Diagrams.Solve.Polynomial:quartForm from diagrams-solve-0.1, B

Percentage Accurate: 100.0% → 100.0%

Time: 6.4s

Alternatives: 8

Speedup: 0.1×

Specification

Math

\[\begin{array}{l} \\ \left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (- (* (/ 1.0 8.0) x) (/ (* y z) 2.0)) t))

C

double code(double x, double y, double z, double t) {
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (((1.0d0 / 8.0d0) * x) - ((y * z) / 2.0d0)) + t
end function

Java

public static double code(double x, double y, double z, double t) {
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}

Python

def code(x, y, z, t):
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(Float64(1.0 / 8.0) * x) - Float64(Float64(y * z) / 2.0)) + t)
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(N[(1.0 / 8.0), $MachinePrecision] * x), $MachinePrecision] - N[(N[(y * z), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (- (* (/ 1.0 8.0) x) (/ (* y z) 2.0)) t))

C

double code(double x, double y, double z, double t) {
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (((1.0d0 / 8.0d0) * x) - ((y * z) / 2.0d0)) + t
end function

Java

public static double code(double x, double y, double z, double t) {
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}

Python

def code(x, y, z, t):
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(Float64(1.0 / 8.0) * x) - Float64(Float64(y * z) / 2.0)) + t)
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(N[(1.0 / 8.0), $MachinePrecision] * x), $MachinePrecision] - N[(N[(y * z), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(z, y \cdot -0.5, \mathsf{fma}\left(0.125, x, t\right)\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (fma z (* y -0.5) (fma 0.125 x t)))

C

double code(double x, double y, double z, double t) {
	return fma(z, (y * -0.5), fma(0.125, x, t));
}

Julia

function code(x, y, z, t)
	return fma(z, Float64(y * -0.5), fma(0.125, x, t))
end

Wolfram

code[x_, y_, z_, t_] := N[(z * N[(y * -0.5), $MachinePrecision] + N[(0.125 * x + t), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
2. Step-by-step derivation

   1. sub-neg 99.7%

      \[\leadsto \color{blue}{\left(\frac{1}{8} \cdot x + \left(-\frac{y \cdot z}{2}\right)\right)} + t \]

   2. +-commutative 99.7%

      \[\leadsto \color{blue}{\left(\left(-\frac{y \cdot z}{2}\right) + \frac{1}{8} \cdot x\right)} + t \]

   3. associate-+l+ 99.7%

      \[\leadsto \color{blue}{\left(-\frac{y \cdot z}{2}\right) + \left(\frac{1}{8} \cdot x + t\right)} \]

   4. associate-/l* 99.9%

      \[\leadsto \left(-\color{blue}{\frac{y}{\frac{2}{z}}}\right) + \left(\frac{1}{8} \cdot x + t\right) \]

   5. distribute-frac-neg 99.9%

      \[\leadsto \color{blue}{\frac{-y}{\frac{2}{z}}} + \left(\frac{1}{8} \cdot x + t\right) \]

   6. associate-/r/ 100.0%

      \[\leadsto \color{blue}{\frac{-y}{2} \cdot z} + \left(\frac{1}{8} \cdot x + t\right) \]

   7. *-commutative 100.0%

      \[\leadsto \color{blue}{z \cdot \frac{-y}{2}} + \left(\frac{1}{8} \cdot x + t\right) \]

   8. +-commutative 100.0%

      \[\leadsto z \cdot \frac{-y}{2} + \color{blue}{\left(t + \frac{1}{8} \cdot x\right)} \]

   9. fma-def 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, \frac{-y}{2}, t + \frac{1}{8} \cdot x\right)} \]

   10. neg-mul-1 100.0%

       \[\leadsto \mathsf{fma}\left(z, \frac{\color{blue}{-1 \cdot y}}{2}, t + \frac{1}{8} \cdot x\right) \]

   11. associate-/l* 99.9%

       \[\leadsto \mathsf{fma}\left(z, \color{blue}{\frac{-1}{\frac{2}{y}}}, t + \frac{1}{8} \cdot x\right) \]

   12. associate-/r/ 100.0%

       \[\leadsto \mathsf{fma}\left(z, \color{blue}{\frac{-1}{2} \cdot y}, t + \frac{1}{8} \cdot x\right) \]

