Diagrams.Solve.Polynomial:cubForm from diagrams-solve-0.1, J

Percentage Accurate: 79.4% → 89.7%

Time: 28.4s

Alternatives: 25

Speedup: 0.8×

• 37.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
}

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 * 9.0d0) * y) - (((z * 4.0d0) * t) * a)) + b) / (z * c)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
}

Python

def code(x, y, z, t, a, b, c):
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c)

Julia

function code(x, y, z, t, a, b, c)
	return Float64(Float64(Float64(Float64(Float64(x * 9.0) * y) - Float64(Float64(Float64(z * 4.0) * t) * a)) + b) / Float64(z * c))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(N[(N[(N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision] - N[(N[(N[(z * 4.0), $MachinePrecision] * t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]

Initial Program: 79.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
}

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 * 9.0d0) * y) - (((z * 4.0d0) * t) * a)) + b) / (z * c)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
}

Python

def code(x, y, z, t, a, b, c):
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c)

Julia

function code(x, y, z, t, a, b, c)
	return Float64(Float64(Float64(Float64(Float64(x * 9.0) * y) - Float64(Float64(Float64(z * 4.0) * t) * a)) + b) / Float64(z * c))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(N[(N[(N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision] - N[(N[(N[(z * 4.0), $MachinePrecision] * t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]

Alternative 1: 89.7% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \sqrt[3]{b + \mathsf{fma}\left(x, 9 \cdot y, a \cdot \left(z \cdot \left(t \cdot -4\right)\right)\right)}\\ \mathbf{if}\;z \leq -6.8 \cdot 10^{-65} \lor \neg \left(z \leq 9.6 \cdot 10^{+104}\right):\\ \;\;\;\;\left(9 \cdot \frac{\frac{x}{\frac{z}{y}}}{c} + \frac{b}{z \cdot c}\right) - 4 \cdot \frac{a \cdot t}{c}\\ \mathbf{else}:\\ \;\;\;\;\frac{{t_1}^{2}}{c} \cdot \frac{t_1}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (cbrt (+ b (fma x (* 9.0 y) (* a (* z (* t -4.0))))))))
   (if (or (<= z -6.8e-65) (not (<= z 9.6e+104)))
     (- (+ (* 9.0 (/ (/ x (/ z y)) c)) (/ b (* z c))) (* 4.0 (/ (* a t) c)))
     (* (/ (pow t_1 2.0) c) (/ t_1 z)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = cbrt((b + fma(x, (9.0 * y), (a * (z * (t * -4.0))))));
	double tmp;
	if ((z <= -6.8e-65) || !(z <= 9.6e+104)) {
		tmp = ((9.0 * ((x / (z / y)) / c)) + (b / (z * c))) - (4.0 * ((a * t) / c));
	} else {
		tmp = (pow(t_1, 2.0) / c) * (t_1 / z);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c)
	t_1 = cbrt(Float64(b + fma(x, Float64(9.0 * y), Float64(a * Float64(z * Float64(t * -4.0))))))
	tmp = 0.0
	if ((z <= -6.8e-65) || !(z <= 9.6e+104))
		tmp = Float64(Float64(Float64(9.0 * Float64(Float64(x / Float64(z / y)) / c)) + Float64(b / Float64(z * c))) - Float64(4.0 * Float64(Float64(a * t) / c)));
	else
		tmp = Float64(Float64((t_1 ^ 2.0) / c) * Float64(t_1 / z));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[Power[N[(b + N[(x * N[(9.0 * y), $MachinePrecision] + N[(a * N[(z * N[(t * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]}, If[Or[LessEqual[z, -6.8e-65], N[Not[LessEqual[z, 9.6e+104]], $MachinePrecision]], N[(N[(N[(9.0 * N[(N[(x / N[(z / y), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision] + N[(b / N[(z * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[Power[t$95$1, 2.0], $MachinePrecision] / c), $MachinePrecision] * N[(t$95$1 / z), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 2: 91.0% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.9 \cdot 10^{-5} \lor \neg \left(z \leq 1.7 \cdot 10^{+105}\right):\\ \;\;\;\;\left(9 \cdot \frac{\frac{x}{\frac{z}{y}}}{c} + \frac{b}{z \cdot c}\right) - 4 \cdot \frac{a \cdot t}{c}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{z} \cdot \frac{b + \mathsf{fma}\left(x, 9 \cdot y, a \cdot \left(z \cdot \left(t \cdot -4\right)\right)\right)}{c}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= z -2.9e-5) (not (<= z 1.7e+105)))
   (- (+ (* 9.0 (/ (/ x (/ z y)) c)) (/ b (* z c))) (* 4.0 (/ (* a t) c)))
   (* (/ 1.0 z) (/ (+ b (fma x (* 9.0 y) (* a (* z (* t -4.0))))) c))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if ((z <= -2.9e-5) || !(z <= 1.7e+105)) {
		tmp = ((9.0 * ((x / (z / y)) / c)) + (b / (z * c))) - (4.0 * ((a * t) / c));
	} else {
		tmp = (1.0 / z) * ((b + fma(x, (9.0 * y), (a * (z * (t * -4.0))))) / c);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((z <= -2.9e-5) || !(z <= 1.7e+105))
		tmp = Float64(Float64(Float64(9.0 * Float64(Float64(x / Float64(z / y)) / c)) + Float64(b / Float64(z * c))) - Float64(4.0 * Float64(Float64(a * t) / c)));
	else
		tmp = Float64(Float64(1.0 / z) * Float64(Float64(b + fma(x, Float64(9.0 * y), Float64(a * Float64(z * Float64(t * -4.0))))) / c));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[z, -2.9e-5], N[Not[LessEqual[z, 1.7e+105]], $MachinePrecision]], N[(N[(N[(9.0 * N[(N[(x / N[(z / y), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision] + N[(b / N[(z * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 / z), $MachinePrecision] * N[(N[(b + N[(x * N[(9.0 * y), $MachinePrecision] + N[(a * N[(z * N[(t * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 3: 89.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 4 \cdot \left(a \cdot t\right)\\ t_2 := y \cdot \left(9 \cdot x\right)\\ \mathbf{if}\;t_2 \leq -\infty:\\ \;\;\;\;\frac{y \cdot \frac{9 \cdot x}{z} - t_1}{c}\\ \mathbf{elif}\;t_2 \leq 5 \cdot 10^{+238}:\\ \;\;\;\;\frac{\frac{b + 9 \cdot \left(x \cdot y\right)}{z} - t_1}{c}\\ \mathbf{else}:\\ \;\;\;\;9 \cdot \left(\frac{y}{c} \cdot \frac{x}{z}\right) - 4 \cdot \frac{a \cdot t}{c}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* 4.0 (* a t))) (t_2 (* y (* 9.0 x))))
   (if (<= t_2 (- INFINITY))
     (/ (- (* y (/ (* 9.0 x) z)) t_1) c)
     (if (<= t_2 5e+238)
       (/ (- (/ (+ b (* 9.0 (* x y))) z) t_1) c)
       (- (* 9.0 (* (/ y c) (/ x z))) (* 4.0 (/ (* a t) c)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = 4.0 * (a * t);
	double t_2 = y * (9.0 * x);
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c;
	} else if (t_2 <= 5e+238) {
		tmp = (((b + (9.0 * (x * y))) / z) - t_1) / c;
	} else {
		tmp = (9.0 * ((y / c) * (x / z))) - (4.0 * ((a * t) / c));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = 4.0 * (a * t);
	double t_2 = y * (9.0 * x);
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c;
	} else if (t_2 <= 5e+238) {
		tmp = (((b + (9.0 * (x * y))) / z) - t_1) / c;
	} else {
		tmp = (9.0 * ((y / c) * (x / z))) - (4.0 * ((a * t) / c));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = 4.0 * (a * t)
	t_2 = y * (9.0 * x)
	tmp = 0
	if t_2 <= -math.inf:
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c
	elif t_2 <= 5e+238:
		tmp = (((b + (9.0 * (x * y))) / z) - t_1) / c
	else:
		tmp = (9.0 * ((y / c) * (x / z))) - (4.0 * ((a * t) / c))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(4.0 * Float64(a * t))
	t_2 = Float64(y * Float64(9.0 * x))
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = Float64(Float64(Float64(y * Float64(Float64(9.0 * x) / z)) - t_1) / c);
	elseif (t_2 <= 5e+238)
		tmp = Float64(Float64(Float64(Float64(b + Float64(9.0 * Float64(x * y))) / z) - t_1) / c);
	else
		tmp = Float64(Float64(9.0 * Float64(Float64(y / c) * Float64(x / z))) - Float64(4.0 * Float64(Float64(a * t) / c)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = 4.0 * (a * t);
	t_2 = y * (9.0 * x);
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c;
	elseif (t_2 <= 5e+238)
		tmp = (((b + (9.0 * (x * y))) / z) - t_1) / c;
	else
		tmp = (9.0 * ((y / c) * (x / z))) - (4.0 * ((a * t) / c));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(9.0 * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], N[(N[(N[(y * N[(N[(9.0 * x), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision] / c), $MachinePrecision], If[LessEqual[t$95$2, 5e+238], N[(N[(N[(N[(b + N[(9.0 * N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision] - t$95$1), $MachinePrecision] / c), $MachinePrecision], N[(N[(9.0 * N[(N[(y / c), $MachinePrecision] * N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 4: 90.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.6 \cdot 10^{-9} \lor \neg \left(z \leq 10^{+105}\right):\\ \;\;\;\;\left(9 \cdot \frac{\frac{x}{\frac{z}{y}}}{c} + \frac{b}{z \cdot c}\right) - 4 \cdot \frac{a \cdot t}{c}\\ \mathbf{else}:\\ \;\;\;\;\frac{b + \left(y \cdot \left(9 \cdot x\right) - a \cdot \left(t \cdot \left(z \cdot 4\right)\right)\right)}{z \cdot c}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= z -8.6e-9) (not (<= z 1e+105)))
   (- (+ (* 9.0 (/ (/ x (/ z y)) c)) (/ b (* z c))) (* 4.0 (/ (* a t) c)))
   (/ (+ b (- (* y (* 9.0 x)) (* a (* t (* z 4.0))))) (* z c))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if ((z <= -8.6e-9) || !(z <= 1e+105)) {
		tmp = ((9.0 * ((x / (z / y)) / c)) + (b / (z * c))) - (4.0 * ((a * t) / c));
	} else {
		tmp = (b + ((y * (9.0 * x)) - (a * (t * (z * 4.0))))) / (z * c);
	}
	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 <= (-8.6d-9)) .or. (.not. (z <= 1d+105))) then
        tmp = ((9.0d0 * ((x / (z / y)) / c)) + (b / (z * c))) - (4.0d0 * ((a * t) / c))
    else
        tmp = (b + ((y * (9.0d0 * x)) - (a * (t * (z * 4.0d0))))) / (z * c)
    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 <= -8.6e-9) || !(z <= 1e+105)) {
		tmp = ((9.0 * ((x / (z / y)) / c)) + (b / (z * c))) - (4.0 * ((a * t) / c));
	} else {
		tmp = (b + ((y * (9.0 * x)) - (a * (t * (z * 4.0))))) / (z * c);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if (z <= -8.6e-9) or not (z <= 1e+105):
		tmp = ((9.0 * ((x / (z / y)) / c)) + (b / (z * c))) - (4.0 * ((a * t) / c))
	else:
		tmp = (b + ((y * (9.0 * x)) - (a * (t * (z * 4.0))))) / (z * c)
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((z <= -8.6e-9) || !(z <= 1e+105))
		tmp = Float64(Float64(Float64(9.0 * Float64(Float64(x / Float64(z / y)) / c)) + Float64(b / Float64(z * c))) - Float64(4.0 * Float64(Float64(a * t) / c)));
	else
		tmp = Float64(Float64(b + Float64(Float64(y * Float64(9.0 * x)) - Float64(a * Float64(t * Float64(z * 4.0))))) / Float64(z * c));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if ((z <= -8.6e-9) || ~((z <= 1e+105)))
		tmp = ((9.0 * ((x / (z / y)) / c)) + (b / (z * c))) - (4.0 * ((a * t) / c));
	else
		tmp = (b + ((y * (9.0 * x)) - (a * (t * (z * 4.0))))) / (z * c);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[z, -8.6e-9], N[Not[LessEqual[z, 1e+105]], $MachinePrecision]], N[(N[(N[(9.0 * N[(N[(x / N[(z / y), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision] + N[(b / N[(z * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(b + N[(N[(y * N[(9.0 * x), $MachinePrecision]), $MachinePrecision] - N[(a * N[(t * N[(z * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 5: 49.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -4 \cdot \left(t \cdot \frac{a}{c}\right)\\ t_2 := 9 \cdot \left(\frac{y}{c} \cdot \frac{x}{z}\right)\\ \mathbf{if}\;y \leq -4 \cdot 10^{-33}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -6.8 \cdot 10^{-290}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 6.4 \cdot 10^{-272}:\\ \;\;\;\;b \cdot \frac{1}{z \cdot c}\\ \mathbf{elif}\;y \leq 2.75 \cdot 10^{-127}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.1 \cdot 10^{-107}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \mathbf{elif}\;y \leq 4.7 \cdot 10^{-55}:\\ \;\;\;\;-4 \cdot \left(a \cdot \frac{t}{c}\right)\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{+87}:\\ \;\;\;\;\frac{b}{z} \cdot \frac{1}{c}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* -4.0 (* t (/ a c)))) (t_2 (* 9.0 (* (/ y c) (/ x z)))))
   (if (<= y -4e-33)
     t_2
     (if (<= y -6.8e-290)
       t_1
       (if (<= y 6.4e-272)
         (* b (/ 1.0 (* z c)))
         (if (<= y 2.75e-127)
           t_1
           (if (<= y 2.1e-107)
             (/ (/ b c) z)
             (if (<= y 4.7e-55)
               (* -4.0 (* a (/ t c)))
               (if (<= y 2.2e+87) (* (/ b z) (/ 1.0 c)) t_2)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = -4.0 * (t * (a / c));
	double t_2 = 9.0 * ((y / c) * (x / z));
	double tmp;
	if (y <= -4e-33) {
		tmp = t_2;
	} else if (y <= -6.8e-290) {
		tmp = t_1;
	} else if (y <= 6.4e-272) {
		tmp = b * (1.0 / (z * c));
	} else if (y <= 2.75e-127) {
		tmp = t_1;
	} else if (y <= 2.1e-107) {
		tmp = (b / c) / z;
	} else if (y <= 4.7e-55) {
		tmp = -4.0 * (a * (t / c));
	} else if (y <= 2.2e+87) {
		tmp = (b / z) * (1.0 / c);
	} 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 = (-4.0d0) * (t * (a / c))
    t_2 = 9.0d0 * ((y / c) * (x / z))
    if (y <= (-4d-33)) then
        tmp = t_2
    else if (y <= (-6.8d-290)) then
        tmp = t_1
    else if (y <= 6.4d-272) then
        tmp = b * (1.0d0 / (z * c))
    else if (y <= 2.75d-127) then
        tmp = t_1
    else if (y <= 2.1d-107) then
        tmp = (b / c) / z
    else if (y <= 4.7d-55) then
        tmp = (-4.0d0) * (a * (t / c))
    else if (y <= 2.2d+87) then
        tmp = (b / z) * (1.0d0 / c)
    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 = -4.0 * (t * (a / c));
	double t_2 = 9.0 * ((y / c) * (x / z));
	double tmp;
	if (y <= -4e-33) {
		tmp = t_2;
	} else if (y <= -6.8e-290) {
		tmp = t_1;
	} else if (y <= 6.4e-272) {
		tmp = b * (1.0 / (z * c));
	} else if (y <= 2.75e-127) {
		tmp = t_1;
	} else if (y <= 2.1e-107) {
		tmp = (b / c) / z;
	} else if (y <= 4.7e-55) {
		tmp = -4.0 * (a * (t / c));
	} else if (y <= 2.2e+87) {
		tmp = (b / z) * (1.0 / c);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = -4.0 * (t * (a / c))
	t_2 = 9.0 * ((y / c) * (x / z))
	tmp = 0
	if y <= -4e-33:
		tmp = t_2
	elif y <= -6.8e-290:
		tmp = t_1
	elif y <= 6.4e-272:
		tmp = b * (1.0 / (z * c))
	elif y <= 2.75e-127:
		tmp = t_1
	elif y <= 2.1e-107:
		tmp = (b / c) / z
	elif y <= 4.7e-55:
		tmp = -4.0 * (a * (t / c))
	elif y <= 2.2e+87:
		tmp = (b / z) * (1.0 / c)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(-4.0 * Float64(t * Float64(a / c)))
	t_2 = Float64(9.0 * Float64(Float64(y / c) * Float64(x / z)))
	tmp = 0.0
	if (y <= -4e-33)
		tmp = t_2;
	elseif (y <= -6.8e-290)
		tmp = t_1;
	elseif (y <= 6.4e-272)
		tmp = Float64(b * Float64(1.0 / Float64(z * c)));
	elseif (y <= 2.75e-127)
		tmp = t_1;
	elseif (y <= 2.1e-107)
		tmp = Float64(Float64(b / c) / z);
	elseif (y <= 4.7e-55)
		tmp = Float64(-4.0 * Float64(a * Float64(t / c)));
	elseif (y <= 2.2e+87)
		tmp = Float64(Float64(b / z) * Float64(1.0 / c));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = -4.0 * (t * (a / c));
	t_2 = 9.0 * ((y / c) * (x / z));
	tmp = 0.0;
	if (y <= -4e-33)
		tmp = t_2;
	elseif (y <= -6.8e-290)
		tmp = t_1;
	elseif (y <= 6.4e-272)
		tmp = b * (1.0 / (z * c));
	elseif (y <= 2.75e-127)
		tmp = t_1;
	elseif (y <= 2.1e-107)
		tmp = (b / c) / z;
	elseif (y <= 4.7e-55)
		tmp = -4.0 * (a * (t / c));
	elseif (y <= 2.2e+87)
		tmp = (b / z) * (1.0 / c);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(-4.0 * N[(t * N[(a / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(9.0 * N[(N[(y / c), $MachinePrecision] * N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -4e-33], t$95$2, If[LessEqual[y, -6.8e-290], t$95$1, If[LessEqual[y, 6.4e-272], N[(b * N[(1.0 / N[(z * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.75e-127], t$95$1, If[LessEqual[y, 2.1e-107], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[y, 4.7e-55], N[(-4.0 * N[(a * N[(t / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.2e+87], N[(N[(b / z), $MachinePrecision] * N[(1.0 / c), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]]

