Linear.Projection:perspective from linear-1.19.1.3, B

Percentage Accurate: 77.4% → 99.8%

Time: 4.6s

Alternatives: 4

Speedup: 0.5×

Specification

Math

\[\begin{array}{l} \\ \frac{\left(x \cdot 2\right) \cdot y}{x - y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (* (* x 2.0) y) (- x y)))

C

double code(double x, double y) {
	return ((x * 2.0) * y) / (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = ((x * 2.0d0) * y) / (x - y)
end function

Java

public static double code(double x, double y) {
	return ((x * 2.0) * y) / (x - y);
}

Python

def code(x, y):
	return ((x * 2.0) * y) / (x - y)

Julia

function code(x, y)
	return Float64(Float64(Float64(x * 2.0) * y) / Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = ((x * 2.0) * y) / (x - y);
end

Wolfram

code[x_, y_] := N[(N[(N[(x * 2.0), $MachinePrecision] * y), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]

Initial Program: 77.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left(x \cdot 2\right) \cdot y}{x - y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (* (* x 2.0) y) (- x y)))

C

double code(double x, double y) {
	return ((x * 2.0) * y) / (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = ((x * 2.0d0) * y) / (x - y)
end function

Java

public static double code(double x, double y) {
	return ((x * 2.0) * y) / (x - y);
}

Python

def code(x, y):
	return ((x * 2.0) * y) / (x - y)

Julia

function code(x, y)
	return Float64(Float64(Float64(x * 2.0) * y) / Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = ((x * 2.0) * y) / (x - y);
end

Wolfram

code[x_, y_] := N[(N[(N[(x * 2.0), $MachinePrecision] * y), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{+32} \lor \neg \left(y \leq 10^{-33}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x \cdot 2}{x - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -5e+32) (not (<= y 1e-33)))
   (* (/ x (+ (/ x y) -1.0)) 2.0)
   (* y (/ (* x 2.0) (- x y)))))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -5e+32) || !(y <= 1e-33)) {
		tmp = (x / ((x / y) + -1.0)) * 2.0;
	} else {
		tmp = y * ((x * 2.0) / (x - y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-5d+32)) .or. (.not. (y <= 1d-33))) then
        tmp = (x / ((x / y) + (-1.0d0))) * 2.0d0
    else
        tmp = y * ((x * 2.0d0) / (x - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -5e+32) || !(y <= 1e-33)) {
		tmp = (x / ((x / y) + -1.0)) * 2.0;
	} else {
		tmp = y * ((x * 2.0) / (x - y));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -5e+32) or not (y <= 1e-33):
		tmp = (x / ((x / y) + -1.0)) * 2.0
	else:
		tmp = y * ((x * 2.0) / (x - y))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -5e+32) || !(y <= 1e-33))
		tmp = Float64(Float64(x / Float64(Float64(x / y) + -1.0)) * 2.0);
	else
		tmp = Float64(y * Float64(Float64(x * 2.0) / Float64(x - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -5e+32) || ~((y <= 1e-33)))
		tmp = (x / ((x / y) + -1.0)) * 2.0;
	else
		tmp = y * ((x * 2.0) / (x - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -5e+32], N[Not[LessEqual[y, 1e-33]], $MachinePrecision]], N[(N[(x / N[(N[(x / y), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision], N[(y * N[(N[(x * 2.0), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -4.9999999999999997e32 or 1.0000000000000001e-33 < y

   1. Initial program 76.9%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Step-by-step derivation

      1. associate-/l* 99.3%

         \[\leadsto \color{blue}{\frac{x \cdot 2}{\frac{x - y}{y}}} \]

      2. associate-*l/ 99.9%

         \[\leadsto \color{blue}{\frac{x}{\frac{x - y}{y}} \cdot 2} \]

      3. div-sub 100.0%

         \[\leadsto \frac{x}{\color{blue}{\frac{x}{y} - \frac{y}{y}}} \cdot 2 \]

      4. *-inverses 100.0%

         \[\leadsto \frac{x}{\frac{x}{y} - \color{blue}{1}} \cdot 2 \]

      5. metadata-eval 100.0%

         \[\leadsto \frac{x}{\frac{x}{y} - \color{blue}{\left(--1\right)}} \cdot 2 \]

      6. sub-neg 100.0%

         \[\leadsto \frac{x}{\color{blue}{\frac{x}{y} + \left(-\left(--1\right)\right)}} \cdot 2 \]

      7. metadata-eval 100.0%

         \[\leadsto \frac{x}{\frac{x}{y} + \left(-\color{blue}{1}\right)} \cdot 2 \]

