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

Percentage Accurate: 60.5% → 85.1%

Time: 16.3s

Alternatives: 14

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: 60.5% 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: 85.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{y}{t_1}\\ t_3 := \left(y + t\right) \cdot a\\ t_4 := \frac{\left(t_3 + z \cdot \left(x + y\right)\right) - y \cdot b}{t_1}\\ t_5 := a \cdot \left(t_2 + \frac{t}{t_1}\right)\\ \mathbf{if}\;t_4 \leq -\infty:\\ \;\;\;\;z + t_5\\ \mathbf{elif}\;t_4 \leq 10^{+193}:\\ \;\;\;\;z \cdot \left(t_2 + \frac{x}{t_1}\right) + \frac{t_3 - y \cdot b}{t_1}\\ \mathbf{else}:\\ \;\;\;\;t_5 + \frac{x}{\frac{x + t}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (/ y t_1))
        (t_3 (* (+ y t) a))
        (t_4 (/ (- (+ t_3 (* z (+ x y))) (* y b)) t_1))
        (t_5 (* a (+ t_2 (/ t t_1)))))
   (if (<= t_4 (- INFINITY))
     (+ z t_5)
     (if (<= t_4 1e+193)
       (+ (* z (+ t_2 (/ x t_1))) (/ (- t_3 (* y b)) t_1))
       (+ t_5 (/ x (/ (+ x t) z)))))))

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 = (y + t) * a;
	double t_4 = ((t_3 + (z * (x + y))) - (y * b)) / t_1;
	double t_5 = a * (t_2 + (t / t_1));
	double tmp;
	if (t_4 <= -((double) INFINITY)) {
		tmp = z + t_5;
	} else if (t_4 <= 1e+193) {
		tmp = (z * (t_2 + (x / t_1))) + ((t_3 - (y * b)) / t_1);
	} else {
		tmp = t_5 + (x / ((x + t) / z));
	}
	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 = (y + t) * a;
	double t_4 = ((t_3 + (z * (x + y))) - (y * b)) / t_1;
	double t_5 = a * (t_2 + (t / t_1));
	double tmp;
	if (t_4 <= -Double.POSITIVE_INFINITY) {
		tmp = z + t_5;
	} else if (t_4 <= 1e+193) {
		tmp = (z * (t_2 + (x / t_1))) + ((t_3 - (y * b)) / t_1);
	} else {
		tmp = t_5 + (x / ((x + t) / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = y / t_1
	t_3 = (y + t) * a
	t_4 = ((t_3 + (z * (x + y))) - (y * b)) / t_1
	t_5 = a * (t_2 + (t / t_1))
	tmp = 0
	if t_4 <= -math.inf:
		tmp = z + t_5
	elif t_4 <= 1e+193:
		tmp = (z * (t_2 + (x / t_1))) + ((t_3 - (y * b)) / t_1)
	else:
		tmp = t_5 + (x / ((x + t) / z))
	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(Float64(y + t) * a)
	t_4 = Float64(Float64(Float64(t_3 + Float64(z * Float64(x + y))) - Float64(y * b)) / t_1)
	t_5 = Float64(a * Float64(t_2 + Float64(t / t_1)))
	tmp = 0.0
	if (t_4 <= Float64(-Inf))
		tmp = Float64(z + t_5);
	elseif (t_4 <= 1e+193)
		tmp = Float64(Float64(z * Float64(t_2 + Float64(x / t_1))) + Float64(Float64(t_3 - Float64(y * b)) / t_1));
	else
		tmp = Float64(t_5 + Float64(x / Float64(Float64(x + t) / z)));
	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 = (y + t) * a;
	t_4 = ((t_3 + (z * (x + y))) - (y * b)) / t_1;
	t_5 = a * (t_2 + (t / t_1));
	tmp = 0.0;
	if (t_4 <= -Inf)
		tmp = z + t_5;
	elseif (t_4 <= 1e+193)
		tmp = (z * (t_2 + (x / t_1))) + ((t_3 - (y * b)) / t_1);
	else
		tmp = t_5 + (x / ((x + t) / z));
	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[(N[(y + t), $MachinePrecision] * a), $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(t$95$3 + N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$5 = N[(a * N[(t$95$2 + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$4, (-Infinity)], N[(z + t$95$5), $MachinePrecision], If[LessEqual[t$95$4, 1e+193], N[(N[(z * N[(t$95$2 + N[(x / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$3 - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision], N[(t$95$5 + N[(x / N[(N[(x + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

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

Alternative 2: 84.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{\left(\left(y + t\right) \cdot a + z \cdot \left(x + y\right)\right) - y \cdot b}{t_1}\\ t_3 := a \cdot \left(\frac{y}{t_1} + \frac{t}{t_1}\right)\\ \mathbf{if}\;t_2 \leq -2 \cdot 10^{+266}:\\ \;\;\;\;z + t_3\\ \mathbf{elif}\;t_2 \leq 10^{+193}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_3 + \frac{x}{\frac{x + t}{z}}\\ \end{array} \end{array} \]

