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

Percentage Accurate: 93.9% → 96.5%

Time: 33.3s

Alternatives: 19

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: 93.9% 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: 96.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z \cdot \sqrt{t + a}}{t}\\ \mathbf{if}\;t\_1 - \left(b - c\right) \cdot \left(\left(a + \frac{5}{6}\right) - \frac{2}{t \cdot 3}\right) \leq \infty:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(t\_1 + \left(b - c\right) \cdot \left(\frac{0.6666666666666666}{t} + \left(-0.8333333333333334 - a\right)\right)\right)}}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (/ (* z (sqrt (+ t a))) t)))
   (if (<=
        (- t_1 (* (- b c) (- (+ a (/ 5.0 6.0)) (/ 2.0 (* t 3.0)))))
        INFINITY)
     (/
      x
      (+
       x
       (*
        y
        (exp
         (*
          2.0
          (+
           t_1
           (*
            (- b c)
            (+ (/ 0.6666666666666666 t) (- -0.8333333333333334 a)))))))))
     1.0)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (z * sqrt((t + a))) / t;
	double tmp;
	if ((t_1 - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0))))) <= ((double) INFINITY)) {
		tmp = x / (x + (y * exp((2.0 * (t_1 + ((b - c) * ((0.6666666666666666 / t) + (-0.8333333333333334 - a))))))));
	} else {
		tmp = 1.0;
	}
	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;
	double tmp;
	if ((t_1 - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0))))) <= Double.POSITIVE_INFINITY) {
		tmp = x / (x + (y * Math.exp((2.0 * (t_1 + ((b - c) * ((0.6666666666666666 / t) + (-0.8333333333333334 - a))))))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (z * math.sqrt((t + a))) / t
	tmp = 0
	if (t_1 - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0))))) <= math.inf:
		tmp = x / (x + (y * math.exp((2.0 * (t_1 + ((b - c) * ((0.6666666666666666 / t) + (-0.8333333333333334 - a))))))))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(z * sqrt(Float64(t + a))) / t)
	tmp = 0.0
	if (Float64(t_1 - Float64(Float64(b - c) * Float64(Float64(a + Float64(5.0 / 6.0)) - Float64(2.0 / Float64(t * 3.0))))) <= Inf)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(t_1 + Float64(Float64(b - c) * Float64(Float64(0.6666666666666666 / t) + Float64(-0.8333333333333334 - a)))))))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (z * sqrt((t + a))) / t;
	tmp = 0.0;
	if ((t_1 - ((b - c) * ((a + (5.0 / 6.0)) - (2.0 / (t * 3.0))))) <= Inf)
		tmp = x / (x + (y * exp((2.0 * (t_1 + ((b - c) * ((0.6666666666666666 / t) + (-0.8333333333333334 - a))))))));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(z * N[Sqrt[N[(t + a), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]}, If[LessEqual[N[(t$95$1 - 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], Infinity], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(t$95$1 + N[(N[(b - c), $MachinePrecision] * N[(N[(0.6666666666666666 / t), $MachinePrecision] + N[(-0.8333333333333334 - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 (*.f64 z (sqrt.f64 (+.f64 t a))) t) (*.f64 (-.f64 b c) (-.f64 (+.f64 a (/.f64 #s(literal 5 binary64) #s(literal 6 binary64))) (/.f64 #s(literal 2 binary64) (*.f64 t #s(literal 3 binary64)))))) < +inf.0

   1. Initial program 98.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   if +inf.0 < (-.f64 (/.f64 (*.f64 z (sqrt.f64 (+.f64 t a))) t) (*.f64 (-.f64 b c) (-.f64 (+.f64 a (/.f64 #s(literal 5 binary64) #s(literal 6 binary64))) (/.f64 #s(literal 2 binary64) (*.f64 t #s(literal 3 binary64))))))

   1. Initial program 0.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

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

Alternative 2: 78.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{0.6666666666666666}{t} - \left(a + 0.8333333333333334\right)\\ t_2 := \frac{x}{x + y \cdot e^{2 \cdot \left(b \cdot t\_1\right)}}\\ t_3 := \frac{x}{x + y \cdot e^{2 \cdot \left(0 - c \cdot t\_1\right)}}\\ \mathbf{if}\;c \leq -1.8 \cdot 10^{+132}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;c \leq 6.6 \cdot 10^{-305}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;c \leq 2.1 \cdot 10^{-199}:\\ \;\;\;\;\frac{1}{\frac{x + y \cdot e^{\frac{2}{\frac{t}{z \cdot \sqrt{t + a}}}}}{x}}\\ \mathbf{elif}\;c \leq 1.22 \cdot 10^{+33}:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (- (/ 0.6666666666666666 t) (+ a 0.8333333333333334)))
        (t_2 (/ x (+ x (* y (exp (* 2.0 (* b t_1)))))))
        (t_3 (/ x (+ x (* y (exp (* 2.0 (- 0.0 (* c t_1)))))))))
   (if (<= c -1.8e+132)
     t_3
     (if (<= c 6.6e-305)
       t_2
       (if (<= c 2.1e-199)
         (/ 1.0 (/ (+ x (* y (exp (/ 2.0 (/ t (* z (sqrt (+ t a)))))))) x))
         (if (<= c 1.22e+33) t_2 t_3))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (0.6666666666666666 / t) - (a + 0.8333333333333334);
	double t_2 = x / (x + (y * exp((2.0 * (b * t_1)))));
	double t_3 = x / (x + (y * exp((2.0 * (0.0 - (c * t_1))))));
	double tmp;
	if (c <= -1.8e+132) {
		tmp = t_3;
	} else if (c <= 6.6e-305) {
		tmp = t_2;
	} else if (c <= 2.1e-199) {
		tmp = 1.0 / ((x + (y * exp((2.0 / (t / (z * sqrt((t + a)))))))) / x);
	} else if (c <= 1.22e+33) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	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) :: t_3
    real(8) :: tmp
    t_1 = (0.6666666666666666d0 / t) - (a + 0.8333333333333334d0)
    t_2 = x / (x + (y * exp((2.0d0 * (b * t_1)))))
    t_3 = x / (x + (y * exp((2.0d0 * (0.0d0 - (c * t_1))))))
    if (c <= (-1.8d+132)) then
        tmp = t_3
    else if (c <= 6.6d-305) then
        tmp = t_2
    else if (c <= 2.1d-199) then
        tmp = 1.0d0 / ((x + (y * exp((2.0d0 / (t / (z * sqrt((t + a)))))))) / x)
    else if (c <= 1.22d+33) then
        tmp = t_2
    else
        tmp = t_3
    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 = (0.6666666666666666 / t) - (a + 0.8333333333333334);
	double t_2 = x / (x + (y * Math.exp((2.0 * (b * t_1)))));
	double t_3 = x / (x + (y * Math.exp((2.0 * (0.0 - (c * t_1))))));
	double tmp;
	if (c <= -1.8e+132) {
		tmp = t_3;
	} else if (c <= 6.6e-305) {
		tmp = t_2;
	} else if (c <= 2.1e-199) {
		tmp = 1.0 / ((x + (y * Math.exp((2.0 / (t / (z * Math.sqrt((t + a)))))))) / x);
	} else if (c <= 1.22e+33) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (0.6666666666666666 / t) - (a + 0.8333333333333334)
	t_2 = x / (x + (y * math.exp((2.0 * (b * t_1)))))
	t_3 = x / (x + (y * math.exp((2.0 * (0.0 - (c * t_1))))))
	tmp = 0
	if c <= -1.8e+132:
		tmp = t_3
	elif c <= 6.6e-305:
		tmp = t_2
	elif c <= 2.1e-199:
		tmp = 1.0 / ((x + (y * math.exp((2.0 / (t / (z * math.sqrt((t + a)))))))) / x)
	elif c <= 1.22e+33:
		tmp = t_2
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(0.6666666666666666 / t) - Float64(a + 0.8333333333333334))
	t_2 = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(b * t_1))))))
	t_3 = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(0.0 - Float64(c * t_1)))))))
	tmp = 0.0
	if (c <= -1.8e+132)
		tmp = t_3;
	elseif (c <= 6.6e-305)
		tmp = t_2;
	elseif (c <= 2.1e-199)
		tmp = Float64(1.0 / Float64(Float64(x + Float64(y * exp(Float64(2.0 / Float64(t / Float64(z * sqrt(Float64(t + a)))))))) / x));
	elseif (c <= 1.22e+33)
		tmp = t_2;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (0.6666666666666666 / t) - (a + 0.8333333333333334);
	t_2 = x / (x + (y * exp((2.0 * (b * t_1)))));
	t_3 = x / (x + (y * exp((2.0 * (0.0 - (c * t_1))))));
	tmp = 0.0;
	if (c <= -1.8e+132)
		tmp = t_3;
	elseif (c <= 6.6e-305)
		tmp = t_2;
	elseif (c <= 2.1e-199)
		tmp = 1.0 / ((x + (y * exp((2.0 / (t / (z * sqrt((t + a)))))))) / x);
	elseif (c <= 1.22e+33)
		tmp = t_2;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(a + 0.8333333333333334), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(b * t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(0.0 - N[(c * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -1.8e+132], t$95$3, If[LessEqual[c, 6.6e-305], t$95$2, If[LessEqual[c, 2.1e-199], N[(1.0 / N[(N[(x + N[(y * N[Exp[N[(2.0 / N[(t / N[(z * N[Sqrt[N[(t + a), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 1.22e+33], t$95$2, t$95$3]]]]]]]

Derivation

1. Split input into 3 regimes

2. if c < -1.80000000000000008e132 or 1.22000000000000005e33 < c

   1. Initial program 89.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.80000000000000008e132 < c < 6.59999999999999965e-305 or 2.10000000000000002e-199 < c < 1.22000000000000005e33

   1. Initial program 96.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 6.59999999999999965e-305 < c < 2.10000000000000002e-199

