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

Percentage Accurate: 60.3% → 88.7%

Time: 17.9s

Alternatives: 15

Speedup: 1.1×

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: 60.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 88.7% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := {\left(x + t\right)}^{2}\\ t_2 := y + \left(x + t\right)\\ t_3 := \frac{\left(\left(y + t\right) \cdot a + \left(x + y\right) \cdot z\right) - y \cdot b}{t_2}\\ \mathbf{if}\;t_3 \leq -\infty:\\ \;\;\;\;\mathsf{fma}\left(y, \left(\left(\frac{a}{x + t} + \frac{z}{x + t}\right) - \frac{b}{x + t}\right) - \left(\frac{a}{\frac{t_1}{t}} + \frac{x}{\frac{t_1}{z}}\right), \frac{x}{\frac{x + t}{z}} + \frac{a}{\frac{x + t}{t}}\right)\\ \mathbf{elif}\;t_3 \leq 10^{+284}:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(\frac{y}{t_2} + \frac{t}{t_2}\right) + \left(z - b\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (pow (+ x t) 2.0))
        (t_2 (+ y (+ x t)))
        (t_3 (/ (- (+ (* (+ y t) a) (* (+ x y) z)) (* y b)) t_2)))
   (if (<= t_3 (- INFINITY))
     (fma
      y
      (-
       (- (+ (/ a (+ x t)) (/ z (+ x t))) (/ b (+ x t)))
       (+ (/ a (/ t_1 t)) (/ x (/ t_1 z))))
      (+ (/ x (/ (+ x t) z)) (/ a (/ (+ x t) t))))
     (if (<= t_3 1e+284) t_3 (+ (* a (+ (/ y t_2) (/ t t_2))) (- z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = pow((x + t), 2.0);
	double t_2 = y + (x + t);
	double t_3 = ((((y + t) * a) + ((x + y) * z)) - (y * b)) / t_2;
	double tmp;
	if (t_3 <= -((double) INFINITY)) {
		tmp = fma(y, ((((a / (x + t)) + (z / (x + t))) - (b / (x + t))) - ((a / (t_1 / t)) + (x / (t_1 / z)))), ((x / ((x + t) / z)) + (a / ((x + t) / t))));
	} else if (t_3 <= 1e+284) {
		tmp = t_3;
	} else {
		tmp = (a * ((y / t_2) + (t / t_2))) + (z - b);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + t) ^ 2.0
	t_2 = Float64(y + Float64(x + t))
	t_3 = Float64(Float64(Float64(Float64(Float64(y + t) * a) + Float64(Float64(x + y) * z)) - Float64(y * b)) / t_2)
	tmp = 0.0
	if (t_3 <= Float64(-Inf))
		tmp = fma(y, Float64(Float64(Float64(Float64(a / Float64(x + t)) + Float64(z / Float64(x + t))) - Float64(b / Float64(x + t))) - Float64(Float64(a / Float64(t_1 / t)) + Float64(x / Float64(t_1 / z)))), Float64(Float64(x / Float64(Float64(x + t) / z)) + Float64(a / Float64(Float64(x + t) / t))));
	elseif (t_3 <= 1e+284)
		tmp = t_3;
	else
		tmp = Float64(Float64(a * Float64(Float64(y / t_2) + Float64(t / t_2))) + Float64(z - b));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[Power[N[(x + t), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$2 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision] + N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$2), $MachinePrecision]}, If[LessEqual[t$95$3, (-Infinity)], N[(y * N[(N[(N[(N[(a / N[(x + t), $MachinePrecision]), $MachinePrecision] + N[(z / N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(b / N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(a / N[(t$95$1 / t), $MachinePrecision]), $MachinePrecision] + N[(x / N[(t$95$1 / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(x / N[(N[(x + t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] + N[(a / N[(N[(x + t), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, 1e+284], t$95$3, N[(N[(a * N[(N[(y / t$95$2), $MachinePrecision] + N[(t / t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(z - b), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 2: 91.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{\left(\left(y + t\right) \cdot a + \left(x + y\right) \cdot z\right) - y \cdot b}{t_1}\\ \mathbf{if}\;t_2 \leq -2 \cdot 10^{+307} \lor \neg \left(t_2 \leq 10^{+284}\right):\\ \;\;\;\;a \cdot \left(\frac{y}{t_1} + \frac{t}{t_1}\right) + \left(z - b\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (/ (- (+ (* (+ y t) a) (* (+ x y) z)) (* y b)) t_1)))
   (if (or (<= t_2 -2e+307) (not (<= t_2 1e+284)))
     (+ (* a (+ (/ y t_1) (/ t t_1))) (- z b))
     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) + ((x + y) * z)) - (y * b)) / t_1;
	double tmp;
	if ((t_2 <= -2e+307) || !(t_2 <= 1e+284)) {
		tmp = (a * ((y / t_1) + (t / t_1))) + (z - b);
	} 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) + ((x + y) * z)) - (y * b)) / t_1
    if ((t_2 <= (-2d+307)) .or. (.not. (t_2 <= 1d+284))) then
        tmp = (a * ((y / t_1) + (t / t_1))) + (z - b)
    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) + ((x + y) * z)) - (y * b)) / t_1;
	double tmp;
	if ((t_2 <= -2e+307) || !(t_2 <= 1e+284)) {
		tmp = (a * ((y / t_1) + (t / t_1))) + (z - b);
	} 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) + ((x + y) * z)) - (y * b)) / t_1
	tmp = 0
	if (t_2 <= -2e+307) or not (t_2 <= 1e+284):
		tmp = (a * ((y / t_1) + (t / t_1))) + (z - b)
	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(Float64(x + y) * z)) - Float64(y * b)) / t_1)
	tmp = 0.0
	if ((t_2 <= -2e+307) || !(t_2 <= 1e+284))
		tmp = Float64(Float64(a * Float64(Float64(y / t_1) + Float64(t / t_1))) + Float64(z - b));
	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) + ((x + y) * z)) - (y * b)) / t_1;
	tmp = 0.0;
	if ((t_2 <= -2e+307) || ~((t_2 <= 1e+284)))
		tmp = (a * ((y / t_1) + (t / t_1))) + (z - b);
	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[(N[(x + y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - N[(y * b), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]}, If[Or[LessEqual[t$95$2, -2e+307], N[Not[LessEqual[t$95$2, 1e+284]], $MachinePrecision]], N[(N[(a * N[(N[(y / t$95$1), $MachinePrecision] + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(z - b), $MachinePrecision]), $MachinePrecision], t$95$2]]]