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))
        (t_3 (* a (+ (/ y t_1) (/ t t_1)))))
   (if (<= t_2 -2e+266)
     (+ z t_3)
     (if (<= t_2 1e+193) t_2 (+ t_3 (/ x (/ (+ x t) z)))))))

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 t_3 = a * ((y / t_1) + (t / t_1));
	double tmp;
	if (t_2 <= -2e+266) {
		tmp = z + t_3;
	} else if (t_2 <= 1e+193) {
		tmp = t_2;
	} else {
		tmp = t_3 + (x / ((x + 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) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = y + (x + t)
    t_2 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / t_1
    t_3 = a * ((y / t_1) + (t / t_1))
    if (t_2 <= (-2d+266)) then
        tmp = z + t_3
    else if (t_2 <= 1d+193) then
        tmp = t_2
    else
        tmp = t_3 + (x / ((x + 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 t_1 = y + (x + t);
	double t_2 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / t_1;
	double t_3 = a * ((y / t_1) + (t / t_1));
	double tmp;
	if (t_2 <= -2e+266) {
		tmp = z + t_3;
	} else if (t_2 <= 1e+193) {
		tmp = t_2;
	} else {
		tmp = t_3 + (x / ((x + t) / z));
	}
	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
	t_3 = a * ((y / t_1) + (t / t_1))
	tmp = 0
	if t_2 <= -2e+266:
		tmp = z + t_3
	elif t_2 <= 1e+193:
		tmp = t_2
	else:
		tmp = t_3 + (x / ((x + t) / z))
	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)
	t_3 = Float64(a * Float64(Float64(y / t_1) + Float64(t / t_1)))
	tmp = 0.0
	if (t_2 <= -2e+266)
		tmp = Float64(z + t_3);
	elseif (t_2 <= 1e+193)
		tmp = t_2;
	else
		tmp = Float64(t_3 + Float64(x / Float64(Float64(x + t) / z)));
	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;
	t_3 = a * ((y / t_1) + (t / t_1));
	tmp = 0.0;
	if (t_2 <= -2e+266)
		tmp = z + t_3;
	elseif (t_2 <= 1e+193)
		tmp = t_2;
	else
		tmp = t_3 + (x / ((x + t) / z));
	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]}, Block[{t$95$3 = N[(a * N[(N[(y / t$95$1), $MachinePrecision] + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -2e+266], N[(z + t$95$3), $MachinePrecision], If[LessEqual[t$95$2, 1e+193], t$95$2, N[(t$95$3 + N[(x / N[(N[(x + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 3: 86.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{\left(\left(y + t\right) \cdot a + z \cdot \left(x + y\right)\right) - y \cdot b}{t_1}\\ \mathbf{if}\;t_2 \leq -2 \cdot 10^{+266} \lor \neg \left(t_2 \leq 10^{+193}\right):\\ \;\;\;\;z + a \cdot \left(\frac{y}{t_1} + \frac{t}{t_1}\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (/ (- (+ (* (+ y t) a) (* z (+ x y))) (* y b)) t_1)))
   (if (or (<= t_2 -2e+266) (not (<= t_2 1e+193)))
     (+ z (* a (+ (/ y t_1) (/ t 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 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / t_1;
	double tmp;
	if ((t_2 <= -2e+266) || !(t_2 <= 1e+193)) {
		tmp = z + (a * ((y / t_1) + (t / 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 = y + (x + t)
    t_2 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / t_1
    if ((t_2 <= (-2d+266)) .or. (.not. (t_2 <= 1d+193))) then
        tmp = z + (a * ((y / t_1) + (t / 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 = y + (x + t);
	double t_2 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / t_1;
	double tmp;
	if ((t_2 <= -2e+266) || !(t_2 <= 1e+193)) {
		tmp = z + (a * ((y / t_1) + (t / t_1)));
	} else {
		tmp = t_2;
	}
	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 <= -2e+266) or not (t_2 <= 1e+193):
		tmp = z + (a * ((y / t_1) + (t / 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(Float64(y + t) * a) + Float64(z * Float64(x + y))) - Float64(y * b)) / t_1)
	tmp = 0.0
	if ((t_2 <= -2e+266) || !(t_2 <= 1e+193))
		tmp = Float64(z + Float64(a * Float64(Float64(y / t_1) + Float64(t / 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 = ((((y + t) * a) + (z * (x + y))) - (y * b)) / t_1;
	tmp = 0.0;
	if ((t_2 <= -2e+266) || ~((t_2 <= 1e+193)))
		tmp = z + (a * ((y / t_1) + (t / 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[(N[(y + t), $MachinePrecision] * a), $MachinePrecision] + N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[Or[LessEqual[t$95$2, -2e+266], N[Not[LessEqual[t$95$2, 1e+193]], $MachinePrecision]], N[(z + N[(a * N[(N[(y / t$95$1), $MachinePrecision] + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]

