Data.Colour.RGB:hslsv from colour-2.3.3, B

Percentage Accurate: 99.5% → 99.7%

Time: 15.5s

Alternatives: 15

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (/ (* 60.0 (- x y)) (- z t)) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
}

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = ((60.0d0 * (x - y)) / (z - t)) + (a * 120.0d0)
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
}

Python

def code(x, y, z, t, a):
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0)

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(60.0 * Float64(x - y)) / Float64(z - t)) + Float64(a * 120.0))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(60.0 * N[(x - y), $MachinePrecision]), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120 \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (/ (* 60.0 (- x y)) (- z t)) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
}

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = ((60.0d0 * (x - y)) / (z - t)) + (a * 120.0d0)
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
}

Python

def code(x, y, z, t, a):
	return ((60.0 * (x - y)) / (z - t)) + (a * 120.0)

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(60.0 * Float64(x - y)) / Float64(z - t)) + Float64(a * 120.0))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = ((60.0 * (x - y)) / (z - t)) + (a * 120.0);
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(60.0 * N[(x - y), $MachinePrecision]), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{60}{\frac{z - t}{x - y}} + a \cdot 120 \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (/ 60.0 (/ (- z t) (- x y))) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0);
}

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = (60.0d0 / ((z - t) / (x - y))) + (a * 120.0d0)
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0);
}

Python

def code(x, y, z, t, a):
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0)

Julia

function code(x, y, z, t, a)
	return Float64(Float64(60.0 / Float64(Float64(z - t) / Float64(x - y))) + Float64(a * 120.0))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = (60.0 / ((z - t) / (x - y))) + (a * 120.0);
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(60.0 / N[(N[(z - t), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 2: 56.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 60 \cdot \frac{x}{z - t}\\ t_2 := 60 \cdot \frac{-y}{z - t}\\ \mathbf{if}\;a \leq -3.4 \cdot 10^{-19}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \leq -7.8 \cdot 10^{-81}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq -1.08 \cdot 10^{-173}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -1.25 \cdot 10^{-239}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq -8.6 \cdot 10^{-295}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 6.1 \cdot 10^{-130}:\\ \;\;\;\;\frac{60}{\frac{z}{x - y}}\\ \mathbf{elif}\;a \leq 4.2:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 2 \cdot 10^{+50}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* 60.0 (/ x (- z t)))) (t_2 (* 60.0 (/ (- y) (- z t)))))
   (if (<= a -3.4e-19)
     (* a 120.0)
     (if (<= a -7.8e-81)
       t_2
       (if (<= a -1.08e-173)
         t_1
         (if (<= a -1.25e-239)
           t_2
           (if (<= a -8.6e-295)
             t_1
             (if (<= a 6.1e-130)
               (/ 60.0 (/ z (- x y)))
               (if (<= a 4.2) t_1 (if (<= a 2e+50) t_2 (* a 120.0)))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * (x / (z - t));
	double t_2 = 60.0 * (-y / (z - t));
	double tmp;
	if (a <= -3.4e-19) {
		tmp = a * 120.0;
	} else if (a <= -7.8e-81) {
		tmp = t_2;
	} else if (a <= -1.08e-173) {
		tmp = t_1;
	} else if (a <= -1.25e-239) {
		tmp = t_2;
	} else if (a <= -8.6e-295) {
		tmp = t_1;
	} else if (a <= 6.1e-130) {
		tmp = 60.0 / (z / (x - y));
	} else if (a <= 4.2) {
		tmp = t_1;
	} else if (a <= 2e+50) {
		tmp = t_2;
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 60.0d0 * (x / (z - t))
    t_2 = 60.0d0 * (-y / (z - t))
    if (a <= (-3.4d-19)) then
        tmp = a * 120.0d0
    else if (a <= (-7.8d-81)) then
        tmp = t_2
    else if (a <= (-1.08d-173)) then
        tmp = t_1
    else if (a <= (-1.25d-239)) then
        tmp = t_2
    else if (a <= (-8.6d-295)) then
        tmp = t_1
    else if (a <= 6.1d-130) then
        tmp = 60.0d0 / (z / (x - y))
    else if (a <= 4.2d0) then
        tmp = t_1
    else if (a <= 2d+50) then
        tmp = t_2
    else
        tmp = a * 120.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * (x / (z - t));
	double t_2 = 60.0 * (-y / (z - t));
	double tmp;
	if (a <= -3.4e-19) {
		tmp = a * 120.0;
	} else if (a <= -7.8e-81) {
		tmp = t_2;
	} else if (a <= -1.08e-173) {
		tmp = t_1;
	} else if (a <= -1.25e-239) {
		tmp = t_2;
	} else if (a <= -8.6e-295) {
		tmp = t_1;
	} else if (a <= 6.1e-130) {
		tmp = 60.0 / (z / (x - y));
	} else if (a <= 4.2) {
		tmp = t_1;
	} else if (a <= 2e+50) {
		tmp = t_2;
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 * (x / (z - t))
	t_2 = 60.0 * (-y / (z - t))
	tmp = 0
	if a <= -3.4e-19:
		tmp = a * 120.0
	elif a <= -7.8e-81:
		tmp = t_2
	elif a <= -1.08e-173:
		tmp = t_1
	elif a <= -1.25e-239:
		tmp = t_2
	elif a <= -8.6e-295:
		tmp = t_1
	elif a <= 6.1e-130:
		tmp = 60.0 / (z / (x - y))
	elif a <= 4.2:
		tmp = t_1
	elif a <= 2e+50:
		tmp = t_2
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 * Float64(x / Float64(z - t)))
	t_2 = Float64(60.0 * Float64(Float64(-y) / Float64(z - t)))
	tmp = 0.0
	if (a <= -3.4e-19)
		tmp = Float64(a * 120.0);
	elseif (a <= -7.8e-81)
		tmp = t_2;
	elseif (a <= -1.08e-173)
		tmp = t_1;
	elseif (a <= -1.25e-239)
		tmp = t_2;
	elseif (a <= -8.6e-295)
		tmp = t_1;
	elseif (a <= 6.1e-130)
		tmp = Float64(60.0 / Float64(z / Float64(x - y)));
	elseif (a <= 4.2)
		tmp = t_1;
	elseif (a <= 2e+50)
		tmp = t_2;
	else
		tmp = Float64(a * 120.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = 60.0 * (x / (z - t));
	t_2 = 60.0 * (-y / (z - t));
	tmp = 0.0;
	if (a <= -3.4e-19)
		tmp = a * 120.0;
	elseif (a <= -7.8e-81)
		tmp = t_2;
	elseif (a <= -1.08e-173)
		tmp = t_1;
	elseif (a <= -1.25e-239)
		tmp = t_2;
	elseif (a <= -8.6e-295)
		tmp = t_1;
	elseif (a <= 6.1e-130)
		tmp = 60.0 / (z / (x - y));
	elseif (a <= 4.2)
		tmp = t_1;
	elseif (a <= 2e+50)
		tmp = t_2;
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(60.0 * N[((-y) / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -3.4e-19], N[(a * 120.0), $MachinePrecision], If[LessEqual[a, -7.8e-81], t$95$2, If[LessEqual[a, -1.08e-173], t$95$1, If[LessEqual[a, -1.25e-239], t$95$2, If[LessEqual[a, -8.6e-295], t$95$1, If[LessEqual[a, 6.1e-130], N[(60.0 / N[(z / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.2], t$95$1, If[LessEqual[a, 2e+50], t$95$2, N[(a * 120.0), $MachinePrecision]]]]]]]]]]]

