdot 10^{+34}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq -2050000:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq -8.5 \cdot 10^{-25}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 3.9 \cdot 10^{-61}:\\ \;\;\;\;z \cdot 5\\ \mathbf{elif}\;x \leq 4.3 \cdot 10^{+58} \lor \neg \left(x \leq 2.8 \cdot 10^{+90}\right) \land x \leq 9.5 \cdot 10^{+250}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;z \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -6.2e+168)
   (* x y)
   (if (<= x -2.2e+142)
     (* z x)
     (if (<= x -4.8e+34)
       (* x y)
       (if (<= x -2050000.0)
         (* z x)
         (if (<= x -8.5e-25)
           (* x y)
           (if (<= x 3.9e-61)
             (* z 5.0)
             (if (or (<= x 4.3e+58) (and (not (<= x 2.8e+90)) (<= x 9.5e+250)))
               (* x y)
               (* z x)))))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -6.2e+168) {
		tmp = x * y;
	} else if (x <= -2.2e+142) {
		tmp = z * x;
	} else if (x <= -4.8e+34) {
		tmp = x * y;
	} else if (x <= -2050000.0) {
		tmp = z * x;
	} else if (x <= -8.5e-25) {
		tmp = x * y;
	} else if (x <= 3.9e-61) {
		tmp = z * 5.0;
	} else if ((x <= 4.3e+58) || (!(x <= 2.8e+90) && (x <= 9.5e+250))) {
		tmp = x * y;
	} else {
		tmp = z * 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 <= (-6.2d+168)) then
        tmp = x * y
    else if (x <= (-2.2d+142)) then
        tmp = z * x
    else if (x <= (-4.8d+34)) then
        tmp = x * y
    else if (x <= (-2050000.0d0)) then
        tmp = z * x
    else if (x <= (-8.5d-25)) then
        tmp = x * y
    else if (x <= 3.9d-61) then
        tmp = z * 5.0d0
    else if ((x <= 4.3d+58) .or. (.not. (x <= 2.8d+90)) .and. (x <= 9.5d+250)) then
        tmp = x * y
    else
        tmp = z * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -6.2e+168) {
		tmp = x * y;
	} else if (x <= -2.2e+142) {
		tmp = z * x;
	} else if (x <= -4.8e+34) {
		tmp = x * y;
	} else if (x <= -2050000.0) {
		tmp = z * x;
	} else if (x <= -8.5e-25) {
		tmp = x * y;
	} else if (x <= 3.9e-61) {
		tmp = z * 5.0;
	} else if ((x <= 4.3e+58) || (!(x <= 2.8e+90) && (x <= 9.5e+250))) {
		tmp = x * y;
	} else {
		tmp = z * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -6.2e+168:
		tmp = x * y
	elif x <= -2.2e+142:
		tmp = z * x
	elif x <= -4.8e+34:
		tmp = x * y
	elif x <= -2050000.0:
		tmp = z * x
	elif x <= -8.5e-25:
		tmp = x * y
	elif x <= 3.9e-61:
		tmp = z * 5.0
	elif (x <= 4.3e+58) or (not (x <= 2.8e+90) and (x <= 9.5e+250)):
		tmp = x * y
	else:
		tmp = z * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -6.2e+168)
		tmp = Float64(x * y);
	elseif (x <= -2.2e+142)
		tmp = Float64(z * x);
	elseif (x <= -4.8e+34)
		tmp = Float64(x * y);
	elseif (x <= -2050000.0)
		tmp = Float64(z * x);
	elseif (x <= -8.5e-25)
		tmp = Float64(x * y);
	elseif (x <= 3.9e-61)
		tmp = Float64(z * 5.0);
	elseif ((x <= 4.3e+58) || (!(x <= 2.8e+90) && (x <= 9.5e+250)))
		tmp = Float64(x * y);
	else
		tmp = Float64(z * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -6.2e+168)
		tmp = x * y;
	elseif (x <= -2.2e+142)
		tmp = z * x;
	elseif (x <= -4.8e+34)
		tmp = x * y;
	elseif (x <= -2050000.0)
		tmp = z * x;
	elseif (x <= -8.5e-25)
		tmp = x * y;
	elseif (x <= 3.9e-61)
		tmp = z * 5.0;
	elseif ((x <= 4.3e+58) || (~((x <= 2.8e+90)) && (x <= 9.5e+250)))
		tmp = x * y;
	else
		tmp = z * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -6.2e+168], N[(x * y), $MachinePrecision], If[LessEqual[x, -2.2e+142], N[(z * x), $MachinePrecision], If[LessEqual[x, -4.8e+34], N[(x * y), $MachinePrecision], If[LessEqual[x, -2050000.0], N[(z * x), $MachinePrecision], If[LessEqual[x, -8.5e-25], N[(x * y), $MachinePrecision], If[LessEqual[x, 3.9e-61], N[(z * 5.0), $MachinePrecision], If[Or[LessEqual[x, 4.3e+58], And[N[Not[LessEqual[x, 2.8e+90]], $MachinePrecision], LessEqual[x, 9.5e+250]]], N[(x * y), $MachinePrecision], N[(z * x), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -6.19999999999999993e168 or -2.19999999999999987e142 < x < -4.79999999999999974e34 or -2.05e6 < x < -8.49999999999999981e-25 or 3.90000000000000033e-61 < x < 4.29999999999999991e58 or 2.8e90 < x < 9.49999999999999957e250

