AI.Clustering.Hierarchical.Internal:ward from clustering-0.2.1

Percentage Accurate: 61.0% → 88.6%

Time: 18.4s

Alternatives: 19

Speedup: 1.6×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 61.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 88.6% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y + t\right) \cdot a\\ t_2 := \frac{\left(t_1 + z \cdot \left(x + y\right)\right) - y \cdot b}{y + \left(x + t\right)}\\ t_3 := {\left(y + t\right)}^{2}\\ \mathbf{if}\;t_2 \leq -\infty:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{elif}\;t_2 \leq 2 \cdot 10^{+303}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x + y, z, t_1\right) - y \cdot b}{x + \left(y + t\right)}\\ \mathbf{else}:\\ \;\;\;\;a + \left(\mathsf{fma}\left(x, \frac{z}{y + t} + \left(\frac{b}{\frac{t_3}{y}} - \left(\frac{y}{\frac{t_3}{z}} + \frac{a}{y + t}\right)\right), \frac{y}{\frac{y + t}{z}}\right) - \frac{b}{\frac{y + t}{y}}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (+ y t) a))
        (t_2 (/ (- (+ t_1 (* z (+ x y))) (* y b)) (+ y (+ x t))))
        (t_3 (pow (+ y t) 2.0)))
   (if (<= t_2 (- INFINITY))
     (- (+ z a) b)
     (if (<= t_2 2e+303)
       (/ (- (fma (+ x y) z t_1) (* y b)) (+ x (+ y t)))
       (+
        a
        (-
         (fma
          x
          (+
           (/ z (+ y t))
           (- (/ b (/ t_3 y)) (+ (/ y (/ t_3 z)) (/ a (+ y t)))))
          (/ y (/ (+ y t) z)))
         (/ b (/ (+ y t) y))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (y + t) * a;
	double t_2 = ((t_1 + (z * (x + y))) - (y * b)) / (y + (x + t));
	double t_3 = pow((y + t), 2.0);
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = (z + a) - b;
	} else if (t_2 <= 2e+303) {
		tmp = (fma((x + y), z, t_1) - (y * b)) / (x + (y + t));
	} else {
		tmp = a + (fma(x, ((z / (y + t)) + ((b / (t_3 / y)) - ((y / (t_3 / z)) + (a / (y + t))))), (y / ((y + t) / z))) - (b / ((y + t) / y)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(y + t) * a)
	t_2 = Float64(Float64(Float64(t_1 + Float64(z * Float64(x + y))) - Float64(y * b)) / Float64(y + Float64(x + t)))
	t_3 = Float64(y + t) ^ 2.0
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = Float64(Float64(z + a) - b);
	elseif (t_2 <= 2e+303)
		tmp = Float64(Float64(fma(Float64(x + y), z, t_1) - Float64(y * b)) / Float64(x + Float64(y + t)));
	else
		tmp = Float64(a + Float64(fma(x, Float64(Float64(z / Float64(y + t)) + Float64(Float64(b / Float64(t_3 / y)) - Float64(Float64(y / Float64(t_3 / z)) + Float64(a / Float64(y + t))))), Float64(y / Float64(Float64(y + t) / z))) - Float64(b / Float64(Float64(y + t) / y))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(t$95$1 + N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[Power[N[(y + t), $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], If[LessEqual[t$95$2, 2e+303], N[(N[(N[(N[(x + y), $MachinePrecision] * z + t$95$1), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / N[(x + N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a + N[(N[(x * N[(N[(z / N[(y + t), $MachinePrecision]), $MachinePrecision] + N[(N[(b / N[(t$95$3 / y), $MachinePrecision]), $MachinePrecision] - N[(N[(y / N[(t$95$3 / z), $MachinePrecision]), $MachinePrecision] + N[(a / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y / N[(N[(y + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(b / N[(N[(y + t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 2: 85.6% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \left(y + t\right) \cdot a\\ t_3 := \frac{\left(t_2 + z \cdot \left(x + y\right)\right) - y \cdot b}{t_1}\\ \mathbf{if}\;t_3 \leq -\infty:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{elif}\;t_3 \leq 2 \cdot 10^{+303}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x + y, z, t_2\right) - y \cdot b}{x + \left(y + t\right)}\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(\frac{y}{t_1} + \frac{t}{t_1}\right) + \frac{y}{\frac{t_1}{-b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (* (+ y t) a))
        (t_3 (/ (- (+ t_2 (* z (+ x y))) (* y b)) t_1)))
   (if (<= t_3 (- INFINITY))
     (- (+ z a) b)
     (if (<= t_3 2e+303)
       (/ (- (fma (+ x y) z t_2) (* y b)) (+ x (+ y t)))
       (+ (* a (+ (/ y t_1) (/ t t_1))) (/ y (/ t_1 (- b))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = (y + t) * a;
	double t_3 = ((t_2 + (z * (x + y))) - (y * b)) / t_1;
	double tmp;
	if (t_3 <= -((double) INFINITY)) {
		tmp = (z + a) - b;
	} else if (t_3 <= 2e+303) {
		tmp = (fma((x + y), z, t_2) - (y * b)) / (x + (y + t));
	} else {
		tmp = (a * ((y / t_1) + (t / t_1))) + (y / (t_1 / -b));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(Float64(y + t) * a)
	t_3 = Float64(Float64(Float64(t_2 + Float64(z * Float64(x + y))) - Float64(y * b)) / t_1)
	tmp = 0.0
	if (t_3 <= Float64(-Inf))
		tmp = Float64(Float64(z + a) - b);
	elseif (t_3 <= 2e+303)
		tmp = Float64(Float64(fma(Float64(x + y), z, t_2) - Float64(y * b)) / Float64(x + Float64(y + t)));
	else
		tmp = Float64(Float64(a * Float64(Float64(y / t_1) + Float64(t / t_1))) + Float64(y / Float64(t_1 / Float64(-b))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(t$95$2 + N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[LessEqual[t$95$3, (-Infinity)], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], If[LessEqual[t$95$3, 2e+303], N[(N[(N[(N[(x + y), $MachinePrecision] * z + t$95$2), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / N[(x + N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * N[(N[(y / t$95$1), $MachinePrecision] + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y / N[(t$95$1 / (-b)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 3: 85.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{\left(\left(y + t\right) \cdot a + z \cdot \left(x + y\right)\right) - y \cdot b}{t_1}\\ \mathbf{if}\;t_2 \leq -\infty:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{elif}\;t_2 \leq 2 \cdot 10^{+303}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(\frac{y}{t_1} + \frac{t}{t_1}\right) + \frac{y}{\frac{t_1}{-b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (/ (- (+ (* (+ y t) a) (* z (+ x y))) (* y b)) t_1)))
   (if (<= t_2 (- INFINITY))
     (- (+ z a) b)
     (if (<= t_2 2e+303)
       t_2
       (+ (* a (+ (/ y t_1) (/ t t_1))) (/ y (/ t_1 (- b))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / t_1;
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = (z + a) - b;
	} else if (t_2 <= 2e+303) {
		tmp = t_2;
	} else {
		tmp = (a * ((y / t_1) + (t / t_1))) + (y / (t_1 / -b));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / t_1;
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = (z + a) - b;
	} else if (t_2 <= 2e+303) {
		tmp = t_2;
	} else {
		tmp = (a * ((y / t_1) + (t / t_1))) + (y / (t_1 / -b));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / t_1
	tmp = 0
	if t_2 <= -math.inf:
		tmp = (z + a) - b
	elif t_2 <= 2e+303:
		tmp = t_2
	else:
		tmp = (a * ((y / t_1) + (t / t_1))) + (y / (t_1 / -b))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(Float64(Float64(Float64(Float64(y + t) * a) + Float64(z * Float64(x + y))) - Float64(y * b)) / t_1)
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = Float64(Float64(z + a) - b);
	elseif (t_2 <= 2e+303)
		tmp = t_2;
	else
		tmp = Float64(Float64(a * Float64(Float64(y / t_1) + Float64(t / t_1))) + Float64(y / Float64(t_1 / Float64(-b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / t_1;
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = (z + a) - b;
	elseif (t_2 <= 2e+303)
		tmp = t_2;
	else
		tmp = (a * ((y / t_1) + (t / t_1))) + (y / (t_1 / -b));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision] + N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], If[LessEqual[t$95$2, 2e+303], t$95$2, N[(N[(a * N[(N[(y / t$95$1), $MachinePrecision] + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y / N[(t$95$1 / (-b)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 4: 87.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\left(\left(y + t\right) \cdot a + z \cdot \left(x + y\right)\right) - y \cdot b}{y + \left(x + t\right)}\\ \mathbf{if}\;t_1 \leq -\infty \lor \neg \left(t_1 \leq 10^{+267}\right):\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- (+ (* (+ y t) a) (* z (+ x y))) (* y b)) (+ y (+ x t)))))
   (if (or (<= t_1 (- INFINITY)) (not (<= t_1 1e+267))) (- (+ z a) b) t_1)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / (y + (x + t));
	double tmp;
	if ((t_1 <= -((double) INFINITY)) || !(t_1 <= 1e+267)) {
		tmp = (z + a) - b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / (y + (x + t));
	double tmp;
	if ((t_1 <= -Double.POSITIVE_INFINITY) || !(t_1 <= 1e+267)) {
		tmp = (z + a) - b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / (y + (x + t))
	tmp = 0
	if (t_1 <= -math.inf) or not (t_1 <= 1e+267):
		tmp = (z + a) - b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(Float64(Float64(y + t) * a) + Float64(z * Float64(x + y))) - Float64(y * b)) / Float64(y + Float64(x + t)))
	tmp = 0.0
	if ((t_1 <= Float64(-Inf)) || !(t_1 <= 1e+267))
		tmp = Float64(Float64(z + a) - b);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / (y + (x + t));
	tmp = 0.0;
	if ((t_1 <= -Inf) || ~((t_1 <= 1e+267)))
		tmp = (z + a) - b;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision] + N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, (-Infinity)], N[Not[LessEqual[t$95$1, 1e+267]], $MachinePrecision]], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], t$95$1]]

