Numeric.SpecFunctions:logGamma from math-functions-0.1.5.2

Percentage Accurate: 56.1% → 84.1%

Time: 28.8s

Alternatives: 17

Speedup: 3.0×

Specification

Math

\[\begin{array}{l} \\ \frac{\left(\left(\left(x \cdot y + z\right) \cdot y + 27464.7644705\right) \cdot y + 230661.510616\right) \cdot y + t}{\left(\left(\left(y + a\right) \cdot y + b\right) \cdot y + c\right) \cdot y + i} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (/
  (+ (* (+ (* (+ (* (+ (* x y) z) y) 27464.7644705) y) 230661.510616) y) t)
  (+ (* (+ (* (+ (* (+ y a) y) b) y) c) y) i)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return ((((((((x * y) + z) * y) + 27464.7644705) * y) + 230661.510616) * y) + t) / (((((((y + a) * y) + b) * y) + c) * y) + i);
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    code = ((((((((x * y) + z) * y) + 27464.7644705d0) * y) + 230661.510616d0) * y) + t) / (((((((y + a) * y) + b) * y) + c) * y) + i)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return ((((((((x * y) + z) * y) + 27464.7644705) * y) + 230661.510616) * y) + t) / (((((((y + a) * y) + b) * y) + c) * y) + i);
}

Python

def code(x, y, z, t, a, b, c, i):
	return ((((((((x * y) + z) * y) + 27464.7644705) * y) + 230661.510616) * y) + t) / (((((((y + a) * y) + b) * y) + c) * y) + i)

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) + z) * y) + 27464.7644705) * y) + 230661.510616) * y) + t) / Float64(Float64(Float64(Float64(Float64(Float64(Float64(y + a) * y) + b) * y) + c) * y) + i))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = ((((((((x * y) + z) * y) + 27464.7644705) * y) + 230661.510616) * y) + t) / (((((((y + a) * y) + b) * y) + c) * y) + i);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(N[(N[(N[(N[(N[(N[(N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision] * y), $MachinePrecision] + 27464.7644705), $MachinePrecision] * y), $MachinePrecision] + 230661.510616), $MachinePrecision] * y), $MachinePrecision] + t), $MachinePrecision] / N[(N[(N[(N[(N[(N[(N[(y + a), $MachinePrecision] * y), $MachinePrecision] + b), $MachinePrecision] * y), $MachinePrecision] + c), $MachinePrecision] * y), $MachinePrecision] + i), $MachinePrecision]), $MachinePrecision]

Initial Program: 56.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left(\left(\left(x \cdot y + z\right) \cdot y + 27464.7644705\right) \cdot y + 230661.510616\right) \cdot y + t}{\left(\left(\left(y + a\right) \cdot y + b\right) \cdot y + c\right) \cdot y + i} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (/
  (+ (* (+ (* (+ (* (+ (* x y) z) y) 27464.7644705) y) 230661.510616) y) t)
  (+ (* (+ (* (+ (* (+ y a) y) b) y) c) y) i)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return ((((((((x * y) + z) * y) + 27464.7644705) * y) + 230661.510616) * y) + t) / (((((((y + a) * y) + b) * y) + c) * y) + i);
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    code = ((((((((x * y) + z) * y) + 27464.7644705d0) * y) + 230661.510616d0) * y) + t) / (((((((y + a) * y) + b) * y) + c) * y) + i)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return ((((((((x * y) + z) * y) + 27464.7644705) * y) + 230661.510616) * y) + t) / (((((((y + a) * y) + b) * y) + c) * y) + i);
}

Python

def code(x, y, z, t, a, b, c, i):
	return ((((((((x * y) + z) * y) + 27464.7644705) * y) + 230661.510616) * y) + t) / (((((((y + a) * y) + b) * y) + c) * y) + i)

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) + z) * y) + 27464.7644705) * y) + 230661.510616) * y) + t) / Float64(Float64(Float64(Float64(Float64(Float64(Float64(y + a) * y) + b) * y) + c) * y) + i))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = ((((((((x * y) + z) * y) + 27464.7644705) * y) + 230661.510616) * y) + t) / (((((((y + a) * y) + b) * y) + c) * y) + i);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(N[(N[(N[(N[(N[(N[(N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision] * y), $MachinePrecision] + 27464.7644705), $MachinePrecision] * y), $MachinePrecision] + 230661.510616), $MachinePrecision] * y), $MachinePrecision] + t), $MachinePrecision] / N[(N[(N[(N[(N[(N[(N[(y + a), $MachinePrecision] * y), $MachinePrecision] + b), $MachinePrecision] * y), $MachinePrecision] + c), $MachinePrecision] * y), $MachinePrecision] + i), $MachinePrecision]), $MachinePrecision]