      8. metadata-eval 100.0%

         \[\leadsto \frac{x}{\frac{x}{y} + \color{blue}{-1}} \cdot 2 \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\frac{x}{\frac{x}{y} + -1} \cdot 2} \]
   4. Add Preprocessing

   if -4.9999999999999997e32 < y < 1.0000000000000001e-33

   1. Initial program 79.7%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Step-by-step derivation

      1. associate-*l/ 100.0%

         \[\leadsto \color{blue}{\frac{x \cdot 2}{x - y} \cdot y} \]

   3. Simplified 100.0%

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

4. Final simplification 100.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{+32} \lor \neg \left(y \leq 10^{-33}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x \cdot 2}{x - y}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 74.2% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.5 \cdot 10^{+15} \lor \neg \left(x \leq 2.45 \cdot 10^{-91}\right) \land \left(x \leq 2 \cdot 10^{-55} \lor \neg \left(x \leq 2 \cdot 10^{+74}\right)\right):\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;x \cdot -2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -4.5e+15)
         (and (not (<= x 2.45e-91)) (or (<= x 2e-55) (not (<= x 2e+74)))))
   (* y 2.0)
   (* x -2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -4.5e+15) || (!(x <= 2.45e-91) && ((x <= 2e-55) || !(x <= 2e+74)))) {
		tmp = y * 2.0;
	} else {
		tmp = x * -2.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x <= (-4.5d+15)) .or. (.not. (x <= 2.45d-91)) .and. (x <= 2d-55) .or. (.not. (x <= 2d+74))) then
        tmp = y * 2.0d0
    else
        tmp = x * (-2.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -4.5e+15) || (!(x <= 2.45e-91) && ((x <= 2e-55) || !(x <= 2e+74)))) {
		tmp = y * 2.0;
	} else {
		tmp = x * -2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -4.5e+15) or (not (x <= 2.45e-91) and ((x <= 2e-55) or not (x <= 2e+74))):
		tmp = y * 2.0
	else:
		tmp = x * -2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -4.5e+15) || (!(x <= 2.45e-91) && ((x <= 2e-55) || !(x <= 2e+74))))
		tmp = Float64(y * 2.0);
	else
		tmp = Float64(x * -2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -4.5e+15) || (~((x <= 2.45e-91)) && ((x <= 2e-55) || ~((x <= 2e+74)))))
		tmp = y * 2.0;
	else
		tmp = x * -2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -4.5e+15], And[N[Not[LessEqual[x, 2.45e-91]], $MachinePrecision], Or[LessEqual[x, 2e-55], N[Not[LessEqual[x, 2e+74]], $MachinePrecision]]]], N[(y * 2.0), $MachinePrecision], N[(x * -2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -4.5e15 or 2.4499999999999999e-91 < x < 1.99999999999999999e-55 or 1.9999999999999999e74 < x

   1. Initial program 74.2%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Step-by-step derivation

      1. associate-/l* 73.6%

         \[\leadsto \color{blue}{\frac{x \cdot 2}{\frac{x - y}{y}}} \]

      2. associate-*l/ 74.4%

         \[\leadsto \color{blue}{\frac{x}{\frac{x - y}{y}} \cdot 2} \]

      3. div-sub 74.4%

         \[\leadsto \frac{x}{\color{blue}{\frac{x}{y} - \frac{y}{y}}} \cdot 2 \]

      4. *-inverses 74.4%

         \[\leadsto \frac{x}{\frac{x}{y} - \color{blue}{1}} \cdot 2 \]

      5. metadata-eval 74.4%

         \[\leadsto \frac{x}{\frac{x}{y} - \color{blue}{\left(--1\right)}} \cdot 2 \]

      6. sub-neg 74.4%

         \[\leadsto \frac{x}{\color{blue}{\frac{x}{y} + \left(-\left(--1\right)\right)}} \cdot 2 \]

      7. metadata-eval 74.4%

         \[\leadsto \frac{x}{\frac{x}{y} + \left(-\color{blue}{1}\right)} \cdot 2 \]

      8. metadata-eval 74.4%

         \[\leadsto \frac{x}{\frac{x}{y} + \color{blue}{-1}} \cdot 2 \]

   3. Simplified 74.4%

      \[\leadsto \color{blue}{\frac{x}{\frac{x}{y} + -1} \cdot 2} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 84.7%

      \[\leadsto \color{blue}{y} \cdot 2 \]

   if -4.5e15 < x < 2.4499999999999999e-91 or 1.99999999999999999e-55 < x < 1.9999999999999999e74

   1. Initial program 81.7%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Step-by-step derivation

      1. associate-*l/ 78.8%

         \[\leadsto \color{blue}{\frac{x \cdot 2}{x - y} \cdot y} \]

