Linear.Projection:perspective from linear-1.19.1.3, B

Percentage Accurate: 77.2% → 99.9%

Time: 9.8s

Alternatives: 6

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: 77.2% 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: 99.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x \cdot 2}{\frac{x - y}{y}}\\ \mathbf{if}\;y \leq -40000000000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 0.001:\\ \;\;\;\;\frac{x \cdot 2}{x - y} \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (* x 2.0) (/ (- x y) y))))
   (if (<= y -40000000000000.0)
     t_0
     (if (<= y 0.001) (* (/ (* x 2.0) (- x y)) y) t_0))))

C

double code(double x, double y) {
	double t_0 = (x * 2.0) / ((x - y) / y);
	double tmp;
	if (y <= -40000000000000.0) {
		tmp = t_0;
	} else if (y <= 0.001) {
		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 = (x * 2.0d0) / ((x - y) / y)
    if (y <= (-40000000000000.0d0)) then
        tmp = t_0
    else if (y <= 0.001d0) 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 = (x * 2.0) / ((x - y) / y);
	double tmp;
	if (y <= -40000000000000.0) {
		tmp = t_0;
	} else if (y <= 0.001) {
		tmp = ((x * 2.0) / (x - y)) * y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (x * 2.0) / ((x - y) / y)
	tmp = 0
	if y <= -40000000000000.0:
		tmp = t_0
	elif y <= 0.001:
		tmp = ((x * 2.0) / (x - y)) * y
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(x * 2.0) / Float64(Float64(x - y) / y))
	tmp = 0.0
	if (y <= -40000000000000.0)
		tmp = t_0;
	elseif (y <= 0.001)
		tmp = Float64(Float64(Float64(x * 2.0) / Float64(x - y)) * y);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (x * 2.0) / ((x - y) / y);
	tmp = 0.0;
	if (y <= -40000000000000.0)
		tmp = t_0;
	elseif (y <= 0.001)
		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[(x * 2.0), $MachinePrecision] / N[(N[(x - y), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -40000000000000.0], t$95$0, If[LessEqual[y, 0.001], N[(N[(N[(x * 2.0), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -4e13 or 1e-3 < y

   1. Initial program 77.5%

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

   4. Applied egg-rr 0

      \[\leadsto expr\]

   if -4e13 < y < 1e-3

   1. Initial program 75.0%

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

   4. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 99.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(y \cdot \frac{2}{x - y}\right) \cdot x\\ \mathbf{if}\;y \leq -3.6 \cdot 10^{+83}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 3.3 \cdot 10^{+30}:\\ \;\;\;\;\frac{x \cdot 2}{x - y} \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (* y (/ 2.0 (- x y))) x)))
   (if (<= y -3.6e+83)
     t_0
     (if (<= y 3.3e+30) (* (/ (* x 2.0) (- x y)) y) t_0))))

C

double code(double x, double y) {
	double t_0 = (y * (2.0 / (x - y))) * x;
	double tmp;
	if (y <= -3.6e+83) {
		tmp = t_0;
	} else if (y <= 3.3e+30) {
		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 = (y * (2.0d0 / (x - y))) * x
    if (y <= (-3.6d+83)) then
        tmp = t_0
    else if (y <= 3.3d+30) 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 = (y * (2.0 / (x - y))) * x;
	double tmp;
	if (y <= -3.6e+83) {
		tmp = t_0;
	} else if (y <= 3.3e+30) {
		tmp = ((x * 2.0) / (x - y)) * y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (y * (2.0 / (x - y))) * x
	tmp = 0
	if y <= -3.6e+83:
		tmp = t_0
	elif y <= 3.3e+30:
		tmp = ((x * 2.0) / (x - y)) * y
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(y * Float64(2.0 / Float64(x - y))) * x)
	tmp = 0.0
	if (y <= -3.6e+83)
		tmp = t_0;
	elseif (y <= 3.3e+30)
		tmp = Float64(Float64(Float64(x * 2.0) / Float64(x - y)) * y);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (y * (2.0 / (x - y))) * x;
	tmp = 0.0;
	if (y <= -3.6e+83)
		tmp = t_0;
	elseif (y <= 3.3e+30)
		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[(y * N[(2.0 / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[y, -3.6e+83], t$95$0, If[LessEqual[y, 3.3e+30], N[(N[(N[(x * 2.0), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -3.5999999999999997e83 or 3.30000000000000026e30 < y

