SynthBasics:oscSampleBasedAux from YampaSynth-0.2

Percentage Accurate: 100.0% → 100.0%

Time: 5.6s

Alternatives: 6

Speedup: 1.0×

• 20.8% 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. Final simplification 100.0%

   \[\leadsto x + y \cdot \left(z - x\right) \]

Alternative 2: 61.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{-32}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 9.2 \cdot 10^{-23}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 5.6 \cdot 10^{+71} \lor \neg \left(y \leq 2.3 \cdot 10^{+118}\right) \land \left(y \leq 1.02 \cdot 10^{+210} \lor \neg \left(y \leq 4.6 \cdot 10^{+262}\right) \land y \leq 1.35 \cdot 10^{+289}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(-y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -9.5e-32)
   (* y z)
   (if (<= y 9.2e-23)
     x
     (if (or (<= y 5.6e+71)
             (and (not (<= y 2.3e+118))
                  (or (<= y 1.02e+210)
                      (and (not (<= y 4.6e+262)) (<= y 1.35e+289)))))
       (* y z)
       (* x (- y))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -9.5e-32) {
		tmp = y * z;
	} else if (y <= 9.2e-23) {
		tmp = x;
	} else if ((y <= 5.6e+71) || (!(y <= 2.3e+118) && ((y <= 1.02e+210) || (!(y <= 4.6e+262) && (y <= 1.35e+289))))) {
		tmp = y * z;
	} else {
		tmp = 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 (y <= (-9.5d-32)) then
        tmp = y * z
    else if (y <= 9.2d-23) then
        tmp = x
    else if ((y <= 5.6d+71) .or. (.not. (y <= 2.3d+118)) .and. (y <= 1.02d+210) .or. (.not. (y <= 4.6d+262)) .and. (y <= 1.35d+289)) then
        tmp = y * z
    else
        tmp = x * -y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -9.5e-32) {
		tmp = y * z;
	} else if (y <= 9.2e-23) {
		tmp = x;
	} else if ((y <= 5.6e+71) || (!(y <= 2.3e+118) && ((y <= 1.02e+210) || (!(y <= 4.6e+262) && (y <= 1.35e+289))))) {
		tmp = y * z;
	} else {
		tmp = x * -y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -9.5e-32:
		tmp = y * z
	elif y <= 9.2e-23:
		tmp = x
	elif (y <= 5.6e+71) or (not (y <= 2.3e+118) and ((y <= 1.02e+210) or (not (y <= 4.6e+262) and (y <= 1.35e+289)))):
		tmp = y * z
	else:
		tmp = x * -y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -9.5e-32)
		tmp = Float64(y * z);
	elseif (y <= 9.2e-23)
		tmp = x;
	elseif ((y <= 5.6e+71) || (!(y <= 2.3e+118) && ((y <= 1.02e+210) || (!(y <= 4.6e+262) && (y <= 1.35e+289)))))
		tmp = Float64(y * z);
	else
		tmp = Float64(x * Float64(-y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -9.5e-32)
		tmp = y * z;
	elseif (y <= 9.2e-23)
		tmp = x;
	elseif ((y <= 5.6e+71) || (~((y <= 2.3e+118)) && ((y <= 1.02e+210) || (~((y <= 4.6e+262)) && (y <= 1.35e+289)))))
		tmp = y * z;
	else
		tmp = x * -y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -9.5e-32], N[(y * z), $MachinePrecision], If[LessEqual[y, 9.2e-23], x, If[Or[LessEqual[y, 5.6e+71], And[N[Not[LessEqual[y, 2.3e+118]], $MachinePrecision], Or[LessEqual[y, 1.02e+210], And[N[Not[LessEqual[y, 4.6e+262]], $MachinePrecision], LessEqual[y, 1.35e+289]]]]], N[(y * z), $MachinePrecision], N[(x * (-y)), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -9.4999999999999999e-32 or 9.2000000000000004e-23 < y < 5.60000000000000004e71 or 2.30000000000000016e118 < y < 1.02000000000000005e210 or 4.5999999999999999e262 < y < 1.35e289

   1. Initial program 100.0%

      \[x + y \cdot \left(z - x\right) \]

   2. Taylor expanded in z around inf 62.2%

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

   3. Taylor expanded in x around 0 60.2%

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

   if -9.4999999999999999e-32 < y < 9.2000000000000004e-23

   1. Initial program 100.0%

      \[x + y \cdot \left(z - x\right) \]

   2. Taylor expanded in z around inf 100.0%

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

   3. Taylor expanded in x around inf 75.3%

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

   if 5.60000000000000004e71 < y < 2.30000000000000016e118 or 1.02000000000000005e210 < y < 4.5999999999999999e262 or 1.35e289 < y

   1. Initial program 99.9%

      \[x + y \cdot \left(z - x\right) \]

   2. Taylor expanded in z around 0 75.9%

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

      1. mul-1-neg 75.9%

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

      2. distribute-rgt-neg-out 75.9%

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

   4. Simplified 75.9%

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

   5. Taylor expanded in y around inf 75.9%

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

      1. mul-1-neg 75.9%

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

      2. distribute-rgt-neg-in 75.9%

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

   7. Simplified 75.9%

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

4. Final simplification 68.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{-32}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 9.2 \cdot 10^{-23}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 5.6 \cdot 10^{+71} \lor \neg \left(y \leq 2.3 \cdot 10^{+118}\right) \land \left(y \leq 1.02 \cdot 10^{+210} \lor \neg \left(y \leq 4.6 \cdot 10^{+262}\right) \land y \leq 1.35 \cdot 10^{+289}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(-y\right)\\ \end{array} \]