   13. *-commutative 100.0%

       \[\leadsto \mathsf{fma}\left(z, \color{blue}{y \cdot \frac{-1}{2}}, t + \frac{1}{8} \cdot x\right) \]

   14. metadata-eval 100.0%

       \[\leadsto \mathsf{fma}\left(z, y \cdot \color{blue}{-0.5}, t + \frac{1}{8} \cdot x\right) \]

   15. +-commutative 100.0%

       \[\leadsto \mathsf{fma}\left(z, y \cdot -0.5, \color{blue}{\frac{1}{8} \cdot x + t}\right) \]

   16. fma-def 100.0%

       \[\leadsto \mathsf{fma}\left(z, y \cdot -0.5, \color{blue}{\mathsf{fma}\left(\frac{1}{8}, x, t\right)}\right) \]

   17. metadata-eval 100.0%

       \[\leadsto \mathsf{fma}\left(z, y \cdot -0.5, \mathsf{fma}\left(\color{blue}{0.125}, x, t\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(z, y \cdot -0.5, \mathsf{fma}\left(0.125, x, t\right)\right)} \]
4. Add Preprocessing

5. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(z, y \cdot -0.5, \mathsf{fma}\left(0.125, x, t\right)\right) \]
6. Add Preprocessing

Alternative 2: 51.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(z \cdot -0.5\right)\\ \mathbf{if}\;t \leq -5.3 \cdot 10^{+129}:\\ \;\;\;\;t\\ \mathbf{elif}\;t \leq -3.25 \cdot 10^{+78}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -1.45 \cdot 10^{+25}:\\ \;\;\;\;t\\ \mathbf{elif}\;t \leq -2.35 \cdot 10^{-142}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -4.8 \cdot 10^{-199}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{elif}\;t \leq 3.5 \cdot 10^{-269}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 2.6 \cdot 10^{-237}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{elif}\;t \leq 7.2 \cdot 10^{-187}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.3 \cdot 10^{-6}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{elif}\;t \leq 4.3 \cdot 10^{+76}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (* z -0.5))))
   (if (<= t -5.3e+129)
     t
     (if (<= t -3.25e+78)
       t_1
       (if (<= t -1.45e+25)
         t
         (if (<= t -2.35e-142)
           t_1
           (if (<= t -4.8e-199)
             (* 0.125 x)
             (if (<= t 3.5e-269)
               t_1
               (if (<= t 2.6e-237)
                 (* 0.125 x)
                 (if (<= t 7.2e-187)
                   t_1
                   (if (<= t 1.3e-6)
                     (* 0.125 x)
                     (if (<= t 4.3e+76) t_1 t))))))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (z * -0.5);
	double tmp;
	if (t <= -5.3e+129) {
		tmp = t;
	} else if (t <= -3.25e+78) {
		tmp = t_1;
	} else if (t <= -1.45e+25) {
		tmp = t;
	} else if (t <= -2.35e-142) {
		tmp = t_1;
	} else if (t <= -4.8e-199) {
		tmp = 0.125 * x;
	} else if (t <= 3.5e-269) {
		tmp = t_1;
	} else if (t <= 2.6e-237) {
		tmp = 0.125 * x;
	} else if (t <= 7.2e-187) {
		tmp = t_1;
	} else if (t <= 1.3e-6) {
		tmp = 0.125 * x;
	} else if (t <= 4.3e+76) {
		tmp = t_1;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (z * (-0.5d0))
    if (t <= (-5.3d+129)) then
        tmp = t
    else if (t <= (-3.25d+78)) then
        tmp = t_1
    else if (t <= (-1.45d+25)) then
        tmp = t
    else if (t <= (-2.35d-142)) then
        tmp = t_1
    else if (t <= (-4.8d-199)) then
        tmp = 0.125d0 * x
    else if (t <= 3.5d-269) then
        tmp = t_1
    else if (t <= 2.6d-237) then
        tmp = 0.125d0 * x
    else if (t <= 7.2d-187) then
