Language.Haskell.HsColour.ColourHighlight:unbase from hscolour-1.23

Percentage Accurate: 99.9% → 99.9%

Time: 5.3s

Alternatives: 6

Speedup: 1.0×

• 28.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

double code(double x, double y, double z, double t) {
	return (((x * y) + z) * y) + 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 = (((x * y) + z) * y) + t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t) {
	return (((x * y) + z) * y) + 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 = (((x * y) + z) * y) + t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t) {
	return (((x * y) + z) * y) + 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 = (((x * y) + z) * y) + t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

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

Alternative 2: 88.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.4 \cdot 10^{+62} \lor \neg \left(y \leq 3 \cdot 10^{-31}\right):\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(z, y, t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -2.4e+62) (not (<= y 3e-31))) (* (fma y x z) y) (fma z y t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -2.4e+62) || !(y <= 3e-31)) {
		tmp = fma(y, x, z) * y;
	} else {
		tmp = fma(z, y, t);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -2.4e+62) || !(y <= 3e-31))
		tmp = Float64(fma(y, x, z) * y);
	else
		tmp = fma(z, y, t);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -2.4e+62], N[Not[LessEqual[y, 3e-31]], $MachinePrecision]], N[(N[(y * x + z), $MachinePrecision] * y), $MachinePrecision], N[(z * y + t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -2.4e62 or 2.99999999999999981e-31 < y

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{{y}^{2} \cdot \left(x + \frac{z}{y}\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. distribute-lft-in N/A

         \[\leadsto \color{blue}{{y}^{2} \cdot \frac{z}{y} + {y}^{2} \cdot x} \]

   5. Applied rewrites 90.6%

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

   if -2.4e62 < y < 2.99999999999999981e-31

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{t + y \cdot z} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 92.7

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

   5. Applied rewrites 92.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, y, t\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 91.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.4 \cdot 10^{+62} \lor \neg \left(y \leq 3 \cdot 10^{-31}\right):\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(z, y, t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 79.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.2 \cdot 10^{+63} \lor \neg \left(y \leq 1.6 \cdot 10^{+82}\right):\\ \;\;\;\;\left(y \cdot x\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(z, y, t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -2.2e+63) (not (<= y 1.6e+82))) (* (* y x) y) (fma z y t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -2.2e+63) || !(y <= 1.6e+82)) {
		tmp = (y * x) * y;
	} else {
		tmp = fma(z, y, t);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -2.2e+63) || !(y <= 1.6e+82))
		tmp = Float64(Float64(y * x) * y);
	else
		tmp = fma(z, y, t);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -2.2e+63], N[Not[LessEqual[y, 1.6e+82]], $MachinePrecision]], N[(N[(y * x), $MachinePrecision] * y), $MachinePrecision], N[(z * y + t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -2.1999999999999999e63 or 1.59999999999999987e82 < y

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{{y}^{2} \cdot \left(x + \frac{z}{y}\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. distribute-lft-in N/A

         \[\leadsto \color{blue}{{y}^{2} \cdot \frac{z}{y} + {y}^{2} \cdot x} \]

   5. Applied rewrites 95.2%

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

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot y\right) \cdot y \]
   7. Step-by-step derivation

   8. Applied rewrites 76.2%

      \[\leadsto \left(y \cdot x\right) \cdot y \]

   if -2.1999999999999999e63 < y < 1.59999999999999987e82

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{t + y \cdot z} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 85.0

