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

Percentage Accurate: 60.4% → 88.0%

Time: 17.5s

Alternatives: 18

Speedup: 1.4×

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: 60.4% 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: 88.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := {\left(y + t\right)}^{2}\\ t_3 := z \cdot \left(x + y\right)\\ t_4 := \frac{\left(t_3 + \left(y + t\right) \cdot a\right) - y \cdot b}{t_1}\\ \mathbf{if}\;t_4 \leq -\infty \lor \neg \left(t_4 \leq 10^{+263}\right):\\ \;\;\;\;\left(a - \frac{b}{\frac{y + t}{y}}\right) + \mathsf{fma}\left(x, \frac{z}{y + t} + \left(\frac{b}{\frac{t_2}{y}} - \left(\frac{a}{y + t} + \frac{y}{\frac{t_2}{z}}\right)\right), \frac{y}{\frac{y + t}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(\frac{y}{t_1} + \frac{t}{t_1}\right) + \frac{t_3 - y \cdot b}{t_1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (pow (+ y t) 2.0))
        (t_3 (* z (+ x y)))
        (t_4 (/ (- (+ t_3 (* (+ y t) a)) (* y b)) t_1)))
   (if (or (<= t_4 (- INFINITY)) (not (<= t_4 1e+263)))
     (+
      (- a (/ b (/ (+ y t) y)))
      (fma
       x
       (+ (/ z (+ y t)) (- (/ b (/ t_2 y)) (+ (/ a (+ y t)) (/ y (/ t_2 z)))))
       (/ y (/ (+ y t) z))))
     (+ (* a (+ (/ y t_1) (/ t t_1))) (/ (- t_3 (* y b)) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = pow((y + t), 2.0);
	double t_3 = z * (x + y);
	double t_4 = ((t_3 + ((y + t) * a)) - (y * b)) / t_1;
	double tmp;
	if ((t_4 <= -((double) INFINITY)) || !(t_4 <= 1e+263)) {
		tmp = (a - (b / ((y + t) / y))) + fma(x, ((z / (y + t)) + ((b / (t_2 / y)) - ((a / (y + t)) + (y / (t_2 / z))))), (y / ((y + t) / z)));
	} else {
		tmp = (a * ((y / t_1) + (t / t_1))) + ((t_3 - (y * b)) / t_1);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(y + t) ^ 2.0
	t_3 = Float64(z * Float64(x + y))
	t_4 = Float64(Float64(Float64(t_3 + Float64(Float64(y + t) * a)) - Float64(y * b)) / t_1)
	tmp = 0.0
	if ((t_4 <= Float64(-Inf)) || !(t_4 <= 1e+263))
		tmp = Float64(Float64(a - Float64(b / Float64(Float64(y + t) / y))) + fma(x, Float64(Float64(z / Float64(y + t)) + Float64(Float64(b / Float64(t_2 / y)) - Float64(Float64(a / Float64(y + t)) + Float64(y / Float64(t_2 / z))))), Float64(y / Float64(Float64(y + t) / z))));
	else
		tmp = Float64(Float64(a * Float64(Float64(y / t_1) + Float64(t / t_1))) + Float64(Float64(t_3 - Float64(y * b)) / t_1));
	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[Power[N[(y + t), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$3 = N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(t$95$3 + N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[Or[LessEqual[t$95$4, (-Infinity)], N[Not[LessEqual[t$95$4, 1e+263]], $MachinePrecision]], N[(N[(a - N[(b / N[(N[(y + t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x * N[(N[(z / N[(y + t), $MachinePrecision]), $MachinePrecision] + N[(N[(b / N[(t$95$2 / y), $MachinePrecision]), $MachinePrecision] - N[(N[(a / N[(y + t), $MachinePrecision]), $MachinePrecision] + N[(y / N[(t$95$2 / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y / N[(N[(y + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * N[(N[(y / t$95$1), $MachinePrecision] + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$3 - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 2: 87.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{y}{t_1}\\ t_3 := z \cdot \left(x + y\right)\\ t_4 := \frac{\left(t_3 + \left(y + t\right) \cdot a\right) - y \cdot b}{t_1}\\ \mathbf{if}\;t_4 \leq -\infty \lor \neg \left(t_4 \leq 10^{+263}\right):\\ \;\;\;\;a + z \cdot \left(t_2 + \frac{x}{t_1}\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(t_2 + \frac{t}{t_1}\right) + \frac{t_3 - y \cdot b}{t_1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (/ y t_1))
        (t_3 (* z (+ x y)))
        (t_4 (/ (- (+ t_3 (* (+ y t) a)) (* y b)) t_1)))
   (if (or (<= t_4 (- INFINITY)) (not (<= t_4 1e+263)))
     (+ a (* z (+ t_2 (/ x t_1))))
     (+ (* a (+ t_2 (/ t t_1))) (/ (- t_3 (* y b)) t_1)))))

