Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, I

Percentage Accurate: 93.5% → 96.9%

Time: 22.8s

Alternatives: 18

Speedup: 0.7×

Specification

Math

\[\begin{array}{l} \\ \frac{x}{x + y \cdot e^{2 \cdot \left(\frac{z \cdot \sqrt{t + a}}{t} - \left(b - c\right) \cdot \left(\left(a + \frac{5}{6}\right) - \frac{2}{t \cdot 3}\right)\right)}} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (/
  x
  (+
   x
   (*
    y
    (exp
     (*
      2.0
      (-
       (/ (* z (sqrt (+ t a))) t)
       (* (- b c) (- (+ a (/ 5.0 6.0)) (/ 2.0 (* t 3.0)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return x / (x + (y * exp((2.0 * (((z * sqrt((t + a))) / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))));
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = x / (x + (y * exp((2.0d0 * (((z * sqrt((t + a))) / t) - ((b - c) * ((a + (5.0d0 / 6.0d0)) - (2.0d0 / (t * 3.0d0)))))))))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return x / (x + (y * Math.exp((2.0 * (((z * Math.sqrt((t + a))) / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))));
}

Python

def code(x, y, z, t, a, b, c):
	return x / (x + (y * math.exp((2.0 * (((z * math.sqrt((t + a))) / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))))

Julia

function code(x, y, z, t, a, b, c)
	return Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(Float64(z * sqrt(Float64(t + a))) / t) - Float64(Float64(b - c) * Float64(Float64(a + Float64(5.0 / 6.0)) - Float64(2.0 / Float64(t * 3.0))))))))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = x / (x + (y * exp((2.0 * (((z * sqrt((t + a))) / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(N[(z * N[Sqrt[N[(t + a), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision] - N[(N[(b - c), $MachinePrecision] * N[(N[(a + N[(5.0 / 6.0), $MachinePrecision]), $MachinePrecision] - N[(2.0 / N[(t * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 93.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x}{x + y \cdot e^{2 \cdot \left(\frac{z \cdot \sqrt{t + a}}{t} - \left(b - c\right) \cdot \left(\left(a + \frac{5}{6}\right) - \frac{2}{t \cdot 3}\right)\right)}} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (/
  x
  (+
   x
   (*
    y
    (exp
     (*
      2.0
      (-
       (/ (* z (sqrt (+ t a))) t)
       (* (- b c) (- (+ a (/ 5.0 6.0)) (/ 2.0 (* t 3.0)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return x / (x + (y * exp((2.0 * (((z * sqrt((t + a))) / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))));
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = x / (x + (y * exp((2.0d0 * (((z * sqrt((t + a))) / t) - ((b - c) * ((a + (5.0d0 / 6.0d0)) - (2.0d0 / (t * 3.0d0)))))))))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return x / (x + (y * Math.exp((2.0 * (((z * Math.sqrt((t + a))) / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))));
}

Python

def code(x, y, z, t, a, b, c):
	return x / (x + (y * math.exp((2.0 * (((z * math.sqrt((t + a))) / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))))