Derivation

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

Alternative 3: 56.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 60 \cdot \frac{-y}{z - t}\\ \mathbf{if}\;a \leq -3.2 \cdot 10^{-19}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \leq -2.35 \cdot 10^{-79}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -2.22 \cdot 10^{-181}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x}}\\ \mathbf{elif}\;a \leq -8 \cdot 10^{-225}:\\ \;\;\;\;60 \cdot \frac{x - y}{z}\\ \mathbf{elif}\;a \leq -2.4 \cdot 10^{-294}:\\ \;\;\;\;60 \cdot \frac{y - x}{t}\\ \mathbf{elif}\;a \leq 10^{-129}:\\ \;\;\;\;\frac{60}{\frac{z}{x - y}}\\ \mathbf{elif}\;a \leq 9.2 \cdot 10^{-10}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;a \leq 2 \cdot 10^{+50}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* 60.0 (/ (- y) (- z t)))))
   (if (<= a -3.2e-19)
     (* a 120.0)
     (if (<= a -2.35e-79)
       t_1
       (if (<= a -2.22e-181)
         (/ 60.0 (/ (- z t) x))
         (if (<= a -8e-225)
           (* 60.0 (/ (- x y) z))
           (if (<= a -2.4e-294)
             (* 60.0 (/ (- y x) t))
             (if (<= a 1e-129)
               (/ 60.0 (/ z (- x y)))
               (if (<= a 9.2e-10)
                 (* 60.0 (/ x (- z t)))
                 (if (<= a 2e+50) t_1 (* a 120.0)))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * (-y / (z - t));
	double tmp;
	if (a <= -3.2e-19) {
		tmp = a * 120.0;
	} else if (a <= -2.35e-79) {
		tmp = t_1;
	} else if (a <= -2.22e-181) {
		tmp = 60.0 / ((z - t) / x);
	} else if (a <= -8e-225) {
		tmp = 60.0 * ((x - y) / z);
	} else if (a <= -2.4e-294) {
		tmp = 60.0 * ((y - x) / t);
	} else if (a <= 1e-129) {
		tmp = 60.0 / (z / (x - y));
	} else if (a <= 9.2e-10) {
		tmp = 60.0 * (x / (z - t));
	} else if (a <= 2e+50) {
		tmp = t_1;
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = 60.0d0 * (-y / (z - t))
    if (a <= (-3.2d-19)) then
        tmp = a * 120.0d0
    else if (a <= (-2.35d-79)) then
        tmp = t_1
    else if (a <= (-2.22d-181)) then
        tmp = 60.0d0 / ((z - t) / x)
    else if (a <= (-8d-225)) then
        tmp = 60.0d0 * ((x - y) / z)
    else if (a <= (-2.4d-294)) then
        tmp = 60.0d0 * ((y - x) / t)
    else if (a <= 1d-129) then
        tmp = 60.0d0 / (z / (x - y))
    else if (a <= 9.2d-10) then
        tmp = 60.0d0 * (x / (z - t))
    else if (a <= 2d+50) then
        tmp = t_1
    else
        tmp = a * 120.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * (-y / (z - t));
	double tmp;
	if (a <= -3.2e-19) {
		tmp = a * 120.0;
	} else if (a <= -2.35e-79) {
		tmp = t_1;
	} else if (a <= -2.22e-181) {
		tmp = 60.0 / ((z - t) / x);
	} else if (a <= -8e-225) {
		tmp = 60.0 * ((x - y) / z);
	} else if (a <= -2.4e-294) {
		tmp = 60.0 * ((y - x) / t);
	} else if (a <= 1e-129) {
		tmp = 60.0 / (z / (x - y));
	} else if (a <= 9.2e-10) {
		tmp = 60.0 * (x / (z - t));
	} else if (a <= 2e+50) {
		tmp = t_1;
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 * (-y / (z - t))
	tmp = 0
	if a <= -3.2e-19:
		tmp = a * 120.0
	elif a <= -2.35e-79:
		tmp = t_1
	elif a <= -2.22e-181:
		tmp = 60.0 / ((z - t) / x)
	elif a <= -8e-225:
		tmp = 60.0 * ((x - y) / z)
	elif a <= -2.4e-294:
		tmp = 60.0 * ((y - x) / t)
	elif a <= 1e-129:
		tmp = 60.0 / (z / (x - y))
	elif a <= 9.2e-10:
		tmp = 60.0 * (x / (z - t))
	elif a <= 2e+50:
		tmp = t_1
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 * Float64(Float64(-y) / Float64(z - t)))
	tmp = 0.0
	if (a <= -3.2e-19)
		tmp = Float64(a * 120.0);
	elseif (a <= -2.35e-79)
		tmp = t_1;
	elseif (a <= -2.22e-181)
		tmp = Float64(60.0 / Float64(Float64(z - t) / x));
	elseif (a <= -8e-225)
		tmp = Float64(60.0 * Float64(Float64(x - y) / z));
	elseif (a <= -2.4e-294)
		tmp = Float64(60.0 * Float64(Float64(y - x) / t));
	elseif (a <= 1e-129)
		tmp = Float64(60.0 / Float64(z / Float64(x - y)));
	elseif (a <= 9.2e-10)
		tmp = Float64(60.0 * Float64(x / Float64(z - t)));
	elseif (a <= 2e+50)
		tmp = t_1;
	else
		tmp = Float64(a * 120.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = 60.0 * (-y / (z - t));
	tmp = 0.0;
	if (a <= -3.2e-19)
		tmp = a * 120.0;
	elseif (a <= -2.35e-79)
		tmp = t_1;
	elseif (a <= -2.22e-181)
		tmp = 60.0 / ((z - t) / x);
	elseif (a <= -8e-225)
		tmp = 60.0 * ((x - y) / z);
	elseif (a <= -2.4e-294)
		tmp = 60.0 * ((y - x) / t);
	elseif (a <= 1e-129)
		tmp = 60.0 / (z / (x - y));
	elseif (a <= 9.2e-10)
		tmp = 60.0 * (x / (z - t));
	elseif (a <= 2e+50)
		tmp = t_1;
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(60.0 * N[((-y) / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -3.2e-19], N[(a * 120.0), $MachinePrecision], If[LessEqual[a, -2.35e-79], t$95$1, If[LessEqual[a, -2.22e-181], N[(60.0 / N[(N[(z - t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -8e-225], N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -2.4e-294], N[(60.0 * N[(N[(y - x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1e-129], N[(60.0 / N[(z / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 9.2e-10], N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2e+50], t$95$1, N[(a * 120.0), $MachinePrecision]]]]]]]]]]