Alternative 3: 86.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.8 \cdot 10^{+21} \lor \neg \left(x \leq 1.75 \cdot 10^{+93}\right):\\ \;\;\;\;x - x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -2.8e+21) (not (<= x 1.75e+93))) (- x (* x y)) (+ x (* y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.8e+21) || !(x <= 1.75e+93)) {
		tmp = x - (x * y);
	} 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 ((x <= (-2.8d+21)) .or. (.not. (x <= 1.75d+93))) then
        tmp = x - (x * y)
    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 ((x <= -2.8e+21) || !(x <= 1.75e+93)) {
		tmp = x - (x * y);
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -2.8e+21) or not (x <= 1.75e+93):
		tmp = x - (x * y)
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -2.8e+21) || !(x <= 1.75e+93))
		tmp = Float64(x - Float64(x * y));
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -2.8e+21) || ~((x <= 1.75e+93)))
		tmp = x - (x * y);
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -2.8e+21], N[Not[LessEqual[x, 1.75e+93]], $MachinePrecision]], N[(x - N[(x * y), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.8e21 or 1.74999999999999999e93 < x

   1. Initial program 100.0%

      \[x + y \cdot \left(z - x\right) \]

   2. Taylor expanded in z around 0 89.8%

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

      1. mul-1-neg 89.8%

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

      2. distribute-rgt-neg-out 89.8%

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

   4. Simplified 89.8%

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

   5. Taylor expanded in x around 0 89.8%

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

      1. neg-mul-1 89.8%

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

      2. +-commutative 89.8%

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

      3. distribute-rgt1-in 89.8%

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

      4. cancel-sign-sub-inv 89.8%

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

   7. Simplified 89.8%

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

   if -2.8e21 < x < 1.74999999999999999e93

   1. Initial program 100.0%

      \[x + y \cdot \left(z - x\right) \]

   2. Taylor expanded in z around inf 85.3%

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

4. Final simplification 87.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.8 \cdot 10^{+21} \lor \neg \left(x \leq 1.75 \cdot 10^{+93}\right):\\ \;\;\;\;x - x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \]

Alternative 4: 61.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{-30}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{-18}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -9.5e-30) (* y z) (if (<= y 4.8e-18) x (* y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -9.5e-30) {
		tmp = y * z;
	} else if (y <= 4.8e-18) {
		tmp = x;
	} else {
		tmp = 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 <= (-9.5d-30)) then
        tmp = y * z
    else if (y <= 4.8d-18) then
        tmp = x
    else
        tmp = y * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -9.5e-30) {
		tmp = y * z;
	} else if (y <= 4.8e-18) {
		tmp = x;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -9.5e-30:
		tmp = y * z
	elif y <= 4.8e-18:
		tmp = x
	else:
		tmp = y * z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -9.5e-30)
		tmp = Float64(y * z);
	elseif (y <= 4.8e-18)
		tmp = x;
	else
		tmp = Float64(y * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -9.5e-30)
		tmp = y * z;
	elseif (y <= 4.8e-18)
		tmp = x;
	else
		tmp = y * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -9.5e-30], N[(y * z), $MachinePrecision], If[LessEqual[y, 4.8e-18], x, N[(y * z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -9.49999999999999939e-30 or 4.79999999999999988e-18 < y

   1. Initial program 100.0%

      \[x + y \cdot \left(z - x\right) \]

   2. Taylor expanded in z around inf 55.4%

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

   3. Taylor expanded in x around 0 54.0%

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

   if -9.49999999999999939e-30 < y < 4.79999999999999988e-18

   1. Initial program 100.0%

      \[x + y \cdot \left(z - x\right) \]

   2. Taylor expanded in z around inf 100.0%

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

   3. Taylor expanded in x around inf 75.3%

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

4. Final simplification 63.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{-30}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{-18}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \]

Alternative 5: 75.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.95 \cdot 10^{+259}:\\ \;\;\;\;x \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1.95e+259) (* x (- y)) (+ x (* y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.95e+259) {
		tmp = x * -y;
	} 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 (x <= (-1.95d+259)) then
        tmp = x * -y
    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 (x <= -1.95e+259) {
		tmp = x * -y;
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -1.95e+259:
		tmp = x * -y
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1.95e+259)
		tmp = Float64(x * Float64(-y));
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -1.95e+259)
		tmp = x * -y;
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1.95e+259], N[(x * (-y)), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.94999999999999993e259

   1. Initial program 100.0%

      \[x + y \cdot \left(z - x\right) \]

   2. Taylor expanded in z around 0 100.0%

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

      1. mul-1-neg 100.0%

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

      2. distribute-rgt-neg-out 100.0%

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

   4. Simplified 100.0%

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

   5. Taylor expanded in y around inf 92.1%

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

      1. mul-1-neg 92.1%

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

      2. distribute-rgt-neg-in 92.1%

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

   7. Simplified 92.1%

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

   if -1.94999999999999993e259 < x

   1. Initial program 100.0%

      \[x + y \cdot \left(z - x\right) \]

   2. Taylor expanded in z around inf 77.5%

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

4. Final simplification 78.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.95 \cdot 10^{+259}:\\ \;\;\;\;x \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \]

Alternative 6: 36.9% 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. Taylor expanded in z around inf 74.7%

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

3. Taylor expanded in x around inf 35.1%

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

4. Final simplification 35.1%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023278 
(FPCore (x y z)
  :name "SynthBasics:oscSampleBasedAux from YampaSynth-0.2"
  :precision binary64
  (+ x (* y (- z x))))