Diagrams.ThreeD.Shapes:frustum from diagrams-lib-1.3.0.3, A

Percentage Accurate: 90.3% → 94.0%

Time: 16.9s

Alternatives: 18

Speedup: 0.5×

• 47.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ 2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (* 2.0 (- (+ (* x y) (* z t)) (* (* (+ a (* b c)) c) i))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    code = 2.0d0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
}

Python

def code(x, y, z, t, a, b, c, i):
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i))

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(2.0 * Float64(Float64(Float64(x * y) + Float64(z * t)) - Float64(Float64(Float64(a + Float64(b * c)) * c) * i)))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(2.0 * N[(N[(N[(x * y), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 90.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(\left(a + b \cdot c\right) \cdot c\right) \cdot i\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (* 2.0 (- (+ (* x y) (* z t)) (* (* (+ a (* b c)) c) i))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    code = 2.0d0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
}

Python

def code(x, y, z, t, a, b, c, i):
	return 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i))

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(2.0 * Float64(Float64(Float64(x * y) + Float64(z * t)) - Float64(Float64(Float64(a + Float64(b * c)) * c) * i)))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = 2.0 * (((x * y) + (z * t)) - (((a + (b * c)) * c) * i));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(2.0 * N[(N[(N[(x * y), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 94.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + b \cdot c\\ t_2 := c \cdot t_1\\ \mathbf{if}\;t_2 \leq -2 \cdot 10^{+154}:\\ \;\;\;\;2 \cdot \mathsf{fma}\left(y, x, z \cdot t - c \cdot \left(\mathsf{fma}\left(b, c, a\right) \cdot i\right)\right)\\ \mathbf{elif}\;t_2 \leq 2 \cdot 10^{+165}:\\ \;\;\;\;2 \cdot \left(\left(y \cdot x + z \cdot t\right) - t_2 \cdot i\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(y \cdot x - c \cdot \left(t_1 \cdot i\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ a (* b c))) (t_2 (* c t_1)))
   (if (<= t_2 -2e+154)
     (* 2.0 (fma y x (- (* z t) (* c (* (fma b c a) i)))))
     (if (<= t_2 2e+165)
       (* 2.0 (- (+ (* y x) (* z t)) (* t_2 i)))
       (* 2.0 (- (* y x) (* c (* t_1 i))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double t_2 = c * t_1;
	double tmp;
	if (t_2 <= -2e+154) {
		tmp = 2.0 * fma(y, x, ((z * t) - (c * (fma(b, c, a) * i))));
	} else if (t_2 <= 2e+165) {
		tmp = 2.0 * (((y * x) + (z * t)) - (t_2 * i));
	} else {
		tmp = 2.0 * ((y * x) - (c * (t_1 * i)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(a + Float64(b * c))
	t_2 = Float64(c * t_1)
	tmp = 0.0
	if (t_2 <= -2e+154)
		tmp = Float64(2.0 * fma(y, x, Float64(Float64(z * t) - Float64(c * Float64(fma(b, c, a) * i)))));
	elseif (t_2 <= 2e+165)
		tmp = Float64(2.0 * Float64(Float64(Float64(y * x) + Float64(z * t)) - Float64(t_2 * i)));
	else
		tmp = Float64(2.0 * Float64(Float64(y * x) - Float64(c * Float64(t_1 * i))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(c * t$95$1), $MachinePrecision]}, If[LessEqual[t$95$2, -2e+154], N[(2.0 * N[(y * x + N[(N[(z * t), $MachinePrecision] - N[(c * N[(N[(b * c + a), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 2e+165], N[(2.0 * N[(N[(N[(y * x), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(t$95$2 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(2.0 * N[(N[(y * x), $MachinePrecision] - N[(c * N[(t$95$1 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 2: 96.2% accurate, 0.5× speedup

Math

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

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ a (* b c))) (t_2 (+ (* y x) (* z t))))
   (if (<= (- t_2 (* (* c t_1) i)) INFINITY)
     (* 2.0 (- t_2 (* t_1 (* c i))))
     (* (* c (* t_1 i)) -2.0))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double t_2 = (y * x) + (z * t);
	double tmp;
	if ((t_2 - ((c * t_1) * i)) <= ((double) INFINITY)) {
		tmp = 2.0 * (t_2 - (t_1 * (c * i)));
	} else {
		tmp = (c * (t_1 * i)) * -2.0;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double t_2 = (y * x) + (z * t);
	double tmp;
	if ((t_2 - ((c * t_1) * i)) <= Double.POSITIVE_INFINITY) {
		tmp = 2.0 * (t_2 - (t_1 * (c * i)));
	} else {
		tmp = (c * (t_1 * i)) * -2.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = a + (b * c)
	t_2 = (y * x) + (z * t)
	tmp = 0
	if (t_2 - ((c * t_1) * i)) <= math.inf:
		tmp = 2.0 * (t_2 - (t_1 * (c * i)))
	else:
		tmp = (c * (t_1 * i)) * -2.0
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(a + Float64(b * c))
	t_2 = Float64(Float64(y * x) + Float64(z * t))
	tmp = 0.0
	if (Float64(t_2 - Float64(Float64(c * t_1) * i)) <= Inf)
		tmp = Float64(2.0 * Float64(t_2 - Float64(t_1 * Float64(c * i))));
	else
		tmp = Float64(Float64(c * Float64(t_1 * i)) * -2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = a + (b * c);
	t_2 = (y * x) + (z * t);
	tmp = 0.0;
	if ((t_2 - ((c * t_1) * i)) <= Inf)
		tmp = 2.0 * (t_2 - (t_1 * (c * i)));
	else
		tmp = (c * (t_1 * i)) * -2.0;
	end
	tmp_2 = tmp;
end