Derivation

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

Alternative 5: 68.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := a + \frac{y}{\frac{t_1}{-b}}\\ t_3 := z + a \cdot \left(\frac{y}{t_1} + \frac{t}{t_1}\right)\\ \mathbf{if}\;x \leq -7 \cdot 10^{+66}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;x \leq -4 \cdot 10^{-238}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -1.25 \cdot 10^{-267}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{elif}\;x \leq 2.15 \cdot 10^{-103}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (+ a (/ y (/ t_1 (- b)))))
        (t_3 (+ z (* a (+ (/ y t_1) (/ t t_1))))))
   (if (<= x -7e+66)
     t_3
     (if (<= x -4e-238)
       t_2
       (if (<= x -1.25e-267) (- (+ z a) b) (if (<= x 2.15e-103) t_2 t_3))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = a + (y / (t_1 / -b));
	double t_3 = z + (a * ((y / t_1) + (t / t_1)));
	double tmp;
	if (x <= -7e+66) {
		tmp = t_3;
	} else if (x <= -4e-238) {
		tmp = t_2;
	} else if (x <= -1.25e-267) {
		tmp = (z + a) - b;
	} else if (x <= 2.15e-103) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = y + (x + t)
    t_2 = a + (y / (t_1 / -b))
    t_3 = z + (a * ((y / t_1) + (t / t_1)))
    if (x <= (-7d+66)) then
        tmp = t_3
    else if (x <= (-4d-238)) then
        tmp = t_2
    else if (x <= (-1.25d-267)) then
        tmp = (z + a) - b
    else if (x <= 2.15d-103) then
        tmp = t_2
    else
        tmp = t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = a + (y / (t_1 / -b));
	double t_3 = z + (a * ((y / t_1) + (t / t_1)));
	double tmp;
	if (x <= -7e+66) {
		tmp = t_3;
	} else if (x <= -4e-238) {
		tmp = t_2;
	} else if (x <= -1.25e-267) {
		tmp = (z + a) - b;
	} else if (x <= 2.15e-103) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = a + (y / (t_1 / -b))
	t_3 = z + (a * ((y / t_1) + (t / t_1)))
	tmp = 0
	if x <= -7e+66:
		tmp = t_3
	elif x <= -4e-238:
		tmp = t_2
	elif x <= -1.25e-267:
		tmp = (z + a) - b
	elif x <= 2.15e-103:
		tmp = t_2
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(a + Float64(y / Float64(t_1 / Float64(-b))))
	t_3 = Float64(z + Float64(a * Float64(Float64(y / t_1) + Float64(t / t_1))))
	tmp = 0.0
	if (x <= -7e+66)
		tmp = t_3;
	elseif (x <= -4e-238)
		tmp = t_2;
	elseif (x <= -1.25e-267)
		tmp = Float64(Float64(z + a) - b);
	elseif (x <= 2.15e-103)
		tmp = t_2;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = a + (y / (t_1 / -b));
	t_3 = z + (a * ((y / t_1) + (t / t_1)));
	tmp = 0.0;
	if (x <= -7e+66)
		tmp = t_3;
	elseif (x <= -4e-238)
		tmp = t_2;
	elseif (x <= -1.25e-267)
		tmp = (z + a) - b;
	elseif (x <= 2.15e-103)
		tmp = t_2;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(a + N[(y / N[(t$95$1 / (-b)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(z + N[(a * N[(N[(y / t$95$1), $MachinePrecision] + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -7e+66], t$95$3, If[LessEqual[x, -4e-238], t$95$2, If[LessEqual[x, -1.25e-267], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], If[LessEqual[x, 2.15e-103], t$95$2, t$95$3]]]]]]]