Julia

function code(x, y, z, t, a, b, c)
	return Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(Float64(z * sqrt(Float64(t + a))) / t) - Float64(Float64(b - c) * Float64(Float64(a + Float64(5.0 / 6.0)) - Float64(2.0 / Float64(t * 3.0))))))))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = x / (x + (y * exp((2.0 * (((z * sqrt((t + a))) / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(N[(z * N[Sqrt[N[(t + a), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision] - N[(N[(b - c), $MachinePrecision] * N[(N[(a + N[(5.0 / 6.0), $MachinePrecision]), $MachinePrecision] - N[(2.0 / N[(t * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 96.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \sqrt{t + a}\\ \mathbf{if}\;\frac{z \cdot t_1}{t} + \left(b - c\right) \cdot \left(\frac{2}{t \cdot 3} - \left(a + 0.8333333333333334\right)\right) \leq \infty:\\ \;\;\;\;\frac{x}{x + y \cdot {\left(e^{2}\right)}^{\left(\frac{z}{\frac{t}{t_1}} + \left(b - c\right) \cdot \left(\left(\frac{\frac{2}{t}}{3} - 0.8333333333333334\right) - a\right)\right)}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(c \cdot \left(a + \left(0.8333333333333334 + \frac{-0.6666666666666666}{t}\right)\right)\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (sqrt (+ t a))))
   (if (<=
        (+
         (/ (* z t_1) t)
         (* (- b c) (- (/ 2.0 (* t 3.0)) (+ a 0.8333333333333334))))
        INFINITY)
     (/
      x
      (+
       x
       (*
        y
        (pow
         (exp 2.0)
         (+
          (/ z (/ t t_1))
          (* (- b c) (- (- (/ (/ 2.0 t) 3.0) 0.8333333333333334) a)))))))
     (/
      x
      (+
       x
       (*
        y
        (exp
         (*
          2.0
          (* c (+ a (+ 0.8333333333333334 (/ -0.6666666666666666 t))))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = sqrt((t + a));
	double tmp;
	if ((((z * t_1) / t) + ((b - c) * ((2.0 / (t * 3.0)) - (a + 0.8333333333333334)))) <= ((double) INFINITY)) {
		tmp = x / (x + (y * pow(exp(2.0), ((z / (t / t_1)) + ((b - c) * ((((2.0 / t) / 3.0) - 0.8333333333333334) - a))))));
	} else {
		tmp = x / (x + (y * exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = Math.sqrt((t + a));
	double tmp;
	if ((((z * t_1) / t) + ((b - c) * ((2.0 / (t * 3.0)) - (a + 0.8333333333333334)))) <= Double.POSITIVE_INFINITY) {
		tmp = x / (x + (y * Math.pow(Math.exp(2.0), ((z / (t / t_1)) + ((b - c) * ((((2.0 / t) / 3.0) - 0.8333333333333334) - a))))));
	} else {
		tmp = x / (x + (y * Math.exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = math.sqrt((t + a))
	tmp = 0
	if (((z * t_1) / t) + ((b - c) * ((2.0 / (t * 3.0)) - (a + 0.8333333333333334)))) <= math.inf:
		tmp = x / (x + (y * math.pow(math.exp(2.0), ((z / (t / t_1)) + ((b - c) * ((((2.0 / t) / 3.0) - 0.8333333333333334) - a))))))
	else:
		tmp = x / (x + (y * math.exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = sqrt(Float64(t + a))
	tmp = 0.0
	if (Float64(Float64(Float64(z * t_1) / t) + Float64(Float64(b - c) * Float64(Float64(2.0 / Float64(t * 3.0)) - Float64(a + 0.8333333333333334)))) <= Inf)
		tmp = Float64(x / Float64(x + Float64(y * (exp(2.0) ^ Float64(Float64(z / Float64(t / t_1)) + Float64(Float64(b - c) * Float64(Float64(Float64(Float64(2.0 / t) / 3.0) - 0.8333333333333334) - a)))))));
	else
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(c * Float64(a + Float64(0.8333333333333334 + Float64(-0.6666666666666666 / t)))))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = sqrt((t + a));
	tmp = 0.0;
	if ((((z * t_1) / t) + ((b - c) * ((2.0 / (t * 3.0)) - (a + 0.8333333333333334)))) <= Inf)
		tmp = x / (x + (y * (exp(2.0) ^ ((z / (t / t_1)) + ((b - c) * ((((2.0 / t) / 3.0) - 0.8333333333333334) - a))))));
	else
		tmp = x / (x + (y * exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[Sqrt[N[(t + a), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[N[(N[(N[(z * t$95$1), $MachinePrecision] / t), $MachinePrecision] + N[(N[(b - c), $MachinePrecision] * N[(N[(2.0 / N[(t * 3.0), $MachinePrecision]), $MachinePrecision] - N[(a + 0.8333333333333334), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], Infinity], N[(x / N[(x + N[(y * N[Power[N[Exp[2.0], $MachinePrecision], N[(N[(z / N[(t / t$95$1), $MachinePrecision]), $MachinePrecision] + N[(N[(b - c), $MachinePrecision] * N[(N[(N[(N[(2.0 / t), $MachinePrecision] / 3.0), $MachinePrecision] - 0.8333333333333334), $MachinePrecision] - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(c * N[(a + N[(0.8333333333333334 + N[(-0.6666666666666666 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 2: 96.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z \cdot \sqrt{t + a}}{t} + \left(b - c\right) \cdot \left(\frac{2}{t \cdot 3} - \left(a + 0.8333333333333334\right)\right)\\ \mathbf{if}\;t_1 \leq \infty:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot t_1}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(c \cdot \left(a + \left(0.8333333333333334 + \frac{-0.6666666666666666}{t}\right)\right)\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1
         (+
          (/ (* z (sqrt (+ t a))) t)
          (* (- b c) (- (/ 2.0 (* t 3.0)) (+ a 0.8333333333333334))))))
   (if (<= t_1 INFINITY)
     (/ x (+ x (* y (exp (* 2.0 t_1)))))
     (/
      x
      (+
       x
       (*
        y
        (exp
         (*
          2.0
          (* c (+ a (+ 0.8333333333333334 (/ -0.6666666666666666 t))))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = ((z * sqrt((t + a))) / t) + ((b - c) * ((2.0 / (t * 3.0)) - (a + 0.8333333333333334)));
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = x / (x + (y * exp((2.0 * t_1))));
	} else {
		tmp = x / (x + (y * exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = ((z * Math.sqrt((t + a))) / t) + ((b - c) * ((2.0 / (t * 3.0)) - (a + 0.8333333333333334)));
	double tmp;
	if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = x / (x + (y * Math.exp((2.0 * t_1))));
	} else {
		tmp = x / (x + (y * Math.exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = ((z * math.sqrt((t + a))) / t) + ((b - c) * ((2.0 / (t * 3.0)) - (a + 0.8333333333333334)))
	tmp = 0
	if t_1 <= math.inf:
		tmp = x / (x + (y * math.exp((2.0 * t_1))))
	else:
		tmp = x / (x + (y * math.exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(Float64(z * sqrt(Float64(t + a))) / t) + Float64(Float64(b - c) * Float64(Float64(2.0 / Float64(t * 3.0)) - Float64(a + 0.8333333333333334))))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * t_1)))));
	else
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(c * Float64(a + Float64(0.8333333333333334 + Float64(-0.6666666666666666 / t)))))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = ((z * sqrt((t + a))) / t) + ((b - c) * ((2.0 / (t * 3.0)) - (a + 0.8333333333333334)));
	tmp = 0.0;
	if (t_1 <= Inf)
		tmp = x / (x + (y * exp((2.0 * t_1))));
	else
		tmp = x / (x + (y * exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(N[(z * N[Sqrt[N[(t + a), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision] + N[(N[(b - c), $MachinePrecision] * N[(N[(2.0 / N[(t * 3.0), $MachinePrecision]), $MachinePrecision] - N[(a + 0.8333333333333334), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * t$95$1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(c * N[(a + N[(0.8333333333333334 + N[(-0.6666666666666666 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 3: 88.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq 2 \cdot 10^{-175}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \frac{z \cdot \sqrt{a} - \left(b - c\right) \cdot -0.6666666666666666}{t}}}\\ \mathbf{elif}\;t \leq 8 \cdot 10^{+73}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(z \cdot \sqrt{\frac{1}{t}} + \left(0.8333333333333334 + \frac{-0.6666666666666666}{t}\right) \cdot \left(c - b\right)\right)}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(\left(a + 0.8333333333333334\right) \cdot \left(c - b\right)\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= t 2e-175)
   (/
    x
    (+
     x
     (*
      y
      (exp (* 2.0 (/ (- (* z (sqrt a)) (* (- b c) -0.6666666666666666)) t))))))
   (if (<= t 8e+73)
     (/
      x
      (+
       x
       (*
        y
        (exp
         (*
          2.0
          (+
           (* z (sqrt (/ 1.0 t)))
           (* (+ 0.8333333333333334 (/ -0.6666666666666666 t)) (- c b))))))))