Wolfram

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

Derivation

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

Alternative 3: 92.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a + b \cdot c\\ t_2 := c \cdot \left(t_1 \cdot i\right)\\ t_3 := c \cdot t_1\\ \mathbf{if}\;t_3 \leq -2 \cdot 10^{+207}:\\ \;\;\;\;2 \cdot \left(z \cdot t - t_2\right)\\ \mathbf{elif}\;t_3 \leq 2 \cdot 10^{+165}:\\ \;\;\;\;2 \cdot \left(\left(y \cdot x + z \cdot t\right) - t_3 \cdot i\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(y \cdot x - t_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ a (* b c))) (t_2 (* c (* t_1 i))) (t_3 (* c t_1)))
   (if (<= t_3 -2e+207)
     (* 2.0 (- (* z t) t_2))
     (if (<= t_3 2e+165)
       (* 2.0 (- (+ (* y x) (* z t)) (* t_3 i)))
       (* 2.0 (- (* y x) t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = a + (b * c);
	double t_2 = c * (t_1 * i);
	double t_3 = c * t_1;
	double tmp;
	if (t_3 <= -2e+207) {
		tmp = 2.0 * ((z * t) - t_2);
	} else if (t_3 <= 2e+165) {
		tmp = 2.0 * (((y * x) + (z * t)) - (t_3 * i));
	} else {
		tmp = 2.0 * ((y * x) - t_2);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = a + (b * c)
    t_2 = c * (t_1 * i)
    t_3 = c * t_1
    if (t_3 <= (-2d+207)) then
        tmp = 2.0d0 * ((z * t) - t_2)
    else if (t_3 <= 2d+165) then
        tmp = 2.0d0 * (((y * x) + (z * t)) - (t_3 * i))
    else
        tmp = 2.0d0 * ((y * x) - 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 c, double i) {
	double t_1 = a + (b * c);
	double t_2 = c * (t_1 * i);
	double t_3 = c * t_1;
	double tmp;
	if (t_3 <= -2e+207) {
		tmp = 2.0 * ((z * t) - t_2);
	} else if (t_3 <= 2e+165) {
		tmp = 2.0 * (((y * x) + (z * t)) - (t_3 * i));
	} else {
		tmp = 2.0 * ((y * x) - t_2);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = a + (b * c)
	t_2 = c * (t_1 * i)
	t_3 = c * t_1
	tmp = 0
	if t_3 <= -2e+207:
		tmp = 2.0 * ((z * t) - t_2)
	elif t_3 <= 2e+165:
		tmp = 2.0 * (((y * x) + (z * t)) - (t_3 * i))
	else:
		tmp = 2.0 * ((y * x) - t_2)
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(a + Float64(b * c))
	t_2 = Float64(c * Float64(t_1 * i))
	t_3 = Float64(c * t_1)
	tmp = 0.0
	if (t_3 <= -2e+207)
		tmp = Float64(2.0 * Float64(Float64(z * t) - t_2));
	elseif (t_3 <= 2e+165)
		tmp = Float64(2.0 * Float64(Float64(Float64(y * x) + Float64(z * t)) - Float64(t_3 * i)));
	else
		tmp = Float64(2.0 * Float64(Float64(y * x) - t_2));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = a + (b * c);
	t_2 = c * (t_1 * i);
	t_3 = c * t_1;
	tmp = 0.0;
	if (t_3 <= -2e+207)
		tmp = 2.0 * ((z * t) - t_2);
	elseif (t_3 <= 2e+165)
		tmp = 2.0 * (((y * x) + (z * t)) - (t_3 * i));
	else
		tmp = 2.0 * ((y * x) - t_2);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(c * N[(t$95$1 * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(c * t$95$1), $MachinePrecision]}, If[LessEqual[t$95$3, -2e+207], N[(2.0 * N[(N[(z * t), $MachinePrecision] - t$95$2), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, 2e+165], N[(2.0 * N[(N[(N[(y * x), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(t$95$3 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(2.0 * N[(N[(y * x), $MachinePrecision] - t$95$2), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 4: 43.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(z \cdot t\right)\\ t_2 := \left(c \cdot i\right) \cdot \left(a \cdot -2\right)\\ t_3 := 2 \cdot \left(y \cdot x\right)\\ \mathbf{if}\;y \cdot x \leq -5 \cdot 10^{+69}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;y \cdot x \leq 4 \cdot 10^{-153}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \cdot x \leq 10^{-68}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \cdot x \leq 2 \cdot 10^{+91}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \cdot x \leq 10^{+161}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (* z t)))
        (t_2 (* (* c i) (* a -2.0)))
        (t_3 (* 2.0 (* y x))))
   (if (<= (* y x) -5e+69)
     t_3
     (if (<= (* y x) 4e-153)
       t_1
       (if (<= (* y x) 1e-68)
         t_2
         (if (<= (* y x) 2e+91) t_1 (if (<= (* y x) 1e+161) t_2 t_3)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * (z * t);
	double t_2 = (c * i) * (a * -2.0);
	double t_3 = 2.0 * (y * x);
	double tmp;
	if ((y * x) <= -5e+69) {
		tmp = t_3;
	} else if ((y * x) <= 4e-153) {
		tmp = t_1;
	} else if ((y * x) <= 1e-68) {
		tmp = t_2;
	} else if ((y * x) <= 2e+91) {
		tmp = t_1;
	} else if ((y * x) <= 1e+161) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = 2.0d0 * (z * t)
    t_2 = (c * i) * (a * (-2.0d0))
    t_3 = 2.0d0 * (y * x)
    if ((y * x) <= (-5d+69)) then
        tmp = t_3
    else if ((y * x) <= 4d-153) then
        tmp = t_1
    else if ((y * x) <= 1d-68) then
        tmp = t_2
    else if ((y * x) <= 2d+91) then
        tmp = t_1
    else if ((y * x) <= 1d+161) 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 c, double i) {
	double t_1 = 2.0 * (z * t);
	double t_2 = (c * i) * (a * -2.0);
	double t_3 = 2.0 * (y * x);
	double tmp;
	if ((y * x) <= -5e+69) {
		tmp = t_3;
	} else if ((y * x) <= 4e-153) {
		tmp = t_1;
	} else if ((y * x) <= 1e-68) {
		tmp = t_2;
	} else if ((y * x) <= 2e+91) {
		tmp = t_1;
	} else if ((y * x) <= 1e+161) {
		tmp = t_2;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * (z * t)
	t_2 = (c * i) * (a * -2.0)
	t_3 = 2.0 * (y * x)
	tmp = 0
	if (y * x) <= -5e+69:
		tmp = t_3
	elif (y * x) <= 4e-153:
		tmp = t_1
	elif (y * x) <= 1e-68:
		tmp = t_2
	elif (y * x) <= 2e+91:
		tmp = t_1
	elif (y * x) <= 1e+161:
		tmp = t_2
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(z * t))
	t_2 = Float64(Float64(c * i) * Float64(a * -2.0))
	t_3 = Float64(2.0 * Float64(y * x))
	tmp = 0.0
	if (Float64(y * x) <= -5e+69)
		tmp = t_3;
	elseif (Float64(y * x) <= 4e-153)
		tmp = t_1;
	elseif (Float64(y * x) <= 1e-68)
		tmp = t_2;
	elseif (Float64(y * x) <= 2e+91)
		tmp = t_1;
	elseif (Float64(y * x) <= 1e+161)
		tmp = t_2;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * (z * t);
	t_2 = (c * i) * (a * -2.0);
	t_3 = 2.0 * (y * x);
	tmp = 0.0;
	if ((y * x) <= -5e+69)
		tmp = t_3;
	elseif ((y * x) <= 4e-153)
		tmp = t_1;
	elseif ((y * x) <= 1e-68)
		tmp = t_2;
	elseif ((y * x) <= 2e+91)
		tmp = t_1;
	elseif ((y * x) <= 1e+161)
		tmp = t_2;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(z * t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(c * i), $MachinePrecision] * N[(a * -2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(2.0 * N[(y * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(y * x), $MachinePrecision], -5e+69], t$95$3, If[LessEqual[N[(y * x), $MachinePrecision], 4e-153], t$95$1, If[LessEqual[N[(y * x), $MachinePrecision], 1e-68], t$95$2, If[LessEqual[N[(y * x), $MachinePrecision], 2e+91], t$95$1, If[LessEqual[N[(y * x), $MachinePrecision], 1e+161], t$95$2, t$95$3]]]]]]]]