Derivation

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

Alternative 6: 62.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \left(z + a\right) - b\\ t_3 := \frac{t \cdot a + x \cdot z}{x + t}\\ \mathbf{if}\;y \leq -1.05 \cdot 10^{+226}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.8 \cdot 10^{-41}:\\ \;\;\;\;a + \frac{y}{\frac{t_1}{-b}}\\ \mathbf{elif}\;y \leq -3.3 \cdot 10^{-140}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq -2.8 \cdot 10^{-163}:\\ \;\;\;\;\frac{a}{\frac{t_1}{y + t}}\\ \mathbf{elif}\;y \leq -8.5 \cdot 10^{-176}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{-69}:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (- (+ z a) b))
        (t_3 (/ (+ (* t a) (* x z)) (+ x t))))
   (if (<= y -1.05e+226)
     t_2
     (if (<= y -1.8e-41)
       (+ a (/ y (/ t_1 (- b))))
       (if (<= y -3.3e-140)
         t_3
         (if (<= y -2.8e-163)
           (/ a (/ t_1 (+ y t)))
           (if (<= y -8.5e-176) z (if (<= y 2.25e-69) t_3 t_2))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = (z + a) - b;
	double t_3 = ((t * a) + (x * z)) / (x + t);
	double tmp;
	if (y <= -1.05e+226) {
		tmp = t_2;
	} else if (y <= -1.8e-41) {
		tmp = a + (y / (t_1 / -b));
	} else if (y <= -3.3e-140) {
		tmp = t_3;
	} else if (y <= -2.8e-163) {
		tmp = a / (t_1 / (y + t));
	} else if (y <= -8.5e-176) {
		tmp = z;
	} else if (y <= 2.25e-69) {
		tmp = t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = y + (x + t)
    t_2 = (z + a) - b
    t_3 = ((t * a) + (x * z)) / (x + t)
    if (y <= (-1.05d+226)) then
        tmp = t_2
    else if (y <= (-1.8d-41)) then
        tmp = a + (y / (t_1 / -b))
    else if (y <= (-3.3d-140)) then
        tmp = t_3
    else if (y <= (-2.8d-163)) then
        tmp = a / (t_1 / (y + t))
    else if (y <= (-8.5d-176)) then
        tmp = z
    else if (y <= 2.25d-69) then
        tmp = t_3
    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 t_1 = y + (x + t);
	double t_2 = (z + a) - b;
	double t_3 = ((t * a) + (x * z)) / (x + t);
	double tmp;
	if (y <= -1.05e+226) {
		tmp = t_2;
	} else if (y <= -1.8e-41) {
		tmp = a + (y / (t_1 / -b));
	} else if (y <= -3.3e-140) {
		tmp = t_3;
	} else if (y <= -2.8e-163) {
		tmp = a / (t_1 / (y + t));
	} else if (y <= -8.5e-176) {
		tmp = z;
	} else if (y <= 2.25e-69) {
		tmp = t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = (z + a) - b
	t_3 = ((t * a) + (x * z)) / (x + t)
	tmp = 0
	if y <= -1.05e+226:
		tmp = t_2
	elif y <= -1.8e-41:
		tmp = a + (y / (t_1 / -b))
	elif y <= -3.3e-140:
		tmp = t_3
	elif y <= -2.8e-163:
		tmp = a / (t_1 / (y + t))
	elif y <= -8.5e-176:
		tmp = z
	elif y <= 2.25e-69:
		tmp = t_3
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(Float64(z + a) - b)
	t_3 = Float64(Float64(Float64(t * a) + Float64(x * z)) / Float64(x + t))
	tmp = 0.0
	if (y <= -1.05e+226)
		tmp = t_2;
	elseif (y <= -1.8e-41)
		tmp = Float64(a + Float64(y / Float64(t_1 / Float64(-b))));
	elseif (y <= -3.3e-140)
		tmp = t_3;
	elseif (y <= -2.8e-163)
		tmp = Float64(a / Float64(t_1 / Float64(y + t)));
	elseif (y <= -8.5e-176)
		tmp = z;
	elseif (y <= 2.25e-69)
		tmp = t_3;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = (z + a) - b;
	t_3 = ((t * a) + (x * z)) / (x + t);
	tmp = 0.0;
	if (y <= -1.05e+226)
		tmp = t_2;
	elseif (y <= -1.8e-41)
		tmp = a + (y / (t_1 / -b));
	elseif (y <= -3.3e-140)
		tmp = t_3;
	elseif (y <= -2.8e-163)
		tmp = a / (t_1 / (y + t));
	elseif (y <= -8.5e-176)
		tmp = z;
	elseif (y <= 2.25e-69)
		tmp = t_3;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(t * a), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.05e+226], t$95$2, If[LessEqual[y, -1.8e-41], N[(a + N[(y / N[(t$95$1 / (-b)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -3.3e-140], t$95$3, If[LessEqual[y, -2.8e-163], N[(a / N[(t$95$1 / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -8.5e-176], z, If[LessEqual[y, 2.25e-69], t$95$3, t$95$2]]]]]]]]]