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around inf 68.3%

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

   if -6.19999999999999993e168 < x < -2.19999999999999987e142 or -4.79999999999999974e34 < x < -2.05e6 or 4.29999999999999991e58 < x < 2.8e90 or 9.49999999999999957e250 < x

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf 95.9%

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

      1. +-commutative 95.9%

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

   4. Simplified 95.9%

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

   5. Taylor expanded in z around inf 77.4%

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

   if -8.49999999999999981e-25 < x < 3.90000000000000033e-61

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around 0 74.4%

      \[\leadsto \color{blue}{5 \cdot z} \]
3. Recombined 3 regimes into one program.

4. Final simplification 72.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -6.2 \cdot 10^{+168}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq -2.2 \cdot 10^{+142}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq -4.8 \cdot 10^{+34}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq -2050000:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq -8.5 \cdot 10^{-25}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 3.9 \cdot 10^{-61}:\\ \;\;\;\;z \cdot 5\\ \mathbf{elif}\;x \leq 4.3 \cdot 10^{+58} \lor \neg \left(x \leq 2.8 \cdot 10^{+90}\right) \land x \leq 9.5 \cdot 10^{+250}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;z \cdot x\\ \end{array} \]

Alternative 3: 84.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{-24} \lor \neg \left(x \leq 4.6 \cdot 10^{-61}\right):\\ \;\;\;\;x \cdot \left(z + y\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot 5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -2e-24) (not (<= x 4.6e-61))) (* x (+ z y)) (* z 5.0)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2e-24) || !(x <= 4.6e-61)) {
		tmp = x * (z + y);
	} else {
		tmp = z * 5.0;
	}
	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 ((x <= (-2d-24)) .or. (.not. (x <= 4.6d-61))) then
        tmp = x * (z + y)
    else
        tmp = z * 5.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2e-24) || !(x <= 4.6e-61)) {
		tmp = x * (z + y);
	} else {
		tmp = z * 5.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -2e-24) or not (x <= 4.6e-61):
		tmp = x * (z + y)
	else:
		tmp = z * 5.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -2e-24) || !(x <= 4.6e-61))
		tmp = Float64(x * Float64(z + y));
	else
		tmp = Float64(z * 5.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -2e-24) || ~((x <= 4.6e-61)))
		tmp = x * (z + y);
	else
		tmp = z * 5.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -2e-24], N[Not[LessEqual[x, 4.6e-61]], $MachinePrecision]], N[(x * N[(z + y), $MachinePrecision]), $MachinePrecision], N[(z * 5.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.99999999999999985e-24 or 4.59999999999999984e-61 < x

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf 96.1%

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

      1. +-commutative 96.1%

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

   4. Simplified 96.1%

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

   if -1.99999999999999985e-24 < x < 4.59999999999999984e-61

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around 0 74.4%

      \[\leadsto \color{blue}{5 \cdot z} \]
3. Recombined 2 regimes into one program.