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_1;
	double t_3 = z * (x + y);
	double t_4 = ((t_3 + ((y + t) * a)) - (y * b)) / t_1;
	double tmp;
	if ((t_4 <= -((double) INFINITY)) || !(t_4 <= 1e+263)) {
		tmp = a + (z * (t_2 + (x / t_1)));
	} else {
		tmp = (a * (t_2 + (t / t_1))) + ((t_3 - (y * b)) / 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 + (x + t);
	double t_2 = y / t_1;
	double t_3 = z * (x + y);
	double t_4 = ((t_3 + ((y + t) * a)) - (y * b)) / t_1;
	double tmp;
	if ((t_4 <= -Double.POSITIVE_INFINITY) || !(t_4 <= 1e+263)) {
		tmp = a + (z * (t_2 + (x / t_1)));
	} else {
		tmp = (a * (t_2 + (t / t_1))) + ((t_3 - (y * b)) / t_1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = y / t_1
	t_3 = z * (x + y)
	t_4 = ((t_3 + ((y + t) * a)) - (y * b)) / t_1
	tmp = 0
	if (t_4 <= -math.inf) or not (t_4 <= 1e+263):
		tmp = a + (z * (t_2 + (x / t_1)))
	else:
		tmp = (a * (t_2 + (t / t_1))) + ((t_3 - (y * b)) / t_1)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(y / t_1)
	t_3 = Float64(z * Float64(x + y))
	t_4 = Float64(Float64(Float64(t_3 + Float64(Float64(y + t) * a)) - Float64(y * b)) / t_1)
	tmp = 0.0
	if ((t_4 <= Float64(-Inf)) || !(t_4 <= 1e+263))
		tmp = Float64(a + Float64(z * Float64(t_2 + Float64(x / t_1))));
	else
		tmp = Float64(Float64(a * Float64(t_2 + Float64(t / t_1))) + Float64(Float64(t_3 - Float64(y * b)) / t_1));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = y / t_1;
	t_3 = z * (x + y);
	t_4 = ((t_3 + ((y + t) * a)) - (y * b)) / t_1;
	tmp = 0.0;
	if ((t_4 <= -Inf) || ~((t_4 <= 1e+263)))
		tmp = a + (z * (t_2 + (x / t_1)));
	else
		tmp = (a * (t_2 + (t / t_1))) + ((t_3 - (y * b)) / t_1);
	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[(y / t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(t$95$3 + N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[Or[LessEqual[t$95$4, (-Infinity)], N[Not[LessEqual[t$95$4, 1e+263]], $MachinePrecision]], N[(a + N[(z * N[(t$95$2 + N[(x / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * N[(t$95$2 + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$3 - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 3: 87.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{\left(z \cdot \left(x + y\right) + \left(y + t\right) \cdot a\right) - y \cdot b}{t_1}\\ \mathbf{if}\;t_2 \leq -\infty \lor \neg \left(t_2 \leq 10^{+263}\right):\\ \;\;\;\;a + z \cdot \left(\frac{y}{t_1} + \frac{x}{t_1}\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (/ (- (+ (* z (+ x y)) (* (+ y t) a)) (* y b)) t_1)))
   (if (or (<= t_2 (- INFINITY)) (not (<= t_2 1e+263)))
     (+ a (* z (+ (/ y t_1) (/ x t_1))))
     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 * (x + y)) + ((y + t) * a)) - (y * b)) / t_1;
	double tmp;
	if ((t_2 <= -((double) INFINITY)) || !(t_2 <= 1e+263)) {
		tmp = a + (z * ((y / t_1) + (x / t_1)));
	} else {
		tmp = t_2;
	}
	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 = (((z * (x + y)) + ((y + t) * a)) - (y * b)) / t_1;
	double tmp;
	if ((t_2 <= -Double.POSITIVE_INFINITY) || !(t_2 <= 1e+263)) {
		tmp = a + (z * ((y / t_1) + (x / t_1)));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = (((z * (x + y)) + ((y + t) * a)) - (y * b)) / t_1
	tmp = 0
	if (t_2 <= -math.inf) or not (t_2 <= 1e+263):
		tmp = a + (z * ((y / t_1) + (x / t_1)))
	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(Float64(Float64(z * Float64(x + y)) + Float64(Float64(y + t) * a)) - Float64(y * b)) / t_1)
	tmp = 0.0
	if ((t_2 <= Float64(-Inf)) || !(t_2 <= 1e+263))
		tmp = Float64(a + Float64(z * Float64(Float64(y / t_1) + Float64(x / t_1))));
	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 * (x + y)) + ((y + t) * a)) - (y * b)) / t_1;
	tmp = 0.0;
	if ((t_2 <= -Inf) || ~((t_2 <= 1e+263)))
		tmp = a + (z * ((y / t_1) + (x / t_1)));
	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[(N[(N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision] + N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[Or[LessEqual[t$95$2, (-Infinity)], N[Not[LessEqual[t$95$2, 1e+263]], $MachinePrecision]], N[(a + N[(z * N[(N[(y / t$95$1), $MachinePrecision] + N[(x / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]

Derivation

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

Alternative 4: 70.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := a + \frac{z \cdot \left(x + y\right) - y \cdot b}{t_1}\\ \mathbf{if}\;y \leq -1.45 \cdot 10^{+92}:\\ \;\;\;\;z + \left(\left(a - b\right) + x \cdot \left(\frac{b}{y} - \frac{a}{y}\right)\right)\\ \mathbf{elif}\;y \leq -4.8 \cdot 10^{-154}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.52 \cdot 10^{-176}:\\ \;\;\;\;z + \frac{y \cdot \left(a - b\right)}{x + y}\\ \mathbf{elif}\;y \leq -2.6 \cdot 10^{-209}:\\ \;\;\;\;\frac{\left(y + t\right) \cdot a - y \cdot b}{t_1}\\ \mathbf{elif}\;y \leq 3.8 \cdot 10^{-181}:\\ \;\;\;\;\frac{t \cdot a + x \cdot z}{x + t}\\ \mathbf{elif}\;y \leq 3.6 \cdot 10^{+64}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;\left(z + a\right) - b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t))) (t_2 (+ a (/ (- (* z (+ x y)) (* y b)) t_1))))
   (if (<= y -1.45e+92)
     (+ z (+ (- a b) (* x (- (/ b y) (/ a y)))))
     (if (<= y -4.8e-154)
       t_2
       (if (<= y -1.52e-176)
         (+ z (/ (* y (- a b)) (+ x y)))
         (if (<= y -2.6e-209)
           (/ (- (* (+ y t) a) (* y b)) t_1)
           (if (<= y 3.8e-181)
             (/ (+ (* t a) (* x z)) (+ x t))
             (if (<= y 3.6e+64) t_2 (- (+ z a) 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 = a + (((z * (x + y)) - (y * b)) / t_1);
	double tmp;
	if (y <= -1.45e+92) {
		tmp = z + ((a - b) + (x * ((b / y) - (a / y))));
	} else if (y <= -4.8e-154) {
		tmp = t_2;
	} else if (y <= -1.52e-176) {
		tmp = z + ((y * (a - b)) / (x + y));
	} else if (y <= -2.6e-209) {
		tmp = (((y + t) * a) - (y * b)) / t_1;
	} else if (y <= 3.8e-181) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else if (y <= 3.6e+64) {
		tmp = t_2;
	} 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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = y + (x + t)
    t_2 = a + (((z * (x + y)) - (y * b)) / t_1)
    if (y <= (-1.45d+92)) then
        tmp = z + ((a - b) + (x * ((b / y) - (a / y))))
    else if (y <= (-4.8d-154)) then
        tmp = t_2
    else if (y <= (-1.52d-176)) then
        tmp = z + ((y * (a - b)) / (x + y))
    else if (y <= (-2.6d-209)) then
        tmp = (((y + t) * a) - (y * b)) / t_1
    else if (y <= 3.8d-181) then
        tmp = ((t * a) + (x * z)) / (x + t)
    else if (y <= 3.6d+64) then
        tmp = t_2
    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 t_1 = y + (x + t);
	double t_2 = a + (((z * (x + y)) - (y * b)) / t_1);
	double tmp;
	if (y <= -1.45e+92) {
		tmp = z + ((a - b) + (x * ((b / y) - (a / y))));
	} else if (y <= -4.8e-154) {
		tmp = t_2;
	} else if (y <= -1.52e-176) {
		tmp = z + ((y * (a - b)) / (x + y));
	} else if (y <= -2.6e-209) {
		tmp = (((y + t) * a) - (y * b)) / t_1;
	} else if (y <= 3.8e-181) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else if (y <= 3.6e+64) {
		tmp = t_2;
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = a + (((z * (x + y)) - (y * b)) / t_1)
	tmp = 0
	if y <= -1.45e+92:
		tmp = z + ((a - b) + (x * ((b / y) - (a / y))))
	elif y <= -4.8e-154:
		tmp = t_2
	elif y <= -1.52e-176:
		tmp = z + ((y * (a - b)) / (x + y))
	elif y <= -2.6e-209:
		tmp = (((y + t) * a) - (y * b)) / t_1
	elif y <= 3.8e-181:
		tmp = ((t * a) + (x * z)) / (x + t)
	elif y <= 3.6e+64:
		tmp = t_2
	else:
		tmp = (z + a) - b
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(a + Float64(Float64(Float64(z * Float64(x + y)) - Float64(y * b)) / t_1))
	tmp = 0.0
	if (y <= -1.45e+92)
		tmp = Float64(z + Float64(Float64(a - b) + Float64(x * Float64(Float64(b / y) - Float64(a / y)))));
	elseif (y <= -4.8e-154)
		tmp = t_2;
	elseif (y <= -1.52e-176)
		tmp = Float64(z + Float64(Float64(y * Float64(a - b)) / Float64(x + y)));
	elseif (y <= -2.6e-209)
		tmp = Float64(Float64(Float64(Float64(y + t) * a) - Float64(y * b)) / t_1);
	elseif (y <= 3.8e-181)
		tmp = Float64(Float64(Float64(t * a) + Float64(x * z)) / Float64(x + t));
	elseif (y <= 3.6e+64)
		tmp = t_2;
	else
		tmp = Float64(Float64(z + a) - b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = a + (((z * (x + y)) - (y * b)) / t_1);
	tmp = 0.0;
	if (y <= -1.45e+92)
		tmp = z + ((a - b) + (x * ((b / y) - (a / y))));
	elseif (y <= -4.8e-154)
		tmp = t_2;
	elseif (y <= -1.52e-176)
		tmp = z + ((y * (a - b)) / (x + y));
	elseif (y <= -2.6e-209)
		tmp = (((y + t) * a) - (y * b)) / t_1;
	elseif (y <= 3.8e-181)
		tmp = ((t * a) + (x * z)) / (x + t);
	elseif (y <= 3.6e+64)
		tmp = t_2;
	else
		tmp = (z + a) - 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[(a + N[(N[(N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.45e+92], N[(z + N[(N[(a - b), $MachinePrecision] + N[(x * N[(N[(b / y), $MachinePrecision] - N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -4.8e-154], t$95$2, If[LessEqual[y, -1.52e-176], N[(z + N[(N[(y * N[(a - b), $MachinePrecision]), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -2.6e-209], N[(N[(N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision], If[LessEqual[y, 3.8e-181], N[(N[(N[(t * a), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3.6e+64], t$95$2, N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]]]]]]]]]