Derivation

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

Alternative 4: 56.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 60 \cdot \frac{x}{z - t}\\ t_2 := 60 \cdot \frac{x - y}{z}\\ \mathbf{if}\;a \leq -2.4 \cdot 10^{-23}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \leq -5.2 \cdot 10^{-142}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq -4.6 \cdot 10^{-180}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.68 \cdot 10^{-130}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 0.25:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 2.5 \cdot 10^{+50}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* 60.0 (/ x (- z t)))) (t_2 (* 60.0 (/ (- x y) z))))
   (if (<= a -2.4e-23)
     (* a 120.0)
     (if (<= a -5.2e-142)
       t_2
       (if (<= a -4.6e-180)
         t_1
         (if (<= a 1.68e-130)
           t_2
           (if (<= a 0.25) t_1 (if (<= a 2.5e+50) t_2 (* a 120.0)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * (x / (z - t));
	double t_2 = 60.0 * ((x - y) / z);
	double tmp;
	if (a <= -2.4e-23) {
		tmp = a * 120.0;
	} else if (a <= -5.2e-142) {
		tmp = t_2;
	} else if (a <= -4.6e-180) {
		tmp = t_1;
	} else if (a <= 1.68e-130) {
		tmp = t_2;
	} else if (a <= 0.25) {
		tmp = t_1;
	} else if (a <= 2.5e+50) {
		tmp = t_2;
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 60.0d0 * (x / (z - t))
    t_2 = 60.0d0 * ((x - y) / z)
    if (a <= (-2.4d-23)) then
        tmp = a * 120.0d0
    else if (a <= (-5.2d-142)) then
        tmp = t_2
    else if (a <= (-4.6d-180)) then
        tmp = t_1
    else if (a <= 1.68d-130) then
        tmp = t_2
    else if (a <= 0.25d0) then
        tmp = t_1
    else if (a <= 2.5d+50) then
        tmp = t_2
    else
        tmp = a * 120.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * (x / (z - t));
	double t_2 = 60.0 * ((x - y) / z);
	double tmp;
	if (a <= -2.4e-23) {
		tmp = a * 120.0;
	} else if (a <= -5.2e-142) {
		tmp = t_2;
	} else if (a <= -4.6e-180) {
		tmp = t_1;
	} else if (a <= 1.68e-130) {
		tmp = t_2;
	} else if (a <= 0.25) {
		tmp = t_1;
	} else if (a <= 2.5e+50) {
		tmp = t_2;
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 * (x / (z - t))
	t_2 = 60.0 * ((x - y) / z)
	tmp = 0
	if a <= -2.4e-23:
		tmp = a * 120.0
	elif a <= -5.2e-142:
		tmp = t_2
	elif a <= -4.6e-180:
		tmp = t_1
	elif a <= 1.68e-130:
		tmp = t_2
	elif a <= 0.25:
		tmp = t_1
	elif a <= 2.5e+50:
		tmp = t_2
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 * Float64(x / Float64(z - t)))
	t_2 = Float64(60.0 * Float64(Float64(x - y) / z))
	tmp = 0.0
	if (a <= -2.4e-23)
		tmp = Float64(a * 120.0);
	elseif (a <= -5.2e-142)
		tmp = t_2;
	elseif (a <= -4.6e-180)
		tmp = t_1;
	elseif (a <= 1.68e-130)
		tmp = t_2;
	elseif (a <= 0.25)
		tmp = t_1;
	elseif (a <= 2.5e+50)
		tmp = t_2;
	else
		tmp = Float64(a * 120.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = 60.0 * (x / (z - t));
	t_2 = 60.0 * ((x - y) / z);
	tmp = 0.0;
	if (a <= -2.4e-23)
		tmp = a * 120.0;
	elseif (a <= -5.2e-142)
		tmp = t_2;
	elseif (a <= -4.6e-180)
		tmp = t_1;
	elseif (a <= 1.68e-130)
		tmp = t_2;
	elseif (a <= 0.25)
		tmp = t_1;
	elseif (a <= 2.5e+50)
		tmp = t_2;
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.4e-23], N[(a * 120.0), $MachinePrecision], If[LessEqual[a, -5.2e-142], t$95$2, If[LessEqual[a, -4.6e-180], t$95$1, If[LessEqual[a, 1.68e-130], t$95$2, If[LessEqual[a, 0.25], t$95$1, If[LessEqual[a, 2.5e+50], t$95$2, N[(a * 120.0), $MachinePrecision]]]]]]]]]

Derivation

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