Alternative 1: 84.1% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.4 \cdot 10^{+53}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 2.3 \cdot 10^{+37}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x, y, z\right), y, 27464.7644705\right), y, 230661.510616\right), y, t\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(y + a, y, b\right), y, c\right), y, i\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\left(1 + \frac{a}{y}\right) + \frac{b}{{y}^{2}}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y -3.4e+53)
   (- (+ x (/ z y)) (/ (* x a) y))
   (if (<= y 2.3e+37)
     (/
      (fma (fma (fma (fma x y z) y 27464.7644705) y 230661.510616) y t)
      (fma (fma (fma (+ y a) y b) y c) y i))
     (/ x (+ (+ 1.0 (/ a y)) (/ b (pow y 2.0)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -3.4e+53) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 2.3e+37) {
		tmp = fma(fma(fma(fma(x, y, z), y, 27464.7644705), y, 230661.510616), y, t) / fma(fma(fma((y + a), y, b), y, c), y, i);
	} else {
		tmp = x / ((1.0 + (a / y)) + (b / pow(y, 2.0)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= -3.4e+53)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 2.3e+37)
		tmp = Float64(fma(fma(fma(fma(x, y, z), y, 27464.7644705), y, 230661.510616), y, t) / fma(fma(fma(Float64(y + a), y, b), y, c), y, i));
	else
		tmp = Float64(x / Float64(Float64(1.0 + Float64(a / y)) + Float64(b / (y ^ 2.0))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, -3.4e+53], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.3e+37], N[(N[(N[(N[(N[(x * y + z), $MachinePrecision] * y + 27464.7644705), $MachinePrecision] * y + 230661.510616), $MachinePrecision] * y + t), $MachinePrecision] / N[(N[(N[(N[(y + a), $MachinePrecision] * y + b), $MachinePrecision] * y + c), $MachinePrecision] * y + i), $MachinePrecision]), $MachinePrecision], N[(x / N[(N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision] + N[(b / N[Power[y, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 84.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(y + a\right)\\ \mathbf{if}\;y \leq -2.1 \cdot 10^{+53}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 5.6 \cdot 10^{+38}:\\ \;\;\;\;\frac{t}{i + y \cdot \left(c + y \cdot \left(b + t_1\right)\right)} + \frac{y \cdot \left(230661.510616 + y \cdot \left(27464.7644705 + y \cdot \left(z + y \cdot x\right)\right)\right)}{i + y \cdot \left(c + \left(y \cdot b + y \cdot t_1\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\left(1 + \frac{a}{y}\right) + \frac{b}{{y}^{2}}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* y (+ y a))))
   (if (<= y -2.1e+53)
     (- (+ x (/ z y)) (/ (* x a) y))
     (if (<= y 5.6e+38)
       (+
        (/ t (+ i (* y (+ c (* y (+ b t_1))))))
        (/
         (* y (+ 230661.510616 (* y (+ 27464.7644705 (* y (+ z (* y x)))))))
         (+ i (* y (+ c (+ (* y b) (* y t_1)))))))
       (/ x (+ (+ 1.0 (/ a y)) (/ b (pow y 2.0))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = y * (y + a);
	double tmp;
	if (y <= -2.1e+53) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 5.6e+38) {
		tmp = (t / (i + (y * (c + (y * (b + t_1)))))) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / (i + (y * (c + ((y * b) + (y * t_1))))));
	} else {
		tmp = x / ((1.0 + (a / y)) + (b / pow(y, 2.0)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (y + a)
    if (y <= (-2.1d+53)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 5.6d+38) then
        tmp = (t / (i + (y * (c + (y * (b + t_1)))))) + ((y * (230661.510616d0 + (y * (27464.7644705d0 + (y * (z + (y * x))))))) / (i + (y * (c + ((y * b) + (y * t_1))))))
    else
        tmp = x / ((1.0d0 + (a / y)) + (b / (y ** 2.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = y * (y + a);
	double tmp;
	if (y <= -2.1e+53) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 5.6e+38) {
		tmp = (t / (i + (y * (c + (y * (b + t_1)))))) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / (i + (y * (c + ((y * b) + (y * t_1))))));
	} else {
		tmp = x / ((1.0 + (a / y)) + (b / Math.pow(y, 2.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = y * (y + a)
	tmp = 0
	if y <= -2.1e+53:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 5.6e+38:
		tmp = (t / (i + (y * (c + (y * (b + t_1)))))) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / (i + (y * (c + ((y * b) + (y * t_1))))))
	else:
		tmp = x / ((1.0 + (a / y)) + (b / math.pow(y, 2.0)))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(y * Float64(y + a))
	tmp = 0.0
	if (y <= -2.1e+53)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 5.6e+38)
		tmp = Float64(Float64(t / Float64(i + Float64(y * Float64(c + Float64(y * Float64(b + t_1)))))) + Float64(Float64(y * Float64(230661.510616 + Float64(y * Float64(27464.7644705 + Float64(y * Float64(z + Float64(y * x))))))) / Float64(i + Float64(y * Float64(c + Float64(Float64(y * b) + Float64(y * t_1)))))));
	else
		tmp = Float64(x / Float64(Float64(1.0 + Float64(a / y)) + Float64(b / (y ^ 2.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = y * (y + a);
	tmp = 0.0;
	if (y <= -2.1e+53)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 5.6e+38)
		tmp = (t / (i + (y * (c + (y * (b + t_1)))))) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / (i + (y * (c + ((y * b) + (y * t_1))))));
	else
