Linear.Projection:perspective from linear-1.19.1.3, B

Percentage Accurate: 76.8% → 99.0%

Time: 3.4s

Alternatives: 4

Speedup: 0.7×

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: 76.8% 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: 99.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.7 \cdot 10^{+98} \lor \neg \left(y \leq 10^{-64}\right):\\ \;\;\;\;\frac{x}{0.5 \cdot \frac{x}{y} - 0.5}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{1 - \frac{y}{x}} \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -4.7e+98) (not (<= y 1e-64)))
   (/ x (- (* 0.5 (/ x y)) 0.5))
   (* (/ y (- 1.0 (/ y x))) 2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -4.7e+98) || !(y <= 1e-64)) {
		tmp = x / ((0.5 * (x / y)) - 0.5);
	} else {
		tmp = (y / (1.0 - (y / 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 ((y <= (-4.7d+98)) .or. (.not. (y <= 1d-64))) then
        tmp = x / ((0.5d0 * (x / y)) - 0.5d0)
    else
        tmp = (y / (1.0d0 - (y / x))) * 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -4.7e+98) || !(y <= 1e-64)) {
		tmp = x / ((0.5 * (x / y)) - 0.5);
	} else {
		tmp = (y / (1.0 - (y / x))) * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -4.7e+98) or not (y <= 1e-64):
		tmp = x / ((0.5 * (x / y)) - 0.5)
	else:
		tmp = (y / (1.0 - (y / x))) * 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -4.7e+98) || !(y <= 1e-64))
		tmp = Float64(x / Float64(Float64(0.5 * Float64(x / y)) - 0.5));
	else
		tmp = Float64(Float64(y / Float64(1.0 - Float64(y / x))) * 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -4.7e+98) || ~((y <= 1e-64)))
		tmp = x / ((0.5 * (x / y)) - 0.5);
	else
		tmp = (y / (1.0 - (y / x))) * 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -4.7e+98], N[Not[LessEqual[y, 1e-64]], $MachinePrecision]], N[(x / N[(N[(0.5 * N[(x / y), $MachinePrecision]), $MachinePrecision] - 0.5), $MachinePrecision]), $MachinePrecision], N[(N[(y / N[(1.0 - N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -4.6999999999999997e98 or 9.99999999999999965e-65 < y

   1. Initial program 77.3%

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

      1. associate-*l/ 79.2%

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

      2. associate-/l* 79.2%

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

      3. associate-/r/ 99.9%

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

   3. Simplified 99.9%

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

   4. Taylor expanded in x around 0 99.9%

      \[\leadsto \frac{x}{\color{blue}{0.5 \cdot \frac{x}{y} - 0.5}} \]

   if -4.6999999999999997e98 < y < 9.99999999999999965e-65

   1. Initial program 75.9%

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

      1. *-commutative 75.9%

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

      2. associate-/l* 99.3%

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

      3. associate-/r* 100.0%

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

      4. associate-/r/ 100.0%

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

      5. div-sub 100.0%

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

      6. *-inverses 100.0%

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

   3. Simplified 100.0%

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

4. Final simplification 100.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.7 \cdot 10^{+98} \lor \neg \left(y \leq 10^{-64}\right):\\ \;\;\;\;\frac{x}{0.5 \cdot \frac{x}{y} - 0.5}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{1 - \frac{y}{x}} \cdot 2\\ \end{array} \]

Alternative 2: 93.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.65 \cdot 10^{-126} \lor \neg \left(x \leq 1.4 \cdot 10^{-236}\right):\\ \;\;\;\;\frac{x}{x - y} \cdot \left(y \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{-0.5}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -2.65e-126) (not (<= x 1.4e-236)))
   (* (/ x (- x y)) (* y 2.0))
   (/ x -0.5)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -2.65e-126) || !(x <= 1.4e-236)) {
		tmp = (x / (x - y)) * (y * 2.0);
	} else {
		tmp = x / -0.5;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x <= (-2.65d-126)) .or. (.not. (x <= 1.4d-236))) then
        tmp = (x / (x - y)) * (y * 2.0d0)
    else
        tmp = x / (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -2.65e-126) || !(x <= 1.4e-236)) {
		tmp = (x / (x - y)) * (y * 2.0);
	} else {
		tmp = x / -0.5;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -2.65e-126) or not (x <= 1.4e-236):
		tmp = (x / (x - y)) * (y * 2.0)
	else:
		tmp = x / -0.5
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -2.65e-126) || !(x <= 1.4e-236))
		tmp = Float64(Float64(x / Float64(x - y)) * Float64(y * 2.0));
	else
		tmp = Float64(x / -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -2.65e-126) || ~((x <= 1.4e-236)))
		tmp = (x / (x - y)) * (y * 2.0);
	else
		tmp = x / -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -2.65e-126], N[Not[LessEqual[x, 1.4e-236]], $MachinePrecision]], N[(N[(x / N[(x - y), $MachinePrecision]), $MachinePrecision] * N[(y * 2.0), $MachinePrecision]), $MachinePrecision], N[(x / -0.5), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.64999999999999997e-126 or 1.39999999999999993e-236 < x

