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

Percentage Accurate: 99.3% → 99.8%

Time: 18.3s

Alternatives: 20

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: 99.3% 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: 99.8% accurate, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 2: 57.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -60 \cdot \frac{y}{z - t}\\ t_2 := 60 \cdot \frac{x}{z - t}\\ \mathbf{if}\;a \leq -1.2 \cdot 10^{+32}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \leq -5.2 \cdot 10^{-288}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 4.6 \cdot 10^{-267}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 9.5 \cdot 10^{-232}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 5.2 \cdot 10^{-161}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 3.4 \cdot 10^{-103}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{-32}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* -60.0 (/ y (- z t)))) (t_2 (* 60.0 (/ x (- z t)))))
   (if (<= a -1.2e+32)
     (* a 120.0)
     (if (<= a -5.2e-288)
       t_2
       (if (<= a 4.6e-267)
         t_1
         (if (<= a 9.5e-232)
           t_2
           (if (<= a 5.2e-161)
             t_1
             (if (<= a 3.4e-103)
               t_2
               (if (<= a 1.02e-32) 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 t_2 = 60.0 * (x / (z - t));
	double tmp;
	if (a <= -1.2e+32) {
		tmp = a * 120.0;
	} else if (a <= -5.2e-288) {
		tmp = t_2;
	} else if (a <= 4.6e-267) {
		tmp = t_1;
	} else if (a <= 9.5e-232) {
		tmp = t_2;
	} else if (a <= 5.2e-161) {
		tmp = t_1;
	} else if (a <= 3.4e-103) {
		tmp = t_2;
	} else if (a <= 1.02e-32) {
		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) :: t_2
    real(8) :: tmp
    t_1 = (-60.0d0) * (y / (z - t))
    t_2 = 60.0d0 * (x / (z - t))
    if (a <= (-1.2d+32)) then
        tmp = a * 120.0d0
    else if (a <= (-5.2d-288)) then
        tmp = t_2
    else if (a <= 4.6d-267) then
        tmp = t_1
    else if (a <= 9.5d-232) then
        tmp = t_2
    else if (a <= 5.2d-161) then
        tmp = t_1
    else if (a <= 3.4d-103) then
        tmp = t_2
    else if (a <= 1.02d-32) 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 t_2 = 60.0 * (x / (z - t));
	double tmp;
	if (a <= -1.2e+32) {
		tmp = a * 120.0;
	} else if (a <= -5.2e-288) {
		tmp = t_2;
	} else if (a <= 4.6e-267) {
		tmp = t_1;
	} else if (a <= 9.5e-232) {
		tmp = t_2;
	} else if (a <= 5.2e-161) {
		tmp = t_1;
	} else if (a <= 3.4e-103) {
		tmp = t_2;
	} else if (a <= 1.02e-32) {
		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))
	t_2 = 60.0 * (x / (z - t))
	tmp = 0
	if a <= -1.2e+32:
		tmp = a * 120.0
	elif a <= -5.2e-288:
		tmp = t_2
	elif a <= 4.6e-267:
		tmp = t_1
	elif a <= 9.5e-232:
		tmp = t_2
	elif a <= 5.2e-161:
		tmp = t_1
	elif a <= 3.4e-103:
		tmp = t_2
	elif a <= 1.02e-32:
		tmp = t_1
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(-60.0 * Float64(y / Float64(z - t)))
	t_2 = Float64(60.0 * Float64(x / Float64(z - t)))
	tmp = 0.0
	if (a <= -1.2e+32)
		tmp = Float64(a * 120.0);
	elseif (a <= -5.2e-288)
		tmp = t_2;
	elseif (a <= 4.6e-267)
		tmp = t_1;
	elseif (a <= 9.5e-232)
		tmp = t_2;
	elseif (a <= 5.2e-161)
		tmp = t_1;
	elseif (a <= 3.4e-103)
		tmp = t_2;
	elseif (a <= 1.02e-32)
		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));
	t_2 = 60.0 * (x / (z - t));
	tmp = 0.0;
	if (a <= -1.2e+32)
		tmp = a * 120.0;
	elseif (a <= -5.2e-288)
		tmp = t_2;
	elseif (a <= 4.6e-267)
		tmp = t_1;
	elseif (a <= 9.5e-232)
		tmp = t_2;
	elseif (a <= 5.2e-161)
		tmp = t_1;
	elseif (a <= 3.4e-103)
		tmp = t_2;
	elseif (a <= 1.02e-32)
		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]}, Block[{t$95$2 = N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.2e+32], N[(a * 120.0), $MachinePrecision], If[LessEqual[a, -5.2e-288], t$95$2, If[LessEqual[a, 4.6e-267], t$95$1, If[LessEqual[a, 9.5e-232], t$95$2, If[LessEqual[a, 5.2e-161], t$95$1, If[LessEqual[a, 3.4e-103], t$95$2, If[LessEqual[a, 1.02e-32], t$95$1, N[(a * 120.0), $MachinePrecision]]]]]]]]]]