Derivation

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

Alternative 5: 68.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ \mathbf{if}\;t \leq -1.6 \cdot 10^{-21}:\\ \;\;\;\;a + \frac{z \cdot \left(x + y\right) - y \cdot b}{t_1}\\ \mathbf{elif}\;t \leq 0.004:\\ \;\;\;\;z + \frac{y \cdot \left(a - b\right)}{x + y}\\ \mathbf{else}:\\ \;\;\;\;a + z \cdot \left(\frac{y}{t_1} + \frac{x}{t_1}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t))))
   (if (<= t -1.6e-21)
     (+ a (/ (- (* z (+ x y)) (* y b)) t_1))
     (if (<= t 0.004)
       (+ z (/ (* y (- a b)) (+ x y)))
       (+ a (* z (+ (/ y t_1) (/ x t_1))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double tmp;
	if (t <= -1.6e-21) {
		tmp = a + (((z * (x + y)) - (y * b)) / t_1);
	} else if (t <= 0.004) {
		tmp = z + ((y * (a - b)) / (x + y));
	} else {
		tmp = a + (z * ((y / t_1) + (x / t_1)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y + (x + t)
    if (t <= (-1.6d-21)) then
        tmp = a + (((z * (x + y)) - (y * b)) / t_1)
    else if (t <= 0.004d0) then
        tmp = z + ((y * (a - b)) / (x + y))
    else
        tmp = a + (z * ((y / t_1) + (x / t_1)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double tmp;
	if (t <= -1.6e-21) {
		tmp = a + (((z * (x + y)) - (y * b)) / t_1);
	} else if (t <= 0.004) {
		tmp = z + ((y * (a - b)) / (x + y));
	} else {
		tmp = a + (z * ((y / t_1) + (x / t_1)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	tmp = 0
	if t <= -1.6e-21:
		tmp = a + (((z * (x + y)) - (y * b)) / t_1)
	elif t <= 0.004:
		tmp = z + ((y * (a - b)) / (x + y))
	else:
		tmp = a + (z * ((y / t_1) + (x / t_1)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	tmp = 0.0
	if (t <= -1.6e-21)
		tmp = Float64(a + Float64(Float64(Float64(z * Float64(x + y)) - Float64(y * b)) / t_1));
	elseif (t <= 0.004)
		tmp = Float64(z + Float64(Float64(y * Float64(a - b)) / Float64(x + y)));
	else
		tmp = Float64(a + Float64(z * Float64(Float64(y / t_1) + Float64(x / t_1))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	tmp = 0.0;
	if (t <= -1.6e-21)
		tmp = a + (((z * (x + y)) - (y * b)) / t_1);
	elseif (t <= 0.004)
		tmp = z + ((y * (a - b)) / (x + y));
	else
		tmp = a + (z * ((y / t_1) + (x / t_1)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.6e-21], N[(a + N[(N[(N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 0.004], N[(z + N[(N[(y * N[(a - b), $MachinePrecision]), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a + N[(z * N[(N[(y / t$95$1), $MachinePrecision] + N[(x / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 6: 64.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -820000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -5 \cdot 10^{-174}:\\ \;\;\;\;z + \frac{y \cdot \left(a - b\right)}{x + y}\\ \mathbf{elif}\;y \leq -5.2 \cdot 10^{-210}:\\ \;\;\;\;\frac{\left(y + t\right) \cdot a - y \cdot b}{y + \left(x + t\right)}\\ \mathbf{elif}\;y \leq 7 \cdot 10^{-136}:\\ \;\;\;\;\frac{t \cdot a + x \cdot z}{x + t}\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{+38}:\\ \;\;\;\;a + z \cdot \frac{y}{y + t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= y -820000.0)
     t_1
     (if (<= y -5e-174)
       (+ z (/ (* y (- a b)) (+ x y)))
       (if (<= y -5.2e-210)
         (/ (- (* (+ y t) a) (* y b)) (+ y (+ x t)))
         (if (<= y 7e-136)
           (/ (+ (* t a) (* x z)) (+ x t))
           (if (<= y 7.2e+38) (+ a (* z (/ y (+ y t)))) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (y <= -820000.0) {
		tmp = t_1;
	} else if (y <= -5e-174) {
		tmp = z + ((y * (a - b)) / (x + y));
	} else if (y <= -5.2e-210) {
		tmp = (((y + t) * a) - (y * b)) / (y + (x + t));
	} else if (y <= 7e-136) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else if (y <= 7.2e+38) {
		tmp = a + (z * (y / (y + t)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z + a) - b
    if (y <= (-820000.0d0)) then
        tmp = t_1
    else if (y <= (-5d-174)) then
        tmp = z + ((y * (a - b)) / (x + y))
    else if (y <= (-5.2d-210)) then
        tmp = (((y + t) * a) - (y * b)) / (y + (x + t))
    else if (y <= 7d-136) then
        tmp = ((t * a) + (x * z)) / (x + t)
    else if (y <= 7.2d+38) then
        tmp = a + (z * (y / (y + t)))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (y <= -820000.0) {
		tmp = t_1;
	} else if (y <= -5e-174) {
		tmp = z + ((y * (a - b)) / (x + y));
	} else if (y <= -5.2e-210) {
		tmp = (((y + t) * a) - (y * b)) / (y + (x + t));
	} else if (y <= 7e-136) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else if (y <= 7.2e+38) {
		tmp = a + (z * (y / (y + t)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if y <= -820000.0:
		tmp = t_1
	elif y <= -5e-174:
		tmp = z + ((y * (a - b)) / (x + y))
	elif y <= -5.2e-210:
		tmp = (((y + t) * a) - (y * b)) / (y + (x + t))
	elif y <= 7e-136:
		tmp = ((t * a) + (x * z)) / (x + t)
	elif y <= 7.2e+38:
		tmp = a + (z * (y / (y + t)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (y <= -820000.0)
		tmp = t_1;
	elseif (y <= -5e-174)
		tmp = Float64(z + Float64(Float64(y * Float64(a - b)) / Float64(x + y)));
	elseif (y <= -5.2e-210)
		tmp = Float64(Float64(Float64(Float64(y + t) * a) - Float64(y * b)) / Float64(y + Float64(x + t)));
	elseif (y <= 7e-136)
		tmp = Float64(Float64(Float64(t * a) + Float64(x * z)) / Float64(x + t));
	elseif (y <= 7.2e+38)
		tmp = Float64(a + Float64(z * Float64(y / Float64(y + t))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	tmp = 0.0;
	if (y <= -820000.0)
		tmp = t_1;
	elseif (y <= -5e-174)
		tmp = z + ((y * (a - b)) / (x + y));
	elseif (y <= -5.2e-210)
		tmp = (((y + t) * a) - (y * b)) / (y + (x + t));
	elseif (y <= 7e-136)
		tmp = ((t * a) + (x * z)) / (x + t);
	elseif (y <= 7.2e+38)
		tmp = a + (z * (y / (y + t)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[y, -820000.0], t$95$1, If[LessEqual[y, -5e-174], N[(z + N[(N[(y * N[(a - b), $MachinePrecision]), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -5.2e-210], N[(N[(N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7e-136], N[(N[(N[(t * a), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7.2e+38], N[(a + N[(z * N[(y / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 7: 59.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -2.1 \cdot 10^{+141}:\\ \;\;\;\;\frac{z}{1 + \frac{t}{x + y}}\\ \mathbf{elif}\;x \leq -8.2 \cdot 10^{+90}:\\ \;\;\;\;z + \frac{y \cdot \left(a - b\right)}{x}\\ \mathbf{elif}\;x \leq -2 \cdot 10^{-8}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -8.5 \cdot 10^{-220}:\\ \;\;\;\;a + z \cdot \frac{y}{y + t}\\ \mathbf{elif}\;x \leq 5 \cdot 10^{+93}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{1 + \frac{t}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= x -2.1e+141)