Derivation

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

Alternative 7: 59.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ t_2 := \frac{z}{\frac{y + \left(x + t\right)}{x + y}}\\ \mathbf{if}\;t \leq -8.6 \cdot 10^{+156}:\\ \;\;\;\;a - y \cdot \frac{b - z}{t}\\ \mathbf{elif}\;t \leq -6.5 \cdot 10^{-100}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -7 \cdot 10^{-213}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 3.9 \cdot 10^{-273}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 5.9 \cdot 10^{-176}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 2.2 \cdot 10^{+180}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a + \frac{y}{\frac{t}{z - b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)) (t_2 (/ z (/ (+ y (+ x t)) (+ x y)))))
   (if (<= t -8.6e+156)
     (- a (* y (/ (- b z) t)))
     (if (<= t -6.5e-100)
       t_1
       (if (<= t -7e-213)
         t_2
         (if (<= t 3.9e-273)
           t_1
           (if (<= t 5.9e-176)
             t_2
             (if (<= t 2.2e+180) t_1 (+ a (/ y (/ t (- z b))))))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double t_2 = z / ((y + (x + t)) / (x + y));
	double tmp;
	if (t <= -8.6e+156) {
		tmp = a - (y * ((b - z) / t));
	} else if (t <= -6.5e-100) {
		tmp = t_1;
	} else if (t <= -7e-213) {
		tmp = t_2;
	} else if (t <= 3.9e-273) {
		tmp = t_1;
	} else if (t <= 5.9e-176) {
		tmp = t_2;
	} else if (t <= 2.2e+180) {
		tmp = t_1;
	} else {
		tmp = a + (y / (t / (z - b)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (z + a) - b
    t_2 = z / ((y + (x + t)) / (x + y))
    if (t <= (-8.6d+156)) then
        tmp = a - (y * ((b - z) / t))
    else if (t <= (-6.5d-100)) then
        tmp = t_1
    else if (t <= (-7d-213)) then
        tmp = t_2
    else if (t <= 3.9d-273) then
        tmp = t_1
    else if (t <= 5.9d-176) then
        tmp = t_2
    else if (t <= 2.2d+180) then
        tmp = t_1
    else
        tmp = a + (y / (t / (z - b)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double t_2 = z / ((y + (x + t)) / (x + y));
	double tmp;
	if (t <= -8.6e+156) {
		tmp = a - (y * ((b - z) / t));
	} else if (t <= -6.5e-100) {
		tmp = t_1;
	} else if (t <= -7e-213) {
		tmp = t_2;
	} else if (t <= 3.9e-273) {
		tmp = t_1;
	} else if (t <= 5.9e-176) {
		tmp = t_2;
	} else if (t <= 2.2e+180) {
		tmp = t_1;
	} else {
		tmp = a + (y / (t / (z - b)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	t_2 = z / ((y + (x + t)) / (x + y))
	tmp = 0
	if t <= -8.6e+156:
		tmp = a - (y * ((b - z) / t))
	elif t <= -6.5e-100:
		tmp = t_1
	elif t <= -7e-213:
		tmp = t_2
	elif t <= 3.9e-273:
		tmp = t_1
	elif t <= 5.9e-176:
		tmp = t_2
	elif t <= 2.2e+180:
		tmp = t_1
	else:
		tmp = a + (y / (t / (z - b)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	t_2 = Float64(z / Float64(Float64(y + Float64(x + t)) / Float64(x + y)))
	tmp = 0.0
	if (t <= -8.6e+156)
		tmp = Float64(a - Float64(y * Float64(Float64(b - z) / t)));
	elseif (t <= -6.5e-100)
		tmp = t_1;
	elseif (t <= -7e-213)
		tmp = t_2;
	elseif (t <= 3.9e-273)
		tmp = t_1;
	elseif (t <= 5.9e-176)
		tmp = t_2;
	elseif (t <= 2.2e+180)
		tmp = t_1;
	else
		tmp = Float64(a + Float64(y / Float64(t / Float64(z - b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	t_2 = z / ((y + (x + t)) / (x + y));
	tmp = 0.0;
	if (t <= -8.6e+156)
		tmp = a - (y * ((b - z) / t));
	elseif (t <= -6.5e-100)
		tmp = t_1;
	elseif (t <= -7e-213)
		tmp = t_2;
	elseif (t <= 3.9e-273)
		tmp = t_1;
	elseif (t <= 5.9e-176)
		tmp = t_2;
	elseif (t <= 2.2e+180)
		tmp = t_1;
	else
		tmp = a + (y / (t / (z - b)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, Block[{t$95$2 = N[(z / N[(N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -8.6e+156], N[(a - N[(y * N[(N[(b - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -6.5e-100], t$95$1, If[LessEqual[t, -7e-213], t$95$2, If[LessEqual[t, 3.9e-273], t$95$1, If[LessEqual[t, 5.9e-176], t$95$2, If[LessEqual[t, 2.2e+180], t$95$1, N[(a + N[(y / N[(t / N[(z - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]