     (/ x (+ x (* y (exp (* 2.0 (* (+ a 0.8333333333333334) (- c b))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (t <= 2e-175) {
		tmp = x / (x + (y * exp((2.0 * (((z * sqrt(a)) - ((b - c) * -0.6666666666666666)) / t)))));
	} else if (t <= 8e+73) {
		tmp = x / (x + (y * exp((2.0 * ((z * sqrt((1.0 / t))) + ((0.8333333333333334 + (-0.6666666666666666 / t)) * (c - b)))))));
	} else {
		tmp = x / (x + (y * exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (t <= 2d-175) then
        tmp = x / (x + (y * exp((2.0d0 * (((z * sqrt(a)) - ((b - c) * (-0.6666666666666666d0))) / t)))))
    else if (t <= 8d+73) then
        tmp = x / (x + (y * exp((2.0d0 * ((z * sqrt((1.0d0 / t))) + ((0.8333333333333334d0 + ((-0.6666666666666666d0) / t)) * (c - b)))))))
    else
        tmp = x / (x + (y * exp((2.0d0 * ((a + 0.8333333333333334d0) * (c - b))))))
    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 tmp;
	if (t <= 2e-175) {
		tmp = x / (x + (y * Math.exp((2.0 * (((z * Math.sqrt(a)) - ((b - c) * -0.6666666666666666)) / t)))));
	} else if (t <= 8e+73) {
		tmp = x / (x + (y * Math.exp((2.0 * ((z * Math.sqrt((1.0 / t))) + ((0.8333333333333334 + (-0.6666666666666666 / t)) * (c - b)))))));
	} else {
		tmp = x / (x + (y * Math.exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if t <= 2e-175:
		tmp = x / (x + (y * math.exp((2.0 * (((z * math.sqrt(a)) - ((b - c) * -0.6666666666666666)) / t)))))
	elif t <= 8e+73:
		tmp = x / (x + (y * math.exp((2.0 * ((z * math.sqrt((1.0 / t))) + ((0.8333333333333334 + (-0.6666666666666666 / t)) * (c - b)))))))
	else:
		tmp = x / (x + (y * math.exp((2.0 * ((a + 0.8333333333333334) * (c - b))))))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (t <= 2e-175)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(Float64(z * sqrt(a)) - Float64(Float64(b - c) * -0.6666666666666666)) / t))))));
	elseif (t <= 8e+73)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(z * sqrt(Float64(1.0 / t))) + Float64(Float64(0.8333333333333334 + Float64(-0.6666666666666666 / t)) * Float64(c - b))))))));
	else
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(a + 0.8333333333333334) * Float64(c - b)))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (t <= 2e-175)
		tmp = x / (x + (y * exp((2.0 * (((z * sqrt(a)) - ((b - c) * -0.6666666666666666)) / t)))));
	elseif (t <= 8e+73)
		tmp = x / (x + (y * exp((2.0 * ((z * sqrt((1.0 / t))) + ((0.8333333333333334 + (-0.6666666666666666 / t)) * (c - b)))))));
	else
		tmp = x / (x + (y * exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[t, 2e-175], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(N[(z * N[Sqrt[a], $MachinePrecision]), $MachinePrecision] - N[(N[(b - c), $MachinePrecision] * -0.6666666666666666), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 8e+73], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(z * N[Sqrt[N[(1.0 / t), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + N[(N[(0.8333333333333334 + N[(-0.6666666666666666 / t), $MachinePrecision]), $MachinePrecision] * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(a + 0.8333333333333334), $MachinePrecision] * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 4: 83.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x + y \cdot e^{2 \cdot \frac{z \cdot \sqrt{a} + -0.6666666666666666 \cdot \left(c - b\right)}{t}}}\\ \mathbf{if}\;t \leq 1.65 \cdot 10^{-87}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 3.2 \cdot 10^{-70}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(a \cdot \left(c - b\right)\right)}}\\ \mathbf{elif}\;t \leq 4 \cdot 10^{-14}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(\left(a + 0.8333333333333334\right) \cdot \left(c - b\right)\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1
         (/
          x
          (+
           x
           (*
            y
            (exp
             (*
              2.0
              (/ (+ (* z (sqrt a)) (* -0.6666666666666666 (- c b))) t))))))))
   (if (<= t 1.65e-87)
     t_1
     (if (<= t 3.2e-70)
       (/ x (+ x (* y (exp (* 2.0 (* a (- c b)))))))
       (if (<= t 4e-14)
         t_1
         (/
          x
          (+ x (* y (exp (* 2.0 (* (+ a 0.8333333333333334) (- c b))))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = x / (x + (y * exp((2.0 * (((z * sqrt(a)) + (-0.6666666666666666 * (c - b))) / t)))));
	double tmp;
	if (t <= 1.65e-87) {
		tmp = t_1;
	} else if (t <= 3.2e-70) {
		tmp = x / (x + (y * exp((2.0 * (a * (c - b))))));
	} else if (t <= 4e-14) {
		tmp = t_1;
	} else {
		tmp = x / (x + (y * exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: tmp
    t_1 = x / (x + (y * exp((2.0d0 * (((z * sqrt(a)) + ((-0.6666666666666666d0) * (c - b))) / t)))))
    if (t <= 1.65d-87) then
        tmp = t_1
    else if (t <= 3.2d-70) then
        tmp = x / (x + (y * exp((2.0d0 * (a * (c - b))))))
    else if (t <= 4d-14) then
        tmp = t_1
    else
        tmp = x / (x + (y * exp((2.0d0 * ((a + 0.8333333333333334d0) * (c - b))))))
    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 t_1 = x / (x + (y * Math.exp((2.0 * (((z * Math.sqrt(a)) + (-0.6666666666666666 * (c - b))) / t)))));
	double tmp;
	if (t <= 1.65e-87) {
		tmp = t_1;
	} else if (t <= 3.2e-70) {
		tmp = x / (x + (y * Math.exp((2.0 * (a * (c - b))))));
	} else if (t <= 4e-14) {
		tmp = t_1;
	} else {
		tmp = x / (x + (y * Math.exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = x / (x + (y * math.exp((2.0 * (((z * math.sqrt(a)) + (-0.6666666666666666 * (c - b))) / t)))))
	tmp = 0
	if t <= 1.65e-87:
		tmp = t_1
	elif t <= 3.2e-70:
		tmp = x / (x + (y * math.exp((2.0 * (a * (c - b))))))
	elif t <= 4e-14:
		tmp = t_1
	else:
		tmp = x / (x + (y * math.exp((2.0 * ((a + 0.8333333333333334) * (c - b))))))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(Float64(z * sqrt(a)) + Float64(-0.6666666666666666 * Float64(c - b))) / t))))))
	tmp = 0.0
	if (t <= 1.65e-87)
		tmp = t_1;
	elseif (t <= 3.2e-70)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(a * Float64(c - b)))))));
	elseif (t <= 4e-14)
		tmp = t_1;
	else
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(a + 0.8333333333333334) * Float64(c - b)))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = x / (x + (y * exp((2.0 * (((z * sqrt(a)) + (-0.6666666666666666 * (c - b))) / t)))));
	tmp = 0.0;
	if (t <= 1.65e-87)
		tmp = t_1;
	elseif (t <= 3.2e-70)
		tmp = x / (x + (y * exp((2.0 * (a * (c - b))))));
	elseif (t <= 4e-14)
		tmp = t_1;
	else
		tmp = x / (x + (y * exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(N[(z * N[Sqrt[a], $MachinePrecision]), $MachinePrecision] + N[(-0.6666666666666666 * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, 1.65e-87], t$95$1, If[LessEqual[t, 3.2e-70], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(a * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 4e-14], t$95$1, N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(a + 0.8333333333333334), $MachinePrecision] * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 5: 70.9% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x + y \cdot e^{2 \cdot \left(a \cdot \left(c - b\right)\right)}}\\ \mathbf{if}\;t \leq -1 \cdot 10^{-296}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 4.2 \cdot 10^{-214}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(-0.6666666666666666 \cdot \frac{c}{t}\right)}}\\ \mathbf{elif}\;t \leq 1.35 \cdot 10^{-113}:\\ \;\;\;\;1\\ \mathbf{elif}\;t \leq 2.5 \cdot 10^{+42} \lor \neg \left(t \leq 1.4 \cdot 10^{+290}\right) \land t \leq 4.3 \cdot 10^{+302}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{1.6666666666666667 \cdot \left(c - b\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (/ x (+ x (* y (exp (* 2.0 (* a (- c b)))))))))
   (if (<= t -1e-296)
     t_1
     (if (<= t 4.2e-214)
       (/ x (+ x (* y (exp (* 2.0 (* -0.6666666666666666 (/ c t)))))))
       (if (<= t 1.35e-113)
         1.0
         (if (or (<= t 2.5e+42) (and (not (<= t 1.4e+290)) (<= t 4.3e+302)))
           t_1
           (/ x (+ x (* y (exp (* 1.6666666666666667 (- c b))))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = x / (x + (y * exp((2.0 * (a * (c - b))))));
	double tmp;
	if (t <= -1e-296) {
		tmp = t_1;
	} else if (t <= 4.2e-214) {
		tmp = x / (x + (y * exp((2.0 * (-0.6666666666666666 * (c / t))))));
	} else if (t <= 1.35e-113) {
		tmp = 1.0;
