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

Percentage Accurate: 60.0% → 86.3%

Time: 17.2s

Alternatives: 16

Speedup: 1.9×

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: 60.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 86.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \left(x + y\right) \cdot z\\ t_3 := \frac{\left(t_2 + \left(y + t\right) \cdot a\right) - y \cdot b}{t_1}\\ t_4 := \frac{a}{\frac{t_1}{y + t}}\\ \mathbf{if}\;t_3 \leq -\infty:\\ \;\;\;\;t_4 + \frac{1}{\frac{\frac{\left(x + y\right) + t}{z}}{x + y}}\\ \mathbf{elif}\;t_3 \leq 10^{+297}:\\ \;\;\;\;t_4 + \frac{t_2 - y \cdot b}{t_1}\\ \mathbf{else}:\\ \;\;\;\;t_4 - \frac{y}{\frac{t_1}{b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (* (+ x y) z))
        (t_3 (/ (- (+ t_2 (* (+ y t) a)) (* y b)) t_1))
        (t_4 (/ a (/ t_1 (+ y t)))))
   (if (<= t_3 (- INFINITY))
     (+ t_4 (/ 1.0 (/ (/ (+ (+ x y) t) z) (+ x y))))
     (if (<= t_3 1e+297)
       (+ t_4 (/ (- t_2 (* y b)) t_1))
       (- t_4 (/ y (/ t_1 b)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = (x + y) * z;
	double t_3 = ((t_2 + ((y + t) * a)) - (y * b)) / t_1;
	double t_4 = a / (t_1 / (y + t));
	double tmp;
	if (t_3 <= -((double) INFINITY)) {
		tmp = t_4 + (1.0 / ((((x + y) + t) / z) / (x + y)));
	} else if (t_3 <= 1e+297) {
		tmp = t_4 + ((t_2 - (y * b)) / t_1);
	} else {
		tmp = t_4 - (y / (t_1 / b));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = (x + y) * z;
	double t_3 = ((t_2 + ((y + t) * a)) - (y * b)) / t_1;
	double t_4 = a / (t_1 / (y + t));
	double tmp;
	if (t_3 <= -Double.POSITIVE_INFINITY) {
		tmp = t_4 + (1.0 / ((((x + y) + t) / z) / (x + y)));
	} else if (t_3 <= 1e+297) {
		tmp = t_4 + ((t_2 - (y * b)) / t_1);
	} else {
		tmp = t_4 - (y / (t_1 / b));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = (x + y) * z
	t_3 = ((t_2 + ((y + t) * a)) - (y * b)) / t_1
	t_4 = a / (t_1 / (y + t))
	tmp = 0
	if t_3 <= -math.inf:
		tmp = t_4 + (1.0 / ((((x + y) + t) / z) / (x + y)))
	elif t_3 <= 1e+297:
		tmp = t_4 + ((t_2 - (y * b)) / t_1)
	else:
		tmp = t_4 - (y / (t_1 / b))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(Float64(x + y) * z)
	t_3 = Float64(Float64(Float64(t_2 + Float64(Float64(y + t) * a)) - Float64(y * b)) / t_1)
	t_4 = Float64(a / Float64(t_1 / Float64(y + t)))
	tmp = 0.0
	if (t_3 <= Float64(-Inf))
		tmp = Float64(t_4 + Float64(1.0 / Float64(Float64(Float64(Float64(x + y) + t) / z) / Float64(x + y))));
	elseif (t_3 <= 1e+297)
		tmp = Float64(t_4 + Float64(Float64(t_2 - Float64(y * b)) / t_1));
	else
		tmp = Float64(t_4 - Float64(y / Float64(t_1 / b)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = (x + y) * z;
	t_3 = ((t_2 + ((y + t) * a)) - (y * b)) / t_1;
	t_4 = a / (t_1 / (y + t));
	tmp = 0.0;
	if (t_3 <= -Inf)
		tmp = t_4 + (1.0 / ((((x + y) + t) / z) / (x + y)));
	elseif (t_3 <= 1e+297)
		tmp = t_4 + ((t_2 - (y * b)) / t_1);
	else
		tmp = t_4 - (y / (t_1 / b));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(t$95$2 + N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$4 = N[(a / N[(t$95$1 / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$3, (-Infinity)], N[(t$95$4 + N[(1.0 / N[(N[(N[(N[(x + y), $MachinePrecision] + t), $MachinePrecision] / z), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, 1e+297], N[(t$95$4 + N[(N[(t$95$2 - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision], N[(t$95$4 - N[(y / N[(t$95$1 / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 2: 87.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\left(\left(x + y\right) \cdot z + \left(y + t\right) \cdot a\right) - y \cdot b}{y + \left(x + t\right)}\\ \mathbf{if}\;t_1 \leq -\infty \lor \neg \left(t_1 \leq 4 \cdot 10^{+241}\right):\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (- (+ (* (+ x y) z) (* (+ y t) a)) (* y b)) (+ y (+ x t)))))
   (if (or (<= t_1 (- INFINITY)) (not (<= t_1 4e+241))) (- (+ z a) b) t_1)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / (y + (x + t));
	double tmp;
	if ((t_1 <= -((double) INFINITY)) || !(t_1 <= 4e+241)) {
		tmp = (z + a) - b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / (y + (x + t));
	double tmp;
	if ((t_1 <= -Double.POSITIVE_INFINITY) || !(t_1 <= 4e+241)) {
		tmp = (z + a) - b;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / (y + (x + t))
	tmp = 0
	if (t_1 <= -math.inf) or not (t_1 <= 4e+241):
		tmp = (z + a) - b
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(Float64(Float64(x + y) * z) + Float64(Float64(y + t) * a)) - Float64(y * b)) / Float64(y + Float64(x + t)))
	tmp = 0.0
	if ((t_1 <= Float64(-Inf)) || !(t_1 <= 4e+241))
		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 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / (y + (x + t));
	tmp = 0.0;
	if ((t_1 <= -Inf) || ~((t_1 <= 4e+241)))
		tmp = (z + a) - b;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision] + N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, (-Infinity)], N[Not[LessEqual[t$95$1, 4e+241]], $MachinePrecision]], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], t$95$1]]