		tmp = x / ((1.0 + (a / y)) + (b / (y ^ 2.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(y * N[(y + a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.1e+53], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 5.6e+38], N[(N[(t / N[(i + N[(y * N[(c + N[(y * N[(b + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(y * N[(230661.510616 + N[(y * N[(27464.7644705 + N[(y * N[(z + N[(y * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(i + N[(y * N[(c + N[(N[(y * b), $MachinePrecision] + N[(y * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision] + N[(b / N[Power[y, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 83.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \left(y + a\right)\\ \mathbf{if}\;y \leq -2.35 \cdot 10^{+53}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{+37}:\\ \;\;\;\;\frac{t}{i + y \cdot \left(c + y \cdot \left(b + t_1\right)\right)} + \frac{y \cdot \left(230661.510616 + y \cdot \left(27464.7644705 + y \cdot \left(z + y \cdot x\right)\right)\right)}{i + y \cdot \left(c + \left(y \cdot b + y \cdot t_1\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* y (+ y a))))
   (if (<= y -2.35e+53)
     (- (+ x (/ z y)) (/ (* x a) y))
     (if (<= y 7.2e+37)
       (+
        (/ t (+ i (* y (+ c (* y (+ b t_1))))))
        (/
         (* y (+ 230661.510616 (* y (+ 27464.7644705 (* y (+ z (* y x)))))))
         (+ i (* y (+ c (+ (* y b) (* y t_1)))))))
       (/ x (+ 1.0 (/ a y)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = y * (y + a);
	double tmp;
	if (y <= -2.35e+53) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 7.2e+37) {
		tmp = (t / (i + (y * (c + (y * (b + t_1)))))) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / (i + (y * (c + ((y * b) + (y * t_1))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (y + a)
    if (y <= (-2.35d+53)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 7.2d+37) then
        tmp = (t / (i + (y * (c + (y * (b + t_1)))))) + ((y * (230661.510616d0 + (y * (27464.7644705d0 + (y * (z + (y * x))))))) / (i + (y * (c + ((y * b) + (y * t_1))))))
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = y * (y + a);
	double tmp;
	if (y <= -2.35e+53) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 7.2e+37) {
		tmp = (t / (i + (y * (c + (y * (b + t_1)))))) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / (i + (y * (c + ((y * b) + (y * t_1))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = y * (y + a)
	tmp = 0
	if y <= -2.35e+53:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 7.2e+37:
		tmp = (t / (i + (y * (c + (y * (b + t_1)))))) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / (i + (y * (c + ((y * b) + (y * t_1))))))
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(y * Float64(y + a))
	tmp = 0.0
	if (y <= -2.35e+53)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 7.2e+37)
		tmp = Float64(Float64(t / Float64(i + Float64(y * Float64(c + Float64(y * Float64(b + t_1)))))) + Float64(Float64(y * Float64(230661.510616 + Float64(y * Float64(27464.7644705 + Float64(y * Float64(z + Float64(y * x))))))) / Float64(i + Float64(y * Float64(c + Float64(Float64(y * b) + Float64(y * t_1)))))));
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = y * (y + a);
	tmp = 0.0;
	if (y <= -2.35e+53)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 7.2e+37)
		tmp = (t / (i + (y * (c + (y * (b + t_1)))))) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / (i + (y * (c + ((y * b) + (y * t_1))))));
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(y * N[(y + a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.35e+53], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7.2e+37], N[(N[(t / N[(i + N[(y * N[(c + N[(y * N[(b + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(y * N[(230661.510616 + N[(y * N[(27464.7644705 + N[(y * N[(z + N[(y * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(i + N[(y * N[(c + N[(N[(y * b), $MachinePrecision] + N[(y * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 83.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := i + y \cdot \left(c + y \cdot \left(b + y \cdot \left(y + a\right)\right)\right)\\ \mathbf{if}\;y \leq -2.6 \cdot 10^{+53}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 5.6 \cdot 10^{+38}:\\ \;\;\;\;\frac{t}{t_1} + \frac{y \cdot \left(230661.510616 + y \cdot \left(27464.7644705 + y \cdot \left(z + y \cdot x\right)\right)\right)}{t_1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ i (* y (+ c (* y (+ b (* y (+ y a)))))))))
   (if (<= y -2.6e+53)
     (- (+ x (/ z y)) (/ (* x a) y))
     (if (<= y 5.6e+38)
       (+
        (/ t t_1)
        (/
         (* y (+ 230661.510616 (* y (+ 27464.7644705 (* y (+ z (* y x)))))))
         t_1))
       (/ x (+ 1.0 (/ a y)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = i + (y * (c + (y * (b + (y * (y + a))))));
	double tmp;
	if (y <= -2.6e+53) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 5.6e+38) {
		tmp = (t / t_1) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / t_1);
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = i + (y * (c + (y * (b + (y * (y + a))))))
    if (y <= (-2.6d+53)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 5.6d+38) then
        tmp = (t / t_1) + ((y * (230661.510616d0 + (y * (27464.7644705d0 + (y * (z + (y * x))))))) / t_1)
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = i + (y * (c + (y * (b + (y * (y + a))))));
	double tmp;
	if (y <= -2.6e+53) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 5.6e+38) {
		tmp = (t / t_1) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / t_1);
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = i + (y * (c + (y * (b + (y * (y + a))))))
	tmp = 0
	if y <= -2.6e+53:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 5.6e+38:
		tmp = (t / t_1) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / t_1)
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(i + Float64(y * Float64(c + Float64(y * Float64(b + Float64(y * Float64(y + a)))))))
