Development.Shake.Progress:decay from shake-0.15.5

Percentage Accurate: 66.7% → 87.5%

Time: 20.0s

Alternatives: 17

Speedup: 0.9×

Specification

Math

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

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (/ (+ (* x y) (* z (- t a))) (+ y (* z (- b y)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x * y) + (z * (t - a))) / (y + (z * (b - y)));
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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
    code = ((x * y) + (z * (t - a))) / (y + (z * (b - y)))
end function

Java

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

Python

def code(x, y, z, t, a, b):
	return ((x * y) + (z * (t - a))) / (y + (z * (b - y)))

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x * y) + Float64(z * Float64(t - a))) / Float64(y + Float64(z * Float64(b - y))))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x * y) + (z * (t - a))) / (y + (z * (b - y)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x * y), $MachinePrecision] + N[(z * N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 66.7% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (/ (+ (* x y) (* z (- t a))) (+ y (* z (- b y)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x * y) + (z * (t - a))) / (y + (z * (b - y)));
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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
    code = ((x * y) + (z * (t - a))) / (y + (z * (b - y)))
end function

Java

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

Python

def code(x, y, z, t, a, b):
	return ((x * y) + (z * (t - a))) / (y + (z * (b - y)))

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x * y) + Float64(z * Float64(t - a))) / Float64(y + Float64(z * Float64(b - y))))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x * y) + (z * (t - a))) / (y + (z * (b - y)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x * y), $MachinePrecision] + N[(z * N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 87.5% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := {\left(b - y\right)}^{2}\\ \mathbf{if}\;z \leq -3.05 \cdot 10^{+16}:\\ \;\;\;\;\left(\frac{t}{b - y} + \frac{x}{z} \cdot \frac{y}{b - y}\right) + \left(\frac{y}{z} \cdot \frac{a - t}{t_1} - \frac{a}{b - y}\right)\\ \mathbf{elif}\;z \leq 1.65 \cdot 10^{+44}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x, y, z \cdot \left(t - a\right)\right)}{\mathsf{fma}\left(z, b - y, y\right)}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{y}{z} \cdot \frac{x}{b - y} + \frac{t - a}{b - y}\right) - \frac{y}{\frac{t_1}{\frac{t - a}{z}}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (pow (- b y) 2.0)))
   (if (<= z -3.05e+16)
     (+
      (+ (/ t (- b y)) (* (/ x z) (/ y (- b y))))
      (- (* (/ y z) (/ (- a t) t_1)) (/ a (- b y))))
     (if (<= z 1.65e+44)
       (/ (fma x y (* z (- t a))) (fma z (- b y) y))
       (-
        (+ (* (/ y z) (/ x (- b y))) (/ (- t a) (- b y)))
        (/ y (/ t_1 (/ (- t a) z))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = pow((b - y), 2.0);
	double tmp;
	if (z <= -3.05e+16) {
		tmp = ((t / (b - y)) + ((x / z) * (y / (b - y)))) + (((y / z) * ((a - t) / t_1)) - (a / (b - y)));
	} else if (z <= 1.65e+44) {
		tmp = fma(x, y, (z * (t - a))) / fma(z, (b - y), y);
	} else {
		tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (t_1 / ((t - a) / z)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(b - y) ^ 2.0
	tmp = 0.0
	if (z <= -3.05e+16)
		tmp = Float64(Float64(Float64(t / Float64(b - y)) + Float64(Float64(x / z) * Float64(y / Float64(b - y)))) + Float64(Float64(Float64(y / z) * Float64(Float64(a - t) / t_1)) - Float64(a / Float64(b - y))));
	elseif (z <= 1.65e+44)
		tmp = Float64(fma(x, y, Float64(z * Float64(t - a))) / fma(z, Float64(b - y), y));
	else
		tmp = Float64(Float64(Float64(Float64(y / z) * Float64(x / Float64(b - y))) + Float64(Float64(t - a) / Float64(b - y))) - Float64(y / Float64(t_1 / Float64(Float64(t - a) / z))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[Power[N[(b - y), $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[z, -3.05e+16], N[(N[(N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision] + N[(N[(x / z), $MachinePrecision] * N[(y / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y / z), $MachinePrecision] * N[(N[(a - t), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] - N[(a / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.65e+44], N[(N[(x * y + N[(z * N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(z * N[(b - y), $MachinePrecision] + y), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(y / z), $MachinePrecision] * N[(x / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(t$95$1 / N[(N[(t - a), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 2: 87.5% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := {\left(b - y\right)}^{2}\\ \mathbf{if}\;z \leq -3.3 \cdot 10^{+18}:\\ \;\;\;\;\left(\frac{t}{b - y} + \frac{x}{z} \cdot \frac{y}{b - y}\right) + \left(\frac{y}{z} \cdot \frac{a - t}{t_1} - \frac{a}{b - y}\right)\\ \mathbf{elif}\;z \leq 1.8 \cdot 10^{+48}:\\ \;\;\;\;\frac{y \cdot x - z \cdot \left(a - t\right)}{y + z \cdot \left(b - y\right)}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{y}{z} \cdot \frac{x}{b - y} + \frac{t - a}{b - y}\right) - \frac{y}{\frac{t_1}{\frac{t - a}{z}}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (pow (- b y) 2.0)))
   (if (<= z -3.3e+18)
     (+
      (+ (/ t (- b y)) (* (/ x z) (/ y (- b y))))
      (- (* (/ y z) (/ (- a t) t_1)) (/ a (- b y))))
     (if (<= z 1.8e+48)
       (/ (- (* y x) (* z (- a t))) (+ y (* z (- b y))))
       (-
        (+ (* (/ y z) (/ x (- b y))) (/ (- t a) (- b y)))