Derivation

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

Alternative 6: 48.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -4 \cdot \left(t \cdot \frac{a}{c}\right)\\ \mathbf{if}\;y \leq -3.3 \cdot 10^{-79}:\\ \;\;\;\;9 \cdot \left(\frac{x}{c} \cdot \frac{y}{z}\right)\\ \mathbf{elif}\;y \leq -3.2 \cdot 10^{-290}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{-268}:\\ \;\;\;\;b \cdot \frac{1}{z \cdot c}\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{-126}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 3.5 \cdot 10^{-108}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \mathbf{elif}\;y \leq 3 \cdot 10^{-60}:\\ \;\;\;\;-4 \cdot \left(a \cdot \frac{t}{c}\right)\\ \mathbf{elif}\;y \leq 1.75 \cdot 10^{+87}:\\ \;\;\;\;\frac{b}{z} \cdot \frac{1}{c}\\ \mathbf{else}:\\ \;\;\;\;9 \cdot \left(\frac{y}{c} \cdot \frac{x}{z}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* -4.0 (* t (/ a c)))))
   (if (<= y -3.3e-79)
     (* 9.0 (* (/ x c) (/ y z)))
     (if (<= y -3.2e-290)
       t_1
       (if (<= y 1.55e-268)
         (* b (/ 1.0 (* z c)))
         (if (<= y 1.15e-126)
           t_1
           (if (<= y 3.5e-108)
             (/ (/ b c) z)
             (if (<= y 3e-60)
               (* -4.0 (* a (/ t c)))
               (if (<= y 1.75e+87)
                 (* (/ b z) (/ 1.0 c))
                 (* 9.0 (* (/ y c) (/ x z))))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = -4.0 * (t * (a / c));
	double tmp;
	if (y <= -3.3e-79) {
		tmp = 9.0 * ((x / c) * (y / z));
	} else if (y <= -3.2e-290) {
		tmp = t_1;
	} else if (y <= 1.55e-268) {
		tmp = b * (1.0 / (z * c));
	} else if (y <= 1.15e-126) {
		tmp = t_1;
	} else if (y <= 3.5e-108) {
		tmp = (b / c) / z;
	} else if (y <= 3e-60) {
		tmp = -4.0 * (a * (t / c));
	} else if (y <= 1.75e+87) {
		tmp = (b / z) * (1.0 / c);
	} else {
		tmp = 9.0 * ((y / c) * (x / z));
	}
	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 = (-4.0d0) * (t * (a / c))
    if (y <= (-3.3d-79)) then
        tmp = 9.0d0 * ((x / c) * (y / z))
    else if (y <= (-3.2d-290)) then
        tmp = t_1
    else if (y <= 1.55d-268) then
        tmp = b * (1.0d0 / (z * c))
    else if (y <= 1.15d-126) then
        tmp = t_1
    else if (y <= 3.5d-108) then
        tmp = (b / c) / z
    else if (y <= 3d-60) then
        tmp = (-4.0d0) * (a * (t / c))
    else if (y <= 1.75d+87) then
        tmp = (b / z) * (1.0d0 / c)
    else
        tmp = 9.0d0 * ((y / c) * (x / 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 c) {
	double t_1 = -4.0 * (t * (a / c));
	double tmp;
	if (y <= -3.3e-79) {
		tmp = 9.0 * ((x / c) * (y / z));
	} else if (y <= -3.2e-290) {
		tmp = t_1;
	} else if (y <= 1.55e-268) {
		tmp = b * (1.0 / (z * c));
	} else if (y <= 1.15e-126) {
		tmp = t_1;
	} else if (y <= 3.5e-108) {
		tmp = (b / c) / z;
	} else if (y <= 3e-60) {
		tmp = -4.0 * (a * (t / c));
	} else if (y <= 1.75e+87) {
		tmp = (b / z) * (1.0 / c);
	} else {
		tmp = 9.0 * ((y / c) * (x / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = -4.0 * (t * (a / c))
	tmp = 0
	if y <= -3.3e-79:
		tmp = 9.0 * ((x / c) * (y / z))
	elif y <= -3.2e-290:
		tmp = t_1
	elif y <= 1.55e-268:
		tmp = b * (1.0 / (z * c))
	elif y <= 1.15e-126:
		tmp = t_1
	elif y <= 3.5e-108:
		tmp = (b / c) / z
	elif y <= 3e-60:
		tmp = -4.0 * (a * (t / c))
	elif y <= 1.75e+87:
		tmp = (b / z) * (1.0 / c)
	else:
		tmp = 9.0 * ((y / c) * (x / z))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(-4.0 * Float64(t * Float64(a / c)))
	tmp = 0.0
	if (y <= -3.3e-79)
		tmp = Float64(9.0 * Float64(Float64(x / c) * Float64(y / z)));
	elseif (y <= -3.2e-290)
		tmp = t_1;
	elseif (y <= 1.55e-268)
		tmp = Float64(b * Float64(1.0 / Float64(z * c)));
	elseif (y <= 1.15e-126)
		tmp = t_1;
	elseif (y <= 3.5e-108)
		tmp = Float64(Float64(b / c) / z);
	elseif (y <= 3e-60)
		tmp = Float64(-4.0 * Float64(a * Float64(t / c)));
	elseif (y <= 1.75e+87)
		tmp = Float64(Float64(b / z) * Float64(1.0 / c));
	else
		tmp = Float64(9.0 * Float64(Float64(y / c) * Float64(x / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = -4.0 * (t * (a / c));
	tmp = 0.0;
	if (y <= -3.3e-79)
		tmp = 9.0 * ((x / c) * (y / z));
	elseif (y <= -3.2e-290)
		tmp = t_1;
	elseif (y <= 1.55e-268)
		tmp = b * (1.0 / (z * c));
	elseif (y <= 1.15e-126)
		tmp = t_1;
	elseif (y <= 3.5e-108)
		tmp = (b / c) / z;
	elseif (y <= 3e-60)
		tmp = -4.0 * (a * (t / c));
	elseif (y <= 1.75e+87)
		tmp = (b / z) * (1.0 / c);
	else
		tmp = 9.0 * ((y / c) * (x / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(-4.0 * N[(t * N[(a / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -3.3e-79], N[(9.0 * N[(N[(x / c), $MachinePrecision] * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -3.2e-290], t$95$1, If[LessEqual[y, 1.55e-268], N[(b * N[(1.0 / N[(z * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.15e-126], t$95$1, If[LessEqual[y, 3.5e-108], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[y, 3e-60], N[(-4.0 * N[(a * N[(t / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.75e+87], N[(N[(b / z), $MachinePrecision] * N[(1.0 / c), $MachinePrecision]), $MachinePrecision], N[(9.0 * N[(N[(y / c), $MachinePrecision] * N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]

