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

Percentage Accurate: 60.7% → 91.7%

Time: 14.1s

Alternatives: 14

Speedup: 1.4×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 60.7% 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: 91.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{\left(\left(x + y\right) \cdot z + \left(y + t\right) \cdot a\right) - y \cdot b}{t_1}\\ \mathbf{if}\;t_2 \leq -\infty \lor \neg \left(t_2 \leq 10^{+300}\right):\\ \;\;\;\;z \cdot \left(\frac{x}{t_1} + \frac{y}{t_1}\right) + \frac{y}{\frac{x + y}{a - b}}\\ \mathbf{else}:\\ \;\;\;\;\frac{t \cdot a + \left(x \cdot z + y \cdot \left(\left(z + a\right) - b\right)\right)}{t_1}\\ \end{array} \end{array} \]

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 (or (<= t_2 (- INFINITY)) (not (<= t_2 1e+300)))
     (+ (* z (+ (/ x t_1) (/ y t_1))) (/ y (/ (+ x y) (- a b))))
     (/ (+ (* t a) (+ (* x z) (* y (- (+ z a) b)))) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	double tmp;
	if ((t_2 <= -((double) INFINITY)) || !(t_2 <= 1e+300)) {
		tmp = (z * ((x / t_1) + (y / t_1))) + (y / ((x + y) / (a - b)));
	} else {
		tmp = ((t * a) + ((x * z) + (y * ((z + a) - b)))) / t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	double tmp;
	if ((t_2 <= -Double.POSITIVE_INFINITY) || !(t_2 <= 1e+300)) {
		tmp = (z * ((x / t_1) + (y / t_1))) + (y / ((x + y) / (a - b)));
	} else {
		tmp = ((t * a) + ((x * z) + (y * ((z + a) - b)))) / t_1;
	}
	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) or not (t_2 <= 1e+300):
		tmp = (z * ((x / t_1) + (y / t_1))) + (y / ((x + y) / (a - b)))
	else:
		tmp = ((t * a) + ((x * z) + (y * ((z + a) - b)))) / t_1
	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)) || !(t_2 <= 1e+300))
		tmp = Float64(Float64(z * Float64(Float64(x / t_1) + Float64(y / t_1))) + Float64(y / Float64(Float64(x + y) / Float64(a - b))));
	else
		tmp = Float64(Float64(Float64(t * a) + Float64(Float64(x * z) + Float64(y * Float64(Float64(z + a) - b)))) / t_1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	tmp = 0.0;
	if ((t_2 <= -Inf) || ~((t_2 <= 1e+300)))
		tmp = (z * ((x / t_1) + (y / t_1))) + (y / ((x + y) / (a - b)));
	else
		tmp = ((t * a) + ((x * z) + (y * ((z + a) - b)))) / t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(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[Or[LessEqual[t$95$2, (-Infinity)], N[Not[LessEqual[t$95$2, 1e+300]], $MachinePrecision]], N[(N[(z * N[(N[(x / t$95$1), $MachinePrecision] + N[(y / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y / N[(N[(x + y), $MachinePrecision] / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(t * a), $MachinePrecision] + N[(N[(x * z), $MachinePrecision] + N[(y * N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision]]]]

Derivation

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

Alternative 2: 87.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ t_2 := \frac{\left(\left(x + y\right) \cdot z + \left(y + t\right) \cdot a\right) - y \cdot b}{t_1}\\ t_3 := \left(z + a\right) - b\\ \mathbf{if}\;t_2 \leq -\infty:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t_2 \leq 2 \cdot 10^{+255}:\\ \;\;\;\;\frac{t \cdot a + \left(x \cdot z + y \cdot t_3\right)}{t_1}\\ \mathbf{else}:\\ \;\;\;\;a + z \cdot \left(\frac{x}{t_1} + \frac{y}{t_1}\right)\\ \end{array} \end{array} \]