Derivation

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

Alternative 3: 85.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{\left(\left(x + y\right) \cdot z + \left(y + t\right) \cdot a\right) - y \cdot b}{t_1}\\ \mathbf{if}\;t_2 \leq -\infty:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{elif}\;t_2 \leq 10^{+297}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;\frac{a}{\frac{t_1}{y + t}} - \frac{y}{\frac{t_1}{b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (/ (- (+ (* (+ x y) z) (* (+ y t) a)) (* y b)) t_1)))
   (if (<= t_2 (- INFINITY))
     (- (+ z a) b)
     (if (<= t_2 1e+297) t_2 (- (/ a (/ t_1 (+ y t))) (/ y (/ t_1 b)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = (z + a) - b;
	} else if (t_2 <= 1e+297) {
		tmp = t_2;
	} else {
		tmp = (a / (t_1 / (y + t))) - (y / (t_1 / b));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = (z + a) - b;
	} else if (t_2 <= 1e+297) {
		tmp = t_2;
	} else {
		tmp = (a / (t_1 / (y + t))) - (y / (t_1 / b));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1
	tmp = 0
	if t_2 <= -math.inf:
		tmp = (z + a) - b
	elif t_2 <= 1e+297:
		tmp = t_2
	else:
		tmp = (a / (t_1 / (y + t))) - (y / (t_1 / b))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(Float64(Float64(Float64(Float64(x + y) * z) + Float64(Float64(y + t) * a)) - Float64(y * b)) / t_1)
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = Float64(Float64(z + a) - b);
	elseif (t_2 <= 1e+297)
		tmp = t_2;
	else
		tmp = Float64(Float64(a / Float64(t_1 / Float64(y + t))) - Float64(y / Float64(t_1 / b)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = (z + a) - b;
	elseif (t_2 <= 1e+297)
		tmp = t_2;
	else
		tmp = (a / (t_1 / (y + t))) - (y / (t_1 / b));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision] + N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], If[LessEqual[t$95$2, 1e+297], t$95$2, N[(N[(a / N[(t$95$1 / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(t$95$1 / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 4: 86.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{\left(\left(x + y\right) \cdot z + \left(y + t\right) \cdot a\right) - y \cdot b}{t_1}\\ t_3 := \frac{a}{\frac{t_1}{y + t}}\\ \mathbf{if}\;t_2 \leq -\infty:\\ \;\;\;\;t_3 + \frac{1}{\frac{\frac{\left(x + y\right) + t}{z}}{x + y}}\\ \mathbf{elif}\;t_2 \leq 10^{+297}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_3 - \frac{y}{\frac{t_1}{b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (/ (- (+ (* (+ x y) z) (* (+ y t) a)) (* y b)) t_1))
        (t_3 (/ a (/ t_1 (+ y t)))))
   (if (<= t_2 (- INFINITY))
     (+ t_3 (/ 1.0 (/ (/ (+ (+ x y) t) z) (+ x y))))
     (if (<= t_2 1e+297) t_2 (- t_3 (/ y (/ t_1 b)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	double t_3 = a / (t_1 / (y + t));
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = t_3 + (1.0 / ((((x + y) + t) / z) / (x + y)));
	} else if (t_2 <= 1e+297) {
		tmp = t_2;
	} else {
		tmp = t_3 - (y / (t_1 / b));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	double t_3 = a / (t_1 / (y + t));
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = t_3 + (1.0 / ((((x + y) + t) / z) / (x + y)));
	} else if (t_2 <= 1e+297) {
		tmp = t_2;
	} else {
		tmp = t_3 - (y / (t_1 / b));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1
	t_3 = a / (t_1 / (y + t))
	tmp = 0
	if t_2 <= -math.inf:
		tmp = t_3 + (1.0 / ((((x + y) + t) / z) / (x + y)))
	elif t_2 <= 1e+297:
		tmp = t_2
	else:
		tmp = t_3 - (y / (t_1 / b))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(Float64(Float64(Float64(Float64(x + y) * z) + Float64(Float64(y + t) * a)) - Float64(y * b)) / t_1)
