Crypto.Random.Test:calculate from crypto-random-0.0.9

Percentage Accurate: 93.0% → 99.9%

Time: 5.7s

Alternatives: 4

Speedup: 1.1×

• 24.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

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 * y) / z)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 93.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 * y) / z)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.1× speedup

Math

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

FPCore

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

C

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

Julia

function code(x, y, z)
	return fma(Float64(y / z), y, x)
end

Wolfram

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

Derivation

1. Initial program 92.6%

   \[x + \frac{y \cdot y}{z} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. +-commutative N/A

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

   3. lift-/.f64 N/A

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

   4. lift-*.f64 N/A

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

   5. associate-*l/ N/A

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

   6. lower-fma.f64 N/A

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

   7. lower-/.f64 99.9

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

4. Applied rewrites 99.9%

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

Alternative 2: 86.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{y \cdot y}{z}\\ \mathbf{if}\;t\_0 \leq -2 \cdot 10^{+134} \lor \neg \left(t\_0 \leq 2 \cdot 10^{-46}\right):\\ \;\;\;\;\frac{y}{z} \cdot y\\ \mathbf{else}:\\ \;\;\;\;-1 \cdot \left(-x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* y y) z)))
   (if (or (<= t_0 -2e+134) (not (<= t_0 2e-46)))
     (* (/ y z) y)
     (* -1.0 (- x)))))

C

double code(double x, double y, double z) {
	double t_0 = (y * y) / z;
	double tmp;
	if ((t_0 <= -2e+134) || !(t_0 <= 2e-46)) {
		tmp = (y / z) * y;
	} else {
		tmp = -1.0 * -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) :: t_0
    real(8) :: tmp
    t_0 = (y * y) / z
    if ((t_0 <= (-2d+134)) .or. (.not. (t_0 <= 2d-46))) then
        tmp = (y / z) * y
    else
        tmp = (-1.0d0) * -x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (y * y) / z;
	double tmp;
	if ((t_0 <= -2e+134) || !(t_0 <= 2e-46)) {
		tmp = (y / z) * y;
	} else {
		tmp = -1.0 * -x;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (y * y) / z
	tmp = 0
	if (t_0 <= -2e+134) or not (t_0 <= 2e-46):
		tmp = (y / z) * y
	else:
		tmp = -1.0 * -x
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(y * y) / z)
	tmp = 0.0
	if ((t_0 <= -2e+134) || !(t_0 <= 2e-46))
		tmp = Float64(Float64(y / z) * y);
	else
		tmp = Float64(-1.0 * Float64(-x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (y * y) / z;
	tmp = 0.0;
	if ((t_0 <= -2e+134) || ~((t_0 <= 2e-46)))
		tmp = (y / z) * y;
	else
		tmp = -1.0 * -x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(y * y), $MachinePrecision] / z), $MachinePrecision]}, If[Or[LessEqual[t$95$0, -2e+134], N[Not[LessEqual[t$95$0, 2e-46]], $MachinePrecision]], N[(N[(y / z), $MachinePrecision] * y), $MachinePrecision], N[(-1.0 * (-x)), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 y y) z) < -1.99999999999999984e134 or 2.00000000000000005e-46 < (/.f64 (*.f64 y y) z)

   1. Initial program 87.5%

      \[x + \frac{y \cdot y}{z} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{{y}^{2}}{z}} \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. unpow2 N/A

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

      3. lower-*.f64 80.3

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

   5. Applied rewrites 80.3%

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

   7. Applied rewrites 91.9%

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

   if -1.99999999999999984e134 < (/.f64 (*.f64 y y) z) < 2.00000000000000005e-46

   1. Initial program 97.7%

      \[x + \frac{y \cdot y}{z} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{{y}^{2}}{z}} \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. unpow2 N/A

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

      3. lower-*.f64 15.5

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

   5. Applied rewrites 15.5%

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

   7. Applied rewrites 17.7%

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

   8. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{{y}^{2}}{x \cdot z} - 1\right)\right)} \]
   9. Step-by-step derivation

      1. associate-*r* N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. associate-/r* N/A

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

      6. associate-/l* N/A

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

      7. lower--.f64 N/A

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

      8. mul-1-neg N/A

         \[\leadsto \left(\frac{\color{blue}{\mathsf{neg}\left(\frac{{y}^{2}}{z}\right)}}{x} - 1\right) \cdot \left(-1 \cdot x\right) \]

      9. distribute-frac-neg N/A

         \[\leadsto \left(\color{blue}{\left(\mathsf{neg}\left(\frac{\frac{{y}^{2}}{z}}{x}\right)\right)} - 1\right) \cdot \left(-1 \cdot x\right) \]

      10. associate-/r* N/A

          \[\leadsto \left(\left(\mathsf{neg}\left(\color{blue}{\frac{{y}^{2}}{z \cdot x}}\right)\right) - 1\right) \cdot \left(-1 \cdot x\right) \]