	} else if ((t <= 2.5e+42) || (!(t <= 1.4e+290) && (t <= 4.3e+302))) {
		tmp = t_1;
	} else {
		tmp = x / (x + (y * exp((1.6666666666666667 * (c - b)))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: tmp
    t_1 = x / (x + (y * exp((2.0d0 * (a * (c - b))))))
    if (t <= (-1d-296)) then
        tmp = t_1
    else if (t <= 4.2d-214) then
        tmp = x / (x + (y * exp((2.0d0 * ((-0.6666666666666666d0) * (c / t))))))
    else if (t <= 1.35d-113) then
        tmp = 1.0d0
    else if ((t <= 2.5d+42) .or. (.not. (t <= 1.4d+290)) .and. (t <= 4.3d+302)) then
        tmp = t_1
    else
        tmp = x / (x + (y * exp((1.6666666666666667d0 * (c - b)))))
    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 t_1 = x / (x + (y * Math.exp((2.0 * (a * (c - b))))));
	double tmp;
	if (t <= -1e-296) {
		tmp = t_1;
	} else if (t <= 4.2e-214) {
		tmp = x / (x + (y * Math.exp((2.0 * (-0.6666666666666666 * (c / t))))));
	} else if (t <= 1.35e-113) {
		tmp = 1.0;
	} else if ((t <= 2.5e+42) || (!(t <= 1.4e+290) && (t <= 4.3e+302))) {
		tmp = t_1;
	} else {
		tmp = x / (x + (y * Math.exp((1.6666666666666667 * (c - b)))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = x / (x + (y * math.exp((2.0 * (a * (c - b))))))
	tmp = 0
	if t <= -1e-296:
		tmp = t_1
	elif t <= 4.2e-214:
		tmp = x / (x + (y * math.exp((2.0 * (-0.6666666666666666 * (c / t))))))
	elif t <= 1.35e-113:
		tmp = 1.0
	elif (t <= 2.5e+42) or (not (t <= 1.4e+290) and (t <= 4.3e+302)):
		tmp = t_1
	else:
		tmp = x / (x + (y * math.exp((1.6666666666666667 * (c - b)))))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(a * Float64(c - b)))))))
	tmp = 0.0
	if (t <= -1e-296)
		tmp = t_1;
	elseif (t <= 4.2e-214)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(-0.6666666666666666 * Float64(c / t)))))));
	elseif (t <= 1.35e-113)
		tmp = 1.0;
	elseif ((t <= 2.5e+42) || (!(t <= 1.4e+290) && (t <= 4.3e+302)))
		tmp = t_1;
	else
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(1.6666666666666667 * Float64(c - b))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = x / (x + (y * exp((2.0 * (a * (c - b))))));
	tmp = 0.0;
	if (t <= -1e-296)
		tmp = t_1;
	elseif (t <= 4.2e-214)
		tmp = x / (x + (y * exp((2.0 * (-0.6666666666666666 * (c / t))))));
	elseif (t <= 1.35e-113)
		tmp = 1.0;
	elseif ((t <= 2.5e+42) || (~((t <= 1.4e+290)) && (t <= 4.3e+302)))
		tmp = t_1;
	else
		tmp = x / (x + (y * exp((1.6666666666666667 * (c - b)))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(a * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1e-296], t$95$1, If[LessEqual[t, 4.2e-214], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(-0.6666666666666666 * N[(c / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.35e-113], 1.0, If[Or[LessEqual[t, 2.5e+42], And[N[Not[LessEqual[t, 1.4e+290]], $MachinePrecision], LessEqual[t, 4.3e+302]]], t$95$1, N[(x / N[(x + N[(y * N[Exp[N[(1.6666666666666667 * N[(c - b), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 6: 78.9% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -9.8 \cdot 10^{-297}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(a \cdot \left(c - b\right)\right)}}\\ \mathbf{elif}\;t \leq 1.35 \cdot 10^{-215}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(-0.6666666666666666 \cdot \frac{c}{t}\right)}}\\ \mathbf{elif}\;t \leq 1.15 \cdot 10^{-147}:\\ \;\;\;\;1\\ \mathbf{elif}\;t \leq 5 \cdot 10^{-15}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(b \cdot \left(\frac{0.6666666666666666}{t} - \left(a + 0.8333333333333334\right)\right)\right)}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(\left(a + 0.8333333333333334\right) \cdot \left(c - b\right)\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= t -9.8e-297)
   (/ x (+ x (* y (exp (* 2.0 (* a (- c b)))))))
   (if (<= t 1.35e-215)
     (/ x (+ x (* y (exp (* 2.0 (* -0.6666666666666666 (/ c t)))))))
     (if (<= t 1.15e-147)
       1.0
       (if (<= t 5e-15)
         (/
          x
          (+
           x
           (*
            y
            (exp
             (*
              2.0
              (* b (- (/ 0.6666666666666666 t) (+ a 0.8333333333333334))))))))
         (/
          x
          (+ x (* y (exp (* 2.0 (* (+ a 0.8333333333333334) (- c b))))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (t <= -9.8e-297) {
		tmp = x / (x + (y * exp((2.0 * (a * (c - b))))));
	} else if (t <= 1.35e-215) {
		tmp = x / (x + (y * exp((2.0 * (-0.6666666666666666 * (c / t))))));
	} else if (t <= 1.15e-147) {
		tmp = 1.0;
	} else if (t <= 5e-15) {
		tmp = x / (x + (y * exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))));
	} else {
		tmp = x / (x + (y * exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (t <= (-9.8d-297)) then
        tmp = x / (x + (y * exp((2.0d0 * (a * (c - b))))))
    else if (t <= 1.35d-215) then
        tmp = x / (x + (y * exp((2.0d0 * ((-0.6666666666666666d0) * (c / t))))))
    else if (t <= 1.15d-147) then
        tmp = 1.0d0
    else if (t <= 5d-15) then
        tmp = x / (x + (y * exp((2.0d0 * (b * ((0.6666666666666666d0 / t) - (a + 0.8333333333333334d0)))))))
    else
        tmp = x / (x + (y * exp((2.0d0 * ((a + 0.8333333333333334d0) * (c - b))))))
    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 tmp;
	if (t <= -9.8e-297) {
		tmp = x / (x + (y * Math.exp((2.0 * (a * (c - b))))));
	} else if (t <= 1.35e-215) {
		tmp = x / (x + (y * Math.exp((2.0 * (-0.6666666666666666 * (c / t))))));
	} else if (t <= 1.15e-147) {
		tmp = 1.0;
	} else if (t <= 5e-15) {
		tmp = x / (x + (y * Math.exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))));
	} else {
		tmp = x / (x + (y * Math.exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if t <= -9.8e-297:
		tmp = x / (x + (y * math.exp((2.0 * (a * (c - b))))))
	elif t <= 1.35e-215:
		tmp = x / (x + (y * math.exp((2.0 * (-0.6666666666666666 * (c / t))))))
	elif t <= 1.15e-147:
		tmp = 1.0
	elif t <= 5e-15:
		tmp = x / (x + (y * math.exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))))
	else:
		tmp = x / (x + (y * math.exp((2.0 * ((a + 0.8333333333333334) * (c - b))))))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (t <= -9.8e-297)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(a * Float64(c - b)))))));
	elseif (t <= 1.35e-215)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(-0.6666666666666666 * Float64(c / t)))))));
	elseif (t <= 1.15e-147)
		tmp = 1.0;
	elseif (t <= 5e-15)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(b * Float64(Float64(0.6666666666666666 / t) - Float64(a + 0.8333333333333334))))))));
	else
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(a + 0.8333333333333334) * Float64(c - b)))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (t <= -9.8e-297)
		tmp = x / (x + (y * exp((2.0 * (a * (c - b))))));
	elseif (t <= 1.35e-215)
		tmp = x / (x + (y * exp((2.0 * (-0.6666666666666666 * (c / t))))));
	elseif (t <= 1.15e-147)
		tmp = 1.0;
	elseif (t <= 5e-15)
		tmp = x / (x + (y * exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))));
	else
		tmp = x / (x + (y * exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[t, -9.8e-297], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(a * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.35e-215], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(-0.6666666666666666 * N[(c / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.15e-147], 1.0, If[LessEqual[t, 5e-15], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(b * N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(a + 0.8333333333333334), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(a + 0.8333333333333334), $MachinePrecision] * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 7: 77.3% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1 \cdot 10^{-296}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(a \cdot \left(c - b\right)\right)}}\\ \mathbf{elif}\;t \leq 2 \cdot 10^{-218}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(-0.6666666666666666 \cdot \frac{c}{t}\right)}}\\ \mathbf{elif}\;t \leq 0.0195:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(\left(a + 0.8333333333333334\right) \cdot \left(c - b\right)\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= t -1e-296)
   (/ x (+ x (* y (exp (* 2.0 (* a (- c b)))))))
   (if (<= t 2e-218)
     (/ x (+ x (* y (exp (* 2.0 (* -0.6666666666666666 (/ c t)))))))
     (if (<= t 0.0195)