Alternative 5: 56.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 60 \cdot \frac{x - y}{z}\\ \mathbf{if}\;a \leq -2.1 \cdot 10^{-23}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \leq -6.8 \cdot 10^{-136}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -3.6 \cdot 10^{-180}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x}}\\ \mathbf{elif}\;a \leq 2.25 \cdot 10^{-131}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 340:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \mathbf{elif}\;a \leq 2 \cdot 10^{+50}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* 60.0 (/ (- x y) z))))
   (if (<= a -2.1e-23)
     (* a 120.0)
     (if (<= a -6.8e-136)
       t_1
       (if (<= a -3.6e-180)
         (/ 60.0 (/ (- z t) x))
         (if (<= a 2.25e-131)
           t_1
           (if (<= a 340.0)
             (* 60.0 (/ x (- z t)))
             (if (<= a 2e+50) t_1 (* a 120.0)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * ((x - y) / z);
	double tmp;
	if (a <= -2.1e-23) {
		tmp = a * 120.0;
	} else if (a <= -6.8e-136) {
		tmp = t_1;
	} else if (a <= -3.6e-180) {
		tmp = 60.0 / ((z - t) / x);
	} else if (a <= 2.25e-131) {
		tmp = t_1;
	} else if (a <= 340.0) {
		tmp = 60.0 * (x / (z - t));
	} else if (a <= 2e+50) {
		tmp = t_1;
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = 60.0d0 * ((x - y) / z)
    if (a <= (-2.1d-23)) then
        tmp = a * 120.0d0
    else if (a <= (-6.8d-136)) then
        tmp = t_1
    else if (a <= (-3.6d-180)) then
        tmp = 60.0d0 / ((z - t) / x)
    else if (a <= 2.25d-131) then
        tmp = t_1
    else if (a <= 340.0d0) then
        tmp = 60.0d0 * (x / (z - t))
    else if (a <= 2d+50) then
        tmp = t_1
    else
        tmp = a * 120.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 * ((x - y) / z);
	double tmp;
	if (a <= -2.1e-23) {
		tmp = a * 120.0;
	} else if (a <= -6.8e-136) {
		tmp = t_1;
	} else if (a <= -3.6e-180) {
		tmp = 60.0 / ((z - t) / x);
	} else if (a <= 2.25e-131) {
		tmp = t_1;
	} else if (a <= 340.0) {
		tmp = 60.0 * (x / (z - t));
	} else if (a <= 2e+50) {
		tmp = t_1;
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 * ((x - y) / z)
	tmp = 0
	if a <= -2.1e-23:
		tmp = a * 120.0
	elif a <= -6.8e-136:
		tmp = t_1
	elif a <= -3.6e-180:
		tmp = 60.0 / ((z - t) / x)
	elif a <= 2.25e-131:
		tmp = t_1
	elif a <= 340.0:
		tmp = 60.0 * (x / (z - t))
	elif a <= 2e+50:
		tmp = t_1
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 * Float64(Float64(x - y) / z))
	tmp = 0.0
	if (a <= -2.1e-23)
		tmp = Float64(a * 120.0);
	elseif (a <= -6.8e-136)
		tmp = t_1;
	elseif (a <= -3.6e-180)
		tmp = Float64(60.0 / Float64(Float64(z - t) / x));
	elseif (a <= 2.25e-131)
		tmp = t_1;
	elseif (a <= 340.0)
		tmp = Float64(60.0 * Float64(x / Float64(z - t)));
	elseif (a <= 2e+50)
		tmp = t_1;
	else
		tmp = Float64(a * 120.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = 60.0 * ((x - y) / z);
	tmp = 0.0;
	if (a <= -2.1e-23)
		tmp = a * 120.0;
	elseif (a <= -6.8e-136)
		tmp = t_1;
	elseif (a <= -3.6e-180)
		tmp = 60.0 / ((z - t) / x);
	elseif (a <= 2.25e-131)
		tmp = t_1;
	elseif (a <= 340.0)
		tmp = 60.0 * (x / (z - t));
	elseif (a <= 2e+50)
		tmp = t_1;
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.1e-23], N[(a * 120.0), $MachinePrecision], If[LessEqual[a, -6.8e-136], t$95$1, If[LessEqual[a, -3.6e-180], N[(60.0 / N[(N[(z - t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.25e-131], t$95$1, If[LessEqual[a, 340.0], N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2e+50], t$95$1, N[(a * 120.0), $MachinePrecision]]]]]]]]