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

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

Alternative 3: 79.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.85 \cdot 10^{+25}:\\ \;\;\;\;\frac{x}{y \cdot e^{-2 \cdot \left(b \cdot \left(0.8333333333333334 + a\right)\right)} + x}\\ \mathbf{elif}\;t \leq 2.1 \cdot 10^{-114}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(0 - c \cdot \left(\frac{0.6666666666666666}{t} - \left(a + 0.8333333333333334\right)\right)\right)}}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(z \cdot \sqrt{\frac{1}{t}} - \left(b - c\right) \cdot \left(a + 0.8333333333333334\right)\right)}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= t -1.85e+25)
   (/ x (+ (* y (exp (* -2.0 (* b (+ 0.8333333333333334 a))))) x))
   (if (<= t 2.1e-114)
     (/
      x
      (+
       x
       (*
        y
        (exp
         (*
          2.0
          (-
           0.0
           (* c (- (/ 0.6666666666666666 t) (+ a 0.8333333333333334)))))))))
     (/
      x
      (+
       x
       (*
        y
        (exp
         (*
          2.0
          (-
           (* z (sqrt (/ 1.0 t)))
           (* (- b c) (+ a 0.8333333333333334)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (t <= -1.85e+25) {
		tmp = x / ((y * exp((-2.0 * (b * (0.8333333333333334 + a))))) + x);
	} else if (t <= 2.1e-114) {
		tmp = x / (x + (y * exp((2.0 * (0.0 - (c * ((0.6666666666666666 / t) - (a + 0.8333333333333334))))))));
	} else {
		tmp = x / (x + (y * exp((2.0 * ((z * sqrt((1.0 / t))) - ((b - c) * (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 (t <= (-1.85d+25)) then
        tmp = x / ((y * exp(((-2.0d0) * (b * (0.8333333333333334d0 + a))))) + x)
    else if (t <= 2.1d-114) then
        tmp = x / (x + (y * exp((2.0d0 * (0.0d0 - (c * ((0.6666666666666666d0 / t) - (a + 0.8333333333333334d0))))))))
    else
        tmp = x / (x + (y * exp((2.0d0 * ((z * sqrt((1.0d0 / t))) - ((b - c) * (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 (t <= -1.85e+25) {
		tmp = x / ((y * Math.exp((-2.0 * (b * (0.8333333333333334 + a))))) + x);
	} else if (t <= 2.1e-114) {
		tmp = x / (x + (y * Math.exp((2.0 * (0.0 - (c * ((0.6666666666666666 / t) - (a + 0.8333333333333334))))))));
	} else {
		tmp = x / (x + (y * Math.exp((2.0 * ((z * Math.sqrt((1.0 / t))) - ((b - c) * (a + 0.8333333333333334)))))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if t <= -1.85e+25:
		tmp = x / ((y * math.exp((-2.0 * (b * (0.8333333333333334 + a))))) + x)
	elif t <= 2.1e-114:
		tmp = x / (x + (y * math.exp((2.0 * (0.0 - (c * ((0.6666666666666666 / t) - (a + 0.8333333333333334))))))))
	else:
		tmp = x / (x + (y * math.exp((2.0 * ((z * math.sqrt((1.0 / t))) - ((b - c) * (a + 0.8333333333333334)))))))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (t <= -1.85e+25)
		tmp = Float64(x / Float64(Float64(y * exp(Float64(-2.0 * Float64(b * Float64(0.8333333333333334 + a))))) + x));
	elseif (t <= 2.1e-114)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(0.0 - Float64(c * Float64(Float64(0.6666666666666666 / t) - Float64(a + 0.8333333333333334)))))))));
	else
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(z * sqrt(Float64(1.0 / t))) - Float64(Float64(b - c) * Float64(a + 0.8333333333333334))))))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (t <= -1.85e+25)
		tmp = x / ((y * exp((-2.0 * (b * (0.8333333333333334 + a))))) + x);
	elseif (t <= 2.1e-114)
		tmp = x / (x + (y * exp((2.0 * (0.0 - (c * ((0.6666666666666666 / t) - (a + 0.8333333333333334))))))));
	else
		tmp = x / (x + (y * exp((2.0 * ((z * sqrt((1.0 / t))) - ((b - c) * (a + 0.8333333333333334)))))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[t, -1.85e+25], N[(x / N[(N[(y * N[Exp[N[(-2.0 * N[(b * N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.1e-114], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(0.0 - N[(c * N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(a + 0.8333333333333334), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 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[(b - c), $MachinePrecision] * N[(a + 0.8333333333333334), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -1.8499999999999999e25

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -1.8499999999999999e25 < t < 2.09999999999999993e-114

   1. Initial program 89.7%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 2.09999999999999993e-114 < t

   1. Initial program 96.1%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

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

Alternative 4: 78.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{0.6666666666666666}{t} - \left(a + 0.8333333333333334\right)\\ t_2 := \frac{x}{x + y \cdot e^{2 \cdot \left(b \cdot t\_1\right)}}\\ t_3 := \frac{x}{x + y \cdot e^{2 \cdot \left(0 - c \cdot t\_1\right)}}\\ \mathbf{if}\;c \leq -4.2 \cdot 10^{+134}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;c \leq 6.6 \cdot 10^{-305}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;c \leq 4.5 \cdot 10^{-199}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(\frac{z}{t} \cdot \sqrt{t + a}\right)}}\\ \mathbf{elif}\;c \leq 1.8 \cdot 10^{+33}:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (- (/ 0.6666666666666666 t) (+ a 0.8333333333333334)))
        (t_2 (/ x (+ x (* y (exp (* 2.0 (* b t_1)))))))
        (t_3 (/ x (+ x (* y (exp (* 2.0 (- 0.0 (* c t_1)))))))))
   (if (<= c -4.2e+134)
     t_3
     (if (<= c 6.6e-305)
       t_2
       (if (<= c 4.5e-199)
         (/ x (+ x (* y (exp (* 2.0 (* (/ z t) (sqrt (+ t a))))))))
         (if (<= c 1.8e+33) t_2 t_3))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (0.6666666666666666 / t) - (a + 0.8333333333333334);
	double t_2 = x / (x + (y * exp((2.0 * (b * t_1)))));
	double t_3 = x / (x + (y * exp((2.0 * (0.0 - (c * t_1))))));
	double tmp;
	if (c <= -4.2e+134) {
		tmp = t_3;
	} else if (c <= 6.6e-305) {
		tmp = t_2;
	} else if (c <= 4.5e-199) {
		tmp = x / (x + (y * exp((2.0 * ((z / t) * sqrt((t + a)))))));
	} else if (c <= 1.8e+33) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	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) :: t_3
    real(8) :: tmp
    t_1 = (0.6666666666666666d0 / t) - (a + 0.8333333333333334d0)
    t_2 = x / (x + (y * exp((2.0d0 * (b * t_1)))))
    t_3 = x / (x + (y * exp((2.0d0 * (0.0d0 - (c * t_1))))))
    if (c <= (-4.2d+134)) then
        tmp = t_3
    else if (c <= 6.6d-305) then
        tmp = t_2
    else if (c <= 4.5d-199) then
        tmp = x / (x + (y * exp((2.0d0 * ((z / t) * sqrt((t + a)))))))
    else if (c <= 1.8d+33) then
        tmp = t_2
    else
        tmp = t_3
    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 = (0.6666666666666666 / t) - (a + 0.8333333333333334);
	double t_2 = x / (x + (y * Math.exp((2.0 * (b * t_1)))));
	double t_3 = x / (x + (y * Math.exp((2.0 * (0.0 - (c * t_1))))));
	double tmp;
	if (c <= -4.2e+134) {
		tmp = t_3;
	} else if (c <= 6.6e-305) {
		tmp = t_2;
	} else if (c <= 4.5e-199) {
		tmp = x / (x + (y * Math.exp((2.0 * ((z / t) * Math.sqrt((t + a)))))));
	} else if (c <= 1.8e+33) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (0.6666666666666666 / t) - (a + 0.8333333333333334)
	t_2 = x / (x + (y * math.exp((2.0 * (b * t_1)))))
	t_3 = x / (x + (y * math.exp((2.0 * (0.0 - (c * t_1))))))
	tmp = 0
	if c <= -4.2e+134:
		tmp = t_3
	elif c <= 6.6e-305:
		tmp = t_2
	elif c <= 4.5e-199:
		tmp = x / (x + (y * math.exp((2.0 * ((z / t) * math.sqrt((t + a)))))))
	elif c <= 1.8e+33:
		tmp = t_2
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(0.6666666666666666 / t) - Float64(a + 0.8333333333333334))
	t_2 = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(b * t_1))))))
	t_3 = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(0.0 - Float64(c * t_1)))))))
	tmp = 0.0
	if (c <= -4.2e+134)
		tmp = t_3;
	elseif (c <= 6.6e-305)
		tmp = t_2;
	elseif (c <= 4.5e-199)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(Float64(z / t) * sqrt(Float64(t + a))))))));
	elseif (c <= 1.8e+33)
		tmp = t_2;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (0.6666666666666666 / t) - (a + 0.8333333333333334);
	t_2 = x / (x + (y * exp((2.0 * (b * t_1)))));
	t_3 = x / (x + (y * exp((2.0 * (0.0 - (c * t_1))))));
	tmp = 0.0;
	if (c <= -4.2e+134)
		tmp = t_3;
	elseif (c <= 6.6e-305)
		tmp = t_2;
	elseif (c <= 4.5e-199)
		tmp = x / (x + (y * exp((2.0 * ((z / t) * sqrt((t + a)))))));
	elseif (c <= 1.8e+33)
		tmp = t_2;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(a + 0.8333333333333334), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(b * t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(0.0 - N[(c * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -4.2e+134], t$95$3, If[LessEqual[c, 6.6e-305], t$95$2, If[LessEqual[c, 4.5e-199], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(N[(z / t), $MachinePrecision] * N[Sqrt[N[(t + a), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 1.8e+33], t$95$2, t$95$3]]]]]]]

Derivation

1. Split input into 3 regimes

2. if c < -4.2000000000000002e134 or 1.8000000000000001e33 < c

   1. Initial program 89.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -4.2000000000000002e134 < c < 6.59999999999999965e-305 or 4.49999999999999998e-199 < c < 1.8000000000000001e33

   1. Initial program 96.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 6.59999999999999965e-305 < c < 4.49999999999999998e-199

   1. Initial program 99.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

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

Alternative 5: 69.0% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{0.6666666666666666}{t} - \left(0.8333333333333334 + a\right)\\ t_2 := \frac{x}{x + y \cdot e^{2 \cdot \left(b \cdot \left(\frac{0.6666666666666666}{t} - \left(a + 0.8333333333333334\right)\right)\right)}}\\ \mathbf{if}\;b \leq -1.25 \cdot 10^{+76}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;b \leq -5.8 \cdot 10^{+26}:\\ \;\;\;\;1\\ \mathbf{elif}\;b \leq -2.7 \cdot 10^{-119}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;b \leq 2.4 \cdot 10^{-226}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(c \cdot \left(0.8333333333333334 + a\right)\right)}}\\ \mathbf{elif}\;b \leq 1.55 \cdot 10^{-83}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + b \cdot \left(2 \cdot \left(b \cdot \left(t\_1 \cdot t\_1\right) + t\_1\right)\right)\right)}\\ \mathbf{elif}\;b \leq 4.5 \cdot 10^{+131}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (- (/ 0.6666666666666666 t) (+ 0.8333333333333334 a)))
        (t_2
         (/
          x
          (+
           x
           (*
            y
            (exp
             (*
              2.0
              (*
               b
               (- (/ 0.6666666666666666 t) (+ a 0.8333333333333334))))))))))
   (if (<= b -1.25e+76)
     t_2
     (if (<= b -5.8e+26)
       1.0
       (if (<= b -2.7e-119)
         t_2
         (if (<= b 2.4e-226)
           (/ x (+ x (* y (exp (* 2.0 (* c (+ 0.8333333333333334 a)))))))
           (if (<= b 1.55e-83)
             (/ x (+ x (* y (+ 1.0 (* b (* 2.0 (+ (* b (* t_1 t_1)) t_1)))))))
             (if (<= b 4.5e+131) 1.0 t_2))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	double t_2 = x / (x + (y * exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))));
	double tmp;
	if (b <= -1.25e+76) {
		tmp = t_2;
	} else if (b <= -5.8e+26) {
		tmp = 1.0;
	} else if (b <= -2.7e-119) {
		tmp = t_2;
	} else if (b <= 2.4e-226) {
		tmp = x / (x + (y * exp((2.0 * (c * (0.8333333333333334 + a))))));
	} else if (b <= 1.55e-83) {
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * (t_1 * t_1)) + t_1))))));
	} else if (b <= 4.5e+131) {
		tmp = 1.0;
	} else {
		tmp = t_2;
	}
	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 = (0.6666666666666666d0 / t) - (0.8333333333333334d0 + a)
    t_2 = x / (x + (y * exp((2.0d0 * (b * ((0.6666666666666666d0 / t) - (a + 0.8333333333333334d0)))))))
    if (b <= (-1.25d+76)) then
        tmp = t_2
    else if (b <= (-5.8d+26)) then
        tmp = 1.0d0
    else if (b <= (-2.7d-119)) then
        tmp = t_2
    else if (b <= 2.4d-226) then
        tmp = x / (x + (y * exp((2.0d0 * (c * (0.8333333333333334d0 + a))))))
    else if (b <= 1.55d-83) then
        tmp = x / (x + (y * (1.0d0 + (b * (2.0d0 * ((b * (t_1 * t_1)) + t_1))))))
    else if (b <= 4.5d+131) then
        tmp = 1.0d0
    else
        tmp = t_2
    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 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	double t_2 = x / (x + (y * Math.exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))));
	double tmp;
	if (b <= -1.25e+76) {
		tmp = t_2;
	} else if (b <= -5.8e+26) {
		tmp = 1.0;
	} else if (b <= -2.7e-119) {
		tmp = t_2;
	} else if (b <= 2.4e-226) {
		tmp = x / (x + (y * Math.exp((2.0 * (c * (0.8333333333333334 + a))))));
	} else if (b <= 1.55e-83) {
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * (t_1 * t_1)) + t_1))))));
	} else if (b <= 4.5e+131) {
		tmp = 1.0;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a)
	t_2 = x / (x + (y * math.exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))))
	tmp = 0
	if b <= -1.25e+76:
		tmp = t_2
	elif b <= -5.8e+26:
		tmp = 1.0
	elif b <= -2.7e-119:
		tmp = t_2
	elif b <= 2.4e-226:
		tmp = x / (x + (y * math.exp((2.0 * (c * (0.8333333333333334 + a))))))
	elif b <= 1.55e-83:
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * (t_1 * t_1)) + t_1))))))
	elif b <= 4.5e+131:
		tmp = 1.0
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(0.6666666666666666 / t) - Float64(0.8333333333333334 + a))
	t_2 = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(b * Float64(Float64(0.6666666666666666 / t) - Float64(a + 0.8333333333333334))))))))
	tmp = 0.0
	if (b <= -1.25e+76)
		tmp = t_2;
	elseif (b <= -5.8e+26)
		tmp = 1.0;
	elseif (b <= -2.7e-119)
		tmp = t_2;
	elseif (b <= 2.4e-226)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(c * Float64(0.8333333333333334 + a)))))));
	elseif (b <= 1.55e-83)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(b * Float64(2.0 * Float64(Float64(b * Float64(t_1 * t_1)) + t_1)))))));
	elseif (b <= 4.5e+131)
		tmp = 1.0;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	t_2 = x / (x + (y * exp((2.0 * (b * ((0.6666666666666666 / t) - (a + 0.8333333333333334)))))));
	tmp = 0.0;
	if (b <= -1.25e+76)
		tmp = t_2;
	elseif (b <= -5.8e+26)
		tmp = 1.0;
	elseif (b <= -2.7e-119)
		tmp = t_2;
	elseif (b <= 2.4e-226)
		tmp = x / (x + (y * exp((2.0 * (c * (0.8333333333333334 + a))))));
	elseif (b <= 1.55e-83)
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * (t_1 * t_1)) + t_1))))));
	elseif (b <= 4.5e+131)
		tmp = 1.0;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = 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]}, If[LessEqual[b, -1.25e+76], t$95$2, If[LessEqual[b, -5.8e+26], 1.0, If[LessEqual[b, -2.7e-119], t$95$2, If[LessEqual[b, 2.4e-226], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(c * N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.55e-83], N[(x / N[(x + N[(y * N[(1.0 + N[(b * N[(2.0 * N[(N[(b * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 4.5e+131], 1.0, t$95$2]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if b < -1.24999999999999998e76 or -5.8e26 < b < -2.70000000000000027e-119 or 4.5000000000000002e131 < b