     (/ z (+ 1.0 (/ t (+ x y))))
     (if (<= x -8.2e+90)
       (+ z (/ (* y (- a b)) x))
       (if (<= x -2e-8)
         t_1
         (if (<= x -8.5e-220)
           (+ a (* z (/ y (+ y t))))
           (if (<= x 5e+93) t_1 (/ z (+ 1.0 (/ t x))))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (x <= -2.1e+141) {
		tmp = z / (1.0 + (t / (x + y)));
	} else if (x <= -8.2e+90) {
		tmp = z + ((y * (a - b)) / x);
	} else if (x <= -2e-8) {
		tmp = t_1;
	} else if (x <= -8.5e-220) {
		tmp = a + (z * (y / (y + t)));
	} else if (x <= 5e+93) {
		tmp = t_1;
	} else {
		tmp = z / (1.0 + (t / x));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z + a) - b
    if (x <= (-2.1d+141)) then
        tmp = z / (1.0d0 + (t / (x + y)))
    else if (x <= (-8.2d+90)) then
        tmp = z + ((y * (a - b)) / x)
    else if (x <= (-2d-8)) then
        tmp = t_1
    else if (x <= (-8.5d-220)) then
        tmp = a + (z * (y / (y + t)))
    else if (x <= 5d+93) then
        tmp = t_1
    else
        tmp = z / (1.0d0 + (t / x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (x <= -2.1e+141) {
		tmp = z / (1.0 + (t / (x + y)));
	} else if (x <= -8.2e+90) {
		tmp = z + ((y * (a - b)) / x);
	} else if (x <= -2e-8) {
		tmp = t_1;
	} else if (x <= -8.5e-220) {
		tmp = a + (z * (y / (y + t)));
	} else if (x <= 5e+93) {
		tmp = t_1;
	} else {
		tmp = z / (1.0 + (t / x));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if x <= -2.1e+141:
		tmp = z / (1.0 + (t / (x + y)))
	elif x <= -8.2e+90:
		tmp = z + ((y * (a - b)) / x)
	elif x <= -2e-8:
		tmp = t_1
	elif x <= -8.5e-220:
		tmp = a + (z * (y / (y + t)))
	elif x <= 5e+93:
		tmp = t_1
	else:
		tmp = z / (1.0 + (t / x))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -2.1e+141)
		tmp = Float64(z / Float64(1.0 + Float64(t / Float64(x + y))));
	elseif (x <= -8.2e+90)
		tmp = Float64(z + Float64(Float64(y * Float64(a - b)) / x));
	elseif (x <= -2e-8)
		tmp = t_1;
	elseif (x <= -8.5e-220)
		tmp = Float64(a + Float64(z * Float64(y / Float64(y + t))));
	elseif (x <= 5e+93)
		tmp = t_1;
	else
		tmp = Float64(z / Float64(1.0 + Float64(t / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	tmp = 0.0;
	if (x <= -2.1e+141)
		tmp = z / (1.0 + (t / (x + y)));
	elseif (x <= -8.2e+90)
		tmp = z + ((y * (a - b)) / x);
	elseif (x <= -2e-8)
		tmp = t_1;
	elseif (x <= -8.5e-220)
		tmp = a + (z * (y / (y + t)));
	elseif (x <= 5e+93)
		tmp = t_1;
	else
		tmp = z / (1.0 + (t / x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[x, -2.1e+141], N[(z / N[(1.0 + N[(t / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -8.2e+90], N[(z + N[(N[(y * N[(a - b), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -2e-8], t$95$1, If[LessEqual[x, -8.5e-220], N[(a + N[(z * N[(y / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 5e+93], t$95$1, N[(z / N[(1.0 + N[(t / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 8: 64.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -1300000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.5 \cdot 10^{-176}:\\ \;\;\;\;z + \frac{y \cdot \left(a - b\right)}{x + y}\\ \mathbf{elif}\;y \leq 5.2 \cdot 10^{-137}:\\ \;\;\;\;\frac{t \cdot a + x \cdot z}{x + t}\\ \mathbf{elif}\;y \leq 2.8 \cdot 10^{+38}:\\ \;\;\;\;a + z \cdot \frac{y}{y + t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= y -1300000.0)
     t_1
     (if (<= y -1.5e-176)
       (+ z (/ (* y (- a b)) (+ x y)))
       (if (<= y 5.2e-137)
         (/ (+ (* t a) (* x z)) (+ x t))
         (if (<= y 2.8e+38) (+ a (* z (/ y (+ y t)))) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (y <= -1300000.0) {
		tmp = t_1;
	} else if (y <= -1.5e-176) {
		tmp = z + ((y * (a - b)) / (x + y));
	} else if (y <= 5.2e-137) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else if (y <= 2.8e+38) {
		tmp = a + (z * (y / (y + t)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z + a) - b
    if (y <= (-1300000.0d0)) then
        tmp = t_1
    else if (y <= (-1.5d-176)) then
        tmp = z + ((y * (a - b)) / (x + y))
    else if (y <= 5.2d-137) then
        tmp = ((t * a) + (x * z)) / (x + t)
    else if (y <= 2.8d+38) then
        tmp = a + (z * (y / (y + t)))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (y <= -1300000.0) {
		tmp = t_1;
	} else if (y <= -1.5e-176) {
		tmp = z + ((y * (a - b)) / (x + y));
	} else if (y <= 5.2e-137) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else if (y <= 2.8e+38) {
		tmp = a + (z * (y / (y + t)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if y <= -1300000.0:
		tmp = t_1
	elif y <= -1.5e-176:
		tmp = z + ((y * (a - b)) / (x + y))
	elif y <= 5.2e-137:
		tmp = ((t * a) + (x * z)) / (x + t)
	elif y <= 2.8e+38:
		tmp = a + (z * (y / (y + t)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (y <= -1300000.0)
		tmp = t_1;
	elseif (y <= -1.5e-176)
		tmp = Float64(z + Float64(Float64(y * Float64(a - b)) / Float64(x + y)));
	elseif (y <= 5.2e-137)
		tmp = Float64(Float64(Float64(t * a) + Float64(x * z)) / Float64(x + t));
	elseif (y <= 2.8e+38)
		tmp = Float64(a + Float64(z * Float64(y / Float64(y + t))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	tmp = 0.0;
	if (y <= -1300000.0)
		tmp = t_1;
	elseif (y <= -1.5e-176)
		tmp = z + ((y * (a - b)) / (x + y));
	elseif (y <= 5.2e-137)
		tmp = ((t * a) + (x * z)) / (x + t);
	elseif (y <= 2.8e+38)
		tmp = a + (z * (y / (y + t)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[y, -1300000.0], t$95$1, If[LessEqual[y, -1.5e-176], N[(z + N[(N[(y * N[(a - b), $MachinePrecision]), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 5.2e-137], N[(N[(N[(t * a), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.8e+38], N[(a + N[(z * N[(y / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 9: 57.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ t_2 := z + \frac{a}{\frac{x}{y}}\\ \mathbf{if}\;x \leq -4.2 \cdot 10^{+91}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -2.8 \cdot 10^{-85}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -2 \cdot 10^{-184}:\\ \;\;\;\;a\\ \mathbf{elif}\;x \leq 3.5 \cdot 10^{+125}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)) (t_2 (+ z (/ a (/ x y)))))
   (if (<= x -4.2e+91)
     t_2
     (if (<= x -2.8e-85)
       t_1
       (if (<= x -2e-184) a (if (<= x 3.5e+125) t_1 t_2))))))