Derivation

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

Alternative 5: 43.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(z \cdot t\right)\\ t_2 := 2 \cdot \left(y \cdot x\right)\\ \mathbf{if}\;y \cdot x \leq -5 \cdot 10^{+69}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \cdot x \leq 4 \cdot 10^{-153}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \cdot x \leq 10^{-68}:\\ \;\;\;\;2 \cdot \left(i \cdot \left(a \cdot \left(-c\right)\right)\right)\\ \mathbf{elif}\;y \cdot x \leq 2 \cdot 10^{+91}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \cdot x \leq 10^{+161}:\\ \;\;\;\;\left(c \cdot i\right) \cdot \left(a \cdot -2\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (* z t))) (t_2 (* 2.0 (* y x))))
   (if (<= (* y x) -5e+69)
     t_2
     (if (<= (* y x) 4e-153)
       t_1
       (if (<= (* y x) 1e-68)
         (* 2.0 (* i (* a (- c))))
         (if (<= (* y x) 2e+91)
           t_1
           (if (<= (* y x) 1e+161) (* (* c i) (* a -2.0)) t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * (z * t);
	double t_2 = 2.0 * (y * x);
	double tmp;
	if ((y * x) <= -5e+69) {
		tmp = t_2;
	} else if ((y * x) <= 4e-153) {
		tmp = t_1;
	} else if ((y * x) <= 1e-68) {
		tmp = 2.0 * (i * (a * -c));
	} else if ((y * x) <= 2e+91) {
		tmp = t_1;
	} else if ((y * x) <= 1e+161) {
		tmp = (c * i) * (a * -2.0);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 2.0d0 * (z * t)
    t_2 = 2.0d0 * (y * x)
    if ((y * x) <= (-5d+69)) then
        tmp = t_2
    else if ((y * x) <= 4d-153) then
        tmp = t_1
    else if ((y * x) <= 1d-68) then
        tmp = 2.0d0 * (i * (a * -c))
    else if ((y * x) <= 2d+91) then
        tmp = t_1
    else if ((y * x) <= 1d+161) then
        tmp = (c * i) * (a * (-2.0d0))
    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 c, double i) {
	double t_1 = 2.0 * (z * t);
	double t_2 = 2.0 * (y * x);
	double tmp;
	if ((y * x) <= -5e+69) {
		tmp = t_2;
	} else if ((y * x) <= 4e-153) {
		tmp = t_1;
	} else if ((y * x) <= 1e-68) {
		tmp = 2.0 * (i * (a * -c));
	} else if ((y * x) <= 2e+91) {
		tmp = t_1;
	} else if ((y * x) <= 1e+161) {
		tmp = (c * i) * (a * -2.0);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * (z * t)
	t_2 = 2.0 * (y * x)
	tmp = 0
	if (y * x) <= -5e+69:
		tmp = t_2
	elif (y * x) <= 4e-153:
		tmp = t_1
	elif (y * x) <= 1e-68:
		tmp = 2.0 * (i * (a * -c))
	elif (y * x) <= 2e+91:
		tmp = t_1
	elif (y * x) <= 1e+161:
		tmp = (c * i) * (a * -2.0)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(z * t))
	t_2 = Float64(2.0 * Float64(y * x))
	tmp = 0.0
	if (Float64(y * x) <= -5e+69)
		tmp = t_2;
	elseif (Float64(y * x) <= 4e-153)
		tmp = t_1;
	elseif (Float64(y * x) <= 1e-68)
		tmp = Float64(2.0 * Float64(i * Float64(a * Float64(-c))));
	elseif (Float64(y * x) <= 2e+91)
		tmp = t_1;
	elseif (Float64(y * x) <= 1e+161)
		tmp = Float64(Float64(c * i) * Float64(a * -2.0));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * (z * t);
	t_2 = 2.0 * (y * x);
	tmp = 0.0;
	if ((y * x) <= -5e+69)
		tmp = t_2;
	elseif ((y * x) <= 4e-153)
		tmp = t_1;
	elseif ((y * x) <= 1e-68)
		tmp = 2.0 * (i * (a * -c));
	elseif ((y * x) <= 2e+91)
		tmp = t_1;
	elseif ((y * x) <= 1e+161)
		tmp = (c * i) * (a * -2.0);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(z * t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[(y * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(y * x), $MachinePrecision], -5e+69], t$95$2, If[LessEqual[N[(y * x), $MachinePrecision], 4e-153], t$95$1, If[LessEqual[N[(y * x), $MachinePrecision], 1e-68], N[(2.0 * N[(i * N[(a * (-c)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(y * x), $MachinePrecision], 2e+91], t$95$1, If[LessEqual[N[(y * x), $MachinePrecision], 1e+161], N[(N[(c * i), $MachinePrecision] * N[(a * -2.0), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]

Derivation

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

Alternative 6: 85.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c \cdot \left(\left(a + b \cdot c\right) \cdot i\right)\\ t_2 := 2 \cdot \left(y \cdot x - t_1\right)\\ t_3 := 2 \cdot \left(\left(y \cdot x + z \cdot t\right) - i \cdot \left(a \cdot c\right)\right)\\ \mathbf{if}\;c \leq -7 \cdot 10^{-50}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;c \leq 7 \cdot 10^{-43}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;c \leq 3.8 \cdot 10^{+15}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;c \leq 2.8 \cdot 10^{+107}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;c \leq 4.4 \cdot 10^{+212}:\\ \;\;\;\;2 \cdot \left(z \cdot t - t_1\right)\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* c (* (+ a (* b c)) i)))
        (t_2 (* 2.0 (- (* y x) t_1)))
        (t_3 (* 2.0 (- (+ (* y x) (* z t)) (* i (* a c))))))
   (if (<= c -7e-50)
     t_2
     (if (<= c 7e-43)
       t_3
       (if (<= c 3.8e+15)
         t_2
         (if (<= c 2.8e+107)
           t_3
           (if (<= c 4.4e+212) (* 2.0 (- (* z t) t_1)) t_2)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = c * ((a + (b * c)) * i);
	double t_2 = 2.0 * ((y * x) - t_1);
	double t_3 = 2.0 * (((y * x) + (z * t)) - (i * (a * c)));
	double tmp;
	if (c <= -7e-50) {
		tmp = t_2;
	} else if (c <= 7e-43) {
		tmp = t_3;
	} else if (c <= 3.8e+15) {
		tmp = t_2;
	} else if (c <= 2.8e+107) {
		tmp = t_3;
	} else if (c <= 4.4e+212) {
		tmp = 2.0 * ((z * t) - t_1);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = c * ((a + (b * c)) * i)
    t_2 = 2.0d0 * ((y * x) - t_1)
    t_3 = 2.0d0 * (((y * x) + (z * t)) - (i * (a * c)))
    if (c <= (-7d-50)) then
        tmp = t_2
    else if (c <= 7d-43) then
        tmp = t_3
    else if (c <= 3.8d+15) then
        tmp = t_2
    else if (c <= 2.8d+107) then
        tmp = t_3
    else if (c <= 4.4d+212) then
        tmp = 2.0d0 * ((z * t) - t_1)
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = c * ((a + (b * c)) * i);
	double t_2 = 2.0 * ((y * x) - t_1);
	double t_3 = 2.0 * (((y * x) + (z * t)) - (i * (a * c)));
	double tmp;
	if (c <= -7e-50) {
		tmp = t_2;
	} else if (c <= 7e-43) {
		tmp = t_3;
	} else if (c <= 3.8e+15) {
		tmp = t_2;
	} else if (c <= 2.8e+107) {
		tmp = t_3;
	} else if (c <= 4.4e+212) {
		tmp = 2.0 * ((z * t) - t_1);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = c * ((a + (b * c)) * i)
	t_2 = 2.0 * ((y * x) - t_1)
	t_3 = 2.0 * (((y * x) + (z * t)) - (i * (a * c)))
	tmp = 0
	if c <= -7e-50:
		tmp = t_2
	elif c <= 7e-43:
		tmp = t_3
	elif c <= 3.8e+15:
		tmp = t_2
	elif c <= 2.8e+107:
		tmp = t_3
	elif c <= 4.4e+212:
		tmp = 2.0 * ((z * t) - t_1)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(c * Float64(Float64(a + Float64(b * c)) * i))
	t_2 = Float64(2.0 * Float64(Float64(y * x) - t_1))
	t_3 = Float64(2.0 * Float64(Float64(Float64(y * x) + Float64(z * t)) - Float64(i * Float64(a * c))))
	tmp = 0.0
	if (c <= -7e-50)
		tmp = t_2;
	elseif (c <= 7e-43)
		tmp = t_3;
	elseif (c <= 3.8e+15)
		tmp = t_2;
	elseif (c <= 2.8e+107)
		tmp = t_3;
	elseif (c <= 4.4e+212)
		tmp = Float64(2.0 * Float64(Float64(z * t) - t_1));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = c * ((a + (b * c)) * i);
	t_2 = 2.0 * ((y * x) - t_1);
	t_3 = 2.0 * (((y * x) + (z * t)) - (i * (a * c)));
	tmp = 0.0;
	if (c <= -7e-50)
		tmp = t_2;
	elseif (c <= 7e-43)
		tmp = t_3;
	elseif (c <= 3.8e+15)
		tmp = t_2;
	elseif (c <= 2.8e+107)
		tmp = t_3;
	elseif (c <= 4.4e+212)
		tmp = 2.0 * ((z * t) - t_1);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(c * N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[(N[(y * x), $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(2.0 * N[(N[(N[(y * x), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(i * N[(a * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -7e-50], t$95$2, If[LessEqual[c, 7e-43], t$95$3, If[LessEqual[c, 3.8e+15], t$95$2, If[LessEqual[c, 2.8e+107], t$95$3, If[LessEqual[c, 4.4e+212], N[(2.0 * N[(N[(z * t), $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]]