Derivation

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

Alternative 3: 68.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := a + z \cdot \left(\frac{y}{t_1} + \frac{x}{t_1}\right)\\ t_3 := z + \frac{y}{\frac{x}{a - b}}\\ \mathbf{if}\;x \leq -2.45 \cdot 10^{+176}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;x \leq -2 \cdot 10^{-32}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -1 \cdot 10^{-169}:\\ \;\;\;\;\left(z + a\right) - b\\ \mathbf{elif}\;x \leq 3.7 \cdot 10^{+145}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t)))
        (t_2 (+ a (* z (+ (/ y t_1) (/ x t_1)))))
        (t_3 (+ z (/ y (/ x (- a b))))))
   (if (<= x -2.45e+176)
     t_3
     (if (<= x -2e-32)
       t_2
       (if (<= x -1e-169) (- (+ z a) b) (if (<= x 3.7e+145) t_2 t_3))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = a + (z * ((y / t_1) + (x / t_1)));
	double t_3 = z + (y / (x / (a - b)));
	double tmp;
	if (x <= -2.45e+176) {
		tmp = t_3;
	} else if (x <= -2e-32) {
		tmp = t_2;
	} else if (x <= -1e-169) {
		tmp = (z + a) - b;
	} else if (x <= 3.7e+145) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = y + (x + t)
    t_2 = a + (z * ((y / t_1) + (x / t_1)))
    t_3 = z + (y / (x / (a - b)))
    if (x <= (-2.45d+176)) then
        tmp = t_3
    else if (x <= (-2d-32)) then
        tmp = t_2
    else if (x <= (-1d-169)) then
        tmp = (z + a) - b
    else if (x <= 3.7d+145) then
        tmp = t_2
    else
        tmp = t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = a + (z * ((y / t_1) + (x / t_1)));
	double t_3 = z + (y / (x / (a - b)));
	double tmp;
	if (x <= -2.45e+176) {
		tmp = t_3;
	} else if (x <= -2e-32) {
		tmp = t_2;
	} else if (x <= -1e-169) {
		tmp = (z + a) - b;
	} else if (x <= 3.7e+145) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = a + (z * ((y / t_1) + (x / t_1)))
	t_3 = z + (y / (x / (a - b)))
	tmp = 0
	if x <= -2.45e+176:
		tmp = t_3
	elif x <= -2e-32:
		tmp = t_2
	elif x <= -1e-169:
		tmp = (z + a) - b
	elif x <= 3.7e+145:
		tmp = t_2
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(a + Float64(z * Float64(Float64(y / t_1) + Float64(x / t_1))))
	t_3 = Float64(z + Float64(y / Float64(x / Float64(a - b))))
	tmp = 0.0
	if (x <= -2.45e+176)
		tmp = t_3;
	elseif (x <= -2e-32)
		tmp = t_2;
	elseif (x <= -1e-169)
		tmp = Float64(Float64(z + a) - b);
	elseif (x <= 3.7e+145)
		tmp = t_2;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = a + (z * ((y / t_1) + (x / t_1)));
	t_3 = z + (y / (x / (a - b)));
	tmp = 0.0;
	if (x <= -2.45e+176)
		tmp = t_3;
	elseif (x <= -2e-32)
		tmp = t_2;
	elseif (x <= -1e-169)
		tmp = (z + a) - b;
	elseif (x <= 3.7e+145)
		tmp = t_2;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(a + N[(z * N[(N[(y / t$95$1), $MachinePrecision] + N[(x / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(z + N[(y / N[(x / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.45e+176], t$95$3, If[LessEqual[x, -2e-32], t$95$2, If[LessEqual[x, -1e-169], N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision], If[LessEqual[x, 3.7e+145], t$95$2, t$95$3]]]]]]]