   1. Initial program 93.6%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.24999999999999998e76 < b < -5.8e26 or 1.54999999999999996e-83 < b < 4.5000000000000002e131

   1. Initial program 89.6%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -2.70000000000000027e-119 < b < 2.4e-226

   1. Initial program 98.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if 2.4e-226 < b < 1.54999999999999996e-83

   1. Initial program 96.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

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

Alternative 6: 62.0% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{0.6666666666666666}{t} - \left(0.8333333333333334 + a\right)\\ t_2 := t\_1 \cdot t\_1\\ t_3 := \frac{x}{y \cdot e^{-2 \cdot \left(b \cdot \left(0.8333333333333334 + a\right)\right)} + x}\\ t_4 := \frac{x}{x + y \cdot e^{2 \cdot \left(c \cdot \left(0.8333333333333334 + a\right)\right)}}\\ \mathbf{if}\;t \leq -8.5 \cdot 10^{+24}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t \leq -7 \cdot 10^{-275}:\\ \;\;\;\;t\_4\\ \mathbf{elif}\;t \leq 4.4 \cdot 10^{-111}:\\ \;\;\;\;1\\ \mathbf{elif}\;t \leq 4.6 \cdot 10^{-104}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + b \cdot \left(2 \cdot t\_1 + b \cdot \left(\left(1.3333333333333333 \cdot b\right) \cdot \left(t\_1 \cdot t\_2\right) + 2 \cdot t\_2\right)\right)\right)}\\ \mathbf{elif}\;t \leq 4.05 \cdot 10^{-38}:\\ \;\;\;\;1\\ \mathbf{elif}\;t \leq 3.3 \cdot 10^{+141}:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (- (/ 0.6666666666666666 t) (+ 0.8333333333333334 a)))
        (t_2 (* t_1 t_1))
        (t_3 (/ x (+ (* y (exp (* -2.0 (* b (+ 0.8333333333333334 a))))) x)))
        (t_4 (/ x (+ x (* y (exp (* 2.0 (* c (+ 0.8333333333333334 a)))))))))
   (if (<= t -8.5e+24)
     t_3
     (if (<= t -7e-275)
       t_4
       (if (<= t 4.4e-111)
         1.0
         (if (<= t 4.6e-104)
           (/
            x
            (+
             x
             (*
              y
              (+
               1.0
               (*
                b
                (+
                 (* 2.0 t_1)
                 (*
                  b
                  (+
                   (* (* 1.3333333333333333 b) (* t_1 t_2))
                   (* 2.0 t_2)))))))))
           (if (<= t 4.05e-38) 1.0 (if (<= t 3.3e+141) t_3 t_4))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	double t_2 = t_1 * t_1;
	double t_3 = x / ((y * exp((-2.0 * (b * (0.8333333333333334 + a))))) + x);
	double t_4 = x / (x + (y * exp((2.0 * (c * (0.8333333333333334 + a))))));
	double tmp;
	if (t <= -8.5e+24) {
		tmp = t_3;
	} else if (t <= -7e-275) {
		tmp = t_4;
	} else if (t <= 4.4e-111) {
		tmp = 1.0;
	} else if (t <= 4.6e-104) {
		tmp = x / (x + (y * (1.0 + (b * ((2.0 * t_1) + (b * (((1.3333333333333333 * b) * (t_1 * t_2)) + (2.0 * t_2))))))));
	} else if (t <= 4.05e-38) {
		tmp = 1.0;
	} else if (t <= 3.3e+141) {
		tmp = t_3;
	} else {
		tmp = t_4;
	}
	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) :: t_3
    real(8) :: t_4
    real(8) :: tmp
    t_1 = (0.6666666666666666d0 / t) - (0.8333333333333334d0 + a)
    t_2 = t_1 * t_1
    t_3 = x / ((y * exp(((-2.0d0) * (b * (0.8333333333333334d0 + a))))) + x)
    t_4 = x / (x + (y * exp((2.0d0 * (c * (0.8333333333333334d0 + a))))))
    if (t <= (-8.5d+24)) then
        tmp = t_3
    else if (t <= (-7d-275)) then
        tmp = t_4
    else if (t <= 4.4d-111) then
        tmp = 1.0d0
    else if (t <= 4.6d-104) then
        tmp = x / (x + (y * (1.0d0 + (b * ((2.0d0 * t_1) + (b * (((1.3333333333333333d0 * b) * (t_1 * t_2)) + (2.0d0 * t_2))))))))
    else if (t <= 4.05d-38) then
        tmp = 1.0d0
    else if (t <= 3.3d+141) then
        tmp = t_3
    else
        tmp = t_4
    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 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	double t_2 = t_1 * t_1;
	double t_3 = x / ((y * Math.exp((-2.0 * (b * (0.8333333333333334 + a))))) + x);
	double t_4 = x / (x + (y * Math.exp((2.0 * (c * (0.8333333333333334 + a))))));
	double tmp;
	if (t <= -8.5e+24) {
		tmp = t_3;
	} else if (t <= -7e-275) {
		tmp = t_4;
	} else if (t <= 4.4e-111) {
		tmp = 1.0;
	} else if (t <= 4.6e-104) {
		tmp = x / (x + (y * (1.0 + (b * ((2.0 * t_1) + (b * (((1.3333333333333333 * b) * (t_1 * t_2)) + (2.0 * t_2))))))));
	} else if (t <= 4.05e-38) {
		tmp = 1.0;
	} else if (t <= 3.3e+141) {
		tmp = t_3;
	} else {
		tmp = t_4;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a)
	t_2 = t_1 * t_1
	t_3 = x / ((y * math.exp((-2.0 * (b * (0.8333333333333334 + a))))) + x)
	t_4 = x / (x + (y * math.exp((2.0 * (c * (0.8333333333333334 + a))))))
	tmp = 0
	if t <= -8.5e+24:
		tmp = t_3
	elif t <= -7e-275:
		tmp = t_4
	elif t <= 4.4e-111:
		tmp = 1.0
	elif t <= 4.6e-104:
		tmp = x / (x + (y * (1.0 + (b * ((2.0 * t_1) + (b * (((1.3333333333333333 * b) * (t_1 * t_2)) + (2.0 * t_2))))))))
	elif t <= 4.05e-38:
		tmp = 1.0
	elif t <= 3.3e+141:
		tmp = t_3
	else:
		tmp = t_4
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(0.6666666666666666 / t) - Float64(0.8333333333333334 + a))
	t_2 = Float64(t_1 * t_1)
	t_3 = Float64(x / Float64(Float64(y * exp(Float64(-2.0 * Float64(b * Float64(0.8333333333333334 + a))))) + x))
	t_4 = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(c * Float64(0.8333333333333334 + a)))))))
	tmp = 0.0
	if (t <= -8.5e+24)
		tmp = t_3;
	elseif (t <= -7e-275)
		tmp = t_4;
	elseif (t <= 4.4e-111)
		tmp = 1.0;
	elseif (t <= 4.6e-104)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(b * Float64(Float64(2.0 * t_1) + Float64(b * Float64(Float64(Float64(1.3333333333333333 * b) * Float64(t_1 * t_2)) + Float64(2.0 * t_2)))))))));
	elseif (t <= 4.05e-38)
		tmp = 1.0;
	elseif (t <= 3.3e+141)
		tmp = t_3;
	else
		tmp = t_4;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	t_2 = t_1 * t_1;
	t_3 = x / ((y * exp((-2.0 * (b * (0.8333333333333334 + a))))) + x);
	t_4 = x / (x + (y * exp((2.0 * (c * (0.8333333333333334 + a))))));
	tmp = 0.0;
	if (t <= -8.5e+24)
		tmp = t_3;
	elseif (t <= -7e-275)
		tmp = t_4;
	elseif (t <= 4.4e-111)
		tmp = 1.0;
	elseif (t <= 4.6e-104)
		tmp = x / (x + (y * (1.0 + (b * ((2.0 * t_1) + (b * (((1.3333333333333333 * b) * (t_1 * t_2)) + (2.0 * t_2))))))));
	elseif (t <= 4.05e-38)
		tmp = 1.0;
	elseif (t <= 3.3e+141)
		tmp = t_3;
	else
		tmp = t_4;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(x / N[(N[(y * N[Exp[N[(-2.0 * N[(b * N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(c * N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -8.5e+24], t$95$3, If[LessEqual[t, -7e-275], t$95$4, If[LessEqual[t, 4.4e-111], 1.0, If[LessEqual[t, 4.6e-104], N[(x / N[(x + N[(y * N[(1.0 + N[(b * N[(N[(2.0 * t$95$1), $MachinePrecision] + N[(b * N[(N[(N[(1.3333333333333333 * b), $MachinePrecision] * N[(t$95$1 * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(2.0 * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 4.05e-38], 1.0, If[LessEqual[t, 3.3e+141], t$95$3, t$95$4]]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -8.49999999999999959e24 or 4.0499999999999998e-38 < t < 3.2999999999999997e141

   1. Initial program 98.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -8.49999999999999959e24 < t < -6.99999999999999938e-275 or 3.2999999999999997e141 < t

   1. Initial program 92.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -6.99999999999999938e-275 < t < 4.4e-111 or 4.5999999999999999e-104 < t < 4.0499999999999998e-38