        (/ y (/ t_1 (/ (- t a) z))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = pow((b - y), 2.0);
	double tmp;
	if (z <= -3.3e+18) {
		tmp = ((t / (b - y)) + ((x / z) * (y / (b - y)))) + (((y / z) * ((a - t) / t_1)) - (a / (b - y)));
	} else if (z <= 1.8e+48) {
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)));
	} else {
		tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (t_1 / ((t - a) / z)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: t_1
    real(8) :: tmp
    t_1 = (b - y) ** 2.0d0
    if (z <= (-3.3d+18)) then
        tmp = ((t / (b - y)) + ((x / z) * (y / (b - y)))) + (((y / z) * ((a - t) / t_1)) - (a / (b - y)))
    else if (z <= 1.8d+48) then
        tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)))
    else
        tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (t_1 / ((t - a) / z)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = Math.pow((b - y), 2.0);
	double tmp;
	if (z <= -3.3e+18) {
		tmp = ((t / (b - y)) + ((x / z) * (y / (b - y)))) + (((y / z) * ((a - t) / t_1)) - (a / (b - y)));
	} else if (z <= 1.8e+48) {
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)));
	} else {
		tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (t_1 / ((t - a) / z)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = math.pow((b - y), 2.0)
	tmp = 0
	if z <= -3.3e+18:
		tmp = ((t / (b - y)) + ((x / z) * (y / (b - y)))) + (((y / z) * ((a - t) / t_1)) - (a / (b - y)))
	elif z <= 1.8e+48:
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)))
	else:
		tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (t_1 / ((t - a) / z)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(b - y) ^ 2.0
	tmp = 0.0
	if (z <= -3.3e+18)
		tmp = Float64(Float64(Float64(t / Float64(b - y)) + Float64(Float64(x / z) * Float64(y / Float64(b - y)))) + Float64(Float64(Float64(y / z) * Float64(Float64(a - t) / t_1)) - Float64(a / Float64(b - y))));
	elseif (z <= 1.8e+48)
		tmp = Float64(Float64(Float64(y * x) - Float64(z * Float64(a - t))) / Float64(y + Float64(z * Float64(b - y))));
	else
		tmp = Float64(Float64(Float64(Float64(y / z) * Float64(x / Float64(b - y))) + Float64(Float64(t - a) / Float64(b - y))) - Float64(y / Float64(t_1 / Float64(Float64(t - a) / z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (b - y) ^ 2.0;
	tmp = 0.0;
	if (z <= -3.3e+18)
		tmp = ((t / (b - y)) + ((x / z) * (y / (b - y)))) + (((y / z) * ((a - t) / t_1)) - (a / (b - y)));
	elseif (z <= 1.8e+48)
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)));
	else
		tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (t_1 / ((t - a) / z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[Power[N[(b - y), $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[z, -3.3e+18], N[(N[(N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision] + N[(N[(x / z), $MachinePrecision] * N[(y / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y / z), $MachinePrecision] * N[(N[(a - t), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] - N[(a / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.8e+48], N[(N[(N[(y * x), $MachinePrecision] - N[(z * N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(y / z), $MachinePrecision] * N[(x / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(t$95$1 / N[(N[(t - a), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 3: 88.4% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7.2 \cdot 10^{+19} \lor \neg \left(z \leq 1.8 \cdot 10^{+44}\right):\\ \;\;\;\;\left(\frac{y}{z} \cdot \frac{x}{b - y} + \frac{t - a}{b - y}\right) - \frac{y}{\frac{{\left(b - y\right)}^{2}}{\frac{t - a}{z}}}\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot x - z \cdot \left(a - t\right)}{y + z \cdot \left(b - y\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -7.2e+19) (not (<= z 1.8e+44)))
   (-
    (+ (* (/ y z) (/ x (- b y))) (/ (- t a) (- b y)))
    (/ y (/ (pow (- b y) 2.0) (/ (- t a) z))))
   (/ (- (* y x) (* z (- a t))) (+ y (* z (- b y))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -7.2e+19) || !(z <= 1.8e+44)) {
		tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (pow((b - y), 2.0) / ((t - a) / z)));
	} else {
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: tmp
    if ((z <= (-7.2d+19)) .or. (.not. (z <= 1.8d+44))) then
        tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (((b - y) ** 2.0d0) / ((t - a) / z)))
    else
        tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -7.2e+19) || !(z <= 1.8e+44)) {
		tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (Math.pow((b - y), 2.0) / ((t - a) / z)));
	} else {
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -7.2e+19) or not (z <= 1.8e+44):
		tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (math.pow((b - y), 2.0) / ((t - a) / z)))
	else:
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -7.2e+19) || !(z <= 1.8e+44))
		tmp = Float64(Float64(Float64(Float64(y / z) * Float64(x / Float64(b - y))) + Float64(Float64(t - a) / Float64(b - y))) - Float64(y / Float64((Float64(b - y) ^ 2.0) / Float64(Float64(t - a) / z))));
	else
		tmp = Float64(Float64(Float64(y * x) - Float64(z * Float64(a - t))) / Float64(y + Float64(z * Float64(b - y))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -7.2e+19) || ~((z <= 1.8e+44)))
		tmp = (((y / z) * (x / (b - y))) + ((t - a) / (b - y))) - (y / (((b - y) ^ 2.0) / ((t - a) / z)));
	else