Derivation

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

Alternative 3: 57.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-60}{\frac{z - t}{y}}\\ t_2 := 60 \cdot \frac{x}{z - t}\\ \mathbf{if}\;a \leq -5.6 \cdot 10^{+31}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \leq -4.5 \cdot 10^{-296}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 3.65 \cdot 10^{-267}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 7.8 \cdot 10^{-234}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 1.5 \cdot 10^{-145}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.26 \cdot 10^{-108}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 3.3 \cdot 10^{-34}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ -60.0 (/ (- z t) y))) (t_2 (* 60.0 (/ x (- z t)))))
   (if (<= a -5.6e+31)
     (* a 120.0)
     (if (<= a -4.5e-296)
       t_2
       (if (<= a 3.65e-267)
         t_1
         (if (<= a 7.8e-234)
           t_2
           (if (<= a 1.5e-145)
             t_1
             (if (<= a 1.26e-108)
               t_2
               (if (<= a 3.3e-34) t_1 (* a 120.0))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = -60.0 / ((z - t) / y);
	double t_2 = 60.0 * (x / (z - t));
	double tmp;
	if (a <= -5.6e+31) {
		tmp = a * 120.0;
	} else if (a <= -4.5e-296) {
		tmp = t_2;
	} else if (a <= 3.65e-267) {
		tmp = t_1;
	} else if (a <= 7.8e-234) {
		tmp = t_2;
	} else if (a <= 1.5e-145) {
		tmp = t_1;
	} else if (a <= 1.26e-108) {
		tmp = t_2;
	} else if (a <= 3.3e-34) {
		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) :: t_2
    real(8) :: tmp
    t_1 = (-60.0d0) / ((z - t) / y)
    t_2 = 60.0d0 * (x / (z - t))
    if (a <= (-5.6d+31)) then
        tmp = a * 120.0d0
    else if (a <= (-4.5d-296)) then
        tmp = t_2
    else if (a <= 3.65d-267) then
        tmp = t_1
    else if (a <= 7.8d-234) then
        tmp = t_2
    else if (a <= 1.5d-145) then
        tmp = t_1
    else if (a <= 1.26d-108) then
        tmp = t_2
    else if (a <= 3.3d-34) 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 / ((z - t) / y);
	double t_2 = 60.0 * (x / (z - t));
	double tmp;
	if (a <= -5.6e+31) {
		tmp = a * 120.0;
	} else if (a <= -4.5e-296) {
		tmp = t_2;
	} else if (a <= 3.65e-267) {
		tmp = t_1;
	} else if (a <= 7.8e-234) {
		tmp = t_2;
	} else if (a <= 1.5e-145) {
		tmp = t_1;
	} else if (a <= 1.26e-108) {
		tmp = t_2;
	} else if (a <= 3.3e-34) {
		tmp = t_1;
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = -60.0 / ((z - t) / y)
	t_2 = 60.0 * (x / (z - t))
	tmp = 0
	if a <= -5.6e+31:
		tmp = a * 120.0
	elif a <= -4.5e-296:
		tmp = t_2
	elif a <= 3.65e-267:
		tmp = t_1
	elif a <= 7.8e-234:
		tmp = t_2
	elif a <= 1.5e-145:
		tmp = t_1
	elif a <= 1.26e-108:
		tmp = t_2
	elif a <= 3.3e-34:
		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(z - t) / y))
	t_2 = Float64(60.0 * Float64(x / Float64(z - t)))
	tmp = 0.0
	if (a <= -5.6e+31)
		tmp = Float64(a * 120.0);
	elseif (a <= -4.5e-296)
		tmp = t_2;
	elseif (a <= 3.65e-267)
		tmp = t_1;
	elseif (a <= 7.8e-234)
		tmp = t_2;
	elseif (a <= 1.5e-145)
		tmp = t_1;
	elseif (a <= 1.26e-108)
		tmp = t_2;
	elseif (a <= 3.3e-34)
		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 / ((z - t) / y);
	t_2 = 60.0 * (x / (z - t));
	tmp = 0.0;
	if (a <= -5.6e+31)
		tmp = a * 120.0;
	elseif (a <= -4.5e-296)
		tmp = t_2;
	elseif (a <= 3.65e-267)
		tmp = t_1;
	elseif (a <= 7.8e-234)
		tmp = t_2;
	elseif (a <= 1.5e-145)
		tmp = t_1;
	elseif (a <= 1.26e-108)
		tmp = t_2;
	elseif (a <= 3.3e-34)
		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[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -5.6e+31], N[(a * 120.0), $MachinePrecision], If[LessEqual[a, -4.5e-296], t$95$2, If[LessEqual[a, 3.65e-267], t$95$1, If[LessEqual[a, 7.8e-234], t$95$2, If[LessEqual[a, 1.5e-145], t$95$1, If[LessEqual[a, 1.26e-108], t$95$2, If[LessEqual[a, 3.3e-34], t$95$1, N[(a * 120.0), $MachinePrecision]]]]]]]]]]