   1. Initial program 91.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 4.4e-111 < t < 4.5999999999999999e-104

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

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

Alternative 7: 60.1% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{0.6666666666666666}{t} - \left(0.8333333333333334 + a\right)\\ t_2 := t\_1 \cdot t\_1\\ t_3 := \frac{x}{x + y \cdot e^{2 \cdot \left(c \cdot \left(0.8333333333333334 + a\right)\right)}}\\ \mathbf{if}\;t \leq -3.15 \cdot 10^{-263}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t \leq 5.1 \cdot 10^{-111}:\\ \;\;\;\;1\\ \mathbf{elif}\;t \leq 2.3 \cdot 10^{-104}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + b \cdot \left(2 \cdot t\_1 + b \cdot \left(\left(1.3333333333333333 \cdot b\right) \cdot \left(t\_1 \cdot t\_2\right) + 2 \cdot t\_2\right)\right)\right)}\\ \mathbf{elif}\;t \leq 2.4 \cdot 10^{-39}:\\ \;\;\;\;1\\ \mathbf{elif}\;t \leq 2.8 \cdot 10^{+80}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + b \cdot \left(2 \cdot \left(b \cdot t\_2 + t\_1\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (- (/ 0.6666666666666666 t) (+ 0.8333333333333334 a)))
        (t_2 (* t_1 t_1))
        (t_3 (/ x (+ x (* y (exp (* 2.0 (* c (+ 0.8333333333333334 a)))))))))
   (if (<= t -3.15e-263)
     t_3
     (if (<= t 5.1e-111)
       1.0
       (if (<= t 2.3e-104)
         (/
          x
          (+
           x
           (*
            y
            (+
             1.0
             (*
              b
              (+
               (* 2.0 t_1)
               (*
                b
                (+ (* (* 1.3333333333333333 b) (* t_1 t_2)) (* 2.0 t_2)))))))))
         (if (<= t 2.4e-39)
           1.0
           (if (<= t 2.8e+80)
             (/ x (+ x (* y (+ 1.0 (* b (* 2.0 (+ (* b t_2) t_1)))))))
             t_3)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	double t_2 = t_1 * t_1;
	double t_3 = x / (x + (y * exp((2.0 * (c * (0.8333333333333334 + a))))));
	double tmp;
	if (t <= -3.15e-263) {
		tmp = t_3;
	} else if (t <= 5.1e-111) {
		tmp = 1.0;
	} else if (t <= 2.3e-104) {
		tmp = x / (x + (y * (1.0 + (b * ((2.0 * t_1) + (b * (((1.3333333333333333 * b) * (t_1 * t_2)) + (2.0 * t_2))))))));
	} else if (t <= 2.4e-39) {
		tmp = 1.0;
	} else if (t <= 2.8e+80) {
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * t_2) + t_1))))));
	} else {
		tmp = t_3;
	}
	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) :: t_3
    real(8) :: tmp
    t_1 = (0.6666666666666666d0 / t) - (0.8333333333333334d0 + a)
    t_2 = t_1 * t_1
    t_3 = x / (x + (y * exp((2.0d0 * (c * (0.8333333333333334d0 + a))))))
    if (t <= (-3.15d-263)) then
        tmp = t_3
    else if (t <= 5.1d-111) then
        tmp = 1.0d0
    else if (t <= 2.3d-104) then
        tmp = x / (x + (y * (1.0d0 + (b * ((2.0d0 * t_1) + (b * (((1.3333333333333333d0 * b) * (t_1 * t_2)) + (2.0d0 * t_2))))))))
    else if (t <= 2.4d-39) then
        tmp = 1.0d0
    else if (t <= 2.8d+80) then
        tmp = x / (x + (y * (1.0d0 + (b * (2.0d0 * ((b * t_2) + t_1))))))
    else
        tmp = t_3
    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 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	double t_2 = t_1 * t_1;
	double t_3 = x / (x + (y * Math.exp((2.0 * (c * (0.8333333333333334 + a))))));
	double tmp;
	if (t <= -3.15e-263) {
		tmp = t_3;
	} else if (t <= 5.1e-111) {
		tmp = 1.0;
	} else if (t <= 2.3e-104) {
		tmp = x / (x + (y * (1.0 + (b * ((2.0 * t_1) + (b * (((1.3333333333333333 * b) * (t_1 * t_2)) + (2.0 * t_2))))))));
	} else if (t <= 2.4e-39) {
		tmp = 1.0;
	} else if (t <= 2.8e+80) {
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * t_2) + t_1))))));
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a)
	t_2 = t_1 * t_1
	t_3 = x / (x + (y * math.exp((2.0 * (c * (0.8333333333333334 + a))))))
	tmp = 0
	if t <= -3.15e-263:
		tmp = t_3
	elif t <= 5.1e-111:
		tmp = 1.0
	elif t <= 2.3e-104:
		tmp = x / (x + (y * (1.0 + (b * ((2.0 * t_1) + (b * (((1.3333333333333333 * b) * (t_1 * t_2)) + (2.0 * t_2))))))))
	elif t <= 2.4e-39:
		tmp = 1.0
	elif t <= 2.8e+80:
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * t_2) + t_1))))))
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(0.6666666666666666 / t) - Float64(0.8333333333333334 + a))
	t_2 = Float64(t_1 * t_1)
	t_3 = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(c * Float64(0.8333333333333334 + a)))))))
	tmp = 0.0
	if (t <= -3.15e-263)
		tmp = t_3;
	elseif (t <= 5.1e-111)
		tmp = 1.0;
	elseif (t <= 2.3e-104)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(b * Float64(Float64(2.0 * t_1) + Float64(b * Float64(Float64(Float64(1.3333333333333333 * b) * Float64(t_1 * t_2)) + Float64(2.0 * t_2)))))))));
	elseif (t <= 2.4e-39)
		tmp = 1.0;
	elseif (t <= 2.8e+80)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(b * Float64(2.0 * Float64(Float64(b * t_2) + t_1)))))));
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	t_2 = t_1 * t_1;
	t_3 = x / (x + (y * exp((2.0 * (c * (0.8333333333333334 + a))))));
	tmp = 0.0;
	if (t <= -3.15e-263)
		tmp = t_3;
	elseif (t <= 5.1e-111)
		tmp = 1.0;
	elseif (t <= 2.3e-104)
		tmp = x / (x + (y * (1.0 + (b * ((2.0 * t_1) + (b * (((1.3333333333333333 * b) * (t_1 * t_2)) + (2.0 * t_2))))))));
	elseif (t <= 2.4e-39)
		tmp = 1.0;
	elseif (t <= 2.8e+80)
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * t_2) + t_1))))));
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(c * N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -3.15e-263], t$95$3, If[LessEqual[t, 5.1e-111], 1.0, If[LessEqual[t, 2.3e-104], N[(x / N[(x + N[(y * N[(1.0 + N[(b * N[(N[(2.0 * t$95$1), $MachinePrecision] + N[(b * N[(N[(N[(1.3333333333333333 * b), $MachinePrecision] * N[(t$95$1 * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(2.0 * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.4e-39], 1.0, If[LessEqual[t, 2.8e+80], N[(x / N[(x + N[(y * N[(1.0 + N[(b * N[(2.0 * N[(N[(b * t$95$2), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -3.14999999999999986e-263 or 2.79999999999999984e80 < t

   1. Initial program 93.6%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if -3.14999999999999986e-263 < t < 5.10000000000000032e-111 or 2.2999999999999999e-104 < t < 2.40000000000000016e-39

   1. Initial program 91.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 5.10000000000000032e-111 < t < 2.2999999999999999e-104

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   if 2.40000000000000016e-39 < t < 2.79999999999999984e80

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

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

Alternative 8: 79.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{0.6666666666666666}{t} - \left(a + 0.8333333333333334\right)\\ t_2 := \frac{x}{x + y \cdot e^{2 \cdot \left(0 - c \cdot t\_1\right)}}\\ \mathbf{if}\;c \leq -5 \cdot 10^{+134}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;c \leq 1.45 \cdot 10^{+33}:\\ \;\;\;\;\frac{x}{x + y \cdot e^{2 \cdot \left(b \cdot t\_1\right)}}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (- (/ 0.6666666666666666 t) (+ a 0.8333333333333334)))
        (t_2 (/ x (+ x (* y (exp (* 2.0 (- 0.0 (* c t_1)))))))))
   (if (<= c -5e+134)
     t_2
     (if (<= c 1.45e+33) (/ x (+ x (* y (exp (* 2.0 (* b t_1)))))) t_2))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (0.6666666666666666 / t) - (a + 0.8333333333333334);
	double t_2 = x / (x + (y * exp((2.0 * (0.0 - (c * t_1))))));
	double tmp;
	if (c <= -5e+134) {
		tmp = t_2;
	} else if (c <= 1.45e+33) {
		tmp = x / (x + (y * exp((2.0 * (b * t_1)))));
	} else {
		tmp = t_2;
	}
	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 = (0.6666666666666666d0 / t) - (a + 0.8333333333333334d0)
    t_2 = x / (x + (y * exp((2.0d0 * (0.0d0 - (c * t_1))))))
    if (c <= (-5d+134)) then
        tmp = t_2
    else if (c <= 1.45d+33) then
        tmp = x / (x + (y * exp((2.0d0 * (b * t_1)))))
    else
        tmp = t_2
    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 = (0.6666666666666666 / t) - (a + 0.8333333333333334);
	double t_2 = x / (x + (y * Math.exp((2.0 * (0.0 - (c * t_1))))));
	double tmp;
	if (c <= -5e+134) {
		tmp = t_2;
	} else if (c <= 1.45e+33) {
		tmp = x / (x + (y * Math.exp((2.0 * (b * t_1)))));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (0.6666666666666666 / t) - (a + 0.8333333333333334)
	t_2 = x / (x + (y * math.exp((2.0 * (0.0 - (c * t_1))))))
	tmp = 0
	if c <= -5e+134:
		tmp = t_2
	elif c <= 1.45e+33:
		tmp = x / (x + (y * math.exp((2.0 * (b * t_1)))))
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(0.6666666666666666 / t) - Float64(a + 0.8333333333333334))
	t_2 = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(0.0 - Float64(c * t_1)))))))
	tmp = 0.0
	if (c <= -5e+134)
		tmp = t_2;
	elseif (c <= 1.45e+33)
		tmp = Float64(x / Float64(x + Float64(y * exp(Float64(2.0 * Float64(b * t_1))))));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (0.6666666666666666 / t) - (a + 0.8333333333333334);
	t_2 = x / (x + (y * exp((2.0 * (0.0 - (c * t_1))))));
	tmp = 0.0;
	if (c <= -5e+134)
		tmp = t_2;
	elseif (c <= 1.45e+33)
		tmp = x / (x + (y * exp((2.0 * (b * t_1)))));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(a + 0.8333333333333334), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(0.0 - N[(c * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -5e+134], t$95$2, If[LessEqual[c, 1.45e+33], N[(x / N[(x + N[(y * N[Exp[N[(2.0 * N[(b * t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if c < -4.99999999999999981e134 or 1.45000000000000012e33 < c

   1. Initial program 89.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -4.99999999999999981e134 < c < 1.45000000000000012e33