Derivation

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

Alternative 7: 86.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c \cdot \left(\left(a + b \cdot c\right) \cdot i\right)\\ t_2 := 2 \cdot \left(y \cdot x - t_1\right)\\ t_3 := y \cdot x + z \cdot t\\ \mathbf{if}\;c \leq -2.6 \cdot 10^{-50}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;c \leq 7.5 \cdot 10^{-43}:\\ \;\;\;\;2 \cdot \left(t_3 - i \cdot \left(a \cdot c\right)\right)\\ \mathbf{elif}\;c \leq 8 \cdot 10^{+21}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;c \leq 1.75 \cdot 10^{+71}:\\ \;\;\;\;2 \cdot \left(t_3 - i \cdot \left(c \cdot \left(b \cdot c\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(z \cdot t - t_1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* c (* (+ a (* b c)) i)))
        (t_2 (* 2.0 (- (* y x) t_1)))
        (t_3 (+ (* y x) (* z t))))
   (if (<= c -2.6e-50)
     t_2
     (if (<= c 7.5e-43)
       (* 2.0 (- t_3 (* i (* a c))))
       (if (<= c 8e+21)
         t_2
         (if (<= c 1.75e+71)
           (* 2.0 (- t_3 (* i (* c (* b c)))))
           (* 2.0 (- (* z t) t_1))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = c * ((a + (b * c)) * i);
	double t_2 = 2.0 * ((y * x) - t_1);
	double t_3 = (y * x) + (z * t);
	double tmp;
	if (c <= -2.6e-50) {
		tmp = t_2;
	} else if (c <= 7.5e-43) {
		tmp = 2.0 * (t_3 - (i * (a * c)));
	} else if (c <= 8e+21) {
		tmp = t_2;
	} else if (c <= 1.75e+71) {
		tmp = 2.0 * (t_3 - (i * (c * (b * c))));
	} else {
		tmp = 2.0 * ((z * t) - t_1);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = c * ((a + (b * c)) * i)
    t_2 = 2.0d0 * ((y * x) - t_1)
    t_3 = (y * x) + (z * t)
    if (c <= (-2.6d-50)) then
        tmp = t_2
    else if (c <= 7.5d-43) then
        tmp = 2.0d0 * (t_3 - (i * (a * c)))
    else if (c <= 8d+21) then
        tmp = t_2
    else if (c <= 1.75d+71) then
        tmp = 2.0d0 * (t_3 - (i * (c * (b * c))))
    else
        tmp = 2.0d0 * ((z * t) - 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 c, double i) {
	double t_1 = c * ((a + (b * c)) * i);
	double t_2 = 2.0 * ((y * x) - t_1);
	double t_3 = (y * x) + (z * t);
	double tmp;
	if (c <= -2.6e-50) {
		tmp = t_2;
	} else if (c <= 7.5e-43) {
		tmp = 2.0 * (t_3 - (i * (a * c)));
	} else if (c <= 8e+21) {
		tmp = t_2;
	} else if (c <= 1.75e+71) {
		tmp = 2.0 * (t_3 - (i * (c * (b * c))));
	} else {
		tmp = 2.0 * ((z * t) - t_1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = c * ((a + (b * c)) * i)
	t_2 = 2.0 * ((y * x) - t_1)
	t_3 = (y * x) + (z * t)
	tmp = 0
	if c <= -2.6e-50:
		tmp = t_2
	elif c <= 7.5e-43:
		tmp = 2.0 * (t_3 - (i * (a * c)))
	elif c <= 8e+21:
		tmp = t_2
	elif c <= 1.75e+71:
		tmp = 2.0 * (t_3 - (i * (c * (b * c))))
	else:
		tmp = 2.0 * ((z * t) - t_1)
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(c * Float64(Float64(a + Float64(b * c)) * i))
	t_2 = Float64(2.0 * Float64(Float64(y * x) - t_1))
	t_3 = Float64(Float64(y * x) + Float64(z * t))
	tmp = 0.0
	if (c <= -2.6e-50)
		tmp = t_2;
	elseif (c <= 7.5e-43)
		tmp = Float64(2.0 * Float64(t_3 - Float64(i * Float64(a * c))));
	elseif (c <= 8e+21)
		tmp = t_2;
	elseif (c <= 1.75e+71)
		tmp = Float64(2.0 * Float64(t_3 - Float64(i * Float64(c * Float64(b * c)))));
	else
		tmp = Float64(2.0 * Float64(Float64(z * t) - t_1));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = c * ((a + (b * c)) * i);
	t_2 = 2.0 * ((y * x) - t_1);
	t_3 = (y * x) + (z * t);
	tmp = 0.0;
	if (c <= -2.6e-50)
		tmp = t_2;
	elseif (c <= 7.5e-43)
		tmp = 2.0 * (t_3 - (i * (a * c)));
	elseif (c <= 8e+21)
		tmp = t_2;
	elseif (c <= 1.75e+71)
		tmp = 2.0 * (t_3 - (i * (c * (b * c))));
	else
		tmp = 2.0 * ((z * t) - t_1);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(c * N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[(N[(y * x), $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(y * x), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[c, -2.6e-50], t$95$2, If[LessEqual[c, 7.5e-43], N[(2.0 * N[(t$95$3 - N[(i * N[(a * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 8e+21], t$95$2, If[LessEqual[c, 1.75e+71], N[(2.0 * N[(t$95$3 - N[(i * N[(c * N[(b * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(2.0 * N[(N[(z * t), $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