Derivation

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

Alternative 6: 81.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.8 \cdot 10^{+60} \lor \neg \left(y \leq 3 \cdot 10^{+113}\right) \land \left(y \leq 8.5 \cdot 10^{+127} \lor \neg \left(y \leq 5 \cdot 10^{+226}\right)\right):\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{60}{z - t} + a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -4.8e+60)
         (and (not (<= y 3e+113)) (or (<= y 8.5e+127) (not (<= y 5e+226)))))
   (* 60.0 (/ (- x y) (- z t)))
   (+ (* x (/ 60.0 (- z t))) (* a 120.0))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -4.8e+60) || (!(y <= 3e+113) && ((y <= 8.5e+127) || !(y <= 5e+226)))) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((y <= (-4.8d+60)) .or. (.not. (y <= 3d+113)) .and. (y <= 8.5d+127) .or. (.not. (y <= 5d+226))) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    else
        tmp = (x * (60.0d0 / (z - t))) + (a * 120.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((y <= -4.8e+60) || (!(y <= 3e+113) && ((y <= 8.5e+127) || !(y <= 5e+226)))) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else {
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (y <= -4.8e+60) or (not (y <= 3e+113) and ((y <= 8.5e+127) or not (y <= 5e+226))):
		tmp = 60.0 * ((x - y) / (z - t))
	else:
		tmp = (x * (60.0 / (z - t))) + (a * 120.0)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((y <= -4.8e+60) || (!(y <= 3e+113) && ((y <= 8.5e+127) || !(y <= 5e+226))))
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	else
		tmp = Float64(Float64(x * Float64(60.0 / Float64(z - t))) + Float64(a * 120.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((y <= -4.8e+60) || (~((y <= 3e+113)) && ((y <= 8.5e+127) || ~((y <= 5e+226)))))
		tmp = 60.0 * ((x - y) / (z - t));
	else
		tmp = (x * (60.0 / (z - t))) + (a * 120.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[y, -4.8e+60], And[N[Not[LessEqual[y, 3e+113]], $MachinePrecision], Or[LessEqual[y, 8.5e+127], N[Not[LessEqual[y, 5e+226]], $MachinePrecision]]]], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 87.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \frac{60}{z - t} + a \cdot 120\\ \mathbf{if}\;x \leq -1.2 \cdot 10^{+116}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1200000000000:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{elif}\;x \leq 1.45 \cdot 10^{+28}:\\ \;\;\;\;\frac{-60}{\frac{z - t}{y}} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (* x (/ 60.0 (- z t))) (* a 120.0))))
   (if (<= x -1.2e+116)
     t_1
     (if (<= x -1200000000000.0)
       (* 60.0 (/ (- x y) (- z t)))
       (if (<= x 1.45e+28) (+ (/ -60.0 (/ (- z t) y)) (* a 120.0)) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (x * (60.0 / (z - t))) + (a * 120.0);
	double tmp;
	if (x <= -1.2e+116) {
		tmp = t_1;
	} else if (x <= -1200000000000.0) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else if (x <= 1.45e+28) {
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = (x * (60.0d0 / (z - t))) + (a * 120.0d0)
    if (x <= (-1.2d+116)) then
        tmp = t_1
    else if (x <= (-1200000000000.0d0)) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    else if (x <= 1.45d+28) then
        tmp = ((-60.0d0) / ((z - t) / y)) + (a * 120.0d0)
    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 t_1 = (x * (60.0 / (z - t))) + (a * 120.0);
	double tmp;
	if (x <= -1.2e+116) {
		tmp = t_1;
	} else if (x <= -1200000000000.0) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else if (x <= 1.45e+28) {
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (x * (60.0 / (z - t))) + (a * 120.0)
	tmp = 0
	if x <= -1.2e+116:
		tmp = t_1
	elif x <= -1200000000000.0:
		tmp = 60.0 * ((x - y) / (z - t))
	elif x <= 1.45e+28:
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(x * Float64(60.0 / Float64(z - t))) + Float64(a * 120.0))
	tmp = 0.0
	if (x <= -1.2e+116)
		tmp = t_1;
	elseif (x <= -1200000000000.0)
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	elseif (x <= 1.45e+28)
		tmp = Float64(Float64(-60.0 / Float64(Float64(z - t) / y)) + Float64(a * 120.0));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (x * (60.0 / (z - t))) + (a * 120.0);
	tmp = 0.0;
	if (x <= -1.2e+116)
		tmp = t_1;
	elseif (x <= -1200000000000.0)
		tmp = 60.0 * ((x - y) / (z - t));
	elseif (x <= 1.45e+28)
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(x * N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.2e+116], t$95$1, If[LessEqual[x, -1200000000000.0], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.45e+28], N[(N[(-60.0 / N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 8: 87.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{60}{\frac{z - t}{x}} + a \cdot 120\\ \mathbf{if}\;x \leq -1.45 \cdot 10^{+116}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1200000000000:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{elif}\;x \leq 1.3 \cdot 10^{+27}:\\ \;\;\;\;\frac{-60}{\frac{z - t}{y}} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (/ 60.0 (/ (- z t) x)) (* a 120.0))))
   (if (<= x -1.45e+116)
     t_1
     (if (<= x -1200000000000.0)
       (* 60.0 (/ (- x y) (- z t)))
       (if (<= x 1.3e+27) (+ (/ -60.0 (/ (- z t) y)) (* a 120.0)) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (60.0 / ((z - t) / x)) + (a * 120.0);
	double tmp;
	if (x <= -1.45e+116) {
		tmp = t_1;
	} else if (x <= -1200000000000.0) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else if (x <= 1.3e+27) {
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: t_1
    real(8) :: tmp
    t_1 = (60.0d0 / ((z - t) / x)) + (a * 120.0d0)
    if (x <= (-1.45d+116)) then
        tmp = t_1
    else if (x <= (-1200000000000.0d0)) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    else if (x <= 1.3d+27) then
        tmp = ((-60.0d0) / ((z - t) / y)) + (a * 120.0d0)
    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 t_1 = (60.0 / ((z - t) / x)) + (a * 120.0);
	double tmp;
	if (x <= -1.45e+116) {
		tmp = t_1;
	} else if (x <= -1200000000000.0) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else if (x <= 1.3e+27) {