   3. Simplified 78.8%

      \[\leadsto \color{blue}{\frac{x \cdot 2}{x - y} \cdot y} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 82.7%

      \[\leadsto \color{blue}{-2 \cdot x} \]
   6. Step-by-step derivation

      1. *-commutative 82.7%

         \[\leadsto \color{blue}{x \cdot -2} \]

   7. Simplified 82.7%

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

4. Final simplification 83.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.5 \cdot 10^{+15} \lor \neg \left(x \leq 2.45 \cdot 10^{-91}\right) \land \left(x \leq 2 \cdot 10^{-55} \lor \neg \left(x \leq 2 \cdot 10^{+74}\right)\right):\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;x \cdot -2\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 92.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8.5 \cdot 10^{-81} \lor \neg \left(y \leq 2.9 \cdot 10^{-181}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -8.5e-81) (not (<= y 2.9e-181)))
   (* (/ x (+ (/ x y) -1.0)) 2.0)
   (* y 2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -8.5e-81) || !(y <= 2.9e-181)) {
		tmp = (x / ((x / y) + -1.0)) * 2.0;
	} else {
		tmp = y * 2.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-8.5d-81)) .or. (.not. (y <= 2.9d-181))) then
        tmp = (x / ((x / y) + (-1.0d0))) * 2.0d0
    else
        tmp = y * 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -8.5e-81) || !(y <= 2.9e-181)) {
		tmp = (x / ((x / y) + -1.0)) * 2.0;
	} else {
		tmp = y * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -8.5e-81) or not (y <= 2.9e-181):
		tmp = (x / ((x / y) + -1.0)) * 2.0
	else:
		tmp = y * 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -8.5e-81) || !(y <= 2.9e-181))
		tmp = Float64(Float64(x / Float64(Float64(x / y) + -1.0)) * 2.0);
	else
		tmp = Float64(y * 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -8.5e-81) || ~((y <= 2.9e-181)))
		tmp = (x / ((x / y) + -1.0)) * 2.0;
	else
		tmp = y * 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -8.5e-81], N[Not[LessEqual[y, 2.9e-181]], $MachinePrecision]], N[(N[(x / N[(N[(x / y), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision], N[(y * 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -8.5000000000000001e-81 or 2.8999999999999998e-181 < y

   1. Initial program 78.2%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Step-by-step derivation

      1. associate-/l* 98.9%

         \[\leadsto \color{blue}{\frac{x \cdot 2}{\frac{x - y}{y}}} \]

      2. associate-*l/ 99.4%

         \[\leadsto \color{blue}{\frac{x}{\frac{x - y}{y}} \cdot 2} \]

      3. div-sub 99.4%

         \[\leadsto \frac{x}{\color{blue}{\frac{x}{y} - \frac{y}{y}}} \cdot 2 \]

      4. *-inverses 99.4%

         \[\leadsto \frac{x}{\frac{x}{y} - \color{blue}{1}} \cdot 2 \]

      5. metadata-eval 99.4%

         \[\leadsto \frac{x}{\frac{x}{y} - \color{blue}{\left(--1\right)}} \cdot 2 \]

      6. sub-neg 99.4%

         \[\leadsto \frac{x}{\color{blue}{\frac{x}{y} + \left(-\left(--1\right)\right)}} \cdot 2 \]

      7. metadata-eval 99.4%

         \[\leadsto \frac{x}{\frac{x}{y} + \left(-\color{blue}{1}\right)} \cdot 2 \]

      8. metadata-eval 99.4%

         \[\leadsto \frac{x}{\frac{x}{y} + \color{blue}{-1}} \cdot 2 \]

   3. Simplified 99.4%

      \[\leadsto \color{blue}{\frac{x}{\frac{x}{y} + -1} \cdot 2} \]
   4. Add Preprocessing

   if -8.5000000000000001e-81 < y < 2.8999999999999998e-181

   1. Initial program 78.2%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Step-by-step derivation

      1. associate-/l* 55.8%

         \[\leadsto \color{blue}{\frac{x \cdot 2}{\frac{x - y}{y}}} \]

      2. associate-*l/ 55.8%

         \[\leadsto \color{blue}{\frac{x}{\frac{x - y}{y}} \cdot 2} \]

      3. div-sub 55.8%

         \[\leadsto \frac{x}{\color{blue}{\frac{x}{y} - \frac{y}{y}}} \cdot 2 \]

      4. *-inverses 55.8%

         \[\leadsto \frac{x}{\frac{x}{y} - \color{blue}{1}} \cdot 2 \]

      5. metadata-eval 55.8%

         \[\leadsto \frac{x}{\frac{x}{y} - \color{blue}{\left(--1\right)}} \cdot 2 \]

      6. sub-neg 55.8%

         \[\leadsto \frac{x}{\color{blue}{\frac{x}{y} + \left(-\left(--1\right)\right)}} \cdot 2 \]