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -7.2e+19], N[Not[LessEqual[z, 1.8e+44]], $MachinePrecision]], N[(N[(N[(N[(y / z), $MachinePrecision] * N[(x / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(N[Power[N[(b - y), $MachinePrecision], 2.0], $MachinePrecision] / N[(N[(t - a), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(y * x), $MachinePrecision] - N[(z * N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 4: 84.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.1 \cdot 10^{+22}:\\ \;\;\;\;\frac{t - a}{b - y}\\ \mathbf{elif}\;z \leq 5.6 \cdot 10^{+65}:\\ \;\;\;\;\frac{y \cdot x - z \cdot \left(a - t\right)}{y + z \cdot \left(b - y\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{b - y} - \frac{a}{b - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -1.1e+22)
   (/ (- t a) (- b y))
   (if (<= z 5.6e+65)
     (/ (- (* y x) (* z (- a t))) (+ y (* z (- b y))))
     (- (/ t (- b y)) (/ a (- b y))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.1e+22) {
		tmp = (t - a) / (b - y);
	} else if (z <= 5.6e+65) {
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)));
	} else {
		tmp = (t / (b - y)) - (a / (b - y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: tmp
    if (z <= (-1.1d+22)) then
        tmp = (t - a) / (b - y)
    else if (z <= 5.6d+65) then
        tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)))
    else
        tmp = (t / (b - y)) - (a / (b - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.1e+22) {
		tmp = (t - a) / (b - y);
	} else if (z <= 5.6e+65) {
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)));
	} else {
		tmp = (t / (b - y)) - (a / (b - y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -1.1e+22:
		tmp = (t - a) / (b - y)
	elif z <= 5.6e+65:
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)))
	else:
		tmp = (t / (b - y)) - (a / (b - y))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -1.1e+22)
		tmp = Float64(Float64(t - a) / Float64(b - y));
	elseif (z <= 5.6e+65)
		tmp = Float64(Float64(Float64(y * x) - Float64(z * Float64(a - t))) / Float64(y + Float64(z * Float64(b - y))));
	else
		tmp = Float64(Float64(t / Float64(b - y)) - Float64(a / Float64(b - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -1.1e+22)
		tmp = (t - a) / (b - y);
	elseif (z <= 5.6e+65)
		tmp = ((y * x) - (z * (a - t))) / (y + (z * (b - y)));
	else
		tmp = (t / (b - y)) - (a / (b - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -1.1e+22], N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 5.6e+65], N[(N[(N[(y * x), $MachinePrecision] - N[(z * N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision] - N[(a / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 5: 43.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-x}{z}\\ t_2 := \frac{t}{b - y}\\ \mathbf{if}\;z \leq -1.85 \cdot 10^{+97}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -1 \cdot 10^{+35}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -9.2 \cdot 10^{-49}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 8.2 \cdot 10^{-88}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 8.2 \cdot 10^{-53}:\\ \;\;\;\;\frac{-a}{b}\\ \mathbf{elif}\;z \leq 1700 \lor \neg \left(z \leq 2.05 \cdot 10^{+60}\right):\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- x) z)) (t_2 (/ t (- b y))))
   (if (<= z -1.85e+97)
     t_2
     (if (<= z -1e+35)
       t_1
       (if (<= z -9.2e-49)
         t_2
         (if (<= z 8.2e-88)
           x
           (if (<= z 8.2e-53)
             (/ (- a) b)
             (if (or (<= z 1700.0) (not (<= z 2.05e+60))) t_2 t_1))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = -x / z;
	double t_2 = t / (b - y);
	double tmp;
	if (z <= -1.85e+97) {
		tmp = t_2;
	} else if (z <= -1e+35) {
		tmp = t_1;
	} else if (z <= -9.2e-49) {
		tmp = t_2;
	} else if (z <= 8.2e-88) {
		tmp = x;
	} else if (z <= 8.2e-53) {
		tmp = -a / b;
	} else if ((z <= 1700.0) || !(z <= 2.05e+60)) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = -x / z
    t_2 = t / (b - y)
    if (z <= (-1.85d+97)) then
        tmp = t_2
    else if (z <= (-1d+35)) then
        tmp = t_1
    else if (z <= (-9.2d-49)) then
        tmp = t_2
    else if (z <= 8.2d-88) then
        tmp = x
    else if (z <= 8.2d-53) then
        tmp = -a / b
    else if ((z <= 1700.0d0) .or. (.not. (z <= 2.05d+60))) then
        tmp = t_2
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = -x / z;
	double t_2 = t / (b - y);
	double tmp;
	if (z <= -1.85e+97) {
		tmp = t_2;
	} else if (z <= -1e+35) {
		tmp = t_1;
	} else if (z <= -9.2e-49) {
		tmp = t_2;
	} else if (z <= 8.2e-88) {
		tmp = x;
	} else if (z <= 8.2e-53) {
		tmp = -a / b;
	} else if ((z <= 1700.0) || !(z <= 2.05e+60)) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = -x / z
	t_2 = t / (b - y)
	tmp = 0
	if z <= -1.85e+97:
		tmp = t_2
	elif z <= -1e+35:
		tmp = t_1
	elif z <= -9.2e-49:
		tmp = t_2
	elif z <= 8.2e-88:
		tmp = x
	elif z <= 8.2e-53:
		tmp = -a / b
	elif (z <= 1700.0) or not (z <= 2.05e+60):
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(-x) / z)
	t_2 = Float64(t / Float64(b - y))
	tmp = 0.0
	if (z <= -1.85e+97)
		tmp = t_2;
	elseif (z <= -1e+35)
		tmp = t_1;
	elseif (z <= -9.2e-49)
		tmp = t_2;
	elseif (z <= 8.2e-88)
		tmp = x;
	elseif (z <= 8.2e-53)
		tmp = Float64(Float64(-a) / b);
	elseif ((z <= 1700.0) || !(z <= 2.05e+60))
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = -x / z;
	t_2 = t / (b - y);
	tmp = 0.0;
	if (z <= -1.85e+97)
		tmp = t_2;
	elseif (z <= -1e+35)
		tmp = t_1;
	elseif (z <= -9.2e-49)
		tmp = t_2;
	elseif (z <= 8.2e-88)
		tmp = x;
	elseif (z <= 8.2e-53)
		tmp = -a / b;
	elseif ((z <= 1700.0) || ~((z <= 2.05e+60)))
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[((-x) / z), $MachinePrecision]}, Block[{t$95$2 = N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.85e+97], t$95$2, If[LessEqual[z, -1e+35], t$95$1, If[LessEqual[z, -9.2e-49], t$95$2, If[LessEqual[z, 8.2e-88], x, If[LessEqual[z, 8.2e-53], N[((-a) / b), $MachinePrecision], If[Or[LessEqual[z, 1700.0], N[Not[LessEqual[z, 2.05e+60]], $MachinePrecision]], t$95$2, t$95$1]]]]]]]]