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

Alternative 8: 77.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \cdot x \leq -5 \cdot 10^{+69}:\\ \;\;\;\;2 \cdot \left(y \cdot x - c \cdot \left(a \cdot i\right)\right)\\ \mathbf{elif}\;y \cdot x \leq 10^{+108}:\\ \;\;\;\;2 \cdot \left(z \cdot t - c \cdot \left(\left(a + b \cdot c\right) \cdot i\right)\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(y \cdot x - a \cdot \left(c \cdot i\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (<= (* y x) -5e+69)
   (* 2.0 (- (* y x) (* c (* a i))))
   (if (<= (* y x) 1e+108)
     (* 2.0 (- (* z t) (* c (* (+ a (* b c)) i))))
     (* 2.0 (- (* y x) (* a (* c i)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((y * x) <= -5e+69) {
		tmp = 2.0 * ((y * x) - (c * (a * i)));
	} else if ((y * x) <= 1e+108) {
		tmp = 2.0 * ((z * t) - (c * ((a + (b * c)) * i)));
	} else {
		tmp = 2.0 * ((y * x) - (a * (c * i)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if ((y * x) <= (-5d+69)) then
        tmp = 2.0d0 * ((y * x) - (c * (a * i)))
    else if ((y * x) <= 1d+108) then
        tmp = 2.0d0 * ((z * t) - (c * ((a + (b * c)) * i)))
    else
        tmp = 2.0d0 * ((y * x) - (a * (c * i)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((y * x) <= -5e+69) {
		tmp = 2.0 * ((y * x) - (c * (a * i)));
	} else if ((y * x) <= 1e+108) {
		tmp = 2.0 * ((z * t) - (c * ((a + (b * c)) * i)));
	} else {
		tmp = 2.0 * ((y * x) - (a * (c * i)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if (y * x) <= -5e+69:
		tmp = 2.0 * ((y * x) - (c * (a * i)))
	elif (y * x) <= 1e+108:
		tmp = 2.0 * ((z * t) - (c * ((a + (b * c)) * i)))
	else:
		tmp = 2.0 * ((y * x) - (a * (c * i)))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if (Float64(y * x) <= -5e+69)
		tmp = Float64(2.0 * Float64(Float64(y * x) - Float64(c * Float64(a * i))));
	elseif (Float64(y * x) <= 1e+108)
		tmp = Float64(2.0 * Float64(Float64(z * t) - Float64(c * Float64(Float64(a + Float64(b * c)) * i))));
	else
		tmp = Float64(2.0 * Float64(Float64(y * x) - Float64(a * Float64(c * i))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if ((y * x) <= -5e+69)
		tmp = 2.0 * ((y * x) - (c * (a * i)));
	elseif ((y * x) <= 1e+108)
		tmp = 2.0 * ((z * t) - (c * ((a + (b * c)) * i)));
	else
		tmp = 2.0 * ((y * x) - (a * (c * i)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[LessEqual[N[(y * x), $MachinePrecision], -5e+69], N[(2.0 * N[(N[(y * x), $MachinePrecision] - N[(c * N[(a * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(y * x), $MachinePrecision], 1e+108], N[(2.0 * N[(N[(z * t), $MachinePrecision] - N[(c * N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(2.0 * N[(N[(y * x), $MachinePrecision] - N[(a * N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 9: 44.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(z \cdot t\right)\\ t_2 := 2 \cdot \left(y \cdot x\right)\\ \mathbf{if}\;y \cdot x \leq -5 \cdot 10^{+69}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \cdot x \leq 4 \cdot 10^{-153}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \cdot x \leq 10^{-68}:\\ \;\;\;\;-2 \cdot \left(c \cdot \left(a \cdot i\right)\right)\\ \mathbf{elif}\;y \cdot x \leq 4 \cdot 10^{+78}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (* z t))) (t_2 (* 2.0 (* y x))))
   (if (<= (* y x) -5e+69)
     t_2
     (if (<= (* y x) 4e-153)
       t_1
       (if (<= (* y x) 1e-68)
         (* -2.0 (* c (* a i)))
         (if (<= (* y x) 4e+78) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * (z * t);
	double t_2 = 2.0 * (y * x);
	double tmp;
	if ((y * x) <= -5e+69) {
		tmp = t_2;
	} else if ((y * x) <= 4e-153) {
		tmp = t_1;
	} else if ((y * x) <= 1e-68) {
		tmp = -2.0 * (c * (a * i));
	} else if ((y * x) <= 4e+78) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 2.0d0 * (z * t)
    t_2 = 2.0d0 * (y * x)
    if ((y * x) <= (-5d+69)) then
        tmp = t_2
    else if ((y * x) <= 4d-153) then
        tmp = t_1
    else if ((y * x) <= 1d-68) then
        tmp = (-2.0d0) * (c * (a * i))
    else if ((y * x) <= 4d+78) 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 c, double i) {
	double t_1 = 2.0 * (z * t);
	double t_2 = 2.0 * (y * x);
	double tmp;
	if ((y * x) <= -5e+69) {
		tmp = t_2;
	} else if ((y * x) <= 4e-153) {
		tmp = t_1;
	} else if ((y * x) <= 1e-68) {
		tmp = -2.0 * (c * (a * i));
	} else if ((y * x) <= 4e+78) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * (z * t)
	t_2 = 2.0 * (y * x)
	tmp = 0
	if (y * x) <= -5e+69:
		tmp = t_2
	elif (y * x) <= 4e-153:
		tmp = t_1
	elif (y * x) <= 1e-68:
		tmp = -2.0 * (c * (a * i))
	elif (y * x) <= 4e+78:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(z * t))
	t_2 = Float64(2.0 * Float64(y * x))
	tmp = 0.0
	if (Float64(y * x) <= -5e+69)
		tmp = t_2;
	elseif (Float64(y * x) <= 4e-153)
		tmp = t_1;
	elseif (Float64(y * x) <= 1e-68)
		tmp = Float64(-2.0 * Float64(c * Float64(a * i)));
	elseif (Float64(y * x) <= 4e+78)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * (z * t);
	t_2 = 2.0 * (y * x);
	tmp = 0.0;
	if ((y * x) <= -5e+69)
		tmp = t_2;
	elseif ((y * x) <= 4e-153)
		tmp = t_1;
	elseif ((y * x) <= 1e-68)
		tmp = -2.0 * (c * (a * i));
	elseif ((y * x) <= 4e+78)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(z * t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(2.0 * N[(y * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(y * x), $MachinePrecision], -5e+69], t$95$2, If[LessEqual[N[(y * x), $MachinePrecision], 4e-153], t$95$1, If[LessEqual[N[(y * x), $MachinePrecision], 1e-68], N[(-2.0 * N[(c * N[(a * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(y * x), $MachinePrecision], 4e+78], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 10: 85.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot x + z \cdot t\\ \mathbf{if}\;a \leq -245000 \lor \neg \left(a \leq 1.25 \cdot 10^{+40}\right):\\ \;\;\;\;2 \cdot \left(t_1 - i \cdot \left(a \cdot c\right)\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(t_1 - \left(b \cdot c\right) \cdot \left(c \cdot i\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (+ (* y x) (* z t))))
   (if (or (<= a -245000.0) (not (<= a 1.25e+40)))
     (* 2.0 (- t_1 (* i (* a c))))
     (* 2.0 (- t_1 (* (* b c) (* c i)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = (y * x) + (z * t);
	double tmp;
	if ((a <= -245000.0) || !(a <= 1.25e+40)) {
		tmp = 2.0 * (t_1 - (i * (a * c)));
	} else {
		tmp = 2.0 * (t_1 - ((b * c) * (c * i)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (y * x) + (z * t)
    if ((a <= (-245000.0d0)) .or. (.not. (a <= 1.25d+40))) then
        tmp = 2.0d0 * (t_1 - (i * (a * c)))
    else
        tmp = 2.0d0 * (t_1 - ((b * c) * (c * i)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = (y * x) + (z * t);
	double tmp;
	if ((a <= -245000.0) || !(a <= 1.25e+40)) {
		tmp = 2.0 * (t_1 - (i * (a * c)));
	} else {
		tmp = 2.0 * (t_1 - ((b * c) * (c * i)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = (y * x) + (z * t)
	tmp = 0
	if (a <= -245000.0) or not (a <= 1.25e+40):
		tmp = 2.0 * (t_1 - (i * (a * c)))
	else:
		tmp = 2.0 * (t_1 - ((b * c) * (c * i)))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(Float64(y * x) + Float64(z * t))
	tmp = 0.0
	if ((a <= -245000.0) || !(a <= 1.25e+40))
		tmp = Float64(2.0 * Float64(t_1 - Float64(i * Float64(a * c))));
	else
		tmp = Float64(2.0 * Float64(t_1 - Float64(Float64(b * c) * Float64(c * i))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = (y * x) + (z * t);
	tmp = 0.0;
	if ((a <= -245000.0) || ~((a <= 1.25e+40)))
		tmp = 2.0 * (t_1 - (i * (a * c)));
	else
		tmp = 2.0 * (t_1 - ((b * c) * (c * i)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(N[(y * x), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[a, -245000.0], N[Not[LessEqual[a, 1.25e+40]], $MachinePrecision]], N[(2.0 * N[(t$95$1 - N[(i * N[(a * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(2.0 * N[(t$95$1 - N[(N[(b * c), $MachinePrecision] * N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 11: 73.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(y \cdot x + z \cdot t\right)\\ t_2 := \left(c \cdot \left(\left(a + b \cdot c\right) \cdot i\right)\right) \cdot -2\\ \mathbf{if}\;c \leq -7 \cdot 10^{-50}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;c \leq 2.5 \cdot 10^{-50}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;c \leq 8.8 \cdot 10^{+15}:\\ \;\;\;\;2 \cdot \left(y \cdot x - a \cdot \left(c \cdot i\right)\right)\\ \mathbf{elif}\;c \leq 2.8 \cdot 10^{+107}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (+ (* y x) (* z t))))