	tmp = 0.0
	if (y <= -2.6e+53)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 5.6e+38)
		tmp = Float64(Float64(t / t_1) + Float64(Float64(y * Float64(230661.510616 + Float64(y * Float64(27464.7644705 + Float64(y * Float64(z + Float64(y * x))))))) / t_1));
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = i + (y * (c + (y * (b + (y * (y + a))))));
	tmp = 0.0;
	if (y <= -2.6e+53)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 5.6e+38)
		tmp = (t / t_1) + ((y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x))))))) / t_1);
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(i + N[(y * N[(c + N[(y * N[(b + N[(y * N[(y + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.6e+53], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 5.6e+38], N[(N[(t / t$95$1), $MachinePrecision] + N[(N[(y * N[(230661.510616 + N[(y * N[(27464.7644705 + N[(y * N[(z + N[(y * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 83.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.4 \cdot 10^{+53}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 6 \cdot 10^{+38}:\\ \;\;\;\;\frac{t + y \cdot \left(230661.510616 + y \cdot \left(27464.7644705 + y \cdot \left(z + y \cdot x\right)\right)\right)}{i + y \cdot \left(c + y \cdot \left(b + y \cdot \left(y + a\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y -3.4e+53)
   (- (+ x (/ z y)) (/ (* x a) y))
   (if (<= y 6e+38)
     (/
      (+ t (* y (+ 230661.510616 (* y (+ 27464.7644705 (* y (+ z (* y x))))))))
      (+ i (* y (+ c (* y (+ b (* y (+ y a))))))))
     (/ x (+ 1.0 (/ a y))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -3.4e+53) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 6e+38) {
		tmp = (t + (y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x)))))))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= (-3.4d+53)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 6d+38) then
        tmp = (t + (y * (230661.510616d0 + (y * (27464.7644705d0 + (y * (z + (y * x)))))))) / (i + (y * (c + (y * (b + (y * (y + a)))))))
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -3.4e+53) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 6e+38) {
		tmp = (t + (y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x)))))))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= -3.4e+53:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 6e+38:
		tmp = (t + (y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x)))))))) / (i + (y * (c + (y * (b + (y * (y + a)))))))
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= -3.4e+53)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 6e+38)
		tmp = Float64(Float64(t + Float64(y * Float64(230661.510616 + Float64(y * Float64(27464.7644705 + Float64(y * Float64(z + Float64(y * x)))))))) / Float64(i + Float64(y * Float64(c + Float64(y * Float64(b + Float64(y * Float64(y + a))))))));
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= -3.4e+53)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 6e+38)
		tmp = (t + (y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x)))))))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, -3.4e+53], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6e+38], N[(N[(t + N[(y * N[(230661.510616 + N[(y * N[(27464.7644705 + N[(y * N[(z + N[(y * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(i + N[(y * N[(c + N[(y * N[(b + N[(y * N[(y + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 80.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.85 \cdot 10^{+64}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 6 \cdot 10^{+38}:\\ \;\;\;\;\frac{t + \left(y \cdot \left(y \cdot \left(27464.7644705 + y \cdot z\right)\right) + y \cdot 230661.510616\right)}{i + y \cdot \left(c + y \cdot \left(b + y \cdot \left(y + a\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y -1.85e+64)
   (- (+ x (/ z y)) (/ (* x a) y))
   (if (<= y 6e+38)
     (/
      (+ t (+ (* y (* y (+ 27464.7644705 (* y z)))) (* y 230661.510616)))
      (+ i (* y (+ c (* y (+ b (* y (+ y a))))))))
     (/ x (+ 1.0 (/ a y))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -1.85e+64) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 6e+38) {
		tmp = (t + ((y * (y * (27464.7644705 + (y * z)))) + (y * 230661.510616))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= (-1.85d+64)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 6d+38) then
        tmp = (t + ((y * (y * (27464.7644705d0 + (y * z)))) + (y * 230661.510616d0))) / (i + (y * (c + (y * (b + (y * (y + a)))))))
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -1.85e+64) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 6e+38) {
		tmp = (t + ((y * (y * (27464.7644705 + (y * z)))) + (y * 230661.510616))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= -1.85e+64:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 6e+38:
		tmp = (t + ((y * (y * (27464.7644705 + (y * z)))) + (y * 230661.510616))) / (i + (y * (c + (y * (b + (y * (y + a)))))))
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= -1.85e+64)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 6e+38)
		tmp = Float64(Float64(t + Float64(Float64(y * Float64(y * Float64(27464.7644705 + Float64(y * z)))) + Float64(y * 230661.510616))) / Float64(i + Float64(y * Float64(c + Float64(y * Float64(b + Float64(y * Float64(y + a))))))));
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= -1.85e+64)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 6e+38)
