Graphics.Rendering.Chart.Drawing:drawTextsR from Chart-1.5.3

Percentage Accurate: 98.0% → 100.0%

Time: 2.6s

Alternatives: 6

Speedup: 1.3×

• 21.0% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 98.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.3× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 98.0%

   \[x \cdot y + \left(x - 1\right) \cdot z \]
2. Step-by-step derivation

   1. *-commutative 98.0%

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

   2. sub-neg 98.0%

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

   3. distribute-rgt-in 98.0%

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

   4. associate-+r+ 98.0%

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

   5. metadata-eval 98.0%

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

   6. mul-1-neg 98.0%

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

   7. unsub-neg 98.0%

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

   8. distribute-lft-out 100.0%

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

3. Simplified 100.0%

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

4. Final simplification 100.0%

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

Alternative 2: 53.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8 \cdot 10^{+159}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;z \leq -1.8 \cdot 10^{+130}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq -1.25 \cdot 10^{+105}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;z \leq 10^{-76} \lor \neg \left(z \leq 1.7 \cdot 10^{+20}\right) \land z \leq 7.6 \cdot 10^{+82}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -8e+159)
   (* x z)
   (if (<= z -1.8e+130)
     (- z)
     (if (<= z -1.25e+105)
       (* x z)
       (if (or (<= z 1e-76) (and (not (<= z 1.7e+20)) (<= z 7.6e+82)))
         (* x y)
         (- z))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -8e+159) {
		tmp = x * z;
	} else if (z <= -1.8e+130) {
		tmp = -z;
	} else if (z <= -1.25e+105) {
		tmp = x * z;
	} else if ((z <= 1e-76) || (!(z <= 1.7e+20) && (z <= 7.6e+82))) {
		tmp = x * y;
	} else {
		tmp = -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 (z <= (-8d+159)) then
        tmp = x * z
    else if (z <= (-1.8d+130)) then
        tmp = -z
    else if (z <= (-1.25d+105)) then
        tmp = x * z
    else if ((z <= 1d-76) .or. (.not. (z <= 1.7d+20)) .and. (z <= 7.6d+82)) then
        tmp = x * y
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -8e+159) {
		tmp = x * z;
	} else if (z <= -1.8e+130) {
		tmp = -z;
	} else if (z <= -1.25e+105) {
		tmp = x * z;
	} else if ((z <= 1e-76) || (!(z <= 1.7e+20) && (z <= 7.6e+82))) {
		tmp = x * y;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -8e+159:
		tmp = x * z
	elif z <= -1.8e+130:
		tmp = -z
	elif z <= -1.25e+105:
		tmp = x * z
	elif (z <= 1e-76) or (not (z <= 1.7e+20) and (z <= 7.6e+82)):
		tmp = x * y
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -8e+159)
		tmp = Float64(x * z);
	elseif (z <= -1.8e+130)
		tmp = Float64(-z);
	elseif (z <= -1.25e+105)
		tmp = Float64(x * z);
	elseif ((z <= 1e-76) || (!(z <= 1.7e+20) && (z <= 7.6e+82)))
		tmp = Float64(x * y);
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -8e+159)
		tmp = x * z;
	elseif (z <= -1.8e+130)
		tmp = -z;
	elseif (z <= -1.25e+105)
		tmp = x * z;
	elseif ((z <= 1e-76) || (~((z <= 1.7e+20)) && (z <= 7.6e+82)))
		tmp = x * y;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -8e+159], N[(x * z), $MachinePrecision], If[LessEqual[z, -1.8e+130], (-z), If[LessEqual[z, -1.25e+105], N[(x * z), $MachinePrecision], If[Or[LessEqual[z, 1e-76], And[N[Not[LessEqual[z, 1.7e+20]], $MachinePrecision], LessEqual[z, 7.6e+82]]], N[(x * y), $MachinePrecision], (-z)]]]]

Derivation

1. Split input into 3 regimes

2. if z < -7.9999999999999994e159 or -1.8000000000000001e130 < z < -1.25000000000000011e105

   1. Initial program 93.9%

      \[x \cdot y + \left(x - 1\right) \cdot z \]

