SynthBasics:oscSampleBasedAux from YampaSynth-0.2

Percentage Accurate: 100.0% → 100.0%

Time: 4.7s

Alternatives: 6

Speedup: 1.0×

• 20.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

3. Final simplification 100.0%

   \[\leadsto x + y \cdot \left(z - x\right) \]
4. Add Preprocessing

Alternative 2: 62.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(-x\right)\\ \mathbf{if}\;y \leq -3.6 \cdot 10^{+243}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -5.8 \cdot 10^{+212}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -2.45 \cdot 10^{+151}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -1 \cdot 10^{+43}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -1.2 \cdot 10^{-56}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-53}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.12 \cdot 10^{+21}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* y (- x))))
   (if (<= y -3.6e+243)
     (* y z)
     (if (<= y -5.8e+212)
       t_0
       (if (<= y -2.45e+151)
         (* y z)
         (if (<= y -1e+43)
           t_0
           (if (<= y -1.2e-56)
             (* y z)
             (if (<= y 1.3e-53) x (if (<= y 1.12e+21) (* y z) t_0)))))))))

C

double code(double x, double y, double z) {
	double t_0 = y * -x;
	double tmp;
	if (y <= -3.6e+243) {
		tmp = y * z;
	} else if (y <= -5.8e+212) {
		tmp = t_0;
	} else if (y <= -2.45e+151) {
		tmp = y * z;
	} else if (y <= -1e+43) {
		tmp = t_0;
	} else if (y <= -1.2e-56) {
		tmp = y * z;
	} else if (y <= 1.3e-53) {
		tmp = x;
	} else if (y <= 1.12e+21) {
		tmp = y * z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = y * -x
    if (y <= (-3.6d+243)) then
        tmp = y * z
    else if (y <= (-5.8d+212)) then
        tmp = t_0
    else if (y <= (-2.45d+151)) then
        tmp = y * z
    else if (y <= (-1d+43)) then
        tmp = t_0
    else if (y <= (-1.2d-56)) then
        tmp = y * z
    else if (y <= 1.3d-53) then
        tmp = x
    else if (y <= 1.12d+21) then
        tmp = y * z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = y * -x;
	double tmp;
	if (y <= -3.6e+243) {
		tmp = y * z;
	} else if (y <= -5.8e+212) {
		tmp = t_0;
	} else if (y <= -2.45e+151) {
		tmp = y * z;
	} else if (y <= -1e+43) {
		tmp = t_0;
	} else if (y <= -1.2e-56) {
		tmp = y * z;
	} else if (y <= 1.3e-53) {
		tmp = x;
	} else if (y <= 1.12e+21) {
		tmp = y * z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = y * -x
	tmp = 0
	if y <= -3.6e+243:
		tmp = y * z
	elif y <= -5.8e+212:
		tmp = t_0
	elif y <= -2.45e+151:
		tmp = y * z
	elif y <= -1e+43:
		tmp = t_0
	elif y <= -1.2e-56:
		tmp = y * z
	elif y <= 1.3e-53:
		tmp = x
	elif y <= 1.12e+21:
		tmp = y * z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(y * Float64(-x))
	tmp = 0.0
	if (y <= -3.6e+243)
		tmp = Float64(y * z);
	elseif (y <= -5.8e+212)
		tmp = t_0;
	elseif (y <= -2.45e+151)
		tmp = Float64(y * z);
	elseif (y <= -1e+43)
		tmp = t_0;
	elseif (y <= -1.2e-56)
		tmp = Float64(y * z);
	elseif (y <= 1.3e-53)
		tmp = x;
	elseif (y <= 1.12e+21)
		tmp = Float64(y * z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = y * -x;
	tmp = 0.0;
	if (y <= -3.6e+243)
		tmp = y * z;
	elseif (y <= -5.8e+212)
		tmp = t_0;
	elseif (y <= -2.45e+151)
		tmp = y * z;
	elseif (y <= -1e+43)
		tmp = t_0;
	elseif (y <= -1.2e-56)
		tmp = y * z;
	elseif (y <= 1.3e-53)
		tmp = x;
	elseif (y <= 1.12e+21)
		tmp = y * z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(y * (-x)), $MachinePrecision]}, If[LessEqual[y, -3.6e+243], N[(y * z), $MachinePrecision], If[LessEqual[y, -5.8e+212], t$95$0, If[LessEqual[y, -2.45e+151], N[(y * z), $MachinePrecision], If[LessEqual[y, -1e+43], t$95$0, If[LessEqual[y, -1.2e-56], N[(y * z), $MachinePrecision], If[LessEqual[y, 1.3e-53], x, If[LessEqual[y, 1.12e+21], N[(y * z), $MachinePrecision], t$95$0]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -3.5999999999999997e243 or -5.7999999999999997e212 < y < -2.45e151 or -1.00000000000000001e43 < y < -1.2e-56 or 1.29999999999999998e-53 < y < 1.12e21