   1. Initial program 96.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

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

Alternative 9: 55.3% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{0.6666666666666666}{t} - \left(0.8333333333333334 + a\right)\\ \mathbf{if}\;b \leq -1.2 \cdot 10^{+76}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(2 \cdot \left(\left(b \cdot a\right) \cdot \left(b \cdot a\right)\right)\right)}\\ \mathbf{elif}\;b \leq -5.8 \cdot 10^{-29}:\\ \;\;\;\;1\\ \mathbf{elif}\;b \leq -6.2 \cdot 10^{-177}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + c \cdot \left(-2 \cdot t\_1 + c \cdot \left(1.3333333333333333 \cdot \left(\left(a \cdot \left(a \cdot a\right)\right) \cdot c\right)\right)\right)\right)}\\ \mathbf{elif}\;b \leq 5.2 \cdot 10^{-269}:\\ \;\;\;\;1\\ \mathbf{elif}\;b \leq 1.15 \cdot 10^{-83}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + b \cdot \left(2 \cdot \left(b \cdot \left(t\_1 \cdot t\_1\right) + t\_1\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (- (/ 0.6666666666666666 t) (+ 0.8333333333333334 a))))
   (if (<= b -1.2e+76)
     (/ x (+ x (* y (* 2.0 (* (* b a) (* b a))))))
     (if (<= b -5.8e-29)
       1.0
       (if (<= b -6.2e-177)
         (/
          x
          (+
           x
           (*
            y
            (+
             1.0
             (*
              c
              (+
               (* -2.0 t_1)
               (* c (* 1.3333333333333333 (* (* a (* a a)) c)))))))))
         (if (<= b 5.2e-269)
           1.0
           (if (<= b 1.15e-83)
             (/ x (+ x (* y (+ 1.0 (* b (* 2.0 (+ (* b (* t_1 t_1)) t_1)))))))
             1.0)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	double tmp;
	if (b <= -1.2e+76) {
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	} else if (b <= -5.8e-29) {
		tmp = 1.0;
	} else if (b <= -6.2e-177) {
		tmp = x / (x + (y * (1.0 + (c * ((-2.0 * t_1) + (c * (1.3333333333333333 * ((a * (a * a)) * c))))))));
	} else if (b <= 5.2e-269) {
		tmp = 1.0;
	} else if (b <= 1.15e-83) {
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * (t_1 * t_1)) + t_1))))));
	} 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) :: t_1
    real(8) :: tmp
    t_1 = (0.6666666666666666d0 / t) - (0.8333333333333334d0 + a)
    if (b <= (-1.2d+76)) then
        tmp = x / (x + (y * (2.0d0 * ((b * a) * (b * a)))))
    else if (b <= (-5.8d-29)) then
        tmp = 1.0d0
    else if (b <= (-6.2d-177)) then
        tmp = x / (x + (y * (1.0d0 + (c * (((-2.0d0) * t_1) + (c * (1.3333333333333333d0 * ((a * (a * a)) * c))))))))
    else if (b <= 5.2d-269) then
        tmp = 1.0d0
    else if (b <= 1.15d-83) then
        tmp = x / (x + (y * (1.0d0 + (b * (2.0d0 * ((b * (t_1 * t_1)) + t_1))))))
    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 t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	double tmp;
	if (b <= -1.2e+76) {
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	} else if (b <= -5.8e-29) {
		tmp = 1.0;
	} else if (b <= -6.2e-177) {
		tmp = x / (x + (y * (1.0 + (c * ((-2.0 * t_1) + (c * (1.3333333333333333 * ((a * (a * a)) * c))))))));
	} else if (b <= 5.2e-269) {
		tmp = 1.0;
	} else if (b <= 1.15e-83) {
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * (t_1 * t_1)) + t_1))))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a)
	tmp = 0
	if b <= -1.2e+76:
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))))
	elif b <= -5.8e-29:
		tmp = 1.0
	elif b <= -6.2e-177:
		tmp = x / (x + (y * (1.0 + (c * ((-2.0 * t_1) + (c * (1.3333333333333333 * ((a * (a * a)) * c))))))))
	elif b <= 5.2e-269:
		tmp = 1.0
	elif b <= 1.15e-83:
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * (t_1 * t_1)) + t_1))))))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(0.6666666666666666 / t) - Float64(0.8333333333333334 + a))
	tmp = 0.0
	if (b <= -1.2e+76)
		tmp = Float64(x / Float64(x + Float64(y * Float64(2.0 * Float64(Float64(b * a) * Float64(b * a))))));
	elseif (b <= -5.8e-29)
		tmp = 1.0;
	elseif (b <= -6.2e-177)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(c * Float64(Float64(-2.0 * t_1) + Float64(c * Float64(1.3333333333333333 * Float64(Float64(a * Float64(a * a)) * c)))))))));
	elseif (b <= 5.2e-269)
		tmp = 1.0;
	elseif (b <= 1.15e-83)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(b * Float64(2.0 * Float64(Float64(b * Float64(t_1 * t_1)) + t_1)))))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (0.6666666666666666 / t) - (0.8333333333333334 + a);
	tmp = 0.0;
	if (b <= -1.2e+76)
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	elseif (b <= -5.8e-29)
		tmp = 1.0;
	elseif (b <= -6.2e-177)
		tmp = x / (x + (y * (1.0 + (c * ((-2.0 * t_1) + (c * (1.3333333333333333 * ((a * (a * a)) * c))))))));
	elseif (b <= 5.2e-269)
		tmp = 1.0;
	elseif (b <= 1.15e-83)
		tmp = x / (x + (y * (1.0 + (b * (2.0 * ((b * (t_1 * t_1)) + t_1))))));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -1.2e+76], N[(x / N[(x + N[(y * N[(2.0 * N[(N[(b * a), $MachinePrecision] * N[(b * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -5.8e-29], 1.0, If[LessEqual[b, -6.2e-177], N[(x / N[(x + N[(y * N[(1.0 + N[(c * N[(N[(-2.0 * t$95$1), $MachinePrecision] + N[(c * N[(1.3333333333333333 * N[(N[(a * N[(a * a), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 5.2e-269], 1.0, If[LessEqual[b, 1.15e-83], N[(x / N[(x + N[(y * N[(1.0 + N[(b * N[(2.0 * N[(N[(b * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]]]]]]

Derivation

1. Split input into 4 regimes

2. if b < -1.2e76

   1. Initial program 90.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in a around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -1.2e76 < b < -5.80000000000000048e-29 or -6.20000000000000036e-177 < b < 5.2e-269 or 1.14999999999999995e-83 < b

   1. Initial program 93.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -5.80000000000000048e-29 < b < -6.20000000000000036e-177

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in a around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if 5.2e-269 < b < 1.14999999999999995e-83

   1. Initial program 97.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

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

Alternative 10: 49.0% accurate, 3.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x + y \cdot \left(1 + c \cdot \left(-2 \cdot \left(\frac{0.6666666666666666}{t} - \left(0.8333333333333334 + a\right)\right) + c \cdot \left(-0.3950617283950617 \cdot \frac{c}{t \cdot \left(t \cdot t\right)}\right)\right)\right)}\\ \mathbf{if}\;x \leq -9.5 \cdot 10^{+159}:\\ \;\;\;\;\frac{x}{y \cdot \left(1 + \frac{x}{y}\right)}\\ \mathbf{elif}\;x \leq -1.9 \cdot 10^{+22}:\\ \;\;\;\;\frac{x}{x + y \cdot \frac{\left(b \cdot b\right) \cdot 0.8888888888888888}{t \cdot t}}\\ \mathbf{elif}\;x \leq -5.8 \cdot 10^{-161}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq -1.8 \cdot 10^{-289}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 5.1 \cdot 10^{-213}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 7.5 \cdot 10^{-153}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1
         (/
          x
          (+
           x
           (*
            y
            (+
             1.0
             (*
              c
              (+
               (* -2.0 (- (/ 0.6666666666666666 t) (+ 0.8333333333333334 a)))
               (* c (* -0.3950617283950617 (/ c (* t (* t t)))))))))))))
   (if (<= x -9.5e+159)
     (/ x (* y (+ 1.0 (/ x y))))
     (if (<= x -1.9e+22)
       (/ x (+ x (* y (/ (* (* b b) 0.8888888888888888) (* t t)))))
       (if (<= x -5.8e-161)
         1.0
         (if (<= x -1.8e-289)
           t_1
           (if (<= x 5.1e-213) 1.0 (if (<= x 7.5e-153) t_1 1.0))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = x / (x + (y * (1.0 + (c * ((-2.0 * ((0.6666666666666666 / t) - (0.8333333333333334 + a))) + (c * (-0.3950617283950617 * (c / (t * (t * t))))))))));
	double tmp;
	if (x <= -9.5e+159) {
		tmp = x / (y * (1.0 + (x / y)));
	} else if (x <= -1.9e+22) {
		tmp = x / (x + (y * (((b * b) * 0.8888888888888888) / (t * t))));
	} else if (x <= -5.8e-161) {
		tmp = 1.0;
	} else if (x <= -1.8e-289) {
		tmp = t_1;
	} else if (x <= 5.1e-213) {
		tmp = 1.0;
	} else if (x <= 7.5e-153) {
		tmp = t_1;
	} 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) :: t_1
    real(8) :: tmp
    t_1 = x / (x + (y * (1.0d0 + (c * (((-2.0d0) * ((0.6666666666666666d0 / t) - (0.8333333333333334d0 + a))) + (c * ((-0.3950617283950617d0) * (c / (t * (t * t))))))))))
    if (x <= (-9.5d+159)) then
        tmp = x / (y * (1.0d0 + (x / y)))
    else if (x <= (-1.9d+22)) then
        tmp = x / (x + (y * (((b * b) * 0.8888888888888888d0) / (t * t))))
    else if (x <= (-5.8d-161)) then
        tmp = 1.0d0
    else if (x <= (-1.8d-289)) then
        tmp = t_1
    else if (x <= 5.1d-213) then
        tmp = 1.0d0
    else if (x <= 7.5d-153) then
        tmp = t_1
    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 t_1 = x / (x + (y * (1.0 + (c * ((-2.0 * ((0.6666666666666666 / t) - (0.8333333333333334 + a))) + (c * (-0.3950617283950617 * (c / (t * (t * t))))))))));
	double tmp;
	if (x <= -9.5e+159) {
		tmp = x / (y * (1.0 + (x / y)));
	} else if (x <= -1.9e+22) {
		tmp = x / (x + (y * (((b * b) * 0.8888888888888888) / (t * t))));
	} else if (x <= -5.8e-161) {
		tmp = 1.0;
	} else if (x <= -1.8e-289) {
		tmp = t_1;
	} else if (x <= 5.1e-213) {
		tmp = 1.0;
	} else if (x <= 7.5e-153) {
		tmp = t_1;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = x / (x + (y * (1.0 + (c * ((-2.0 * ((0.6666666666666666 / t) - (0.8333333333333334 + a))) + (c * (-0.3950617283950617 * (c / (t * (t * t))))))))))
	tmp = 0
	if x <= -9.5e+159:
		tmp = x / (y * (1.0 + (x / y)))
	elif x <= -1.9e+22:
		tmp = x / (x + (y * (((b * b) * 0.8888888888888888) / (t * t))))
	elif x <= -5.8e-161:
		tmp = 1.0
	elif x <= -1.8e-289:
		tmp = t_1
	elif x <= 5.1e-213:
		tmp = 1.0
	elif x <= 7.5e-153:
		tmp = t_1
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(c * Float64(Float64(-2.0 * Float64(Float64(0.6666666666666666 / t) - Float64(0.8333333333333334 + a))) + Float64(c * Float64(-0.3950617283950617 * Float64(c / Float64(t * Float64(t * t)))))))))))
	tmp = 0.0
	if (x <= -9.5e+159)
		tmp = Float64(x / Float64(y * Float64(1.0 + Float64(x / y))));
	elseif (x <= -1.9e+22)
		tmp = Float64(x / Float64(x + Float64(y * Float64(Float64(Float64(b * b) * 0.8888888888888888) / Float64(t * t)))));
	elseif (x <= -5.8e-161)
		tmp = 1.0;
	elseif (x <= -1.8e-289)
		tmp = t_1;
	elseif (x <= 5.1e-213)
		tmp = 1.0;
	elseif (x <= 7.5e-153)
		tmp = t_1;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = x / (x + (y * (1.0 + (c * ((-2.0 * ((0.6666666666666666 / t) - (0.8333333333333334 + a))) + (c * (-0.3950617283950617 * (c / (t * (t * t))))))))));
	tmp = 0.0;
	if (x <= -9.5e+159)
		tmp = x / (y * (1.0 + (x / y)));
	elseif (x <= -1.9e+22)
		tmp = x / (x + (y * (((b * b) * 0.8888888888888888) / (t * t))));
	elseif (x <= -5.8e-161)
		tmp = 1.0;
	elseif (x <= -1.8e-289)
		tmp = t_1;
	elseif (x <= 5.1e-213)
		tmp = 1.0;
	elseif (x <= 7.5e-153)
		tmp = t_1;
	else
		tmp = 1.0;
	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[(1.0 + N[(c * N[(N[(-2.0 * N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(c * N[(-0.3950617283950617 * N[(c / N[(t * N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -9.5e+159], N[(x / N[(y * N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -1.9e+22], N[(x / N[(x + N[(y * N[(N[(N[(b * b), $MachinePrecision] * 0.8888888888888888), $MachinePrecision] / N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -5.8e-161], 1.0, If[LessEqual[x, -1.8e-289], t$95$1, If[LessEqual[x, 5.1e-213], 1.0, If[LessEqual[x, 7.5e-153], t$95$1, 1.0]]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -9.5000000000000003e159

