Linear.Projection:perspective from linear-1.19.1.3, B

Percentage Accurate: 75.8% → 99.6%

Time: 3.0s

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: 75.8% 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: 99.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.45 \cdot 10^{+78} \lor \neg \left(y \leq 50000000000\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x \cdot 2}{x - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -1.45e+78) (not (<= y 50000000000.0)))
   (* (/ x (+ (/ x y) -1.0)) 2.0)
   (* y (/ (* x 2.0) (- x y)))))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -1.45e+78) || !(y <= 50000000000.0)) {
		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 <= (-1.45d+78)) .or. (.not. (y <= 50000000000.0d0))) 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 <= -1.45e+78) || !(y <= 50000000000.0)) {
		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 <= -1.45e+78) or not (y <= 50000000000.0):
		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 <= -1.45e+78) || !(y <= 50000000000.0))
		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 <= -1.45e+78) || ~((y <= 50000000000.0)))
		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, -1.45e+78], N[Not[LessEqual[y, 50000000000.0]], $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 < -1.45000000000000008e78 or 5e10 < y

   1. Initial program 70.5%

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

      1. associate-/l* 99.9%

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

      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 -1.45000000000000008e78 < y < 5e10

   1. Initial program 79.4%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   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%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.45 \cdot 10^{+78} \lor \neg \left(y \leq 50000000000\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x \cdot 2}{x - y}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 93.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.08 \cdot 10^{-182} \lor \neg \left(y \leq 5.2 \cdot 10^{-211}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -1.08e-182) (not (<= y 5.2e-211)))
   (* (/ x (+ (/ x y) -1.0)) 2.0)
   (* y 2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -1.08e-182) || !(y <= 5.2e-211)) {
		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 <= (-1.08d-182)) .or. (.not. (y <= 5.2d-211))) 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 <= -1.08e-182) || !(y <= 5.2e-211)) {
		tmp = (x / ((x / y) + -1.0)) * 2.0;
	} else {
		tmp = y * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -1.08e-182) or not (y <= 5.2e-211):
		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 <= -1.08e-182) || !(y <= 5.2e-211))
		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 <= -1.08e-182) || ~((y <= 5.2e-211)))
		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, -1.08e-182], N[Not[LessEqual[y, 5.2e-211]], $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 < -1.08000000000000003e-182 or 5.2e-211 < y

   1. Initial program 77.5%

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

      1. associate-/l* 96.7%

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

      2. associate-*l/ 96.7%

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

      3. div-sub 96.7%

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

      4. *-inverses 96.7%

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

      5. metadata-eval 96.7%

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

      6. sub-neg 96.7%

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

      7. metadata-eval 96.7%

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

      8. metadata-eval 96.7%

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

   3. Simplified 96.7%

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

   if -1.08000000000000003e-182 < y < 5.2e-211

   1. Initial program 68.0%

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

      1. associate-/l* 60.1%

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

      2. associate-*l/ 60.1%

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

      3. div-sub 60.1%

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

      4. *-inverses 60.1%

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

      5. metadata-eval 60.1%

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

      6. sub-neg 60.1%

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

      7. metadata-eval 60.1%

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

      8. metadata-eval 60.1%

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

   3. Simplified 60.1%

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

   5. Taylor expanded in x around inf 92.5%

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

4. Final simplification 95.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.08 \cdot 10^{-182} \lor \neg \left(y \leq 5.2 \cdot 10^{-211}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 75.0% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6 \cdot 10^{+14} \lor \neg \left(x \leq 1.18 \cdot 10^{-36}\right):\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;x \cdot -2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -6e+14) (not (<= x 1.18e-36))) (* y 2.0) (* x -2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -6e+14) || !(x <= 1.18e-36)) {
		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 <= (-6d+14)) .or. (.not. (x <= 1.18d-36))) 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 <= -6e+14) || !(x <= 1.18e-36)) {
		tmp = y * 2.0;
	} else {
		tmp = x * -2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -6e+14) or not (x <= 1.18e-36):
		tmp = y * 2.0
	else:
		tmp = x * -2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -6e+14) || !(x <= 1.18e-36))
		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 <= -6e+14) || ~((x <= 1.18e-36)))
		tmp = y * 2.0;
	else
		tmp = x * -2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -6e+14], N[Not[LessEqual[x, 1.18e-36]], $MachinePrecision]], N[(y * 2.0), $MachinePrecision], N[(x * -2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -6e14 or 1.1799999999999999e-36 < x

   1. Initial program 73.5%

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

      1. associate-/l* 79.2%

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

      2. associate-*l/ 79.2%

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

      3. div-sub 79.2%

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

      4. *-inverses 79.2%

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

      5. metadata-eval 79.2%

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

      6. sub-neg 79.2%

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

      7. metadata-eval 79.2%

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

      8. metadata-eval 79.2%

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

   3. Simplified 79.2%

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

   5. Taylor expanded in x around inf 74.8%

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

   if -6e14 < x < 1.1799999999999999e-36

   1. Initial program 77.7%

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

      1. associate-*l/ 79.0%

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

   3. Simplified 79.0%

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

   5. Taylor expanded in x around 0 78.6%

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

      1. *-commutative 78.6%

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

   7. Simplified 78.6%

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

4. Final simplification 76.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -6 \cdot 10^{+14} \lor \neg \left(x \leq 1.18 \cdot 10^{-36}\right):\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;x \cdot -2\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 50.3% 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 75.6%

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

   1. associate-*l/ 89.6%

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

3. Simplified 89.6%

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

5. Taylor expanded in x around 0 52.3%

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

   1. *-commutative 52.3%

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

7. Simplified 52.3%

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

8. Final simplification 52.3%

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

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 2024010 
(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)))