      11. *-commutative N/A

          \[\leadsto \left(\left(\mathsf{neg}\left(\frac{{y}^{2}}{\color{blue}{x \cdot z}}\right)\right) - 1\right) \cdot \left(-1 \cdot x\right) \]

      12. unpow2 N/A

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

      13. associate-/l* N/A

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

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

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

      15. lower-*.f64 N/A

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

      16. lower-neg.f64 N/A

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

      17. lower-/.f64 N/A

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

      18. lower-*.f64 N/A

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

      19. mul-1-neg N/A

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

      20. lower-neg.f64 95.1

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

   10. Applied rewrites 95.1%

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

   11. Taylor expanded in x around inf

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

   13. Applied rewrites 85.1%

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

4. Final simplification 88.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{y \cdot y}{z} \leq -2 \cdot 10^{+134} \lor \neg \left(\frac{y \cdot y}{z} \leq 2 \cdot 10^{-46}\right):\\ \;\;\;\;\frac{y}{z} \cdot y\\ \mathbf{else}:\\ \;\;\;\;-1 \cdot \left(-x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 50.9% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ -1 \cdot \left(-x\right) \end{array} \]

FPCore

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

C

double code(double x, double y, double z) {
	return -1.0 * -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 = (-1.0d0) * -x
end function

Java

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

Python

def code(x, y, z):
	return -1.0 * -x

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 92.6%

   \[x + \frac{y \cdot y}{z} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{{y}^{2}}{z}} \]
4. Step-by-step derivation

   1. lower-/.f64 N/A

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

   2. unpow2 N/A

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

   3. lower-*.f64 48.2

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

5. Applied rewrites 48.2%

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

7. Applied rewrites 55.1%

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

8. Taylor expanded in x around -inf

   \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{{y}^{2}}{x \cdot z} - 1\right)\right)} \]
9. Step-by-step derivation

   1. associate-*r* N/A

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

   2. *-commutative N/A

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

   3. lower-*.f64 N/A

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

   4. *-commutative N/A

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

   5. associate-/r* N/A

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

   6. associate-/l* N/A

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

   7. lower--.f64 N/A

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

   8. mul-1-neg N/A

      \[\leadsto \left(\frac{\color{blue}{\mathsf{neg}\left(\frac{{y}^{2}}{z}\right)}}{x} - 1\right) \cdot \left(-1 \cdot x\right) \]

   9. distribute-frac-neg N/A

      \[\leadsto \left(\color{blue}{\left(\mathsf{neg}\left(\frac{\frac{{y}^{2}}{z}}{x}\right)\right)} - 1\right) \cdot \left(-1 \cdot x\right) \]

   10. associate-/r* N/A

       \[\leadsto \left(\left(\mathsf{neg}\left(\color{blue}{\frac{{y}^{2}}{z \cdot x}}\right)\right) - 1\right) \cdot \left(-1 \cdot x\right) \]

   11. *-commutative N/A

       \[\leadsto \left(\left(\mathsf{neg}\left(\frac{{y}^{2}}{\color{blue}{x \cdot z}}\right)\right) - 1\right) \cdot \left(-1 \cdot x\right) \]

   12. unpow2 N/A

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

   13. associate-/l* N/A

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

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

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

   15. lower-*.f64 N/A

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

   16. lower-neg.f64 N/A

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

   17. lower-/.f64 N/A

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

   18. lower-*.f64 N/A

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

   19. mul-1-neg N/A

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

   20. lower-neg.f64 88.2

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

10. Applied rewrites 88.2%

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

11. Taylor expanded in x around inf

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

13. Applied rewrites 47.0%

    \[\leadsto -1 \cdot \left(-\color{blue}{x}\right) \]
14. Add Preprocessing

Alternative 4: 2.2% accurate, 6.7× 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 Float64(-x)
end

MATLAB

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

Wolfram

code[x_, y_, z_] := (-x)

Derivation

1. Initial program 92.6%

   \[x + \frac{y \cdot y}{z} \]
2. Add Preprocessing

4. Applied rewrites 9.7%

   \[\leadsto \color{blue}{\frac{{\left(\mathsf{fma}\left(\frac{y}{z}, y, x\right)\right)}^{2}}{{\left(\frac{y \cdot y}{z}\right)}^{3} - {x}^{3}} \cdot \mathsf{fma}\left(\frac{y}{z}, y \cdot \mathsf{fma}\left(\frac{y}{z}, y, x\right), x \cdot x\right)} \]

5. Taylor expanded in x around inf

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

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(x\right)} \]

   2. lower-neg.f64 2.4

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

7. Applied rewrites 2.4%

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

Developer Target 1: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 * (y / z))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024338 
(FPCore (x y z)
  :name "Crypto.Random.Test:calculate from crypto-random-0.0.9"
  :precision binary64

  :alt
  (! :herbie-platform default (+ x (* y (/ y z))))

  (+ x (/ (* y y) z)))