Derivation

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

Alternative 7: 47.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -4 \cdot \left(t \cdot \frac{a}{c}\right)\\ \mathbf{if}\;y \leq -3.3 \cdot 10^{-79}:\\ \;\;\;\;9 \cdot \left(\frac{x}{c} \cdot \frac{y}{z}\right)\\ \mathbf{elif}\;y \leq -4.1 \cdot 10^{-289}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 4.4 \cdot 10^{-268}:\\ \;\;\;\;b \cdot \frac{1}{z \cdot c}\\ \mathbf{elif}\;y \leq 3.6 \cdot 10^{-128}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 5.5 \cdot 10^{-108}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \mathbf{elif}\;y \leq 5 \cdot 10^{-61}:\\ \;\;\;\;-4 \cdot \left(a \cdot \frac{t}{c}\right)\\ \mathbf{elif}\;y \leq 5.5 \cdot 10^{+86}:\\ \;\;\;\;\frac{b}{z} \cdot \frac{1}{c}\\ \mathbf{else}:\\ \;\;\;\;\frac{9}{z \cdot \frac{\frac{c}{x}}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* -4.0 (* t (/ a c)))))
   (if (<= y -3.3e-79)
     (* 9.0 (* (/ x c) (/ y z)))
     (if (<= y -4.1e-289)
       t_1
       (if (<= y 4.4e-268)
         (* b (/ 1.0 (* z c)))
         (if (<= y 3.6e-128)
           t_1
           (if (<= y 5.5e-108)
             (/ (/ b c) z)
             (if (<= y 5e-61)
               (* -4.0 (* a (/ t c)))
               (if (<= y 5.5e+86)
                 (* (/ b z) (/ 1.0 c))
                 (/ 9.0 (* z (/ (/ c x) y))))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = -4.0 * (t * (a / c));
	double tmp;
	if (y <= -3.3e-79) {
		tmp = 9.0 * ((x / c) * (y / z));
	} else if (y <= -4.1e-289) {
		tmp = t_1;
	} else if (y <= 4.4e-268) {
		tmp = b * (1.0 / (z * c));
	} else if (y <= 3.6e-128) {
		tmp = t_1;
	} else if (y <= 5.5e-108) {
		tmp = (b / c) / z;
	} else if (y <= 5e-61) {
		tmp = -4.0 * (a * (t / c));
	} else if (y <= 5.5e+86) {
		tmp = (b / z) * (1.0 / c);
	} else {
		tmp = 9.0 / (z * ((c / x) / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (-4.0d0) * (t * (a / c))
    if (y <= (-3.3d-79)) then
        tmp = 9.0d0 * ((x / c) * (y / z))
    else if (y <= (-4.1d-289)) then
        tmp = t_1
    else if (y <= 4.4d-268) then
        tmp = b * (1.0d0 / (z * c))
    else if (y <= 3.6d-128) then
        tmp = t_1
    else if (y <= 5.5d-108) then
        tmp = (b / c) / z
    else if (y <= 5d-61) then
        tmp = (-4.0d0) * (a * (t / c))
    else if (y <= 5.5d+86) then
        tmp = (b / z) * (1.0d0 / c)
    else
        tmp = 9.0d0 / (z * ((c / x) / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = -4.0 * (t * (a / c));
	double tmp;
	if (y <= -3.3e-79) {
		tmp = 9.0 * ((x / c) * (y / z));
	} else if (y <= -4.1e-289) {
		tmp = t_1;
	} else if (y <= 4.4e-268) {
		tmp = b * (1.0 / (z * c));
	} else if (y <= 3.6e-128) {
		tmp = t_1;
	} else if (y <= 5.5e-108) {
		tmp = (b / c) / z;
	} else if (y <= 5e-61) {
		tmp = -4.0 * (a * (t / c));
	} else if (y <= 5.5e+86) {
		tmp = (b / z) * (1.0 / c);
	} else {
		tmp = 9.0 / (z * ((c / x) / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = -4.0 * (t * (a / c))
	tmp = 0
	if y <= -3.3e-79:
		tmp = 9.0 * ((x / c) * (y / z))
	elif y <= -4.1e-289:
		tmp = t_1
	elif y <= 4.4e-268:
		tmp = b * (1.0 / (z * c))
	elif y <= 3.6e-128:
		tmp = t_1
	elif y <= 5.5e-108:
		tmp = (b / c) / z
	elif y <= 5e-61:
		tmp = -4.0 * (a * (t / c))
	elif y <= 5.5e+86:
		tmp = (b / z) * (1.0 / c)
	else:
		tmp = 9.0 / (z * ((c / x) / y))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(-4.0 * Float64(t * Float64(a / c)))
	tmp = 0.0
	if (y <= -3.3e-79)
		tmp = Float64(9.0 * Float64(Float64(x / c) * Float64(y / z)));
	elseif (y <= -4.1e-289)
		tmp = t_1;
	elseif (y <= 4.4e-268)
		tmp = Float64(b * Float64(1.0 / Float64(z * c)));
	elseif (y <= 3.6e-128)
		tmp = t_1;
	elseif (y <= 5.5e-108)
		tmp = Float64(Float64(b / c) / z);
	elseif (y <= 5e-61)
		tmp = Float64(-4.0 * Float64(a * Float64(t / c)));
	elseif (y <= 5.5e+86)
		tmp = Float64(Float64(b / z) * Float64(1.0 / c));
	else
		tmp = Float64(9.0 / Float64(z * Float64(Float64(c / x) / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = -4.0 * (t * (a / c));
	tmp = 0.0;
	if (y <= -3.3e-79)
		tmp = 9.0 * ((x / c) * (y / z));
	elseif (y <= -4.1e-289)
		tmp = t_1;
	elseif (y <= 4.4e-268)
		tmp = b * (1.0 / (z * c));
	elseif (y <= 3.6e-128)
		tmp = t_1;
	elseif (y <= 5.5e-108)
		tmp = (b / c) / z;
	elseif (y <= 5e-61)
		tmp = -4.0 * (a * (t / c));
	elseif (y <= 5.5e+86)
		tmp = (b / z) * (1.0 / c);
	else
		tmp = 9.0 / (z * ((c / x) / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(-4.0 * N[(t * N[(a / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -3.3e-79], N[(9.0 * N[(N[(x / c), $MachinePrecision] * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -4.1e-289], t$95$1, If[LessEqual[y, 4.4e-268], N[(b * N[(1.0 / N[(z * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3.6e-128], t$95$1, If[LessEqual[y, 5.5e-108], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[y, 5e-61], N[(-4.0 * N[(a * N[(t / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 5.5e+86], N[(N[(b / z), $MachinePrecision] * N[(1.0 / c), $MachinePrecision]), $MachinePrecision], N[(9.0 / N[(z * N[(N[(c / x), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]