Derivation

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

Alternative 4: 67.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{a}{\frac{t_1}{y + t}}\\ t_3 := \frac{t \cdot a + x \cdot z}{x + t}\\ t_4 := \left(z + a\right) - b\\ t_5 := a + \frac{z \cdot \left(x + y\right) - y \cdot b}{t_1}\\ \mathbf{if}\;y \leq -1.7 \cdot 10^{+160}:\\ \;\;\;\;t_4\\ \mathbf{elif}\;y \leq -1.8 \cdot 10^{+92}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -2.45 \cdot 10^{-77}:\\ \;\;\;\;t_5\\ \mathbf{elif}\;y \leq -2.4 \cdot 10^{-157}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq -3.5 \cdot 10^{-211}:\\ \;\;\;\;a + \frac{x}{\frac{x + t}{z}}\\ \mathbf{elif}\;y \leq -1.45 \cdot 10^{-276}:\\ \;\;\;\;z - \frac{y \cdot b}{x}\\ \mathbf{elif}\;y \leq 2.9 \cdot 10^{-202}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \leq 1.85 \cdot 10^{-168}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq 5.5 \cdot 10^{+106}:\\ \;\;\;\;t_5\\ \mathbf{else}:\\ \;\;\;\;t_4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (/ a (/ t_1 (+ y t))))
        (t_3 (/ (+ (* t a) (* x z)) (+ x t)))
        (t_4 (- (+ z a) b))
        (t_5 (+ a (/ (- (* z (+ x y)) (* y b)) t_1))))
   (if (<= y -1.7e+160)
     t_4
     (if (<= y -1.8e+92)
       t_2
       (if (<= y -2.45e-77)
         t_5
         (if (<= y -2.4e-157)
           t_3
           (if (<= y -3.5e-211)
             (+ a (/ x (/ (+ x t) z)))
             (if (<= y -1.45e-276)
               (- z (/ (* y b) x))
               (if (<= y 2.9e-202)
                 t_3
                 (if (<= y 1.85e-168)
                   t_2
                   (if (<= y 5.5e+106) t_5 t_4)))))))))))