	t_3 = Float64(a / Float64(t_1 / Float64(y + t)))
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = Float64(t_3 + Float64(1.0 / Float64(Float64(Float64(Float64(x + y) + t) / z) / Float64(x + y))));
	elseif (t_2 <= 1e+297)
		tmp = t_2;
	else
		tmp = Float64(t_3 - Float64(y / Float64(t_1 / b)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	t_3 = a / (t_1 / (y + t));
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = t_3 + (1.0 / ((((x + y) + t) / z) / (x + y)));
	elseif (t_2 <= 1e+297)
		tmp = t_2;
	else
		tmp = t_3 - (y / (t_1 / b));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision] + N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(a / N[(t$95$1 / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], N[(t$95$3 + N[(1.0 / N[(N[(N[(N[(x + y), $MachinePrecision] + t), $MachinePrecision] / z), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+297], t$95$2, N[(t$95$3 - N[(y / N[(t$95$1 / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 5: 70.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -2.65 \cdot 10^{+84}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -3.3 \cdot 10^{-11}:\\ \;\;\;\;\frac{\left(x + y\right) \cdot z + y \cdot \left(a - b\right)}{x + y}\\ \mathbf{elif}\;y \leq 6 \cdot 10^{-65}:\\ \;\;\;\;\frac{a}{\frac{y + \left(x + t\right)}{y + t}} + \frac{x}{\frac{x + t}{z}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= y -2.65e+84)
     t_1
     (if (<= y -3.3e-11)
       (/ (+ (* (+ x y) z) (* y (- a b))) (+ x y))
       (if (<= y 6e-65)
         (+ (/ a (/ (+ y (+ x t)) (+ y t))) (/ 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 <= -2.65e+84) {
		tmp = t_1;
	} else if (y <= -3.3e-11) {
		tmp = (((x + y) * z) + (y * (a - b))) / (x + y);
	} else if (y <= 6e-65) {
		tmp = (a / ((y + (x + t)) / (y + t))) + (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 <= (-2.65d+84)) then
        tmp = t_1
    else if (y <= (-3.3d-11)) then
        tmp = (((x + y) * z) + (y * (a - b))) / (x + y)
    else if (y <= 6d-65) then
        tmp = (a / ((y + (x + t)) / (y + t))) + (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 <= -2.65e+84) {
		tmp = t_1;
	} else if (y <= -3.3e-11) {
		tmp = (((x + y) * z) + (y * (a - b))) / (x + y);
	} else if (y <= 6e-65) {
		tmp = (a / ((y + (x + t)) / (y + t))) + (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 <= -2.65e+84:
		tmp = t_1
	elif y <= -3.3e-11:
		tmp = (((x + y) * z) + (y * (a - b))) / (x + y)
	elif y <= 6e-65:
		tmp = (a / ((y + (x + t)) / (y + t))) + (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 <= -2.65e+84)
		tmp = t_1;
	elseif (y <= -3.3e-11)
		tmp = Float64(Float64(Float64(Float64(x + y) * z) + Float64(y * Float64(a - b))) / Float64(x + y));
	elseif (y <= 6e-65)
		tmp = Float64(Float64(a / Float64(Float64(y + Float64(x + t)) / Float64(y + t))) + 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 <= -2.65e+84)
		tmp = t_1;
	elseif (y <= -3.3e-11)
		tmp = (((x + y) * z) + (y * (a - b))) / (x + y);
	elseif (y <= 6e-65)
		tmp = (a / ((y + (x + t)) / (y + t))) + (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, -2.65e+84], t$95$1, If[LessEqual[y, -3.3e-11], N[(N[(N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision] + N[(y * N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6e-65], N[(N[(a / N[(N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision] / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x / N[(N[(x + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 6: 64.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ t_2 := y + \left(x + t\right)\\ t_3 := z + \frac{a}{\frac{t_2}{y + t}}\\ \mathbf{if}\;a \leq -4.2 \cdot 10^{-19}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;a \leq -2.75 \cdot 10^{-235}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 5.3 \cdot 10^{-89}:\\ \;\;\;\;\frac{\left(x + y\right) \cdot z - y \cdot b}{t_2}\\ \mathbf{elif}\;a \leq 2.55 \cdot 10^{+95}:\\ \;\;\;\;\frac{\left(y + t\right) \cdot a - y \cdot b}{t_2}\\ \mathbf{elif}\;a \leq 1.4 \cdot 10^{+146}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b))