		tmp = (t + ((y * (y * (27464.7644705 + (y * z)))) + (y * 230661.510616))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, -1.85e+64], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6e+38], N[(N[(t + N[(N[(y * N[(y * N[(27464.7644705 + N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y * 230661.510616), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(i + N[(y * N[(c + N[(y * N[(b + N[(y * N[(y + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 80.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.65 \cdot 10^{+63}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 5.4 \cdot 10^{+38}:\\ \;\;\;\;\frac{t + y \cdot \left(230661.510616 + y \cdot \left(27464.7644705 + y \cdot z\right)\right)}{i + y \cdot \left(c + y \cdot \left(b + y \cdot \left(y + a\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y -1.65e+63)
   (- (+ x (/ z y)) (/ (* x a) y))
   (if (<= y 5.4e+38)
     (/
      (+ t (* y (+ 230661.510616 (* y (+ 27464.7644705 (* y z))))))
      (+ i (* y (+ c (* y (+ b (* y (+ y a))))))))
     (/ x (+ 1.0 (/ a y))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -1.65e+63) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 5.4e+38) {
		tmp = (t + (y * (230661.510616 + (y * (27464.7644705 + (y * z)))))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= (-1.65d+63)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 5.4d+38) then
        tmp = (t + (y * (230661.510616d0 + (y * (27464.7644705d0 + (y * z)))))) / (i + (y * (c + (y * (b + (y * (y + a)))))))
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -1.65e+63) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 5.4e+38) {
		tmp = (t + (y * (230661.510616 + (y * (27464.7644705 + (y * z)))))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= -1.65e+63:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 5.4e+38:
		tmp = (t + (y * (230661.510616 + (y * (27464.7644705 + (y * z)))))) / (i + (y * (c + (y * (b + (y * (y + a)))))))
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= -1.65e+63)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 5.4e+38)
		tmp = Float64(Float64(t + Float64(y * Float64(230661.510616 + Float64(y * Float64(27464.7644705 + Float64(y * z)))))) / Float64(i + Float64(y * Float64(c + Float64(y * Float64(b + Float64(y * Float64(y + a))))))));
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= -1.65e+63)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 5.4e+38)
		tmp = (t + (y * (230661.510616 + (y * (27464.7644705 + (y * z)))))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, -1.65e+63], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 5.4e+38], N[(N[(t + N[(y * N[(230661.510616 + N[(y * N[(27464.7644705 + N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(i + N[(y * N[(c + N[(y * N[(b + N[(y * N[(y + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 75.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.45 \cdot 10^{+27}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 9.5 \cdot 10^{+36}:\\ \;\;\;\;\frac{t + y \cdot \left(230661.510616 + y \cdot 27464.7644705\right)}{i + y \cdot \left(c + y \cdot \left(b + y \cdot \left(y + a\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y -1.45e+27)
   (- (+ x (/ z y)) (/ (* x a) y))
   (if (<= y 9.5e+36)
     (/
      (+ t (* y (+ 230661.510616 (* y 27464.7644705))))
      (+ i (* y (+ c (* y (+ b (* y (+ y a))))))))
     (/ x (+ 1.0 (/ a y))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -1.45e+27) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 9.5e+36) {
		tmp = (t + (y * (230661.510616 + (y * 27464.7644705)))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= (-1.45d+27)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 9.5d+36) then
        tmp = (t + (y * (230661.510616d0 + (y * 27464.7644705d0)))) / (i + (y * (c + (y * (b + (y * (y + a)))))))
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -1.45e+27) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 9.5e+36) {
		tmp = (t + (y * (230661.510616 + (y * 27464.7644705)))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= -1.45e+27:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 9.5e+36:
		tmp = (t + (y * (230661.510616 + (y * 27464.7644705)))) / (i + (y * (c + (y * (b + (y * (y + a)))))))
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= -1.45e+27)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 9.5e+36)
		tmp = Float64(Float64(t + Float64(y * Float64(230661.510616 + Float64(y * 27464.7644705)))) / Float64(i + Float64(y * Float64(c + Float64(y * Float64(b + Float64(y * Float64(y + a))))))));
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= -1.45e+27)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 9.5e+36)
		tmp = (t + (y * (230661.510616 + (y * 27464.7644705)))) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, -1.45e+27], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 9.5e+36], N[(N[(t + N[(y * N[(230661.510616 + N[(y * 27464.7644705), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(i + N[(y * N[(c + N[(y * N[(b + N[(y * N[(y + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 9: 65.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t + y \cdot \left(230661.510616 + y \cdot \left(27464.7644705 + y \cdot \left(z + y \cdot x\right)\right)\right)\\ \mathbf{if}\;y \leq -2.15 \cdot 10^{+43}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq -4 \cdot 10^{-70}:\\ \;\;\;\;\frac{t_1}{y \cdot c}\\ \mathbf{elif}\;y \leq -6 \cdot 10^{-147}:\\ \;\;\;\;\frac{t_1}{i}\\ \mathbf{elif}\;y \leq 8 \cdot 10^{+22}:\\ \;\;\;\;\frac{t}{i + y \cdot \left(c + y \cdot \left(b + y \cdot \left(y + a\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1
         (+
          t
          (*
           y
           (+ 230661.510616 (* y (+ 27464.7644705 (* y (+ z (* y x))))))))))
