Linear.Projection:perspective from linear-1.19.1.3, B

Percentage Accurate: 76.9% → 93.4%

Time: 7.8s

Alternatives: 5

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: 76.9% 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: 93.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.6 \cdot 10^{+249} \lor \neg \left(x \leq 1.35 \cdot 10^{+166}\right):\\ \;\;\;\;y + y\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{y}{x - y} \cdot x\right) \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -3.6e+249) (not (<= x 1.35e+166)))
   (+ y y)
   (* (* (/ y (- x y)) x) 2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -3.6e+249) || !(x <= 1.35e+166)) {
		tmp = y + y;
	} else {
		tmp = ((y / (x - 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 ((x <= (-3.6d+249)) .or. (.not. (x <= 1.35d+166))) then
        tmp = y + y
    else
        tmp = ((y / (x - y)) * x) * 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -3.6e+249) || !(x <= 1.35e+166)) {
		tmp = y + y;
	} else {
		tmp = ((y / (x - y)) * x) * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -3.6e+249) or not (x <= 1.35e+166):
		tmp = y + y
	else:
		tmp = ((y / (x - y)) * x) * 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -3.6e+249) || !(x <= 1.35e+166))
		tmp = Float64(y + y);
	else
		tmp = Float64(Float64(Float64(y / Float64(x - y)) * x) * 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -3.6e+249) || ~((x <= 1.35e+166)))
		tmp = y + y;
	else
		tmp = ((y / (x - y)) * x) * 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -3.6e+249], N[Not[LessEqual[x, 1.35e+166]], $MachinePrecision]], N[(y + y), $MachinePrecision], N[(N[(N[(y / N[(x - y), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision] * 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -3.5999999999999997e249 or 1.35000000000000006e166 < x

   1. Initial program 79.8%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. lower-*.f64 89.7

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

   5. Applied rewrites 89.7%

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

   7. Applied rewrites 89.7%

      \[\leadsto y + \color{blue}{y} \]

   if -3.5999999999999997e249 < x < 1.35000000000000006e166

   1. Initial program 75.8%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. lift-*.f64 N/A

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

      5. *-commutative N/A

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

      6. associate-*l* N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 N/A

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

      9. *-commutative N/A

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

      10. lower-*.f64 N/A

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

      11. lower-/.f64 98.3

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

   4. Applied rewrites 98.3%

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

4. Final simplification 97.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.6 \cdot 10^{+249} \lor \neg \left(x \leq 1.35 \cdot 10^{+166}\right):\\ \;\;\;\;y + y\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{y}{x - y} \cdot x\right) \cdot 2\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 87.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\left(x + x\right) \cdot y}{x - y}\\ t_1 := \frac{\left(x \cdot 2\right) \cdot y}{x - y}\\ \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;-2 \cdot x\\ \mathbf{elif}\;t\_1 \leq -5 \cdot 10^{-308}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq 0:\\ \;\;\;\;\mathsf{fma}\left(x, \frac{x}{y}, x\right) \cdot -2\\ \mathbf{elif}\;t\_1 \leq 4 \cdot 10^{+139}:\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;-2 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (* (+ x x) y) (- x y))) (t_1 (/ (* (* x 2.0) y) (- x y))))
   (if (<= t_1 (- INFINITY))
     (* -2.0 x)
     (if (<= t_1 -5e-308)
       t_0
       (if (<= t_1 0.0)
         (* (fma x (/ x y) x) -2.0)
         (if (<= t_1 4e+139) t_0 (* -2.0 x)))))))

C

double code(double x, double y) {
	double t_0 = ((x + x) * y) / (x - y);
	double t_1 = ((x * 2.0) * y) / (x - y);
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = -2.0 * x;
	} else if (t_1 <= -5e-308) {
		tmp = t_0;
	} else if (t_1 <= 0.0) {
		tmp = fma(x, (x / y), x) * -2.0;
	} else if (t_1 <= 4e+139) {
		tmp = t_0;
	} else {
		tmp = -2.0 * x;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(Float64(Float64(x + x) * y) / Float64(x - y))
	t_1 = Float64(Float64(Float64(x * 2.0) * y) / Float64(x - y))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(-2.0 * x);
	elseif (t_1 <= -5e-308)
		tmp = t_0;
	elseif (t_1 <= 0.0)
		tmp = Float64(fma(x, Float64(x / y), x) * -2.0);
	elseif (t_1 <= 4e+139)
		tmp = t_0;
	else
		tmp = Float64(-2.0 * x);
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[(x + x), $MachinePrecision] * y), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(x * 2.0), $MachinePrecision] * y), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(-2.0 * x), $MachinePrecision], If[LessEqual[t$95$1, -5e-308], t$95$0, If[LessEqual[t$95$1, 0.0], N[(N[(x * N[(x / y), $MachinePrecision] + x), $MachinePrecision] * -2.0), $MachinePrecision], If[LessEqual[t$95$1, 4e+139], t$95$0, N[(-2.0 * x), $MachinePrecision]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (*.f64 (*.f64 x #s(literal 2 binary64)) y) (-.f64 x y)) < -inf.0 or 4.00000000000000013e139 < (/.f64 (*.f64 (*.f64 x #s(literal 2 binary64)) y) (-.f64 x y))