Derivation

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

Alternative 6: 64.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -2.5 \cdot 10^{-28}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{-103}:\\ \;\;\;\;\frac{y \cdot x}{y + z \cdot b}\\ \mathbf{elif}\;z \leq -7 \cdot 10^{-122}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4 \cdot 10^{-88}:\\ \;\;\;\;\frac{x}{1 - z}\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{b - y} - \frac{a}{b - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- t a) (- b y))))
   (if (<= z -2.5e-28)
     t_1
     (if (<= z -4.2e-103)
       (/ (* y x) (+ y (* z b)))
       (if (<= z -7e-122)
         t_1
         (if (<= z 4e-88) (/ x (- 1.0 z)) (- (/ t (- b y)) (/ a (- b y)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (t - a) / (b - y);
	double tmp;
	if (z <= -2.5e-28) {
		tmp = t_1;
	} else if (z <= -4.2e-103) {
		tmp = (y * x) / (y + (z * b));
	} else if (z <= -7e-122) {
		tmp = t_1;
	} else if (z <= 4e-88) {
		tmp = x / (1.0 - z);
	} else {
		tmp = (t / (b - y)) - (a / (b - y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: t_1
    real(8) :: tmp
    t_1 = (t - a) / (b - y)
    if (z <= (-2.5d-28)) then
        tmp = t_1
    else if (z <= (-4.2d-103)) then
        tmp = (y * x) / (y + (z * b))
    else if (z <= (-7d-122)) then
        tmp = t_1
    else if (z <= 4d-88) then
        tmp = x / (1.0d0 - z)
    else
        tmp = (t / (b - y)) - (a / (b - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (t - a) / (b - y);
	double tmp;
	if (z <= -2.5e-28) {
		tmp = t_1;
	} else if (z <= -4.2e-103) {
		tmp = (y * x) / (y + (z * b));
	} else if (z <= -7e-122) {
		tmp = t_1;
	} else if (z <= 4e-88) {
		tmp = x / (1.0 - z);
	} else {
		tmp = (t / (b - y)) - (a / (b - y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (t - a) / (b - y)
	tmp = 0
	if z <= -2.5e-28:
		tmp = t_1
	elif z <= -4.2e-103:
		tmp = (y * x) / (y + (z * b))
	elif z <= -7e-122:
		tmp = t_1
	elif z <= 4e-88:
		tmp = x / (1.0 - z)
	else:
		tmp = (t / (b - y)) - (a / (b - y))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(t - a) / Float64(b - y))
	tmp = 0.0
	if (z <= -2.5e-28)
		tmp = t_1;
	elseif (z <= -4.2e-103)
		tmp = Float64(Float64(y * x) / Float64(y + Float64(z * b)));
	elseif (z <= -7e-122)
		tmp = t_1;
	elseif (z <= 4e-88)
		tmp = Float64(x / Float64(1.0 - z));
	else
		tmp = Float64(Float64(t / Float64(b - y)) - Float64(a / Float64(b - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (t - a) / (b - y);
	tmp = 0.0;
	if (z <= -2.5e-28)
		tmp = t_1;
	elseif (z <= -4.2e-103)
		tmp = (y * x) / (y + (z * b));
	elseif (z <= -7e-122)
		tmp = t_1;
	elseif (z <= 4e-88)
		tmp = x / (1.0 - z);
	else
		tmp = (t / (b - y)) - (a / (b - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.5e-28], t$95$1, If[LessEqual[z, -4.2e-103], N[(N[(y * x), $MachinePrecision] / N[(y + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -7e-122], t$95$1, If[LessEqual[z, 4e-88], N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision], N[(N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision] - N[(a / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 7: 64.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.5 \cdot 10^{-28}:\\ \;\;\;\;\frac{t - a}{b - y}\\ \mathbf{elif}\;z \leq -4.6 \cdot 10^{-103}:\\ \;\;\;\;\frac{y \cdot x}{y + z \cdot b}\\ \mathbf{elif}\;z \leq -4.5 \cdot 10^{-123}:\\ \;\;\;\;\frac{z \cdot t}{y + z \cdot \left(b - y\right)}\\ \mathbf{elif}\;z \leq 1.26 \cdot 10^{-86}:\\ \;\;\;\;\frac{x}{1 - z}\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{b - y} - \frac{a}{b - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -2.5e-28)
   (/ (- t a) (- b y))
   (if (<= z -4.6e-103)
     (/ (* y x) (+ y (* z b)))
     (if (<= z -4.5e-123)
       (/ (* z t) (+ y (* z (- b y))))
       (if (<= z 1.26e-86) (/ x (- 1.0 z)) (- (/ t (- b y)) (/ a (- b y))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.5e-28) {
		tmp = (t - a) / (b - y);
	} else if (z <= -4.6e-103) {
		tmp = (y * x) / (y + (z * b));
	} else if (z <= -4.5e-123) {
		tmp = (z * t) / (y + (z * (b - y)));
	} else if (z <= 1.26e-86) {
		tmp = x / (1.0 - z);
	} else {
		tmp = (t / (b - y)) - (a / (b - y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: tmp
    if (z <= (-2.5d-28)) then
        tmp = (t - a) / (b - y)
    else if (z <= (-4.6d-103)) then
        tmp = (y * x) / (y + (z * b))
    else if (z <= (-4.5d-123)) then
        tmp = (z * t) / (y + (z * (b - y)))
    else if (z <= 1.26d-86) then
        tmp = x / (1.0d0 - z)
    else
        tmp = (t / (b - y)) - (a / (b - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -2.5e-28) {
		tmp = (t - a) / (b - y);
	} else if (z <= -4.6e-103) {
		tmp = (y * x) / (y + (z * b));
	} else if (z <= -4.5e-123) {
		tmp = (z * t) / (y + (z * (b - y)));
	} else if (z <= 1.26e-86) {
		tmp = x / (1.0 - z);
	} else {
		tmp = (t / (b - y)) - (a / (b - y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -2.5e-28:
		tmp = (t - a) / (b - y)
	elif z <= -4.6e-103:
		tmp = (y * x) / (y + (z * b))
	elif z <= -4.5e-123:
		tmp = (z * t) / (y + (z * (b - y)))
	elif z <= 1.26e-86:
		tmp = x / (1.0 - z)
	else:
		tmp = (t / (b - y)) - (a / (b - y))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -2.5e-28)
		tmp = Float64(Float64(t - a) / Float64(b - y));
	elseif (z <= -4.6e-103)
		tmp = Float64(Float64(y * x) / Float64(y + Float64(z * b)));
	elseif (z <= -4.5e-123)
		tmp = Float64(Float64(z * t) / Float64(y + Float64(z * Float64(b - y))));
	elseif (z <= 1.26e-86)
		tmp = Float64(x / Float64(1.0 - z));
	else
		tmp = Float64(Float64(t / Float64(b - y)) - Float64(a / Float64(b - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -2.5e-28)
		tmp = (t - a) / (b - y);
	elseif (z <= -4.6e-103)
		tmp = (y * x) / (y + (z * b));