      7. metadata-eval 55.8%

         \[\leadsto \frac{x}{\frac{x}{y} + \left(-\color{blue}{1}\right)} \cdot 2 \]

      8. metadata-eval 55.8%

         \[\leadsto \frac{x}{\frac{x}{y} + \color{blue}{-1}} \cdot 2 \]

   3. Simplified 55.8%

      \[\leadsto \color{blue}{\frac{x}{\frac{x}{y} + -1} \cdot 2} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 91.5%

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

4. Final simplification 97.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -8.5 \cdot 10^{-81} \lor \neg \left(y \leq 2.9 \cdot 10^{-181}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 50.5% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ x \cdot -2 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x -2.0))

C

double code(double x, double y) {
	return x * -2.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (-2.0d0)
end function

Java

public static double code(double x, double y) {
	return x * -2.0;
}

Python

def code(x, y):
	return x * -2.0

Julia

function code(x, y)
	return Float64(x * -2.0)
end

MATLAB

function tmp = code(x, y)
	tmp = x * -2.0;
end

Wolfram

code[x_, y_] := N[(x * -2.0), $MachinePrecision]

Derivation

1. Initial program 78.2%

   \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
2. Step-by-step derivation

   1. associate-*l/ 88.4%

      \[\leadsto \color{blue}{\frac{x \cdot 2}{x - y} \cdot y} \]

3. Simplified 88.4%

   \[\leadsto \color{blue}{\frac{x \cdot 2}{x - y} \cdot y} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 51.9%

   \[\leadsto \color{blue}{-2 \cdot x} \]
6. Step-by-step derivation

   1. *-commutative 51.9%

      \[\leadsto \color{blue}{x \cdot -2} \]

7. Simplified 51.9%

   \[\leadsto \color{blue}{x \cdot -2} \]

8. Final simplification 51.9%

   \[\leadsto x \cdot -2 \]
9. Add Preprocessing

Developer target: 99.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{2 \cdot x}{x - y} \cdot y\\ \mathbf{if}\;x < -1.7210442634149447 \cdot 10^{+81}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x < 83645045635564430:\\ \;\;\;\;\frac{x \cdot 2}{\frac{x - y}{y}}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (/ (* 2.0 x) (- x y)) y)))
   (if (< x -1.7210442634149447e+81)
     t_0
     (if (< x 83645045635564430.0) (/ (* x 2.0) (/ (- x y) y)) t_0))))

C

double code(double x, double y) {
	double t_0 = ((2.0 * x) / (x - y)) * y;
	double tmp;
	if (x < -1.7210442634149447e+81) {
		tmp = t_0;
	} else if (x < 83645045635564430.0) {
		tmp = (x * 2.0) / ((x - y) / y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((2.0d0 * x) / (x - y)) * y
    if (x < (-1.7210442634149447d+81)) then
        tmp = t_0
    else if (x < 83645045635564430.0d0) then
        tmp = (x * 2.0d0) / ((x - y) / y)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = ((2.0 * x) / (x - y)) * y;
	double tmp;
	if (x < -1.7210442634149447e+81) {
		tmp = t_0;
	} else if (x < 83645045635564430.0) {
		tmp = (x * 2.0) / ((x - y) / y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = ((2.0 * x) / (x - y)) * y
	tmp = 0
	if x < -1.7210442634149447e+81:
		tmp = t_0
	elif x < 83645045635564430.0:
		tmp = (x * 2.0) / ((x - y) / y)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(Float64(2.0 * x) / Float64(x - y)) * y)
	tmp = 0.0
	if (x < -1.7210442634149447e+81)
		tmp = t_0;
	elseif (x < 83645045635564430.0)
		tmp = Float64(Float64(x * 2.0) / Float64(Float64(x - y) / y));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = ((2.0 * x) / (x - y)) * y;
	tmp = 0.0;
	if (x < -1.7210442634149447e+81)
		tmp = t_0;
	elseif (x < 83645045635564430.0)
		tmp = (x * 2.0) / ((x - y) / y);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[(2.0 * x), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]}, If[Less[x, -1.7210442634149447e+81], t$95$0, If[Less[x, 83645045635564430.0], N[(N[(x * 2.0), $MachinePrecision] / N[(N[(x - y), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Reproduce

herbie shell --seed 2024026 
(FPCore (x y)
  :name "Linear.Projection:perspective from linear-1.19.1.3, B"
  :precision binary64

  :herbie-target
  (if (< x -1.7210442634149447e+81) (* (/ (* 2.0 x) (- x y)) y) (if (< x 83645045635564430.0) (/ (* x 2.0) (/ (- x y) y)) (* (/ (* 2.0 x) (- x y)) y)))

  (/ (* (* x 2.0) y) (- x y)))