   2. Taylor expanded in x around inf 73.5%

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

      1. +-commutative 73.5%

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

   4. Simplified 73.5%

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

   5. Taylor expanded in z around inf 67.8%

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

   if -7.9999999999999994e159 < z < -1.8000000000000001e130 or 9.99999999999999927e-77 < z < 1.7e20 or 7.60000000000000067e82 < z

   1. Initial program 98.7%

      \[x \cdot y + \left(x - 1\right) \cdot z \]

   2. Taylor expanded in x around 0 62.1%

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

      1. neg-mul-1 62.1%

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

   4. Simplified 62.1%

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

   if -1.25000000000000011e105 < z < 9.99999999999999927e-77 or 1.7e20 < z < 7.60000000000000067e82

   1. Initial program 98.6%

      \[x \cdot y + \left(x - 1\right) \cdot z \]

   2. Taylor expanded in y around inf 72.7%

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

4. Final simplification 68.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -8 \cdot 10^{+159}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;z \leq -1.8 \cdot 10^{+130}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq -1.25 \cdot 10^{+105}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;z \leq 10^{-76} \lor \neg \left(z \leq 1.7 \cdot 10^{+20}\right) \land z \leq 7.6 \cdot 10^{+82}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 3: 52.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5 \cdot 10^{+129}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq 2 \cdot 10^{-76} \lor \neg \left(z \leq 10500000000\right) \land z \leq 8.2 \cdot 10^{+82}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -5e+129)
   (- z)
   (if (or (<= z 2e-76) (and (not (<= z 10500000000.0)) (<= z 8.2e+82)))
     (* x y)
     (- z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -5e+129) {
		tmp = -z;
	} else if ((z <= 2e-76) || (!(z <= 10500000000.0) && (z <= 8.2e+82))) {
		tmp = x * y;
	} else {
		tmp = -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 (z <= (-5d+129)) then
        tmp = -z
    else if ((z <= 2d-76) .or. (.not. (z <= 10500000000.0d0)) .and. (z <= 8.2d+82)) then
        tmp = x * y
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -5e+129) {
		tmp = -z;
	} else if ((z <= 2e-76) || (!(z <= 10500000000.0) && (z <= 8.2e+82))) {
		tmp = x * y;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -5e+129:
		tmp = -z
	elif (z <= 2e-76) or (not (z <= 10500000000.0) and (z <= 8.2e+82)):
		tmp = x * y
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -5e+129)
		tmp = Float64(-z);
	elseif ((z <= 2e-76) || (!(z <= 10500000000.0) && (z <= 8.2e+82)))
		tmp = Float64(x * y);
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -5e+129)
		tmp = -z;
	elseif ((z <= 2e-76) || (~((z <= 10500000000.0)) && (z <= 8.2e+82)))
		tmp = x * y;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -5e+129], (-z), If[Or[LessEqual[z, 2e-76], And[N[Not[LessEqual[z, 10500000000.0]], $MachinePrecision], LessEqual[z, 8.2e+82]]], N[(x * y), $MachinePrecision], (-z)]]

Derivation

1. Split input into 2 regimes

2. if z < -5.0000000000000003e129 or 1.99999999999999985e-76 < z < 1.05e10 or 8.1999999999999999e82 < z

   1. Initial program 97.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]

   2. Taylor expanded in x around 0 54.2%

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

      1. neg-mul-1 54.2%

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

   4. Simplified 54.2%

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

   if -5.0000000000000003e129 < z < 1.99999999999999985e-76 or 1.05e10 < z < 8.1999999999999999e82

   1. Initial program 98.7%

      \[x \cdot y + \left(x - 1\right) \cdot z \]