Derivation

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

Alternative 4: 67.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\left(y + t\right) \cdot a + \left(x + y\right) \cdot z}{y + \left(x + t\right)}\\ t_2 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -2.95 \cdot 10^{+60}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq 1.32 \cdot 10^{-174}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{-89}:\\ \;\;\;\;a + x \cdot \frac{z}{x + t}\\ \mathbf{elif}\;y \leq 8.5 \cdot 10^{+121}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ (+ (* (+ y t) a) (* (+ x y) z)) (+ y (+ x t))))
        (t_2 (- (+ z a) b)))
   (if (<= y -2.95e+60)
     t_2
     (if (<= y 1.32e-174)
       t_1
       (if (<= y 2.2e-89)
         (+ a (* x (/ z (+ x t))))
         (if (<= y 8.5e+121) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (((y + t) * a) + ((x + y) * z)) / (y + (x + t));
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -2.95e+60) {
		tmp = t_2;
	} else if (y <= 1.32e-174) {
		tmp = t_1;
	} else if (y <= 2.2e-89) {
		tmp = a + (x * (z / (x + t)));
	} else if (y <= 8.5e+121) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (((y + t) * a) + ((x + y) * z)) / (y + (x + t))
    t_2 = (z + a) - b
    if (y <= (-2.95d+60)) then
        tmp = t_2
    else if (y <= 1.32d-174) then
        tmp = t_1
    else if (y <= 2.2d-89) then
        tmp = a + (x * (z / (x + t)))
    else if (y <= 8.5d+121) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (((y + t) * a) + ((x + y) * z)) / (y + (x + t));
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -2.95e+60) {
		tmp = t_2;
	} else if (y <= 1.32e-174) {
		tmp = t_1;
	} else if (y <= 2.2e-89) {
		tmp = a + (x * (z / (x + t)));
	} else if (y <= 8.5e+121) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (((y + t) * a) + ((x + y) * z)) / (y + (x + t))
	t_2 = (z + a) - b
	tmp = 0
	if y <= -2.95e+60:
		tmp = t_2
	elif y <= 1.32e-174:
		tmp = t_1
	elif y <= 2.2e-89:
		tmp = a + (x * (z / (x + t)))
	elif y <= 8.5e+121:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(Float64(y + t) * a) + Float64(Float64(x + y) * z)) / Float64(y + Float64(x + t)))
	t_2 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (y <= -2.95e+60)
		tmp = t_2;
	elseif (y <= 1.32e-174)
		tmp = t_1;
	elseif (y <= 2.2e-89)
		tmp = Float64(a + Float64(x * Float64(z / Float64(x + t))));
	elseif (y <= 8.5e+121)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (((y + t) * a) + ((x + y) * z)) / (y + (x + t));
	t_2 = (z + a) - b;
	tmp = 0.0;
	if (y <= -2.95e+60)
		tmp = t_2;
	elseif (y <= 1.32e-174)
		tmp = t_1;
	elseif (y <= 2.2e-89)
		tmp = a + (x * (z / (x + t)));
	elseif (y <= 8.5e+121)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(N[(y + t), $MachinePrecision] * a), $MachinePrecision] + N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] / N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[y, -2.95e+60], t$95$2, If[LessEqual[y, 1.32e-174], t$95$1, If[LessEqual[y, 2.2e-89], N[(a + N[(x * N[(z / N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 8.5e+121], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 5: 73.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{y}{t_1}\\ \mathbf{if}\;t \leq -1.6 \cdot 10^{-45} \lor \neg \left(t \leq 1.95 \cdot 10^{-40}\right):\\ \;\;\;\;a + z \cdot \left(t_2 + \frac{x}{t_1}\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(t_2 + \frac{t}{t_1}\right) + \left(z - b\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t))) (t_2 (/ y t_1)))
   (if (or (<= t -1.6e-45) (not (<= t 1.95e-40)))
     (+ a (* z (+ t_2 (/ x t_1))))
     (+ (* a (+ t_2 (/ t t_1))) (- z b)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = y / t_1;
	double tmp;
	if ((t <= -1.6e-45) || !(t <= 1.95e-40)) {
		tmp = a + (z * (t_2 + (x / t_1)));
	} else {
		tmp = (a * (t_2 + (t / t_1))) + (z - b);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = y + (x + t)
    t_2 = y / t_1
    if ((t <= (-1.6d-45)) .or. (.not. (t <= 1.95d-40))) then
        tmp = a + (z * (t_2 + (x / t_1)))
    else
        tmp = (a * (t_2 + (t / t_1))) + (z - b)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = y / t_1;
	double tmp;
	if ((t <= -1.6e-45) || !(t <= 1.95e-40)) {
		tmp = a + (z * (t_2 + (x / t_1)));
	} else {
		tmp = (a * (t_2 + (t / t_1))) + (z - b);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	t_2 = y / t_1
	tmp = 0