        (t_2 (+ y (+ x t)))
        (t_3 (+ z (/ a (/ t_2 (+ y t))))))
   (if (<= a -4.2e-19)
     t_3
     (if (<= a -2.75e-235)
       t_1
       (if (<= a 5.3e-89)
         (/ (- (* (+ x y) z) (* y b)) t_2)
         (if (<= a 2.55e+95)
           (/ (- (* (+ y t) a) (* y b)) t_2)
           (if (<= a 1.4e+146) t_1 t_3)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double t_2 = y + (x + t);
	double t_3 = z + (a / (t_2 / (y + t)));
	double tmp;
	if (a <= -4.2e-19) {
		tmp = t_3;
	} else if (a <= -2.75e-235) {
		tmp = t_1;
	} else if (a <= 5.3e-89) {
		tmp = (((x + y) * z) - (y * b)) / t_2;
	} else if (a <= 2.55e+95) {
		tmp = (((y + t) * a) - (y * b)) / t_2;
	} else if (a <= 1.4e+146) {
		tmp = t_1;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = (z + a) - b
    t_2 = y + (x + t)
    t_3 = z + (a / (t_2 / (y + t)))
    if (a <= (-4.2d-19)) then
        tmp = t_3
    else if (a <= (-2.75d-235)) then
        tmp = t_1
    else if (a <= 5.3d-89) then
        tmp = (((x + y) * z) - (y * b)) / t_2
    else if (a <= 2.55d+95) then
        tmp = (((y + t) * a) - (y * b)) / t_2
    else if (a <= 1.4d+146) then
        tmp = t_1
    else
        tmp = t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double t_2 = y + (x + t);
	double t_3 = z + (a / (t_2 / (y + t)));
	double tmp;
	if (a <= -4.2e-19) {
		tmp = t_3;
	} else if (a <= -2.75e-235) {
		tmp = t_1;
	} else if (a <= 5.3e-89) {
		tmp = (((x + y) * z) - (y * b)) / t_2;
	} else if (a <= 2.55e+95) {
		tmp = (((y + t) * a) - (y * b)) / t_2;
	} else if (a <= 1.4e+146) {
		tmp = t_1;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	t_2 = y + (x + t)
	t_3 = z + (a / (t_2 / (y + t)))
	tmp = 0
	if a <= -4.2e-19:
		tmp = t_3
	elif a <= -2.75e-235:
		tmp = t_1
	elif a <= 5.3e-89:
		tmp = (((x + y) * z) - (y * b)) / t_2
	elif a <= 2.55e+95:
		tmp = (((y + t) * a) - (y * b)) / t_2
	elif a <= 1.4e+146:
		tmp = t_1
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	t_2 = Float64(y + Float64(x + t))
	t_3 = Float64(z + Float64(a / Float64(t_2 / Float64(y + t))))
	tmp = 0.0
	if (a <= -4.2e-19)
		tmp = t_3;
	elseif (a <= -2.75e-235)
		tmp = t_1;
	elseif (a <= 5.3e-89)
		tmp = Float64(Float64(Float64(Float64(x + y) * z) - Float64(y * b)) / t_2);
	elseif (a <= 2.55e+95)
		tmp = Float64(Float64(Float64(Float64(y + t) * a) - Float64(y * b)) / t_2);
	elseif (a <= 1.4e+146)
		tmp = t_1;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	t_2 = y + (x + t);
	t_3 = z + (a / (t_2 / (y + t)));
	tmp = 0.0;
	if (a <= -4.2e-19)
		tmp = t_3;
	elseif (a <= -2.75e-235)
		tmp = t_1;
	elseif (a <= 5.3e-89)
		tmp = (((x + y) * z) - (y * b)) / t_2;
	elseif (a <= 2.55e+95)
		tmp = (((y + t) * a) - (y * b)) / t_2;
	elseif (a <= 1.4e+146)
		tmp = t_1;
	else
		tmp = t_3;
	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[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(z + N[(a / N[(t$95$2 / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -4.2e-19], t$95$3, If[LessEqual[a, -2.75e-235], t$95$1, If[LessEqual[a, 5.3e-89], N[(N[(N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$2), $MachinePrecision], If[LessEqual[a, 2.55e+95], N[(N[(N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$2), $MachinePrecision], If[LessEqual[a, 1.4e+146], t$95$1, t$95$3]]]]]]]]