        (t_2 (* (* c (* (+ a (* b c)) i)) -2.0)))
   (if (<= c -7e-50)
     t_2
     (if (<= c 2.5e-50)
       t_1
       (if (<= c 8.8e+15)
         (* 2.0 (- (* y x) (* a (* c i))))
         (if (<= c 2.8e+107) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((y * x) + (z * t));
	double t_2 = (c * ((a + (b * c)) * i)) * -2.0;
	double tmp;
	if (c <= -7e-50) {
		tmp = t_2;
	} else if (c <= 2.5e-50) {
		tmp = t_1;
	} else if (c <= 8.8e+15) {
		tmp = 2.0 * ((y * x) - (a * (c * i)));
	} else if (c <= 2.8e+107) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 2.0d0 * ((y * x) + (z * t))
    t_2 = (c * ((a + (b * c)) * i)) * (-2.0d0)
    if (c <= (-7d-50)) then
        tmp = t_2
    else if (c <= 2.5d-50) then
        tmp = t_1
    else if (c <= 8.8d+15) then
        tmp = 2.0d0 * ((y * x) - (a * (c * i)))
    else if (c <= 2.8d+107) 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 c, double i) {
	double t_1 = 2.0 * ((y * x) + (z * t));
	double t_2 = (c * ((a + (b * c)) * i)) * -2.0;
	double tmp;
	if (c <= -7e-50) {
		tmp = t_2;
	} else if (c <= 2.5e-50) {
		tmp = t_1;
	} else if (c <= 8.8e+15) {
		tmp = 2.0 * ((y * x) - (a * (c * i)));
	} else if (c <= 2.8e+107) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * ((y * x) + (z * t))
	t_2 = (c * ((a + (b * c)) * i)) * -2.0
	tmp = 0
	if c <= -7e-50:
		tmp = t_2
	elif c <= 2.5e-50:
		tmp = t_1
	elif c <= 8.8e+15:
		tmp = 2.0 * ((y * x) - (a * (c * i)))
	elif c <= 2.8e+107:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(Float64(y * x) + Float64(z * t)))
	t_2 = Float64(Float64(c * Float64(Float64(a + Float64(b * c)) * i)) * -2.0)
	tmp = 0.0
	if (c <= -7e-50)
		tmp = t_2;
	elseif (c <= 2.5e-50)
		tmp = t_1;
	elseif (c <= 8.8e+15)
		tmp = Float64(2.0 * Float64(Float64(y * x) - Float64(a * Float64(c * i))));
	elseif (c <= 2.8e+107)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * ((y * x) + (z * t));
	t_2 = (c * ((a + (b * c)) * i)) * -2.0;
	tmp = 0.0;
	if (c <= -7e-50)
		tmp = t_2;
	elseif (c <= 2.5e-50)
		tmp = t_1;
	elseif (c <= 8.8e+15)
		tmp = 2.0 * ((y * x) - (a * (c * i)));
	elseif (c <= 2.8e+107)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(N[(y * x), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(c * N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision]}, If[LessEqual[c, -7e-50], t$95$2, If[LessEqual[c, 2.5e-50], t$95$1, If[LessEqual[c, 8.8e+15], N[(2.0 * N[(N[(y * x), $MachinePrecision] - N[(a * N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 2.8e+107], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 12: 73.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 2 \cdot \left(y \cdot x + z \cdot t\right)\\ t_2 := \left(c \cdot \left(\left(a + b \cdot c\right) \cdot i\right)\right) \cdot -2\\ \mathbf{if}\;c \leq -8 \cdot 10^{-57}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;c \leq 4 \cdot 10^{-49}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;c \leq 3.3 \cdot 10^{+15}:\\ \;\;\;\;2 \cdot \left(y \cdot x - c \cdot \left(a \cdot i\right)\right)\\ \mathbf{elif}\;c \leq 3.1 \cdot 10^{+107}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* 2.0 (+ (* y x) (* z t))))
        (t_2 (* (* c (* (+ a (* b c)) i)) -2.0)))
   (if (<= c -8e-57)
     t_2
     (if (<= c 4e-49)
       t_1
       (if (<= c 3.3e+15)
         (* 2.0 (- (* y x) (* c (* a i))))
         (if (<= c 3.1e+107) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = 2.0 * ((y * x) + (z * t));
	double t_2 = (c * ((a + (b * c)) * i)) * -2.0;
	double tmp;
	if (c <= -8e-57) {
		tmp = t_2;
	} else if (c <= 4e-49) {
		tmp = t_1;
	} else if (c <= 3.3e+15) {
		tmp = 2.0 * ((y * x) - (c * (a * i)));
	} else if (c <= 3.1e+107) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 2.0d0 * ((y * x) + (z * t))
    t_2 = (c * ((a + (b * c)) * i)) * (-2.0d0)
    if (c <= (-8d-57)) then
        tmp = t_2
    else if (c <= 4d-49) then
        tmp = t_1
    else if (c <= 3.3d+15) then
        tmp = 2.0d0 * ((y * x) - (c * (a * i)))
    else if (c <= 3.1d+107) 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 c, double i) {
	double t_1 = 2.0 * ((y * x) + (z * t));
	double t_2 = (c * ((a + (b * c)) * i)) * -2.0;
	double tmp;
	if (c <= -8e-57) {
		tmp = t_2;
	} else if (c <= 4e-49) {
		tmp = t_1;
	} else if (c <= 3.3e+15) {
		tmp = 2.0 * ((y * x) - (c * (a * i)));
	} else if (c <= 3.1e+107) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = 2.0 * ((y * x) + (z * t))
	t_2 = (c * ((a + (b * c)) * i)) * -2.0
	tmp = 0
	if c <= -8e-57:
		tmp = t_2
	elif c <= 4e-49:
		tmp = t_1
	elif c <= 3.3e+15:
		tmp = 2.0 * ((y * x) - (c * (a * i)))
	elif c <= 3.1e+107:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(2.0 * Float64(Float64(y * x) + Float64(z * t)))
	t_2 = Float64(Float64(c * Float64(Float64(a + Float64(b * c)) * i)) * -2.0)
	tmp = 0.0
	if (c <= -8e-57)
		tmp = t_2;
	elseif (c <= 4e-49)
		tmp = t_1;
	elseif (c <= 3.3e+15)
		tmp = Float64(2.0 * Float64(Float64(y * x) - Float64(c * Float64(a * i))));
	elseif (c <= 3.1e+107)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = 2.0 * ((y * x) + (z * t));
	t_2 = (c * ((a + (b * c)) * i)) * -2.0;
	tmp = 0.0;
	if (c <= -8e-57)
		tmp = t_2;
	elseif (c <= 4e-49)
		tmp = t_1;
	elseif (c <= 3.3e+15)
		tmp = 2.0 * ((y * x) - (c * (a * i)));
	elseif (c <= 3.1e+107)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(2.0 * N[(N[(y * x), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(c * N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision]}, If[LessEqual[c, -8e-57], t$95$2, If[LessEqual[c, 4e-49], t$95$1, If[LessEqual[c, 3.3e+15], N[(2.0 * N[(N[(y * x), $MachinePrecision] - N[(c * N[(a * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 3.1e+107], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 13: 76.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c \cdot \left(\left(a + b \cdot c\right) \cdot i\right)\\ \mathbf{if}\;z \leq -5.5 \cdot 10^{+87} \lor \neg \left(z \leq 3.9 \cdot 10^{-99}\right):\\ \;\;\;\;2 \cdot \left(z \cdot t - t_1\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(y \cdot x - t_1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* c (* (+ a (* b c)) i))))
   (if (or (<= z -5.5e+87) (not (<= z 3.9e-99)))
     (* 2.0 (- (* z t) t_1))
     (* 2.0 (- (* y x) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = c * ((a + (b * c)) * i);
	double tmp;
	if ((z <= -5.5e+87) || !(z <= 3.9e-99)) {
		tmp = 2.0 * ((z * t) - t_1);
	} else {
		tmp = 2.0 * ((y * x) - t_1);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = c * ((a + (b * c)) * i)
    if ((z <= (-5.5d+87)) .or. (.not. (z <= 3.9d-99))) then
        tmp = 2.0d0 * ((z * t) - t_1)
    else
        tmp = 2.0d0 * ((y * x) - t_1)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = c * ((a + (b * c)) * i);
	double tmp;
	if ((z <= -5.5e+87) || !(z <= 3.9e-99)) {
		tmp = 2.0 * ((z * t) - t_1);
	} else {
		tmp = 2.0 * ((y * x) - t_1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = c * ((a + (b * c)) * i)
	tmp = 0
	if (z <= -5.5e+87) or not (z <= 3.9e-99):
		tmp = 2.0 * ((z * t) - t_1)
	else:
		tmp = 2.0 * ((y * x) - t_1)
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(c * Float64(Float64(a + Float64(b * c)) * i))
	tmp = 0.0
	if ((z <= -5.5e+87) || !(z <= 3.9e-99))
		tmp = Float64(2.0 * Float64(Float64(z * t) - t_1));
	else
		tmp = Float64(2.0 * Float64(Float64(y * x) - t_1));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = c * ((a + (b * c)) * i);
	tmp = 0.0;
	if ((z <= -5.5e+87) || ~((z <= 3.9e-99)))
		tmp = 2.0 * ((z * t) - t_1);
	else
		tmp = 2.0 * ((y * x) - t_1);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(c * N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[z, -5.5e+87], N[Not[LessEqual[z, 3.9e-99]], $MachinePrecision]], N[(2.0 * N[(N[(z * t), $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision], N[(2.0 * N[(N[(y * x), $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 14: 66.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(b \cdot \left(i \cdot \left(c \cdot c\right)\right)\right) \cdot \left(-2\right)\\ \mathbf{if}\;c \leq -1.45 \cdot 10^{+110}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;c \leq -7 \cdot 10^{-50}:\\ \;\;\;\;-2 \cdot \left(c \cdot \left(a \cdot i\right)\right)\\ \mathbf{elif}\;c \leq 2.85 \cdot 10^{+107}:\\ \;\;\;\;2 \cdot \left(y \cdot x + z \cdot t\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (let* ((t_1 (* (* b (* i (* c c))) (- 2.0))))