   1. Initial program 89.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in y around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -9.5000000000000003e159 < x < -1.9000000000000002e22

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in t around 0 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -1.9000000000000002e22 < x < -5.8e-161 or -1.8e-289 < x < 5.0999999999999997e-213 or 7.5e-153 < x

   1. Initial program 96.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -5.8e-161 < x < -1.8e-289 or 5.0999999999999997e-213 < x < 7.5e-153

   1. Initial program 88.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in t around 0 0

       \[\leadsto expr\]

   13. Simplified 0

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

Alternative 11: 55.1% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -2 \cdot \left(\frac{0.6666666666666666}{t} - \left(0.8333333333333334 + a\right)\right)\\ \mathbf{if}\;b \leq -1.05 \cdot 10^{+76}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(2 \cdot \left(\left(b \cdot a\right) \cdot \left(b \cdot a\right)\right)\right)}\\ \mathbf{elif}\;b \leq -2.35 \cdot 10^{-29}:\\ \;\;\;\;1\\ \mathbf{elif}\;b \leq -5.2 \cdot 10^{-177}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + c \cdot \left(t\_1 + c \cdot \left(1.3333333333333333 \cdot \left(\left(a \cdot \left(a \cdot a\right)\right) \cdot c\right)\right)\right)\right)}\\ \mathbf{elif}\;b \leq -1.02 \cdot 10^{-276}:\\ \;\;\;\;1\\ \mathbf{elif}\;b \leq 4.1 \cdot 10^{-66}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + c \cdot \left(t\_1 + c \cdot \left(-0.3950617283950617 \cdot \frac{c}{t \cdot \left(t \cdot t\right)}\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* -2.0 (- (/ 0.6666666666666666 t) (+ 0.8333333333333334 a)))))
   (if (<= b -1.05e+76)
     (/ x (+ x (* y (* 2.0 (* (* b a) (* b a))))))
     (if (<= b -2.35e-29)
       1.0
       (if (<= b -5.2e-177)
         (/
          x
          (+
           x
           (*
            y
            (+
             1.0
             (* c (+ t_1 (* c (* 1.3333333333333333 (* (* a (* a a)) c)))))))))
         (if (<= b -1.02e-276)
           1.0
           (if (<= b 4.1e-66)
             (/
              x
              (+
               x
               (*
                y
                (+
                 1.0
                 (*
                  c
                  (+
                   t_1
                   (* c (* -0.3950617283950617 (/ c (* t (* t t)))))))))))
             1.0)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = -2.0 * ((0.6666666666666666 / t) - (0.8333333333333334 + a));
	double tmp;
	if (b <= -1.05e+76) {
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	} else if (b <= -2.35e-29) {
		tmp = 1.0;
	} else if (b <= -5.2e-177) {
		tmp = x / (x + (y * (1.0 + (c * (t_1 + (c * (1.3333333333333333 * ((a * (a * a)) * c))))))));
	} else if (b <= -1.02e-276) {
		tmp = 1.0;
	} else if (b <= 4.1e-66) {
		tmp = x / (x + (y * (1.0 + (c * (t_1 + (c * (-0.3950617283950617 * (c / (t * (t * t))))))))));
	} 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) :: t_1
    real(8) :: tmp
    t_1 = (-2.0d0) * ((0.6666666666666666d0 / t) - (0.8333333333333334d0 + a))
    if (b <= (-1.05d+76)) then
        tmp = x / (x + (y * (2.0d0 * ((b * a) * (b * a)))))
    else if (b <= (-2.35d-29)) then
        tmp = 1.0d0
    else if (b <= (-5.2d-177)) then
        tmp = x / (x + (y * (1.0d0 + (c * (t_1 + (c * (1.3333333333333333d0 * ((a * (a * a)) * c))))))))
    else if (b <= (-1.02d-276)) then
        tmp = 1.0d0
    else if (b <= 4.1d-66) then
        tmp = x / (x + (y * (1.0d0 + (c * (t_1 + (c * ((-0.3950617283950617d0) * (c / (t * (t * t))))))))))
    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 t_1 = -2.0 * ((0.6666666666666666 / t) - (0.8333333333333334 + a));
	double tmp;
	if (b <= -1.05e+76) {
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	} else if (b <= -2.35e-29) {
		tmp = 1.0;
	} else if (b <= -5.2e-177) {
		tmp = x / (x + (y * (1.0 + (c * (t_1 + (c * (1.3333333333333333 * ((a * (a * a)) * c))))))));
	} else if (b <= -1.02e-276) {
		tmp = 1.0;
	} else if (b <= 4.1e-66) {
		tmp = x / (x + (y * (1.0 + (c * (t_1 + (c * (-0.3950617283950617 * (c / (t * (t * t))))))))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = -2.0 * ((0.6666666666666666 / t) - (0.8333333333333334 + a))
	tmp = 0
	if b <= -1.05e+76:
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))))
	elif b <= -2.35e-29:
		tmp = 1.0
	elif b <= -5.2e-177:
		tmp = x / (x + (y * (1.0 + (c * (t_1 + (c * (1.3333333333333333 * ((a * (a * a)) * c))))))))
	elif b <= -1.02e-276:
		tmp = 1.0
	elif b <= 4.1e-66:
		tmp = x / (x + (y * (1.0 + (c * (t_1 + (c * (-0.3950617283950617 * (c / (t * (t * t))))))))))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(-2.0 * Float64(Float64(0.6666666666666666 / t) - Float64(0.8333333333333334 + a)))
	tmp = 0.0
	if (b <= -1.05e+76)
		tmp = Float64(x / Float64(x + Float64(y * Float64(2.0 * Float64(Float64(b * a) * Float64(b * a))))));
	elseif (b <= -2.35e-29)
		tmp = 1.0;
	elseif (b <= -5.2e-177)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(c * Float64(t_1 + Float64(c * Float64(1.3333333333333333 * Float64(Float64(a * Float64(a * a)) * c)))))))));
	elseif (b <= -1.02e-276)
		tmp = 1.0;
	elseif (b <= 4.1e-66)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(c * Float64(t_1 + Float64(c * Float64(-0.3950617283950617 * Float64(c / Float64(t * Float64(t * t)))))))))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = -2.0 * ((0.6666666666666666 / t) - (0.8333333333333334 + a));
	tmp = 0.0;
	if (b <= -1.05e+76)
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	elseif (b <= -2.35e-29)
		tmp = 1.0;
	elseif (b <= -5.2e-177)
		tmp = x / (x + (y * (1.0 + (c * (t_1 + (c * (1.3333333333333333 * ((a * (a * a)) * c))))))));
	elseif (b <= -1.02e-276)
		tmp = 1.0;
	elseif (b <= 4.1e-66)
		tmp = x / (x + (y * (1.0 + (c * (t_1 + (c * (-0.3950617283950617 * (c / (t * (t * t))))))))));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(-2.0 * N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[b, -1.05e+76], N[(x / N[(x + N[(y * N[(2.0 * N[(N[(b * a), $MachinePrecision] * N[(b * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -2.35e-29], 1.0, If[LessEqual[b, -5.2e-177], N[(x / N[(x + N[(y * N[(1.0 + N[(c * N[(t$95$1 + N[(c * N[(1.3333333333333333 * N[(N[(a * N[(a * a), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -1.02e-276], 1.0, If[LessEqual[b, 4.1e-66], N[(x / N[(x + N[(y * N[(1.0 + N[(c * N[(t$95$1 + N[(c * N[(-0.3950617283950617 * N[(c / N[(t * N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]]]]]]

Derivation

1. Split input into 4 regimes

2. if b < -1.05000000000000003e76

   1. Initial program 90.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in a around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -1.05000000000000003e76 < b < -2.3499999999999999e-29 or -5.2000000000000002e-177 < b < -1.02e-276 or 4.09999999999999998e-66 < b

   1. Initial program 93.1%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -2.3499999999999999e-29 < b < -5.2000000000000002e-177

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in a around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -1.02e-276 < b < 4.09999999999999998e-66

   1. Initial program 96.1%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in t around 0 0

       \[\leadsto expr\]

   13. Simplified 0

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

Alternative 12: 52.6% accurate, 5.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -1.6 \cdot 10^{+76}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(2 \cdot \left(\left(b \cdot a\right) \cdot \left(b \cdot a\right)\right)\right)}\\ \mathbf{elif}\;b \leq -6.6 \cdot 10^{-43}:\\ \;\;\;\;1\\ \mathbf{elif}\;b \leq -5 \cdot 10^{-111}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + \left(2 \cdot c\right) \cdot \left(0.8333333333333334 + a\right)\right)}\\ \mathbf{elif}\;b \leq -6.2 \cdot 10^{-177}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(-0.3950617283950617 \cdot \frac{c \cdot \left(c \cdot c\right)}{t \cdot \left(t \cdot t\right)}\right)}\\ \mathbf{elif}\;b \leq 1.15 \cdot 10^{-263}:\\ \;\;\;\;1\\ \mathbf{elif}\;b \leq 2.7 \cdot 10^{-93}:\\ \;\;\;\;\frac{x}{y \cdot \left(1 + \frac{x}{y}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= b -1.6e+76)
   (/ x (+ x (* y (* 2.0 (* (* b a) (* b a))))))
   (if (<= b -6.6e-43)
     1.0
     (if (<= b -5e-111)
       (/ x (+ x (* y (+ 1.0 (* (* 2.0 c) (+ 0.8333333333333334 a))))))
       (if (<= b -6.2e-177)
         (/
          x
          (+ x (* y (* -0.3950617283950617 (/ (* c (* c c)) (* t (* t t)))))))
         (if (<= b 1.15e-263)
           1.0
           (if (<= b 2.7e-93) (/ x (* y (+ 1.0 (/ x y)))) 1.0)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (b <= -1.6e+76) {
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	} else if (b <= -6.6e-43) {
		tmp = 1.0;
	} else if (b <= -5e-111) {
		tmp = x / (x + (y * (1.0 + ((2.0 * c) * (0.8333333333333334 + a)))));
	} else if (b <= -6.2e-177) {
		tmp = x / (x + (y * (-0.3950617283950617 * ((c * (c * c)) / (t * (t * t))))));
	} else if (b <= 1.15e-263) {
		tmp = 1.0;
	} else if (b <= 2.7e-93) {
		tmp = x / (y * (1.0 + (x / y)));
	} 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 (b <= (-1.6d+76)) then
        tmp = x / (x + (y * (2.0d0 * ((b * a) * (b * a)))))
    else if (b <= (-6.6d-43)) then
        tmp = 1.0d0
    else if (b <= (-5d-111)) then
        tmp = x / (x + (y * (1.0d0 + ((2.0d0 * c) * (0.8333333333333334d0 + a)))))
    else if (b <= (-6.2d-177)) then
        tmp = x / (x + (y * ((-0.3950617283950617d0) * ((c * (c * c)) / (t * (t * t))))))
    else if (b <= 1.15d-263) then
        tmp = 1.0d0
    else if (b <= 2.7d-93) then
        tmp = x / (y * (1.0d0 + (x / y)))
    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 (b <= -1.6e+76) {
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	} else if (b <= -6.6e-43) {
		tmp = 1.0;
	} else if (b <= -5e-111) {
		tmp = x / (x + (y * (1.0 + ((2.0 * c) * (0.8333333333333334 + a)))));
	} else if (b <= -6.2e-177) {
		tmp = x / (x + (y * (-0.3950617283950617 * ((c * (c * c)) / (t * (t * t))))));
	} else if (b <= 1.15e-263) {
		tmp = 1.0;
	} else if (b <= 2.7e-93) {
		tmp = x / (y * (1.0 + (x / y)));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if b <= -1.6e+76:
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))))
	elif b <= -6.6e-43:
		tmp = 1.0
	elif b <= -5e-111:
		tmp = x / (x + (y * (1.0 + ((2.0 * c) * (0.8333333333333334 + a)))))
	elif b <= -6.2e-177:
		tmp = x / (x + (y * (-0.3950617283950617 * ((c * (c * c)) / (t * (t * t))))))
	elif b <= 1.15e-263:
		tmp = 1.0
	elif b <= 2.7e-93:
		tmp = x / (y * (1.0 + (x / y)))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (b <= -1.6e+76)
		tmp = Float64(x / Float64(x + Float64(y * Float64(2.0 * Float64(Float64(b * a) * Float64(b * a))))));
	elseif (b <= -6.6e-43)
		tmp = 1.0;
	elseif (b <= -5e-111)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(Float64(2.0 * c) * Float64(0.8333333333333334 + a))))));
	elseif (b <= -6.2e-177)
		tmp = Float64(x / Float64(x + Float64(y * Float64(-0.3950617283950617 * Float64(Float64(c * Float64(c * c)) / Float64(t * Float64(t * t)))))));
	elseif (b <= 1.15e-263)
		tmp = 1.0;
	elseif (b <= 2.7e-93)
		tmp = Float64(x / Float64(y * Float64(1.0 + Float64(x / y))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (b <= -1.6e+76)
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	elseif (b <= -6.6e-43)
		tmp = 1.0;
	elseif (b <= -5e-111)
		tmp = x / (x + (y * (1.0 + ((2.0 * c) * (0.8333333333333334 + a)))));
	elseif (b <= -6.2e-177)
		tmp = x / (x + (y * (-0.3950617283950617 * ((c * (c * c)) / (t * (t * t))))));
	elseif (b <= 1.15e-263)
		tmp = 1.0;
	elseif (b <= 2.7e-93)
		tmp = x / (y * (1.0 + (x / y)));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[b, -1.6e+76], N[(x / N[(x + N[(y * N[(2.0 * N[(N[(b * a), $MachinePrecision] * N[(b * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -6.6e-43], 1.0, If[LessEqual[b, -5e-111], N[(x / N[(x + N[(y * N[(1.0 + N[(N[(2.0 * c), $MachinePrecision] * N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -6.2e-177], N[(x / N[(x + N[(y * N[(-0.3950617283950617 * N[(N[(c * N[(c * c), $MachinePrecision]), $MachinePrecision] / N[(t * N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.15e-263], 1.0, If[LessEqual[b, 2.7e-93], N[(x / N[(y * N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]]]]]]