4. Final simplification 87.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{-24} \lor \neg \left(x \leq 4.6 \cdot 10^{-61}\right):\\ \;\;\;\;x \cdot \left(z + y\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot 5\\ \end{array} \]

Alternative 4: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(z + y\right) + z \cdot 5 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* x (+ z y)) (* z 5.0)))

C

double code(double x, double y, double z) {
	return (x * (z + y)) + (z * 5.0);
}

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 * (z + y)) + (z * 5.0d0)
end function

Java

public static double code(double x, double y, double z) {
	return (x * (z + y)) + (z * 5.0);
}

Python

def code(x, y, z):
	return (x * (z + y)) + (z * 5.0)

Julia

function code(x, y, z)
	return Float64(Float64(x * Float64(z + y)) + Float64(z * 5.0))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * (z + y)) + (z * 5.0);
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[(z + y), $MachinePrecision]), $MachinePrecision] + N[(z * 5.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x \cdot \left(y + z\right) + z \cdot 5 \]

2. Final simplification 99.9%

   \[\leadsto x \cdot \left(z + y\right) + z \cdot 5 \]

Alternative 5: 60.2% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3 \cdot 10^{-24} \lor \neg \left(x \leq 7.6 \cdot 10^{-66}\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;z \cdot 5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -3e-24) (not (<= x 7.6e-66))) (* x y) (* z 5.0)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -3e-24) || !(x <= 7.6e-66)) {
		tmp = x * y;
	} else {
		tmp = z * 5.0;
	}
	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 ((x <= (-3d-24)) .or. (.not. (x <= 7.6d-66))) then
        tmp = x * y
    else
        tmp = z * 5.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -3e-24) || !(x <= 7.6e-66)) {
		tmp = x * y;
	} else {
		tmp = z * 5.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -3e-24) or not (x <= 7.6e-66):
		tmp = x * y
	else:
		tmp = z * 5.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -3e-24) || !(x <= 7.6e-66))
		tmp = Float64(x * y);
	else
		tmp = Float64(z * 5.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -3e-24) || ~((x <= 7.6e-66)))
		tmp = x * y;
	else
		tmp = z * 5.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -3e-24], N[Not[LessEqual[x, 7.6e-66]], $MachinePrecision]], N[(x * y), $MachinePrecision], N[(z * 5.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.99999999999999995e-24 or 7.5999999999999995e-66 < x

   1. Initial program 100.0%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around inf 57.2%

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

   if -2.99999999999999995e-24 < x < 7.5999999999999995e-66

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around 0 74.4%

      \[\leadsto \color{blue}{5 \cdot z} \]
3. Recombined 2 regimes into one program.

4. Final simplification 64.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3 \cdot 10^{-24} \lor \neg \left(x \leq 7.6 \cdot 10^{-66}\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;z \cdot 5\\ \end{array} \]

Alternative 6: 35.7% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ z \cdot 5 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* z 5.0))

C

double code(double x, double y, double z) {
	return z * 5.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = z * 5.0d0
end function

Java

public static double code(double x, double y, double z) {
	return z * 5.0;
}

Python

def code(x, y, z):
	return z * 5.0

Julia

function code(x, y, z)
	return Float64(z * 5.0)
end

MATLAB

function tmp = code(x, y, z)
	tmp = z * 5.0;
end

Wolfram

code[x_, y_, z_] := N[(z * 5.0), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x \cdot \left(y + z\right) + z \cdot 5 \]

2. Taylor expanded in x around 0 33.8%

   \[\leadsto \color{blue}{5 \cdot z} \]

3. Final simplification 33.8%

   \[\leadsto z \cdot 5 \]

Developer target: 97.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x + 5\right) \cdot z + x \cdot y \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* (+ x 5.0) z) (* x y)))

C

double code(double x, double y, double z) {
	return ((x + 5.0) * z) + (x * 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 + 5.0d0) * z) + (x * y)
end function

Java

public static double code(double x, double y, double z) {
	return ((x + 5.0) * z) + (x * y);
}

Python

def code(x, y, z):
	return ((x + 5.0) * z) + (x * y)

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x + 5.0) * z) + Float64(x * y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((x + 5.0) * z) + (x * y);
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x + 5.0), $MachinePrecision] * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023311 
(FPCore (x y z)
  :name "Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendOutside from plot-0.2.3.4, C"
  :precision binary64

  :herbie-target
  (+ (* (+ x 5.0) z) (* x y))

  (+ (* x (+ y z)) (* z 5.0)))