   1. Initial program 74.4%

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

   4. Applied egg-rr 0

      \[\leadsto expr\]

   6. Applied egg-rr 0

      \[\leadsto expr\]

   if -3.5999999999999997e83 < y < 3.30000000000000026e30

   1. Initial program 77.2%

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

   4. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 93.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(y \cdot \frac{2}{x - y}\right) \cdot x\\ \mathbf{if}\;y \leq -5 \cdot 10^{-203}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{-206}:\\ \;\;\;\;y \cdot \left(2 + \frac{y \cdot 2}{x}\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (* y (/ 2.0 (- x y))) x)))
   (if (<= y -5e-203)
     t_0
     (if (<= y 4.4e-206) (* y (+ 2.0 (/ (* y 2.0) x))) t_0))))

C

double code(double x, double y) {
	double t_0 = (y * (2.0 / (x - y))) * x;
	double tmp;
	if (y <= -5e-203) {
		tmp = t_0;
	} else if (y <= 4.4e-206) {
		tmp = y * (2.0 + ((y * 2.0) / x));
	} 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 = (y * (2.0d0 / (x - y))) * x
    if (y <= (-5d-203)) then
        tmp = t_0
    else if (y <= 4.4d-206) then
        tmp = y * (2.0d0 + ((y * 2.0d0) / x))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (y * (2.0 / (x - y))) * x;
	double tmp;
	if (y <= -5e-203) {
		tmp = t_0;
	} else if (y <= 4.4e-206) {
		tmp = y * (2.0 + ((y * 2.0) / x));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (y * (2.0 / (x - y))) * x
	tmp = 0
	if y <= -5e-203:
		tmp = t_0
	elif y <= 4.4e-206:
		tmp = y * (2.0 + ((y * 2.0) / x))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(y * Float64(2.0 / Float64(x - y))) * x)
	tmp = 0.0
	if (y <= -5e-203)
		tmp = t_0;
	elseif (y <= 4.4e-206)
		tmp = Float64(y * Float64(2.0 + Float64(Float64(y * 2.0) / x)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (y * (2.0 / (x - y))) * x;
	tmp = 0.0;
	if (y <= -5e-203)
		tmp = t_0;
	elseif (y <= 4.4e-206)
		tmp = y * (2.0 + ((y * 2.0) / x));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(y * N[(2.0 / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[y, -5e-203], t$95$0, If[LessEqual[y, 4.4e-206], N[(y * N[(2.0 + N[(N[(y * 2.0), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -5.0000000000000002e-203 or 4.3999999999999997e-206 < y

   1. Initial program 79.0%

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

   4. Applied egg-rr 0

      \[\leadsto expr\]

   6. Applied egg-rr 0

      \[\leadsto expr\]