	if (t <= -1.6e-45) or not (t <= 1.95e-40):
		tmp = a + (z * (t_2 + (x / t_1)))
	else:
		tmp = (a * (t_2 + (t / t_1))) + (z - b)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	t_2 = Float64(y / t_1)
	tmp = 0.0
	if ((t <= -1.6e-45) || !(t <= 1.95e-40))
		tmp = Float64(a + Float64(z * Float64(t_2 + Float64(x / t_1))));
	else
		tmp = Float64(Float64(a * Float64(t_2 + Float64(t / t_1))) + Float64(z - b));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = y / t_1;
	tmp = 0.0;
	if ((t <= -1.6e-45) || ~((t <= 1.95e-40)))
		tmp = a + (z * (t_2 + (x / t_1)));
	else
		tmp = (a * (t_2 + (t / t_1))) + (z - 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[(y / t$95$1), $MachinePrecision]}, If[Or[LessEqual[t, -1.6e-45], N[Not[LessEqual[t, 1.95e-40]], $MachinePrecision]], N[(a + N[(z * N[(t$95$2 + N[(x / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * N[(t$95$2 + N[(t / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(z - b), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 6: 59.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z + \frac{y}{\frac{x}{a - b}}\\ t_2 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -1.55 \cdot 10^{+128}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.16 \cdot 10^{-31}:\\ \;\;\;\;a + \frac{x \cdot z}{x + t}\\ \mathbf{elif}\;x \leq -3.3 \cdot 10^{-308}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 3.8 \cdot 10^{-163}:\\ \;\;\;\;\frac{a}{\frac{x + t}{t}}\\ \mathbf{elif}\;x \leq 7.1 \cdot 10^{+115}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ z (/ y (/ x (- a b))))) (t_2 (- (+ z a) b)))
   (if (<= x -1.55e+128)
     t_1
     (if (<= x -1.16e-31)
       (+ a (/ (* x z) (+ x t)))
       (if (<= x -3.3e-308)
         t_2
         (if (<= x 3.8e-163)
           (/ a (/ (+ x t) t))
           (if (<= x 7.1e+115) t_2 t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z + (y / (x / (a - b)));
	double t_2 = (z + a) - b;
	double tmp;
	if (x <= -1.55e+128) {
		tmp = t_1;
	} else if (x <= -1.16e-31) {
		tmp = a + ((x * z) / (x + t));
	} else if (x <= -3.3e-308) {
		tmp = t_2;
	} else if (x <= 3.8e-163) {
		tmp = a / ((x + t) / t);
	} else if (x <= 7.1e+115) {
		tmp = t_2;
	} 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) :: t_2
    real(8) :: tmp
    t_1 = z + (y / (x / (a - b)))
    t_2 = (z + a) - b
    if (x <= (-1.55d+128)) then
        tmp = t_1
    else if (x <= (-1.16d-31)) then
        tmp = a + ((x * z) / (x + t))
    else if (x <= (-3.3d-308)) then
        tmp = t_2
    else if (x <= 3.8d-163) then
        tmp = a / ((x + t) / t)
    else if (x <= 7.1d+115) then
        tmp = t_2
    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 + (y / (x / (a - b)));
	double t_2 = (z + a) - b;
	double tmp;
	if (x <= -1.55e+128) {
		tmp = t_1;
	} else if (x <= -1.16e-31) {
		tmp = a + ((x * z) / (x + t));
	} else if (x <= -3.3e-308) {
		tmp = t_2;
	} else if (x <= 3.8e-163) {
		tmp = a / ((x + t) / t);
	} else if (x <= 7.1e+115) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z + (y / (x / (a - b)))
	t_2 = (z + a) - b
	tmp = 0
	if x <= -1.55e+128:
		tmp = t_1
	elif x <= -1.16e-31:
		tmp = a + ((x * z) / (x + t))
	elif x <= -3.3e-308:
		tmp = t_2
	elif x <= 3.8e-163:
		tmp = a / ((x + t) / t)
	elif x <= 7.1e+115:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z + Float64(y / Float64(x / Float64(a - b))))
	t_2 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -1.55e+128)
		tmp = t_1;
	elseif (x <= -1.16e-31)
		tmp = Float64(a + Float64(Float64(x * z) / Float64(x + t)));
	elseif (x <= -3.3e-308)
		tmp = t_2;
	elseif (x <= 3.8e-163)
		tmp = Float64(a / Float64(Float64(x + t) / t));
	elseif (x <= 7.1e+115)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z + (y / (x / (a - b)));
	t_2 = (z + a) - b;
	tmp = 0.0;
	if (x <= -1.55e+128)
		tmp = t_1;
	elseif (x <= -1.16e-31)
		tmp = a + ((x * z) / (x + t));
	elseif (x <= -3.3e-308)
		tmp = t_2;
	elseif (x <= 3.8e-163)
		tmp = a / ((x + t) / t);
	elseif (x <= 7.1e+115)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z + N[(y / N[(x / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[x, -1.55e+128], t$95$1, If[LessEqual[x, -1.16e-31], N[(a + N[(N[(x * z), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -3.3e-308], t$95$2, If[LessEqual[x, 3.8e-163], N[(a / N[(N[(x + t), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 7.1e+115], t$95$2, t$95$1]]]]]]]