	elseif (z <= -4.5e-123)
		tmp = (z * t) / (y + (z * (b - y)));
	elseif (z <= 1.26e-86)
		tmp = x / (1.0 - z);
	else
		tmp = (t / (b - y)) - (a / (b - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -2.5e-28], N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -4.6e-103], N[(N[(y * x), $MachinePrecision] / N[(y + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -4.5e-123], N[(N[(z * t), $MachinePrecision] / N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.26e-86], N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision], N[(N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision] - N[(a / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 8: 67.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y \cdot x - z \cdot \left(a - t\right)}{y}\\ \mathbf{if}\;z \leq -1.5 \cdot 10^{-38}:\\ \;\;\;\;\frac{t - a}{b - y}\\ \mathbf{elif}\;z \leq -3.5 \cdot 10^{-274}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4 \cdot 10^{-116}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 1.9 \cdot 10^{-82}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{b - y} - \frac{a}{b - y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- (* y x) (* z (- a t))) y)))
   (if (<= z -1.5e-38)
     (/ (- t a) (- b y))
     (if (<= z -3.5e-274)
       t_1
       (if (<= z 4e-116)
         x
         (if (<= z 1.9e-82) t_1 (- (/ t (- b y)) (/ a (- b y)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((y * x) - (z * (a - t))) / y;
	double tmp;
	if (z <= -1.5e-38) {
		tmp = (t - a) / (b - y);
	} else if (z <= -3.5e-274) {
		tmp = t_1;
	} else if (z <= 4e-116) {
		tmp = x;
	} else if (z <= 1.9e-82) {
		tmp = t_1;
	} else {
		tmp = (t / (b - y)) - (a / (b - y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: t_1
    real(8) :: tmp
    t_1 = ((y * x) - (z * (a - t))) / y
    if (z <= (-1.5d-38)) then
        tmp = (t - a) / (b - y)
    else if (z <= (-3.5d-274)) then
        tmp = t_1
    else if (z <= 4d-116) then
        tmp = x
    else if (z <= 1.9d-82) then
        tmp = t_1
    else
        tmp = (t / (b - y)) - (a / (b - y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((y * x) - (z * (a - t))) / y;
	double tmp;
	if (z <= -1.5e-38) {
		tmp = (t - a) / (b - y);
	} else if (z <= -3.5e-274) {
		tmp = t_1;
	} else if (z <= 4e-116) {
		tmp = x;
	} else if (z <= 1.9e-82) {
		tmp = t_1;
	} else {
		tmp = (t / (b - y)) - (a / (b - y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((y * x) - (z * (a - t))) / y
	tmp = 0
	if z <= -1.5e-38:
		tmp = (t - a) / (b - y)
	elif z <= -3.5e-274:
		tmp = t_1
	elif z <= 4e-116:
		tmp = x
	elif z <= 1.9e-82:
		tmp = t_1
	else:
		tmp = (t / (b - y)) - (a / (b - y))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(y * x) - Float64(z * Float64(a - t))) / y)
	tmp = 0.0
	if (z <= -1.5e-38)
		tmp = Float64(Float64(t - a) / Float64(b - y));
	elseif (z <= -3.5e-274)
		tmp = t_1;
	elseif (z <= 4e-116)
		tmp = x;
	elseif (z <= 1.9e-82)
		tmp = t_1;
	else
		tmp = Float64(Float64(t / Float64(b - y)) - Float64(a / Float64(b - y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((y * x) - (z * (a - t))) / y;
	tmp = 0.0;
	if (z <= -1.5e-38)
		tmp = (t - a) / (b - y);
	elseif (z <= -3.5e-274)
		tmp = t_1;
	elseif (z <= 4e-116)
		tmp = x;
	elseif (z <= 1.9e-82)
		tmp = t_1;
	else
		tmp = (t / (b - y)) - (a / (b - y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(y * x), $MachinePrecision] - N[(z * N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[z, -1.5e-38], N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -3.5e-274], t$95$1, If[LessEqual[z, 4e-116], x, If[LessEqual[z, 1.9e-82], t$95$1, N[(N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision] - N[(a / N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 9: 41.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 - z}\\ \mathbf{if}\;y \leq -1.95 \cdot 10^{+202}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -7.5 \cdot 10^{+156}:\\ \;\;\;\;\frac{a - t}{y}\\ \mathbf{elif}\;y \leq -9 \cdot 10^{-6}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 4.6 \cdot 10^{-220}:\\ \;\;\;\;\frac{-a}{b}\\ \mathbf{elif}\;y \leq 8 \cdot 10^{+53}:\\ \;\;\;\;\frac{t}{b - y}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (- 1.0 z))))
   (if (<= y -1.95e+202)
     t_1
     (if (<= y -7.5e+156)
       (/ (- a t) y)
       (if (<= y -9e-6)
         t_1
         (if (<= y 4.6e-220)
           (/ (- a) b)
           (if (<= y 8e+53) (/ t (- b y)) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double tmp;
	if (y <= -1.95e+202) {
		tmp = t_1;
	} else if (y <= -7.5e+156) {
		tmp = (a - t) / y;
	} else if (y <= -9e-6) {
		tmp = t_1;
	} else if (y <= 4.6e-220) {
		tmp = -a / b;
	} else if (y <= 8e+53) {
		tmp = t / (b - y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: t_1
    real(8) :: tmp
    t_1 = x / (1.0d0 - z)
    if (y <= (-1.95d+202)) then
        tmp = t_1
    else if (y <= (-7.5d+156)) then
        tmp = (a - t) / y
    else if (y <= (-9d-6)) then
        tmp = t_1
    else if (y <= 4.6d-220) then
        tmp = -a / b
    else if (y <= 8d+53) then
        tmp = t / (b - y)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double tmp;
	if (y <= -1.95e+202) {
		tmp = t_1;
	} else if (y <= -7.5e+156) {
		tmp = (a - t) / y;
	} else if (y <= -9e-6) {
		tmp = t_1;
	} else if (y <= 4.6e-220) {
		tmp = -a / b;
	} else if (y <= 8e+53) {
		tmp = t / (b - y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 - z)
	tmp = 0
	if y <= -1.95e+202:
		tmp = t_1
	elif y <= -7.5e+156:
		tmp = (a - t) / y
	elif y <= -9e-6:
		tmp = t_1
	elif y <= 4.6e-220:
		tmp = -a / b
	elif y <= 8e+53:
		tmp = t / (b - y)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 - z))
	tmp = 0.0
	if (y <= -1.95e+202)
		tmp = t_1;
	elseif (y <= -7.5e+156)
		tmp = Float64(Float64(a - t) / y);
	elseif (y <= -9e-6)