       1.0
       (/ x (+ x (* y (exp (* 2.0 (* (+ a 0.8333333333333334) (- c b)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (t <= -1e-296) {
		tmp = x / (x + (y * exp((2.0 * (a * (c - b))))));
	} else if (t <= 2e-218) {
		tmp = x / (x + (y * exp((2.0 * (-0.6666666666666666 * (c / t))))));
	} else if (t <= 0.0195) {
		tmp = 1.0;
	} else {
		tmp = x / (x + (y * exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (t <= (-1d-296)) then
        tmp = x / (x + (y * exp((2.0d0 * (a * (c - b))))))
    else if (t <= 2d-218) then
        tmp = x / (x + (y * exp((2.0d0 * ((-0.6666666666666666d0) * (c / t))))))
    else if (t <= 0.0195d0) then
        tmp = 1.0d0
    else
        tmp = x / (x + (y * exp((2.0d0 * ((a + 0.8333333333333334d0) * (c - b))))))
    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 tmp;
	if (t <= -1e-296) {
		tmp = x / (x + (y * Math.exp((2.0 * (a * (c - b))))));
	} else if (t <= 2e-218) {
		tmp = x / (x + (y * Math.exp((2.0 * (-0.6666666666666666 * (c / t))))));
	} else if (t <= 0.0195) {
		tmp = 1.0;
	} else {
		tmp = x / (x + (y * Math.exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if t <= -1e-296:
		tmp = x / (x + (y * math.exp((2.0 * (a * (c - b))))))
	elif t <= 2e-218:
		tmp = x / (x + (y * math.exp((2.0 * (-0.6666666666666666 * (c / t))))))
	elif t <= 0.0195:
		tmp = 1.0
	else:
		tmp = x / (x + (y * math.exp((2.0 * ((a + 0.8333333333333334) * (c - b))))))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (t <= -1e-296)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(a * Float64(c - b)))))));
	elseif (t <= 2e-218)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(-0.6666666666666666 * Float64(c / t)))))));
	elseif (t <= 0.0195)
		tmp = 1.0;
	else
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(a + 0.8333333333333334) * Float64(c - b)))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (t <= -1e-296)
		tmp = x / (x + (y * exp((2.0 * (a * (c - b))))));
	elseif (t <= 2e-218)
		tmp = x / (x + (y * exp((2.0 * (-0.6666666666666666 * (c / t))))));
	elseif (t <= 0.0195)
		tmp = 1.0;
	else
		tmp = x / (x + (y * exp((2.0 * ((a + 0.8333333333333334) * (c - b))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[t, -1e-296], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(a * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2e-218], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(-0.6666666666666666 * N[(c / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.0195], 1.0, N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(a + 0.8333333333333334), $MachinePrecision] * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 8: 78.9% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq -4 \cdot 10^{-38} \lor \neg \left(c \leq 1.8 \cdot 10^{+122}\right):\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(c \cdot \left(a + \left(0.8333333333333334 + \frac{-0.6666666666666666}{t}\right)\right)\right)}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(b \cdot \left(\frac{0.6666666666666666}{t} - \left(a + 0.8333333333333334\right)\right)\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= c -4e-38) (not (<= c 1.8e+122)))
   (/
    x
    (+
     x
     (*
      y
      (exp
       (* 2.0 (* c (+ a (+ 0.8333333333333334 (/ -0.6666666666666666 t)))))))))
   (/
    x
    (+
     x
     (*
      y
      (exp
       (*
        2.0
        (* b (- (/ 0.6666666666666666 t) (+ a 0.8333333333333334))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if ((c <= -4e-38) || !(c <= 1.8e+122)) {
		tmp = x / (x + (y * exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))));
	} else {
		tmp = x / (x + (y * exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if ((c <= (-4d-38)) .or. (.not. (c <= 1.8d+122))) then
        tmp = x / (x + (y * exp((2.0d0 * (c * (a + (0.8333333333333334d0 + ((-0.6666666666666666d0) / t))))))))
    else
        tmp = x / (x + (y * exp((2.0d0 * (b * ((0.6666666666666666d0 / t) - (a + 0.8333333333333334d0)))))))
    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 tmp;
	if ((c <= -4e-38) || !(c <= 1.8e+122)) {
		tmp = x / (x + (y * Math.exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))));
	} else {
		tmp = x / (x + (y * Math.exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if (c <= -4e-38) or not (c <= 1.8e+122):
		tmp = x / (x + (y * math.exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))))
	else:
		tmp = x / (x + (y * math.exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((c <= -4e-38) || !(c <= 1.8e+122))
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(c * Float64(a + Float64(0.8333333333333334 + Float64(-0.6666666666666666 / t)))))))));
	else
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(b * Float64(Float64(0.6666666666666666 / t) - Float64(a + 0.8333333333333334))))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if ((c <= -4e-38) || ~((c <= 1.8e+122)))
		tmp = x / (x + (y * exp((2.0 * (c * (a + (0.8333333333333334 + (-0.6666666666666666 / t))))))));
	else
		tmp = x / (x + (y * exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[c, -4e-38], N[Not[LessEqual[c, 1.8e+122]], $MachinePrecision]], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(c * N[(a + N[(0.8333333333333334 + N[(-0.6666666666666666 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(b * N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(a + 0.8333333333333334), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 9: 70.6% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x + y \cdot e^{\left(c - b\right) \cdot 1.6666666666666667}}\\ \mathbf{if}\;t \leq -1.12 \cdot 10^{-296}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.9 \cdot 10^{-214}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(-0.6666666666666666 \cdot \frac{c}{t}\right)}}\\ \mathbf{elif}\;t \leq 0.0105:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (/ x (+ x (* y (exp (* (- c b) 1.6666666666666667)))))))
   (if (<= t -1.12e-296)
     t_1
     (if (<= t 1.9e-214)
       (/ x (+ x (* y (exp (* 2.0 (* -0.6666666666666666 (/ c t)))))))
       (if (<= t 0.0105) 1.0 t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = x / (x + (y * exp(((c - b) * 1.6666666666666667))));
	double tmp;
	if (t <= -1.12e-296) {
		tmp = t_1;
	} else if (t <= 1.9e-214) {
		tmp = x / (x + (y * exp((2.0 * (-0.6666666666666666 * (c / t))))));
	} else if (t <= 0.0105) {
		tmp = 1.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: tmp
    t_1 = x / (x + (y * exp(((c - b) * 1.6666666666666667d0))))
    if (t <= (-1.12d-296)) then
        tmp = t_1
    else if (t <= 1.9d-214) then
        tmp = x / (x + (y * exp((2.0d0 * ((-0.6666666666666666d0) * (c / t))))))
    else if (t <= 0.0105d0) then
        tmp = 1.0d0
    else
        tmp = t_1
    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 t_1 = x / (x + (y * Math.exp(((c - b) * 1.6666666666666667))));
	double tmp;
	if (t <= -1.12e-296) {
		tmp = t_1;
	} else if (t <= 1.9e-214) {
		tmp = x / (x + (y * Math.exp((2.0 * (-0.6666666666666666 * (c / t))))));
	} else if (t <= 0.0105) {
		tmp = 1.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = x / (x + (y * math.exp(((c - b) * 1.6666666666666667))))
	tmp = 0
	if t <= -1.12e-296:
		tmp = t_1
	elif t <= 1.9e-214:
		tmp = x / (x + (y * math.exp((2.0 * (-0.6666666666666666 * (c / t))))))
	elif t <= 0.0105:
		tmp = 1.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(x / Float64(x + Float64(y * exp(Float64(Float64(c - b) * 1.6666666666666667)))))
	tmp = 0.0
	if (t <= -1.12e-296)
		tmp = t_1;
	elseif (t <= 1.9e-214)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(-0.6666666666666666 * Float64(c / t)))))));
	elseif (t <= 0.0105)
		tmp = 1.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = x / (x + (y * exp(((c - b) * 1.6666666666666667))));
	tmp = 0.0;
	if (t <= -1.12e-296)
		tmp = t_1;
	elseif (t <= 1.9e-214)
		tmp = x / (x + (y * exp((2.0 * (-0.6666666666666666 * (c / t))))));
	elseif (t <= 0.0105)
		tmp = 1.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(x / N[(x + N[(y * N[Exp[N[(N[(c - b), $MachinePrecision] * 1.6666666666666667), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.12e-296], t$95$1, If[LessEqual[t, 1.9e-214], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(-0.6666666666666666 * N[(c / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.0105], 1.0, t$95$1]]]]