Derivation

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

Alternative 7: 60.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z + \frac{y}{\frac{x}{a - b}}\\ t_2 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -2.2 \cdot 10^{+130}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -2.45 \cdot 10^{-29}:\\ \;\;\;\;a + \frac{x \cdot z}{x + t}\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{-287}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 4.5 \cdot 10^{-163}:\\ \;\;\;\;a + \frac{\left(x + y\right) \cdot z}{t}\\ \mathbf{elif}\;x \leq 9.6 \cdot 10^{+114}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ z (/ y (/ x (- a b))))) (t_2 (- (+ z a) b)))
   (if (<= x -2.2e+130)
     t_1
     (if (<= x -2.45e-29)
       (+ a (/ (* x z) (+ x t)))
       (if (<= x 2.05e-287)
         t_2
         (if (<= x 4.5e-163)
           (+ a (/ (* (+ x y) z) t))
           (if (<= x 9.6e+114) t_2 t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z + (y / (x / (a - b)));
	double t_2 = (z + a) - b;
	double tmp;
	if (x <= -2.2e+130) {
		tmp = t_1;
	} else if (x <= -2.45e-29) {
		tmp = a + ((x * z) / (x + t));
	} else if (x <= 2.05e-287) {
		tmp = t_2;
	} else if (x <= 4.5e-163) {
		tmp = a + (((x + y) * z) / t);
	} else if (x <= 9.6e+114) {
		tmp = t_2;
	} 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) :: t_2
    real(8) :: tmp
    t_1 = z + (y / (x / (a - b)))
    t_2 = (z + a) - b
    if (x <= (-2.2d+130)) then
        tmp = t_1
    else if (x <= (-2.45d-29)) then
        tmp = a + ((x * z) / (x + t))
    else if (x <= 2.05d-287) then
        tmp = t_2
    else if (x <= 4.5d-163) then
        tmp = a + (((x + y) * z) / t)
    else if (x <= 9.6d+114) then
        tmp = t_2
    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 + (y / (x / (a - b)));
	double t_2 = (z + a) - b;
	double tmp;
	if (x <= -2.2e+130) {
		tmp = t_1;
	} else if (x <= -2.45e-29) {
		tmp = a + ((x * z) / (x + t));
	} else if (x <= 2.05e-287) {
		tmp = t_2;
	} else if (x <= 4.5e-163) {
		tmp = a + (((x + y) * z) / t);
	} else if (x <= 9.6e+114) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z + (y / (x / (a - b)))
	t_2 = (z + a) - b
	tmp = 0
	if x <= -2.2e+130:
		tmp = t_1
	elif x <= -2.45e-29:
		tmp = a + ((x * z) / (x + t))
	elif x <= 2.05e-287:
		tmp = t_2
	elif x <= 4.5e-163:
		tmp = a + (((x + y) * z) / t)
	elif x <= 9.6e+114:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z + Float64(y / Float64(x / Float64(a - b))))
	t_2 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -2.2e+130)
		tmp = t_1;
	elseif (x <= -2.45e-29)
		tmp = Float64(a + Float64(Float64(x * z) / Float64(x + t)));
	elseif (x <= 2.05e-287)
		tmp = t_2;
	elseif (x <= 4.5e-163)
		tmp = Float64(a + Float64(Float64(Float64(x + y) * z) / t));
	elseif (x <= 9.6e+114)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = z + (y / (x / (a - b)));
	t_2 = (z + a) - b;
	tmp = 0.0;
	if (x <= -2.2e+130)
		tmp = t_1;
	elseif (x <= -2.45e-29)
		tmp = a + ((x * z) / (x + t));
	elseif (x <= 2.05e-287)
		tmp = t_2;
	elseif (x <= 4.5e-163)
		tmp = a + (((x + y) * z) / t);
	elseif (x <= 9.6e+114)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(z + N[(y / N[(x / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[x, -2.2e+130], t$95$1, If[LessEqual[x, -2.45e-29], N[(a + N[(N[(x * z), $MachinePrecision] / N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.05e-287], t$95$2, If[LessEqual[x, 4.5e-163], N[(a + N[(N[(N[(x + y), $MachinePrecision] * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 9.6e+114], t$95$2, t$95$1]]]]]]]