		tmp = t_1;
	elseif (y <= 4.6e-220)
		tmp = Float64(Float64(-a) / b);
	elseif (y <= 8e+53)
		tmp = Float64(t / Float64(b - y));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 - z);
	tmp = 0.0;
	if (y <= -1.95e+202)
		tmp = t_1;
	elseif (y <= -7.5e+156)
		tmp = (a - t) / y;
	elseif (y <= -9e-6)
		tmp = t_1;
	elseif (y <= 4.6e-220)
		tmp = -a / b;
	elseif (y <= 8e+53)
		tmp = t / (b - y);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.95e+202], t$95$1, If[LessEqual[y, -7.5e+156], N[(N[(a - t), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[y, -9e-6], t$95$1, If[LessEqual[y, 4.6e-220], N[((-a) / b), $MachinePrecision], If[LessEqual[y, 8e+53], N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 10: 64.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t - a}{b - y}\\ \mathbf{if}\;z \leq -2.5 \cdot 10^{-28}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2.65 \cdot 10^{-101}:\\ \;\;\;\;\frac{y \cdot x}{y + z \cdot b}\\ \mathbf{elif}\;z \leq -1.8 \cdot 10^{-122} \lor \neg \left(z \leq 1.65 \cdot 10^{-87}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- t a) (- b y))))
   (if (<= z -2.5e-28)
     t_1
     (if (<= z -2.65e-101)
       (/ (* y x) (+ y (* z b)))
       (if (or (<= z -1.8e-122) (not (<= z 1.65e-87))) t_1 (/ x (- 1.0 z)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (t - a) / (b - y);
	double tmp;
	if (z <= -2.5e-28) {
		tmp = t_1;
	} else if (z <= -2.65e-101) {
		tmp = (y * x) / (y + (z * b));
	} else if ((z <= -1.8e-122) || !(z <= 1.65e-87)) {
		tmp = t_1;
	} else {
		tmp = x / (1.0 - z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: t_1
    real(8) :: tmp
    t_1 = (t - a) / (b - y)
    if (z <= (-2.5d-28)) then
        tmp = t_1
    else if (z <= (-2.65d-101)) then
        tmp = (y * x) / (y + (z * b))
    else if ((z <= (-1.8d-122)) .or. (.not. (z <= 1.65d-87))) then
        tmp = t_1
    else
        tmp = x / (1.0d0 - z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (t - a) / (b - y);
	double tmp;
	if (z <= -2.5e-28) {
		tmp = t_1;
	} else if (z <= -2.65e-101) {
		tmp = (y * x) / (y + (z * b));
	} else if ((z <= -1.8e-122) || !(z <= 1.65e-87)) {
		tmp = t_1;
	} else {
		tmp = x / (1.0 - z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (t - a) / (b - y)
	tmp = 0
	if z <= -2.5e-28:
		tmp = t_1
	elif z <= -2.65e-101:
		tmp = (y * x) / (y + (z * b))
	elif (z <= -1.8e-122) or not (z <= 1.65e-87):
		tmp = t_1
	else:
		tmp = x / (1.0 - z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(t - a) / Float64(b - y))
	tmp = 0.0
	if (z <= -2.5e-28)
		tmp = t_1;
	elseif (z <= -2.65e-101)
		tmp = Float64(Float64(y * x) / Float64(y + Float64(z * b)));
	elseif ((z <= -1.8e-122) || !(z <= 1.65e-87))
		tmp = t_1;
	else
		tmp = Float64(x / Float64(1.0 - z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (t - a) / (b - y);
	tmp = 0.0;
	if (z <= -2.5e-28)
		tmp = t_1;
	elseif (z <= -2.65e-101)
		tmp = (y * x) / (y + (z * b));
	elseif ((z <= -1.8e-122) || ~((z <= 1.65e-87)))
		tmp = t_1;
	else
		tmp = x / (1.0 - z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.5e-28], t$95$1, If[LessEqual[z, -2.65e-101], N[(N[(y * x), $MachinePrecision] / N[(y + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, -1.8e-122], N[Not[LessEqual[z, 1.65e-87]], $MachinePrecision]], t$95$1, N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 11: 52.7% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 - z}\\ \mathbf{if}\;y \leq -3.6 \cdot 10^{+202}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -5.4 \cdot 10^{+157}:\\ \;\;\;\;\frac{a - t}{y}\\ \mathbf{elif}\;y \leq -4.2 \cdot 10^{+26} \lor \neg \left(y \leq 1.45 \cdot 10^{+85}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{t - a}{b}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (- 1.0 z))))
   (if (<= y -3.6e+202)
     t_1
     (if (<= y -5.4e+157)
       (/ (- a t) y)
       (if (or (<= y -4.2e+26) (not (<= y 1.45e+85))) t_1 (/ (- t a) b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double tmp;
	if (y <= -3.6e+202) {
		tmp = t_1;
	} else if (y <= -5.4e+157) {
		tmp = (a - t) / y;
	} else if ((y <= -4.2e+26) || !(y <= 1.45e+85)) {
		tmp = t_1;
	} else {
		tmp = (t - a) / b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: t_1
    real(8) :: tmp
    t_1 = x / (1.0d0 - z)
    if (y <= (-3.6d+202)) then
        tmp = t_1
    else if (y <= (-5.4d+157)) then
        tmp = (a - t) / y
    else if ((y <= (-4.2d+26)) .or. (.not. (y <= 1.45d+85))) then
        tmp = t_1
    else
        tmp = (t - a) / b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double tmp;
	if (y <= -3.6e+202) {
		tmp = t_1;
	} else if (y <= -5.4e+157) {
		tmp = (a - t) / y;
	} else if ((y <= -4.2e+26) || !(y <= 1.45e+85)) {
		tmp = t_1;
	} else {
		tmp = (t - a) / b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 - z)
	tmp = 0
	if y <= -3.6e+202:
		tmp = t_1
	elif y <= -5.4e+157:
		tmp = (a - t) / y
	elif (y <= -4.2e+26) or not (y <= 1.45e+85):
		tmp = t_1
	else:
		tmp = (t - a) / b
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 - z))
	tmp = 0.0
	if (y <= -3.6e+202)
		tmp = t_1;
	elseif (y <= -5.4e+157)
		tmp = Float64(Float64(a - t) / y);
	elseif ((y <= -4.2e+26) || !(y <= 1.45e+85))
		tmp = t_1;
	else
		tmp = Float64(Float64(t - a) / b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 - z);
	tmp = 0.0;
	if (y <= -3.6e+202)
		tmp = t_1;
	elseif (y <= -5.4e+157)
		tmp = (a - t) / y;
	elseif ((y <= -4.2e+26) || ~((y <= 1.45e+85)))
		tmp = t_1;
	else
		tmp = (t - a) / b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -3.6e+202], t$95$1, If[LessEqual[y, -5.4e+157], N[(N[(a - t), $MachinePrecision] / y), $MachinePrecision], If[Or[LessEqual[y, -4.2e+26], N[Not[LessEqual[y, 1.45e+85]], $MachinePrecision]], t$95$1, N[(N[(t - a), $MachinePrecision] / b), $MachinePrecision]]]]]