Derivation

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

Alternative 7: 72.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ \mathbf{if}\;x \leq -5.3 \cdot 10^{+191}:\\ \;\;\;\;z + y \cdot \left(\frac{a}{x} - \frac{b}{x}\right)\\ \mathbf{elif}\;x \leq -6.6 \cdot 10^{-10} \lor \neg \left(x \leq 1.1 \cdot 10^{+46}\right):\\ \;\;\;\;z + \frac{a}{\frac{t_1}{y + t}}\\ \mathbf{else}:\\ \;\;\;\;a + \frac{\left(x + y\right) \cdot z - y \cdot b}{t_1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t))))
   (if (<= x -5.3e+191)
     (+ z (* y (- (/ a x) (/ b x))))
     (if (or (<= x -6.6e-10) (not (<= x 1.1e+46)))
       (+ z (/ a (/ t_1 (+ y t))))
       (+ a (/ (- (* (+ x y) z) (* 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 <= -5.3e+191) {
		tmp = z + (y * ((a / x) - (b / x)));
	} else if ((x <= -6.6e-10) || !(x <= 1.1e+46)) {
		tmp = z + (a / (t_1 / (y + t)));
	} else {
		tmp = a + ((((x + y) * z) - (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 <= (-5.3d+191)) then
        tmp = z + (y * ((a / x) - (b / x)))
    else if ((x <= (-6.6d-10)) .or. (.not. (x <= 1.1d+46))) then
        tmp = z + (a / (t_1 / (y + t)))
    else
        tmp = a + ((((x + y) * z) - (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 <= -5.3e+191) {
		tmp = z + (y * ((a / x) - (b / x)));
	} else if ((x <= -6.6e-10) || !(x <= 1.1e+46)) {
		tmp = z + (a / (t_1 / (y + t)));
	} else {
		tmp = a + ((((x + y) * z) - (y * b)) / t_1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	tmp = 0
	if x <= -5.3e+191:
		tmp = z + (y * ((a / x) - (b / x)))
	elif (x <= -6.6e-10) or not (x <= 1.1e+46):
		tmp = z + (a / (t_1 / (y + t)))
	else:
		tmp = a + ((((x + y) * z) - (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 <= -5.3e+191)
		tmp = Float64(z + Float64(y * Float64(Float64(a / x) - Float64(b / x))));
	elseif ((x <= -6.6e-10) || !(x <= 1.1e+46))
		tmp = Float64(z + Float64(a / Float64(t_1 / Float64(y + t))));
	else
		tmp = Float64(a + Float64(Float64(Float64(Float64(x + y) * z) - 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 <= -5.3e+191)
		tmp = z + (y * ((a / x) - (b / x)));
	elseif ((x <= -6.6e-10) || ~((x <= 1.1e+46)))
		tmp = z + (a / (t_1 / (y + t)));
	else
		tmp = a + ((((x + y) * z) - (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[LessEqual[x, -5.3e+191], N[(z + N[(y * N[(N[(a / x), $MachinePrecision] - N[(b / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[x, -6.6e-10], N[Not[LessEqual[x, 1.1e+46]], $MachinePrecision]], N[(z + N[(a / N[(t$95$1 / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a + N[(N[(N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 8: 67.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.1 \cdot 10^{+14} \lor \neg \left(y \leq 1.3 \cdot 10^{-67}\right):\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;z + \frac{a}{\frac{y + \left(x + t\right)}{y + t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= y -3.1e+14) (not (<= y 1.3e-67)))
   (- (+ z a) b)