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (60.0 / ((z - t) / x)) + (a * 120.0)
	tmp = 0
	if x <= -1.45e+116:
		tmp = t_1
	elif x <= -1200000000000.0:
		tmp = 60.0 * ((x - y) / (z - t))
	elif x <= 1.3e+27:
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(60.0 / Float64(Float64(z - t) / x)) + Float64(a * 120.0))
	tmp = 0.0
	if (x <= -1.45e+116)
		tmp = t_1;
	elseif (x <= -1200000000000.0)
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	elseif (x <= 1.3e+27)
		tmp = Float64(Float64(-60.0 / Float64(Float64(z - t) / y)) + Float64(a * 120.0));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (60.0 / ((z - t) / x)) + (a * 120.0);
	tmp = 0.0;
	if (x <= -1.45e+116)
		tmp = t_1;
	elseif (x <= -1200000000000.0)
		tmp = 60.0 * ((x - y) / (z - t));
	elseif (x <= 1.3e+27)
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(60.0 / N[(N[(z - t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.45e+116], t$95$1, If[LessEqual[x, -1200000000000.0], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.3e+27], N[(N[(-60.0 / N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 9: 87.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.9 \cdot 10^{+116}:\\ \;\;\;\;\frac{60 \cdot x}{z - t} + a \cdot 120\\ \mathbf{elif}\;x \leq -1200000000000:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{elif}\;x \leq 3.8 \cdot 10^{+27}:\\ \;\;\;\;\frac{-60}{\frac{z - t}{y}} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x}} + a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -3.9e+116)
   (+ (/ (* 60.0 x) (- z t)) (* a 120.0))
   (if (<= x -1200000000000.0)
     (* 60.0 (/ (- x y) (- z t)))
     (if (<= x 3.8e+27)
       (+ (/ -60.0 (/ (- z t) y)) (* a 120.0))
       (+ (/ 60.0 (/ (- z t) x)) (* a 120.0))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -3.9e+116) {
		tmp = ((60.0 * x) / (z - t)) + (a * 120.0);
	} else if (x <= -1200000000000.0) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else if (x <= 3.8e+27) {
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	} else {
		tmp = (60.0 / ((z - t) / x)) + (a * 120.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if (x <= (-3.9d+116)) then
        tmp = ((60.0d0 * x) / (z - t)) + (a * 120.0d0)
    else if (x <= (-1200000000000.0d0)) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    else if (x <= 3.8d+27) then
        tmp = ((-60.0d0) / ((z - t) / y)) + (a * 120.0d0)
    else
        tmp = (60.0d0 / ((z - t) / x)) + (a * 120.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -3.9e+116) {
		tmp = ((60.0 * x) / (z - t)) + (a * 120.0);
	} else if (x <= -1200000000000.0) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else if (x <= 3.8e+27) {
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	} else {
		tmp = (60.0 / ((z - t) / x)) + (a * 120.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -3.9e+116:
		tmp = ((60.0 * x) / (z - t)) + (a * 120.0)
	elif x <= -1200000000000.0:
		tmp = 60.0 * ((x - y) / (z - t))
	elif x <= 3.8e+27:
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0)
	else:
		tmp = (60.0 / ((z - t) / x)) + (a * 120.0)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (x <= -3.9e+116)
		tmp = Float64(Float64(Float64(60.0 * x) / Float64(z - t)) + Float64(a * 120.0));
	elseif (x <= -1200000000000.0)
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	elseif (x <= 3.8e+27)
		tmp = Float64(Float64(-60.0 / Float64(Float64(z - t) / y)) + Float64(a * 120.0));
	else
		tmp = Float64(Float64(60.0 / Float64(Float64(z - t) / x)) + Float64(a * 120.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (x <= -3.9e+116)
		tmp = ((60.0 * x) / (z - t)) + (a * 120.0);
	elseif (x <= -1200000000000.0)
		tmp = 60.0 * ((x - y) / (z - t));
	elseif (x <= 3.8e+27)
		tmp = (-60.0 / ((z - t) / y)) + (a * 120.0);
	else
		tmp = (60.0 / ((z - t) / x)) + (a * 120.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[x, -3.9e+116], N[(N[(N[(60.0 * x), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -1200000000000.0], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.8e+27], N[(N[(-60.0 / N[(N[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], N[(N[(60.0 / N[(N[(z - t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 10: 73.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot 120 \leq -5 \cdot 10^{-17}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{x}{t}\\ \mathbf{elif}\;a \cdot 120 \leq 4 \cdot 10^{+52}:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= (* a 120.0) -5e-17)
   (+ (* a 120.0) (* -60.0 (/ x t)))
   (if (<= (* a 120.0) 4e+52) (* 60.0 (/ (- x y) (- z t))) (* a 120.0))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a * 120.0) <= -5e-17) {
		tmp = (a * 120.0) + (-60.0 * (x / t));
	} else if ((a * 120.0) <= 4e+52) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((a * 120.0d0) <= (-5d-17)) then
        tmp = (a * 120.0d0) + ((-60.0d0) * (x / t))
    else if ((a * 120.0d0) <= 4d+52) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    else
        tmp = a * 120.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a * 120.0) <= -5e-17) {
		tmp = (a * 120.0) + (-60.0 * (x / t));
	} else if ((a * 120.0) <= 4e+52) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a * 120.0) <= -5e-17:
		tmp = (a * 120.0) + (-60.0 * (x / t))
	elif (a * 120.0) <= 4e+52:
		tmp = 60.0 * ((x - y) / (z - t))
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (Float64(a * 120.0) <= -5e-17)
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(x / t)));
	elseif (Float64(a * 120.0) <= 4e+52)
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	else
		tmp = Float64(a * 120.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a * 120.0) <= -5e-17)
		tmp = (a * 120.0) + (-60.0 * (x / t));
	elseif ((a * 120.0) <= 4e+52)
		tmp = 60.0 * ((x - y) / (z - t));
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[N[(a * 120.0), $MachinePrecision], -5e-17], N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(x / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(a * 120.0), $MachinePrecision], 4e+52], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]]