Derivation

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

Alternative 12: 35.2% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-a}{b}\\ \mathbf{if}\;z \leq -6.5 \cdot 10^{+152}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{-11}:\\ \;\;\;\;\frac{a}{y}\\ \mathbf{elif}\;z \leq 1.1 \cdot 10^{-84}:\\ \;\;\;\;x + z \cdot x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- a) b)))
   (if (<= z -6.5e+152)
     t_1
     (if (<= z -4.2e-11) (/ a y) (if (<= z 1.1e-84) (+ x (* z x)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = -a / b;
	double tmp;
	if (z <= -6.5e+152) {
		tmp = t_1;
	} else if (z <= -4.2e-11) {
		tmp = a / y;
	} else if (z <= 1.1e-84) {
		tmp = x + (z * x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: t_1
    real(8) :: tmp
    t_1 = -a / b
    if (z <= (-6.5d+152)) then
        tmp = t_1
    else if (z <= (-4.2d-11)) then
        tmp = a / y
    else if (z <= 1.1d-84) then
        tmp = x + (z * x)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = -a / b;
	double tmp;
	if (z <= -6.5e+152) {
		tmp = t_1;
	} else if (z <= -4.2e-11) {
		tmp = a / y;
	} else if (z <= 1.1e-84) {
		tmp = x + (z * x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = -a / b
	tmp = 0
	if z <= -6.5e+152:
		tmp = t_1
	elif z <= -4.2e-11:
		tmp = a / y
	elif z <= 1.1e-84:
		tmp = x + (z * x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(-a) / b)
	tmp = 0.0
	if (z <= -6.5e+152)
		tmp = t_1;
	elseif (z <= -4.2e-11)
		tmp = Float64(a / y);
	elseif (z <= 1.1e-84)
		tmp = Float64(x + Float64(z * x));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = -a / b;
	tmp = 0.0;
	if (z <= -6.5e+152)
		tmp = t_1;
	elseif (z <= -4.2e-11)
		tmp = a / y;
	elseif (z <= 1.1e-84)
		tmp = x + (z * x);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[((-a) / b), $MachinePrecision]}, If[LessEqual[z, -6.5e+152], t$95$1, If[LessEqual[z, -4.2e-11], N[(a / y), $MachinePrecision], If[LessEqual[z, 1.1e-84], N[(x + N[(z * x), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 13: 42.4% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{1 - z}\\ \mathbf{if}\;y \leq -1.9 \cdot 10^{-8}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{-220}:\\ \;\;\;\;\frac{-a}{b}\\ \mathbf{elif}\;y \leq 3.1 \cdot 10^{+55}:\\ \;\;\;\;\frac{t}{b - y}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ x (- 1.0 z))))
   (if (<= y -1.9e-8)
     t_1
     (if (<= y 2.25e-220) (/ (- a) b) (if (<= y 3.1e+55) (/ t (- b y)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double tmp;
	if (y <= -1.9e-8) {
		tmp = t_1;
	} else if (y <= 2.25e-220) {
		tmp = -a / b;
	} else if (y <= 3.1e+55) {
		tmp = t / (b - y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: t_1
    real(8) :: tmp
    t_1 = x / (1.0d0 - z)
    if (y <= (-1.9d-8)) then
        tmp = t_1
    else if (y <= 2.25d-220) then
        tmp = -a / b
    else if (y <= 3.1d+55) then
        tmp = t / (b - y)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x / (1.0 - z);
	double tmp;
	if (y <= -1.9e-8) {
		tmp = t_1;
	} else if (y <= 2.25e-220) {
		tmp = -a / b;
	} else if (y <= 3.1e+55) {
		tmp = t / (b - y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x / (1.0 - z)
	tmp = 0
	if y <= -1.9e-8:
		tmp = t_1
	elif y <= 2.25e-220:
		tmp = -a / b
	elif y <= 3.1e+55:
		tmp = t / (b - y)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x / Float64(1.0 - z))
	tmp = 0.0
	if (y <= -1.9e-8)
		tmp = t_1;
	elseif (y <= 2.25e-220)
		tmp = Float64(Float64(-a) / b);
	elseif (y <= 3.1e+55)
		tmp = Float64(t / Float64(b - y));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x / (1.0 - z);
	tmp = 0.0;
	if (y <= -1.9e-8)
		tmp = t_1;
	elseif (y <= 2.25e-220)
		tmp = -a / b;
	elseif (y <= 3.1e+55)
		tmp = t / (b - y);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.9e-8], t$95$1, If[LessEqual[y, 2.25e-220], N[((-a) / b), $MachinePrecision], If[LessEqual[y, 3.1e+55], N[(t / N[(b - y), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 14: 64.6% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7.2 \cdot 10^{-51} \lor \neg \left(z \leq 4.1 \cdot 10^{-86}\right):\\ \;\;\;\;\frac{t - a}{b - y}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{1 - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -7.2e-51) (not (<= z 4.1e-86)))
   (/ (- t a) (- b y))
   (/ x (- 1.0 z))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -7.2e-51) || !(z <= 4.1e-86)) {
		tmp = (t - a) / (b - y);
	} else {
		tmp = x / (1.0 - z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: tmp
    if ((z <= (-7.2d-51)) .or. (.not. (z <= 4.1d-86))) then
        tmp = (t - a) / (b - y)
    else
        tmp = x / (1.0d0 - z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -7.2e-51) || !(z <= 4.1e-86)) {
		tmp = (t - a) / (b - y);
	} else {
		tmp = x / (1.0 - z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -7.2e-51) or not (z <= 4.1e-86):
		tmp = (t - a) / (b - y)
	else:
		tmp = x / (1.0 - z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -7.2e-51) || !(z <= 4.1e-86))
		tmp = Float64(Float64(t - a) / Float64(b - y));
	else
		tmp = Float64(x / Float64(1.0 - z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -7.2e-51) || ~((z <= 4.1e-86)))
		tmp = (t - a) / (b - y);
	else
		tmp = x / (1.0 - z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -7.2e-51], N[Not[LessEqual[z, 4.1e-86]], $MachinePrecision]], N[(N[(t - a), $MachinePrecision] / N[(b - y), $MachinePrecision]), $MachinePrecision], N[(x / N[(1.0 - z), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 15: 35.2% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-a}{b}\\ \mathbf{if}\;z \leq -5 \cdot 10^{+152}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -5.5 \cdot 10^{-11}:\\ \;\;\;\;\frac{a}{y}\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{-85}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- a) b)))
   (if (<= z -5e+152)
     t_1
     (if (<= z -5.5e-11) (/ a y) (if (<= z 2.2e-85) x t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = -a / b;
	double tmp;
	if (z <= -5e+152) {
		tmp = t_1;
	} else if (z <= -5.5e-11) {
		tmp = a / y;
	} else if (z <= 2.2e-85) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: t_1
    real(8) :: tmp
    t_1 = -a / b
    if (z <= (-5d+152)) then
        tmp = t_1
    else if (z <= (-5.5d-11)) then
        tmp = a / y
    else if (z <= 2.2d-85) then
        tmp = x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = -a / b;
	double tmp;
	if (z <= -5e+152) {
		tmp = t_1;
	} else if (z <= -5.5e-11) {
		tmp = a / y;
	} else if (z <= 2.2e-85) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = -a / b
	tmp = 0
	if z <= -5e+152:
		tmp = t_1
	elif z <= -5.5e-11:
		tmp = a / y
	elif z <= 2.2e-85:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(-a) / b)
	tmp = 0.0
	if (z <= -5e+152)
		tmp = t_1;
	elseif (z <= -5.5e-11)
		tmp = Float64(a / y);
	elseif (z <= 2.2e-85)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = -a / b;
	tmp = 0.0;
	if (z <= -5e+152)
		tmp = t_1;
	elseif (z <= -5.5e-11)
		tmp = a / y;
	elseif (z <= 2.2e-85)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[((-a) / b), $MachinePrecision]}, If[LessEqual[z, -5e+152], t$95$1, If[LessEqual[z, -5.5e-11], N[(a / y), $MachinePrecision], If[LessEqual[z, 2.2e-85], x, t$95$1]]]]

