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

Percentage Accurate: 97.8% → 100.0%

Time: 4.0s

Alternatives: 6

Speedup: 1.3×

• 20.3% 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: 97.8% 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 98.4%

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

   1. *-commutative 98.4%

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

   2. sub-neg 98.4%

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

   3. distribute-rgt-in 98.4%

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

   4. metadata-eval 98.4%

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

   5. neg-mul-1 98.4%

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

   6. associate-+r+ 98.4%

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

   7. unsub-neg 98.4%

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

   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. Add Preprocessing

5. Final simplification 100.0%

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

Alternative 2: 59.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq -2.6 \cdot 10^{-101}:\\ \;\;\;\;-z\\ \mathbf{elif}\;x \leq -2.65 \cdot 10^{-142}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 2.65 \cdot 10^{-17}:\\ \;\;\;\;-z\\ \mathbf{elif}\;x \leq 480000 \lor \neg \left(x \leq 1.06 \cdot 10^{+64}\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1.0)
   (* x z)
   (if (<= x -2.6e-101)
     (- z)
     (if (<= x -2.65e-142)
       (* x y)
       (if (<= x 2.65e-17)
         (- z)
         (if (or (<= x 480000.0) (not (<= x 1.06e+64))) (* x y) (* x z)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.0) {
		tmp = x * z;
	} else if (x <= -2.6e-101) {
		tmp = -z;
	} else if (x <= -2.65e-142) {
		tmp = x * y;
	} else if (x <= 2.65e-17) {
		tmp = -z;
	} else if ((x <= 480000.0) || !(x <= 1.06e+64)) {
		tmp = x * y;
	} else {
		tmp = x * 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 <= (-1.0d0)) then
        tmp = x * z
    else if (x <= (-2.6d-101)) then
        tmp = -z
    else if (x <= (-2.65d-142)) then
        tmp = x * y
    else if (x <= 2.65d-17) then
        tmp = -z
    else if ((x <= 480000.0d0) .or. (.not. (x <= 1.06d+64))) then
        tmp = x * y
    else
        tmp = x * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.0) {
		tmp = x * z;
	} else if (x <= -2.6e-101) {
		tmp = -z;
	} else if (x <= -2.65e-142) {
		tmp = x * y;
	} else if (x <= 2.65e-17) {
		tmp = -z;
	} else if ((x <= 480000.0) || !(x <= 1.06e+64)) {
		tmp = x * y;
	} else {
		tmp = x * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -1.0:
		tmp = x * z
	elif x <= -2.6e-101:
		tmp = -z
	elif x <= -2.65e-142:
		tmp = x * y
	elif x <= 2.65e-17:
		tmp = -z
	elif (x <= 480000.0) or not (x <= 1.06e+64):
		tmp = x * y
	else:
		tmp = x * z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1.0)
		tmp = Float64(x * z);
	elseif (x <= -2.6e-101)
		tmp = Float64(-z);
	elseif (x <= -2.65e-142)
		tmp = Float64(x * y);
	elseif (x <= 2.65e-17)
		tmp = Float64(-z);
	elseif ((x <= 480000.0) || !(x <= 1.06e+64))
		tmp = Float64(x * y);
	else
		tmp = Float64(x * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -1.0)
		tmp = x * z;
	elseif (x <= -2.6e-101)
		tmp = -z;
	elseif (x <= -2.65e-142)
		tmp = x * y;
	elseif (x <= 2.65e-17)
		tmp = -z;
	elseif ((x <= 480000.0) || ~((x <= 1.06e+64)))
		tmp = x * y;
	else
		tmp = x * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1.0], N[(x * z), $MachinePrecision], If[LessEqual[x, -2.6e-101], (-z), If[LessEqual[x, -2.65e-142], N[(x * y), $MachinePrecision], If[LessEqual[x, 2.65e-17], (-z), If[Or[LessEqual[x, 480000.0], N[Not[LessEqual[x, 1.06e+64]], $MachinePrecision]], N[(x * y), $MachinePrecision], N[(x * z), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1 or 4.8e5 < x < 1.06e64

   1. Initial program 96.1%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 66.1%

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

   4. Taylor expanded in x around inf 65.0%

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

      1. *-commutative 65.0%

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

   6. Simplified 65.0%

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

   if -1 < x < -2.6000000000000001e-101 or -2.6499999999999999e-142 < x < 2.6499999999999999e-17

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 84.6%

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

      1. mul-1-neg 84.6%

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

   5. Simplified 84.6%

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

   if -2.6000000000000001e-101 < x < -2.6499999999999999e-142 or 2.6499999999999999e-17 < x < 4.8e5 or 1.06e64 < x

