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

Percentage Accurate: 93.3% → 99.9%

Time: 5.9s

Alternatives: 5

Speedup: 1.0×

• 25.3% 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: 93.3% 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, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 93.4%

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

   1. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(x, \color{blue}{\left(\frac{y \cdot y}{z}\right)}\right) \]

   2. associate-/l* N/A

      \[\leadsto \mathsf{+.f64}\left(x, \left(y \cdot \color{blue}{\frac{y}{z}}\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \color{blue}{\left(\frac{y}{z}\right)}\right)\right) \]

   4. /-lowering-/.f64 99.9%

      \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \mathsf{/.f64}\left(y, \color{blue}{z}\right)\right)\right) \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{x + y \cdot \frac{y}{z}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. +-commutative N/A

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

   2. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(y \cdot \frac{y}{z}\right), \color{blue}{x}\right) \]

   3. clear-num N/A

      \[\leadsto \mathsf{+.f64}\left(\left(y \cdot \frac{1}{\frac{z}{y}}\right), x\right) \]

   4. un-div-inv N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{y}{\frac{z}{y}}\right), x\right) \]

   5. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(y, \left(\frac{z}{y}\right)\right), x\right) \]

   6. /-lowering-/.f64 99.9%

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(y, \mathsf{/.f64}\left(z, y\right)\right), x\right) \]

6. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{\frac{y}{\frac{z}{y}} + x} \]
7. Add Preprocessing

Alternative 2: 87.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{y \cdot y}{z}\\ \mathbf{if}\;t\_0 \leq -4 \cdot 10^{+102}:\\ \;\;\;\;y \cdot \frac{y}{z}\\ \mathbf{elif}\;t\_0 \leq 10^{+20}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{z}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* y y) z)))
   (if (<= t_0 -4e+102) (* y (/ y z)) (if (<= t_0 1e+20) x (/ y (/ z y))))))

C

double code(double x, double y, double z) {
	double t_0 = (y * y) / z;
	double tmp;
	if (t_0 <= -4e+102) {
		tmp = y * (y / z);
	} else if (t_0 <= 1e+20) {
		tmp = x;
	} else {
		tmp = y / (z / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (y * y) / z
    if (t_0 <= (-4d+102)) then
        tmp = y * (y / z)
    else if (t_0 <= 1d+20) then
        tmp = x
    else
        tmp = y / (z / y)
    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 <= -4e+102) {
		tmp = y * (y / z);
	} else if (t_0 <= 1e+20) {
		tmp = x;
	} else {
		tmp = y / (z / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (y * y) / z
	tmp = 0
	if t_0 <= -4e+102:
		tmp = y * (y / z)
	elif t_0 <= 1e+20:
		tmp = x
	else:
		tmp = y / (z / y)
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(y * y) / z)
	tmp = 0.0
	if (t_0 <= -4e+102)
		tmp = Float64(y * Float64(y / z));
	elseif (t_0 <= 1e+20)
		tmp = x;
	else
		tmp = Float64(y / Float64(z / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (y * y) / z;
	tmp = 0.0;
	if (t_0 <= -4e+102)
		tmp = y * (y / z);
	elseif (t_0 <= 1e+20)
		tmp = x;
	else
		tmp = y / (z / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(y * y), $MachinePrecision] / z), $MachinePrecision]}, If[LessEqual[t$95$0, -4e+102], N[(y * N[(y / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 1e+20], x, N[(y / N[(z / y), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (*.f64 y y) z) < -3.99999999999999991e102

   1. Initial program 86.2%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(x, \color{blue}{\left(\frac{y \cdot y}{z}\right)}\right) \]

      2. associate-/l* N/A

         \[\leadsto \mathsf{+.f64}\left(x, \left(y \cdot \color{blue}{\frac{y}{z}}\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \color{blue}{\left(\frac{y}{z}\right)}\right)\right) \]

      4. /-lowering-/.f64 99.8%

         \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \mathsf{/.f64}\left(y, \color{blue}{z}\right)\right)\right) \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x + y \cdot \frac{y}{z}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

      1. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\left({y}^{2}\right), \color{blue}{z}\right) \]

      2. unpow2 N/A

         \[\leadsto \mathsf{/.f64}\left(\left(y \cdot y\right), z\right) \]

      3. *-lowering-*.f64 82.6%

         \[\leadsto \mathsf{/.f64}\left(\mathsf{*.f64}\left(y, y\right), z\right) \]

   7. Simplified 82.6%

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

      1. associate-*l/ N/A

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

      2. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(\left(\frac{y}{z}\right), \color{blue}{y}\right) \]

      3. /-lowering-/.f64 92.9%

         \[\leadsto \mathsf{*.f64}\left(\mathsf{/.f64}\left(y, z\right), y\right) \]

   9. Applied egg-rr 92.9%

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

   if -3.99999999999999991e102 < (/.f64 (*.f64 y y) z) < 1e20

   1. Initial program 98.2%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(x, \color{blue}{\left(\frac{y \cdot y}{z}\right)}\right) \]

      2. associate-/l* N/A

         \[\leadsto \mathsf{+.f64}\left(x, \left(y \cdot \color{blue}{\frac{y}{z}}\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \color{blue}{\left(\frac{y}{z}\right)}\right)\right) \]

      4. /-lowering-/.f64 99.9%

         \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \mathsf{/.f64}\left(y, \color{blue}{z}\right)\right)\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x + y \cdot \frac{y}{z}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

   7. Simplified 86.3%

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

   if 1e20 < (/.f64 (*.f64 y y) z)

   1. Initial program 88.0%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(x, \color{blue}{\left(\frac{y \cdot y}{z}\right)}\right) \]

