SynthBasics:oscSampleBasedAux from YampaSynth-0.2

Percentage Accurate: 100.0% → 100.0%

Time: 8.7s

Alternatives: 8

Speedup: 1.0×

• 21.3% 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, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[x + y \cdot \left(z - x\right) \]
2. Step-by-step derivation

   1. +-commutative 100.0%

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

   2. fma-define 100.0%

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

3. Simplified 100.0%

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

Alternative 2: 99.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -115 \lor \neg \left(y \leq 1\right):\\ \;\;\;\;y \cdot \left(z - x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -115.0) (not (<= y 1.0))) (* y (- z x)) (+ x (* y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -115.0) || !(y <= 1.0)) {
		tmp = y * (z - x);
	} else {
		tmp = x + (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 <= (-115.0d0)) .or. (.not. (y <= 1.0d0))) then
        tmp = y * (z - x)
    else
        tmp = x + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -115.0) || !(y <= 1.0)) {
		tmp = y * (z - x);
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -115.0) or not (y <= 1.0):
		tmp = y * (z - x)
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -115.0) || !(y <= 1.0))
		tmp = Float64(y * Float64(z - x));
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -115.0) || ~((y <= 1.0)))
		tmp = y * (z - x);
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -115.0], N[Not[LessEqual[y, 1.0]], $MachinePrecision]], N[(y * N[(z - x), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -115 or 1 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 96.8%

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

      1. mul-1-neg 96.8%

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

      2. unsub-neg 96.8%

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

   5. Simplified 96.8%

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

   6. Taylor expanded in y around inf 94.7%

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

   7. Taylor expanded in z around 0 94.0%

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

      1. associate-*r* 94.0%

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

      2. neg-mul-1 94.0%

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

      3. *-commutative 94.0%

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

      4. distribute-rgt-out 97.5%

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

      5. +-commutative 97.5%

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

      6. sub-neg 97.5%

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

   9. Simplified 97.5%

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

   if -115 < y < 1

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 98.4%

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

4. Final simplification 98.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -115 \lor \neg \left(y \leq 1\right):\\ \;\;\;\;y \cdot \left(z - x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 78.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.4 \cdot 10^{+36} \lor \neg \left(z \leq 4.8 \cdot 10^{-24}\right):\\ \;\;\;\;y \cdot \left(z - x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -1.4e+36) (not (<= z 4.8e-24))) (* y (- z x)) (* x (- 1.0 y))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -1.4e+36) || !(z <= 4.8e-24)) {
		tmp = y * (z - x);
	} else {
		tmp = x * (1.0 - 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 <= (-1.4d+36)) .or. (.not. (z <= 4.8d-24))) then
        tmp = y * (z - x)
    else
        tmp = x * (1.0d0 - y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -1.4e+36) || !(z <= 4.8e-24)) {
		tmp = y * (z - x);
	} else {
		tmp = x * (1.0 - y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -1.4e+36) or not (z <= 4.8e-24):
		tmp = y * (z - x)
	else:
		tmp = x * (1.0 - y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -1.4e+36) || !(z <= 4.8e-24))
		tmp = Float64(y * Float64(z - x));
	else
		tmp = Float64(x * Float64(1.0 - y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -1.4e+36) || ~((z <= 4.8e-24)))
		tmp = y * (z - x);
	else
		tmp = x * (1.0 - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -1.4e+36], N[Not[LessEqual[z, 4.8e-24]], $MachinePrecision]], N[(y * N[(z - x), $MachinePrecision]), $MachinePrecision], N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -1.4e36 or 4.7999999999999996e-24 < z

