Linear.Projection:perspective from linear-1.19.1.3, B

Percentage Accurate: 75.9% → 98.7%

Time: 3.5s

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: 75.9% 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: 98.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.25 \cdot 10^{-72} \lor \neg \left(y \leq 4.5 \cdot 10^{+135}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x \cdot 2}{x - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -3.25e-72) (not (<= y 4.5e+135)))
   (* (/ x (+ (/ x y) -1.0)) 2.0)
   (* y (/ (* x 2.0) (- x y)))))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -3.25e-72) || !(y <= 4.5e+135)) {
		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 <= (-3.25d-72)) .or. (.not. (y <= 4.5d+135))) 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 <= -3.25e-72) || !(y <= 4.5e+135)) {
		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 <= -3.25e-72) or not (y <= 4.5e+135):
		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 <= -3.25e-72) || !(y <= 4.5e+135))
		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 <= -3.25e-72) || ~((y <= 4.5e+135)))
		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, -3.25e-72], N[Not[LessEqual[y, 4.5e+135]], $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 < -3.2499999999999998e-72 or 4.50000000000000007e135 < y

   1. Initial program 75.5%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   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 99.9%

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

      4. *-inverses 99.9%

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

      5. metadata-eval 99.9%

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

      6. sub-neg 99.9%

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

      7. metadata-eval 99.9%

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

      8. metadata-eval 99.9%

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

   3. Simplified 99.9%

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

   if -3.2499999999999998e-72 < y < 4.50000000000000007e135

   1. Initial program 72.8%

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

      1. associate-*l/ 99.9%

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

   3. Simplified 99.9%

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

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.25 \cdot 10^{-72} \lor \neg \left(y \leq 4.5 \cdot 10^{+135}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x \cdot 2}{x - y}\\ \end{array} \]

Alternative 2: 93.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{-225} \lor \neg \left(y \leq 1.75 \cdot 10^{-164}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -1.55e-225) (not (<= y 1.75e-164)))
   (* (/ x (+ (/ x y) -1.0)) 2.0)
   (* y 2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -1.55e-225) || !(y <= 1.75e-164)) {
		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 <= (-1.55d-225)) .or. (.not. (y <= 1.75d-164))) 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 <= -1.55e-225) || !(y <= 1.75e-164)) {
		tmp = (x / ((x / y) + -1.0)) * 2.0;
	} else {
		tmp = y * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -1.55e-225) or not (y <= 1.75e-164):
		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 <= -1.55e-225) || !(y <= 1.75e-164))
		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 <= -1.55e-225) || ~((y <= 1.75e-164)))
		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, -1.55e-225], N[Not[LessEqual[y, 1.75e-164]], $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 < -1.54999999999999998e-225 or 1.75e-164 < y

   1. Initial program 76.0%

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

      1. associate-/l* 98.0%

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

      2. associate-*l/ 98.0%

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

      3. div-sub 97.9%

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

      4. *-inverses 97.9%

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

      5. metadata-eval 97.9%

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

      6. sub-neg 97.9%

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

      7. metadata-eval 97.9%

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

      8. metadata-eval 97.9%

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

   3. Simplified 97.9%

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

   if -1.54999999999999998e-225 < y < 1.75e-164

   1. Initial program 67.2%

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

      1. associate-/l* 59.5%

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

      2. associate-*l/ 59.5%

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

      3. div-sub 59.5%

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

      4. *-inverses 59.5%

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

      5. metadata-eval 59.5%

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

      6. sub-neg 59.5%

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

      7. metadata-eval 59.5%

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

      8. metadata-eval 59.5%

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

   3. Simplified 59.5%

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

   4. Taylor expanded in x around inf 91.3%

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

4. Final simplification 96.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{-225} \lor \neg \left(y \leq 1.75 \cdot 10^{-164}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \]

Alternative 3: 74.0% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.8 \cdot 10^{+15} \lor \neg \left(y \leq 1.22 \cdot 10^{+64}\right):\\ \;\;\;\;x \cdot -2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -4.8e+15) (not (<= y 1.22e+64))) (* x -2.0) (* y 2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -4.8e+15) || !(y <= 1.22e+64)) {
		tmp = x * -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 <= (-4.8d+15)) .or. (.not. (y <= 1.22d+64))) then
        tmp = x * (-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 <= -4.8e+15) || !(y <= 1.22e+64)) {
		tmp = x * -2.0;
	} else {
		tmp = y * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -4.8e+15) or not (y <= 1.22e+64):
		tmp = x * -2.0
	else:
		tmp = y * 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -4.8e+15) || !(y <= 1.22e+64))
		tmp = Float64(x * -2.0);
	else
		tmp = Float64(y * 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -4.8e+15) || ~((y <= 1.22e+64)))
		tmp = x * -2.0;
	else
		tmp = y * 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -4.8e+15], N[Not[LessEqual[y, 1.22e+64]], $MachinePrecision]], N[(x * -2.0), $MachinePrecision], N[(y * 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -4.8e15 or 1.21999999999999994e64 < y

   1. Initial program 72.3%

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

      1. associate-*l/ 77.9%

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

   3. Simplified 77.9%

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

   4. Taylor expanded in x around 0 76.4%

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

      1. *-commutative 76.4%

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

   6. Simplified 76.4%

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

   if -4.8e15 < y < 1.21999999999999994e64

   1. Initial program 75.2%

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

      1. associate-/l* 82.3%

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

      2. associate-*l/ 82.3%

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

      3. div-sub 82.3%

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

      4. *-inverses 82.3%

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

      5. metadata-eval 82.3%

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

      6. sub-neg 82.3%

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

      7. metadata-eval 82.3%

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

      8. metadata-eval 82.3%

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

   3. Simplified 82.3%

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

   4. Taylor expanded in x around inf 76.6%

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

4. Final simplification 76.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.8 \cdot 10^{+15} \lor \neg \left(y \leq 1.22 \cdot 10^{+64}\right):\\ \;\;\;\;x \cdot -2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \]

Alternative 4: 50.2% 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 74.0%

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

   1. associate-*l/ 91.1%

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

3. Simplified 91.1%

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

4. Taylor expanded in x around 0 44.9%

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

   1. *-commutative 44.9%

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

6. Simplified 44.9%

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

7. Final simplification 44.9%

   \[\leadsto x \cdot -2 \]

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 2023322 
(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)))