   2. Taylor expanded in y around inf 71.4%

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

4. Final simplification 64.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5 \cdot 10^{+129}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq 2 \cdot 10^{-76} \lor \neg \left(z \leq 10500000000\right) \land z \leq 8.2 \cdot 10^{+82}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 4: 83.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.45 \cdot 10^{-8} \lor \neg \left(x \leq 7 \cdot 10^{-118}\right):\\ \;\;\;\;x \cdot \left(y + z\right)\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -2.45e-8) (not (<= x 7e-118))) (* x (+ y z)) (- z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.45e-8) || !(x <= 7e-118)) {
		tmp = x * (y + z);
	} else {
		tmp = -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 <= (-2.45d-8)) .or. (.not. (x <= 7d-118))) then
        tmp = x * (y + z)
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2.45e-8) || !(x <= 7e-118)) {
		tmp = x * (y + z);
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -2.45e-8) or not (x <= 7e-118):
		tmp = x * (y + z)
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -2.45e-8) || !(x <= 7e-118))
		tmp = Float64(x * Float64(y + z));
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -2.45e-8) || ~((x <= 7e-118)))
		tmp = x * (y + z);
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -2.45e-8], N[Not[LessEqual[x, 7e-118]], $MachinePrecision]], N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision], (-z)]

Derivation

1. Split input into 2 regimes

2. if x < -2.4500000000000001e-8 or 7e-118 < x

   1. Initial program 97.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]

   2. Taylor expanded in x around inf 89.3%

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

      1. +-commutative 89.3%

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

   4. Simplified 89.3%

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

   if -2.4500000000000001e-8 < x < 7e-118

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]

   2. Taylor expanded in x around 0 75.0%

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

      1. neg-mul-1 75.0%

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

   4. Simplified 75.0%

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

4. Final simplification 84.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.45 \cdot 10^{-8} \lor \neg \left(x \leq 7 \cdot 10^{-118}\right):\\ \;\;\;\;x \cdot \left(y + z\right)\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 5: 78.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.2 \cdot 10^{+129} \lor \neg \left(z \leq 1.8 \cdot 10^{-76}\right):\\ \;\;\;\;z \cdot \left(x + -1\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y + z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -4.2e+129) (not (<= z 1.8e-76)))
   (* z (+ x -1.0))
   (* x (+ y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -4.2e+129) || !(z <= 1.8e-76)) {
		tmp = z * (x + -1.0);
	} 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 ((z <= (-4.2d+129)) .or. (.not. (z <= 1.8d-76))) then
        tmp = z * (x + (-1.0d0))
    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 ((z <= -4.2e+129) || !(z <= 1.8e-76)) {
		tmp = z * (x + -1.0);
	} else {
		tmp = x * (y + z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -4.2e+129) or not (z <= 1.8e-76):
		tmp = z * (x + -1.0)
	else:
		tmp = x * (y + z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -4.2e+129) || !(z <= 1.8e-76))
		tmp = Float64(z * Float64(x + -1.0));
	else
		tmp = Float64(x * Float64(y + z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -4.2e+129) || ~((z <= 1.8e-76)))
		tmp = z * (x + -1.0);
	else
		tmp = x * (y + z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -4.2e+129], N[Not[LessEqual[z, 1.8e-76]], $MachinePrecision]], N[(z * N[(x + -1.0), $MachinePrecision]), $MachinePrecision], N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -4.19999999999999993e129 or 1.8e-76 < z

   1. Initial program 96.5%

      \[x \cdot y + \left(x - 1\right) \cdot z \]

   2. Taylor expanded in y around 0 90.7%

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

   if -4.19999999999999993e129 < z < 1.8e-76

   1. Initial program 99.3%

      \[x \cdot y + \left(x - 1\right) \cdot z \]

   2. Taylor expanded in x around inf 82.5%

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

      1. +-commutative 82.5%

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

   4. Simplified 82.5%

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

4. Final simplification 86.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.2 \cdot 10^{+129} \lor \neg \left(z \leq 1.8 \cdot 10^{-76}\right):\\ \;\;\;\;z \cdot \left(x + -1\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y + z\right)\\ \end{array} \]

Alternative 6: 35.9% accurate, 4.5× speedup

Math

\[\begin{array}{l} \\ -z \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 98.0%

   \[x \cdot y + \left(x - 1\right) \cdot z \]

2. Taylor expanded in x around 0 33.2%

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

   1. neg-mul-1 33.2%

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

4. Simplified 33.2%

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

5. Final simplification 33.2%

   \[\leadsto -z \]

Reproduce

herbie shell --seed 2023230 
(FPCore (x y z)
  :name "Graphics.Rendering.Chart.Drawing:drawTextsR from Chart-1.5.3"
  :precision binary64
  (+ (* x y) (* (- x 1.0) z)))