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 / (t_1 / (y + t));
	double t_3 = ((t * a) + (x * z)) / (x + t);
	double t_4 = (z + a) - b;
	double t_5 = a + (((z * (x + y)) - (y * b)) / t_1);
	double tmp;
	if (y <= -1.7e+160) {
		tmp = t_4;
	} else if (y <= -1.8e+92) {
		tmp = t_2;
	} else if (y <= -2.45e-77) {
		tmp = t_5;
	} else if (y <= -2.4e-157) {
		tmp = t_3;
	} else if (y <= -3.5e-211) {
		tmp = a + (x / ((x + t) / z));
	} else if (y <= -1.45e-276) {
		tmp = z - ((y * b) / x);
	} else if (y <= 2.9e-202) {
		tmp = t_3;
	} else if (y <= 1.85e-168) {
		tmp = t_2;
	} else if (y <= 5.5e+106) {
		tmp = t_5;
	} 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) :: t_5
    real(8) :: tmp
    t_1 = y + (x + t)
    t_2 = a / (t_1 / (y + t))
    t_3 = ((t * a) + (x * z)) / (x + t)
    t_4 = (z + a) - b
    t_5 = a + (((z * (x + y)) - (y * b)) / t_1)
    if (y <= (-1.7d+160)) then
        tmp = t_4
    else if (y <= (-1.8d+92)) then
        tmp = t_2
    else if (y <= (-2.45d-77)) then
        tmp = t_5
    else if (y <= (-2.4d-157)) then
        tmp = t_3
    else if (y <= (-3.5d-211)) then
        tmp = a + (x / ((x + t) / z))
    else if (y <= (-1.45d-276)) then
        tmp = z - ((y * b) / x)
    else if (y <= 2.9d-202) then
        tmp = t_3
    else if (y <= 1.85d-168) then
        tmp = t_2
    else if (y <= 5.5d+106) then
        tmp = t_5
    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 = y + (x + t);
	double t_2 = a / (t_1 / (y + t));
	double t_3 = ((t * a) + (x * z)) / (x + t);
	double t_4 = (z + a) - b;
	double t_5 = a + (((z * (x + y)) - (y * b)) / t_1);
	double tmp;
	if (y <= -1.7e+160) {
		tmp = t_4;
	} else if (y <= -1.8e+92) {
		tmp = t_2;
	} else if (y <= -2.45e-77) {
		tmp = t_5;
	} else if (y <= -2.4e-157) {
		tmp = t_3;
	} else if (y <= -3.5e-211) {
		tmp = a + (x / ((x + t) / z));
	} else if (y <= -1.45e-276) {
		tmp = z - ((y * b) / x);
	} else if (y <= 2.9e-202) {
		tmp = t_3;
	} else if (y <= 1.85e-168) {
		tmp = t_2;
	} else if (y <= 5.5e+106) {
		tmp = t_5;
	} else {
		tmp = t_4;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = a / (t_1 / (y + t))
	t_3 = ((t * a) + (x * z)) / (x + t)
	t_4 = (z + a) - b
	t_5 = a + (((z * (x + y)) - (y * b)) / t_1)
	tmp = 0
	if y <= -1.7e+160:
		tmp = t_4
	elif y <= -1.8e+92:
		tmp = t_2
	elif y <= -2.45e-77:
		tmp = t_5
	elif y <= -2.4e-157:
		tmp = t_3
	elif y <= -3.5e-211:
		tmp = a + (x / ((x + t) / z))
	elif y <= -1.45e-276:
		tmp = z - ((y * b) / x)
	elif y <= 2.9e-202:
		tmp = t_3
	elif y <= 1.85e-168:
		tmp = t_2
	elif y <= 5.5e+106:
		tmp = t_5
	else:
		tmp = t_4
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(a / Float64(t_1 / Float64(y + t)))
	t_3 = Float64(Float64(Float64(t * a) + Float64(x * z)) / Float64(x + t))
	t_4 = Float64(Float64(z + a) - b)
	t_5 = Float64(a + Float64(Float64(Float64(z * Float64(x + y)) - Float64(y * b)) / t_1))
	tmp = 0.0
	if (y <= -1.7e+160)
		tmp = t_4;
	elseif (y <= -1.8e+92)
		tmp = t_2;
	elseif (y <= -2.45e-77)
		tmp = t_5;
	elseif (y <= -2.4e-157)
		tmp = t_3;
	elseif (y <= -3.5e-211)
		tmp = Float64(a + Float64(x / Float64(Float64(x + t) / z)));
	elseif (y <= -1.45e-276)
		tmp = Float64(z - Float64(Float64(y * b) / x));
	elseif (y <= 2.9e-202)
		tmp = t_3;
	elseif (y <= 1.85e-168)
		tmp = t_2;
	elseif (y <= 5.5e+106)
		tmp = t_5;
	else
		tmp = t_4;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = a / (t_1 / (y + t));
	t_3 = ((t * a) + (x * z)) / (x + t);
	t_4 = (z + a) - b;
	t_5 = a + (((z * (x + y)) - (y * b)) / t_1);
	tmp = 0.0;
	if (y <= -1.7e+160)
		tmp = t_4;
	elseif (y <= -1.8e+92)
		tmp = t_2;
	elseif (y <= -2.45e-77)
		tmp = t_5;
	elseif (y <= -2.4e-157)
		tmp = t_3;
	elseif (y <= -3.5e-211)
		tmp = a + (x / ((x + t) / z));
	elseif (y <= -1.45e-276)
		tmp = z - ((y * b) / x);
	elseif (y <= 2.9e-202)
		tmp = t_3;
	elseif (y <= 1.85e-168)
		tmp = t_2;
	elseif (y <= 5.5e+106)
		tmp = t_5;
	else
		tmp = t_4;
	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[(t$95$1 / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(t * a), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, Block[{t$95$5 = N[(a + N[(N[(N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.7e+160], t$95$4, If[LessEqual[y, -1.8e+92], t$95$2, If[LessEqual[y, -2.45e-77], t$95$5, If[LessEqual[y, -2.4e-157], t$95$3, If[LessEqual[y, -3.5e-211], N[(a + N[(x / N[(N[(x + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -1.45e-276], N[(z - N[(N[(y * b), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.9e-202], t$95$3, If[LessEqual[y, 1.85e-168], t$95$2, If[LessEqual[y, 5.5e+106], t$95$5, t$95$4]]]]]]]]]]]]]]