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 84.8%

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

   4. Taylor expanded in x around 0 69.6%

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

   if -3.5999999999999997e243 < y < -5.7999999999999997e212 or -2.45e151 < y < -1.00000000000000001e43 or 1.12e21 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in z around 0 70.9%

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

      1. mul-1-neg 70.9%

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

      2. distribute-lft-neg-out 70.9%

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

      3. *-commutative 70.9%

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

   5. Simplified 70.9%

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

   6. Taylor expanded in y around inf 70.9%

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

      1. associate-*r* 70.9%

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

      2. mul-1-neg 70.9%

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

   8. Simplified 70.9%

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

   if -1.2e-56 < y < 1.29999999999999998e-53

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 100.0%

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

   4. Taylor expanded in x around inf 77.4%

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

4. Final simplification 73.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.6 \cdot 10^{+243}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -5.8 \cdot 10^{+212}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \mathbf{elif}\;y \leq -2.45 \cdot 10^{+151}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq -1 \cdot 10^{+43}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \mathbf{elif}\;y \leq -1.2 \cdot 10^{-56}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-53}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.12 \cdot 10^{+21}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 76.5% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.1 \cdot 10^{+243} \lor \neg \left(y \leq -7.4 \cdot 10^{+210}\right) \land \left(y \leq -2.05 \cdot 10^{+150} \lor \neg \left(y \leq -1.1 \cdot 10^{+43}\right) \land y \leq 8.6 \cdot 10^{+19}\right):\\ \;\;\;\;x + y \cdot z\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -2.1e+243)
         (and (not (<= y -7.4e+210))
              (or (<= y -2.05e+150)
                  (and (not (<= y -1.1e+43)) (<= y 8.6e+19)))))
   (+ x (* y z))
   (* y (- x))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -2.1e+243) || (!(y <= -7.4e+210) && ((y <= -2.05e+150) || (!(y <= -1.1e+43) && (y <= 8.6e+19))))) {
		tmp = x + (y * z);
	} else {
		tmp = y * -x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((y <= (-2.1d+243)) .or. (.not. (y <= (-7.4d+210))) .and. (y <= (-2.05d+150)) .or. (.not. (y <= (-1.1d+43))) .and. (y <= 8.6d+19)) then
        tmp = x + (y * z)
    else
        tmp = y * -x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -2.1e+243) || (!(y <= -7.4e+210) && ((y <= -2.05e+150) || (!(y <= -1.1e+43) && (y <= 8.6e+19))))) {
		tmp = x + (y * z);
	} else {
		tmp = y * -x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -2.1e+243) or (not (y <= -7.4e+210) and ((y <= -2.05e+150) or (not (y <= -1.1e+43) and (y <= 8.6e+19)))):
		tmp = x + (y * z)
	else:
		tmp = y * -x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -2.1e+243) || (!(y <= -7.4e+210) && ((y <= -2.05e+150) || (!(y <= -1.1e+43) && (y <= 8.6e+19)))))
		tmp = Float64(x + Float64(y * z));
	else
		tmp = Float64(y * Float64(-x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -2.1e+243) || (~((y <= -7.4e+210)) && ((y <= -2.05e+150) || (~((y <= -1.1e+43)) && (y <= 8.6e+19)))))
		tmp = x + (y * z);
	else
		tmp = y * -x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -2.1e+243], And[N[Not[LessEqual[y, -7.4e+210]], $MachinePrecision], Or[LessEqual[y, -2.05e+150], And[N[Not[LessEqual[y, -1.1e+43]], $MachinePrecision], LessEqual[y, 8.6e+19]]]]], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision], N[(y * (-x)), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -2.0999999999999999e243 or -7.39999999999999996e210 < y < -2.04999999999999997e150 or -1.1e43 < y < 8.6e19

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 94.4%

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

   if -2.0999999999999999e243 < y < -7.39999999999999996e210 or -2.04999999999999997e150 < y < -1.1e43 or 8.6e19 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in z around 0 70.9%

