Linear.Projection:perspective from linear-1.19.1.3, B

Percentage Accurate: 77.1% → 99.8%

Time: 3.7s

Alternatives: 4

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: 77.1% 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: 99.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1 \cdot 10^{+29} \lor \neg \left(y \leq 5 \cdot 10^{-38}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x \cdot 2}{x - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -1e+29) (not (<= y 5e-38)))
   (* (/ x (+ (/ x y) -1.0)) 2.0)
   (* y (/ (* x 2.0) (- x y)))))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -1e+29) || !(y <= 5e-38)) {
		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 <= (-1d+29)) .or. (.not. (y <= 5d-38))) 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 <= -1e+29) || !(y <= 5e-38)) {
		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 <= -1e+29) or not (y <= 5e-38):
		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 <= -1e+29) || !(y <= 5e-38))
		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 <= -1e+29) || ~((y <= 5e-38)))
		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, -1e+29], N[Not[LessEqual[y, 5e-38]], $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 < -9.99999999999999914e28 or 5.00000000000000033e-38 < y

   1. Initial program 79.7%

      \[\frac{\left(x \cdot 2\right) \cdot y}{x - y} \]
   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 -9.99999999999999914e28 < y < 5.00000000000000033e-38

   1. Initial program 77.6%

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

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1 \cdot 10^{+29} \lor \neg \left(y \leq 5 \cdot 10^{-38}\right):\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x \cdot 2}{x - y}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 73.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.25 \cdot 10^{+124} \lor \neg \left(y \leq -4.6 \cdot 10^{+111} \lor \neg \left(y \leq -2.2 \cdot 10^{+17}\right) \land \left(y \leq 1.45 \cdot 10^{+43} \lor \neg \left(y \leq 2.1 \cdot 10^{+70}\right) \land y \leq 5.6 \cdot 10^{+135}\right)\right):\\ \;\;\;\;x \cdot -2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -1.25e+124)
         (not
          (or (<= y -4.6e+111)
              (and (not (<= y -2.2e+17))
                   (or (<= y 1.45e+43)
                       (and (not (<= y 2.1e+70)) (<= y 5.6e+135)))))))
   (* x -2.0)
   (* y 2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -1.25e+124) || !((y <= -4.6e+111) || (!(y <= -2.2e+17) && ((y <= 1.45e+43) || (!(y <= 2.1e+70) && (y <= 5.6e+135)))))) {
		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 <= (-1.25d+124)) .or. (.not. (y <= (-4.6d+111)) .or. (.not. (y <= (-2.2d+17))) .and. (y <= 1.45d+43) .or. (.not. (y <= 2.1d+70)) .and. (y <= 5.6d+135))) 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 <= -1.25e+124) || !((y <= -4.6e+111) || (!(y <= -2.2e+17) && ((y <= 1.45e+43) || (!(y <= 2.1e+70) && (y <= 5.6e+135)))))) {
		tmp = x * -2.0;
	} else {
		tmp = y * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -1.25e+124) or not ((y <= -4.6e+111) or (not (y <= -2.2e+17) and ((y <= 1.45e+43) or (not (y <= 2.1e+70) and (y <= 5.6e+135))))):
		tmp = x * -2.0
	else:
		tmp = y * 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -1.25e+124) || !((y <= -4.6e+111) || (!(y <= -2.2e+17) && ((y <= 1.45e+43) || (!(y <= 2.1e+70) && (y <= 5.6e+135))))))
		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 <= -1.25e+124) || ~(((y <= -4.6e+111) || (~((y <= -2.2e+17)) && ((y <= 1.45e+43) || (~((y <= 2.1e+70)) && (y <= 5.6e+135)))))))
		tmp = x * -2.0;
	else
		tmp = y * 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -1.25e+124], N[Not[Or[LessEqual[y, -4.6e+111], And[N[Not[LessEqual[y, -2.2e+17]], $MachinePrecision], Or[LessEqual[y, 1.45e+43], And[N[Not[LessEqual[y, 2.1e+70]], $MachinePrecision], LessEqual[y, 5.6e+135]]]]]], $MachinePrecision]], N[(x * -2.0), $MachinePrecision], N[(y * 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.2499999999999999e124 or -4.60000000000000004e111 < y < -2.2e17 or 1.4500000000000001e43 < y < 2.10000000000000008e70 or 5.60000000000000004e135 < y