   (if (<= y -2.15e+43)
     (- (+ x (/ z y)) (/ (* x a) y))
     (if (<= y -4e-70)
       (/ t_1 (* y c))
       (if (<= y -6e-147)
         (/ t_1 i)
         (if (<= y 8e+22)
           (/ t (+ i (* y (+ c (* y (+ b (* y (+ y a))))))))
           (/ x (+ 1.0 (/ a y)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = t + (y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x)))))));
	double tmp;
	if (y <= -2.15e+43) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= -4e-70) {
		tmp = t_1 / (y * c);
	} else if (y <= -6e-147) {
		tmp = t_1 / i;
	} else if (y <= 8e+22) {
		tmp = t / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t + (y * (230661.510616d0 + (y * (27464.7644705d0 + (y * (z + (y * x)))))))
    if (y <= (-2.15d+43)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= (-4d-70)) then
        tmp = t_1 / (y * c)
    else if (y <= (-6d-147)) then
        tmp = t_1 / i
    else if (y <= 8d+22) then
        tmp = t / (i + (y * (c + (y * (b + (y * (y + a)))))))
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = t + (y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x)))))));
	double tmp;
	if (y <= -2.15e+43) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= -4e-70) {
		tmp = t_1 / (y * c);
	} else if (y <= -6e-147) {
		tmp = t_1 / i;
	} else if (y <= 8e+22) {
		tmp = t / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = t + (y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x)))))))
	tmp = 0
	if y <= -2.15e+43:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= -4e-70:
		tmp = t_1 / (y * c)
	elif y <= -6e-147:
		tmp = t_1 / i
	elif y <= 8e+22:
		tmp = t / (i + (y * (c + (y * (b + (y * (y + a)))))))
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(t + Float64(y * Float64(230661.510616 + Float64(y * Float64(27464.7644705 + Float64(y * Float64(z + Float64(y * x))))))))
	tmp = 0.0
	if (y <= -2.15e+43)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= -4e-70)
		tmp = Float64(t_1 / Float64(y * c));
	elseif (y <= -6e-147)
		tmp = Float64(t_1 / i);
	elseif (y <= 8e+22)
		tmp = Float64(t / Float64(i + Float64(y * Float64(c + Float64(y * Float64(b + Float64(y * Float64(y + a))))))));
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = t + (y * (230661.510616 + (y * (27464.7644705 + (y * (z + (y * x)))))));
	tmp = 0.0;
	if (y <= -2.15e+43)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= -4e-70)
		tmp = t_1 / (y * c);
	elseif (y <= -6e-147)
		tmp = t_1 / i;
	elseif (y <= 8e+22)
		tmp = t / (i + (y * (c + (y * (b + (y * (y + a)))))));
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(t + N[(y * N[(230661.510616 + N[(y * N[(27464.7644705 + N[(y * N[(z + N[(y * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.15e+43], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -4e-70], N[(t$95$1 / N[(y * c), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -6e-147], N[(t$95$1 / i), $MachinePrecision], If[LessEqual[y, 8e+22], N[(t / N[(i + N[(y * N[(c + N[(y * N[(b + N[(y * N[(y + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 10: 75.2% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.7 \cdot 10^{+19}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 2.5 \cdot 10^{+20}:\\ \;\;\;\;\frac{t + y \cdot 230661.510616}{i + y \cdot \left(c + y \cdot \left(b + y \cdot \left(y + a\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y -3.7e+19)
   (- (+ x (/ z y)) (/ (* x a) y))
   (if (<= y 2.5e+20)
     (/ (+ t (* y 230661.510616)) (+ i (* y (+ c (* y (+ b (* y (+ y a))))))))
     (/ x (+ 1.0 (/ a y))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -3.7e+19) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 2.5e+20) {
		tmp = (t + (y * 230661.510616)) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= (-3.7d+19)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 2.5d+20) then
        tmp = (t + (y * 230661.510616d0)) / (i + (y * (c + (y * (b + (y * (y + a)))))))
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -3.7e+19) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 2.5e+20) {
		tmp = (t + (y * 230661.510616)) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= -3.7e+19:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 2.5e+20:
		tmp = (t + (y * 230661.510616)) / (i + (y * (c + (y * (b + (y * (y + a)))))))
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= -3.7e+19)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 2.5e+20)
		tmp = Float64(Float64(t + Float64(y * 230661.510616)) / Float64(i + Float64(y * Float64(c + Float64(y * Float64(b + Float64(y * Float64(y + a))))))));
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= -3.7e+19)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 2.5e+20)
		tmp = (t + (y * 230661.510616)) / (i + (y * (c + (y * (b + (y * (y + a)))))));
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, -3.7e+19], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.5e+20], N[(N[(t + N[(y * 230661.510616), $MachinePrecision]), $MachinePrecision] / N[(i + N[(y * N[(c + N[(y * N[(b + N[(y * N[(y + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 11: 68.1% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.2 \cdot 10^{+19}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 2.7 \cdot 10^{+21}:\\ \;\;\;\;\frac{t}{i + y \cdot \left(c + y \cdot \left(b + y \cdot \left(y + a\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y -5.2e+19)
   (- (+ x (/ z y)) (/ (* x a) y))
   (if (<= y 2.7e+21)
     (/ t (+ i (* y (+ c (* y (+ b (* y (+ y a))))))))
     (/ x (+ 1.0 (/ a y))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -5.2e+19) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 2.7e+21) {