Derivation

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

Alternative 5: 74.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ \mathbf{if}\;x \leq -4.8 \cdot 10^{+55} \lor \neg \left(x \leq 8.2 \cdot 10^{+61}\right):\\ \;\;\;\;z + a \cdot \left(\frac{y}{t_1} + \frac{t}{t_1}\right)\\ \mathbf{else}:\\ \;\;\;\;a + \frac{z \cdot \left(x + y\right) - y \cdot b}{t_1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t))))
   (if (or (<= x -4.8e+55) (not (<= x 8.2e+61)))
     (+ z (* a (+ (/ y t_1) (/ t t_1))))
     (+ a (/ (- (* z (+ x y)) (* 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 tmp;
	if ((x <= -4.8e+55) || !(x <= 8.2e+61)) {
		tmp = z + (a * ((y / t_1) + (t / t_1)));
	} else {
		tmp = a + (((z * (x + y)) - (y * b)) / 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 ((x <= (-4.8d+55)) .or. (.not. (x <= 8.2d+61))) then
        tmp = z + (a * ((y / t_1) + (t / t_1)))
    else
        tmp = a + (((z * (x + y)) - (y * b)) / 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 ((x <= -4.8e+55) || !(x <= 8.2e+61)) {
		tmp = z + (a * ((y / t_1) + (t / t_1)));
	} else {
		tmp = a + (((z * (x + y)) - (y * b)) / t_1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	tmp = 0
	if (x <= -4.8e+55) or not (x <= 8.2e+61):
		tmp = z + (a * ((y / t_1) + (t / t_1)))
	else:
		tmp = a + (((z * (x + y)) - (y * b)) / t_1)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	tmp = 0.0
	if ((x <= -4.8e+55) || !(x <= 8.2e+61))
		tmp = Float64(z + Float64(a * Float64(Float64(y / t_1) + Float64(t / t_1))));
	else
		tmp = Float64(a + Float64(Float64(Float64(z * Float64(x + y)) - 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);
	tmp = 0.0;
	if ((x <= -4.8e+55) || ~((x <= 8.2e+61)))
		tmp = z + (a * ((y / t_1) + (t / t_1)));
	else
		tmp = a + (((z * (x + y)) - (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]}, If[Or[LessEqual[x, -4.8e+55], N[Not[LessEqual[x, 8.2e+61]], $MachinePrecision]], N[(z + N[(a * N[(N[(y / t$95$1), $MachinePrecision] + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a + N[(N[(N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 6: 60.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{a}{\frac{y + \left(x + t\right)}{y + t}}\\ t_2 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -1.8 \cdot 10^{+160}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -4 \cdot 10^{-27}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 1.85 \cdot 10^{-209}:\\ \;\;\;\;\frac{t \cdot a + x \cdot z}{x + t}\\ \mathbf{elif}\;y \leq 6.2 \cdot 10^{-172}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 5.2 \cdot 10^{+62}:\\ \;\;\;\;a + \frac{x}{\frac{x + t}{z}}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ a (/ (+ y (+ x t)) (+ y t)))) (t_2 (- (+ z a) b)))
   (if (<= y -1.8e+160)
     t_2
     (if (<= y -4e-27)
       t_1
       (if (<= y 1.85e-209)
         (/ (+ (* t a) (* x z)) (+ x t))
         (if (<= y 6.2e-172)
           t_1
           (if (<= y 5.2e+62) (+ a (/ x (/ (+ x t) z))) t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a / ((y + (x + t)) / (y + t));
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -1.8e+160) {
		tmp = t_2;
	} else if (y <= -4e-27) {
		tmp = t_1;
	} else if (y <= 1.85e-209) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else if (y <= 6.2e-172) {
		tmp = t_1;
	} else if (y <= 5.2e+62) {
		tmp = a + (x / ((x + t) / z));
	} 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 = a / ((y + (x + t)) / (y + t))
    t_2 = (z + a) - b
    if (y <= (-1.8d+160)) then
        tmp = t_2
    else if (y <= (-4d-27)) then
        tmp = t_1
    else if (y <= 1.85d-209) then
        tmp = ((t * a) + (x * z)) / (x + t)
    else if (y <= 6.2d-172) then
        tmp = t_1
    else if (y <= 5.2d+62) then
        tmp = a + (x / ((x + t) / z))
    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 = a / ((y + (x + t)) / (y + t));
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -1.8e+160) {
		tmp = t_2;
	} else if (y <= -4e-27) {
		tmp = t_1;
	} else if (y <= 1.85e-209) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else if (y <= 6.2e-172) {
		tmp = t_1;
	} else if (y <= 5.2e+62) {
		tmp = a + (x / ((x + t) / z));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a / ((y + (x + t)) / (y + t))
	t_2 = (z + a) - b
	tmp = 0
	if y <= -1.8e+160:
		tmp = t_2
	elif y <= -4e-27:
		tmp = t_1
	elif y <= 1.85e-209:
		tmp = ((t * a) + (x * z)) / (x + t)
	elif y <= 6.2e-172:
		tmp = t_1
	elif y <= 5.2e+62:
		tmp = a + (x / ((x + t) / z))
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a / Float64(Float64(y + Float64(x + t)) / Float64(y + t)))
	t_2 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (y <= -1.8e+160)
		tmp = t_2;
	elseif (y <= -4e-27)
		tmp = t_1;
	elseif (y <= 1.85e-209)
		tmp = Float64(Float64(Float64(t * a) + Float64(x * z)) / Float64(x + t));
	elseif (y <= 6.2e-172)
		tmp = t_1;
	elseif (y <= 5.2e+62)
		tmp = Float64(a + Float64(x / Float64(Float64(x + t) / z)));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a / ((y + (x + t)) / (y + t));
	t_2 = (z + a) - b;
	tmp = 0.0;
	if (y <= -1.8e+160)
		tmp = t_2;
	elseif (y <= -4e-27)
		tmp = t_1;
	elseif (y <= 1.85e-209)
		tmp = ((t * a) + (x * z)) / (x + t);
	elseif (y <= 6.2e-172)
		tmp = t_1;
	elseif (y <= 5.2e+62)
		tmp = a + (x / ((x + t) / z));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a / N[(N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision] / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[y, -1.8e+160], t$95$2, If[LessEqual[y, -4e-27], t$95$1, If[LessEqual[y, 1.85e-209], N[(N[(N[(t * a), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6.2e-172], t$95$1, If[LessEqual[y, 5.2e+62], N[(a + N[(x / N[(N[(x + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]