Derivation

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

Alternative 8: 48.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -4 \cdot \left(t \cdot \frac{a}{c}\right)\\ \mathbf{if}\;y \leq -7 \cdot 10^{-81}:\\ \;\;\;\;9 \cdot \left(\frac{x}{c} \cdot \frac{y}{z}\right)\\ \mathbf{elif}\;y \leq -6 \cdot 10^{-290}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.35 \cdot 10^{-272}:\\ \;\;\;\;b \cdot \frac{1}{z \cdot c}\\ \mathbf{elif}\;y \leq 1.48 \cdot 10^{-128}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 5.5 \cdot 10^{-108}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \mathbf{elif}\;y \leq 5.3 \cdot 10^{-52}:\\ \;\;\;\;-4 \cdot \left(a \cdot \frac{t}{c}\right)\\ \mathbf{elif}\;y \leq 1.6 \cdot 10^{+86}:\\ \;\;\;\;\frac{b}{z} \cdot \frac{1}{c}\\ \mathbf{else}:\\ \;\;\;\;\frac{9}{\frac{z \cdot \frac{c}{x}}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* -4.0 (* t (/ a c)))))
   (if (<= y -7e-81)
     (* 9.0 (* (/ x c) (/ y z)))
     (if (<= y -6e-290)
       t_1
       (if (<= y 2.35e-272)
         (* b (/ 1.0 (* z c)))
         (if (<= y 1.48e-128)
           t_1
           (if (<= y 5.5e-108)
             (/ (/ b c) z)
             (if (<= y 5.3e-52)
               (* -4.0 (* a (/ t c)))
               (if (<= y 1.6e+86)
                 (* (/ b z) (/ 1.0 c))
                 (/ 9.0 (/ (* z (/ c x)) y)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = -4.0 * (t * (a / c));
	double tmp;
	if (y <= -7e-81) {
		tmp = 9.0 * ((x / c) * (y / z));
	} else if (y <= -6e-290) {
		tmp = t_1;
	} else if (y <= 2.35e-272) {
		tmp = b * (1.0 / (z * c));
	} else if (y <= 1.48e-128) {
		tmp = t_1;
	} else if (y <= 5.5e-108) {
		tmp = (b / c) / z;
	} else if (y <= 5.3e-52) {
		tmp = -4.0 * (a * (t / c));
	} else if (y <= 1.6e+86) {
		tmp = (b / z) * (1.0 / c);
	} else {
		tmp = 9.0 / ((z * (c / x)) / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (-4.0d0) * (t * (a / c))
    if (y <= (-7d-81)) then
        tmp = 9.0d0 * ((x / c) * (y / z))
    else if (y <= (-6d-290)) then
        tmp = t_1
    else if (y <= 2.35d-272) then
        tmp = b * (1.0d0 / (z * c))
    else if (y <= 1.48d-128) then
        tmp = t_1
    else if (y <= 5.5d-108) then
        tmp = (b / c) / z
    else if (y <= 5.3d-52) then
        tmp = (-4.0d0) * (a * (t / c))
    else if (y <= 1.6d+86) then
        tmp = (b / z) * (1.0d0 / c)
    else
        tmp = 9.0d0 / ((z * (c / x)) / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = -4.0 * (t * (a / c));
	double tmp;
	if (y <= -7e-81) {
		tmp = 9.0 * ((x / c) * (y / z));
	} else if (y <= -6e-290) {
		tmp = t_1;
	} else if (y <= 2.35e-272) {
		tmp = b * (1.0 / (z * c));
	} else if (y <= 1.48e-128) {
		tmp = t_1;
	} else if (y <= 5.5e-108) {
		tmp = (b / c) / z;
	} else if (y <= 5.3e-52) {
		tmp = -4.0 * (a * (t / c));
	} else if (y <= 1.6e+86) {
		tmp = (b / z) * (1.0 / c);
	} else {
		tmp = 9.0 / ((z * (c / x)) / y);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = -4.0 * (t * (a / c))
	tmp = 0
	if y <= -7e-81:
		tmp = 9.0 * ((x / c) * (y / z))
	elif y <= -6e-290:
		tmp = t_1
	elif y <= 2.35e-272:
		tmp = b * (1.0 / (z * c))
	elif y <= 1.48e-128:
		tmp = t_1
	elif y <= 5.5e-108:
		tmp = (b / c) / z
	elif y <= 5.3e-52:
		tmp = -4.0 * (a * (t / c))
	elif y <= 1.6e+86:
		tmp = (b / z) * (1.0 / c)
	else:
		tmp = 9.0 / ((z * (c / x)) / y)
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(-4.0 * Float64(t * Float64(a / c)))
	tmp = 0.0
	if (y <= -7e-81)
		tmp = Float64(9.0 * Float64(Float64(x / c) * Float64(y / z)));
	elseif (y <= -6e-290)
		tmp = t_1;
	elseif (y <= 2.35e-272)
		tmp = Float64(b * Float64(1.0 / Float64(z * c)));
	elseif (y <= 1.48e-128)
		tmp = t_1;
	elseif (y <= 5.5e-108)
		tmp = Float64(Float64(b / c) / z);
	elseif (y <= 5.3e-52)
		tmp = Float64(-4.0 * Float64(a * Float64(t / c)));
	elseif (y <= 1.6e+86)
		tmp = Float64(Float64(b / z) * Float64(1.0 / c));
	else
		tmp = Float64(9.0 / Float64(Float64(z * Float64(c / x)) / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = -4.0 * (t * (a / c));
	tmp = 0.0;
	if (y <= -7e-81)
		tmp = 9.0 * ((x / c) * (y / z));
	elseif (y <= -6e-290)
		tmp = t_1;
	elseif (y <= 2.35e-272)
		tmp = b * (1.0 / (z * c));
	elseif (y <= 1.48e-128)
		tmp = t_1;
	elseif (y <= 5.5e-108)
		tmp = (b / c) / z;
	elseif (y <= 5.3e-52)
		tmp = -4.0 * (a * (t / c));
	elseif (y <= 1.6e+86)
		tmp = (b / z) * (1.0 / c);
	else
		tmp = 9.0 / ((z * (c / x)) / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(-4.0 * N[(t * N[(a / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -7e-81], N[(9.0 * N[(N[(x / c), $MachinePrecision] * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -6e-290], t$95$1, If[LessEqual[y, 2.35e-272], N[(b * N[(1.0 / N[(z * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.48e-128], t$95$1, If[LessEqual[y, 5.5e-108], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[y, 5.3e-52], N[(-4.0 * N[(a * N[(t / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.6e+86], N[(N[(b / z), $MachinePrecision] * N[(1.0 / c), $MachinePrecision]), $MachinePrecision], N[(9.0 / N[(N[(z * N[(c / x), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]]]]]]]]]