		tmp = t / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= (-5.2d+19)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 2.7d+21) then
        tmp = t / (i + (y * (c + (y * (b + (y * (y + a)))))))
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -5.2e+19) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 2.7e+21) {
		tmp = t / (i + (y * (c + (y * (b + (y * (y + a)))))));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= -5.2e+19:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 2.7e+21:
		tmp = t / (i + (y * (c + (y * (b + (y * (y + a)))))))
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= -5.2e+19)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 2.7e+21)
		tmp = Float64(t / Float64(i + Float64(y * Float64(c + Float64(y * Float64(b + Float64(y * Float64(y + a))))))));
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= -5.2e+19)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 2.7e+21)
		tmp = t / (i + (y * (c + (y * (b + (y * (y + a)))))));
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, -5.2e+19], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.7e+21], N[(t / N[(i + N[(y * N[(c + N[(y * N[(b + N[(y * N[(y + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 12: 57.8% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.3 \cdot 10^{+19}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 8.2 \cdot 10^{-95}:\\ \;\;\;\;\frac{t}{i}\\ \mathbf{elif}\;y \leq 1.8 \cdot 10^{+20}:\\ \;\;\;\;\frac{t + y \cdot 230661.510616}{y \cdot \left(c + y \cdot b\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y -1.3e+19)
   (- (+ x (/ z y)) (/ (* x a) y))
   (if (<= y 8.2e-95)
     (/ t i)
     (if (<= y 1.8e+20)
       (/ (+ t (* y 230661.510616)) (* y (+ c (* y b))))
       (/ x (+ 1.0 (/ a y)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -1.3e+19) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 8.2e-95) {
		tmp = t / i;
	} else if (y <= 1.8e+20) {
		tmp = (t + (y * 230661.510616)) / (y * (c + (y * b)));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= (-1.3d+19)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 8.2d-95) then
        tmp = t / i
    else if (y <= 1.8d+20) then
        tmp = (t + (y * 230661.510616d0)) / (y * (c + (y * b)))
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -1.3e+19) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 8.2e-95) {
		tmp = t / i;
	} else if (y <= 1.8e+20) {
		tmp = (t + (y * 230661.510616)) / (y * (c + (y * b)));
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= -1.3e+19:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 8.2e-95:
		tmp = t / i
	elif y <= 1.8e+20:
		tmp = (t + (y * 230661.510616)) / (y * (c + (y * b)))
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= -1.3e+19)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 8.2e-95)
		tmp = Float64(t / i);
	elseif (y <= 1.8e+20)
		tmp = Float64(Float64(t + Float64(y * 230661.510616)) / Float64(y * Float64(c + Float64(y * b))));
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= -1.3e+19)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 8.2e-95)
		tmp = t / i;
	elseif (y <= 1.8e+20)
		tmp = (t + (y * 230661.510616)) / (y * (c + (y * b)));
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, -1.3e+19], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 8.2e-95], N[(t / i), $MachinePrecision], If[LessEqual[y, 1.8e+20], N[(N[(t + N[(y * 230661.510616), $MachinePrecision]), $MachinePrecision] / N[(y * N[(c + N[(y * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 13: 48.4% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{\frac{a}{y}}\\ \mathbf{if}\;y \leq -2.6 \cdot 10^{+189}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq -16500000000000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 3.7 \cdot 10^{-7}:\\ \;\;\;\;\frac{t}{i}\\ \mathbf{elif}\;y \leq 8.5 \cdot 10^{+151}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (/ x (/ a y))))
   (if (<= y -2.6e+189)
     x
     (if (<= y -16500000000000.0)
       t_1
       (if (<= y 3.7e-7) (/ t i) (if (<= y 8.5e+151) t_1 x))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = x / (a / y);
	double tmp;
	if (y <= -2.6e+189) {
		tmp = x;
	} else if (y <= -16500000000000.0) {
		tmp = t_1;
	} else if (y <= 3.7e-7) {
		tmp = t / i;
	} else if (y <= 8.5e+151) {
		tmp = t_1;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x / (a / y)
    if (y <= (-2.6d+189)) then
        tmp = x
    else if (y <= (-16500000000000.0d0)) then
        tmp = t_1
    else if (y <= 3.7d-7) then
        tmp = t / i
    else if (y <= 8.5d+151) then
        tmp = t_1
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = x / (a / y);
	double tmp;
	if (y <= -2.6e+189) {
		tmp = x;
	} else if (y <= -16500000000000.0) {
		tmp = t_1;
	} else if (y <= 3.7e-7) {
		tmp = t / i;
	} else if (y <= 8.5e+151) {
		tmp = t_1;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = x / (a / y)
	tmp = 0
	if y <= -2.6e+189:
		tmp = x
	elif y <= -16500000000000.0:
		tmp = t_1
	elif y <= 3.7e-7:
		tmp = t / i
	elif y <= 8.5e+151:
		tmp = t_1
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(x / Float64(a / y))
	tmp = 0.0
	if (y <= -2.6e+189)
		tmp = x;
	elseif (y <= -16500000000000.0)
		tmp = t_1;
	elseif (y <= 3.7e-7)
		tmp = Float64(t / i);
	elseif (y <= 8.5e+151)
		tmp = t_1;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = x / (a / y);
	tmp = 0.0;
	if (y <= -2.6e+189)
		tmp = x;
	elseif (y <= -16500000000000.0)
		tmp = t_1;
	elseif (y <= 3.7e-7)
		tmp = t / i;
	elseif (y <= 8.5e+151)