   1. Initial program 76.7%

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

      1. associate-*l/ 97.2%

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

      2. associate-/l* 97.7%

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

      3. associate-/r/ 88.0%

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

   3. Simplified 88.0%

      \[\leadsto \color{blue}{\frac{x}{\frac{\frac{x - y}{2}}{y}}} \]
   4. Step-by-step derivation

      1. associate-/l/ 88.0%

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

      2. associate-/r/ 97.7%

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

   5. Applied egg-rr 97.7%

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

   if -2.64999999999999997e-126 < x < 1.39999999999999993e-236

   1. Initial program 76.0%

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

      1. associate-*l/ 54.2%

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

      2. associate-/l* 54.2%

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

      3. associate-/r/ 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in x around 0 91.5%

      \[\leadsto \frac{x}{\color{blue}{-0.5}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 96.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.65 \cdot 10^{-126} \lor \neg \left(x \leq 1.4 \cdot 10^{-236}\right):\\ \;\;\;\;\frac{x}{x - y} \cdot \left(y \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{-0.5}\\ \end{array} \]

Alternative 3: 74.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{-23}:\\ \;\;\;\;\frac{x}{-0.5}\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-15} \lor \neg \left(y \leq 3 \cdot 10^{+18}\right) \land y \leq 3.2 \cdot 10^{+50}:\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{-0.5}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.35e-23)
   (/ x -0.5)
   (if (or (<= y 4e-15) (and (not (<= y 3e+18)) (<= y 3.2e+50)))
     (* y 2.0)
     (/ x -0.5))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.35e-23) {
		tmp = x / -0.5;
	} else if ((y <= 4e-15) || (!(y <= 3e+18) && (y <= 3.2e+50))) {
		tmp = y * 2.0;
	} else {
		tmp = x / -0.5;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-1.35d-23)) then
        tmp = x / (-0.5d0)
    else if ((y <= 4d-15) .or. (.not. (y <= 3d+18)) .and. (y <= 3.2d+50)) then
        tmp = y * 2.0d0
    else
        tmp = x / (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -1.35e-23) {
		tmp = x / -0.5;
	} else if ((y <= 4e-15) || (!(y <= 3e+18) && (y <= 3.2e+50))) {
		tmp = y * 2.0;
	} else {
		tmp = x / -0.5;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.35e-23:
		tmp = x / -0.5
	elif (y <= 4e-15) or (not (y <= 3e+18) and (y <= 3.2e+50)):
		tmp = y * 2.0
	else:
		tmp = x / -0.5
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.35e-23)
		tmp = Float64(x / -0.5);
	elseif ((y <= 4e-15) || (!(y <= 3e+18) && (y <= 3.2e+50)))
		tmp = Float64(y * 2.0);
	else
		tmp = Float64(x / -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1.35e-23)
		tmp = x / -0.5;
	elseif ((y <= 4e-15) || (~((y <= 3e+18)) && (y <= 3.2e+50)))
		tmp = y * 2.0;
	else
		tmp = x / -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.35e-23], N[(x / -0.5), $MachinePrecision], If[Or[LessEqual[y, 4e-15], And[N[Not[LessEqual[y, 3e+18]], $MachinePrecision], LessEqual[y, 3.2e+50]]], N[(y * 2.0), $MachinePrecision], N[(x / -0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.34999999999999992e-23 or 4.0000000000000003e-15 < y < 3e18 or 3.19999999999999983e50 < y

   1. Initial program 77.0%

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

      1. associate-*l/ 79.5%

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

      2. associate-/l* 80.3%

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

      3. associate-/r/ 99.2%

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

   3. Simplified 99.2%

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

   4. Taylor expanded in x around 0 79.1%

      \[\leadsto \frac{x}{\color{blue}{-0.5}} \]

   if -1.34999999999999992e-23 < y < 4.0000000000000003e-15 or 3e18 < y < 3.19999999999999983e50

   1. Initial program 76.1%

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

      1. *-commutative 76.1%

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

      2. associate-/l* 100.0%

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

      3. associate-/r* 100.0%

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

      4. associate-/r/ 100.0%

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

      5. div-sub 100.0%

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

      6. *-inverses 100.0%

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

   3. Simplified 100.0%

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

   4. Taylor expanded in y around 0 81.4%

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

4. Final simplification 80.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{-23}:\\ \;\;\;\;\frac{x}{-0.5}\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-15} \lor \neg \left(y \leq 3 \cdot 10^{+18}\right) \land y \leq 3.2 \cdot 10^{+50}:\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{-0.5}\\ \end{array} \]

Alternative 4: 51.3% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ y \cdot 2 \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 76.5%

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

   1. *-commutative 76.5%

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

   2. associate-/l* 89.3%

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

   3. associate-/r* 89.7%

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

   4. associate-/r/ 89.7%

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

   5. div-sub 89.7%

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

   6. *-inverses 89.7%

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

3. Simplified 89.7%

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

4. Taylor expanded in y around 0 51.5%

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

5. Final simplification 51.5%

   \[\leadsto y \cdot 2 \]

Developer target: 99.5% accurate, 0.7× 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 2023271 
(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)))