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 + (a / (x / y));
	double tmp;
	if (x <= -4.2e+91) {
		tmp = t_2;
	} else if (x <= -2.8e-85) {
		tmp = t_1;
	} else if (x <= -2e-184) {
		tmp = a;
	} else if (x <= 3.5e+125) {
		tmp = t_1;
	} 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 = (z + a) - b
    t_2 = z + (a / (x / y))
    if (x <= (-4.2d+91)) then
        tmp = t_2
    else if (x <= (-2.8d-85)) then
        tmp = t_1
    else if (x <= (-2d-184)) then
        tmp = a
    else if (x <= 3.5d+125) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double t_2 = z + (a / (x / y));
	double tmp;
	if (x <= -4.2e+91) {
		tmp = t_2;
	} else if (x <= -2.8e-85) {
		tmp = t_1;
	} else if (x <= -2e-184) {
		tmp = a;
	} else if (x <= 3.5e+125) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	t_2 = z + (a / (x / y))
	tmp = 0
	if x <= -4.2e+91:
		tmp = t_2
	elif x <= -2.8e-85:
		tmp = t_1
	elif x <= -2e-184:
		tmp = a
	elif x <= 3.5e+125:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	t_2 = Float64(z + Float64(a / Float64(x / y)))
	tmp = 0.0
	if (x <= -4.2e+91)
		tmp = t_2;
	elseif (x <= -2.8e-85)
		tmp = t_1;
	elseif (x <= -2e-184)
		tmp = a;
	elseif (x <= 3.5e+125)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	t_2 = z + (a / (x / y));
	tmp = 0.0;
	if (x <= -4.2e+91)
		tmp = t_2;
	elseif (x <= -2.8e-85)
		tmp = t_1;
	elseif (x <= -2e-184)
		tmp = a;
	elseif (x <= 3.5e+125)
		tmp = t_1;
	else
		tmp = t_2;
	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[(a / N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -4.2e+91], t$95$2, If[LessEqual[x, -2.8e-85], t$95$1, If[LessEqual[x, -2e-184], a, If[LessEqual[x, 3.5e+125], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 10: 57.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -4.4 \cdot 10^{+91}:\\ \;\;\;\;z + \frac{a}{\frac{x}{y}}\\ \mathbf{elif}\;x \leq -2.8 \cdot 10^{-85}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.2 \cdot 10^{-181}:\\ \;\;\;\;a\\ \mathbf{elif}\;x \leq 3.7 \cdot 10^{+93}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;z - \frac{b}{\frac{x}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= x -4.4e+91)
     (+ z (/ a (/ x y)))
     (if (<= x -2.8e-85)
       t_1
       (if (<= x -1.2e-181) a (if (<= x 3.7e+93) t_1 (- z (/ b (/ x y)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (x <= -4.4e+91) {
		tmp = z + (a / (x / y));
	} else if (x <= -2.8e-85) {
		tmp = t_1;
	} else if (x <= -1.2e-181) {
		tmp = a;
	} else if (x <= 3.7e+93) {
		tmp = t_1;
	} else {
		tmp = z - (b / (x / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z + a) - b
    if (x <= (-4.4d+91)) then
        tmp = z + (a / (x / y))
    else if (x <= (-2.8d-85)) then
        tmp = t_1
    else if (x <= (-1.2d-181)) then
        tmp = a
    else if (x <= 3.7d+93) then
        tmp = t_1
    else
        tmp = z - (b / (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 t_1 = (z + a) - b;
	double tmp;
	if (x <= -4.4e+91) {
		tmp = z + (a / (x / y));
	} else if (x <= -2.8e-85) {
		tmp = t_1;
	} else if (x <= -1.2e-181) {
		tmp = a;
	} else if (x <= 3.7e+93) {
		tmp = t_1;
	} else {
		tmp = z - (b / (x / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if x <= -4.4e+91:
		tmp = z + (a / (x / y))
	elif x <= -2.8e-85:
		tmp = t_1
	elif x <= -1.2e-181:
		tmp = a
	elif x <= 3.7e+93:
		tmp = t_1
	else:
		tmp = z - (b / (x / y))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -4.4e+91)
		tmp = Float64(z + Float64(a / Float64(x / y)));
	elseif (x <= -2.8e-85)
		tmp = t_1;
	elseif (x <= -1.2e-181)
		tmp = a;
	elseif (x <= 3.7e+93)
		tmp = t_1;
	else
		tmp = Float64(z - Float64(b / Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	tmp = 0.0;
	if (x <= -4.4e+91)
		tmp = z + (a / (x / y));
	elseif (x <= -2.8e-85)
		tmp = t_1;
	elseif (x <= -1.2e-181)
		tmp = a;
	elseif (x <= 3.7e+93)
		tmp = t_1;
	else
		tmp = z - (b / (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[x, -4.4e+91], N[(z + N[(a / N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -2.8e-85], t$95$1, If[LessEqual[x, -1.2e-181], a, If[LessEqual[x, 3.7e+93], t$95$1, N[(z - N[(b / N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 11: 57.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -6.8 \cdot 10^{+86}:\\ \;\;\;\;z + \frac{a}{\frac{x}{y}}\\ \mathbf{elif}\;x \leq -3.1 \cdot 10^{-85}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -4.5 \cdot 10^{-183}:\\ \;\;\;\;a\\ \mathbf{elif}\;x \leq 3.6 \cdot 10^{+93}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{1 + \frac{t}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= x -6.8e+86)
     (+ z (/ a (/ x y)))
     (if (<= x -3.1e-85)
       t_1
       (if (<= x -4.5e-183)
         a
         (if (<= x 3.6e+93) t_1 (/ z (+ 1.0 (/ t x)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (x <= -6.8e+86) {
		tmp = z + (a / (x / y));
	} else if (x <= -3.1e-85) {
		tmp = t_1;
	} else if (x <= -4.5e-183) {
		tmp = a;
	} else if (x <= 3.6e+93) {
		tmp = t_1;
	} else {
		tmp = z / (1.0 + (t / x));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z + a) - b
    if (x <= (-6.8d+86)) then
        tmp = z + (a / (x / y))
    else if (x <= (-3.1d-85)) then
        tmp = t_1
    else if (x <= (-4.5d-183)) then
        tmp = a
    else if (x <= 3.6d+93) then
        tmp = t_1
    else
        tmp = z / (1.0d0 + (t / x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (x <= -6.8e+86) {
		tmp = z + (a / (x / y));