		tmp = t_1;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(x / N[(a / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.6e+189], x, If[LessEqual[y, -16500000000000.0], t$95$1, If[LessEqual[y, 3.7e-7], N[(t / i), $MachinePrecision], If[LessEqual[y, 8.5e+151], t$95$1, x]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 14: 57.1% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8.5 \cdot 10^{+18}:\\ \;\;\;\;\left(x + \frac{z}{y}\right) - \frac{x \cdot a}{y}\\ \mathbf{elif}\;y \leq 1.72 \cdot 10^{-6}:\\ \;\;\;\;\frac{t}{i}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y -8.5e+18)
   (- (+ x (/ z y)) (/ (* x a) y))
   (if (<= y 1.72e-6) (/ t i) (/ x (+ 1.0 (/ a y))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -8.5e+18) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 1.72e-6) {
		tmp = t / i;
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= (-8.5d+18)) then
        tmp = (x + (z / y)) - ((x * a) / y)
    else if (y <= 1.72d-6) then
        tmp = t / i
    else
        tmp = x / (1.0d0 + (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -8.5e+18) {
		tmp = (x + (z / y)) - ((x * a) / y);
	} else if (y <= 1.72e-6) {
		tmp = t / i;
	} else {
		tmp = x / (1.0 + (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= -8.5e+18:
		tmp = (x + (z / y)) - ((x * a) / y)
	elif y <= 1.72e-6:
		tmp = t / i
	else:
		tmp = x / (1.0 + (a / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= -8.5e+18)
		tmp = Float64(Float64(x + Float64(z / y)) - Float64(Float64(x * a) / y));
	elseif (y <= 1.72e-6)
		tmp = Float64(t / i);
	else
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= -8.5e+18)
		tmp = (x + (z / y)) - ((x * a) / y);
	elseif (y <= 1.72e-6)
		tmp = t / i;
	else
		tmp = x / (1.0 + (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, -8.5e+18], N[(N[(x + N[(z / y), $MachinePrecision]), $MachinePrecision] - N[(N[(x * a), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.72e-6], N[(t / i), $MachinePrecision], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 15: 58.0% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.15 \cdot 10^{+14} \lor \neg \left(y \leq 0.0096\right):\\ \;\;\;\;\frac{x}{1 + \frac{a}{y}}\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{i}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (or (<= y -2.15e+14) (not (<= y 0.0096))) (/ x (+ 1.0 (/ a y))) (/ t i)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((y <= -2.15e+14) || !(y <= 0.0096)) {
		tmp = x / (1.0 + (a / y));
	} else {
		tmp = t / i;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if ((y <= (-2.15d+14)) .or. (.not. (y <= 0.0096d0))) then
        tmp = x / (1.0d0 + (a / y))
    else
        tmp = t / i
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((y <= -2.15e+14) || !(y <= 0.0096)) {
		tmp = x / (1.0 + (a / y));
	} else {
		tmp = t / i;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if (y <= -2.15e+14) or not (y <= 0.0096):
		tmp = x / (1.0 + (a / y))
	else:
		tmp = t / i
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if ((y <= -2.15e+14) || !(y <= 0.0096))
		tmp = Float64(x / Float64(1.0 + Float64(a / y)));
	else
		tmp = Float64(t / i);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if ((y <= -2.15e+14) || ~((y <= 0.0096)))
		tmp = x / (1.0 + (a / y));
	else
		tmp = t / i;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[Or[LessEqual[y, -2.15e+14], N[Not[LessEqual[y, 0.0096]], $MachinePrecision]], N[(x / N[(1.0 + N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t / i), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 16: 51.4% accurate, 4.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{+18}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.85 \cdot 10^{+20}:\\ \;\;\;\;\frac{t}{i}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= y -1.55e+18) x (if (<= y 1.85e+20) (/ t i) x)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -1.55e+18) {
		tmp = x;
	} else if (y <= 1.85e+20) {
		tmp = t / i;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (y <= (-1.55d+18)) then
        tmp = x
    else if (y <= 1.85d+20) then
        tmp = t / i
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (y <= -1.55e+18) {
		tmp = x;
	} else if (y <= 1.85e+20) {
		tmp = t / i;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if y <= -1.55e+18:
		tmp = x
	elif y <= 1.85e+20:
		tmp = t / i
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (y <= -1.55e+18)
		tmp = x;
	elseif (y <= 1.85e+20)
		tmp = Float64(t / i);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (y <= -1.55e+18)
		tmp = x;
	elseif (y <= 1.85e+20)
		tmp = t / i;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[y, -1.55e+18], x, If[LessEqual[y, 1.85e+20], N[(t / i), $MachinePrecision], x]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 17: 26.1% accurate, 33.0× speedup

Math

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

FPCore

(FPCore (x y z t a b c i) :precision binary64 x)

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return x;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    code = x
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return x;
}

Python

def code(x, y, z, t, a, b, c, i):
	return x

Julia

function code(x, y, z, t, a, b, c, i)
	return x
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = x;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := x

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Reproduce

herbie shell --seed 2023350 
(FPCore (x y z t a b c i)
  :name "Numeric.SpecFunctions:logGamma from math-functions-0.1.5.2"
  :precision binary64
  (/ (+ (* (+ (* (+ (* (+ (* x y) z) y) 27464.7644705) y) 230661.510616) y) t) (+ (* (+ (* (+ (* (+ y a) y) b) y) c) y) i)))