Derivation

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

Alternative 8: 63.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -8.2 \cdot 10^{+45}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.3 \cdot 10^{+28}:\\ \;\;\;\;\frac{z \cdot \left(x + y\right) - y \cdot b}{t_1}\\ \mathbf{elif}\;y \leq -3400000000000:\\ \;\;\;\;\left(y + t\right) \cdot \frac{a}{t_1}\\ \mathbf{elif}\;y \leq -1.5 \cdot 10^{-42}:\\ \;\;\;\;a + \frac{y}{\frac{t_1}{-b}}\\ \mathbf{elif}\;y \leq 8.5 \cdot 10^{-69}:\\ \;\;\;\;\frac{t \cdot a + x \cdot z}{x + t}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t))) (t_2 (- (+ z a) b)))
   (if (<= y -8.2e+45)
     t_2
     (if (<= y -1.3e+28)
       (/ (- (* z (+ x y)) (* y b)) t_1)
       (if (<= y -3400000000000.0)
         (* (+ y t) (/ a t_1))
         (if (<= y -1.5e-42)
           (+ a (/ y (/ t_1 (- b))))
           (if (<= y 8.5e-69) (/ (+ (* t a) (* x z)) (+ x t)) t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -8.2e+45) {
		tmp = t_2;
	} else if (y <= -1.3e+28) {
		tmp = ((z * (x + y)) - (y * b)) / t_1;
	} else if (y <= -3400000000000.0) {
		tmp = (y + t) * (a / t_1);
	} else if (y <= -1.5e-42) {
		tmp = a + (y / (t_1 / -b));
	} else if (y <= 8.5e-69) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = y + (x + t)
    t_2 = (z + a) - b
    if (y <= (-8.2d+45)) then
        tmp = t_2
    else if (y <= (-1.3d+28)) then
        tmp = ((z * (x + y)) - (y * b)) / t_1
    else if (y <= (-3400000000000.0d0)) then
        tmp = (y + t) * (a / t_1)
    else if (y <= (-1.5d-42)) then
        tmp = a + (y / (t_1 / -b))
    else if (y <= 8.5d-69) then
        tmp = ((t * a) + (x * z)) / (x + t)
    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 t_1 = y + (x + t);
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -8.2e+45) {
		tmp = t_2;
	} else if (y <= -1.3e+28) {
		tmp = ((z * (x + y)) - (y * b)) / t_1;
	} else if (y <= -3400000000000.0) {
		tmp = (y + t) * (a / t_1);
	} else if (y <= -1.5e-42) {
		tmp = a + (y / (t_1 / -b));
	} else if (y <= 8.5e-69) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = (z + a) - b
	tmp = 0
	if y <= -8.2e+45:
		tmp = t_2
	elif y <= -1.3e+28:
		tmp = ((z * (x + y)) - (y * b)) / t_1
	elif y <= -3400000000000.0:
		tmp = (y + t) * (a / t_1)
	elif y <= -1.5e-42:
		tmp = a + (y / (t_1 / -b))
	elif y <= 8.5e-69:
		tmp = ((t * a) + (x * z)) / (x + t)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (y <= -8.2e+45)
		tmp = t_2;
	elseif (y <= -1.3e+28)
		tmp = Float64(Float64(Float64(z * Float64(x + y)) - Float64(y * b)) / t_1);
	elseif (y <= -3400000000000.0)
		tmp = Float64(Float64(y + t) * Float64(a / t_1));
	elseif (y <= -1.5e-42)
		tmp = Float64(a + Float64(y / Float64(t_1 / Float64(-b))));
	elseif (y <= 8.5e-69)
		tmp = Float64(Float64(Float64(t * a) + Float64(x * z)) / Float64(x + t));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = (z + a) - b;
	tmp = 0.0;
	if (y <= -8.2e+45)
		tmp = t_2;
	elseif (y <= -1.3e+28)
		tmp = ((z * (x + y)) - (y * b)) / t_1;
	elseif (y <= -3400000000000.0)
		tmp = (y + t) * (a / t_1);
	elseif (y <= -1.5e-42)
		tmp = a + (y / (t_1 / -b));
	elseif (y <= 8.5e-69)
		tmp = ((t * a) + (x * z)) / (x + t);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[y, -8.2e+45], t$95$2, If[LessEqual[y, -1.3e+28], N[(N[(N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision], If[LessEqual[y, -3400000000000.0], N[(N[(y + t), $MachinePrecision] * N[(a / t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -1.5e-42], N[(a + N[(y / N[(t$95$1 / (-b)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 8.5e-69], N[(N[(N[(t * a), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]

Derivation

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

Alternative 9: 59.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -75000000000:\\ \;\;\;\;a + \frac{y}{\frac{y + \left(x + t\right)}{-b}}\\ \mathbf{elif}\;t \leq 1.25 \cdot 10^{-304}:\\ \;\;\;\;\frac{z \cdot \left(x + y\right) - y \cdot \left(b - a\right)}{x + y}\\ \mathbf{elif}\;t \leq 2.8 \cdot 10^{+181}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;a + \frac{y}{\frac{t}{z - b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -75000000000.0)
   (+ a (/ y (/ (+ y (+ x t)) (- b))))
   (if (<= t 1.25e-304)
     (/ (- (* z (+ x y)) (* y (- b a))) (+ x y))
     (if (<= t 2.8e+181) (- (+ z a) b) (+ a (/ y (/ t (- z b))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -75000000000.0) {
		tmp = a + (y / ((y + (x + t)) / -b));
	} else if (t <= 1.25e-304) {
		tmp = ((z * (x + y)) - (y * (b - a))) / (x + y);
	} else if (t <= 2.8e+181) {
		tmp = (z + a) - b;
	} else {
		tmp = a + (y / (t / (z - b)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= (-75000000000.0d0)) then
        tmp = a + (y / ((y + (x + t)) / -b))
    else if (t <= 1.25d-304) then
        tmp = ((z * (x + y)) - (y * (b - a))) / (x + y)
    else if (t <= 2.8d+181) then
        tmp = (z + a) - b
    else
        tmp = a + (y / (t / (z - 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 tmp;
	if (t <= -75000000000.0) {
		tmp = a + (y / ((y + (x + t)) / -b));
	} else if (t <= 1.25e-304) {
		tmp = ((z * (x + y)) - (y * (b - a))) / (x + y);
	} else if (t <= 2.8e+181) {
		tmp = (z + a) - b;
	} else {
		tmp = a + (y / (t / (z - b)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -75000000000.0:
		tmp = a + (y / ((y + (x + t)) / -b))
	elif t <= 1.25e-304:
		tmp = ((z * (x + y)) - (y * (b - a))) / (x + y)
	elif t <= 2.8e+181:
		tmp = (z + a) - b
	else:
		tmp = a + (y / (t / (z - b)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -75000000000.0)
		tmp = Float64(a + Float64(y / Float64(Float64(y + Float64(x + t)) / Float64(-b))));
	elseif (t <= 1.25e-304)
		tmp = Float64(Float64(Float64(z * Float64(x + y)) - Float64(y * Float64(b - a))) / Float64(x + y));
	elseif (t <= 2.8e+181)
		tmp = Float64(Float64(z + a) - b);
	else
		tmp = Float64(a + Float64(y / Float64(t / Float64(z - b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -75000000000.0)
		tmp = a + (y / ((y + (x + t)) / -b));
	elseif (t <= 1.25e-304)
		tmp = ((z * (x + y)) - (y * (b - a))) / (x + y);
	elseif (t <= 2.8e+181)
		tmp = (z + a) - b;
	else
		tmp = a + (y / (t / (z - b)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -75000000000.0], N[(a + N[(y / N[(N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision] / (-b)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.25e-304], N[(N[(N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision] - N[(y * N[(b - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.8e+181], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], N[(a + N[(y / N[(t / N[(z - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 10: 63.2% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.4 \cdot 10^{+44} \lor \neg \left(y \leq 1.75 \cdot 10^{-69}\right):\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;\frac{t \cdot a + x \cdot z}{x + t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -5.4e+44) (not (<= y 1.75e-69)))
   (- (+ z a) b)
   (/ (+ (* t a) (* x z)) (+ x t))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -5.4e+44) || !(y <= 1.75e-69)) {
		tmp = (z + a) - b;
	} else {
		tmp = ((t * a) + (x * z)) / (x + t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((y <= (-5.4d+44)) .or. (.not. (y <= 1.75d-69))) then
        tmp = (z + a) - b
    else
        tmp = ((t * a) + (x * z)) / (x + 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 tmp;
	if ((y <= -5.4e+44) || !(y <= 1.75e-69)) {
		tmp = (z + a) - b;
	} else {
		tmp = ((t * a) + (x * z)) / (x + t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -5.4e+44) or not (y <= 1.75e-69):
		tmp = (z + a) - b
	else:
		tmp = ((t * a) + (x * z)) / (x + t)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -5.4e+44) || !(y <= 1.75e-69))
		tmp = Float64(Float64(z + a) - b);
	else
		tmp = Float64(Float64(Float64(t * a) + Float64(x * z)) / Float64(x + t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -5.4e+44) || ~((y <= 1.75e-69)))
		tmp = (z + a) - b;
	else
		tmp = ((t * a) + (x * z)) / (x + t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -5.4e+44], N[Not[LessEqual[y, 1.75e-69]], $MachinePrecision]], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], N[(N[(N[(t * a), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 56.1% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.5 \cdot 10^{+194}:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq 1.7 \cdot 10^{+201}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{elif}\;t \leq 8.5 \cdot 10^{+250}:\\ \;\;\;\;y \cdot \frac{z - b}{t}\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -2.5e+194)
   a
   (if (<= t 1.7e+201)
     (- (+ z a) b)
     (if (<= t 8.5e+250) (* y (/ (- z b) t)) a))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -2.5e+194) {
		tmp = a;
	} else if (t <= 1.7e+201) {
		tmp = (z + a) - b;
	} else if (t <= 8.5e+250) {
		tmp = y * ((z - b) / t);
	} else {
		tmp = a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= (-2.5d+194)) then
        tmp = a
    else if (t <= 1.7d+201) then
        tmp = (z + a) - b
    else if (t <= 8.5d+250) then
        tmp = y * ((z - b) / t)
    else
        tmp = a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -2.5e+194) {
		tmp = a;
	} else if (t <= 1.7e+201) {
		tmp = (z + a) - b;
	} else if (t <= 8.5e+250) {
		tmp = y * ((z - b) / t);
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -2.5e+194:
		tmp = a
	elif t <= 1.7e+201:
		tmp = (z + a) - b
	elif t <= 8.5e+250:
		tmp = y * ((z - b) / t)
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -2.5e+194)
		tmp = a;
	elseif (t <= 1.7e+201)
		tmp = Float64(Float64(z + a) - b);
	elseif (t <= 8.5e+250)
		tmp = Float64(y * Float64(Float64(z - b) / t));
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -2.5e+194)
		tmp = a;
	elseif (t <= 1.7e+201)
		tmp = (z + a) - b;
	elseif (t <= 8.5e+250)
		tmp = y * ((z - b) / t);
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -2.5e+194], a, If[LessEqual[t, 1.7e+201], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], If[LessEqual[t, 8.5e+250], N[(y * N[(N[(z - b), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], a]]]