Derivation

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

Alternative 9: 89.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq 3.5 \cdot 10^{+37}:\\ \;\;\;\;\frac{\frac{b + 9 \cdot \left(x \cdot y\right)}{z} - 4 \cdot \left(a \cdot t\right)}{c}\\ \mathbf{else}:\\ \;\;\;\;\frac{9 \cdot \frac{x \cdot y}{c} + \frac{b}{c}}{z} - 4 \cdot \left(a \cdot \frac{t}{c}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= c 3.5e+37)
   (/ (- (/ (+ b (* 9.0 (* x y))) z) (* 4.0 (* a t))) c)
   (- (/ (+ (* 9.0 (/ (* x y) c)) (/ b c)) z) (* 4.0 (* a (/ t c))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (c <= 3.5e+37) {
		tmp = (((b + (9.0 * (x * y))) / z) - (4.0 * (a * t))) / c;
	} else {
		tmp = (((9.0 * ((x * y) / c)) + (b / c)) / z) - (4.0 * (a * (t / c)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: tmp
    if (c <= 3.5d+37) then
        tmp = (((b + (9.0d0 * (x * y))) / z) - (4.0d0 * (a * t))) / c
    else
        tmp = (((9.0d0 * ((x * y) / c)) + (b / c)) / z) - (4.0d0 * (a * (t / c)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (c <= 3.5e+37) {
		tmp = (((b + (9.0 * (x * y))) / z) - (4.0 * (a * t))) / c;
	} else {
		tmp = (((9.0 * ((x * y) / c)) + (b / c)) / z) - (4.0 * (a * (t / c)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if c <= 3.5e+37:
		tmp = (((b + (9.0 * (x * y))) / z) - (4.0 * (a * t))) / c
	else:
		tmp = (((9.0 * ((x * y) / c)) + (b / c)) / z) - (4.0 * (a * (t / c)))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (c <= 3.5e+37)
		tmp = Float64(Float64(Float64(Float64(b + Float64(9.0 * Float64(x * y))) / z) - Float64(4.0 * Float64(a * t))) / c);
	else
		tmp = Float64(Float64(Float64(Float64(9.0 * Float64(Float64(x * y) / c)) + Float64(b / c)) / z) - Float64(4.0 * Float64(a * Float64(t / c))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (c <= 3.5e+37)
		tmp = (((b + (9.0 * (x * y))) / z) - (4.0 * (a * t))) / c;
	else
		tmp = (((9.0 * ((x * y) / c)) + (b / c)) / z) - (4.0 * (a * (t / c)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[c, 3.5e+37], N[(N[(N[(N[(b + N[(9.0 * N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision] - N[(4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision], N[(N[(N[(N[(9.0 * N[(N[(x * y), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision] + N[(b / c), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision] - N[(4.0 * N[(a * N[(t / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 74.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 4 \cdot \left(a \cdot t\right)\\ \mathbf{if}\;y \leq -2.9 \cdot 10^{-39}:\\ \;\;\;\;\frac{y \cdot \frac{9 \cdot x}{z} - t_1}{c}\\ \mathbf{elif}\;y \leq 9.5 \cdot 10^{+86}:\\ \;\;\;\;\frac{\frac{b}{z} - t_1}{c}\\ \mathbf{else}:\\ \;\;\;\;9 \cdot \left(\frac{y}{c} \cdot \frac{x}{z}\right) - 4 \cdot \frac{a \cdot t}{c}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* 4.0 (* a t))))
   (if (<= y -2.9e-39)
     (/ (- (* y (/ (* 9.0 x) z)) t_1) c)
     (if (<= y 9.5e+86)
       (/ (- (/ b z) t_1) c)
       (- (* 9.0 (* (/ y c) (/ x z))) (* 4.0 (/ (* a t) c)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = 4.0 * (a * t);
	double tmp;
	if (y <= -2.9e-39) {
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c;
	} else if (y <= 9.5e+86) {
		tmp = ((b / z) - t_1) / c;
	} else {
		tmp = (9.0 * ((y / c) * (x / z))) - (4.0 * ((a * t) / c));
	}
	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 = 4.0d0 * (a * t)
    if (y <= (-2.9d-39)) then
        tmp = ((y * ((9.0d0 * x) / z)) - t_1) / c
    else if (y <= 9.5d+86) then
        tmp = ((b / z) - t_1) / c
    else
        tmp = (9.0d0 * ((y / c) * (x / z))) - (4.0d0 * ((a * t) / c))
    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 = 4.0 * (a * t);
	double tmp;
	if (y <= -2.9e-39) {
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c;
	} else if (y <= 9.5e+86) {
		tmp = ((b / z) - t_1) / c;
	} else {
		tmp = (9.0 * ((y / c) * (x / z))) - (4.0 * ((a * t) / c));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = 4.0 * (a * t)
	tmp = 0
	if y <= -2.9e-39:
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c
	elif y <= 9.5e+86:
		tmp = ((b / z) - t_1) / c
	else:
		tmp = (9.0 * ((y / c) * (x / z))) - (4.0 * ((a * t) / c))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(4.0 * Float64(a * t))
	tmp = 0.0
	if (y <= -2.9e-39)
		tmp = Float64(Float64(Float64(y * Float64(Float64(9.0 * x) / z)) - t_1) / c);
	elseif (y <= 9.5e+86)
		tmp = Float64(Float64(Float64(b / z) - t_1) / c);
	else
		tmp = Float64(Float64(9.0 * Float64(Float64(y / c) * Float64(x / z))) - Float64(4.0 * Float64(Float64(a * t) / c)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = 4.0 * (a * t);
	tmp = 0.0;
	if (y <= -2.9e-39)
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c;
	elseif (y <= 9.5e+86)
		tmp = ((b / z) - t_1) / c;
	else
		tmp = (9.0 * ((y / c) * (x / z))) - (4.0 * ((a * t) / c));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -2.9e-39], N[(N[(N[(y * N[(N[(9.0 * x), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision] / c), $MachinePrecision], If[LessEqual[y, 9.5e+86], N[(N[(N[(b / z), $MachinePrecision] - t$95$1), $MachinePrecision] / c), $MachinePrecision], N[(N[(9.0 * N[(N[(y / c), $MachinePrecision] * N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 11: 62.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{b + x \cdot \left(9 \cdot y\right)}{z \cdot c}\\ \mathbf{if}\;a \leq -7.2 \cdot 10^{-53}:\\ \;\;\;\;-4 \cdot \left(t \cdot \frac{a}{c}\right)\\ \mathbf{elif}\;a \leq 1.52 \cdot 10^{-92}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.82 \cdot 10^{-53}:\\ \;\;\;\;-4 \cdot \frac{a}{\frac{c}{t}}\\ \mathbf{elif}\;a \leq 3.35 \cdot 10^{+184}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{-4}{\frac{c}{a \cdot t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (/ (+ b (* x (* 9.0 y))) (* z c))))
   (if (<= a -7.2e-53)
     (* -4.0 (* t (/ a c)))
     (if (<= a 1.52e-92)
       t_1
       (if (<= a 1.82e-53)
         (* -4.0 (/ a (/ c t)))
         (if (<= a 3.35e+184) t_1 (/ -4.0 (/ c (* a t)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (b + (x * (9.0 * y))) / (z * c);
	double tmp;
	if (a <= -7.2e-53) {
		tmp = -4.0 * (t * (a / c));
	} else if (a <= 1.52e-92) {
		tmp = t_1;
	} else if (a <= 1.82e-53) {
		tmp = -4.0 * (a / (c / t));
	} else if (a <= 3.35e+184) {
		tmp = t_1;
	} else {
		tmp = -4.0 / (c / (a * t));
	}
	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 = (b + (x * (9.0d0 * y))) / (z * c)
    if (a <= (-7.2d-53)) then
        tmp = (-4.0d0) * (t * (a / c))
    else if (a <= 1.52d-92) then
        tmp = t_1
    else if (a <= 1.82d-53) then
        tmp = (-4.0d0) * (a / (c / t))
    else if (a <= 3.35d+184) then
        tmp = t_1
    else
        tmp = (-4.0d0) / (c / (a * t))
    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 = (b + (x * (9.0 * y))) / (z * c);
	double tmp;
	if (a <= -7.2e-53) {
		tmp = -4.0 * (t * (a / c));
	} else if (a <= 1.52e-92) {
		tmp = t_1;
	} else if (a <= 1.82e-53) {
		tmp = -4.0 * (a / (c / t));
	} else if (a <= 3.35e+184) {
		tmp = t_1;
	} else {
		tmp = -4.0 / (c / (a * t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (b + (x * (9.0 * y))) / (z * c)
	tmp = 0
	if a <= -7.2e-53:
		tmp = -4.0 * (t * (a / c))
	elif a <= 1.52e-92:
		tmp = t_1
	elif a <= 1.82e-53:
		tmp = -4.0 * (a / (c / t))
	elif a <= 3.35e+184:
		tmp = t_1
	else:
		tmp = -4.0 / (c / (a * t))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(b + Float64(x * Float64(9.0 * y))) / Float64(z * c))
	tmp = 0.0
	if (a <= -7.2e-53)
		tmp = Float64(-4.0 * Float64(t * Float64(a / c)));
	elseif (a <= 1.52e-92)
		tmp = t_1;
	elseif (a <= 1.82e-53)
		tmp = Float64(-4.0 * Float64(a / Float64(c / t)));
	elseif (a <= 3.35e+184)
		tmp = t_1;
	else
		tmp = Float64(-4.0 / Float64(c / Float64(a * t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (b + (x * (9.0 * y))) / (z * c);
	tmp = 0.0;
	if (a <= -7.2e-53)
		tmp = -4.0 * (t * (a / c));
	elseif (a <= 1.52e-92)
		tmp = t_1;
	elseif (a <= 1.82e-53)
		tmp = -4.0 * (a / (c / t));
	elseif (a <= 3.35e+184)
		tmp = t_1;
	else
		tmp = -4.0 / (c / (a * t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(b + N[(x * N[(9.0 * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -7.2e-53], N[(-4.0 * N[(t * N[(a / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.52e-92], t$95$1, If[LessEqual[a, 1.82e-53], N[(-4.0 * N[(a / N[(c / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3.35e+184], t$95$1, N[(-4.0 / N[(c / N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 12: 63.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -7.5 \cdot 10^{-49}:\\ \;\;\;\;-4 \cdot \left(t \cdot \frac{a}{c}\right)\\ \mathbf{elif}\;a \leq 1.52 \cdot 10^{-92}:\\ \;\;\;\;\frac{b + x \cdot \left(9 \cdot y\right)}{z \cdot c}\\ \mathbf{elif}\;a \leq 2.1 \cdot 10^{-52}:\\ \;\;\;\;-4 \cdot \frac{a}{\frac{c}{t}}\\ \mathbf{elif}\;a \leq 3.1 \cdot 10^{+184}:\\ \;\;\;\;\frac{b + y \cdot \left(9 \cdot x\right)}{z \cdot c}\\ \mathbf{else}:\\ \;\;\;\;\frac{-4}{\frac{c}{a \cdot t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= a -7.5e-49)
   (* -4.0 (* t (/ a c)))
   (if (<= a 1.52e-92)
     (/ (+ b (* x (* 9.0 y))) (* z c))
     (if (<= a 2.1e-52)
       (* -4.0 (/ a (/ c t)))
       (if (<= a 3.1e+184)
         (/ (+ b (* y (* 9.0 x))) (* z c))
         (/ -4.0 (/ c (* a t))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (a <= -7.5e-49) {
		tmp = -4.0 * (t * (a / c));
	} else if (a <= 1.52e-92) {
		tmp = (b + (x * (9.0 * y))) / (z * c);
	} else if (a <= 2.1e-52) {
		tmp = -4.0 * (a / (c / t));
	} else if (a <= 3.1e+184) {
		tmp = (b + (y * (9.0 * x))) / (z * c);
	} else {
		tmp = -4.0 / (c / (a * t));
	}
	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 (a <= (-7.5d-49)) then
        tmp = (-4.0d0) * (t * (a / c))
    else if (a <= 1.52d-92) then
        tmp = (b + (x * (9.0d0 * y))) / (z * c)
    else if (a <= 2.1d-52) then
        tmp = (-4.0d0) * (a / (c / t))
    else if (a <= 3.1d+184) then
        tmp = (b + (y * (9.0d0 * x))) / (z * c)
    else
        tmp = (-4.0d0) / (c / (a * t))
    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 (a <= -7.5e-49) {
		tmp = -4.0 * (t * (a / c));
	} else if (a <= 1.52e-92) {
		tmp = (b + (x * (9.0 * y))) / (z * c);
	} else if (a <= 2.1e-52) {
		tmp = -4.0 * (a / (c / t));
	} else if (a <= 3.1e+184) {
		tmp = (b + (y * (9.0 * x))) / (z * c);
	} else {
		tmp = -4.0 / (c / (a * t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if a <= -7.5e-49:
		tmp = -4.0 * (t * (a / c))
	elif a <= 1.52e-92:
		tmp = (b + (x * (9.0 * y))) / (z * c)
	elif a <= 2.1e-52:
		tmp = -4.0 * (a / (c / t))
	elif a <= 3.1e+184:
		tmp = (b + (y * (9.0 * x))) / (z * c)
	else:
		tmp = -4.0 / (c / (a * t))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (a <= -7.5e-49)
		tmp = Float64(-4.0 * Float64(t * Float64(a / c)));
	elseif (a <= 1.52e-92)
		tmp = Float64(Float64(b + Float64(x * Float64(9.0 * y))) / Float64(z * c));
	elseif (a <= 2.1e-52)
		tmp = Float64(-4.0 * Float64(a / Float64(c / t)));
	elseif (a <= 3.1e+184)
		tmp = Float64(Float64(b + Float64(y * Float64(9.0 * x))) / Float64(z * c));
	else
		tmp = Float64(-4.0 / Float64(c / Float64(a * t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (a <= -7.5e-49)
		tmp = -4.0 * (t * (a / c));
	elseif (a <= 1.52e-92)
		tmp = (b + (x * (9.0 * y))) / (z * c);
	elseif (a <= 2.1e-52)
		tmp = -4.0 * (a / (c / t));
	elseif (a <= 3.1e+184)
		tmp = (b + (y * (9.0 * x))) / (z * c);
	else
		tmp = -4.0 / (c / (a * t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[a, -7.5e-49], N[(-4.0 * N[(t * N[(a / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.52e-92], N[(N[(b + N[(x * N[(9.0 * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.1e-52], N[(-4.0 * N[(a / N[(c / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3.1e+184], N[(N[(b + N[(y * N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision], N[(-4.0 / N[(c / N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 13: 68.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{-11}:\\ \;\;\;\;9 \cdot \left(\frac{x}{c} \cdot \frac{y}{z}\right)\\ \mathbf{elif}\;y \leq 2.5 \cdot 10^{+94}:\\ \;\;\;\;\frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c}\\ \mathbf{elif}\;y \leq 5.8 \cdot 10^{+235}:\\ \;\;\;\;\frac{b + y \cdot \left(9 \cdot x\right)}{z \cdot c}\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{+301}:\\ \;\;\;\;\frac{9}{\frac{z}{y} \cdot \frac{c}{x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{b + x \cdot \left(9 \cdot y\right)}{z \cdot c}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= y -9e-11)
   (* 9.0 (* (/ x c) (/ y z)))
   (if (<= y 2.5e+94)
     (/ (- (/ b z) (* 4.0 (* a t))) c)
     (if (<= y 5.8e+235)
       (/ (+ b (* y (* 9.0 x))) (* z c))
       (if (<= y 4.2e+301)
         (/ 9.0 (* (/ z y) (/ c x)))
         (/ (+ b (* x (* 9.0 y))) (* z c)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (y <= -9e-11) {
		tmp = 9.0 * ((x / c) * (y / z));
	} else if (y <= 2.5e+94) {
		tmp = ((b / z) - (4.0 * (a * t))) / c;
	} else if (y <= 5.8e+235) {
		tmp = (b + (y * (9.0 * x))) / (z * c);
	} else if (y <= 4.2e+301) {
		tmp = 9.0 / ((z / y) * (c / x));
	} else {
		tmp = (b + (x * (9.0 * y))) / (z * c);
	}
	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 (y <= (-9d-11)) then
        tmp = 9.0d0 * ((x / c) * (y / z))
    else if (y <= 2.5d+94) then
        tmp = ((b / z) - (4.0d0 * (a * t))) / c
    else if (y <= 5.8d+235) then
        tmp = (b + (y * (9.0d0 * x))) / (z * c)
    else if (y <= 4.2d+301) then
        tmp = 9.0d0 / ((z / y) * (c / x))
    else
        tmp = (b + (x * (9.0d0 * y))) / (z * c)
    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 (y <= -9e-11) {
		tmp = 9.0 * ((x / c) * (y / z));
	} else if (y <= 2.5e+94) {
		tmp = ((b / z) - (4.0 * (a * t))) / c;
	} else if (y <= 5.8e+235) {
		tmp = (b + (y * (9.0 * x))) / (z * c);
	} else if (y <= 4.2e+301) {
		tmp = 9.0 / ((z / y) * (c / x));
	} else {
		tmp = (b + (x * (9.0 * y))) / (z * c);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if y <= -9e-11:
		tmp = 9.0 * ((x / c) * (y / z))
	elif y <= 2.5e+94:
		tmp = ((b / z) - (4.0 * (a * t))) / c
	elif y <= 5.8e+235:
		tmp = (b + (y * (9.0 * x))) / (z * c)
	elif y <= 4.2e+301:
		tmp = 9.0 / ((z / y) * (c / x))
	else:
		tmp = (b + (x * (9.0 * y))) / (z * c)
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (y <= -9e-11)
		tmp = Float64(9.0 * Float64(Float64(x / c) * Float64(y / z)));
	elseif (y <= 2.5e+94)
		tmp = Float64(Float64(Float64(b / z) - Float64(4.0 * Float64(a * t))) / c);
	elseif (y <= 5.8e+235)
		tmp = Float64(Float64(b + Float64(y * Float64(9.0 * x))) / Float64(z * c));
	elseif (y <= 4.2e+301)
		tmp = Float64(9.0 / Float64(Float64(z / y) * Float64(c / x)));
	else
		tmp = Float64(Float64(b + Float64(x * Float64(9.0 * y))) / Float64(z * c));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (y <= -9e-11)
		tmp = 9.0 * ((x / c) * (y / z));
	elseif (y <= 2.5e+94)
		tmp = ((b / z) - (4.0 * (a * t))) / c;
	elseif (y <= 5.8e+235)
		tmp = (b + (y * (9.0 * x))) / (z * c);
	elseif (y <= 4.2e+301)
		tmp = 9.0 / ((z / y) * (c / x));
	else
		tmp = (b + (x * (9.0 * y))) / (z * c);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[y, -9e-11], N[(9.0 * N[(N[(x / c), $MachinePrecision] * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.5e+94], N[(N[(N[(b / z), $MachinePrecision] - N[(4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision], If[LessEqual[y, 5.8e+235], N[(N[(b + N[(y * N[(9.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 4.2e+301], N[(9.0 / N[(N[(z / y), $MachinePrecision] * N[(c / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(b + N[(x * N[(9.0 * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 14: 74.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 4 \cdot \left(a \cdot t\right)\\ \mathbf{if}\;y \leq -4.8 \cdot 10^{-41} \lor \neg \left(y \leq 1.65 \cdot 10^{+87}\right):\\ \;\;\;\;\frac{y \cdot \frac{9 \cdot x}{z} - t_1}{c}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{z} - t_1}{c}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* 4.0 (* a t))))
   (if (or (<= y -4.8e-41) (not (<= y 1.65e+87)))
     (/ (- (* y (/ (* 9.0 x) z)) t_1) c)
     (/ (- (/ b z) t_1) c))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = 4.0 * (a * t);
	double tmp;
	if ((y <= -4.8e-41) || !(y <= 1.65e+87)) {
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c;
	} else {
		tmp = ((b / z) - t_1) / c;
	}
	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 = 4.0d0 * (a * t)
    if ((y <= (-4.8d-41)) .or. (.not. (y <= 1.65d+87))) then
        tmp = ((y * ((9.0d0 * x) / z)) - t_1) / c
    else
        tmp = ((b / z) - t_1) / c
    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 = 4.0 * (a * t);
	double tmp;
	if ((y <= -4.8e-41) || !(y <= 1.65e+87)) {
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c;
	} else {
		tmp = ((b / z) - t_1) / c;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = 4.0 * (a * t)
	tmp = 0
	if (y <= -4.8e-41) or not (y <= 1.65e+87):
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c
	else:
		tmp = ((b / z) - t_1) / c
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(4.0 * Float64(a * t))
	tmp = 0.0
	if ((y <= -4.8e-41) || !(y <= 1.65e+87))
		tmp = Float64(Float64(Float64(y * Float64(Float64(9.0 * x) / z)) - t_1) / c);
	else
		tmp = Float64(Float64(Float64(b / z) - t_1) / c);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = 4.0 * (a * t);
	tmp = 0.0;
	if ((y <= -4.8e-41) || ~((y <= 1.65e+87)))
		tmp = ((y * ((9.0 * x) / z)) - t_1) / c;
	else
		tmp = ((b / z) - t_1) / c;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[y, -4.8e-41], N[Not[LessEqual[y, 1.65e+87]], $MachinePrecision]], N[(N[(N[(y * N[(N[(9.0 * x), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision] / c), $MachinePrecision], N[(N[(N[(b / z), $MachinePrecision] - t$95$1), $MachinePrecision] / c), $MachinePrecision]]]