   (if (<= c -1.45e+110)
     t_1
     (if (<= c -7e-50)
       (* -2.0 (* c (* a i)))
       (if (<= c 2.85e+107) (* 2.0 (+ (* y x) (* z t))) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = (b * (i * (c * c))) * -2.0;
	double tmp;
	if (c <= -1.45e+110) {
		tmp = t_1;
	} else if (c <= -7e-50) {
		tmp = -2.0 * (c * (a * i));
	} else if (c <= 2.85e+107) {
		tmp = 2.0 * ((y * x) + (z * t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (b * (i * (c * c))) * -2.0d0
    if (c <= (-1.45d+110)) then
        tmp = t_1
    else if (c <= (-7d-50)) then
        tmp = (-2.0d0) * (c * (a * i))
    else if (c <= 2.85d+107) then
        tmp = 2.0d0 * ((y * x) + (z * t))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double t_1 = (b * (i * (c * c))) * -2.0;
	double tmp;
	if (c <= -1.45e+110) {
		tmp = t_1;
	} else if (c <= -7e-50) {
		tmp = -2.0 * (c * (a * i));
	} else if (c <= 2.85e+107) {
		tmp = 2.0 * ((y * x) + (z * t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	t_1 = (b * (i * (c * c))) * -2.0
	tmp = 0
	if c <= -1.45e+110:
		tmp = t_1
	elif c <= -7e-50:
		tmp = -2.0 * (c * (a * i))
	elif c <= 2.85e+107:
		tmp = 2.0 * ((y * x) + (z * t))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	t_1 = Float64(Float64(b * Float64(i * Float64(c * c))) * Float64(-2.0))
	tmp = 0.0
	if (c <= -1.45e+110)
		tmp = t_1;
	elseif (c <= -7e-50)
		tmp = Float64(-2.0 * Float64(c * Float64(a * i)));
	elseif (c <= 2.85e+107)
		tmp = Float64(2.0 * Float64(Float64(y * x) + Float64(z * t)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	t_1 = (b * (i * (c * c))) * -2.0;
	tmp = 0.0;
	if (c <= -1.45e+110)
		tmp = t_1;
	elseif (c <= -7e-50)
		tmp = -2.0 * (c * (a * i));
	elseif (c <= 2.85e+107)
		tmp = 2.0 * ((y * x) + (z * t));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := Block[{t$95$1 = N[(N[(b * N[(i * N[(c * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * (-2.0)), $MachinePrecision]}, If[LessEqual[c, -1.45e+110], t$95$1, If[LessEqual[c, -7e-50], N[(-2.0 * N[(c * N[(a * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[c, 2.85e+107], N[(2.0 * N[(N[(y * x), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 15: 74.2% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;c \leq -7 \cdot 10^{-50} \lor \neg \left(c \leq 7.5 \cdot 10^{-43}\right):\\ \;\;\;\;\left(c \cdot \left(\left(a + b \cdot c\right) \cdot i\right)\right) \cdot -2\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(y \cdot x + z \cdot t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (or (<= c -7e-50) (not (<= c 7.5e-43)))
   (* (* c (* (+ a (* b c)) i)) -2.0)
   (* 2.0 (+ (* y x) (* z t)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((c <= -7e-50) || !(c <= 7.5e-43)) {
		tmp = (c * ((a + (b * c)) * i)) * -2.0;
	} else {
		tmp = 2.0 * ((y * x) + (z * t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if ((c <= (-7d-50)) .or. (.not. (c <= 7.5d-43))) then
        tmp = (c * ((a + (b * c)) * i)) * (-2.0d0)
    else
        tmp = 2.0d0 * ((y * x) + (z * 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 c, double i) {
	double tmp;
	if ((c <= -7e-50) || !(c <= 7.5e-43)) {
		tmp = (c * ((a + (b * c)) * i)) * -2.0;
	} else {
		tmp = 2.0 * ((y * x) + (z * t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if (c <= -7e-50) or not (c <= 7.5e-43):
		tmp = (c * ((a + (b * c)) * i)) * -2.0
	else:
		tmp = 2.0 * ((y * x) + (z * t))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if ((c <= -7e-50) || !(c <= 7.5e-43))
		tmp = Float64(Float64(c * Float64(Float64(a + Float64(b * c)) * i)) * -2.0);
	else
		tmp = Float64(2.0 * Float64(Float64(y * x) + Float64(z * t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if ((c <= -7e-50) || ~((c <= 7.5e-43)))
		tmp = (c * ((a + (b * c)) * i)) * -2.0;
	else
		tmp = 2.0 * ((y * x) + (z * t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[Or[LessEqual[c, -7e-50], N[Not[LessEqual[c, 7.5e-43]], $MachinePrecision]], N[(N[(c * N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision], N[(2.0 * N[(N[(y * x), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 16: 45.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \cdot x \leq -5 \cdot 10^{+69} \lor \neg \left(y \cdot x \leq 4 \cdot 10^{+78}\right):\\ \;\;\;\;2 \cdot \left(y \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(z \cdot t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (or (<= (* y x) -5e+69) (not (<= (* y x) 4e+78)))
   (* 2.0 (* y x))
   (* 2.0 (* z t))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if (((y * x) <= -5e+69) || !((y * x) <= 4e+78)) {
		tmp = 2.0 * (y * x);
	} else {
		tmp = 2.0 * (z * t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if (((y * x) <= (-5d+69)) .or. (.not. ((y * x) <= 4d+78))) then
        tmp = 2.0d0 * (y * x)
    else
        tmp = 2.0d0 * (z * 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 c, double i) {
	double tmp;
	if (((y * x) <= -5e+69) || !((y * x) <= 4e+78)) {
		tmp = 2.0 * (y * x);
	} else {
		tmp = 2.0 * (z * t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if ((y * x) <= -5e+69) or not ((y * x) <= 4e+78):
		tmp = 2.0 * (y * x)
	else:
		tmp = 2.0 * (z * t)
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if ((Float64(y * x) <= -5e+69) || !(Float64(y * x) <= 4e+78))
		tmp = Float64(2.0 * Float64(y * x));
	else
		tmp = Float64(2.0 * Float64(z * t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if (((y * x) <= -5e+69) || ~(((y * x) <= 4e+78)))
		tmp = 2.0 * (y * x);
	else
		tmp = 2.0 * (z * t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[Or[LessEqual[N[(y * x), $MachinePrecision], -5e+69], N[Not[LessEqual[N[(y * x), $MachinePrecision], 4e+78]], $MachinePrecision]], N[(2.0 * N[(y * x), $MachinePrecision]), $MachinePrecision], N[(2.0 * N[(z * t), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 17: 56.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;i \leq -1.2 \cdot 10^{+114} \lor \neg \left(i \leq 3.1 \cdot 10^{+99}\right):\\ \;\;\;\;\left(c \cdot i\right) \cdot \left(a \cdot -2\right)\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(y \cdot x + z \cdot t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (if (or (<= i -1.2e+114) (not (<= i 3.1e+99)))
   (* (* c i) (* a -2.0))
   (* 2.0 (+ (* y x) (* z t)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	double tmp;
	if ((i <= -1.2e+114) || !(i <= 3.1e+99)) {
		tmp = (c * i) * (a * -2.0);
	} else {
		tmp = 2.0 * ((y * x) + (z * t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    real(8) :: tmp
    if ((i <= (-1.2d+114)) .or. (.not. (i <= 3.1d+99))) then
        tmp = (c * i) * (a * (-2.0d0))
    else
        tmp = 2.0d0 * ((y * x) + (z * 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 c, double i) {
	double tmp;
	if ((i <= -1.2e+114) || !(i <= 3.1e+99)) {
		tmp = (c * i) * (a * -2.0);
	} else {
		tmp = 2.0 * ((y * x) + (z * t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i):
	tmp = 0
	if (i <= -1.2e+114) or not (i <= 3.1e+99):
		tmp = (c * i) * (a * -2.0)
	else:
		tmp = 2.0 * ((y * x) + (z * t))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i)
	tmp = 0.0
	if ((i <= -1.2e+114) || !(i <= 3.1e+99))
		tmp = Float64(Float64(c * i) * Float64(a * -2.0));
	else
		tmp = Float64(2.0 * Float64(Float64(y * x) + Float64(z * t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i)
	tmp = 0.0;
	if ((i <= -1.2e+114) || ~((i <= 3.1e+99)))
		tmp = (c * i) * (a * -2.0);
	else
		tmp = 2.0 * ((y * x) + (z * t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := If[Or[LessEqual[i, -1.2e+114], N[Not[LessEqual[i, 3.1e+99]], $MachinePrecision]], N[(N[(c * i), $MachinePrecision] * N[(a * -2.0), $MachinePrecision]), $MachinePrecision], N[(2.0 * N[(N[(y * x), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 18: 29.6% accurate, 3.8× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(z \cdot t\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i) :precision binary64 (* 2.0 (* z t)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (z * t);
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    code = 2.0d0 * (z * t)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (z * t);
}