   1. Initial program 80.3%

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

      1. associate-*l/ 70.1%

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

   3. Simplified 70.1%

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

   5. Taylor expanded in x around 0 90.2%

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

      1. *-commutative 90.2%

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

   7. Simplified 90.2%

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

   if -1.2499999999999999e124 < y < -4.60000000000000004e111 or -2.2e17 < y < 1.4500000000000001e43 or 2.10000000000000008e70 < y < 5.60000000000000004e135

   1. Initial program 77.6%

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

      1. associate-/l* 77.9%

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

      2. associate-*l/ 77.9%

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

      3. div-sub 77.9%

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

      4. *-inverses 77.9%

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

      5. metadata-eval 77.9%

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

      6. sub-neg 77.9%

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

      7. metadata-eval 77.9%

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

      8. metadata-eval 77.9%

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

   3. Simplified 77.9%

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

   5. Taylor expanded in x around inf 81.0%

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

4. Final simplification 84.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.25 \cdot 10^{+124} \lor \neg \left(y \leq -4.6 \cdot 10^{+111} \lor \neg \left(y \leq -2.2 \cdot 10^{+17}\right) \land \left(y \leq 1.45 \cdot 10^{+43} \lor \neg \left(y \leq 2.1 \cdot 10^{+70}\right) \land y \leq 5.6 \cdot 10^{+135}\right)\right):\\ \;\;\;\;x \cdot -2\\ \mathbf{else}:\\ \;\;\;\;y \cdot 2\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 93.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{+165} \lor \neg \left(x \leq 1.9 \cdot 10^{+215}\right):\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= x -3.8e+165) (not (<= x 1.9e+215)))
   (* y 2.0)
   (* (/ x (+ (/ x y) -1.0)) 2.0)))

C

double code(double x, double y) {
	double tmp;
	if ((x <= -3.8e+165) || !(x <= 1.9e+215)) {
		tmp = y * 2.0;
	} else {
		tmp = (x / ((x / y) + -1.0)) * 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 <= (-3.8d+165)) .or. (.not. (x <= 1.9d+215))) then
        tmp = y * 2.0d0
    else
        tmp = (x / ((x / y) + (-1.0d0))) * 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x <= -3.8e+165) || !(x <= 1.9e+215)) {
		tmp = y * 2.0;
	} else {
		tmp = (x / ((x / y) + -1.0)) * 2.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x <= -3.8e+165) or not (x <= 1.9e+215):
		tmp = y * 2.0
	else:
		tmp = (x / ((x / y) + -1.0)) * 2.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((x <= -3.8e+165) || !(x <= 1.9e+215))
		tmp = Float64(y * 2.0);
	else
		tmp = Float64(Float64(x / Float64(Float64(x / y) + -1.0)) * 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x <= -3.8e+165) || ~((x <= 1.9e+215)))
		tmp = y * 2.0;
	else
		tmp = (x / ((x / y) + -1.0)) * 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[x, -3.8e+165], N[Not[LessEqual[x, 1.9e+215]], $MachinePrecision]], N[(y * 2.0), $MachinePrecision], N[(N[(x / N[(N[(x / y), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -3.7999999999999999e165 or 1.89999999999999984e215 < x

   1. Initial program 72.8%

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

      1. associate-/l* 46.2%

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

      2. associate-*l/ 46.2%

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

      3. div-sub 46.2%

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

      4. *-inverses 46.2%

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

      5. metadata-eval 46.2%

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

      6. sub-neg 46.2%

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

      7. metadata-eval 46.2%

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

      8. metadata-eval 46.2%

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

   3. Simplified 46.2%

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

   5. Taylor expanded in x around inf 94.7%

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

   if -3.7999999999999999e165 < x < 1.89999999999999984e215

   1. Initial program 80.1%

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

      1. associate-/l* 96.8%

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

      2. associate-*l/ 96.8%

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

      3. div-sub 96.8%

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

      4. *-inverses 96.8%

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

      5. metadata-eval 96.8%

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

      6. sub-neg 96.8%

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

      7. metadata-eval 96.8%

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

      8. metadata-eval 96.8%

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

   3. Simplified 96.8%

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

4. Final simplification 96.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{+165} \lor \neg \left(x \leq 1.9 \cdot 10^{+215}\right):\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{x}{y} + -1} \cdot 2\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 50.5% 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 78.7%

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

   1. associate-*l/ 87.5%

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

3. Simplified 87.5%

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

5. Taylor expanded in x around 0 49.7%

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

   1. *-commutative 49.7%

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

7. Simplified 49.7%

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

8. Final simplification 49.7%

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

Developer target: 99.4% 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 2024027 
(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)))