      2. associate-/l* N/A

         \[\leadsto \mathsf{+.f64}\left(x, \left(y \cdot \color{blue}{\frac{y}{z}}\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \color{blue}{\left(\frac{y}{z}\right)}\right)\right) \]

      4. /-lowering-/.f64 99.9%

         \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \mathsf{/.f64}\left(y, \color{blue}{z}\right)\right)\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x + y \cdot \frac{y}{z}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

      1. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\left({y}^{2}\right), \color{blue}{z}\right) \]

      2. unpow2 N/A

         \[\leadsto \mathsf{/.f64}\left(\left(y \cdot y\right), z\right) \]

      3. *-lowering-*.f64 80.9%

         \[\leadsto \mathsf{/.f64}\left(\mathsf{*.f64}\left(y, y\right), z\right) \]

   7. Simplified 80.9%

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

      1. associate-*l/ N/A

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

      2. associate-/r/ N/A

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

      3. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(y, \color{blue}{\left(\frac{z}{y}\right)}\right) \]

      4. /-lowering-/.f64 91.1%

         \[\leadsto \mathsf{/.f64}\left(y, \mathsf{/.f64}\left(z, \color{blue}{y}\right)\right) \]

   9. Applied egg-rr 91.1%

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

4. Final simplification 88.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{y \cdot y}{z} \leq -4 \cdot 10^{+102}:\\ \;\;\;\;y \cdot \frac{y}{z}\\ \mathbf{elif}\;\frac{y \cdot y}{z} \leq 10^{+20}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{\frac{z}{y}}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 66.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 62:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (if (<= y 62.0) x (* y (/ y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 62.0) {
		tmp = x;
	} else {
		tmp = y * (y / z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= 62.0d0) then
        tmp = x
    else
        tmp = y * (y / z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= 62.0) {
		tmp = x;
	} else {
		tmp = y * (y / z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= 62.0:
		tmp = x
	else:
		tmp = y * (y / z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 62.0)
		tmp = x;
	else
		tmp = Float64(y * Float64(y / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= 62.0)
		tmp = x;
	else
		tmp = y * (y / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 62.0], x, N[(y * N[(y / z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 62

   1. Initial program 95.3%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(x, \color{blue}{\left(\frac{y \cdot y}{z}\right)}\right) \]

      2. associate-/l* N/A

         \[\leadsto \mathsf{+.f64}\left(x, \left(y \cdot \color{blue}{\frac{y}{z}}\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \color{blue}{\left(\frac{y}{z}\right)}\right)\right) \]

      4. /-lowering-/.f64 99.9%

         \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \mathsf{/.f64}\left(y, \color{blue}{z}\right)\right)\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x + y \cdot \frac{y}{z}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf

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

   7. Simplified 63.8%

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

   if 62 < y

   1. Initial program 87.3%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(x, \color{blue}{\left(\frac{y \cdot y}{z}\right)}\right) \]

      2. associate-/l* N/A

         \[\leadsto \mathsf{+.f64}\left(x, \left(y \cdot \color{blue}{\frac{y}{z}}\right)\right) \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \color{blue}{\left(\frac{y}{z}\right)}\right)\right) \]

      4. /-lowering-/.f64 99.8%

         \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \mathsf{/.f64}\left(y, \color{blue}{z}\right)\right)\right) \]

   3. Simplified 99.8%

      \[\leadsto \color{blue}{x + y \cdot \frac{y}{z}} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0

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

      1. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\left({y}^{2}\right), \color{blue}{z}\right) \]

      2. unpow2 N/A

         \[\leadsto \mathsf{/.f64}\left(\left(y \cdot y\right), z\right) \]

      3. *-lowering-*.f64 74.3%

         \[\leadsto \mathsf{/.f64}\left(\mathsf{*.f64}\left(y, y\right), z\right) \]

   7. Simplified 74.3%

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

      1. associate-*l/ N/A

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

      2. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(\left(\frac{y}{z}\right), \color{blue}{y}\right) \]

      3. /-lowering-/.f64 82.2%

         \[\leadsto \mathsf{*.f64}\left(\mathsf{/.f64}\left(y, z\right), y\right) \]

   9. Applied egg-rr 82.2%

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

4. Final simplification 68.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 62:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{y}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 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]

Derivation

1. Initial program 93.4%

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

   1. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(x, \color{blue}{\left(\frac{y \cdot y}{z}\right)}\right) \]

   2. associate-/l* N/A

      \[\leadsto \mathsf{+.f64}\left(x, \left(y \cdot \color{blue}{\frac{y}{z}}\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \color{blue}{\left(\frac{y}{z}\right)}\right)\right) \]

   4. /-lowering-/.f64 99.9%

      \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \mathsf{/.f64}\left(y, \color{blue}{z}\right)\right)\right) \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{x + y \cdot \frac{y}{z}} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 5: 51.6% accurate, 7.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 93.4%

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

   1. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(x, \color{blue}{\left(\frac{y \cdot y}{z}\right)}\right) \]

   2. associate-/l* N/A

      \[\leadsto \mathsf{+.f64}\left(x, \left(y \cdot \color{blue}{\frac{y}{z}}\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \color{blue}{\left(\frac{y}{z}\right)}\right)\right) \]

   4. /-lowering-/.f64 99.9%

      \[\leadsto \mathsf{+.f64}\left(x, \mathsf{*.f64}\left(y, \mathsf{/.f64}\left(y, \color{blue}{z}\right)\right)\right) \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{x + y \cdot \frac{y}{z}} \]
4. Add Preprocessing

5. Taylor expanded in x around inf

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

7. Simplified 53.1%

   \[\leadsto \color{blue}{x} \]
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 2024161 
(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)))