	} else if (x <= -3.1e-85) {
		tmp = t_1;
	} else if (x <= -4.5e-183) {
		tmp = a;
	} else if (x <= 3.6e+93) {
		tmp = t_1;
	} else {
		tmp = z / (1.0 + (t / x));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if x <= -6.8e+86:
		tmp = z + (a / (x / y))
	elif x <= -3.1e-85:
		tmp = t_1
	elif x <= -4.5e-183:
		tmp = a
	elif x <= 3.6e+93:
		tmp = t_1
	else:
		tmp = z / (1.0 + (t / x))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -6.8e+86)
		tmp = Float64(z + Float64(a / Float64(x / y)));
	elseif (x <= -3.1e-85)
		tmp = t_1;
	elseif (x <= -4.5e-183)
		tmp = a;
	elseif (x <= 3.6e+93)
		tmp = t_1;
	else
		tmp = Float64(z / Float64(1.0 + Float64(t / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	tmp = 0.0;
	if (x <= -6.8e+86)
		tmp = z + (a / (x / y));
	elseif (x <= -3.1e-85)
		tmp = t_1;
	elseif (x <= -4.5e-183)
		tmp = a;
	elseif (x <= 3.6e+93)
		tmp = t_1;
	else
		tmp = z / (1.0 + (t / x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[x, -6.8e+86], N[(z + N[(a / N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -3.1e-85], t$95$1, If[LessEqual[x, -4.5e-183], a, If[LessEqual[x, 3.6e+93], t$95$1, N[(z / N[(1.0 + N[(t / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 12: 58.7% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -1.5 \cdot 10^{+86}:\\ \;\;\;\;z + \frac{y}{\frac{x}{a - b}}\\ \mathbf{elif}\;x \leq -3 \cdot 10^{-85}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -8.2 \cdot 10^{-182}:\\ \;\;\;\;a\\ \mathbf{elif}\;x \leq 2.5 \cdot 10^{+93}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{1 + \frac{t}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= x -1.5e+86)
     (+ z (/ y (/ x (- a b))))
     (if (<= x -3e-85)
       t_1
       (if (<= x -8.2e-182)
         a
         (if (<= x 2.5e+93) t_1 (/ z (+ 1.0 (/ t x)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (x <= -1.5e+86) {
		tmp = z + (y / (x / (a - b)));
	} else if (x <= -3e-85) {
		tmp = t_1;
	} else if (x <= -8.2e-182) {
		tmp = a;
	} else if (x <= 2.5e+93) {
		tmp = t_1;
	} else {
		tmp = z / (1.0 + (t / x));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z + a) - b
    if (x <= (-1.5d+86)) then
        tmp = z + (y / (x / (a - b)))
    else if (x <= (-3d-85)) then
        tmp = t_1
    else if (x <= (-8.2d-182)) then
        tmp = a
    else if (x <= 2.5d+93) then
        tmp = t_1
    else
        tmp = z / (1.0d0 + (t / x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (x <= -1.5e+86) {
		tmp = z + (y / (x / (a - b)));
	} else if (x <= -3e-85) {
		tmp = t_1;
	} else if (x <= -8.2e-182) {
		tmp = a;
	} else if (x <= 2.5e+93) {
		tmp = t_1;
	} else {
		tmp = z / (1.0 + (t / x));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if x <= -1.5e+86:
		tmp = z + (y / (x / (a - b)))
	elif x <= -3e-85:
		tmp = t_1
	elif x <= -8.2e-182:
		tmp = a
	elif x <= 2.5e+93:
		tmp = t_1
	else:
		tmp = z / (1.0 + (t / x))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -1.5e+86)
		tmp = Float64(z + Float64(y / Float64(x / Float64(a - b))));
	elseif (x <= -3e-85)
		tmp = t_1;
	elseif (x <= -8.2e-182)
		tmp = a;
	elseif (x <= 2.5e+93)
		tmp = t_1;
	else
		tmp = Float64(z / Float64(1.0 + Float64(t / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	tmp = 0.0;
	if (x <= -1.5e+86)
		tmp = z + (y / (x / (a - b)));
	elseif (x <= -3e-85)
		tmp = t_1;
	elseif (x <= -8.2e-182)
		tmp = a;
	elseif (x <= 2.5e+93)
		tmp = t_1;
	else
		tmp = z / (1.0 + (t / x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[x, -1.5e+86], N[(z + N[(y / N[(x / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -3e-85], t$95$1, If[LessEqual[x, -8.2e-182], a, If[LessEqual[x, 2.5e+93], t$95$1, N[(z / N[(1.0 + N[(t / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 13: 60.6% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -4.2 \cdot 10^{+88}:\\ \;\;\;\;z + \frac{y}{\frac{x}{a - b}}\\ \mathbf{elif}\;x \leq -4.8 \cdot 10^{-9}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.38 \cdot 10^{-219}:\\ \;\;\;\;a + z \cdot \frac{y}{y + t}\\ \mathbf{elif}\;x \leq 6.4 \cdot 10^{+93}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{1 + \frac{t}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= x -4.2e+88)
     (+ z (/ y (/ x (- a b))))
     (if (<= x -4.8e-9)
       t_1
       (if (<= x -1.38e-219)
         (+ a (* z (/ y (+ y t))))
         (if (<= x 6.4e+93) t_1 (/ z (+ 1.0 (/ t x)))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (x <= -4.2e+88) {
		tmp = z + (y / (x / (a - b)));
	} else if (x <= -4.8e-9) {
		tmp = t_1;
	} else if (x <= -1.38e-219) {
		tmp = a + (z * (y / (y + t)));
	} else if (x <= 6.4e+93) {
		tmp = t_1;
	} else {
		tmp = z / (1.0 + (t / x));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z + a) - b
    if (x <= (-4.2d+88)) then
        tmp = z + (y / (x / (a - b)))
    else if (x <= (-4.8d-9)) then
        tmp = t_1
    else if (x <= (-1.38d-219)) then
        tmp = a + (z * (y / (y + t)))
    else if (x <= 6.4d+93) then
        tmp = t_1
    else
        tmp = z / (1.0d0 + (t / x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (x <= -4.2e+88) {
		tmp = z + (y / (x / (a - b)));
	} else if (x <= -4.8e-9) {
		tmp = t_1;
	} else if (x <= -1.38e-219) {
		tmp = a + (z * (y / (y + t)));
	} else if (x <= 6.4e+93) {
		tmp = t_1;
	} else {
		tmp = z / (1.0 + (t / x));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if x <= -4.2e+88:
		tmp = z + (y / (x / (a - b)))
	elif x <= -4.8e-9:
		tmp = t_1
	elif x <= -1.38e-219:
		tmp = a + (z * (y / (y + t)))
	elif x <= 6.4e+93:
		tmp = t_1
	else:
		tmp = z / (1.0 + (t / x))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -4.2e+88)
		tmp = Float64(z + Float64(y / Float64(x / Float64(a - b))));
	elseif (x <= -4.8e-9)
		tmp = t_1;
	elseif (x <= -1.38e-219)
		tmp = Float64(a + Float64(z * Float64(y / Float64(y + t))));
	elseif (x <= 6.4e+93)
		tmp = t_1;
	else
		tmp = Float64(z / Float64(1.0 + Float64(t / x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	tmp = 0.0;
	if (x <= -4.2e+88)
		tmp = z + (y / (x / (a - b)));
	elseif (x <= -4.8e-9)
		tmp = t_1;
	elseif (x <= -1.38e-219)
		tmp = a + (z * (y / (y + t)));
	elseif (x <= 6.4e+93)
		tmp = t_1;
	else
		tmp = z / (1.0 + (t / x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[x, -4.2e+88], N[(z + N[(y / N[(x / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -4.8e-9], t$95$1, If[LessEqual[x, -1.38e-219], N[(a + N[(z * N[(y / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 6.4e+93], t$95$1, N[(z / N[(1.0 + N[(t / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 14: 64.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + z \cdot \frac{y}{y + t}\\ \mathbf{if}\;t \leq -90000000000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.15 \cdot 10^{-26}:\\ \;\;\;\;z + \frac{y \cdot \left(a - b\right)}{x + y}\\ \mathbf{elif}\;t \leq 6.2 \cdot 10^{+124}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ a (* z (/ y (+ y t))))))
   (if (<= t -90000000000.0)
     t_1
     (if (<= t 1.15e-26)
       (+ z (/ (* y (- a b)) (+ x y)))
       (if (<= t 6.2e+124) (- (+ z a) b) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a + (z * (y / (y + t)));
	double tmp;
	if (t <= -90000000000.0) {
		tmp = t_1;
	} else if (t <= 1.15e-26) {
		tmp = z + ((y * (a - b)) / (x + y));
	} else if (t <= 6.2e+124) {
		tmp = (z + a) - b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a + (z * (y / (y + t)))
    if (t <= (-90000000000.0d0)) then
        tmp = t_1
    else if (t <= 1.15d-26) then
        tmp = z + ((y * (a - b)) / (x + y))
    else if (t <= 6.2d+124) then
        tmp = (z + a) - b
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a + (z * (y / (y + t)));
	double tmp;
	if (t <= -90000000000.0) {
		tmp = t_1;
	} else if (t <= 1.15e-26) {
		tmp = z + ((y * (a - b)) / (x + y));
	} else if (t <= 6.2e+124) {
		tmp = (z + a) - b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a + (z * (y / (y + t)))
	tmp = 0
	if t <= -90000000000.0:
		tmp = t_1
	elif t <= 1.15e-26:
		tmp = z + ((y * (a - b)) / (x + y))
	elif t <= 6.2e+124:
		tmp = (z + a) - b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a + Float64(z * Float64(y / Float64(y + t))))
	tmp = 0.0
	if (t <= -90000000000.0)
		tmp = t_1;
	elseif (t <= 1.15e-26)
		tmp = Float64(z + Float64(Float64(y * Float64(a - b)) / Float64(x + y)));
	elseif (t <= 6.2e+124)
		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 = a + (z * (y / (y + t)));
	tmp = 0.0;
	if (t <= -90000000000.0)
		tmp = t_1;
	elseif (t <= 1.15e-26)
		tmp = z + ((y * (a - b)) / (x + y));
	elseif (t <= 6.2e+124)
		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[(a + N[(z * N[(y / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -90000000000.0], t$95$1, If[LessEqual[t, 1.15e-26], N[(z + N[(N[(y * N[(a - b), $MachinePrecision]), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 6.2e+124], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 15: 56.6% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -1.4 \cdot 10^{+211}:\\ \;\;\;\;z\\ \mathbf{elif}\;x \leq -7.1 \cdot 10^{-81}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -8.5 \cdot 10^{-182}:\\ \;\;\;\;a\\ \mathbf{elif}\;x \leq 7.5 \cdot 10^{+127}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= x -1.4e+211)
     z
     (if (<= x -7.1e-81)
       t_1
       (if (<= x -8.5e-182) a (if (<= x 7.5e+127) t_1 z))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (x <= -1.4e+211) {
		tmp = z;
	} else if (x <= -7.1e-81) {
		tmp = t_1;
	} else if (x <= -8.5e-182) {
		tmp = a;
	} else if (x <= 7.5e+127) {
		tmp = t_1;
	} 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) :: t_1
    real(8) :: tmp
    t_1 = (z + a) - b
    if (x <= (-1.4d+211)) then
        tmp = z
    else if (x <= (-7.1d-81)) then
        tmp = t_1
    else if (x <= (-8.5d-182)) then
        tmp = a
    else if (x <= 7.5d+127) then
        tmp = t_1
    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 t_1 = (z + a) - b;
	double tmp;
	if (x <= -1.4e+211) {
		tmp = z;
	} else if (x <= -7.1e-81) {
		tmp = t_1;
	} else if (x <= -8.5e-182) {
		tmp = a;
	} else if (x <= 7.5e+127) {
		tmp = t_1;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if x <= -1.4e+211:
		tmp = z
	elif x <= -7.1e-81:
		tmp = t_1
	elif x <= -8.5e-182:
		tmp = a
	elif x <= 7.5e+127:
		tmp = t_1
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -1.4e+211)
		tmp = z;
	elseif (x <= -7.1e-81)
		tmp = t_1;
	elseif (x <= -8.5e-182)
		tmp = a;
	elseif (x <= 7.5e+127)
		tmp = t_1;
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	tmp = 0.0;
	if (x <= -1.4e+211)
		tmp = z;
	elseif (x <= -7.1e-81)
		tmp = t_1;
	elseif (x <= -8.5e-182)
		tmp = a;
	elseif (x <= 7.5e+127)
		tmp = t_1;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[x, -1.4e+211], z, If[LessEqual[x, -7.1e-81], t$95$1, If[LessEqual[x, -8.5e-182], a, If[LessEqual[x, 7.5e+127], t$95$1, z]]]]]