Derivation

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

Alternative 10: 69.2% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -9.8 \cdot 10^{-297} \lor \neg \left(t \leq 0.0102\right):\\ \;\;\;\;\frac{x}{x + y \cdot e^{1.6666666666666667 \cdot \left(c - b\right)}}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= t -9.8e-297) (not (<= t 0.0102)))
   (/ x (+ x (* y (exp (* 1.6666666666666667 (- c b))))))
   1.0))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if ((t <= -9.8e-297) || !(t <= 0.0102)) {
		tmp = x / (x + (y * exp((1.6666666666666667 * (c - b)))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if ((t <= (-9.8d-297)) .or. (.not. (t <= 0.0102d0))) then
        tmp = x / (x + (y * exp((1.6666666666666667d0 * (c - b)))))
    else
        tmp = 1.0d0
    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 tmp;
	if ((t <= -9.8e-297) || !(t <= 0.0102)) {
		tmp = x / (x + (y * Math.exp((1.6666666666666667 * (c - b)))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if (t <= -9.8e-297) or not (t <= 0.0102):
		tmp = x / (x + (y * math.exp((1.6666666666666667 * (c - b)))))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((t <= -9.8e-297) || !(t <= 0.0102))
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(1.6666666666666667 * Float64(c - b))))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if ((t <= -9.8e-297) || ~((t <= 0.0102)))
		tmp = x / (x + (y * exp((1.6666666666666667 * (c - b)))));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[t, -9.8e-297], N[Not[LessEqual[t, 0.0102]], $MachinePrecision]], N[(x / N[(x + N[(y * N[Exp[N[(1.6666666666666667 * N[(c - b), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]