Derivation

1. Split input into 5 regimes

2. if b < -1.59999999999999988e76

   1. Initial program 90.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in a around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -1.59999999999999988e76 < b < -6.60000000000000031e-43 or -6.20000000000000036e-177 < b < 1.15000000000000001e-263 or 2.7000000000000001e-93 < b

   1. Initial program 93.6%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -6.60000000000000031e-43 < b < -5.0000000000000003e-111

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in t around 0 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   14. Taylor expanded in t around inf 0

       \[\leadsto expr\]

   16. Simplified 0

       \[\leadsto expr\]

   if -5.0000000000000003e-111 < b < -6.20000000000000036e-177

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in t around 0 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if 1.15000000000000001e-263 < b < 2.7000000000000001e-93

   1. Initial program 96.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in y around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 13: 49.0% accurate, 5.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5 \cdot 10^{+159}:\\ \;\;\;\;\frac{x}{y \cdot \left(1 + \frac{x}{y}\right)}\\ \mathbf{elif}\;x \leq -6 \cdot 10^{+21}:\\ \;\;\;\;\frac{x}{x + y \cdot \frac{\left(b \cdot b\right) \cdot 0.8888888888888888}{t \cdot t}}\\ \mathbf{elif}\;x \leq -2.4 \cdot 10^{-161}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq -1.45 \cdot 10^{-289}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + -2 \cdot \left(c \cdot \left(\frac{0.6666666666666666}{t} - \left(0.8333333333333334 + a\right)\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= x -5e+159)
   (/ x (* y (+ 1.0 (/ x y))))
   (if (<= x -6e+21)
     (/ x (+ x (* y (/ (* (* b b) 0.8888888888888888) (* t t)))))
     (if (<= x -2.4e-161)
       1.0
       (if (<= x -1.45e-289)
         (/
          x
          (+
           x
           (*
            y
            (+
             1.0
             (*
              -2.0
              (* c (- (/ 0.6666666666666666 t) (+ 0.8333333333333334 a))))))))
         1.0)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (x <= -5e+159) {
		tmp = x / (y * (1.0 + (x / y)));
	} else if (x <= -6e+21) {
		tmp = x / (x + (y * (((b * b) * 0.8888888888888888) / (t * t))));
	} else if (x <= -2.4e-161) {
		tmp = 1.0;
	} else if (x <= -1.45e-289) {
		tmp = x / (x + (y * (1.0 + (-2.0 * (c * ((0.6666666666666666 / t) - (0.8333333333333334 + a)))))));
	} 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 (x <= (-5d+159)) then
        tmp = x / (y * (1.0d0 + (x / y)))
    else if (x <= (-6d+21)) then
        tmp = x / (x + (y * (((b * b) * 0.8888888888888888d0) / (t * t))))
    else if (x <= (-2.4d-161)) then
        tmp = 1.0d0
    else if (x <= (-1.45d-289)) then
        tmp = x / (x + (y * (1.0d0 + ((-2.0d0) * (c * ((0.6666666666666666d0 / t) - (0.8333333333333334d0 + a)))))))
    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 (x <= -5e+159) {
		tmp = x / (y * (1.0 + (x / y)));
	} else if (x <= -6e+21) {
		tmp = x / (x + (y * (((b * b) * 0.8888888888888888) / (t * t))));
	} else if (x <= -2.4e-161) {
		tmp = 1.0;
	} else if (x <= -1.45e-289) {
		tmp = x / (x + (y * (1.0 + (-2.0 * (c * ((0.6666666666666666 / t) - (0.8333333333333334 + a)))))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if x <= -5e+159:
		tmp = x / (y * (1.0 + (x / y)))
	elif x <= -6e+21:
		tmp = x / (x + (y * (((b * b) * 0.8888888888888888) / (t * t))))
	elif x <= -2.4e-161:
		tmp = 1.0
	elif x <= -1.45e-289:
		tmp = x / (x + (y * (1.0 + (-2.0 * (c * ((0.6666666666666666 / t) - (0.8333333333333334 + a)))))))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (x <= -5e+159)
		tmp = Float64(x / Float64(y * Float64(1.0 + Float64(x / y))));
	elseif (x <= -6e+21)
		tmp = Float64(x / Float64(x + Float64(y * Float64(Float64(Float64(b * b) * 0.8888888888888888) / Float64(t * t)))));
	elseif (x <= -2.4e-161)
		tmp = 1.0;
	elseif (x <= -1.45e-289)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(-2.0 * Float64(c * Float64(Float64(0.6666666666666666 / t) - Float64(0.8333333333333334 + a))))))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (x <= -5e+159)
		tmp = x / (y * (1.0 + (x / y)));
	elseif (x <= -6e+21)
		tmp = x / (x + (y * (((b * b) * 0.8888888888888888) / (t * t))));
	elseif (x <= -2.4e-161)
		tmp = 1.0;
	elseif (x <= -1.45e-289)
		tmp = x / (x + (y * (1.0 + (-2.0 * (c * ((0.6666666666666666 / t) - (0.8333333333333334 + a)))))));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[x, -5e+159], N[(x / N[(y * N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -6e+21], N[(x / N[(x + N[(y * N[(N[(N[(b * b), $MachinePrecision] * 0.8888888888888888), $MachinePrecision] / N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -2.4e-161], 1.0, If[LessEqual[x, -1.45e-289], N[(x / N[(x + N[(y * N[(1.0 + N[(-2.0 * N[(c * N[(N[(0.6666666666666666 / t), $MachinePrecision] - N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]]]]

Derivation

1. Split input into 4 regimes

2. if x < -5.00000000000000003e159

   1. Initial program 89.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in y around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -5.00000000000000003e159 < x < -6e21

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in t around 0 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -6e21 < x < -2.39999999999999999e-161 or -1.45000000000000003e-289 < x

   1. Initial program 95.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -2.39999999999999999e-161 < x < -1.45000000000000003e-289

   1. Initial program 89.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

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

Alternative 14: 53.3% accurate, 6.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -2.2 \cdot 10^{+76}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(2 \cdot \left(\left(b \cdot a\right) \cdot \left(b \cdot a\right)\right)\right)}\\ \mathbf{elif}\;b \leq -1.25 \cdot 10^{-42}:\\ \;\;\;\;1\\ \mathbf{elif}\;b \leq -2.5 \cdot 10^{-154}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(1 + \left(2 \cdot c\right) \cdot \left(0.8333333333333334 + a\right)\right)}\\ \mathbf{elif}\;b \leq 3.3 \cdot 10^{-276}:\\ \;\;\;\;1\\ \mathbf{elif}\;b \leq 9.8 \cdot 10^{-89}:\\ \;\;\;\;\frac{x}{y \cdot \left(1 + \frac{x}{y}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= b -2.2e+76)
   (/ x (+ x (* y (* 2.0 (* (* b a) (* b a))))))
   (if (<= b -1.25e-42)
     1.0
     (if (<= b -2.5e-154)
       (/ x (+ x (* y (+ 1.0 (* (* 2.0 c) (+ 0.8333333333333334 a))))))
       (if (<= b 3.3e-276)
         1.0
         (if (<= b 9.8e-89) (/ x (* y (+ 1.0 (/ x y)))) 1.0))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (b <= -2.2e+76) {
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	} else if (b <= -1.25e-42) {
		tmp = 1.0;
	} else if (b <= -2.5e-154) {
		tmp = x / (x + (y * (1.0 + ((2.0 * c) * (0.8333333333333334 + a)))));
	} else if (b <= 3.3e-276) {
		tmp = 1.0;
	} else if (b <= 9.8e-89) {
		tmp = x / (y * (1.0 + (x / y)));
	} 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 (b <= (-2.2d+76)) then
        tmp = x / (x + (y * (2.0d0 * ((b * a) * (b * a)))))
    else if (b <= (-1.25d-42)) then
        tmp = 1.0d0
    else if (b <= (-2.5d-154)) then
        tmp = x / (x + (y * (1.0d0 + ((2.0d0 * c) * (0.8333333333333334d0 + a)))))
    else if (b <= 3.3d-276) then
        tmp = 1.0d0
    else if (b <= 9.8d-89) then
        tmp = x / (y * (1.0d0 + (x / y)))
    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 (b <= -2.2e+76) {
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	} else if (b <= -1.25e-42) {
		tmp = 1.0;
	} else if (b <= -2.5e-154) {
		tmp = x / (x + (y * (1.0 + ((2.0 * c) * (0.8333333333333334 + a)))));
	} else if (b <= 3.3e-276) {
		tmp = 1.0;
	} else if (b <= 9.8e-89) {
		tmp = x / (y * (1.0 + (x / y)));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if b <= -2.2e+76:
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))))
	elif b <= -1.25e-42:
		tmp = 1.0
	elif b <= -2.5e-154:
		tmp = x / (x + (y * (1.0 + ((2.0 * c) * (0.8333333333333334 + a)))))
	elif b <= 3.3e-276:
		tmp = 1.0
	elif b <= 9.8e-89:
		tmp = x / (y * (1.0 + (x / y)))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (b <= -2.2e+76)
		tmp = Float64(x / Float64(x + Float64(y * Float64(2.0 * Float64(Float64(b * a) * Float64(b * a))))));
	elseif (b <= -1.25e-42)
		tmp = 1.0;
	elseif (b <= -2.5e-154)
		tmp = Float64(x / Float64(x + Float64(y * Float64(1.0 + Float64(Float64(2.0 * c) * Float64(0.8333333333333334 + a))))));
	elseif (b <= 3.3e-276)
		tmp = 1.0;
	elseif (b <= 9.8e-89)
		tmp = Float64(x / Float64(y * Float64(1.0 + Float64(x / y))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (b <= -2.2e+76)
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	elseif (b <= -1.25e-42)
		tmp = 1.0;
	elseif (b <= -2.5e-154)
		tmp = x / (x + (y * (1.0 + ((2.0 * c) * (0.8333333333333334 + a)))));
	elseif (b <= 3.3e-276)
		tmp = 1.0;
	elseif (b <= 9.8e-89)
		tmp = x / (y * (1.0 + (x / y)));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[b, -2.2e+76], N[(x / N[(x + N[(y * N[(2.0 * N[(N[(b * a), $MachinePrecision] * N[(b * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -1.25e-42], 1.0, If[LessEqual[b, -2.5e-154], N[(x / N[(x + N[(y * N[(1.0 + N[(N[(2.0 * c), $MachinePrecision] * N[(0.8333333333333334 + a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 3.3e-276], 1.0, If[LessEqual[b, 9.8e-89], N[(x / N[(y * N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]]]]]