   1. Initial program 98.6%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf 62.7%

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

4. Final simplification 72.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq -2.6 \cdot 10^{-101}:\\ \;\;\;\;-z\\ \mathbf{elif}\;x \leq -2.65 \cdot 10^{-142}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \leq 2.65 \cdot 10^{-17}:\\ \;\;\;\;-z\\ \mathbf{elif}\;x \leq 480000 \lor \neg \left(x \leq 1.06 \cdot 10^{+64}\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 84.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.0012 \lor \neg \left(x \leq -2.6 \cdot 10^{-101} \lor \neg \left(x \leq -2.65 \cdot 10^{-142}\right) \land x \leq 2.45 \cdot 10^{-17}\right):\\ \;\;\;\;x \cdot \left(y + z\right)\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -0.0012)
         (not
          (or (<= x -2.6e-101) (and (not (<= x -2.65e-142)) (<= x 2.45e-17)))))
   (* x (+ y z))
   (- z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -0.0012) || !((x <= -2.6e-101) || (!(x <= -2.65e-142) && (x <= 2.45e-17)))) {
		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 <= (-0.0012d0)) .or. (.not. (x <= (-2.6d-101)) .or. (.not. (x <= (-2.65d-142))) .and. (x <= 2.45d-17))) 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 <= -0.0012) || !((x <= -2.6e-101) || (!(x <= -2.65e-142) && (x <= 2.45e-17)))) {
		tmp = x * (y + z);
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -0.0012) or not ((x <= -2.6e-101) or (not (x <= -2.65e-142) and (x <= 2.45e-17))):
		tmp = x * (y + z)
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -0.0012) || !((x <= -2.6e-101) || (!(x <= -2.65e-142) && (x <= 2.45e-17))))
		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 <= -0.0012) || ~(((x <= -2.6e-101) || (~((x <= -2.65e-142)) && (x <= 2.45e-17)))))
		tmp = x * (y + z);
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -0.0012], N[Not[Or[LessEqual[x, -2.6e-101], And[N[Not[LessEqual[x, -2.65e-142]], $MachinePrecision], LessEqual[x, 2.45e-17]]]], $MachinePrecision]], N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision], (-z)]

Derivation

1. Split input into 2 regimes

2. if x < -0.00119999999999999989 or -2.6000000000000001e-101 < x < -2.6499999999999999e-142 or 2.45000000000000006e-17 < x

   1. Initial program 97.3%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 95.5%

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

      1. +-commutative 95.5%

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

   5. Simplified 95.5%

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

   if -0.00119999999999999989 < x < -2.6000000000000001e-101 or -2.6499999999999999e-142 < x < 2.45000000000000006e-17

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 84.6%

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

      1. mul-1-neg 84.6%

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

   5. Simplified 84.6%

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

4. Final simplification 91.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.0012 \lor \neg \left(x \leq -2.6 \cdot 10^{-101} \lor \neg \left(x \leq -2.65 \cdot 10^{-142}\right) \land x \leq 2.45 \cdot 10^{-17}\right):\\ \;\;\;\;x \cdot \left(y + z\right)\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 83.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6 \cdot 10^{+24} \lor \neg \left(x \leq -9 \cdot 10^{-100}\right) \land \left(x \leq -2.25 \cdot 10^{-142} \lor \neg \left(x \leq 1.7 \cdot 10^{-27}\right)\right):\\ \;\;\;\;x \cdot \left(y + z\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(x + -1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -6e+24)
         (and (not (<= x -9e-100))
              (or (<= x -2.25e-142) (not (<= x 1.7e-27)))))
   (* x (+ y z))
   (* z (+ x -1.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -6e+24) || (!(x <= -9e-100) && ((x <= -2.25e-142) || !(x <= 1.7e-27)))) {
		tmp = x * (y + z);
	} else {
		tmp = z * (x + -1.0);
	}
	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) :: tmp
    if ((x <= (-6d+24)) .or. (.not. (x <= (-9d-100))) .and. (x <= (-2.25d-142)) .or. (.not. (x <= 1.7d-27))) then
        tmp = x * (y + z)
    else
        tmp = z * (x + (-1.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -6e+24) || (!(x <= -9e-100) && ((x <= -2.25e-142) || !(x <= 1.7e-27)))) {
		tmp = x * (y + z);
	} else {
		tmp = z * (x + -1.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -6e+24) or (not (x <= -9e-100) and ((x <= -2.25e-142) or not (x <= 1.7e-27))):
		tmp = x * (y + z)
	else:
		tmp = z * (x + -1.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -6e+24) || (!(x <= -9e-100) && ((x <= -2.25e-142) || !(x <= 1.7e-27))))
		tmp = Float64(x * Float64(y + z));
	else
		tmp = Float64(z * Float64(x + -1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -6e+24) || (~((x <= -9e-100)) && ((x <= -2.25e-142) || ~((x <= 1.7e-27)))))
		tmp = x * (y + z);
	else
		tmp = z * (x + -1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -6e+24], And[N[Not[LessEqual[x, -9e-100]], $MachinePrecision], Or[LessEqual[x, -2.25e-142], N[Not[LessEqual[x, 1.7e-27]], $MachinePrecision]]]], N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision], N[(z * N[(x + -1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -5.9999999999999999e24 or -9.0000000000000002e-100 < x < -2.25000000000000009e-142 or 1.69999999999999985e-27 < x