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 (- (+ z a) b)))
   (if (<= t_2 (- INFINITY))
     t_3
     (if (<= t_2 2e+255)
       (/ (+ (* t a) (+ (* x z) (* y t_3))) t_1)
       (+ a (* z (+ (/ x t_1) (/ y t_1))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	double t_3 = (z + a) - b;
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = t_3;
	} else if (t_2 <= 2e+255) {
		tmp = ((t * a) + ((x * z) + (y * t_3))) / t_1;
	} else {
		tmp = a + (z * ((x / t_1) + (y / t_1)));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double t_2 = ((((x + y) * z) + ((y + t) * a)) - (y * b)) / t_1;
	double t_3 = (z + a) - b;
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else if (t_2 <= 2e+255) {
		tmp = ((t * a) + ((x * z) + (y * t_3))) / t_1;
	} else {
		tmp = a + (z * ((x / t_1) + (y / t_1)));
	}
	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 = (z + a) - b
	tmp = 0
	if t_2 <= -math.inf:
		tmp = t_3
	elif t_2 <= 2e+255:
		tmp = ((t * a) + ((x * z) + (y * t_3))) / t_1
	else:
		tmp = a + (z * ((x / t_1) + (y / t_1)))
	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(Float64(z + a) - b)
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = t_3;
	elseif (t_2 <= 2e+255)
		tmp = Float64(Float64(Float64(t * a) + Float64(Float64(x * z) + Float64(y * t_3))) / t_1);
	else
		tmp = Float64(a + Float64(z * Float64(Float64(x / t_1) + Float64(y / t_1))));
	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 = (z + a) - b;
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = t_3;
	elseif (t_2 <= 2e+255)
		tmp = ((t * a) + ((x * z) + (y * t_3))) / t_1;
	else
		tmp = a + (z * ((x / t_1) + (y / t_1)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(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[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], t$95$3, If[LessEqual[t$95$2, 2e+255], N[(N[(N[(t * a), $MachinePrecision] + N[(N[(x * z), $MachinePrecision] + N[(y * t$95$3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t$95$1), $MachinePrecision], N[(a + N[(z * N[(N[(x / t$95$1), $MachinePrecision] + N[(y / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 3: 79.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y + \left(x + t\right)\\ \mathbf{if}\;t \leq -1.16 \cdot 10^{+61} \lor \neg \left(t \leq 1.75 \cdot 10^{+112}\right):\\ \;\;\;\;a + z \cdot \left(\frac{x}{t_1} + \frac{y}{t_1}\right)\\ \mathbf{else}:\\ \;\;\;\;z + \frac{y}{\frac{x + y}{a - b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ y (+ x t))))
   (if (or (<= t -1.16e+61) (not (<= t 1.75e+112)))
     (+ a (* z (+ (/ x t_1) (/ y t_1))))
     (+ z (/ y (/ (+ x y) (- a b)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double tmp;
	if ((t <= -1.16e+61) || !(t <= 1.75e+112)) {
		tmp = a + (z * ((x / t_1) + (y / t_1)));
	} else {
		tmp = z + (y / ((x + y) / (a - b)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y + (x + t)
    if ((t <= (-1.16d+61)) .or. (.not. (t <= 1.75d+112))) then
        tmp = a + (z * ((x / t_1) + (y / t_1)))
    else
        tmp = z + (y / ((x + y) / (a - b)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = y + (x + t);
	double tmp;
	if ((t <= -1.16e+61) || !(t <= 1.75e+112)) {
		tmp = a + (z * ((x / t_1) + (y / t_1)));
	} else {
		tmp = z + (y / ((x + y) / (a - b)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = y + (x + t)
	tmp = 0
	if (t <= -1.16e+61) or not (t <= 1.75e+112):
		tmp = a + (z * ((x / t_1) + (y / t_1)))
	else:
		tmp = z + (y / ((x + y) / (a - b)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(y + Float64(x + t))
	tmp = 0.0
	if ((t <= -1.16e+61) || !(t <= 1.75e+112))
		tmp = Float64(a + Float64(z * Float64(Float64(x / t_1) + Float64(y / t_1))));
	else
		tmp = Float64(z + Float64(y / Float64(Float64(x + y) / Float64(a - b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = y + (x + t);
	tmp = 0.0;
	if ((t <= -1.16e+61) || ~((t <= 1.75e+112)))
		tmp = a + (z * ((x / t_1) + (y / t_1)));
	else