        tmp = t_1
    else if (t <= 1.3d-6) then
        tmp = 0.125d0 * x
    else if (t <= 4.3d+76) then
        tmp = t_1
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * (z * -0.5);
	double tmp;
	if (t <= -5.3e+129) {
		tmp = t;
	} else if (t <= -3.25e+78) {
		tmp = t_1;
	} else if (t <= -1.45e+25) {
		tmp = t;
	} else if (t <= -2.35e-142) {
		tmp = t_1;
	} else if (t <= -4.8e-199) {
		tmp = 0.125 * x;
	} else if (t <= 3.5e-269) {
		tmp = t_1;
	} else if (t <= 2.6e-237) {
		tmp = 0.125 * x;
	} else if (t <= 7.2e-187) {
		tmp = t_1;
	} else if (t <= 1.3e-6) {
		tmp = 0.125 * x;
	} else if (t <= 4.3e+76) {
		tmp = t_1;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (z * -0.5)
	tmp = 0
	if t <= -5.3e+129:
		tmp = t
	elif t <= -3.25e+78:
		tmp = t_1
	elif t <= -1.45e+25:
		tmp = t
	elif t <= -2.35e-142:
		tmp = t_1
	elif t <= -4.8e-199:
		tmp = 0.125 * x
	elif t <= 3.5e-269:
		tmp = t_1
	elif t <= 2.6e-237:
		tmp = 0.125 * x
	elif t <= 7.2e-187:
		tmp = t_1
	elif t <= 1.3e-6:
		tmp = 0.125 * x
	elif t <= 4.3e+76:
		tmp = t_1
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(z * -0.5))
	tmp = 0.0
	if (t <= -5.3e+129)
		tmp = t;
	elseif (t <= -3.25e+78)
		tmp = t_1;
	elseif (t <= -1.45e+25)
		tmp = t;
	elseif (t <= -2.35e-142)
		tmp = t_1;
	elseif (t <= -4.8e-199)
		tmp = Float64(0.125 * x);
	elseif (t <= 3.5e-269)
		tmp = t_1;
	elseif (t <= 2.6e-237)
		tmp = Float64(0.125 * x);
	elseif (t <= 7.2e-187)
		tmp = t_1;
	elseif (t <= 1.3e-6)
		tmp = Float64(0.125 * x);
	elseif (t <= 4.3e+76)
		tmp = t_1;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (z * -0.5);
	tmp = 0.0;
	if (t <= -5.3e+129)
		tmp = t;
	elseif (t <= -3.25e+78)
		tmp = t_1;
	elseif (t <= -1.45e+25)
		tmp = t;
	elseif (t <= -2.35e-142)
		tmp = t_1;
	elseif (t <= -4.8e-199)
		tmp = 0.125 * x;
	elseif (t <= 3.5e-269)
		tmp = t_1;
	elseif (t <= 2.6e-237)
		tmp = 0.125 * x;
	elseif (t <= 7.2e-187)
		tmp = t_1;
	elseif (t <= 1.3e-6)
		tmp = 0.125 * x;
	elseif (t <= 4.3e+76)
		tmp = t_1;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(z * -0.5), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.3e+129], t, If[LessEqual[t, -3.25e+78], t$95$1, If[LessEqual[t, -1.45e+25], t, If[LessEqual[t, -2.35e-142], t$95$1, If[LessEqual[t, -4.8e-199], N[(0.125 * x), $MachinePrecision], If[LessEqual[t, 3.5e-269], t$95$1, If[LessEqual[t, 2.6e-237], N[(0.125 * x), $MachinePrecision], If[LessEqual[t, 7.2e-187], t$95$1, If[LessEqual[t, 1.3e-6], N[(0.125 * x), $MachinePrecision], If[LessEqual[t, 4.3e+76], t$95$1, t]]]]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -5.2999999999999999e129 or -3.25000000000000018e78 < t < -1.44999999999999995e25 or 4.29999999999999978e76 < t

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 70.6%

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

   if -5.2999999999999999e129 < t < -3.25000000000000018e78 or -1.44999999999999995e25 < t < -2.34999999999999995e-142 or -4.79999999999999991e-199 < t < 3.50000000000000019e-269 or 2.6000000000000002e-237 < t < 7.19999999999999989e-187 or 1.30000000000000005e-6 < t < 4.29999999999999978e76

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 99.9%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 78.0%

      \[\leadsto \color{blue}{-0.5 \cdot \left(y \cdot z\right)} + t \]
   6. Step-by-step derivation