Derivation

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

Alternative 11: 73.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot 120 \leq -5 \cdot 10^{-17}:\\ \;\;\;\;x \cdot \frac{-60}{t} + a \cdot 120\\ \mathbf{elif}\;a \cdot 120 \leq 4 \cdot 10^{+52}:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= (* a 120.0) -5e-17)
   (+ (* x (/ -60.0 t)) (* a 120.0))
   (if (<= (* a 120.0) 4e+52) (* 60.0 (/ (- x y) (- z t))) (* a 120.0))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a * 120.0) <= -5e-17) {
		tmp = (x * (-60.0 / t)) + (a * 120.0);
	} else if ((a * 120.0) <= 4e+52) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((a * 120.0d0) <= (-5d-17)) then
        tmp = (x * ((-60.0d0) / t)) + (a * 120.0d0)
    else if ((a * 120.0d0) <= 4d+52) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    else
        tmp = a * 120.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a * 120.0) <= -5e-17) {
		tmp = (x * (-60.0 / t)) + (a * 120.0);
	} else if ((a * 120.0) <= 4e+52) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a * 120.0) <= -5e-17:
		tmp = (x * (-60.0 / t)) + (a * 120.0)
	elif (a * 120.0) <= 4e+52:
		tmp = 60.0 * ((x - y) / (z - t))
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (Float64(a * 120.0) <= -5e-17)
		tmp = Float64(Float64(x * Float64(-60.0 / t)) + Float64(a * 120.0));
	elseif (Float64(a * 120.0) <= 4e+52)
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	else
		tmp = Float64(a * 120.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a * 120.0) <= -5e-17)
		tmp = (x * (-60.0 / t)) + (a * 120.0);
	elseif ((a * 120.0) <= 4e+52)
		tmp = 60.0 * ((x - y) / (z - t));
	else
		tmp = a * 120.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[N[(a * 120.0), $MachinePrecision], -5e-17], N[(N[(x * N[(-60.0 / t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(a * 120.0), $MachinePrecision], 4e+52], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]]