   1. Initial program 4.8%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. lower-*.f64 78.5

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

   5. Applied rewrites 78.5%

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

   if -inf.0 < (/.f64 (*.f64 (*.f64 x #s(literal 2 binary64)) y) (-.f64 x y)) < -4.99999999999999955e-308 or 0.0 < (/.f64 (*.f64 (*.f64 x #s(literal 2 binary64)) y) (-.f64 x y)) < 4.00000000000000013e139

   1. Initial program 99.1%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. count-2-rev N/A

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

      4. lower-+.f64 99.1

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

   4. Applied rewrites 99.1%

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

   if -4.99999999999999955e-308 < (/.f64 (*.f64 (*.f64 x #s(literal 2 binary64)) y) (-.f64 x y)) < 0.0

   1. Initial program 8.1%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

      1. distribute-lft-out N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. +-commutative N/A

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

      5. unpow2 N/A

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

      6. associate-/l* N/A

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

      7. lower-fma.f64 N/A

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

      8. lower-/.f64 64.3

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

   5. Applied rewrites 64.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, \frac{x}{y}, x\right) \cdot -2} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 74.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{-45} \lor \neg \left(y \leq 140000\right):\\ \;\;\;\;-2 \cdot x\\ \mathbf{else}:\\ \;\;\;\;y + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -9e-45) (not (<= y 140000.0))) (* -2.0 x) (+ y y)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -9e-45) || !(y <= 140000.0)) {
		tmp = -2.0 * x;
	} else {
		tmp = y + 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 <= (-9d-45)) .or. (.not. (y <= 140000.0d0))) then
        tmp = (-2.0d0) * x
    else
        tmp = y + y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -9e-45) || !(y <= 140000.0)) {
		tmp = -2.0 * x;
	} else {
		tmp = y + y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -9e-45) or not (y <= 140000.0):
		tmp = -2.0 * x
	else:
		tmp = y + y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -9e-45) || !(y <= 140000.0))
		tmp = Float64(-2.0 * x);
	else
		tmp = Float64(y + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -9e-45) || ~((y <= 140000.0)))
		tmp = -2.0 * x;
	else
		tmp = y + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -9e-45], N[Not[LessEqual[y, 140000.0]], $MachinePrecision]], N[(-2.0 * x), $MachinePrecision], N[(y + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -8.9999999999999997e-45 or 1.4e5 < y

   1. Initial program 80.1%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. lower-*.f64 84.1

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

   5. Applied rewrites 84.1%

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

   if -8.9999999999999997e-45 < y < 1.4e5

   1. Initial program 72.6%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. lower-*.f64 72.3

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

   5. Applied rewrites 72.3%

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

   7. Applied rewrites 72.3%

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

4. Final simplification 78.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{-45} \lor \neg \left(y \leq 140000\right):\\ \;\;\;\;-2 \cdot x\\ \mathbf{else}:\\ \;\;\;\;y + y\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 49.8% accurate, 6.3× speedup

Math

\[\begin{array}{l} \\ y + y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ y y))

C

double code(double x, double y) {
	return y + y;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = y + y
end function

Java

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

Python

def code(x, y):
	return y + y

Julia

function code(x, y)
	return Float64(y + y)
end

MATLAB

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

Wolfram

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

Derivation

1. Initial program 76.4%

   \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

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

   1. lower-*.f64 44.8

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

5. Applied rewrites 44.8%

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

7. Applied rewrites 44.8%

   \[\leadsto y + \color{blue}{y} \]
8. Add Preprocessing

Alternative 5: 3.5% accurate, 6.3× speedup

Math

\[\begin{array}{l} \\ x + x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x x))

C

double code(double x, double y) {
	return x + x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + x
end function

Java

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

Python

def code(x, y):
	return x + x

Julia

function code(x, y)
	return Float64(x + x)
end

MATLAB

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

Wolfram

code[x_, y_] := N[(x + x), $MachinePrecision]

Derivation

1. Initial program 76.4%

   \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

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

   1. lower-*.f64 44.8

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

5. Applied rewrites 44.8%

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

7. Applied rewrites 44.8%

   \[\leadsto y + \color{blue}{y} \]
8. Step-by-step derivation

9. Applied rewrites 3.4%

   \[\leadsto x + \color{blue}{x} \]
10. Add Preprocessing

Developer Target 1: 99.4% accurate, 0.6× 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 2024338 
(FPCore (x y)
  :name "Linear.Projection:perspective from linear-1.19.1.3, B"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< x -1721044263414944700000000000000000000000000000000000000000000000000000000000000000) (* (/ (* 2 x) (- x y)) y) (if (< x 83645045635564430) (/ (* x 2) (/ (- x y) y)) (* (/ (* 2 x) (- x y)) y))))

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