Derivation

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

Alternative 15: 47.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -8.5 \cdot 10^{-110} \lor \neg \left(a \leq 1.5 \cdot 10^{-104}\right) \land \left(a \leq 10^{-43} \lor \neg \left(a \leq 0.000165\right)\right):\\ \;\;\;\;-4 \cdot \left(a \cdot \frac{t}{c}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{z}}{c}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= a -8.5e-110)
         (and (not (<= a 1.5e-104)) (or (<= a 1e-43) (not (<= a 0.000165)))))
   (* -4.0 (* a (/ t c)))
   (/ (/ b z) c)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if ((a <= -8.5e-110) || (!(a <= 1.5e-104) && ((a <= 1e-43) || !(a <= 0.000165)))) {
		tmp = -4.0 * (a * (t / c));
	} else {
		tmp = (b / z) / c;
	}
	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 ((a <= (-8.5d-110)) .or. (.not. (a <= 1.5d-104)) .and. (a <= 1d-43) .or. (.not. (a <= 0.000165d0))) then
        tmp = (-4.0d0) * (a * (t / c))
    else
        tmp = (b / z) / c
    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 ((a <= -8.5e-110) || (!(a <= 1.5e-104) && ((a <= 1e-43) || !(a <= 0.000165)))) {
		tmp = -4.0 * (a * (t / c));
	} else {
		tmp = (b / z) / c;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if (a <= -8.5e-110) or (not (a <= 1.5e-104) and ((a <= 1e-43) or not (a <= 0.000165))):
		tmp = -4.0 * (a * (t / c))
	else:
		tmp = (b / z) / c
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((a <= -8.5e-110) || (!(a <= 1.5e-104) && ((a <= 1e-43) || !(a <= 0.000165))))
		tmp = Float64(-4.0 * Float64(a * Float64(t / c)));
	else
		tmp = Float64(Float64(b / z) / c);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if ((a <= -8.5e-110) || (~((a <= 1.5e-104)) && ((a <= 1e-43) || ~((a <= 0.000165)))))
		tmp = -4.0 * (a * (t / c));
	else
		tmp = (b / z) / c;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[a, -8.5e-110], And[N[Not[LessEqual[a, 1.5e-104]], $MachinePrecision], Or[LessEqual[a, 1e-43], N[Not[LessEqual[a, 0.000165]], $MachinePrecision]]]], N[(-4.0 * N[(a * N[(t / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(b / z), $MachinePrecision] / c), $MachinePrecision]]

Derivation

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

Alternative 16: 71.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -52 \lor \neg \left(y \leq 2.5 \cdot 10^{+94}\right):\\ \;\;\;\;\frac{\frac{b + 9 \cdot \left(x \cdot y\right)}{c}}{z}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= y -52.0) (not (<= y 2.5e+94)))
   (/ (/ (+ b (* 9.0 (* x y))) c) z)
   (/ (- (/ b z) (* 4.0 (* a t))) c)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if ((y <= -52.0) || !(y <= 2.5e+94)) {
		tmp = ((b + (9.0 * (x * y))) / c) / z;
	} else {
		tmp = ((b / z) - (4.0 * (a * t))) / c;
	}
	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 ((y <= (-52.0d0)) .or. (.not. (y <= 2.5d+94))) then
        tmp = ((b + (9.0d0 * (x * y))) / c) / z
    else
        tmp = ((b / z) - (4.0d0 * (a * t))) / c
    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 ((y <= -52.0) || !(y <= 2.5e+94)) {
		tmp = ((b + (9.0 * (x * y))) / c) / z;
	} else {
		tmp = ((b / z) - (4.0 * (a * t))) / c;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if (y <= -52.0) or not (y <= 2.5e+94):
		tmp = ((b + (9.0 * (x * y))) / c) / z
	else:
		tmp = ((b / z) - (4.0 * (a * t))) / c
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((y <= -52.0) || !(y <= 2.5e+94))
		tmp = Float64(Float64(Float64(b + Float64(9.0 * Float64(x * y))) / c) / z);
	else
		tmp = Float64(Float64(Float64(b / z) - Float64(4.0 * Float64(a * t))) / c);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if ((y <= -52.0) || ~((y <= 2.5e+94)))
		tmp = ((b + (9.0 * (x * y))) / c) / z;
	else
		tmp = ((b / z) - (4.0 * (a * t))) / c;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[y, -52.0], N[Not[LessEqual[y, 2.5e+94]], $MachinePrecision]], N[(N[(N[(b + N[(9.0 * N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision] / z), $MachinePrecision], N[(N[(N[(b / z), $MachinePrecision] - N[(4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]]

Derivation

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

Alternative 17: 49.0% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -7.6 \cdot 10^{-28}:\\ \;\;\;\;\frac{b}{z \cdot c}\\ \mathbf{elif}\;b \leq 1.65 \cdot 10^{+118}:\\ \;\;\;\;\frac{a \cdot t}{c} \cdot -4\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= b -7.6e-28)
   (/ b (* z c))
   (if (<= b 1.65e+118) (* (/ (* a t) c) -4.0) (/ (/ b c) z))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (b <= -7.6e-28) {
		tmp = b / (z * c);
	} else if (b <= 1.65e+118) {
		tmp = ((a * t) / c) * -4.0;
	} else {
		tmp = (b / c) / z;
	}
	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 <= (-7.6d-28)) then
        tmp = b / (z * c)
    else if (b <= 1.65d+118) then
        tmp = ((a * t) / c) * (-4.0d0)
    else
        tmp = (b / c) / 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 c) {
	double tmp;
	if (b <= -7.6e-28) {
		tmp = b / (z * c);
	} else if (b <= 1.65e+118) {
		tmp = ((a * t) / c) * -4.0;
	} else {
		tmp = (b / c) / z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if b <= -7.6e-28:
		tmp = b / (z * c)
	elif b <= 1.65e+118:
		tmp = ((a * t) / c) * -4.0
	else:
		tmp = (b / c) / z
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (b <= -7.6e-28)
		tmp = Float64(b / Float64(z * c));
	elseif (b <= 1.65e+118)
		tmp = Float64(Float64(Float64(a * t) / c) * -4.0);
	else
		tmp = Float64(Float64(b / c) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (b <= -7.6e-28)
		tmp = b / (z * c);
	elseif (b <= 1.65e+118)
		tmp = ((a * t) / c) * -4.0;
	else
		tmp = (b / c) / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[b, -7.6e-28], N[(b / N[(z * c), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.65e+118], N[(N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision] * -4.0), $MachinePrecision], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision]]]