Derivation

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

Alternative 12: 74.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -0.006 \lor \neg \left(a \leq 3.55 \cdot 10^{+50}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -0.006) (not (<= a 3.55e+50)))
   (* a 120.0)
   (* 60.0 (/ (- x y) (- z t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -0.006) || !(a <= 3.55e+50)) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * ((x - y) / (z - t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((a <= (-0.006d0)) .or. (.not. (a <= 3.55d+50))) then
        tmp = a * 120.0d0
    else
        tmp = 60.0d0 * ((x - y) / (z - t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -0.006) || !(a <= 3.55e+50)) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * ((x - y) / (z - t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -0.006) or not (a <= 3.55e+50):
		tmp = a * 120.0
	else:
		tmp = 60.0 * ((x - y) / (z - t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -0.006) || !(a <= 3.55e+50))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -0.006) || ~((a <= 3.55e+50)))
		tmp = a * 120.0;
	else
		tmp = 60.0 * ((x - y) / (z - t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -0.006], N[Not[LessEqual[a, 3.55e+50]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 57.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.7 \cdot 10^{-115} \lor \neg \left(a \leq 2.6 \cdot 10^{-11}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x}{z - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -2.7e-115) (not (<= a 2.6e-11)))
   (* a 120.0)
   (* 60.0 (/ x (- z t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -2.7e-115) || !(a <= 2.6e-11)) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * (x / (z - t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((a <= (-2.7d-115)) .or. (.not. (a <= 2.6d-11))) then
        tmp = a * 120.0d0
    else
        tmp = 60.0d0 * (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 tmp;
	if ((a <= -2.7e-115) || !(a <= 2.6e-11)) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * (x / (z - t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -2.7e-115) or not (a <= 2.6e-11):
		tmp = a * 120.0
	else:
		tmp = 60.0 * (x / (z - t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -2.7e-115) || !(a <= 2.6e-11))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(60.0 * Float64(x / Float64(z - t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -2.7e-115) || ~((a <= 2.6e-11)))
		tmp = a * 120.0;
	else
		tmp = 60.0 * (x / (z - t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -2.7e-115], N[Not[LessEqual[a, 2.6e-11]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 14: 50.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.1 \cdot 10^{-155} \lor \neg \left(a \leq 7.8 \cdot 10^{-11}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -1.1e-155) (not (<= a 7.8e-11))) (* a 120.0) (* 60.0 (/ x z))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -1.1e-155) || !(a <= 7.8e-11)) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * (x / z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    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) :: tmp
    if ((a <= (-1.1d-155)) .or. (.not. (a <= 7.8d-11))) then
        tmp = a * 120.0d0
    else
        tmp = 60.0d0 * (x / z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -1.1e-155) || !(a <= 7.8e-11)) {
		tmp = a * 120.0;
	} else {
		tmp = 60.0 * (x / z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -1.1e-155) or not (a <= 7.8e-11):
		tmp = a * 120.0
	else:
		tmp = 60.0 * (x / z)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -1.1e-155) || !(a <= 7.8e-11))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(60.0 * Float64(x / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -1.1e-155) || ~((a <= 7.8e-11)))
		tmp = a * 120.0;
	else
		tmp = 60.0 * (x / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -1.1e-155], N[Not[LessEqual[a, 7.8e-11]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(60.0 * N[(x / z), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 15: 50.7% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ a \cdot 120 \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (* a 120.0))

C

double code(double x, double y, double z, double t, double a) {
	return a * 120.0;
}

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = a * 120.0d0
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return a * 120.0;
}

Python

def code(x, y, z, t, a):
	return a * 120.0

Julia

function code(x, y, z, t, a)
	return Float64(a * 120.0)
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = a * 120.0;
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(a * 120.0), $MachinePrecision]

Derivation

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

Developer target: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{60}{\frac{z - t}{x - y}} + a \cdot 120 \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (+ (/ 60.0 (/ (- z t) (- x y))) (* a 120.0)))

C

double code(double x, double y, double z, double t, double a) {
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0);
}

Fortran

real(8) function code(x, y, z, t, a)
    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
    code = (60.0d0 / ((z - t) / (x - y))) + (a * 120.0d0)
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0);
}

Python

def code(x, y, z, t, a):
	return (60.0 / ((z - t) / (x - y))) + (a * 120.0)

Julia

function code(x, y, z, t, a)
	return Float64(Float64(60.0 / Float64(Float64(z - t) / Float64(x - y))) + Float64(a * 120.0))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = (60.0 / ((z - t) / (x - y))) + (a * 120.0);
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(60.0 / N[(N[(z - t), $MachinePrecision] / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024008 
(FPCore (x y z t a)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, B"
  :precision binary64

  :herbie-target
  (+ (/ 60.0 (/ (- z t) (- x y))) (* a 120.0))

  (+ (/ (* 60.0 (- x y)) (- z t)) (* a 120.0)))