Derivation

1. Split input into 4 regimes

2. if b < -2.2e76

   1. Initial program 90.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in a around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -2.2e76 < b < -1.25000000000000001e-42 or -2.5000000000000001e-154 < b < 3.29999999999999991e-276 or 9.8e-89 < b

   1. Initial program 93.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.25000000000000001e-42 < b < -2.5000000000000001e-154

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in t around 0 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   14. Taylor expanded in t around inf 0

       \[\leadsto expr\]

   16. Simplified 0

       \[\leadsto expr\]

   if 3.29999999999999991e-276 < b < 9.8e-89

   1. Initial program 96.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in y around inf 0

       \[\leadsto expr\]

   13. Simplified 0

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

Alternative 15: 48.9% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.7 \cdot 10^{+187}:\\ \;\;\;\;1\\ \mathbf{elif}\;z \leq -4.1 \cdot 10^{+64}:\\ \;\;\;\;\frac{x}{x + y \cdot \left(2 \cdot \left(\left(b \cdot a\right) \cdot \left(b \cdot a\right)\right)\right)}\\ \mathbf{elif}\;z \leq -2.95 \cdot 10^{-186}:\\ \;\;\;\;1\\ \mathbf{elif}\;z \leq 3.4 \cdot 10^{-227}:\\ \;\;\;\;\frac{x}{\frac{y \cdot y - x \cdot x}{y - x}}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= z -5.7e+187)
   1.0
   (if (<= z -4.1e+64)
     (/ x (+ x (* y (* 2.0 (* (* b a) (* b a))))))
     (if (<= z -2.95e-186)
       1.0
       (if (<= z 3.4e-227) (/ x (/ (- (* y y) (* x x)) (- y x))) 1.0)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (z <= -5.7e+187) {
		tmp = 1.0;
	} else if (z <= -4.1e+64) {
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	} else if (z <= -2.95e-186) {
		tmp = 1.0;
	} else if (z <= 3.4e-227) {
		tmp = x / (((y * y) - (x * x)) / (y - x));
	} 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 (z <= (-5.7d+187)) then
        tmp = 1.0d0
    else if (z <= (-4.1d+64)) then
        tmp = x / (x + (y * (2.0d0 * ((b * a) * (b * a)))))
    else if (z <= (-2.95d-186)) then
        tmp = 1.0d0
    else if (z <= 3.4d-227) then
        tmp = x / (((y * y) - (x * x)) / (y - x))
    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 (z <= -5.7e+187) {
		tmp = 1.0;
	} else if (z <= -4.1e+64) {
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	} else if (z <= -2.95e-186) {
		tmp = 1.0;
	} else if (z <= 3.4e-227) {
		tmp = x / (((y * y) - (x * x)) / (y - x));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if z <= -5.7e+187:
		tmp = 1.0
	elif z <= -4.1e+64:
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))))
	elif z <= -2.95e-186:
		tmp = 1.0
	elif z <= 3.4e-227:
		tmp = x / (((y * y) - (x * x)) / (y - x))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (z <= -5.7e+187)
		tmp = 1.0;
	elseif (z <= -4.1e+64)
		tmp = Float64(x / Float64(x + Float64(y * Float64(2.0 * Float64(Float64(b * a) * Float64(b * a))))));
	elseif (z <= -2.95e-186)
		tmp = 1.0;
	elseif (z <= 3.4e-227)
		tmp = Float64(x / Float64(Float64(Float64(y * y) - Float64(x * x)) / Float64(y - x)));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (z <= -5.7e+187)
		tmp = 1.0;
	elseif (z <= -4.1e+64)
		tmp = x / (x + (y * (2.0 * ((b * a) * (b * a)))));
	elseif (z <= -2.95e-186)
		tmp = 1.0;
	elseif (z <= 3.4e-227)
		tmp = x / (((y * y) - (x * x)) / (y - x));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[z, -5.7e+187], 1.0, If[LessEqual[z, -4.1e+64], N[(x / N[(x + N[(y * N[(2.0 * N[(N[(b * a), $MachinePrecision] * N[(b * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -2.95e-186], 1.0, If[LessEqual[z, 3.4e-227], N[(x / N[(N[(N[(y * y), $MachinePrecision] - N[(x * x), $MachinePrecision]), $MachinePrecision] / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]]]]

Derivation

1. Split input into 3 regimes

2. if z < -5.7000000000000004e187 or -4.09999999999999978e64 < z < -2.95e-186 or 3.39999999999999979e-227 < z

   1. Initial program 93.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -5.7000000000000004e187 < z < -4.09999999999999978e64

   1. Initial program 93.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in b around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in a around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -2.95e-186 < z < 3.39999999999999979e-227

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

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

Alternative 16: 50.4% accurate, 10.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.45 \cdot 10^{+187}:\\ \;\;\;\;\frac{x}{y \cdot \left(1 + \frac{x}{y}\right)}\\ \mathbf{elif}\;x \leq -4 \cdot 10^{-15}:\\ \;\;\;\;\frac{x}{\frac{y \cdot y - x \cdot x}{y - x}}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= x -1.45e+187)
   (/ x (* y (+ 1.0 (/ x y))))
   (if (<= x -4e-15) (/ x (/ (- (* y y) (* x x)) (- y x))) 1.0)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (x <= -1.45e+187) {
		tmp = x / (y * (1.0 + (x / y)));
	} else if (x <= -4e-15) {
		tmp = x / (((y * y) - (x * x)) / (y - x));
	} 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 (x <= (-1.45d+187)) then
        tmp = x / (y * (1.0d0 + (x / y)))
    else if (x <= (-4d-15)) then
        tmp = x / (((y * y) - (x * x)) / (y - x))
    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 (x <= -1.45e+187) {
		tmp = x / (y * (1.0 + (x / y)));
	} else if (x <= -4e-15) {
		tmp = x / (((y * y) - (x * x)) / (y - x));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if x <= -1.45e+187:
		tmp = x / (y * (1.0 + (x / y)))
	elif x <= -4e-15:
		tmp = x / (((y * y) - (x * x)) / (y - x))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (x <= -1.45e+187)
		tmp = Float64(x / Float64(y * Float64(1.0 + Float64(x / y))));
	elseif (x <= -4e-15)
		tmp = Float64(x / Float64(Float64(Float64(y * y) - Float64(x * x)) / Float64(y - x)));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (x <= -1.45e+187)
		tmp = x / (y * (1.0 + (x / y)));
	elseif (x <= -4e-15)
		tmp = x / (((y * y) - (x * x)) / (y - x));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[x, -1.45e+187], N[(x / N[(y * N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -4e-15], N[(x / N[(N[(N[(y * y), $MachinePrecision] - N[(x * x), $MachinePrecision]), $MachinePrecision] / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]]

Derivation

1. Split input into 3 regimes

2. if x < -1.45e187

   1. Initial program 87.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in y around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -1.45e187 < x < -4.0000000000000003e-15

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

       \[\leadsto expr\]

   if -4.0000000000000003e-15 < x

   1. Initial program 94.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

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

Alternative 17: 50.4% accurate, 10.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+186}:\\ \;\;\;\;\frac{x}{y \cdot \left(1 + \frac{x}{y}\right)}\\ \mathbf{elif}\;x \leq -2.35 \cdot 10^{-14}:\\ \;\;\;\;\frac{x}{y \cdot y - x \cdot x} \cdot \left(y - x\right)\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= x -4e+186)
   (/ x (* y (+ 1.0 (/ x y))))
   (if (<= x -2.35e-14) (* (/ x (- (* y y) (* x x))) (- y x)) 1.0)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (x <= -4e+186) {
		tmp = x / (y * (1.0 + (x / y)));
	} else if (x <= -2.35e-14) {
		tmp = (x / ((y * y) - (x * x))) * (y - x);
	} 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 (x <= (-4d+186)) then
        tmp = x / (y * (1.0d0 + (x / y)))
    else if (x <= (-2.35d-14)) then
        tmp = (x / ((y * y) - (x * x))) * (y - x)
    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 (x <= -4e+186) {
		tmp = x / (y * (1.0 + (x / y)));
	} else if (x <= -2.35e-14) {
		tmp = (x / ((y * y) - (x * x))) * (y - x);
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if x <= -4e+186:
		tmp = x / (y * (1.0 + (x / y)))
	elif x <= -2.35e-14:
		tmp = (x / ((y * y) - (x * x))) * (y - x)
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (x <= -4e+186)
		tmp = Float64(x / Float64(y * Float64(1.0 + Float64(x / y))));
	elseif (x <= -2.35e-14)
		tmp = Float64(Float64(x / Float64(Float64(y * y) - Float64(x * x))) * Float64(y - x));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (x <= -4e+186)
		tmp = x / (y * (1.0 + (x / y)));
	elseif (x <= -2.35e-14)
		tmp = (x / ((y * y) - (x * x))) * (y - x);
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[x, -4e+186], N[(x / N[(y * N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -2.35e-14], N[(N[(x / N[(N[(y * y), $MachinePrecision] - N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(y - x), $MachinePrecision]), $MachinePrecision], 1.0]]

Derivation

1. Split input into 3 regimes

2. if x < -3.99999999999999992e186

   1. Initial program 87.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in y around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -3.99999999999999992e186 < x < -2.3500000000000001e-14

   1. Initial program 100.0%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   12. Applied egg-rr 0

       \[\leadsto expr\]

   if -2.3500000000000001e-14 < x

   1. Initial program 94.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

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

Alternative 18: 50.4% accurate, 16.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.6 \cdot 10^{+96}:\\ \;\;\;\;\frac{x}{y \cdot \left(1 + \frac{x}{y}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= x -6.6e+96) (/ x (* y (+ 1.0 (/ x y)))) 1.0))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (x <= -6.6e+96) {
		tmp = x / (y * (1.0 + (x / y)));
	} 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 (x <= (-6.6d+96)) then
        tmp = x / (y * (1.0d0 + (x / y)))
    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 (x <= -6.6e+96) {
		tmp = x / (y * (1.0 + (x / y)));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if x <= -6.6e+96:
		tmp = x / (y * (1.0 + (x / y)))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (x <= -6.6e+96)
		tmp = Float64(x / Float64(y * Float64(1.0 + Float64(x / y))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (x <= -6.6e+96)
		tmp = x / (y * (1.0 + (x / y)));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[x, -6.6e+96], N[(x / N[(y * N[(1.0 + N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]

Derivation

1. Split input into 2 regimes

2. if x < -6.59999999999999969e96

   1. Initial program 91.7%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in c around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in c around 0 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]

   11. Taylor expanded in y around inf 0

       \[\leadsto expr\]

   13. Simplified 0

       \[\leadsto expr\]

   if -6.59999999999999969e96 < x

   1. Initial program 94.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   7. Simplified 0

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

Alternative 19: 51.9% 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

1. Initial program 94.2%

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

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing

5. Taylor expanded in x around inf 0

   \[\leadsto expr\]

7. Simplified 0

   \[\leadsto expr\]
8. Add Preprocessing

Developer target: 94.9% 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 2024110 
(FPCore (x y z t a b c)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, I"
  :precision binary64

  :alt
  (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)))))))))))