      1. *-commutative 78.0%

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot -0.5} + t \]

      2. associate-*l* 78.0%

         \[\leadsto \color{blue}{y \cdot \left(z \cdot -0.5\right)} + t \]

   7. Simplified 78.0%

      \[\leadsto \color{blue}{y \cdot \left(z \cdot -0.5\right)} + t \]

   8. Taylor expanded in y around inf 65.7%

      \[\leadsto \color{blue}{-0.5 \cdot \left(y \cdot z\right)} \]
   9. Step-by-step derivation

      1. *-commutative 65.7%

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot -0.5} \]

      2. associate-*r* 65.7%

         \[\leadsto \color{blue}{y \cdot \left(z \cdot -0.5\right)} \]

      3. *-commutative 65.7%

         \[\leadsto y \cdot \color{blue}{\left(-0.5 \cdot z\right)} \]

   10. Simplified 65.7%

       \[\leadsto \color{blue}{y \cdot \left(-0.5 \cdot z\right)} \]

   if -2.34999999999999995e-142 < t < -4.79999999999999991e-199 or 3.50000000000000019e-269 < t < 2.6000000000000002e-237 or 7.19999999999999989e-187 < t < 1.30000000000000005e-6

   1. Initial program 98.8%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 98.8%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 99.9%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 90.8%

      \[\leadsto \color{blue}{0.125 \cdot x - 0.5 \cdot \left(y \cdot z\right)} \]

   6. Taylor expanded in x around inf 69.8%

      \[\leadsto \color{blue}{0.125 \cdot x} \]
3. Recombined 3 regimes into one program.

4. Final simplification 68.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -5.3 \cdot 10^{+129}:\\ \;\;\;\;t\\ \mathbf{elif}\;t \leq -3.25 \cdot 10^{+78}:\\ \;\;\;\;y \cdot \left(z \cdot -0.5\right)\\ \mathbf{elif}\;t \leq -1.45 \cdot 10^{+25}:\\ \;\;\;\;t\\ \mathbf{elif}\;t \leq -2.35 \cdot 10^{-142}:\\ \;\;\;\;y \cdot \left(z \cdot -0.5\right)\\ \mathbf{elif}\;t \leq -4.8 \cdot 10^{-199}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{elif}\;t \leq 3.5 \cdot 10^{-269}:\\ \;\;\;\;y \cdot \left(z \cdot -0.5\right)\\ \mathbf{elif}\;t \leq 2.6 \cdot 10^{-237}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{elif}\;t \leq 7.2 \cdot 10^{-187}:\\ \;\;\;\;y \cdot \left(z \cdot -0.5\right)\\ \mathbf{elif}\;t \leq 1.3 \cdot 10^{-6}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{elif}\;t \leq 4.3 \cdot 10^{+76}:\\ \;\;\;\;y \cdot \left(z \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 86.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.5 \cdot 10^{+57} \lor \neg \left(t \leq 3.05\right):\\ \;\;\;\;t + y \cdot \left(z \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;0.125 \cdot x - 0.5 \cdot \left(z \cdot y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= t -2.5e+57) (not (<= t 3.05)))
   (+ t (* y (* z -0.5)))
   (- (* 0.125 x) (* 0.5 (* z y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -2.5e+57) || !(t <= 3.05)) {
		tmp = t + (y * (z * -0.5));
	} else {
		tmp = (0.125 * x) - (0.5 * (z * y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((t <= (-2.5d+57)) .or. (.not. (t <= 3.05d0))) then
        tmp = t + (y * (z * (-0.5d0)))
    else
        tmp = (0.125d0 * x) - (0.5d0 * (z * y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((t <= -2.5e+57) || !(t <= 3.05)) {
		tmp = t + (y * (z * -0.5));
	} else {
		tmp = (0.125 * x) - (0.5 * (z * y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (t <= -2.5e+57) or not (t <= 3.05):
		tmp = t + (y * (z * -0.5))
	else:
		tmp = (0.125 * x) - (0.5 * (z * y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((t <= -2.5e+57) || !(t <= 3.05))
		tmp = Float64(t + Float64(y * Float64(z * -0.5)));
	else
		tmp = Float64(Float64(0.125 * x) - Float64(0.5 * Float64(z * y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((t <= -2.5e+57) || ~((t <= 3.05)))
		tmp = t + (y * (z * -0.5));
	else
		tmp = (0.125 * x) - (0.5 * (z * y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[t, -2.5e+57], N[Not[LessEqual[t, 3.05]], $MachinePrecision]], N[(t + N[(y * N[(z * -0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(0.125 * x), $MachinePrecision] - N[(0.5 * N[(z * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -2.49999999999999986e57 or 3.0499999999999998 < t