   (+ z (/ a (/ (+ y (+ x t)) (+ y t))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -3.1e+14) || !(y <= 1.3e-67)) {
		tmp = (z + a) - b;
	} else {
		tmp = z + (a / ((y + (x + t)) / (y + t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((y <= (-3.1d+14)) .or. (.not. (y <= 1.3d-67))) then
        tmp = (z + a) - b
    else
        tmp = z + (a / ((y + (x + t)) / (y + t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((y <= -3.1e+14) || !(y <= 1.3e-67)) {
		tmp = (z + a) - b;
	} else {
		tmp = z + (a / ((y + (x + t)) / (y + t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (y <= -3.1e+14) or not (y <= 1.3e-67):
		tmp = (z + a) - b
	else:
		tmp = z + (a / ((y + (x + t)) / (y + t)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((y <= -3.1e+14) || !(y <= 1.3e-67))
		tmp = Float64(Float64(z + a) - b);
	else
		tmp = Float64(z + Float64(a / Float64(Float64(y + Float64(x + t)) / Float64(y + t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((y <= -3.1e+14) || ~((y <= 1.3e-67)))
		tmp = (z + a) - b;
	else
		tmp = z + (a / ((y + (x + t)) / (y + t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[y, -3.1e+14], N[Not[LessEqual[y, 1.3e-67]], $MachinePrecision]], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], N[(z + N[(a / N[(N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision] / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 9: 61.7% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -8.8 \cdot 10^{+117} \lor \neg \left(x \leq 7.2 \cdot 10^{+190}\right):\\ \;\;\;\;z + y \cdot \left(\frac{a}{x} - \frac{b}{x}\right)\\ \mathbf{else}:\\ \;\;\;\;\left(z + a\right) - b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= x -8.8e+117) (not (<= x 7.2e+190)))
   (+ z (* y (- (/ a x) (/ b x))))
   (- (+ z a) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((x <= -8.8e+117) || !(x <= 7.2e+190)) {
		tmp = z + (y * ((a / x) - (b / x)));
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((x <= (-8.8d+117)) .or. (.not. (x <= 7.2d+190))) then
        tmp = z + (y * ((a / x) - (b / x)))
    else
        tmp = (z + a) - b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((x <= -8.8e+117) || !(x <= 7.2e+190)) {
		tmp = z + (y * ((a / x) - (b / x)));
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (x <= -8.8e+117) or not (x <= 7.2e+190):
		tmp = z + (y * ((a / x) - (b / x)))
	else:
		tmp = (z + a) - b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((x <= -8.8e+117) || !(x <= 7.2e+190))
		tmp = Float64(z + Float64(y * Float64(Float64(a / x) - Float64(b / x))));
	else
		tmp = Float64(Float64(z + a) - b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((x <= -8.8e+117) || ~((x <= 7.2e+190)))
		tmp = z + (y * ((a / x) - (b / x)));
	else
		tmp = (z + a) - b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[x, -8.8e+117], N[Not[LessEqual[x, 7.2e+190]], $MachinePrecision]], N[(z + N[(y * N[(N[(a / x), $MachinePrecision] - N[(b / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]]