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 100.0%

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

      1. mul-1-neg 100.0%

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

      2. unsub-neg 100.0%

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

   5. Simplified 100.0%

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

   6. Taylor expanded in y around inf 79.7%

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

   7. Taylor expanded in z around 0 76.6%

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

      1. associate-*r* 76.6%

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

      2. neg-mul-1 76.6%

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

      3. *-commutative 76.6%

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

      4. distribute-rgt-out 79.6%

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

      5. +-commutative 79.6%

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

      6. sub-neg 79.6%

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

   9. Simplified 79.6%

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

   if -1.4e36 < z < 4.7999999999999996e-24

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 88.0%

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

      1. mul-1-neg 88.0%

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

      2. unsub-neg 88.0%

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

   5. Simplified 88.0%

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

4. Final simplification 83.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.4 \cdot 10^{+36} \lor \neg \left(z \leq 4.8 \cdot 10^{-24}\right):\\ \;\;\;\;y \cdot \left(z - x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 73.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.8 \cdot 10^{+44} \lor \neg \left(z \leq 1.96 \cdot 10^{-17}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -4.8e+44) (not (<= z 1.96e-17))) (* y z) (* x (- 1.0 y))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -4.8e+44) || !(z <= 1.96e-17)) {
		tmp = y * z;
	} else {
		tmp = x * (1.0 - 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 <= (-4.8d+44)) .or. (.not. (z <= 1.96d-17))) then
        tmp = y * z
    else
        tmp = x * (1.0d0 - y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -4.8e+44) || !(z <= 1.96e-17)) {
		tmp = y * z;
	} else {
		tmp = x * (1.0 - y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -4.8e+44) or not (z <= 1.96e-17):
		tmp = y * z
	else:
		tmp = x * (1.0 - y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -4.8e+44) || !(z <= 1.96e-17))
		tmp = Float64(y * z);
	else
		tmp = Float64(x * Float64(1.0 - y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -4.8e+44) || ~((z <= 1.96e-17)))
		tmp = y * z;
	else
		tmp = x * (1.0 - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -4.8e+44], N[Not[LessEqual[z, 1.96e-17]], $MachinePrecision]], N[(y * z), $MachinePrecision], N[(x * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -4.80000000000000026e44 or 1.96e-17 < z

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 92.7%

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

   4. Taylor expanded in x around 0 73.8%

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

   if -4.80000000000000026e44 < z < 1.96e-17

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 87.0%

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

      1. mul-1-neg 87.0%

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

      2. unsub-neg 87.0%

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

   5. Simplified 87.0%

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

4. Final simplification 80.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.8 \cdot 10^{+44} \lor \neg \left(z \leq 1.96 \cdot 10^{-17}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 54.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5 \cdot 10^{+35} \lor \neg \left(z \leq 1.25 \cdot 10^{-90}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -5e+35) (not (<= z 1.25e-90))) (* y z) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -5e+35) || !(z <= 1.25e-90)) {
		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 ((z <= (-5d+35)) .or. (.not. (z <= 1.25d-90))) 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 ((z <= -5e+35) || !(z <= 1.25e-90)) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -5e+35) or not (z <= 1.25e-90):
		tmp = y * z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -5e+35) || !(z <= 1.25e-90))
		tmp = Float64(y * z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -5e+35) || ~((z <= 1.25e-90)))
		tmp = y * z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -5e+35], N[Not[LessEqual[z, 1.25e-90]], $MachinePrecision]], N[(y * z), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -5.00000000000000021e35 or 1.25000000000000005e-90 < z

   1. Initial program 100.0%

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

   3. Taylor expanded in z around inf 90.6%

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

   4. Taylor expanded in x around 0 70.6%

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

   if -5.00000000000000021e35 < z < 1.25000000000000005e-90

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0 57.5%

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

4. Final simplification 64.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5 \cdot 10^{+35} \lor \neg \left(z \leq 1.25 \cdot 10^{-90}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 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. Add Preprocessing

Alternative 7: 36.6% 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 100.0%

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

3. Taylor expanded in y around 0 38.1%

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

Alternative 8: 2.6% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 0.0)

C

double code(double x, double y, double z) {
	return 0.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 0.0d0
end function

Java

public static double code(double x, double y, double z) {
	return 0.0;
}

Python

def code(x, y, z):
	return 0.0

Julia

function code(x, y, z)
	return 0.0
end

MATLAB

function tmp = code(x, y, z)
	tmp = 0.0;
end

Wolfram

code[x_, y_, z_] := 0.0

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around inf 60.6%

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

   1. mul-1-neg 60.6%

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

   2. unsub-neg 60.6%

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

5. Simplified 60.6%

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

6. Taylor expanded in y around inf 23.7%

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

   1. associate-*r* 23.7%

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

   2. neg-mul-1 23.7%

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

8. Simplified 23.7%

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

   1. add-log-exp 17.3%

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

   2. add-sqr-sqrt 17.3%

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

   3. sqrt-unprod 17.3%

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

   4. distribute-lft-neg-out 17.3%

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

   5. distribute-rgt-neg-in 17.3%

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

   6. add-sqr-sqrt 9.6%

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

   7. sqrt-unprod 10.6%

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

   8. sqr-neg 10.6%

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

   9. sqrt-unprod 0.9%

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

   10. add-sqr-sqrt 1.9%

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

   11. exp-prod 4.4%

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

   12. pow-flip 4.4%

       \[\leadsto \log \left(\sqrt{e^{\left(-x\right) \cdot y} \cdot \color{blue}{\frac{1}{{\left(e^{-x}\right)}^{y}}}}\right) \]

   13. exp-prod 1.8%

       \[\leadsto \log \left(\sqrt{e^{\left(-x\right) \cdot y} \cdot \frac{1}{\color{blue}{e^{\left(-x\right) \cdot y}}}}\right) \]

   14. rgt-mult-inverse 2.6%

       \[\leadsto \log \left(\sqrt{\color{blue}{1}}\right) \]

   15. metadata-eval 2.6%

       \[\leadsto \log \color{blue}{1} \]

   16. metadata-eval 2.6%

       \[\leadsto \color{blue}{0} \]

10. Applied egg-rr 2.6%

    \[\leadsto \color{blue}{0} \]
11. Add Preprocessing

Reproduce

herbie shell --seed 2024133 
(FPCore (x y z)
  :name "SynthBasics:oscSampleBasedAux from YampaSynth-0.2"
  :precision binary64
  (+ x (* y (- z x))))