		tmp = z + (y / ((x + y) / (a - b)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(y + N[(x + t), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t, -1.16e+61], N[Not[LessEqual[t, 1.75e+112]], $MachinePrecision]], N[(a + N[(z * N[(N[(x / t$95$1), $MachinePrecision] + N[(y / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(z + N[(y / N[(N[(x + y), $MachinePrecision] / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 4: 60.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ t_2 := a + y \cdot \left(\frac{z}{t} - \frac{b}{t}\right)\\ \mathbf{if}\;t \leq -1.1 \cdot 10^{+116}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 2.3 \cdot 10^{-252}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 2.2 \cdot 10^{-94}:\\ \;\;\;\;z + \frac{a}{\frac{x}{t}}\\ \mathbf{elif}\;t \leq 6.2 \cdot 10^{+111}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)) (t_2 (+ a (* y (- (/ z t) (/ b t))))))
   (if (<= t -1.1e+116)
     t_2
     (if (<= t 2.3e-252)
       t_1
       (if (<= t 2.2e-94) (+ z (/ a (/ x t))) (if (<= t 6.2e+111) 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 = a + (y * ((z / t) - (b / t)));
	double tmp;
	if (t <= -1.1e+116) {
		tmp = t_2;
	} else if (t <= 2.3e-252) {
		tmp = t_1;
	} else if (t <= 2.2e-94) {
		tmp = z + (a / (x / t));
	} else if (t <= 6.2e+111) {
		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 = a + (y * ((z / t) - (b / t)))
    if (t <= (-1.1d+116)) then
        tmp = t_2
    else if (t <= 2.3d-252) then
        tmp = t_1
    else if (t <= 2.2d-94) then
        tmp = z + (a / (x / t))
    else if (t <= 6.2d+111) 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 = a + (y * ((z / t) - (b / t)));
	double tmp;
	if (t <= -1.1e+116) {
		tmp = t_2;
	} else if (t <= 2.3e-252) {
		tmp = t_1;
	} else if (t <= 2.2e-94) {
		tmp = z + (a / (x / t));
	} else if (t <= 6.2e+111) {
		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 = a + (y * ((z / t) - (b / t)))
	tmp = 0
	if t <= -1.1e+116:
		tmp = t_2
	elif t <= 2.3e-252:
		tmp = t_1
	elif t <= 2.2e-94:
		tmp = z + (a / (x / t))
	elif t <= 6.2e+111:
		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(a + Float64(y * Float64(Float64(z / t) - Float64(b / t))))
	tmp = 0.0
	if (t <= -1.1e+116)
		tmp = t_2;
	elseif (t <= 2.3e-252)
		tmp = t_1;
	elseif (t <= 2.2e-94)
		tmp = Float64(z + Float64(a / Float64(x / t)));
	elseif (t <= 6.2e+111)
		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 = a + (y * ((z / t) - (b / t)));
	tmp = 0.0;
	if (t <= -1.1e+116)
		tmp = t_2;
	elseif (t <= 2.3e-252)
		tmp = t_1;
	elseif (t <= 2.2e-94)
		tmp = z + (a / (x / t));
	elseif (t <= 6.2e+111)
		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[(a + N[(y * N[(N[(z / t), $MachinePrecision] - N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.1e+116], t$95$2, If[LessEqual[t, 2.3e-252], t$95$1, If[LessEqual[t, 2.2e-94], N[(z + N[(a / N[(x / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 6.2e+111], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 5: 75.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -3.2 \cdot 10^{+114}:\\ \;\;\;\;a + y \cdot \left(\frac{z}{t} - \frac{b}{t}\right)\\ \mathbf{elif}\;t \leq 1.52 \cdot 10^{+115}:\\ \;\;\;\;z + \frac{y}{\frac{x + y}{a - b}}\\ \mathbf{else}:\\ \;\;\;\;a + \left(\frac{y}{\frac{t}{z}} - y \cdot \frac{b}{t}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -3.2e+114)
   (+ a (* y (- (/ z t) (/ b t))))
   (if (<= t 1.52e+115)
     (+ z (/ y (/ (+ x y) (- a b))))
     (+ a (- (/ y (/ t z)) (* y (/ b t)))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -3.2e+114) {
		tmp = a + (y * ((z / t) - (b / t)));
	} else if (t <= 1.52e+115) {
		tmp = z + (y / ((x + y) / (a - b)));
	} else {
		tmp = a + ((y / (t / z)) - (y * (b / 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 <= (-3.2d+114)) then
        tmp = a + (y * ((z / t) - (b / t)))
    else if (t <= 1.52d+115) then
        tmp = z + (y / ((x + y) / (a - b)))
    else
        tmp = a + ((y / (t / z)) - (y * (b / 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 <= -3.2e+114) {
		tmp = a + (y * ((z / t) - (b / t)));
	} else if (t <= 1.52e+115) {
		tmp = z + (y / ((x + y) / (a - b)));
	} else {
		tmp = a + ((y / (t / z)) - (y * (b / t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -3.2e+114:
		tmp = a + (y * ((z / t) - (b / t)))
	elif t <= 1.52e+115:
		tmp = z + (y / ((x + y) / (a - b)))