Derivation

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

Alternative 16: 34.2% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.5 \cdot 10^{-11} \lor \neg \left(z \leq 1.85 \cdot 10^{-10}\right):\\ \;\;\;\;\frac{a}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -5.5e-11) (not (<= z 1.85e-10))) (/ a y) x))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -5.5e-11) || !(z <= 1.85e-10)) {
		tmp = a / y;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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) :: tmp
    if ((z <= (-5.5d-11)) .or. (.not. (z <= 1.85d-10))) then
        tmp = a / y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -5.5e-11) || !(z <= 1.85e-10)) {
		tmp = a / y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -5.5e-11) or not (z <= 1.85e-10):
		tmp = a / y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -5.5e-11) || !(z <= 1.85e-10))
		tmp = Float64(a / y);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -5.5e-11) || ~((z <= 1.85e-10)))
		tmp = a / y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -5.5e-11], N[Not[LessEqual[z, 1.85e-10]], $MachinePrecision]], N[(a / y), $MachinePrecision], x]

Derivation

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

Alternative 17: 25.2% accurate, 17.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 73.6% accurate, 0.7× speedup

Math

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

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (- (/ (+ (* z t) (* y x)) (+ y (* z (- b y)))) (/ a (+ (- b y) (/ y z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return (((z * t) + (y * x)) / (y + (z * (b - y)))) - (a / ((b - y) + (y / z)));
}

Fortran

real(8) function code(x, y, z, t, a, b)
    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
    code = (((z * t) + (y * x)) / (y + (z * (b - y)))) - (a / ((b - y) + (y / z)))
end function

Java

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

Python

def code(x, y, z, t, a, b):
	return (((z * t) + (y * x)) / (y + (z * (b - y)))) - (a / ((b - y) + (y / z)))

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(Float64(z * t) + Float64(y * x)) / Float64(y + Float64(z * Float64(b - y)))) - Float64(a / Float64(Float64(b - y) + Float64(y / z))))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = (((z * t) + (y * x)) / (y + (z * (b - y)))) - (a / ((b - y) + (y / z)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(N[(z * t), $MachinePrecision] + N[(y * x), $MachinePrecision]), $MachinePrecision] / N[(y + N[(z * N[(b - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(a / N[(N[(b - y), $MachinePrecision] + N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z t a b)
  :name "Development.Shake.Progress:decay from shake-0.15.5"
  :precision binary64

  :herbie-target
  (- (/ (+ (* z t) (* y x)) (+ y (* z (- b y)))) (/ a (+ (- b y) (/ y z))))

  (/ (+ (* x y) (* z (- t a))) (+ y (* z (- b y)))))