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 92.9%

      \[\leadsto \color{blue}{-0.5 \cdot \left(y \cdot z\right)} + t \]
   6. Step-by-step derivation

      1. *-commutative 92.9%

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot -0.5} + t \]

      2. associate-*l* 92.9%

         \[\leadsto \color{blue}{y \cdot \left(z \cdot -0.5\right)} + t \]

   7. Simplified 92.9%

      \[\leadsto \color{blue}{y \cdot \left(z \cdot -0.5\right)} + t \]

   if -2.49999999999999986e57 < t < 3.0499999999999998

   1. Initial program 99.4%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 99.4%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 99.9%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 90.5%

      \[\leadsto \color{blue}{0.125 \cdot x - 0.5 \cdot \left(y \cdot z\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 91.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -2.5 \cdot 10^{+57} \lor \neg \left(t \leq 3.05\right):\\ \;\;\;\;t + y \cdot \left(z \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;0.125 \cdot x - 0.5 \cdot \left(z \cdot y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 80.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.5 \cdot 10^{-78} \lor \neg \left(z \leq 3.05 \cdot 10^{-75}\right):\\ \;\;\;\;t + y \cdot \left(z \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;t + 0.125 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -3.5e-78) (not (<= z 3.05e-75)))
   (+ t (* y (* z -0.5)))
   (+ t (* 0.125 x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -3.5e-78) || !(z <= 3.05e-75)) {
		tmp = t + (y * (z * -0.5));
	} else {
		tmp = t + (0.125 * x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-3.5d-78)) .or. (.not. (z <= 3.05d-75))) then
        tmp = t + (y * (z * (-0.5d0)))
    else
        tmp = t + (0.125d0 * x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -3.5e-78) || !(z <= 3.05e-75)) {
		tmp = t + (y * (z * -0.5));
	} else {
		tmp = t + (0.125 * x);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -3.5e-78) or not (z <= 3.05e-75):
		tmp = t + (y * (z * -0.5))
	else:
		tmp = t + (0.125 * x)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -3.5e-78) || !(z <= 3.05e-75))
		tmp = Float64(t + Float64(y * Float64(z * -0.5)));
	else
		tmp = Float64(t + Float64(0.125 * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -3.5e-78) || ~((z <= 3.05e-75)))
		tmp = t + (y * (z * -0.5));
	else
		tmp = t + (0.125 * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -3.5e-78], N[Not[LessEqual[z, 3.05e-75]], $MachinePrecision]], N[(t + N[(y * N[(z * -0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t + N[(0.125 * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -3.4999999999999999e-78 or 3.05000000000000021e-75 < z

   1. Initial program 99.5%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 99.5%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 99.9%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 74.3%

      \[\leadsto \color{blue}{-0.5 \cdot \left(y \cdot z\right)} + t \]
   6. Step-by-step derivation

      1. *-commutative 74.3%

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot -0.5} + t \]

      2. associate-*l* 74.8%

         \[\leadsto \color{blue}{y \cdot \left(z \cdot -0.5\right)} + t \]

   7. Simplified 74.8%

      \[\leadsto \color{blue}{y \cdot \left(z \cdot -0.5\right)} + t \]

   if -3.4999999999999999e-78 < z < 3.05000000000000021e-75

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 99.9%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 86.2%

      \[\leadsto \color{blue}{0.125 \cdot x} + t \]
3. Recombined 2 regimes into one program.