	else:
		tmp = a + ((y / (t / z)) - (y * (b / t)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -3.2e+114)
		tmp = Float64(a + Float64(y * Float64(Float64(z / t) - Float64(b / t))));
	elseif (t <= 1.52e+115)
		tmp = Float64(z + Float64(y / Float64(Float64(x + y) / Float64(a - b))));
	else
		tmp = Float64(a + Float64(Float64(y / Float64(t / z)) - Float64(y * Float64(b / t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -3.2e+114)
		tmp = a + (y * ((z / t) - (b / t)));
	elseif (t <= 1.52e+115)
		tmp = z + (y / ((x + y) / (a - b)));
	else
		tmp = a + ((y / (t / z)) - (y * (b / t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -3.2e+114], N[(a + N[(y * N[(N[(z / t), $MachinePrecision] - N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.52e+115], N[(z + N[(y / N[(N[(x + y), $MachinePrecision] / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a + N[(N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision] - N[(y * N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 6: 57.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z + t \cdot \frac{a}{x}\\ t_2 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -8.6 \cdot 10^{-99}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -3.8 \cdot 10^{-296}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{-237}:\\ \;\;\;\;a\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{-150}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ z (* t (/ a x)))) (t_2 (- (+ z a) b)))
   (if (<= y -8.6e-99)
     t_2
     (if (<= y -3.8e-296)
       t_1
       (if (<= y 7.2e-237) a (if (<= y 1.9e-150) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z + (t * (a / x));
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -8.6e-99) {
		tmp = t_2;
	} else if (y <= -3.8e-296) {
		tmp = t_1;
	} else if (y <= 7.2e-237) {
		tmp = a;
	} else if (y <= 1.9e-150) {
		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 + (t * (a / x))
    t_2 = (z + a) - b
    if (y <= (-8.6d-99)) then
        tmp = t_2
    else if (y <= (-3.8d-296)) then
        tmp = t_1
    else if (y <= 7.2d-237) then
        tmp = a
    else if (y <= 1.9d-150) 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 + (t * (a / x));
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -8.6e-99) {
		tmp = t_2;
	} else if (y <= -3.8e-296) {
		tmp = t_1;
	} else if (y <= 7.2e-237) {
		tmp = a;
	} else if (y <= 1.9e-150) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z + (t * (a / x))
	t_2 = (z + a) - b
	tmp = 0
	if y <= -8.6e-99:
		tmp = t_2
	elif y <= -3.8e-296:
		tmp = t_1
	elif y <= 7.2e-237:
		tmp = a
	elif y <= 1.9e-150:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z + Float64(t * Float64(a / x)))
	t_2 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (y <= -8.6e-99)
		tmp = t_2;
	elseif (y <= -3.8e-296)
		tmp = t_1;
	elseif (y <= 7.2e-237)
		tmp = a;
	elseif (y <= 1.9e-150)
		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 + (t * (a / x));
	t_2 = (z + a) - b;
	tmp = 0.0;
	if (y <= -8.6e-99)
		tmp = t_2;
	elseif (y <= -3.8e-296)
		tmp = t_1;
	elseif (y <= 7.2e-237)
		tmp = a;
	elseif (y <= 1.9e-150)
		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[(z + N[(t * N[(a / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[y, -8.6e-99], t$95$2, If[LessEqual[y, -3.8e-296], t$95$1, If[LessEqual[y, 7.2e-237], a, If[LessEqual[y, 1.9e-150], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 7: 57.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z + \frac{a}{\frac{x}{t}}\\ t_2 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -1.15 \cdot 10^{-97}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -9.8 \cdot 10^{-296}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 1.42 \cdot 10^{-236}:\\ \;\;\;\;a\\ \mathbf{elif}\;y \leq 1.2 \cdot 10^{-153}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ z (/ a (/ x t)))) (t_2 (- (+ z a) b)))
   (if (<= y -1.15e-97)
     t_2
     (if (<= y -9.8e-296)
       t_1
       (if (<= y 1.42e-236) a (if (<= y 1.2e-153) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z + (a / (x / t));
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -1.15e-97) {
		tmp = t_2;