Derivation

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

Alternative 16: 52.6% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -3.9 \cdot 10^{+206}:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq 3.6 \cdot 10^{+125}:\\ \;\;\;\;z + a\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -3.9e+206) a (if (<= t 3.6e+125) (+ z a) a)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -3.9e+206) {
		tmp = a;
	} else if (t <= 3.6e+125) {
		tmp = z + a;
	} 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 <= (-3.9d+206)) then
        tmp = a
    else if (t <= 3.6d+125) then
        tmp = z + a
    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 <= -3.9e+206) {
		tmp = a;
	} else if (t <= 3.6e+125) {
		tmp = z + a;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -3.9e+206:
		tmp = a
	elif t <= 3.6e+125:
		tmp = z + a
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -3.9e+206)
		tmp = a;
	elseif (t <= 3.6e+125)
		tmp = Float64(z + a);
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -3.9e+206)
		tmp = a;
	elseif (t <= 3.6e+125)
		tmp = z + a;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -3.9e+206], a, If[LessEqual[t, 3.6e+125], N[(z + a), $MachinePrecision], a]]

Derivation

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

Alternative 17: 44.7% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.8 \cdot 10^{+90}:\\ \;\;\;\;z\\ \mathbf{elif}\;x \leq 8000:\\ \;\;\;\;a\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= x -4.8e+90) z (if (<= x 8000.0) a z)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -4.8e+90) {
		tmp = z;
	} else if (x <= 8000.0) {
		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 <= (-4.8d+90)) then
        tmp = z
    else if (x <= 8000.0d0) 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 <= -4.8e+90) {
		tmp = z;
	} else if (x <= 8000.0) {
		tmp = a;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if x <= -4.8e+90:
		tmp = z
	elif x <= 8000.0:
		tmp = a
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -4.8e+90)
		tmp = z;
	elseif (x <= 8000.0)
		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 <= -4.8e+90)
		tmp = z;
	elseif (x <= 8000.0)
		tmp = a;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -4.8e+90], z, If[LessEqual[x, 8000.0], a, z]]

Derivation

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

Alternative 18: 32.6% 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 2023343 
(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)))