4. Final simplification 78.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3.5 \cdot 10^{-78} \lor \neg \left(z \leq 3.05 \cdot 10^{-75}\right):\\ \;\;\;\;t + y \cdot \left(z \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;t + 0.125 \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 67.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.1 \cdot 10^{+187} \lor \neg \left(y \leq 2.15 \cdot 10^{-113}\right):\\ \;\;\;\;y \cdot \left(z \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;t + 0.125 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -3.1e+187) (not (<= y 2.15e-113)))
   (* y (* z -0.5))
   (+ t (* 0.125 x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -3.1e+187) || !(y <= 2.15e-113)) {
		tmp = y * (z * -0.5);
	} else {
		tmp = t + (0.125 * x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-3.1d+187)) .or. (.not. (y <= 2.15d-113))) then
        tmp = y * (z * (-0.5d0))
    else
        tmp = t + (0.125d0 * x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -3.1e+187) || !(y <= 2.15e-113)) {
		tmp = y * (z * -0.5);
	} else {
		tmp = t + (0.125 * x);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -3.1e+187) or not (y <= 2.15e-113):
		tmp = y * (z * -0.5)
	else:
		tmp = t + (0.125 * x)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -3.1e+187) || !(y <= 2.15e-113))
		tmp = Float64(y * Float64(z * -0.5));
	else
		tmp = Float64(t + Float64(0.125 * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -3.1e+187) || ~((y <= 2.15e-113)))
		tmp = y * (z * -0.5);
	else
		tmp = t + (0.125 * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -3.1e+187], N[Not[LessEqual[y, 2.15e-113]], $MachinePrecision]], N[(y * N[(z * -0.5), $MachinePrecision]), $MachinePrecision], N[(t + N[(0.125 * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -3.10000000000000012e187 or 2.15e-113 < y

   1. Initial program 99.3%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 99.3%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 99.9%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 80.5%

      \[\leadsto \color{blue}{-0.5 \cdot \left(y \cdot z\right)} + t \]
   6. Step-by-step derivation

      1. *-commutative 80.5%

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot -0.5} + t \]

      2. associate-*l* 81.2%

         \[\leadsto \color{blue}{y \cdot \left(z \cdot -0.5\right)} + t \]

   7. Simplified 81.2%

      \[\leadsto \color{blue}{y \cdot \left(z \cdot -0.5\right)} + t \]

   8. Taylor expanded in y around inf 59.3%

      \[\leadsto \color{blue}{-0.5 \cdot \left(y \cdot z\right)} \]
   9. Step-by-step derivation

      1. *-commutative 59.3%

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot -0.5} \]

      2. associate-*r* 60.0%

         \[\leadsto \color{blue}{y \cdot \left(z \cdot -0.5\right)} \]

      3. *-commutative 60.0%

         \[\leadsto y \cdot \color{blue}{\left(-0.5 \cdot z\right)} \]

   10. Simplified 60.0%

       \[\leadsto \color{blue}{y \cdot \left(-0.5 \cdot z\right)} \]

   if -3.10000000000000012e187 < y < 2.15e-113

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 76.8%

      \[\leadsto \color{blue}{0.125 \cdot x} + t \]
3. Recombined 2 regimes into one program.

4. Final simplification 69.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.1 \cdot 10^{+187} \lor \neg \left(y \leq 2.15 \cdot 10^{-113}\right):\\ \;\;\;\;y \cdot \left(z \cdot -0.5\right)\\ \mathbf{else}:\\ \;\;\;\;t + 0.125 \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 50.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -4.9 \cdot 10^{+54}:\\ \;\;\;\;t\\ \mathbf{elif}\;t \leq 4.8 \cdot 10^{-5}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= t -4.9e+54) t (if (<= t 4.8e-5) (* 0.125 x) t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -4.9e+54) {
		tmp = t;
	} else if (t <= 4.8e-5) {
		tmp = 0.125 * x;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (t <= (-4.9d+54)) then
        tmp = t
    else if (t <= 4.8d-5) then
        tmp = 0.125d0 * x
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -4.9e+54) {
		tmp = t;
	} else if (t <= 4.8e-5) {
		tmp = 0.125 * x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if t <= -4.9e+54:
		tmp = t
	elif t <= 4.8e-5:
		tmp = 0.125 * x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (t <= -4.9e+54)
		tmp = t;
	elseif (t <= 4.8e-5)
		tmp = Float64(0.125 * x);
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (t <= -4.9e+54)
		tmp = t;
	elseif (t <= 4.8e-5)
		tmp = 0.125 * x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[t, -4.9e+54], t, If[LessEqual[t, 4.8e-5], N[(0.125 * x), $MachinePrecision], t]]