	} else if (y <= -9.8e-296) {
		tmp = t_1;
	} else if (y <= 1.42e-236) {
		tmp = a;
	} else if (y <= 1.2e-153) {
		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 / (x / t))
    t_2 = (z + a) - b
    if (y <= (-1.15d-97)) then
        tmp = t_2
    else if (y <= (-9.8d-296)) then
        tmp = t_1
    else if (y <= 1.42d-236) then
        tmp = a
    else if (y <= 1.2d-153) 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 / (x / t));
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -1.15e-97) {
		tmp = t_2;
	} else if (y <= -9.8e-296) {
		tmp = t_1;
	} else if (y <= 1.42e-236) {
		tmp = a;
	} else if (y <= 1.2e-153) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z + (a / (x / t))
	t_2 = (z + a) - b
	tmp = 0
	if y <= -1.15e-97:
		tmp = t_2
	elif y <= -9.8e-296:
		tmp = t_1
	elif y <= 1.42e-236:
		tmp = a
	elif y <= 1.2e-153:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z + Float64(a / Float64(x / t)))
	t_2 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (y <= -1.15e-97)
		tmp = t_2;
	elseif (y <= -9.8e-296)
		tmp = t_1;
	elseif (y <= 1.42e-236)
		tmp = a;
	elseif (y <= 1.2e-153)
		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 / (x / t));
	t_2 = (z + a) - b;
	tmp = 0.0;
	if (y <= -1.15e-97)
		tmp = t_2;
	elseif (y <= -9.8e-296)
		tmp = t_1;
	elseif (y <= 1.42e-236)
		tmp = a;
	elseif (y <= 1.2e-153)
		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[(z + N[(a / N[(x / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[y, -1.15e-97], t$95$2, If[LessEqual[y, -9.8e-296], t$95$1, If[LessEqual[y, 1.42e-236], a, If[LessEqual[y, 1.2e-153], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 8: 58.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z}{\frac{x + t}{x}}\\ t_2 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -3.7 \cdot 10^{-104}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-281}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq 2.95 \cdot 10^{-238}:\\ \;\;\;\;a \cdot \left(1 - \frac{x}{t}\right)\\ \mathbf{elif}\;y \leq 1.1 \cdot 10^{-81}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (/ z (/ (+ x t) x))) (t_2 (- (+ z a) b)))
   (if (<= y -3.7e-104)
     t_2
     (if (<= y 1.3e-281)
       t_1
       (if (<= y 2.95e-238)
         (* a (- 1.0 (/ x t)))
         (if (<= y 1.1e-81) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = z / ((x + t) / x);
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -3.7e-104) {
		tmp = t_2;
	} else if (y <= 1.3e-281) {
		tmp = t_1;
	} else if (y <= 2.95e-238) {
		tmp = a * (1.0 - (x / t));
	} else if (y <= 1.1e-81) {
		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 / ((x + t) / x)
    t_2 = (z + a) - b
    if (y <= (-3.7d-104)) then
        tmp = t_2
    else if (y <= 1.3d-281) then
        tmp = t_1
    else if (y <= 2.95d-238) then
        tmp = a * (1.0d0 - (x / t))
    else if (y <= 1.1d-81) 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 / ((x + t) / x);
	double t_2 = (z + a) - b;
	double tmp;
	if (y <= -3.7e-104) {
		tmp = t_2;
	} else if (y <= 1.3e-281) {
		tmp = t_1;
	} else if (y <= 2.95e-238) {
		tmp = a * (1.0 - (x / t));
	} else if (y <= 1.1e-81) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = z / ((x + t) / x)
	t_2 = (z + a) - b
	tmp = 0
	if y <= -3.7e-104:
		tmp = t_2
	elif y <= 1.3e-281:
		tmp = t_1
	elif y <= 2.95e-238:
		tmp = a * (1.0 - (x / t))
	elif y <= 1.1e-81:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(z / Float64(Float64(x + t) / x))
	t_2 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (y <= -3.7e-104)
		tmp = t_2;
	elseif (y <= 1.3e-281)
		tmp = t_1;
	elseif (y <= 2.95e-238)
		tmp = Float64(a * Float64(1.0 - Float64(x / t)));
	elseif (y <= 1.1e-81)
		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 / ((x + t) / x);
	t_2 = (z + a) - b;
	tmp = 0.0;
	if (y <= -3.7e-104)
		tmp = t_2;
	elseif (y <= 1.3e-281)
		tmp = t_1;
	elseif (y <= 2.95e-238)
		tmp = a * (1.0 - (x / t));
	elseif (y <= 1.1e-81)