Derivation

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

Alternative 18: 48.9% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -5.2 \cdot 10^{-28}:\\ \;\;\;\;b \cdot \frac{1}{z \cdot c}\\ \mathbf{elif}\;b \leq 9.8 \cdot 10^{+119}:\\ \;\;\;\;\frac{a \cdot t}{c} \cdot -4\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= b -5.2e-28)
   (* b (/ 1.0 (* z c)))
   (if (<= b 9.8e+119) (* (/ (* a t) c) -4.0) (/ (/ b c) z))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (b <= -5.2e-28) {
		tmp = b * (1.0 / (z * c));
	} else if (b <= 9.8e+119) {
		tmp = ((a * t) / c) * -4.0;
	} else {
		tmp = (b / c) / z;
	}
	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 <= (-5.2d-28)) then
        tmp = b * (1.0d0 / (z * c))
    else if (b <= 9.8d+119) then
        tmp = ((a * t) / c) * (-4.0d0)
    else
        tmp = (b / c) / 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 c) {
	double tmp;
	if (b <= -5.2e-28) {
		tmp = b * (1.0 / (z * c));
	} else if (b <= 9.8e+119) {
		tmp = ((a * t) / c) * -4.0;
	} else {
		tmp = (b / c) / z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if b <= -5.2e-28:
		tmp = b * (1.0 / (z * c))
	elif b <= 9.8e+119:
		tmp = ((a * t) / c) * -4.0
	else:
		tmp = (b / c) / z
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (b <= -5.2e-28)
		tmp = Float64(b * Float64(1.0 / Float64(z * c)));
	elseif (b <= 9.8e+119)
		tmp = Float64(Float64(Float64(a * t) / c) * -4.0);
	else
		tmp = Float64(Float64(b / c) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (b <= -5.2e-28)
		tmp = b * (1.0 / (z * c));
	elseif (b <= 9.8e+119)
		tmp = ((a * t) / c) * -4.0;
	else
		tmp = (b / c) / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[b, -5.2e-28], N[(b * N[(1.0 / N[(z * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 9.8e+119], N[(N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision] * -4.0), $MachinePrecision], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision]]]

Derivation

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

Alternative 19: 49.0% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -4.9 \cdot 10^{-29}:\\ \;\;\;\;b \cdot \frac{\frac{1}{z}}{c}\\ \mathbf{elif}\;b \leq 1.18 \cdot 10^{+120}:\\ \;\;\;\;\frac{a \cdot t}{c} \cdot -4\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= b -4.9e-29)
   (* b (/ (/ 1.0 z) c))
   (if (<= b 1.18e+120) (* (/ (* a t) c) -4.0) (/ (/ b c) z))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (b <= -4.9e-29) {
		tmp = b * ((1.0 / z) / c);
	} else if (b <= 1.18e+120) {
		tmp = ((a * t) / c) * -4.0;
	} else {
		tmp = (b / c) / z;
	}
	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 <= (-4.9d-29)) then
        tmp = b * ((1.0d0 / z) / c)
    else if (b <= 1.18d+120) then
        tmp = ((a * t) / c) * (-4.0d0)
    else
        tmp = (b / c) / 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 c) {
	double tmp;
	if (b <= -4.9e-29) {
		tmp = b * ((1.0 / z) / c);
	} else if (b <= 1.18e+120) {
		tmp = ((a * t) / c) * -4.0;
	} else {
		tmp = (b / c) / z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if b <= -4.9e-29:
		tmp = b * ((1.0 / z) / c)
	elif b <= 1.18e+120:
		tmp = ((a * t) / c) * -4.0
	else:
		tmp = (b / c) / z
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (b <= -4.9e-29)
		tmp = Float64(b * Float64(Float64(1.0 / z) / c));
	elseif (b <= 1.18e+120)
		tmp = Float64(Float64(Float64(a * t) / c) * -4.0);
	else
		tmp = Float64(Float64(b / c) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (b <= -4.9e-29)
		tmp = b * ((1.0 / z) / c);
	elseif (b <= 1.18e+120)
		tmp = ((a * t) / c) * -4.0;
	else
		tmp = (b / c) / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[b, -4.9e-29], N[(b * N[(N[(1.0 / z), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.18e+120], N[(N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision] * -4.0), $MachinePrecision], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision]]]

Derivation

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

Alternative 20: 49.6% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -5.6 \cdot 10^{-28}:\\ \;\;\;\;b \cdot \frac{\frac{1}{z}}{c}\\ \mathbf{elif}\;b \leq 2.4 \cdot 10^{+118}:\\ \;\;\;\;t \cdot \left(a \cdot \frac{-4}{c}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= b -5.6e-28)
   (* b (/ (/ 1.0 z) c))
   (if (<= b 2.4e+118) (* t (* a (/ -4.0 c))) (/ (/ b c) z))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (b <= -5.6e-28) {
		tmp = b * ((1.0 / z) / c);
	} else if (b <= 2.4e+118) {
		tmp = t * (a * (-4.0 / c));
	} else {
		tmp = (b / c) / z;
	}
	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 <= (-5.6d-28)) then
        tmp = b * ((1.0d0 / z) / c)
    else if (b <= 2.4d+118) then
        tmp = t * (a * ((-4.0d0) / c))
    else
        tmp = (b / c) / 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 c) {
	double tmp;
	if (b <= -5.6e-28) {
		tmp = b * ((1.0 / z) / c);
	} else if (b <= 2.4e+118) {
		tmp = t * (a * (-4.0 / c));
	} else {
		tmp = (b / c) / z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if b <= -5.6e-28:
		tmp = b * ((1.0 / z) / c)
	elif b <= 2.4e+118:
		tmp = t * (a * (-4.0 / c))
	else:
		tmp = (b / c) / z
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (b <= -5.6e-28)
		tmp = Float64(b * Float64(Float64(1.0 / z) / c));
	elseif (b <= 2.4e+118)
		tmp = Float64(t * Float64(a * Float64(-4.0 / c)));
	else
		tmp = Float64(Float64(b / c) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (b <= -5.6e-28)
		tmp = b * ((1.0 / z) / c);
	elseif (b <= 2.4e+118)
		tmp = t * (a * (-4.0 / c));
	else
		tmp = (b / c) / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[b, -5.6e-28], N[(b * N[(N[(1.0 / z), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 2.4e+118], N[(t * N[(a * N[(-4.0 / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision]]]

Derivation

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

Alternative 21: 49.6% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -2.4 \cdot 10^{-28}:\\ \;\;\;\;b \cdot \frac{\frac{1}{z}}{c}\\ \mathbf{elif}\;b \leq 2.5 \cdot 10^{+119}:\\ \;\;\;\;-4 \cdot \left(t \cdot \frac{a}{c}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= b -2.4e-28)
   (* b (/ (/ 1.0 z) c))
   (if (<= b 2.5e+119) (* -4.0 (* t (/ a c))) (/ (/ b c) z))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (b <= -2.4e-28) {
		tmp = b * ((1.0 / z) / c);
	} else if (b <= 2.5e+119) {
		tmp = -4.0 * (t * (a / c));
	} else {
		tmp = (b / c) / z;
	}
	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 <= (-2.4d-28)) then
        tmp = b * ((1.0d0 / z) / c)
    else if (b <= 2.5d+119) then
        tmp = (-4.0d0) * (t * (a / c))
    else
        tmp = (b / c) / 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 c) {
	double tmp;
	if (b <= -2.4e-28) {
		tmp = b * ((1.0 / z) / c);
	} else if (b <= 2.5e+119) {
		tmp = -4.0 * (t * (a / c));
	} else {
		tmp = (b / c) / z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if b <= -2.4e-28:
		tmp = b * ((1.0 / z) / c)
	elif b <= 2.5e+119:
		tmp = -4.0 * (t * (a / c))
	else:
		tmp = (b / c) / z
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (b <= -2.4e-28)
		tmp = Float64(b * Float64(Float64(1.0 / z) / c));
	elseif (b <= 2.5e+119)
		tmp = Float64(-4.0 * Float64(t * Float64(a / c)));
	else
		tmp = Float64(Float64(b / c) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (b <= -2.4e-28)
		tmp = b * ((1.0 / z) / c);
	elseif (b <= 2.5e+119)
		tmp = -4.0 * (t * (a / c));
	else
		tmp = (b / c) / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[b, -2.4e-28], N[(b * N[(N[(1.0 / z), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 2.5e+119], N[(-4.0 * N[(t * N[(a / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision]]]

Derivation

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

Alternative 22: 49.6% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -7.6 \cdot 10^{-28}:\\ \;\;\;\;b \cdot \frac{\frac{1}{z}}{c}\\ \mathbf{elif}\;b \leq 4.1 \cdot 10^{+119}:\\ \;\;\;\;-4 \cdot \left(t \cdot \frac{a}{c}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{z \cdot \frac{c}{b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= b -7.6e-28)
   (* b (/ (/ 1.0 z) c))
   (if (<= b 4.1e+119) (* -4.0 (* t (/ a c))) (/ 1.0 (* z (/ c b))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (b <= -7.6e-28) {
		tmp = b * ((1.0 / z) / c);
	} else if (b <= 4.1e+119) {
		tmp = -4.0 * (t * (a / c));
	} else {
		tmp = 1.0 / (z * (c / b));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: tmp
    if (b <= (-7.6d-28)) then
        tmp = b * ((1.0d0 / z) / c)
    else if (b <= 4.1d+119) then
        tmp = (-4.0d0) * (t * (a / c))
    else
        tmp = 1.0d0 / (z * (c / b))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (b <= -7.6e-28) {
		tmp = b * ((1.0 / z) / c);
	} else if (b <= 4.1e+119) {
		tmp = -4.0 * (t * (a / c));
	} else {
		tmp = 1.0 / (z * (c / b));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if b <= -7.6e-28:
		tmp = b * ((1.0 / z) / c)
	elif b <= 4.1e+119:
		tmp = -4.0 * (t * (a / c))
	else:
		tmp = 1.0 / (z * (c / b))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (b <= -7.6e-28)
		tmp = Float64(b * Float64(Float64(1.0 / z) / c));
	elseif (b <= 4.1e+119)
		tmp = Float64(-4.0 * Float64(t * Float64(a / c)));
	else
		tmp = Float64(1.0 / Float64(z * Float64(c / b)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (b <= -7.6e-28)
		tmp = b * ((1.0 / z) / c);
	elseif (b <= 4.1e+119)
		tmp = -4.0 * (t * (a / c));
	else
		tmp = 1.0 / (z * (c / b));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[b, -7.6e-28], N[(b * N[(N[(1.0 / z), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 4.1e+119], N[(-4.0 * N[(t * N[(a / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(z * N[(c / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 23: 34.6% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -9 \cdot 10^{+29}:\\ \;\;\;\;\frac{b}{z \cdot c}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= t -9e+29) (/ b (* z c)) (/ (/ b c) z)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (t <= -9e+29) {
		tmp = b / (z * c);
	} else {
		tmp = (b / c) / z;
	}
	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 <= (-9d+29)) then
        tmp = b / (z * c)
    else
        tmp = (b / c) / 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 c) {
	double tmp;
	if (t <= -9e+29) {
		tmp = b / (z * c);
	} else {
		tmp = (b / c) / z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if t <= -9e+29:
		tmp = b / (z * c)
	else:
		tmp = (b / c) / z
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (t <= -9e+29)
		tmp = Float64(b / Float64(z * c));
	else
		tmp = Float64(Float64(b / c) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (t <= -9e+29)
		tmp = b / (z * c);
	else
		tmp = (b / c) / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[t, -9e+29], N[(b / N[(z * c), $MachinePrecision]), $MachinePrecision], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision]]