Derivation

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

Alternative 10: 56.5% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.75 \cdot 10^{-29} \lor \neg \left(a \leq 4.2 \cdot 10^{+150}\right):\\ \;\;\;\;\frac{a}{1 + \frac{x}{y + t}}\\ \mathbf{else}:\\ \;\;\;\;\left(z + a\right) - b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= a -1.75e-29) (not (<= a 4.2e+150)))
   (/ a (+ 1.0 (/ x (+ y t))))
   (- (+ z a) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -1.75e-29) || !(a <= 4.2e+150)) {
		tmp = a / (1.0 + (x / (y + t)));
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((a <= (-1.75d-29)) .or. (.not. (a <= 4.2d+150))) then
        tmp = a / (1.0d0 + (x / (y + t)))
    else
        tmp = (z + a) - b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -1.75e-29) || !(a <= 4.2e+150)) {
		tmp = a / (1.0 + (x / (y + t)));
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (a <= -1.75e-29) or not (a <= 4.2e+150):
		tmp = a / (1.0 + (x / (y + t)))
	else:
		tmp = (z + a) - b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((a <= -1.75e-29) || !(a <= 4.2e+150))
		tmp = Float64(a / Float64(1.0 + Float64(x / Float64(y + t))));
	else
		tmp = Float64(Float64(z + a) - b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((a <= -1.75e-29) || ~((a <= 4.2e+150)))
		tmp = a / (1.0 + (x / (y + t)));
	else
		tmp = (z + a) - b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[a, -1.75e-29], N[Not[LessEqual[a, 4.2e+150]], $MachinePrecision]], N[(a / N[(1.0 + N[(x / N[(y + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]]

Derivation

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

Alternative 11: 45.1% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.25 \cdot 10^{+215}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq -2.1 \cdot 10^{+148}:\\ \;\;\;\;a\\ \mathbf{elif}\;z \leq -1.1 \cdot 10^{+103}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{+96}:\\ \;\;\;\;a - b\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -3.25e+215)
   z
   (if (<= z -2.1e+148)
     a
     (if (<= z -1.1e+103) z (if (<= z 3.8e+96) (- a b) z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -3.25e+215) {
		tmp = z;
	} else if (z <= -2.1e+148) {
		tmp = a;
	} else if (z <= -1.1e+103) {
		tmp = z;
	} else if (z <= 3.8e+96) {
		tmp = a - b;
	} 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 (z <= (-3.25d+215)) then
        tmp = z
    else if (z <= (-2.1d+148)) then
        tmp = a
    else if (z <= (-1.1d+103)) then
        tmp = z
    else if (z <= 3.8d+96) then
        tmp = a - b
    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 (z <= -3.25e+215) {
		tmp = z;
	} else if (z <= -2.1e+148) {
		tmp = a;
	} else if (z <= -1.1e+103) {
		tmp = z;
	} else if (z <= 3.8e+96) {
		tmp = a - b;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -3.25e+215:
		tmp = z
	elif z <= -2.1e+148:
		tmp = a
	elif z <= -1.1e+103:
		tmp = z
	elif z <= 3.8e+96:
		tmp = a - b
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -3.25e+215)
		tmp = z;
	elseif (z <= -2.1e+148)
		tmp = a;
	elseif (z <= -1.1e+103)
		tmp = z;
	elseif (z <= 3.8e+96)
		tmp = Float64(a - b);
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -3.25e+215)
		tmp = z;
	elseif (z <= -2.1e+148)
		tmp = a;
	elseif (z <= -1.1e+103)
		tmp = z;
	elseif (z <= 3.8e+96)
		tmp = a - b;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -3.25e+215], z, If[LessEqual[z, -2.1e+148], a, If[LessEqual[z, -1.1e+103], z, If[LessEqual[z, 3.8e+96], N[(a - b), $MachinePrecision], z]]]]