		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[(z / N[(N[(x + t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[y, -3.7e-104], t$95$2, If[LessEqual[y, 1.3e-281], t$95$1, If[LessEqual[y, 2.95e-238], N[(a * N[(1.0 - N[(x / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.1e-81], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 9: 75.3% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -5.8 \cdot 10^{+116} \lor \neg \left(t \leq 2.3 \cdot 10^{+114}\right):\\ \;\;\;\;a + y \cdot \left(\frac{z}{t} - \frac{b}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;z + \frac{y}{\frac{x + y}{a - b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= t -5.8e+116) (not (<= t 2.3e+114)))
   (+ a (* y (- (/ z t) (/ b t))))
   (+ z (/ y (/ (+ x y) (- a b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -5.8e+116) || !(t <= 2.3e+114)) {
		tmp = a + (y * ((z / t) - (b / t)));
	} else {
		tmp = z + (y / ((x + y) / (a - b)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((t <= (-5.8d+116)) .or. (.not. (t <= 2.3d+114))) then
        tmp = a + (y * ((z / t) - (b / t)))
    else
        tmp = z + (y / ((x + y) / (a - b)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((t <= -5.8e+116) || !(t <= 2.3e+114)) {
		tmp = a + (y * ((z / t) - (b / t)));
	} else {
		tmp = z + (y / ((x + y) / (a - b)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (t <= -5.8e+116) or not (t <= 2.3e+114):
		tmp = a + (y * ((z / t) - (b / t)))
	else:
		tmp = z + (y / ((x + y) / (a - b)))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((t <= -5.8e+116) || !(t <= 2.3e+114))
		tmp = Float64(a + Float64(y * Float64(Float64(z / t) - Float64(b / t))));
	else
		tmp = Float64(z + Float64(y / Float64(Float64(x + y) / Float64(a - b))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((t <= -5.8e+116) || ~((t <= 2.3e+114)))
		tmp = a + (y * ((z / t) - (b / t)));
	else
		tmp = z + (y / ((x + y) / (a - b)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[t, -5.8e+116], N[Not[LessEqual[t, 2.3e+114]], $MachinePrecision]], N[(a + N[(y * N[(N[(z / t), $MachinePrecision] - N[(b / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(z + N[(y / N[(N[(x + y), $MachinePrecision] / N[(a - b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 55.9% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z + a\right) - b\\ \mathbf{if}\;y \leq -1.65 \cdot 10^{-104}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -5.4 \cdot 10^{-297}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq 3.7 \cdot 10^{-236}:\\ \;\;\;\;a\\ \mathbf{elif}\;y \leq 2.7 \cdot 10^{-154}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (- (+ z a) b)))
   (if (<= y -1.65e-104)
     t_1
     (if (<= y -5.4e-297)
       z
       (if (<= y 3.7e-236) a (if (<= y 2.7e-154) z t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (y <= -1.65e-104) {
		tmp = t_1;
	} else if (y <= -5.4e-297) {
		tmp = z;
	} else if (y <= 3.7e-236) {
		tmp = a;
	} else if (y <= 2.7e-154) {
		tmp = z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z + a) - b
    if (y <= (-1.65d-104)) then
        tmp = t_1
    else if (y <= (-5.4d-297)) then
        tmp = z
    else if (y <= 3.7d-236) then
        tmp = a
    else if (y <= 2.7d-154) then
        tmp = z
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z + a) - b;
	double tmp;
	if (y <= -1.65e-104) {
		tmp = t_1;
	} else if (y <= -5.4e-297) {
		tmp = z;
	} else if (y <= 3.7e-236) {
		tmp = a;
	} else if (y <= 2.7e-154) {
		tmp = z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z + a) - b
	tmp = 0
	if y <= -1.65e-104:
		tmp = t_1
	elif y <= -5.4e-297:
		tmp = z
	elif y <= 3.7e-236:
		tmp = a
	elif y <= 2.7e-154:
		tmp = z
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z + a) - b)
	tmp = 0.0
	if (y <= -1.65e-104)
		tmp = t_1;
	elseif (y <= -5.4e-297)
		tmp = z;
	elseif (y <= 3.7e-236)
		tmp = a;
	elseif (y <= 2.7e-154)
		tmp = z;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z + a) - b;
	tmp = 0.0;
	if (y <= -1.65e-104)
		tmp = t_1;
	elseif (y <= -5.4e-297)
		tmp = z;
	elseif (y <= 3.7e-236)
		tmp = a;
	elseif (y <= 2.7e-154)
		tmp = z;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z + a), $MachinePrecision] - b), $MachinePrecision]}, If[LessEqual[y, -1.65e-104], t$95$1, If[LessEqual[y, -5.4e-297], z, If[LessEqual[y, 3.7e-236], a, If[LessEqual[y, 2.7e-154], z, t$95$1]]]]]