Derivation

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

Alternative 24: 34.2% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq 2 \cdot 10^{+40}:\\ \;\;\;\;\frac{\frac{b}{z}}{c}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= c 2e+40) (/ (/ b z) c) (/ (/ b c) z)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (c <= 2e+40) {
		tmp = (b / z) / c;
	} else {
		tmp = (b / c) / z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: tmp
    if (c <= 2d+40) then
        tmp = (b / z) / c
    else
        tmp = (b / c) / 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 c) {
	double tmp;
	if (c <= 2e+40) {
		tmp = (b / z) / c;
	} else {
		tmp = (b / c) / z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if c <= 2e+40:
		tmp = (b / z) / c
	else:
		tmp = (b / c) / z
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (c <= 2e+40)
		tmp = Float64(Float64(b / z) / c);
	else
		tmp = Float64(Float64(b / c) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (c <= 2e+40)
		tmp = (b / z) / c;
	else
		tmp = (b / c) / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[c, 2e+40], N[(N[(b / z), $MachinePrecision] / c), $MachinePrecision], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision]]

Derivation

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

Alternative 25: 34.9% accurate, 3.8× speedup

Math

\[\begin{array}{l} \\ \frac{b}{z \cdot c} \end{array} \]

FPCore

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

C

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

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 = b / (z * c)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(b / N[(z * c), $MachinePrecision]), $MachinePrecision]

Derivation

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

Developer target: 80.4% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{b}{c \cdot z}\\ t_2 := 4 \cdot \frac{a \cdot t}{c}\\ t_3 := \left(x \cdot 9\right) \cdot y\\ t_4 := \left(t_3 - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b\\ t_5 := \frac{t_4}{z \cdot c}\\ t_6 := \frac{\left(t_3 - \left(z \cdot 4\right) \cdot \left(t \cdot a\right)\right) + b}{z \cdot c}\\ \mathbf{if}\;t_5 < -1.100156740804105 \cdot 10^{-171}:\\ \;\;\;\;t_6\\ \mathbf{elif}\;t_5 < 0:\\ \;\;\;\;\frac{\frac{t_4}{z}}{c}\\ \mathbf{elif}\;t_5 < 1.1708877911747488 \cdot 10^{-53}:\\ \;\;\;\;t_6\\ \mathbf{elif}\;t_5 < 2.876823679546137 \cdot 10^{+130}:\\ \;\;\;\;\left(\left(9 \cdot \frac{y}{c}\right) \cdot \frac{x}{z} + t_1\right) - t_2\\ \mathbf{elif}\;t_5 < 1.3838515042456319 \cdot 10^{+158}:\\ \;\;\;\;t_6\\ \mathbf{else}:\\ \;\;\;\;\left(9 \cdot \left(\frac{y}{c \cdot z} \cdot x\right) + t_1\right) - t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (/ b (* c z)))
        (t_2 (* 4.0 (/ (* a t) c)))
        (t_3 (* (* x 9.0) y))
        (t_4 (+ (- t_3 (* (* (* z 4.0) t) a)) b))
        (t_5 (/ t_4 (* z c)))
        (t_6 (/ (+ (- t_3 (* (* z 4.0) (* t a))) b) (* z c))))
   (if (< t_5 -1.100156740804105e-171)
     t_6
     (if (< t_5 0.0)
       (/ (/ t_4 z) c)
       (if (< t_5 1.1708877911747488e-53)
         t_6
         (if (< t_5 2.876823679546137e+130)
           (- (+ (* (* 9.0 (/ y c)) (/ x z)) t_1) t_2)
           (if (< t_5 1.3838515042456319e+158)
             t_6
             (- (+ (* 9.0 (* (/ y (* c z)) x)) t_1) t_2))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = b / (c * z);
	double t_2 = 4.0 * ((a * t) / c);
	double t_3 = (x * 9.0) * y;
	double t_4 = (t_3 - (((z * 4.0) * t) * a)) + b;
	double t_5 = t_4 / (z * c);
	double t_6 = ((t_3 - ((z * 4.0) * (t * a))) + b) / (z * c);
	double tmp;
	if (t_5 < -1.100156740804105e-171) {
		tmp = t_6;
	} else if (t_5 < 0.0) {
		tmp = (t_4 / z) / c;
	} else if (t_5 < 1.1708877911747488e-53) {
		tmp = t_6;
	} else if (t_5 < 2.876823679546137e+130) {
		tmp = (((9.0 * (y / c)) * (x / z)) + t_1) - t_2;
	} else if (t_5 < 1.3838515042456319e+158) {
		tmp = t_6;
	} else {
		tmp = ((9.0 * ((y / (c * z)) * x)) + t_1) - 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) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: tmp
    t_1 = b / (c * z)
    t_2 = 4.0d0 * ((a * t) / c)
    t_3 = (x * 9.0d0) * y
    t_4 = (t_3 - (((z * 4.0d0) * t) * a)) + b
    t_5 = t_4 / (z * c)
    t_6 = ((t_3 - ((z * 4.0d0) * (t * a))) + b) / (z * c)
    if (t_5 < (-1.100156740804105d-171)) then
        tmp = t_6
    else if (t_5 < 0.0d0) then
        tmp = (t_4 / z) / c
    else if (t_5 < 1.1708877911747488d-53) then
        tmp = t_6
    else if (t_5 < 2.876823679546137d+130) then
        tmp = (((9.0d0 * (y / c)) * (x / z)) + t_1) - t_2
    else if (t_5 < 1.3838515042456319d+158) then
        tmp = t_6
    else
        tmp = ((9.0d0 * ((y / (c * z)) * x)) + t_1) - 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 = b / (c * z);
	double t_2 = 4.0 * ((a * t) / c);
	double t_3 = (x * 9.0) * y;
	double t_4 = (t_3 - (((z * 4.0) * t) * a)) + b;
	double t_5 = t_4 / (z * c);
	double t_6 = ((t_3 - ((z * 4.0) * (t * a))) + b) / (z * c);
	double tmp;
	if (t_5 < -1.100156740804105e-171) {
		tmp = t_6;
	} else if (t_5 < 0.0) {
		tmp = (t_4 / z) / c;
	} else if (t_5 < 1.1708877911747488e-53) {
		tmp = t_6;
	} else if (t_5 < 2.876823679546137e+130) {
		tmp = (((9.0 * (y / c)) * (x / z)) + t_1) - t_2;
	} else if (t_5 < 1.3838515042456319e+158) {
		tmp = t_6;
	} else {
		tmp = ((9.0 * ((y / (c * z)) * x)) + t_1) - t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = b / (c * z)
	t_2 = 4.0 * ((a * t) / c)
	t_3 = (x * 9.0) * y
	t_4 = (t_3 - (((z * 4.0) * t) * a)) + b
	t_5 = t_4 / (z * c)
	t_6 = ((t_3 - ((z * 4.0) * (t * a))) + b) / (z * c)
	tmp = 0
	if t_5 < -1.100156740804105e-171:
		tmp = t_6
	elif t_5 < 0.0:
		tmp = (t_4 / z) / c
	elif t_5 < 1.1708877911747488e-53:
		tmp = t_6
	elif t_5 < 2.876823679546137e+130:
		tmp = (((9.0 * (y / c)) * (x / z)) + t_1) - t_2
	elif t_5 < 1.3838515042456319e+158:
		tmp = t_6
	else:
		tmp = ((9.0 * ((y / (c * z)) * x)) + t_1) - t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(b / Float64(c * z))
	t_2 = Float64(4.0 * Float64(Float64(a * t) / c))
	t_3 = Float64(Float64(x * 9.0) * y)
	t_4 = Float64(Float64(t_3 - Float64(Float64(Float64(z * 4.0) * t) * a)) + b)
	t_5 = Float64(t_4 / Float64(z * c))
	t_6 = Float64(Float64(Float64(t_3 - Float64(Float64(z * 4.0) * Float64(t * a))) + b) / Float64(z * c))
	tmp = 0.0
	if (t_5 < -1.100156740804105e-171)
		tmp = t_6;
	elseif (t_5 < 0.0)
		tmp = Float64(Float64(t_4 / z) / c);
	elseif (t_5 < 1.1708877911747488e-53)
		tmp = t_6;
	elseif (t_5 < 2.876823679546137e+130)
		tmp = Float64(Float64(Float64(Float64(9.0 * Float64(y / c)) * Float64(x / z)) + t_1) - t_2);
	elseif (t_5 < 1.3838515042456319e+158)
		tmp = t_6;
	else
		tmp = Float64(Float64(Float64(9.0 * Float64(Float64(y / Float64(c * z)) * x)) + t_1) - t_2);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = b / (c * z);
	t_2 = 4.0 * ((a * t) / c);
	t_3 = (x * 9.0) * y;
	t_4 = (t_3 - (((z * 4.0) * t) * a)) + b;
	t_5 = t_4 / (z * c);
	t_6 = ((t_3 - ((z * 4.0) * (t * a))) + b) / (z * c);
	tmp = 0.0;
	if (t_5 < -1.100156740804105e-171)
		tmp = t_6;
	elseif (t_5 < 0.0)
		tmp = (t_4 / z) / c;
	elseif (t_5 < 1.1708877911747488e-53)
		tmp = t_6;
	elseif (t_5 < 2.876823679546137e+130)
		tmp = (((9.0 * (y / c)) * (x / z)) + t_1) - t_2;
	elseif (t_5 < 1.3838515042456319e+158)
		tmp = t_6;
	else
		tmp = ((9.0 * ((y / (c * z)) * x)) + t_1) - t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(b / N[(c * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(4.0 * N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$4 = N[(N[(t$95$3 - N[(N[(N[(z * 4.0), $MachinePrecision] * t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision]}, Block[{t$95$5 = N[(t$95$4 / N[(z * c), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(N[(N[(t$95$3 - N[(N[(z * 4.0), $MachinePrecision] * N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]}, If[Less[t$95$5, -1.100156740804105e-171], t$95$6, If[Less[t$95$5, 0.0], N[(N[(t$95$4 / z), $MachinePrecision] / c), $MachinePrecision], If[Less[t$95$5, 1.1708877911747488e-53], t$95$6, If[Less[t$95$5, 2.876823679546137e+130], N[(N[(N[(N[(9.0 * N[(y / c), $MachinePrecision]), $MachinePrecision] * N[(x / z), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision] - t$95$2), $MachinePrecision], If[Less[t$95$5, 1.3838515042456319e+158], t$95$6, N[(N[(N[(9.0 * N[(N[(y / N[(c * z), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision] - t$95$2), $MachinePrecision]]]]]]]]]]]]

Reproduce

herbie shell --seed 2024008 
(FPCore (x y z t a b c)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, J"
  :precision binary64

  :herbie-target
  (if (< (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)) -1.100156740804105e-171) (/ (+ (- (* (* x 9.0) y) (* (* z 4.0) (* t a))) b) (* z c)) (if (< (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)) 0.0) (/ (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) z) c) (if (< (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)) 1.1708877911747488e-53) (/ (+ (- (* (* x 9.0) y) (* (* z 4.0) (* t a))) b) (* z c)) (if (< (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)) 2.876823679546137e+130) (- (+ (* (* 9.0 (/ y c)) (/ x z)) (/ b (* c z))) (* 4.0 (/ (* a t) c))) (if (< (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)) 1.3838515042456319e+158) (/ (+ (- (* (* x 9.0) y) (* (* z 4.0) (* t a))) b) (* z c)) (- (+ (* 9.0 (* (/ y (* c z)) x)) (/ b (* c z))) (* 4.0 (/ (* a t) c))))))))

  (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)))