Derivation

1. Split input into 2 regimes

2. if t < -4.90000000000000001e54 or 4.8000000000000001e-5 < t

   1. Initial program 100.0%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 100.0%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 100.0%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 60.8%

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

   if -4.90000000000000001e54 < t < 4.8000000000000001e-5

   1. Initial program 99.4%

      \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   2. Step-by-step derivation

      1. metadata-eval 99.4%

         \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

      2. associate-/l* 99.9%

         \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
   4. Add Preprocessing

   5. Taylor expanded in t around 0 90.4%

      \[\leadsto \color{blue}{0.125 \cdot x - 0.5 \cdot \left(y \cdot z\right)} \]

   6. Taylor expanded in x around inf 48.1%

      \[\leadsto \color{blue}{0.125 \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 53.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -4.9 \cdot 10^{+54}:\\ \;\;\;\;t\\ \mathbf{elif}\;t \leq 4.8 \cdot 10^{-5}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 99.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ t + \left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (+ t (- (* 0.125 x) (/ y (/ 2.0 z)))))

C

double code(double x, double y, double z, double t) {
	return t + ((0.125 * x) - (y / (2.0 / z)));
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = t + ((0.125d0 * x) - (y / (2.0d0 / z)))
end function

Java

public static double code(double x, double y, double z, double t) {
	return t + ((0.125 * x) - (y / (2.0 / z)));
}

Python

def code(x, y, z, t):
	return t + ((0.125 * x) - (y / (2.0 / z)))

Julia

function code(x, y, z, t)
	return Float64(t + Float64(Float64(0.125 * x) - Float64(y / Float64(2.0 / z))))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = t + ((0.125 * x) - (y / (2.0 / z)));
end

Wolfram

code[x_, y_, z_, t_] := N[(t + N[(N[(0.125 * x), $MachinePrecision] - N[(y / N[(2.0 / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
2. Step-by-step derivation

   1. metadata-eval 99.7%

      \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

   2. associate-/l* 99.9%

      \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
4. Add Preprocessing

5. Final simplification 99.9%

   \[\leadsto t + \left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) \]
6. Add Preprocessing

Alternative 8: 33.1% accurate, 13.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := t

Derivation

1. Initial program 99.7%

   \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
2. Step-by-step derivation

   1. metadata-eval 99.7%

      \[\leadsto \left(\color{blue}{0.125} \cdot x - \frac{y \cdot z}{2}\right) + t \]

   2. associate-/l* 99.9%

      \[\leadsto \left(0.125 \cdot x - \color{blue}{\frac{y}{\frac{2}{z}}}\right) + t \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{\left(0.125 \cdot x - \frac{y}{\frac{2}{z}}\right) + t} \]
4. Add Preprocessing

5. Taylor expanded in t around inf 32.2%

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

6. Final simplification 32.2%

   \[\leadsto t \]
7. Add Preprocessing

Developer target: 100.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \left(\frac{x}{8} + t\right) - \frac{z}{2} \cdot y \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (- (+ (/ x 8.0) t) (* (/ z 2.0) y)))

C

double code(double x, double y, double z, double t) {
	return ((x / 8.0) + t) - ((z / 2.0) * y);
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = ((x / 8.0d0) + t) - ((z / 2.0d0) * y)
end function

Java

public static double code(double x, double y, double z, double t) {
	return ((x / 8.0) + t) - ((z / 2.0) * y);
}

Python

def code(x, y, z, t):
	return ((x / 8.0) + t) - ((z / 2.0) * y)

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(x / 8.0) + t) - Float64(Float64(z / 2.0) * y))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = ((x / 8.0) + t) - ((z / 2.0) * y);
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(x / 8.0), $MachinePrecision] + t), $MachinePrecision] - N[(N[(z / 2.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024019 
(FPCore (x y z t)
  :name "Diagrams.Solve.Polynomial:quartForm  from diagrams-solve-0.1, B"
  :precision binary64

  :herbie-target
  (- (+ (/ x 8.0) t) (* (/ z 2.0) y))

  (+ (- (* (/ 1.0 8.0) x) (/ (* y z) 2.0)) t))