Derivation

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

Alternative 11: 56.8% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq 1.65 \cdot 10^{-59}:\\ \;\;\;\;1\\ \mathbf{elif}\;c \leq 11600000000000:\\ \;\;\;\;\frac{x}{\left(x + y\right) + \left(a \cdot \left(b \cdot y\right)\right) \cdot -2}\\ \mathbf{elif}\;c \leq 1.22 \cdot 10^{+42}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y \cdot e^{c \cdot 1.6666666666666667}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= c 1.65e-59)
   1.0
   (if (<= c 11600000000000.0)
     (/ x (+ (+ x y) (* (* a (* b y)) -2.0)))
     (if (<= c 1.22e+42) 1.0 (/ x (* y (exp (* c 1.6666666666666667))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (c <= 1.65e-59) {
		tmp = 1.0;
	} else if (c <= 11600000000000.0) {
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0));
	} else if (c <= 1.22e+42) {
		tmp = 1.0;
	} else {
		tmp = x / (y * exp((c * 1.6666666666666667)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (c <= 1.65d-59) then
        tmp = 1.0d0
    else if (c <= 11600000000000.0d0) then
        tmp = x / ((x + y) + ((a * (b * y)) * (-2.0d0)))
    else if (c <= 1.22d+42) then
        tmp = 1.0d0
    else
        tmp = x / (y * exp((c * 1.6666666666666667d0)))
    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 tmp;
	if (c <= 1.65e-59) {
		tmp = 1.0;
	} else if (c <= 11600000000000.0) {
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0));
	} else if (c <= 1.22e+42) {
		tmp = 1.0;
	} else {
		tmp = x / (y * Math.exp((c * 1.6666666666666667)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if c <= 1.65e-59:
		tmp = 1.0
	elif c <= 11600000000000.0:
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0))
	elif c <= 1.22e+42:
		tmp = 1.0
	else:
		tmp = x / (y * math.exp((c * 1.6666666666666667)))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (c <= 1.65e-59)
		tmp = 1.0;
	elseif (c <= 11600000000000.0)
		tmp = Float64(x / Float64(Float64(x + y) + Float64(Float64(a * Float64(b * y)) * -2.0)));
	elseif (c <= 1.22e+42)
		tmp = 1.0;
	else
		tmp = Float64(x / Float64(y * exp(Float64(c * 1.6666666666666667))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (c <= 1.65e-59)
		tmp = 1.0;
	elseif (c <= 11600000000000.0)
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0));
	elseif (c <= 1.22e+42)
		tmp = 1.0;
	else
		tmp = x / (y * exp((c * 1.6666666666666667)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[c, 1.65e-59], 1.0, If[LessEqual[c, 11600000000000.0], N[(x / N[(N[(x + y), $MachinePrecision] + N[(N[(a * N[(b * y), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 1.22e+42], 1.0, N[(x / N[(y * N[Exp[N[(c * 1.6666666666666667), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 12: 53.2% accurate, 10.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq 1.65 \cdot 10^{-59}:\\ \;\;\;\;1\\ \mathbf{elif}\;c \leq 3500000000000:\\ \;\;\;\;\frac{x}{\left(x + y\right) + \left(a \cdot \left(b \cdot y\right)\right) \cdot -2}\\ \mathbf{elif}\;c \leq 1.2 \cdot 10^{+105}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(2 \cdot \left(a \cdot \left(c - b\right)\right) + 1\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= c 1.65e-59)
   1.0
   (if (<= c 3500000000000.0)
     (/ x (+ (+ x y) (* (* a (* b y)) -2.0)))
     (if (<= c 1.2e+105)
       1.0
       (/ x (+ x (* y (+ (* 2.0 (* a (- c b))) 1.0))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (c <= 1.65e-59) {
		tmp = 1.0;
	} else if (c <= 3500000000000.0) {
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0));
	} else if (c <= 1.2e+105) {
		tmp = 1.0;
	} else {
		tmp = x / (x + (y * ((2.0 * (a * (c - b))) + 1.0)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (c <= 1.65d-59) then
        tmp = 1.0d0
    else if (c <= 3500000000000.0d0) then
        tmp = x / ((x + y) + ((a * (b * y)) * (-2.0d0)))
    else if (c <= 1.2d+105) then
        tmp = 1.0d0
    else
        tmp = x / (x + (y * ((2.0d0 * (a * (c - b))) + 1.0d0)))
    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 tmp;
	if (c <= 1.65e-59) {
		tmp = 1.0;
	} else if (c <= 3500000000000.0) {
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0));
	} else if (c <= 1.2e+105) {
		tmp = 1.0;
	} else {
		tmp = x / (x + (y * ((2.0 * (a * (c - b))) + 1.0)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if c <= 1.65e-59:
		tmp = 1.0
	elif c <= 3500000000000.0:
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0))
	elif c <= 1.2e+105:
		tmp = 1.0
	else:
		tmp = x / (x + (y * ((2.0 * (a * (c - b))) + 1.0)))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (c <= 1.65e-59)
		tmp = 1.0;
	elseif (c <= 3500000000000.0)
		tmp = Float64(x / Float64(Float64(x + y) + Float64(Float64(a * Float64(b * y)) * -2.0)));
	elseif (c <= 1.2e+105)
		tmp = 1.0;
	else
		tmp = Float64(x / Float64(x + Float64(y * Float64(Float64(2.0 * Float64(a * Float64(c - b))) + 1.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (c <= 1.65e-59)
		tmp = 1.0;
	elseif (c <= 3500000000000.0)
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0));
	elseif (c <= 1.2e+105)
		tmp = 1.0;
	else
		tmp = x / (x + (y * ((2.0 * (a * (c - b))) + 1.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[c, 1.65e-59], 1.0, If[LessEqual[c, 3500000000000.0], N[(x / N[(N[(x + y), $MachinePrecision] + N[(N[(a * N[(b * y), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 1.2e+105], 1.0, N[(x / N[(x + N[(y * N[(N[(2.0 * N[(a * N[(c - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 13: 53.2% accurate, 12.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq 1.65 \cdot 10^{-59}:\\ \;\;\;\;1\\ \mathbf{elif}\;c \leq 31000000000000:\\ \;\;\;\;\frac{x}{\left(x + y\right) + \left(a \cdot \left(b \cdot y\right)\right) \cdot -2}\\ \mathbf{elif}\;c \leq 3 \cdot 10^{+107}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\left(x + y\right) + \left(a \cdot 2\right) \cdot \left(c \cdot y\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= c 1.65e-59)
   1.0
   (if (<= c 31000000000000.0)
     (/ x (+ (+ x y) (* (* a (* b y)) -2.0)))
     (if (<= c 3e+107) 1.0 (/ x (+ (+ x y) (* (* a 2.0) (* c y))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (c <= 1.65e-59) {
		tmp = 1.0;
	} else if (c <= 31000000000000.0) {
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0));
	} else if (c <= 3e+107) {
		tmp = 1.0;
	} else {
		tmp = x / ((x + y) + ((a * 2.0) * (c * y)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (c <= 1.65d-59) then
        tmp = 1.0d0
    else if (c <= 31000000000000.0d0) then
        tmp = x / ((x + y) + ((a * (b * y)) * (-2.0d0)))
    else if (c <= 3d+107) then
        tmp = 1.0d0
    else
        tmp = x / ((x + y) + ((a * 2.0d0) * (c * 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 tmp;
	if (c <= 1.65e-59) {
		tmp = 1.0;
	} else if (c <= 31000000000000.0) {
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0));
	} else if (c <= 3e+107) {
		tmp = 1.0;
	} else {
		tmp = x / ((x + y) + ((a * 2.0) * (c * y)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if c <= 1.65e-59:
		tmp = 1.0
	elif c <= 31000000000000.0:
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0))
	elif c <= 3e+107:
		tmp = 1.0
	else:
		tmp = x / ((x + y) + ((a * 2.0) * (c * y)))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (c <= 1.65e-59)
		tmp = 1.0;
	elseif (c <= 31000000000000.0)
		tmp = Float64(x / Float64(Float64(x + y) + Float64(Float64(a * Float64(b * y)) * -2.0)));
	elseif (c <= 3e+107)
		tmp = 1.0;
	else
		tmp = Float64(x / Float64(Float64(x + y) + Float64(Float64(a * 2.0) * Float64(c * y))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (c <= 1.65e-59)
		tmp = 1.0;
	elseif (c <= 31000000000000.0)
		tmp = x / ((x + y) + ((a * (b * y)) * -2.0));
	elseif (c <= 3e+107)
		tmp = 1.0;
	else
		tmp = x / ((x + y) + ((a * 2.0) * (c * y)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[c, 1.65e-59], 1.0, If[LessEqual[c, 31000000000000.0], N[(x / N[(N[(x + y), $MachinePrecision] + N[(N[(a * N[(b * y), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 3e+107], 1.0, N[(x / N[(N[(x + y), $MachinePrecision] + N[(N[(a * 2.0), $MachinePrecision] * N[(c * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 14: 53.6% accurate, 15.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq 3.2 \cdot 10^{+111}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\left(x + y\right) + \left(a \cdot 2\right) \cdot \left(c \cdot y\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= c 3.2e+111) 1.0 (/ x (+ (+ x y) (* (* a 2.0) (* c y))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (c <= 3.2e+111) {
		tmp = 1.0;
	} else {
		tmp = x / ((x + y) + ((a * 2.0) * (c * y)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (c <= 3.2d+111) then
        tmp = 1.0d0
    else
        tmp = x / ((x + y) + ((a * 2.0d0) * (c * 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 tmp;
	if (c <= 3.2e+111) {
		tmp = 1.0;
	} else {
		tmp = x / ((x + y) + ((a * 2.0) * (c * y)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if c <= 3.2e+111:
		tmp = 1.0
	else:
		tmp = x / ((x + y) + ((a * 2.0) * (c * y)))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (c <= 3.2e+111)
		tmp = 1.0;
	else
		tmp = Float64(x / Float64(Float64(x + y) + Float64(Float64(a * 2.0) * Float64(c * y))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (c <= 3.2e+111)
		tmp = 1.0;
	else
		tmp = x / ((x + y) + ((a * 2.0) * (c * y)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[c, 3.2e+111], 1.0, N[(x / N[(N[(x + y), $MachinePrecision] + N[(N[(a * 2.0), $MachinePrecision] * N[(c * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 15: 52.2% accurate, 17.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq 7 \cdot 10^{+115}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y \cdot \left(2 \cdot \left(a \cdot c\right) + 1\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= c 7e+115) 1.0 (/ x (* y (+ (* 2.0 (* a c)) 1.0)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (c <= 7e+115) {
		tmp = 1.0;
	} else {
		tmp = x / (y * ((2.0 * (a * c)) + 1.0));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (c <= 7d+115) then
        tmp = 1.0d0
    else
        tmp = x / (y * ((2.0d0 * (a * c)) + 1.0d0))
    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 tmp;
	if (c <= 7e+115) {
		tmp = 1.0;
	} else {
		tmp = x / (y * ((2.0 * (a * c)) + 1.0));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if c <= 7e+115:
		tmp = 1.0
	else:
		tmp = x / (y * ((2.0 * (a * c)) + 1.0))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (c <= 7e+115)
		tmp = 1.0;
	else
		tmp = Float64(x / Float64(y * Float64(Float64(2.0 * Float64(a * c)) + 1.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (c <= 7e+115)
		tmp = 1.0;
	else
		tmp = x / (y * ((2.0 * (a * c)) + 1.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[c, 7e+115], 1.0, N[(x / N[(y * N[(N[(2.0 * N[(a * c), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 16: 51.8% accurate, 20.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq 6.5 \cdot 10^{+134}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot \frac{\frac{x}{a}}{c \cdot y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= c 6.5e+134) 1.0 (* 0.5 (/ (/ x a) (* c y)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (c <= 6.5e+134) {
		tmp = 1.0;
	} else {
		tmp = 0.5 * ((x / a) / (c * y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (c <= 6.5d+134) then
        tmp = 1.0d0
    else
        tmp = 0.5d0 * ((x / a) / (c * 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 tmp;
	if (c <= 6.5e+134) {
		tmp = 1.0;
	} else {
		tmp = 0.5 * ((x / a) / (c * y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if c <= 6.5e+134:
		tmp = 1.0
	else:
		tmp = 0.5 * ((x / a) / (c * y))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (c <= 6.5e+134)
		tmp = 1.0;
	else
		tmp = Float64(0.5 * Float64(Float64(x / a) / Float64(c * y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (c <= 6.5e+134)
		tmp = 1.0;
	else
		tmp = 0.5 * ((x / a) / (c * y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[c, 6.5e+134], 1.0, N[(0.5 * N[(N[(x / a), $MachinePrecision] / N[(c * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 17: 52.4% accurate, 20.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq 1.45 \cdot 10^{+136}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot 0.5}{a \cdot \left(c \cdot y\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= c 1.45e+136) 1.0 (/ (* x 0.5) (* a (* c y)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (c <= 1.45e+136) {
		tmp = 1.0;
	} else {
		tmp = (x * 0.5) / (a * (c * y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (c <= 1.45d+136) then
        tmp = 1.0d0
    else
        tmp = (x * 0.5d0) / (a * (c * 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 tmp;
	if (c <= 1.45e+136) {
		tmp = 1.0;
	} else {
		tmp = (x * 0.5) / (a * (c * y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if c <= 1.45e+136:
		tmp = 1.0
	else:
		tmp = (x * 0.5) / (a * (c * y))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (c <= 1.45e+136)
		tmp = 1.0;
	else
		tmp = Float64(Float64(x * 0.5) / Float64(a * Float64(c * y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (c <= 1.45e+136)
		tmp = 1.0;
	else
		tmp = (x * 0.5) / (a * (c * y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[c, 1.45e+136], 1.0, N[(N[(x * 0.5), $MachinePrecision] / N[(a * N[(c * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 18: 51.8% accurate, 231.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x y z t a b c) :precision binary64 1.0)