Derivation

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

Alternative 11: 48.4% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{+105}:\\ \;\;\;\;z\\ \mathbf{elif}\;x \leq 4.6 \cdot 10^{+113}:\\ \;\;\;\;a - b\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= x -9e+105) z (if (<= x 4.6e+113) (- a b) z)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -9e+105) {
		tmp = z;
	} else if (x <= 4.6e+113) {
		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 (x <= (-9d+105)) then
        tmp = z
    else if (x <= 4.6d+113) 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 (x <= -9e+105) {
		tmp = z;
	} else if (x <= 4.6e+113) {
		tmp = a - b;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if x <= -9e+105:
		tmp = z
	elif x <= 4.6e+113:
		tmp = a - b
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -9e+105)
		tmp = z;
	elseif (x <= 4.6e+113)
		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 (x <= -9e+105)
		tmp = z;
	elseif (x <= 4.6e+113)
		tmp = a - b;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -9e+105], z, If[LessEqual[x, 4.6e+113], N[(a - b), $MachinePrecision], z]]

Derivation

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

Alternative 12: 48.3% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -6.5 \cdot 10^{+51}:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq 3.2 \cdot 10^{+144}:\\ \;\;\;\;z - b\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -6.5e+51) a (if (<= t 3.2e+144) (- z b) a)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -6.5e+51) {
		tmp = a;
	} else if (t <= 3.2e+144) {
		tmp = z - 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 <= (-6.5d+51)) then
        tmp = a
    else if (t <= 3.2d+144) then
        tmp = z - 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 <= -6.5e+51) {
		tmp = a;
	} else if (t <= 3.2e+144) {
		tmp = z - b;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -6.5e+51:
		tmp = a
	elif t <= 3.2e+144:
		tmp = z - b
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -6.5e+51)
		tmp = a;
	elseif (t <= 3.2e+144)
		tmp = Float64(z - b);
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -6.5e+51)
		tmp = a;
	elseif (t <= 3.2e+144)
		tmp = z - b;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -6.5e+51], a, If[LessEqual[t, 3.2e+144], N[(z - b), $MachinePrecision], a]]

Derivation

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

Alternative 13: 45.6% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2 \cdot 10^{+22}:\\ \;\;\;\;a\\ \mathbf{elif}\;t \leq 2.3 \cdot 10^{+66}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= t -2e+22) a (if (<= t 2.3e+66) z a)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -2e+22) {
		tmp = a;
	} else if (t <= 2.3e+66) {
		tmp = z;
	} else {
		tmp = a;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (t <= (-2d+22)) then
        tmp = a
    else if (t <= 2.3d+66) then
        tmp = z
    else
        tmp = a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (t <= -2e+22) {
		tmp = a;
	} else if (t <= 2.3e+66) {
		tmp = z;
	} else {
		tmp = a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if t <= -2e+22:
		tmp = a
	elif t <= 2.3e+66:
		tmp = z
	else:
		tmp = a
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (t <= -2e+22)
		tmp = a;
	elseif (t <= 2.3e+66)
		tmp = z;
	else
		tmp = a;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (t <= -2e+22)
		tmp = a;
	elseif (t <= 2.3e+66)
		tmp = z;
	else
		tmp = a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[t, -2e+22], a, If[LessEqual[t, 2.3e+66], z, a]]

Derivation

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

Alternative 14: 32.8% accurate, 21.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 81.9% 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 2024008 
(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)))