Derivation

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

Alternative 8: 61.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + x \cdot \frac{z}{x + t}\\ t_2 := z + \frac{y}{\frac{x}{a - b}}\\ t_3 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -7 \cdot 10^{+161}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -1.7 \cdot 10^{-29}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -5.7 \cdot 10^{-306}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;x \leq 4.5 \cdot 10^{-163}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 8.5 \cdot 10^{+114}:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ a (* x (/ z (+ x t)))))
        (t_2 (+ z (/ y (/ x (- a b)))))
        (t_3 (- (+ z a) b)))
   (if (<= x -7e+161)
     t_2
     (if (<= x -1.7e-29)
       t_1
       (if (<= x -5.7e-306)
         t_3
         (if (<= x 4.5e-163) t_1 (if (<= x 8.5e+114) t_3 t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a + (x * (z / (x + t)));
	double t_2 = z + (y / (x / (a - b)));
	double t_3 = (z + a) - b;
	double tmp;
	if (x <= -7e+161) {
		tmp = t_2;
	} else if (x <= -1.7e-29) {
		tmp = t_1;
	} else if (x <= -5.7e-306) {
		tmp = t_3;
	} else if (x <= 4.5e-163) {
		tmp = t_1;
	} else if (x <= 8.5e+114) {
		tmp = t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = a + (x * (z / (x + t)))
    t_2 = z + (y / (x / (a - b)))
    t_3 = (z + a) - b
    if (x <= (-7d+161)) then
        tmp = t_2
    else if (x <= (-1.7d-29)) then
        tmp = t_1
    else if (x <= (-5.7d-306)) then
        tmp = t_3
    else if (x <= 4.5d-163) then
        tmp = t_1
    else if (x <= 8.5d+114) then
        tmp = t_3
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a + (x * (z / (x + t)));
	double t_2 = z + (y / (x / (a - b)));
	double t_3 = (z + a) - b;
	double tmp;
	if (x <= -7e+161) {
		tmp = t_2;
	} else if (x <= -1.7e-29) {
		tmp = t_1;
	} else if (x <= -5.7e-306) {
		tmp = t_3;
	} else if (x <= 4.5e-163) {
		tmp = t_1;
	} else if (x <= 8.5e+114) {
		tmp = t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a + (x * (z / (x + t)))
	t_2 = z + (y / (x / (a - b)))
	t_3 = (z + a) - b
	tmp = 0
	if x <= -7e+161:
		tmp = t_2
	elif x <= -1.7e-29:
		tmp = t_1
	elif x <= -5.7e-306:
		tmp = t_3
	elif x <= 4.5e-163:
		tmp = t_1
	elif x <= 8.5e+114:
		tmp = t_3
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a + Float64(x * Float64(z / Float64(x + t))))
	t_2 = Float64(z + Float64(y / Float64(x / Float64(a - b))))
	t_3 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -7e+161)
		tmp = t_2;
	elseif (x <= -1.7e-29)
		tmp = t_1;
	elseif (x <= -5.7e-306)
		tmp = t_3;
	elseif (x <= 4.5e-163)
		tmp = t_1;
	elseif (x <= 8.5e+114)
		tmp = t_3;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a + (x * (z / (x + t)));
	t_2 = z + (y / (x / (a - b)));
	t_3 = (z + a) - b;
	tmp = 0.0;
	if (x <= -7e+161)
		tmp = t_2;
	elseif (x <= -1.7e-29)
		tmp = t_1;
	elseif (x <= -5.7e-306)
		tmp = t_3;
	elseif (x <= 4.5e-163)
		tmp = t_1;
	elseif (x <= 8.5e+114)
		tmp = t_3;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a + N[(x * N[(z / N[(x + t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(z + N[(y / N[(x / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[x, -7e+161], t$95$2, If[LessEqual[x, -1.7e-29], t$95$1, If[LessEqual[x, -5.7e-306], t$95$3, If[LessEqual[x, 4.5e-163], t$95$1, If[LessEqual[x, 8.5e+114], t$95$3, t$95$2]]]]]]]]