Derivation

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

Alternative 12: 61.4% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -6.5 \cdot 10^{+156} \lor \neg \left(t \leq 1.65 \cdot 10^{+180}\right):\\ \;\;\;\;a - y \cdot \frac{b - z}{t}\\ \mathbf{else}:\\ \;\;\;\;\left(z + a\right) - b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= t -6.5e+156) (not (<= t 1.65e+180)))
   (- a (* y (/ (- b z) t)))
   (- (+ z a) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -6.5e+156) || !(t <= 1.65e+180)) {
		tmp = a - (y * ((b - z) / t));
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((t <= (-6.5d+156)) .or. (.not. (t <= 1.65d+180))) then
        tmp = a - (y * ((b - z) / t))
    else
        tmp = (z + a) - b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -6.5e+156) || !(t <= 1.65e+180)) {
		tmp = a - (y * ((b - z) / t));
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (t <= -6.5e+156) or not (t <= 1.65e+180):
		tmp = a - (y * ((b - z) / t))
	else:
		tmp = (z + a) - b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((t <= -6.5e+156) || !(t <= 1.65e+180))
		tmp = Float64(a - Float64(y * Float64(Float64(b - z) / t)));
	else
		tmp = Float64(Float64(z + a) - b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((t <= -6.5e+156) || ~((t <= 1.65e+180)))
		tmp = a - (y * ((b - z) / t));
	else
		tmp = (z + a) - b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[t, -6.5e+156], N[Not[LessEqual[t, 1.65e+180]], $MachinePrecision]], N[(a - N[(y * N[(N[(b - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]]