Derivation

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

Alternative 12: 60.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{+118} \lor \neg \left(x \leq 9.5 \cdot 10^{+192}\right):\\ \;\;\;\;z - y \cdot \frac{b}{x}\\ \mathbf{else}:\\ \;\;\;\;\left(z + a\right) - b\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= x -2e+118) (not (<= x 9.5e+192)))
   (- z (* y (/ b x)))
   (- (+ z a) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((x <= -2e+118) || !(x <= 9.5e+192)) {
		tmp = z - (y * (b / x));
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((x <= (-2d+118)) .or. (.not. (x <= 9.5d+192))) then
        tmp = z - (y * (b / x))
    else
        tmp = (z + a) - b
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((x <= -2e+118) || !(x <= 9.5e+192)) {
		tmp = z - (y * (b / x));
	} else {
		tmp = (z + a) - b;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (x <= -2e+118) or not (x <= 9.5e+192):
		tmp = z - (y * (b / x))
	else:
		tmp = (z + a) - b
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((x <= -2e+118) || !(x <= 9.5e+192))
		tmp = Float64(z - Float64(y * Float64(b / x)));
	else
		tmp = Float64(Float64(z + a) - b);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((x <= -2e+118) || ~((x <= 9.5e+192)))
		tmp = z - (y * (b / x));
	else
		tmp = (z + a) - b;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[x, -2e+118], N[Not[LessEqual[x, 9.5e+192]], $MachinePrecision]], N[(z - N[(y * N[(b / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]]

Derivation

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

Alternative 13: 56.6% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq 6 \cdot 10^{+117}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;\frac{a}{\frac{x + t}{t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t 6e+117) (- (+ z a) b) (/ a (/ (+ x t) t))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= 6e+117) {
		tmp = (z + a) - b;
	} else {
		tmp = a / ((x + t) / t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= 6d+117) then
        tmp = (z + a) - b
    else
        tmp = a / ((x + t) / t)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= 6e+117) {
		tmp = (z + a) - b;
	} else {
		tmp = a / ((x + t) / t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= 6e+117:
		tmp = (z + a) - b
	else:
		tmp = a / ((x + t) / t)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= 6e+117)
		tmp = Float64(Float64(z + a) - b);
	else
		tmp = Float64(a / Float64(Float64(x + t) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= 6e+117)
		tmp = (z + a) - b;
	else
		tmp = a / ((x + t) / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, 6e+117], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], N[(a / N[(N[(x + t), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 14: 56.0% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq 6 \cdot 10^{+121}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b) :precision binary64 (if (<= t 6e+121) (- (+ z a) b) a))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= 6e+121) {
		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 <= 6d+121) 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 <= 6e+121) {
		tmp = (z + a) - b;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= 6e+121:
		tmp = (z + a) - b
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= 6e+121)
		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 <= 6e+121)
		tmp = (z + a) - b;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, 6e+121], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], a]

Derivation

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

Alternative 15: 43.2% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.9 \cdot 10^{-43}:\\ \;\;\;\;a\\ \mathbf{elif}\;a \leq 3.6 \cdot 10^{-81}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= a -2.9e-43) a (if (<= a 3.6e-81) z a)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -2.9e-43) {
		tmp = a;
	} else if (a <= 3.6e-81) {
		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 (a <= (-2.9d-43)) then
        tmp = a
    else if (a <= 3.6d-81) 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 (a <= -2.9e-43) {
		tmp = a;
	} else if (a <= 3.6e-81) {
		tmp = z;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if a <= -2.9e-43:
		tmp = a
	elif a <= 3.6e-81:
		tmp = z
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (a <= -2.9e-43)
		tmp = a;
	elseif (a <= 3.6e-81)
		tmp = z;
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (a <= -2.9e-43)
		tmp = a;
	elseif (a <= 3.6e-81)
		tmp = z;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[a, -2.9e-43], a, If[LessEqual[a, 3.6e-81], z, a]]

Derivation

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

Alternative 16: 31.6% 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: 81.7% 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 2023350 
(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)))