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return 1.0;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = 1.0d0
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return 1.0;
}

Python

def code(x, y, z, t, a, b, c):
	return 1.0

Julia

function code(x, y, z, t, a, b, c)
	return 1.0
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = 1.0;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := 1.0

Derivation

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

Developer target: 95.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \sqrt{t + a}\\ t_2 := a - \frac{5}{6}\\ \mathbf{if}\;t < -2.118326644891581 \cdot 10^{-50}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(\left(a \cdot c + 0.8333333333333334 \cdot c\right) - a \cdot b\right)}}\\ \mathbf{elif}\;t < 5.196588770651547 \cdot 10^{-123}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \frac{t_1 \cdot \left(\left(3 \cdot t\right) \cdot t_2\right) - \left(\left(\frac{5}{6} + a\right) \cdot \left(3 \cdot t\right) - 2\right) \cdot \left(t_2 \cdot \left(\left(b - c\right) \cdot t\right)\right)}{\left(\left(t \cdot t\right) \cdot 3\right) \cdot t_2}}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(\frac{t_1}{t} - \left(b - c\right) \cdot \left(\left(a + \frac{5}{6}\right) - \frac{2}{t \cdot 3}\right)\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* z (sqrt (+ t a)))) (t_2 (- a (/ 5.0 6.0))))
   (if (< t -2.118326644891581e-50)
     (/
      x
      (+
       x
       (* y (exp (* 2.0 (- (+ (* a c) (* 0.8333333333333334 c)) (* a b)))))))
     (if (< t 5.196588770651547e-123)
       (/
        x
        (+
         x
         (*
          y
          (exp
           (*
            2.0
            (/
             (-
              (* t_1 (* (* 3.0 t) t_2))
              (*
               (- (* (+ (/ 5.0 6.0) a) (* 3.0 t)) 2.0)
               (* t_2 (* (- b c) t))))
             (* (* (* t t) 3.0) t_2)))))))
       (/
        x
        (+
         x
         (*
          y
          (exp
           (*
            2.0
            (-
             (/ t_1 t)
             (* (- b c) (- (+ a (/ 5.0 6.0)) (/ 2.0 (* t 3.0))))))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = z * sqrt((t + a));
	double t_2 = a - (5.0 / 6.0);
	double tmp;
	if (t < -2.118326644891581e-50) {
		tmp = x / (x + (y * exp((2.0 * (((a * c) + (0.8333333333333334 * c)) - (a * b))))));
	} else if (t < 5.196588770651547e-123) {
		tmp = x / (x + (y * exp((2.0 * (((t_1 * ((3.0 * t) * t_2)) - (((((5.0 / 6.0) + a) * (3.0 * t)) - 2.0) * (t_2 * ((b - c) * t)))) / (((t * t) * 3.0) * t_2))))));
	} else {
		tmp = x / (x + (y * exp((2.0 * ((t_1 / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = z * sqrt((t + a))
    t_2 = a - (5.0d0 / 6.0d0)
    if (t < (-2.118326644891581d-50)) then
        tmp = x / (x + (y * exp((2.0d0 * (((a * c) + (0.8333333333333334d0 * c)) - (a * b))))))
    else if (t < 5.196588770651547d-123) then
        tmp = x / (x + (y * exp((2.0d0 * (((t_1 * ((3.0d0 * t) * t_2)) - (((((5.0d0 / 6.0d0) + a) * (3.0d0 * t)) - 2.0d0) * (t_2 * ((b - c) * t)))) / (((t * t) * 3.0d0) * t_2))))))
    else
        tmp = x / (x + (y * exp((2.0d0 * ((t_1 / t) - ((b - c) * ((a + (5.0d0 / 6.0d0)) - (2.0d0 / (t * 3.0d0)))))))))
    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 t_1 = z * Math.sqrt((t + a));
	double t_2 = a - (5.0 / 6.0);
	double tmp;
	if (t < -2.118326644891581e-50) {
		tmp = x / (x + (y * Math.exp((2.0 * (((a * c) + (0.8333333333333334 * c)) - (a * b))))));
	} else if (t < 5.196588770651547e-123) {
		tmp = x / (x + (y * Math.exp((2.0 * (((t_1 * ((3.0 * t) * t_2)) - (((((5.0 / 6.0) + a) * (3.0 * t)) - 2.0) * (t_2 * ((b - c) * t)))) / (((t * t) * 3.0) * t_2))))));
	} else {
		tmp = x / (x + (y * Math.exp((2.0 * ((t_1 / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = z * math.sqrt((t + a))
	t_2 = a - (5.0 / 6.0)
	tmp = 0
	if t < -2.118326644891581e-50:
		tmp = x / (x + (y * math.exp((2.0 * (((a * c) + (0.8333333333333334 * c)) - (a * b))))))
	elif t < 5.196588770651547e-123:
		tmp = x / (x + (y * math.exp((2.0 * (((t_1 * ((3.0 * t) * t_2)) - (((((5.0 / 6.0) + a) * (3.0 * t)) - 2.0) * (t_2 * ((b - c) * t)))) / (((t * t) * 3.0) * t_2))))))
	else:
		tmp = x / (x + (y * math.exp((2.0 * ((t_1 / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(z * sqrt(Float64(t + a)))
	t_2 = Float64(a - Float64(5.0 / 6.0))
	tmp = 0.0
	if (t < -2.118326644891581e-50)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(Float64(a * c) + Float64(0.8333333333333334 * c)) - Float64(a * b)))))));
	elseif (t < 5.196588770651547e-123)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(Float64(t_1 * Float64(Float64(3.0 * t) * t_2)) - Float64(Float64(Float64(Float64(Float64(5.0 / 6.0) + a) * Float64(3.0 * t)) - 2.0) * Float64(t_2 * Float64(Float64(b - c) * t)))) / Float64(Float64(Float64(t * t) * 3.0) * t_2)))))));
	else
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(t_1 / t) - Float64(Float64(b - c) * Float64(Float64(a + Float64(5.0 / 6.0)) - Float64(2.0 / Float64(t * 3.0))))))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = z * sqrt((t + a));
	t_2 = a - (5.0 / 6.0);
	tmp = 0.0;
	if (t < -2.118326644891581e-50)
		tmp = x / (x + (y * exp((2.0 * (((a * c) + (0.8333333333333334 * c)) - (a * b))))));
	elseif (t < 5.196588770651547e-123)
		tmp = x / (x + (y * exp((2.0 * (((t_1 * ((3.0 * t) * t_2)) - (((((5.0 / 6.0) + a) * (3.0 * t)) - 2.0) * (t_2 * ((b - c) * t)))) / (((t * t) * 3.0) * t_2))))));
	else
		tmp = x / (x + (y * exp((2.0 * ((t_1 / t) - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0)))))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(z * N[Sqrt[N[(t + a), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(a - N[(5.0 / 6.0), $MachinePrecision]), $MachinePrecision]}, If[Less[t, -2.118326644891581e-50], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(N[(a * c), $MachinePrecision] + N[(0.8333333333333334 * c), $MachinePrecision]), $MachinePrecision] - N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Less[t, 5.196588770651547e-123], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(N[(t$95$1 * N[(N[(3.0 * t), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(N[(N[(5.0 / 6.0), $MachinePrecision] + a), $MachinePrecision] * N[(3.0 * t), $MachinePrecision]), $MachinePrecision] - 2.0), $MachinePrecision] * N[(t$95$2 * N[(N[(b - c), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(t * t), $MachinePrecision] * 3.0), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(t$95$1 / t), $MachinePrecision] - N[(N[(b - c), $MachinePrecision] * N[(N[(a + N[(5.0 / 6.0), $MachinePrecision]), $MachinePrecision] - N[(2.0 / N[(t * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Reproduce

herbie shell --seed 2023350 
(FPCore (x y z t a b c)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, I"
  :precision binary64

  :herbie-target
  (if (< t -2.118326644891581e-50) (/ x (+ x (* y (exp (* 2.0 (- (+ (* a c) (* 0.8333333333333334 c)) (* a b))))))) (if (< t 5.196588770651547e-123) (/ x (+ x (* y (exp (* 2.0 (/ (- (* (* z (sqrt (+ t a))) (* (* 3.0 t) (- a (/ 5.0 6.0)))) (* (- (* (+ (/ 5.0 6.0) a) (* 3.0 t)) 2.0) (* (- a (/ 5.0 6.0)) (* (- b c) t)))) (* (* (* t t) 3.0) (- a (/ 5.0 6.0))))))))) (/ x (+ x (* y (exp (* 2.0 (- (/ (* z (sqrt (+ t a))) t) (* (- b c) (- (+ a (/ 5.0 6.0)) (/ 2.0 (* t 3.0))))))))))))

  (/ x (+ x (* y (exp (* 2.0 (- (/ (* z (sqrt (+ t a))) t) (* (- b c) (- (+ a (/ 5.0 6.0)) (/ 2.0 (* t 3.0)))))))))))