Derivation

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

Alternative 13: 61.4% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.35 \cdot 10^{+157}:\\ \;\;\;\;a - y \cdot \frac{b - z}{t}\\ \mathbf{elif}\;t \leq 9.5 \cdot 10^{+179}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;a + \frac{y}{\frac{t}{z - b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -1.35e+157)
   (- a (* y (/ (- b z) t)))
   (if (<= t 9.5e+179) (- (+ z a) b) (+ a (/ y (/ t (- z b)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -1.35e+157) {
		tmp = a - (y * ((b - z) / t));
	} else if (t <= 9.5e+179) {
		tmp = (z + a) - b;
	} else {
		tmp = a + (y / (t / (z - b)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= (-1.35d+157)) then
        tmp = a - (y * ((b - z) / t))
    else if (t <= 9.5d+179) then
        tmp = (z + a) - b
    else
        tmp = a + (y / (t / (z - 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 tmp;
	if (t <= -1.35e+157) {
		tmp = a - (y * ((b - z) / t));
	} else if (t <= 9.5e+179) {
		tmp = (z + a) - b;
	} else {
		tmp = a + (y / (t / (z - b)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -1.35e+157:
		tmp = a - (y * ((b - z) / t))
	elif t <= 9.5e+179:
		tmp = (z + a) - b
	else:
		tmp = a + (y / (t / (z - b)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -1.35e+157)
		tmp = Float64(a - Float64(y * Float64(Float64(b - z) / t)));
	elseif (t <= 9.5e+179)
		tmp = Float64(Float64(z + a) - b);
	else
		tmp = Float64(a + Float64(y / Float64(t / Float64(z - b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -1.35e+157)
		tmp = a - (y * ((b - z) / t));
	elseif (t <= 9.5e+179)
		tmp = (z + a) - b;
	else
		tmp = a + (y / (t / (z - b)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -1.35e+157], N[(a - N[(y * N[(N[(b - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 9.5e+179], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], N[(a + N[(y / N[(t / N[(z - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 14: 56.5% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.3 \cdot 10^{+195}:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq 8 \cdot 10^{+226}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{elif}\;t \leq 8.5 \cdot 10^{+250}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -2.3e+195)
   a
   (if (<= t 8e+226) (- (+ z a) b) (if (<= t 8.5e+250) (* y (/ z t)) a))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -2.3e+195) {
		tmp = a;
	} else if (t <= 8e+226) {
		tmp = (z + a) - b;
	} else if (t <= 8.5e+250) {
		tmp = y * (z / t);
	} else {
		tmp = a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= (-2.3d+195)) then
        tmp = a
    else if (t <= 8d+226) then
        tmp = (z + a) - b
    else if (t <= 8.5d+250) then
        tmp = y * (z / t)
    else
        tmp = a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -2.3e+195) {
		tmp = a;
	} else if (t <= 8e+226) {
		tmp = (z + a) - b;
	} else if (t <= 8.5e+250) {
		tmp = y * (z / t);
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -2.3e+195:
		tmp = a
	elif t <= 8e+226:
		tmp = (z + a) - b
	elif t <= 8.5e+250:
		tmp = y * (z / t)
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -2.3e+195)
		tmp = a;
	elseif (t <= 8e+226)
		tmp = Float64(Float64(z + a) - b);
	elseif (t <= 8.5e+250)
		tmp = Float64(y * Float64(z / t));
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -2.3e+195)
		tmp = a;
	elseif (t <= 8e+226)
		tmp = (z + a) - b;
	elseif (t <= 8.5e+250)
		tmp = y * (z / t);
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -2.3e+195], a, If[LessEqual[t, 8e+226], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], If[LessEqual[t, 8.5e+250], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], a]]]

Derivation

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

Alternative 15: 58.9% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.45 \cdot 10^{+194} \lor \neg \left(t \leq 3.6 \cdot 10^{+181}\right):\\ \;\;\;\;a + \frac{y}{\frac{t}{z}}\\ \mathbf{else}:\\ \;\;\;\;\left(z + a\right) - b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= t -1.45e+194) (not (<= t 3.6e+181)))
   (+ a (/ y (/ t z)))
   (- (+ z a) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -1.45e+194) || !(t <= 3.6e+181)) {
		tmp = a + (y / (t / z));
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((t <= (-1.45d+194)) .or. (.not. (t <= 3.6d+181))) then
        tmp = a + (y / (t / z))
    else
        tmp = (z + a) - b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -1.45e+194) || !(t <= 3.6e+181)) {
		tmp = a + (y / (t / z));
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (t <= -1.45e+194) or not (t <= 3.6e+181):
		tmp = a + (y / (t / z))
	else:
		tmp = (z + a) - b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((t <= -1.45e+194) || !(t <= 3.6e+181))
		tmp = Float64(a + Float64(y / Float64(t / z)));
	else
		tmp = Float64(Float64(z + a) - b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((t <= -1.45e+194) || ~((t <= 3.6e+181)))
		tmp = a + (y / (t / z));
	else
		tmp = (z + a) - b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[t, -1.45e+194], N[Not[LessEqual[t, 3.6e+181]], $MachinePrecision]], N[(a + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]]

Derivation

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

Alternative 16: 59.4% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.15 \cdot 10^{+158}:\\ \;\;\;\;a - b \cdot \frac{y}{t}\\ \mathbf{elif}\;t \leq 3 \cdot 10^{+181}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;a + \frac{y}{\frac{t}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -1.15e+158)
   (- a (* b (/ y t)))
   (if (<= t 3e+181) (- (+ z a) b) (+ a (/ y (/ t z))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -1.15e+158) {
		tmp = a - (b * (y / t));
	} else if (t <= 3e+181) {
		tmp = (z + a) - b;
	} else {
		tmp = a + (y / (t / z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= (-1.15d+158)) then
        tmp = a - (b * (y / t))
    else if (t <= 3d+181) then
        tmp = (z + a) - b
    else
        tmp = a + (y / (t / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -1.15e+158) {
		tmp = a - (b * (y / t));
	} else if (t <= 3e+181) {
		tmp = (z + a) - b;
	} else {
		tmp = a + (y / (t / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -1.15e+158:
		tmp = a - (b * (y / t))
	elif t <= 3e+181:
		tmp = (z + a) - b
	else:
		tmp = a + (y / (t / z))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -1.15e+158)
		tmp = Float64(a - Float64(b * Float64(y / t)));
	elseif (t <= 3e+181)
		tmp = Float64(Float64(z + a) - b);
	else
		tmp = Float64(a + Float64(y / Float64(t / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -1.15e+158)
		tmp = a - (b * (y / t));
	elseif (t <= 3e+181)
		tmp = (z + a) - b;
	else
		tmp = a + (y / (t / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -1.15e+158], N[(a - N[(b * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3e+181], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], N[(a + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 17: 59.4% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.25 \cdot 10^{+157}:\\ \;\;\;\;a - \frac{b}{\frac{t}{y}}\\ \mathbf{elif}\;t \leq 1.7 \cdot 10^{+180}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;a + \frac{y}{\frac{t}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -2.25e+157)
   (- a (/ b (/ t y)))
   (if (<= t 1.7e+180) (- (+ z a) b) (+ a (/ y (/ t z))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -2.25e+157) {
		tmp = a - (b / (t / y));
	} else if (t <= 1.7e+180) {
		tmp = (z + a) - b;
	} else {
		tmp = a + (y / (t / z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= (-2.25d+157)) then
        tmp = a - (b / (t / y))
    else if (t <= 1.7d+180) then
        tmp = (z + a) - b
    else
        tmp = a + (y / (t / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -2.25e+157) {
		tmp = a - (b / (t / y));
	} else if (t <= 1.7e+180) {
		tmp = (z + a) - b;
	} else {
		tmp = a + (y / (t / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -2.25e+157:
		tmp = a - (b / (t / y))
	elif t <= 1.7e+180:
		tmp = (z + a) - b
	else:
		tmp = a + (y / (t / z))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -2.25e+157)
		tmp = Float64(a - Float64(b / Float64(t / y)));
	elseif (t <= 1.7e+180)
		tmp = Float64(Float64(z + a) - b);
	else
		tmp = Float64(a + Float64(y / Float64(t / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -2.25e+157)
		tmp = a - (b / (t / y));
	elseif (t <= 1.7e+180)
		tmp = (z + a) - b;
	else
		tmp = a + (y / (t / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -2.25e+157], N[(a - N[(b / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.7e+180], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], N[(a + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 18: 44.2% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.2 \cdot 10^{+66}:\\ \;\;\;\;z\\ \mathbf{elif}\;x \leq 3.4 \cdot 10^{+73}:\\ \;\;\;\;a\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= x -6.2e+66) z (if (<= x 3.4e+73) a z)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -6.2e+66) {
		tmp = z;
	} else if (x <= 3.4e+73) {
		tmp = a;
	} else {
		tmp = z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (x <= (-6.2d+66)) then
        tmp = z
    else if (x <= 3.4d+73) then
        tmp = a
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -6.2e+66) {
		tmp = z;
	} else if (x <= 3.4e+73) {
		tmp = a;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if x <= -6.2e+66:
		tmp = z
	elif x <= 3.4e+73:
		tmp = a
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -6.2e+66)
		tmp = z;
	elseif (x <= 3.4e+73)
		tmp = a;
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (x <= -6.2e+66)
		tmp = z;
	elseif (x <= 3.4e+73)
		tmp = a;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -6.2e+66], z, If[LessEqual[x, 3.4e+73], a, z]]