   if -5.0000000000000002e-203 < y < 4.3999999999999997e-206

   1. Initial program 63.9%

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

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 75.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.05 \cdot 10^{-37}:\\ \;\;\;\;-2 \cdot x\\ \mathbf{elif}\;y \leq 6.8 \cdot 10^{+28}:\\ \;\;\;\;y \cdot \left(2 + \frac{y \cdot 2}{x}\right)\\ \mathbf{else}:\\ \;\;\;\;-2 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1.05e-37)
   (* -2.0 x)
   (if (<= y 6.8e+28) (* y (+ 2.0 (/ (* y 2.0) x))) (* -2.0 x))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1.05e-37) {
		tmp = -2.0 * x;
	} else if (y <= 6.8e+28) {
		tmp = y * (2.0 + ((y * 2.0) / x));
	} else {
		tmp = -2.0 * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-1.05d-37)) then
        tmp = (-2.0d0) * x
    else if (y <= 6.8d+28) then
        tmp = y * (2.0d0 + ((y * 2.0d0) / x))
    else
        tmp = (-2.0d0) * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -1.05e-37) {
		tmp = -2.0 * x;
	} else if (y <= 6.8e+28) {
		tmp = y * (2.0 + ((y * 2.0) / x));
	} else {
		tmp = -2.0 * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1.05e-37:
		tmp = -2.0 * x
	elif y <= 6.8e+28:
		tmp = y * (2.0 + ((y * 2.0) / x))
	else:
		tmp = -2.0 * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1.05e-37)
		tmp = Float64(-2.0 * x);
	elseif (y <= 6.8e+28)
		tmp = Float64(y * Float64(2.0 + Float64(Float64(y * 2.0) / x)));
	else
		tmp = Float64(-2.0 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1.05e-37)
		tmp = -2.0 * x;
	elseif (y <= 6.8e+28)
		tmp = y * (2.0 + ((y * 2.0) / x));
	else
		tmp = -2.0 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1.05e-37], N[(-2.0 * x), $MachinePrecision], If[LessEqual[y, 6.8e+28], N[(y * N[(2.0 + N[(N[(y * 2.0), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(-2.0 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.05e-37 or 6.8e28 < y

   1. Initial program 77.9%

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

   3. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -1.05e-37 < y < 6.8e28

   1. Initial program 74.5%

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

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 75.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.2 \cdot 10^{-36}:\\ \;\;\;\;-2 \cdot x\\ \mathbf{elif}\;y \leq 2.9 \cdot 10^{+29}:\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;-2 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -4.2e-36) (* -2.0 x) (if (<= y 2.9e+29) (* y 2.0) (* -2.0 x))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -4.2e-36) {
		tmp = -2.0 * x;
	} else if (y <= 2.9e+29) {
		tmp = y * 2.0;
	} else {
		tmp = -2.0 * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-4.2d-36)) then
        tmp = (-2.0d0) * x
    else if (y <= 2.9d+29) then
        tmp = y * 2.0d0
    else
        tmp = (-2.0d0) * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -4.2e-36) {
		tmp = -2.0 * x;
	} else if (y <= 2.9e+29) {
		tmp = y * 2.0;
	} else {
		tmp = -2.0 * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -4.2e-36:
		tmp = -2.0 * x
	elif y <= 2.9e+29:
		tmp = y * 2.0
	else:
		tmp = -2.0 * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -4.2e-36)
		tmp = Float64(-2.0 * x);
	elseif (y <= 2.9e+29)
		tmp = Float64(y * 2.0);
	else
		tmp = Float64(-2.0 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -4.2e-36)
		tmp = -2.0 * x;
	elseif (y <= 2.9e+29)
		tmp = y * 2.0;
	else
		tmp = -2.0 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -4.2e-36], N[(-2.0 * x), $MachinePrecision], If[LessEqual[y, 2.9e+29], N[(y * 2.0), $MachinePrecision], N[(-2.0 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -4.19999999999999982e-36 or 2.8999999999999999e29 < y

   1. Initial program 77.9%

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

   3. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -4.19999999999999982e-36 < y < 2.8999999999999999e29

   1. Initial program 74.5%

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

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 50.9% accurate, 3.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 76.1%

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

3. Taylor expanded in x around 0 0

   \[\leadsto expr\]

5. Simplified 0

   \[\leadsto expr\]
6. Add Preprocessing

Developer target: 99.5% 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 2024110 
(FPCore (x y)
  :name "Linear.Projection:perspective from linear-1.19.1.3, B"
  :precision binary64

  :alt
  (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)))