Derivation

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

Alternative 7: 62.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -2.65 \cdot 10^{+140}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -1.52 \cdot 10^{-27}:\\ \;\;\;\;\frac{\left(y + t\right) \cdot a - y \cdot b}{t_1}\\ \mathbf{elif}\;y \leq 6.3 \cdot 10^{-205}:\\ \;\;\;\;\frac{t \cdot a + x \cdot z}{x + t}\\ \mathbf{elif}\;y \leq 6.2 \cdot 10^{-172}:\\ \;\;\;\;\frac{a}{\frac{t_1}{y + t}}\\ \mathbf{elif}\;y \leq 4.5 \cdot 10^{+63}:\\ \;\;\;\;a + \frac{x}{\frac{x + t}{z}}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t))) (t_2 (- (+ z a) b)))
   (if (<= y -2.65e+140)
     t_2
     (if (<= y -1.52e-27)
       (/ (- (* (+ y t) a) (* y b)) t_1)
       (if (<= y 6.3e-205)
         (/ (+ (* t a) (* x z)) (+ x t))
         (if (<= y 6.2e-172)
           (/ a (/ t_1 (+ y t)))
           (if (<= y 4.5e+63) (+ a (/ x (/ (+ x t) z))) 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 <= -2.65e+140) {
		tmp = t_2;
	} else if (y <= -1.52e-27) {
		tmp = (((y + t) * a) - (y * b)) / t_1;
	} else if (y <= 6.3e-205) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else if (y <= 6.2e-172) {
		tmp = a / (t_1 / (y + t));
	} else if (y <= 4.5e+63) {
		tmp = a + (x / ((x + t) / z));
	} 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 <= (-2.65d+140)) then
        tmp = t_2
    else if (y <= (-1.52d-27)) then
        tmp = (((y + t) * a) - (y * b)) / t_1
    else if (y <= 6.3d-205) then
        tmp = ((t * a) + (x * z)) / (x + t)
    else if (y <= 6.2d-172) then
        tmp = a / (t_1 / (y + t))
    else if (y <= 4.5d+63) then
        tmp = a + (x / ((x + t) / z))
    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 <= -2.65e+140) {
		tmp = t_2;
	} else if (y <= -1.52e-27) {
		tmp = (((y + t) * a) - (y * b)) / t_1;
	} else if (y <= 6.3e-205) {
		tmp = ((t * a) + (x * z)) / (x + t);
	} else if (y <= 6.2e-172) {
		tmp = a / (t_1 / (y + t));
	} else if (y <= 4.5e+63) {
		tmp = a + (x / ((x + t) / z));
	} 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 <= -2.65e+140:
		tmp = t_2
	elif y <= -1.52e-27:
		tmp = (((y + t) * a) - (y * b)) / t_1
	elif y <= 6.3e-205:
		tmp = ((t * a) + (x * z)) / (x + t)
	elif y <= 6.2e-172:
		tmp = a / (t_1 / (y + t))
	elif y <= 4.5e+63:
		tmp = a + (x / ((x + t) / z))
	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 <= -2.65e+140)
		tmp = t_2;
	elseif (y <= -1.52e-27)
		tmp = Float64(Float64(Float64(Float64(y + t) * a) - Float64(y * b)) / t_1);
	elseif (y <= 6.3e-205)
		tmp = Float64(Float64(Float64(t * a) + Float64(x * z)) / Float64(x + t));
	elseif (y <= 6.2e-172)
		tmp = Float64(a / Float64(t_1 / Float64(y + t)));
	elseif (y <= 4.5e+63)
		tmp = Float64(a + Float64(x / Float64(Float64(x + t) / z)));
	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 <= -2.65e+140)
		tmp = t_2;
	elseif (y <= -1.52e-27)
		tmp = (((y + t) * a) - (y * b)) / t_1;
	elseif (y <= 6.3e-205)
		tmp = ((t * a) + (x * z)) / (x + t);
	elseif (y <= 6.2e-172)
		tmp = a / (t_1 / (y + t));
	elseif (y <= 4.5e+63)
		tmp = a + (x / ((x + t) / z));
	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, -2.65e+140], t$95$2, If[LessEqual[y, -1.52e-27], N[(N[(N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision], If[LessEqual[y, 6.3e-205], N[(N[(N[(t * a), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6.2e-172], N[(a / N[(t$95$1 / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 4.5e+63], N[(a + N[(x / N[(N[(x + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]