   1. Initial program 97.3%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 95.5%

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

      1. +-commutative 95.5%

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

   5. Simplified 95.5%

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

   if -5.9999999999999999e24 < x < -9.0000000000000002e-100 or -2.25000000000000009e-142 < x < 1.69999999999999985e-27

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 86.5%

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

4. Final simplification 91.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -6 \cdot 10^{+24} \lor \neg \left(x \leq -9 \cdot 10^{-100}\right) \land \left(x \leq -2.25 \cdot 10^{-142} \lor \neg \left(x \leq 1.7 \cdot 10^{-27}\right)\right):\\ \;\;\;\;x \cdot \left(y + z\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot \left(x + -1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 59.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.15 \cdot 10^{+46} \lor \neg \left(x \leq -2.75 \cdot 10^{-101}\right) \land \left(x \leq -2.65 \cdot 10^{-142} \lor \neg \left(x \leq 1.62 \cdot 10^{-18}\right)\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -1.15e+46)
         (and (not (<= x -2.75e-101))
              (or (<= x -2.65e-142) (not (<= x 1.62e-18)))))
   (* x y)
   (- z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.15e+46) || (!(x <= -2.75e-101) && ((x <= -2.65e-142) || !(x <= 1.62e-18)))) {
		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 ((x <= (-1.15d+46)) .or. (.not. (x <= (-2.75d-101))) .and. (x <= (-2.65d-142)) .or. (.not. (x <= 1.62d-18))) 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 ((x <= -1.15e+46) || (!(x <= -2.75e-101) && ((x <= -2.65e-142) || !(x <= 1.62e-18)))) {
		tmp = x * y;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -1.15e+46) or (not (x <= -2.75e-101) and ((x <= -2.65e-142) or not (x <= 1.62e-18))):
		tmp = x * y
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -1.15e+46) || (!(x <= -2.75e-101) && ((x <= -2.65e-142) || !(x <= 1.62e-18))))
		tmp = Float64(x * y);
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -1.15e+46) || (~((x <= -2.75e-101)) && ((x <= -2.65e-142) || ~((x <= 1.62e-18)))))
		tmp = x * y;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -1.15e+46], And[N[Not[LessEqual[x, -2.75e-101]], $MachinePrecision], Or[LessEqual[x, -2.65e-142], N[Not[LessEqual[x, 1.62e-18]], $MachinePrecision]]]], N[(x * y), $MachinePrecision], (-z)]

Derivation

1. Split input into 2 regimes

2. if x < -1.15e46 or -2.74999999999999986e-101 < x < -2.6499999999999999e-142 or 1.62000000000000005e-18 < x

   1. Initial program 97.2%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf 53.3%

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

   if -1.15e46 < x < -2.74999999999999986e-101 or -2.6499999999999999e-142 < x < 1.62000000000000005e-18

   1. Initial program 100.0%

      \[x \cdot y + \left(x - 1\right) \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 81.1%

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

      1. mul-1-neg 81.1%

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

   5. Simplified 81.1%

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

4. Final simplification 65.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.15 \cdot 10^{+46} \lor \neg \left(x \leq -2.75 \cdot 10^{-101}\right) \land \left(x \leq -2.65 \cdot 10^{-142} \lor \neg \left(x \leq 1.62 \cdot 10^{-18}\right)\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 36.2% 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 98.4%

   \[x \cdot y + \left(x - 1\right) \cdot z \]
2. Add Preprocessing

3. Taylor expanded in x around 0 38.5%

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

   1. mul-1-neg 38.5%

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

5. Simplified 38.5%

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

6. Final simplification 38.5%

   \[\leadsto -z \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024079 
(FPCore (x y z)
  :name "Graphics.Rendering.Chart.Drawing:drawTextsR from Chart-1.5.3"
  :precision binary64
  (+ (* x y) (* (- x 1.0) z)))