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

   5. Applied rewrites 85.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, y, t\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 81.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.2 \cdot 10^{+63} \lor \neg \left(y \leq 1.6 \cdot 10^{+82}\right):\\ \;\;\;\;\left(y \cdot x\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(z, y, t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 51.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.6 \cdot 10^{+65} \lor \neg \left(z \leq 1.7 \cdot 10^{+36}\right):\\ \;\;\;\;z \cdot y\\ \mathbf{else}:\\ \;\;\;\;1 \cdot t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -4.6e+65) (not (<= z 1.7e+36))) (* z y) (* 1.0 t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -4.6e+65) || !(z <= 1.7e+36)) {
		tmp = z * y;
	} else {
		tmp = 1.0 * 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 ((z <= (-4.6d+65)) .or. (.not. (z <= 1.7d+36))) then
        tmp = z * y
    else
        tmp = 1.0d0 * t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -4.6e+65) || !(z <= 1.7e+36)) {
		tmp = z * y;
	} else {
		tmp = 1.0 * t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -4.6e+65) or not (z <= 1.7e+36):
		tmp = z * y
	else:
		tmp = 1.0 * t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -4.6e+65) || !(z <= 1.7e+36))
		tmp = Float64(z * y);
	else
		tmp = Float64(1.0 * t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -4.6e+65) || ~((z <= 1.7e+36)))
		tmp = z * y;
	else
		tmp = 1.0 * t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -4.6e+65], N[Not[LessEqual[z, 1.7e+36]], $MachinePrecision]], N[(z * y), $MachinePrecision], N[(1.0 * t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -4.6e65 or 1.6999999999999999e36 < z

   1. Initial program 99.9%

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

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{{y}^{2} \cdot \left(x + \frac{z}{y}\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. distribute-lft-in N/A

         \[\leadsto \color{blue}{{y}^{2} \cdot \frac{z}{y} + {y}^{2} \cdot x} \]

   5. Applied rewrites 81.8%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 63.7%

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

   if -4.6e65 < z < 1.6999999999999999e36

   1. Initial program 99.9%

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

   3. Taylor expanded in t around -inf

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

      1. associate-*r* N/A

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

      2. lower-*.f64 N/A

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

      3. mul-1-neg N/A

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

      4. lower-neg.f64 N/A

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

      5. lower--.f64 N/A

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

      6. mul-1-neg N/A

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

      7. *-commutative N/A

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

      8. associate-/l* N/A

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

      9. *-commutative N/A

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

      10. distribute-lft-neg-in N/A

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

      11. lower-*.f64 N/A

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

      12. distribute-frac-neg N/A

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

      13. mul-1-neg N/A

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

      14. lower-/.f64 N/A

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

      15. mul-1-neg N/A

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

      16. lower-neg.f64 N/A

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

      17. +-commutative N/A

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

      18. *-commutative N/A

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

      19. lower-fma.f64 94.3

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

   5. Applied rewrites 94.3%

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

   6. Taylor expanded in t around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. associate-/l* N/A

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

      6. lower-fma.f64 N/A

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

      7. +-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. lower-/.f64 94.3

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

   8. Applied rewrites 94.3%

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

   9. Taylor expanded in y around 0

      \[\leadsto 1 \cdot t \]
   10. Step-by-step derivation

   11. Applied rewrites 49.0%

       \[\leadsto 1 \cdot t \]
3. Recombined 2 regimes into one program.

4. Final simplification 55.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.6 \cdot 10^{+65} \lor \neg \left(z \leq 1.7 \cdot 10^{+36}\right):\\ \;\;\;\;z \cdot y\\ \mathbf{else}:\\ \;\;\;\;1 \cdot t\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 66.2% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(z, y, t\right) \end{array} \]

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.9%

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

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{t + y \cdot z} \]
4. Step-by-step derivation

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 66.5

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

5. Applied rewrites 66.5%

   \[\leadsto \color{blue}{\mathsf{fma}\left(z, y, t\right)} \]
6. Add Preprocessing

Alternative 6: 29.2% accurate, 2.8× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t) {
	return z * 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 = z * y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

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

3. Taylor expanded in y around inf

   \[\leadsto \color{blue}{{y}^{2} \cdot \left(x + \frac{z}{y}\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

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

   2. distribute-lft-in N/A

      \[\leadsto \color{blue}{{y}^{2} \cdot \frac{z}{y} + {y}^{2} \cdot x} \]

5. Applied rewrites 66.0%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 33.5%

   \[\leadsto z \cdot \color{blue}{y} \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2024339 
(FPCore (x y z t)
  :name "Language.Haskell.HsColour.ColourHighlight:unbase from hscolour-1.23"
  :precision binary64
  (+ (* (+ (* x y) z) y) t))