Derivation

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

Alternative 9: 59.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ t_2 := z + \frac{y}{\frac{x}{a - b}}\\ \mathbf{if}\;x \leq -1.9 \cdot 10^{+130}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -3.3 \cdot 10^{-308}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{-164}:\\ \;\;\;\;\frac{a}{\frac{x + t}{t}}\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{+115}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)) (t_2 (+ z (/ y (/ x (- a b))))))
   (if (<= x -1.9e+130)
     t_2
     (if (<= x -3.3e-308)
       t_1
       (if (<= x 2.6e-164)
         (/ a (/ (+ x t) t))
         (if (<= x 2.05e+115) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double t_2 = z + (y / (x / (a - b)));
	double tmp;
	if (x <= -1.9e+130) {
		tmp = t_2;
	} else if (x <= -3.3e-308) {
		tmp = t_1;
	} else if (x <= 2.6e-164) {
		tmp = a / ((x + t) / t);
	} else if (x <= 2.05e+115) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (z + a) - b
    t_2 = z + (y / (x / (a - b)))
    if (x <= (-1.9d+130)) then
        tmp = t_2
    else if (x <= (-3.3d-308)) then
        tmp = t_1
    else if (x <= 2.6d-164) then
        tmp = a / ((x + t) / t)
    else if (x <= 2.05d+115) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double t_2 = z + (y / (x / (a - b)));
	double tmp;
	if (x <= -1.9e+130) {
		tmp = t_2;
	} else if (x <= -3.3e-308) {
		tmp = t_1;
	} else if (x <= 2.6e-164) {
		tmp = a / ((x + t) / t);
	} else if (x <= 2.05e+115) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	t_2 = z + (y / (x / (a - b)))
	tmp = 0
	if x <= -1.9e+130:
		tmp = t_2
	elif x <= -3.3e-308:
		tmp = t_1
	elif x <= 2.6e-164:
		tmp = a / ((x + t) / t)
	elif x <= 2.05e+115:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	t_2 = Float64(z + Float64(y / Float64(x / Float64(a - b))))
	tmp = 0.0
	if (x <= -1.9e+130)
		tmp = t_2;
	elseif (x <= -3.3e-308)
		tmp = t_1;
	elseif (x <= 2.6e-164)
		tmp = Float64(a / Float64(Float64(x + t) / t));
	elseif (x <= 2.05e+115)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	t_2 = z + (y / (x / (a - b)));
	tmp = 0.0;
	if (x <= -1.9e+130)
		tmp = t_2;
	elseif (x <= -3.3e-308)
		tmp = t_1;
	elseif (x <= 2.6e-164)
		tmp = a / ((x + t) / t);
	elseif (x <= 2.05e+115)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, Block[{t$95$2 = N[(z + N[(y / N[(x / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.9e+130], t$95$2, If[LessEqual[x, -3.3e-308], t$95$1, If[LessEqual[x, 2.6e-164], N[(a / N[(N[(x + t), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.05e+115], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 10: 56.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ t_2 := \frac{z}{\frac{x + t}{x}}\\ \mathbf{if}\;x \leq -1.1 \cdot 10^{+139}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -3.3 \cdot 10^{-308}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 3.4 \cdot 10^{-164}:\\ \;\;\;\;\frac{a}{\frac{x + t}{t}}\\ \mathbf{elif}\;x \leq 4.2 \cdot 10^{+118}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)) (t_2 (/ z (/ (+ x t) x))))
   (if (<= x -1.1e+139)
     t_2
     (if (<= x -3.3e-308)
       t_1
       (if (<= x 3.4e-164)
         (/ a (/ (+ x t) t))
         (if (<= x 4.2e+118) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double t_2 = z / ((x + t) / x);
	double tmp;
	if (x <= -1.1e+139) {
		tmp = t_2;
	} else if (x <= -3.3e-308) {
		tmp = t_1;
	} else if (x <= 3.4e-164) {
		tmp = a / ((x + t) / t);
	} else if (x <= 4.2e+118) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (z + a) - b
    t_2 = z / ((x + t) / x)
    if (x <= (-1.1d+139)) then
        tmp = t_2
    else if (x <= (-3.3d-308)) then
        tmp = t_1
    else if (x <= 3.4d-164) then
        tmp = a / ((x + t) / t)
    else if (x <= 4.2d+118) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double t_2 = z / ((x + t) / x);
	double tmp;
	if (x <= -1.1e+139) {
		tmp = t_2;
	} else if (x <= -3.3e-308) {
		tmp = t_1;
	} else if (x <= 3.4e-164) {
		tmp = a / ((x + t) / t);
	} else if (x <= 4.2e+118) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	t_2 = z / ((x + t) / x)
	tmp = 0
	if x <= -1.1e+139:
		tmp = t_2
	elif x <= -3.3e-308:
		tmp = t_1
	elif x <= 3.4e-164:
		tmp = a / ((x + t) / t)
	elif x <= 4.2e+118:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	t_2 = Float64(z / Float64(Float64(x + t) / x))
	tmp = 0.0
	if (x <= -1.1e+139)
		tmp = t_2;
	elseif (x <= -3.3e-308)
		tmp = t_1;
	elseif (x <= 3.4e-164)
		tmp = Float64(a / Float64(Float64(x + t) / t));
	elseif (x <= 4.2e+118)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	t_2 = z / ((x + t) / x);
	tmp = 0.0;
	if (x <= -1.1e+139)
		tmp = t_2;
	elseif (x <= -3.3e-308)
		tmp = t_1;
	elseif (x <= 3.4e-164)
		tmp = a / ((x + t) / t);
	elseif (x <= 4.2e+118)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, Block[{t$95$2 = N[(z / N[(N[(x + t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.1e+139], t$95$2, If[LessEqual[x, -3.3e-308], t$95$1, If[LessEqual[x, 3.4e-164], N[(a / N[(N[(x + t), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.2e+118], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 11: 55.3% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -9.8 \cdot 10^{+103}:\\ \;\;\;\;z\\ \mathbf{elif}\;x \leq -3.3 \cdot 10^{-308}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{-164}:\\ \;\;\;\;a\\ \mathbf{elif}\;x \leq 1.42 \cdot 10^{+116}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;z + a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= x -9.8e+103)
     z
     (if (<= x -3.3e-308)
       t_1
       (if (<= x 2.6e-164) a (if (<= x 1.42e+116) t_1 (+ z 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 (x <= -9.8e+103) {
		tmp = z;
	} else if (x <= -3.3e-308) {
		tmp = t_1;
	} else if (x <= 2.6e-164) {
		tmp = a;
	} else if (x <= 1.42e+116) {
		tmp = t_1;
	} else {
		tmp = z + 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 (x <= (-9.8d+103)) then
        tmp = z
    else if (x <= (-3.3d-308)) then
        tmp = t_1
    else if (x <= 2.6d-164) then
        tmp = a
    else if (x <= 1.42d+116) then
        tmp = t_1
    else
        tmp = z + 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 (x <= -9.8e+103) {
		tmp = z;
	} else if (x <= -3.3e-308) {
		tmp = t_1;
	} else if (x <= 2.6e-164) {
		tmp = a;
	} else if (x <= 1.42e+116) {
		tmp = t_1;
	} else {
		tmp = z + a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if x <= -9.8e+103:
		tmp = z
	elif x <= -3.3e-308:
		tmp = t_1
	elif x <= 2.6e-164:
		tmp = a
	elif x <= 1.42e+116:
		tmp = t_1
	else:
		tmp = z + a
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -9.8e+103)
		tmp = z;
	elseif (x <= -3.3e-308)
		tmp = t_1;
	elseif (x <= 2.6e-164)
		tmp = a;
	elseif (x <= 1.42e+116)
		tmp = t_1;
	else
		tmp = Float64(z + 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 (x <= -9.8e+103)
		tmp = z;
	elseif (x <= -3.3e-308)
		tmp = t_1;
	elseif (x <= 2.6e-164)
		tmp = a;
	elseif (x <= 1.42e+116)
		tmp = t_1;
	else
		tmp = z + 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[x, -9.8e+103], z, If[LessEqual[x, -3.3e-308], t$95$1, If[LessEqual[x, 2.6e-164], a, If[LessEqual[x, 1.42e+116], t$95$1, N[(z + a), $MachinePrecision]]]]]]