Derivation

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

Alternative 19: 31.7% accurate, 21.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 81.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(x + t\right) + y\\ t_2 := \left(\left(x + y\right) \cdot z + \left(t + y\right) \cdot a\right) - y \cdot b\\ t_3 := \frac{t_2}{t_1}\\ t_4 := \left(z + a\right) - b\\ \mathbf{if}\;t_3 < -3.5813117084150564 \cdot 10^{+153}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;t_3 < 1.2285964308315609 \cdot 10^{+82}:\\ \;\;\;\;\frac{1}{\frac{t_1}{t_2}}\\ \mathbf{else}:\\ \;\;\;\;t_4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (+ x t) y))
        (t_2 (- (+ (* (+ x y) z) (* (+ t y) a)) (* y b)))
        (t_3 (/ t_2 t_1))
        (t_4 (- (+ z a) b)))
   (if (< t_3 -3.5813117084150564e+153)
     t_4
     (if (< t_3 1.2285964308315609e+82) (/ 1.0 (/ t_1 t_2)) t_4))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + t) + y;
	double t_2 = (((x + y) * z) + ((t + y) * a)) - (y * b);
	double t_3 = t_2 / t_1;
	double t_4 = (z + a) - b;
	double tmp;
	if (t_3 < -3.5813117084150564e+153) {
		tmp = t_4;
	} else if (t_3 < 1.2285964308315609e+82) {
		tmp = 1.0 / (t_1 / t_2);
	} else {
		tmp = t_4;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: tmp
    t_1 = (x + t) + y
    t_2 = (((x + y) * z) + ((t + y) * a)) - (y * b)
    t_3 = t_2 / t_1
    t_4 = (z + a) - b
    if (t_3 < (-3.5813117084150564d+153)) then
        tmp = t_4
    else if (t_3 < 1.2285964308315609d+82) then
        tmp = 1.0d0 / (t_1 / t_2)
    else
        tmp = t_4
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + t) + y;
	double t_2 = (((x + y) * z) + ((t + y) * a)) - (y * b);
	double t_3 = t_2 / t_1;
	double t_4 = (z + a) - b;
	double tmp;
	if (t_3 < -3.5813117084150564e+153) {
		tmp = t_4;
	} else if (t_3 < 1.2285964308315609e+82) {
		tmp = 1.0 / (t_1 / t_2);
	} else {
		tmp = t_4;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + t) + y
	t_2 = (((x + y) * z) + ((t + y) * a)) - (y * b)
	t_3 = t_2 / t_1
	t_4 = (z + a) - b
	tmp = 0
	if t_3 < -3.5813117084150564e+153:
		tmp = t_4
	elif t_3 < 1.2285964308315609e+82:
		tmp = 1.0 / (t_1 / t_2)
	else:
		tmp = t_4
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + t) + y)
	t_2 = Float64(Float64(Float64(Float64(x + y) * z) + Float64(Float64(t + y) * a)) - Float64(y * b))
	t_3 = Float64(t_2 / t_1)
	t_4 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (t_3 < -3.5813117084150564e+153)
		tmp = t_4;
	elseif (t_3 < 1.2285964308315609e+82)
		tmp = Float64(1.0 / Float64(t_1 / t_2));
	else
		tmp = t_4;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (x + t) + y;
	t_2 = (((x + y) * z) + ((t + y) * a)) - (y * b);
	t_3 = t_2 / t_1;
	t_4 = (z + a) - b;
	tmp = 0.0;
	if (t_3 < -3.5813117084150564e+153)
		tmp = t_4;
	elseif (t_3 < 1.2285964308315609e+82)
		tmp = 1.0 / (t_1 / t_2);
	else
		tmp = t_4;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x + t), $MachinePrecision] + y), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision] + N[(N[(t + y), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$2 / t$95$1), $MachinePrecision]}, Block[{t$95$4 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[Less[t$95$3, -3.5813117084150564e+153], t$95$4, If[Less[t$95$3, 1.2285964308315609e+82], N[(1.0 / N[(t$95$1 / t$95$2), $MachinePrecision]), $MachinePrecision], t$95$4]]]]]]

Reproduce

herbie shell --seed 2023364 
(FPCore (x y z t a b)
  :name "AI.Clustering.Hierarchical.Internal:ward from clustering-0.2.1"
  :precision binary64

  :herbie-target
  (if (< (/ (- (+ (* (+ x y) z) (* (+ t y) a)) (* y b)) (+ (+ x t) y)) -3.5813117084150564e+153) (- (+ z a) b) (if (< (/ (- (+ (* (+ x y) z) (* (+ t y) a)) (* y b)) (+ (+ x t) y)) 1.2285964308315609e+82) (/ 1.0 (/ (+ (+ x t) y) (- (+ (* (+ x y) z) (* (+ t y) a)) (* y b)))) (- (+ z a) b)))

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