Derivation

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

Alternative 8: 62.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -1.7 \cdot 10^{+160}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -2.5 \cdot 10^{+56}:\\ \;\;\;\;\frac{a}{\frac{y + \left(x + t\right)}{y + t}}\\ \mathbf{elif}\;y \leq 5.8 \cdot 10^{+57}:\\ \;\;\;\;a + \frac{x}{\frac{x + t}{z}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= y -1.7e+160)
     t_1
     (if (<= y -2.5e+56)
       (/ a (/ (+ y (+ x t)) (+ y t)))
       (if (<= y 5.8e+57) (+ a (/ x (/ (+ x t) z))) 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 <= -1.7e+160) {
		tmp = t_1;
	} else if (y <= -2.5e+56) {
		tmp = a / ((y + (x + t)) / (y + t));
	} else if (y <= 5.8e+57) {
		tmp = a + (x / ((x + t) / z));
	} 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 <= (-1.7d+160)) then
        tmp = t_1
    else if (y <= (-2.5d+56)) then
        tmp = a / ((y + (x + t)) / (y + t))
    else if (y <= 5.8d+57) then
        tmp = a + (x / ((x + t) / z))
    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 <= -1.7e+160) {
		tmp = t_1;
	} else if (y <= -2.5e+56) {
		tmp = a / ((y + (x + t)) / (y + t));
	} else if (y <= 5.8e+57) {
		tmp = a + (x / ((x + t) / z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if y <= -1.7e+160:
		tmp = t_1
	elif y <= -2.5e+56:
		tmp = a / ((y + (x + t)) / (y + t))
	elif y <= 5.8e+57:
		tmp = a + (x / ((x + t) / z))
	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 <= -1.7e+160)
		tmp = t_1;
	elseif (y <= -2.5e+56)
		tmp = Float64(a / Float64(Float64(y + Float64(x + t)) / Float64(y + t)));
	elseif (y <= 5.8e+57)
		tmp = Float64(a + Float64(x / Float64(Float64(x + t) / z)));
	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 <= -1.7e+160)
		tmp = t_1;
	elseif (y <= -2.5e+56)
		tmp = a / ((y + (x + t)) / (y + t));
	elseif (y <= 5.8e+57)
		tmp = a + (x / ((x + t) / z));
	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, -1.7e+160], t$95$1, If[LessEqual[y, -2.5e+56], N[(a / N[(N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision] / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 5.8e+57], N[(a + N[(x / N[(N[(x + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 9: 57.4% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;t \leq -1.45 \cdot 10^{+142}:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq -3.8 \cdot 10^{-287}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.95 \cdot 10^{-250}:\\ \;\;\;\;z + \frac{y \cdot a}{x}\\ \mathbf{elif}\;t \leq 1.4 \cdot 10^{+146}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= t -1.45e+142)
     a
     (if (<= t -3.8e-287)
       t_1
       (if (<= t 1.95e-250) (+ z (/ (* y a) x)) (if (<= t 1.4e+146) t_1 a))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (t <= -1.45e+142) {
		tmp = a;
	} else if (t <= -3.8e-287) {
		tmp = t_1;
	} else if (t <= 1.95e-250) {
		tmp = z + ((y * a) / x);
	} else if (t <= 1.4e+146) {
		tmp = t_1;
	} 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) :: t_1
    real(8) :: tmp
    t_1 = (z + a) - b
    if (t <= (-1.45d+142)) then
        tmp = a
    else if (t <= (-3.8d-287)) then
        tmp = t_1
    else if (t <= 1.95d-250) then
        tmp = z + ((y * a) / x)
    else if (t <= 1.4d+146) then
        tmp = t_1
    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 t_1 = (z + a) - b;
	double tmp;
	if (t <= -1.45e+142) {
		tmp = a;
	} else if (t <= -3.8e-287) {
		tmp = t_1;
	} else if (t <= 1.95e-250) {
		tmp = z + ((y * a) / x);
	} else if (t <= 1.4e+146) {
		tmp = t_1;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if t <= -1.45e+142:
		tmp = a
	elif t <= -3.8e-287:
		tmp = t_1
	elif t <= 1.95e-250:
		tmp = z + ((y * a) / x)
	elif t <= 1.4e+146:
		tmp = t_1
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (t <= -1.45e+142)
		tmp = a;
	elseif (t <= -3.8e-287)
		tmp = t_1;
	elseif (t <= 1.95e-250)
		tmp = Float64(z + Float64(Float64(y * a) / x));
	elseif (t <= 1.4e+146)
		tmp = t_1;
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	tmp = 0.0;
	if (t <= -1.45e+142)
		tmp = a;
	elseif (t <= -3.8e-287)
		tmp = t_1;
	elseif (t <= 1.95e-250)
		tmp = z + ((y * a) / x);
	elseif (t <= 1.4e+146)
		tmp = t_1;
	else
		tmp = a;
	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[t, -1.45e+142], a, If[LessEqual[t, -3.8e-287], t$95$1, If[LessEqual[t, 1.95e-250], N[(z + N[(N[(y * a), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.4e+146], t$95$1, a]]]]]