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

      1. mul-1-neg 70.9%

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

      2. distribute-lft-neg-out 70.9%

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

      3. *-commutative 70.9%

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

   5. Simplified 70.9%

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

   6. Taylor expanded in y around inf 70.9%

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

      1. associate-*r* 70.9%

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

      2. mul-1-neg 70.9%

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

   8. Simplified 70.9%

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

4. Final simplification 86.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.1 \cdot 10^{+243} \lor \neg \left(y \leq -7.4 \cdot 10^{+210}\right) \land \left(y \leq -2.05 \cdot 10^{+150} \lor \neg \left(y \leq -1.1 \cdot 10^{+43}\right) \land y \leq 8.6 \cdot 10^{+19}\right):\\ \;\;\;\;x + y \cdot z\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 86.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.65 \cdot 10^{-79} \lor \neg \left(z \leq 2.2 \cdot 10^{-35}\right):\\ \;\;\;\;x + y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x - x \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -2.65e-79) (not (<= z 2.2e-35))) (+ x (* y z)) (- x (* x y))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2.65e-79) || !(z <= 2.2e-35)) {
		tmp = x + (y * z);
	} else {
		tmp = x - (x * 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) :: tmp
    if ((z <= (-2.65d-79)) .or. (.not. (z <= 2.2d-35))) then
        tmp = x + (y * z)
    else
        tmp = x - (x * y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2.65e-79) || !(z <= 2.2e-35)) {
		tmp = x + (y * z);
	} else {
		tmp = x - (x * y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -2.65e-79) or not (z <= 2.2e-35):
		tmp = x + (y * z)
	else:
		tmp = x - (x * y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -2.65e-79) || !(z <= 2.2e-35))
		tmp = Float64(x + Float64(y * z));
	else
		tmp = Float64(x - Float64(x * y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -2.65e-79) || ~((z <= 2.2e-35)))
		tmp = x + (y * z);
	else
		tmp = x - (x * y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -2.65e-79], N[Not[LessEqual[z, 2.2e-35]], $MachinePrecision]], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision], N[(x - N[(x * y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -2.6499999999999999e-79 or 2.19999999999999994e-35 < z

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 89.5%

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

   if -2.6499999999999999e-79 < z < 2.19999999999999994e-35

   1. Initial program 100.0%

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

   3. Taylor expanded in z around 0 92.6%

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

      1. mul-1-neg 92.6%

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

      2. distribute-lft-neg-out 92.6%

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

      3. *-commutative 92.6%

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

   5. Simplified 92.6%

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

      1. *-commutative 92.6%

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

      2. distribute-lft-neg-out 92.6%

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

      3. unsub-neg 92.6%

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

   7. Applied egg-rr 92.6%

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

4. Final simplification 90.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.65 \cdot 10^{-79} \lor \neg \left(z \leq 2.2 \cdot 10^{-35}\right):\\ \;\;\;\;x + y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x - x \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 61.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{-48} \lor \neg \left(y \leq 1.85 \cdot 10^{-58}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -1.35e-48) (not (<= y 1.85e-58))) (* y z) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.35e-48) || !(y <= 1.85e-58)) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((y <= (-1.35d-48)) .or. (.not. (y <= 1.85d-58))) then
        tmp = y * z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.35e-48) || !(y <= 1.85e-58)) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -1.35e-48) or not (y <= 1.85e-58):
		tmp = y * z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -1.35e-48) || !(y <= 1.85e-58))
		tmp = Float64(y * z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -1.35e-48) || ~((y <= 1.85e-58)))
		tmp = y * z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -1.35e-48], N[Not[LessEqual[y, 1.85e-58]], $MachinePrecision]], N[(y * z), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if y < -1.35000000000000006e-48 or 1.8500000000000001e-58 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 60.0%

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

   4. Taylor expanded in x around 0 53.2%

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

   if -1.35000000000000006e-48 < y < 1.8500000000000001e-58

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 100.0%

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

   4. Taylor expanded in x around inf 77.4%

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

4. Final simplification 63.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{-48} \lor \neg \left(y \leq 1.85 \cdot 10^{-58}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 36.8% 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 100.0%

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

3. Taylor expanded in z around inf 77.2%

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

4. Taylor expanded in x around inf 38.3%

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

5. Final simplification 38.3%

   \[\leadsto x \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024024 
(FPCore (x y z)
  :name "SynthBasics:oscSampleBasedAux from YampaSynth-0.2"
  :precision binary64
  (+ x (* y (- z x))))