Derivation

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

Alternative 12: 54.8% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;x \leq -1.46 \cdot 10^{+105}:\\ \;\;\;\;z\\ \mathbf{elif}\;x \leq -3.45 \cdot 10^{-308}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{-164}:\\ \;\;\;\;\frac{a}{\frac{x + t}{t}}\\ \mathbf{elif}\;x \leq 6.5 \cdot 10^{+114}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;z + a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= x -1.46e+105)
     z
     (if (<= x -3.45e-308)
       t_1
       (if (<= x 2.6e-164)
         (/ a (/ (+ x t) t))
         (if (<= x 6.5e+114) t_1 (+ z 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 (x <= -1.46e+105) {
		tmp = z;
	} else if (x <= -3.45e-308) {
		tmp = t_1;
	} else if (x <= 2.6e-164) {
		tmp = a / ((x + t) / t);
	} else if (x <= 6.5e+114) {
		tmp = t_1;
	} else {
		tmp = z + 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 (x <= (-1.46d+105)) then
        tmp = z
    else if (x <= (-3.45d-308)) then
        tmp = t_1
    else if (x <= 2.6d-164) then
        tmp = a / ((x + t) / t)
    else if (x <= 6.5d+114) then
        tmp = t_1
    else
        tmp = z + 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 (x <= -1.46e+105) {
		tmp = z;
	} else if (x <= -3.45e-308) {
		tmp = t_1;
	} else if (x <= 2.6e-164) {
		tmp = a / ((x + t) / t);
	} else if (x <= 6.5e+114) {
		tmp = t_1;
	} else {
		tmp = z + a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if x <= -1.46e+105:
		tmp = z
	elif x <= -3.45e-308:
		tmp = t_1
	elif x <= 2.6e-164:
		tmp = a / ((x + t) / t)
	elif x <= 6.5e+114:
		tmp = t_1
	else:
		tmp = z + a
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (x <= -1.46e+105)
		tmp = z;
	elseif (x <= -3.45e-308)
		tmp = t_1;
	elseif (x <= 2.6e-164)
		tmp = Float64(a / Float64(Float64(x + t) / t));
	elseif (x <= 6.5e+114)
		tmp = t_1;
	else
		tmp = Float64(z + 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 (x <= -1.46e+105)
		tmp = z;
	elseif (x <= -3.45e-308)
		tmp = t_1;
	elseif (x <= 2.6e-164)
		tmp = a / ((x + t) / t);
	elseif (x <= 6.5e+114)
		tmp = t_1;
	else
		tmp = z + 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[x, -1.46e+105], z, If[LessEqual[x, -3.45e-308], t$95$1, If[LessEqual[x, 2.6e-164], N[(a / N[(N[(x + t), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 6.5e+114], t$95$1, N[(z + a), $MachinePrecision]]]]]]

Derivation

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

Alternative 13: 44.2% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.9 \cdot 10^{+125}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq 1.15 \cdot 10^{-12}:\\ \;\;\;\;a\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -1.9e+125) z (if (<= z 1.15e-12) a z)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.9e+125) {
		tmp = z;
	} else if (z <= 1.15e-12) {
		tmp = a;
	} else {
		tmp = z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (z <= (-1.9d+125)) then
        tmp = z
    else if (z <= 1.15d-12) then
        tmp = a
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -1.9e+125) {
		tmp = z;
	} else if (z <= 1.15e-12) {
		tmp = a;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if z <= -1.9e+125:
		tmp = z
	elif z <= 1.15e-12:
		tmp = a
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -1.9e+125)
		tmp = z;
	elseif (z <= 1.15e-12)
		tmp = a;
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (z <= -1.9e+125)
		tmp = z;
	elseif (z <= 1.15e-12)
		tmp = a;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -1.9e+125], z, If[LessEqual[z, 1.15e-12], a, z]]

Derivation

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

Alternative 14: 52.5% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.35 \cdot 10^{+154}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;z + a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b) :precision binary64 (if (<= x -1.35e+154) z (+ z a)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -1.35e+154) {
		tmp = z;
	} else {
		tmp = z + 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 (x <= (-1.35d+154)) then
        tmp = z
    else
        tmp = z + 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 (x <= -1.35e+154) {
		tmp = z;
	} else {
		tmp = z + a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if x <= -1.35e+154:
		tmp = z
	else:
		tmp = z + a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -1.35e+154)
		tmp = z;
	else
		tmp = Float64(z + a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (x <= -1.35e+154)
		tmp = z;
	else
		tmp = z + a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -1.35e+154], z, N[(z + a), $MachinePrecision]]

Derivation

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

Alternative 15: 32.0% 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: 82.2% 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 2024003 
(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)))