Derivation

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

Alternative 10: 64.2% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.3 \cdot 10^{+114} \lor \neg \left(y \leq 3.1 \cdot 10^{+63}\right):\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;a + \frac{x}{\frac{x + t}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -1.3e+114) (not (<= y 3.1e+63)))
   (- (+ z a) b)
   (+ a (/ x (/ (+ x t) z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -1.3e+114) || !(y <= 3.1e+63)) {
		tmp = (z + a) - b;
	} else {
		tmp = a + (x / ((x + 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 ((y <= (-1.3d+114)) .or. (.not. (y <= 3.1d+63))) then
        tmp = (z + a) - b
    else
        tmp = a + (x / ((x + 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 ((y <= -1.3e+114) || !(y <= 3.1e+63)) {
		tmp = (z + a) - b;
	} else {
		tmp = a + (x / ((x + t) / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -1.3e+114) or not (y <= 3.1e+63):
		tmp = (z + a) - b
	else:
		tmp = a + (x / ((x + t) / z))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -1.3e+114) || !(y <= 3.1e+63))
		tmp = Float64(Float64(z + a) - b);
	else
		tmp = Float64(a + Float64(x / Float64(Float64(x + t) / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -1.3e+114) || ~((y <= 3.1e+63)))
		tmp = (z + a) - b;
	else
		tmp = a + (x / ((x + t) / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -1.3e+114], N[Not[LessEqual[y, 3.1e+63]], $MachinePrecision]], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], N[(a + N[(x / N[(N[(x + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 42.4% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -960000000:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq -4.4 \cdot 10^{-132}:\\ \;\;\;\;z\\ \mathbf{elif}\;t \leq -2.4 \cdot 10^{-286}:\\ \;\;\;\;a - b\\ \mathbf{elif}\;t \leq 1.45 \cdot 10^{+85}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -960000000.0)
   a
   (if (<= t -4.4e-132)
     z
     (if (<= t -2.4e-286) (- a b) (if (<= t 1.45e+85) z a)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -960000000.0) {
		tmp = a;
	} else if (t <= -4.4e-132) {
		tmp = z;
	} else if (t <= -2.4e-286) {
		tmp = a - b;
	} else if (t <= 1.45e+85) {
		tmp = z;
	} 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 <= (-960000000.0d0)) then
        tmp = a
    else if (t <= (-4.4d-132)) then
        tmp = z
    else if (t <= (-2.4d-286)) then
        tmp = a - b
    else if (t <= 1.45d+85) then
        tmp = z
    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 <= -960000000.0) {
		tmp = a;
	} else if (t <= -4.4e-132) {
		tmp = z;
	} else if (t <= -2.4e-286) {
		tmp = a - b;
	} else if (t <= 1.45e+85) {
		tmp = z;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -960000000.0:
		tmp = a
	elif t <= -4.4e-132:
		tmp = z
	elif t <= -2.4e-286:
		tmp = a - b
	elif t <= 1.45e+85:
		tmp = z
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -960000000.0)
		tmp = a;
	elseif (t <= -4.4e-132)
		tmp = z;
	elseif (t <= -2.4e-286)
		tmp = Float64(a - b);
	elseif (t <= 1.45e+85)
		tmp = z;
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -960000000.0)
		tmp = a;
	elseif (t <= -4.4e-132)
		tmp = z;
	elseif (t <= -2.4e-286)
		tmp = a - b;
	elseif (t <= 1.45e+85)
		tmp = z;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -960000000.0], a, If[LessEqual[t, -4.4e-132], z, If[LessEqual[t, -2.4e-286], N[(a - b), $MachinePrecision], If[LessEqual[t, 1.45e+85], z, a]]]]

Derivation

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

Alternative 12: 58.5% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.7 \cdot 10^{+141}:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq 7.8 \cdot 10^{+146}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -1.7e+141) a (if (<= t 7.8e+146) (- (+ z a) b) a)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -1.7e+141) {
		tmp = a;
	} else if (t <= 7.8e+146) {
		tmp = (z + a) - b;
	} 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 <= (-1.7d+141)) then
        tmp = a
    else if (t <= 7.8d+146) then
        tmp = (z + a) - b
    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 <= -1.7e+141) {
		tmp = a;
	} else if (t <= 7.8e+146) {
		tmp = (z + a) - b;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -1.7e+141:
		tmp = a
	elif t <= 7.8e+146:
		tmp = (z + a) - b
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -1.7e+141)
		tmp = a;
	elseif (t <= 7.8e+146)
		tmp = Float64(Float64(z + a) - b);
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -1.7e+141)
		tmp = a;
	elseif (t <= 7.8e+146)
		tmp = (z + a) - b;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -1.7e+141], a, If[LessEqual[t, 7.8e+146], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], a]]

Derivation

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

Alternative 13: 44.3% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -4000000000:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq 3.5 \cdot 10^{+84}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -4000000000.0) a (if (<= t 3.5e+84) z a)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -4000000000.0) {
		tmp = a;
	} else if (t <= 3.5e+84) {
		tmp = z;
	} 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 <= (-4000000000.0d0)) then
        tmp = a
    else if (t <= 3.5d+84) then
        tmp = z
    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 <= -4000000000.0) {
		tmp = a;
	} else if (t <= 3.5e+84) {
		tmp = z;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -4000000000.0:
		tmp = a
	elif t <= 3.5e+84:
		tmp = z
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -4000000000.0)
		tmp = a;
	elseif (t <= 3.5e+84)
		tmp = z;
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -4000000000.0)
		tmp = a;
	elseif (t <= 3.5e+84)
		tmp = z;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -4000000000.0], a, If[LessEqual[t, 3.5e+84], z, a]]

Derivation

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

Alternative 14: 31.8% 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: 82.1% 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 2023347 
(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)))