Python

def code(x, y, z, t, a, b, c, i):
	return 2.0 * (z * t)

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(2.0 * Float64(z * t))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = 2.0 * (z * t);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(2.0 * N[(z * t), $MachinePrecision]), $MachinePrecision]

Derivation

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

Developer target: 94.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(\left(x \cdot y + z \cdot t\right) - \left(a + b \cdot c\right) \cdot \left(c \cdot i\right)\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i)
 :precision binary64
 (* 2.0 (- (+ (* x y) (* z t)) (* (+ a (* b c)) (* c i)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - ((a + (b * c)) * (c * i)));
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i)
    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), intent (in) :: c
    real(8), intent (in) :: i
    code = 2.0d0 * (((x * y) + (z * t)) - ((a + (b * c)) * (c * i)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i) {
	return 2.0 * (((x * y) + (z * t)) - ((a + (b * c)) * (c * i)));
}

Python

def code(x, y, z, t, a, b, c, i):
	return 2.0 * (((x * y) + (z * t)) - ((a + (b * c)) * (c * i)))

Julia

function code(x, y, z, t, a, b, c, i)
	return Float64(2.0 * Float64(Float64(Float64(x * y) + Float64(z * t)) - Float64(Float64(a + Float64(b * c)) * Float64(c * i))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i)
	tmp = 2.0 * (((x * y) + (z * t)) - ((a + (b * c)) * (c * i)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_] := N[(2.0 * N[(N[(N[(x * y), $MachinePrecision] + N[(z * t), $MachinePrecision]), $MachinePrecision] - N[(N[(a + N[(b * c), $MachinePrecision]), $MachinePrecision] * N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023350 
(FPCore (x y z t a b c i)
  :name "Diagrams.ThreeD.Shapes:frustum from diagrams-lib-1.3.0.3, A"
  :precision binary64

  :herbie-target
  (* 2.0 (- (+ (* x y) (* z t)) (* (+ a (* b c)) (* c i))))

  (* 2.0 (- (+ (* x y) (* z t)) (* (* (+ a (* b c)) c) i))))