Derivation

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

Alternative 4: 57.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-60}{\frac{z - t}{y}}\\ t_2 := 60 \cdot \frac{x}{z - t}\\ \mathbf{if}\;a \leq -5.8 \cdot 10^{+31}:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \leq -1.15 \cdot 10^{-294}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 4.4 \cdot 10^{-268}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 9 \cdot 10^{-239}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 1.2 \cdot 10^{-155}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 7.4 \cdot 10^{-103}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 2.1 \cdot 10^{-36}:\\ \;\;\;\;\frac{y \cdot -60}{z - t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ -60.0 (/ (- z t) y))) (t_2 (* 60.0 (/ x (- z t)))))
   (if (<= a -5.8e+31)
     (* a 120.0)
     (if (<= a -1.15e-294)
       t_2
       (if (<= a 4.4e-268)
         t_1
         (if (<= a 9e-239)
           t_2
           (if (<= a 1.2e-155)
             t_1
             (if (<= a 7.4e-103)
               t_2
               (if (<= a 2.1e-36) (/ (* y -60.0) (- z t)) (* a 120.0))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = -60.0 / ((z - t) / y);
	double t_2 = 60.0 * (x / (z - t));
	double tmp;
	if (a <= -5.8e+31) {
		tmp = a * 120.0;
	} else if (a <= -1.15e-294) {
		tmp = t_2;
	} else if (a <= 4.4e-268) {
		tmp = t_1;
	} else if (a <= 9e-239) {
		tmp = t_2;
	} else if (a <= 1.2e-155) {
		tmp = t_1;
	} else if (a <= 7.4e-103) {
		tmp = t_2;
	} else if (a <= 2.1e-36) {
		tmp = (y * -60.0) / (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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (-60.0d0) / ((z - t) / y)
    t_2 = 60.0d0 * (x / (z - t))
    if (a <= (-5.8d+31)) then
        tmp = a * 120.0d0
    else if (a <= (-1.15d-294)) then
        tmp = t_2
    else if (a <= 4.4d-268) then
        tmp = t_1
    else if (a <= 9d-239) then
        tmp = t_2
    else if (a <= 1.2d-155) then
        tmp = t_1
    else if (a <= 7.4d-103) then
        tmp = t_2
    else if (a <= 2.1d-36) then
        tmp = (y * (-60.0d0)) / (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 t_1 = -60.0 / ((z - t) / y);
	double t_2 = 60.0 * (x / (z - t));
	double tmp;
	if (a <= -5.8e+31) {
		tmp = a * 120.0;
	} else if (a <= -1.15e-294) {
		tmp = t_2;
	} else if (a <= 4.4e-268) {
		tmp = t_1;
	} else if (a <= 9e-239) {
		tmp = t_2;
	} else if (a <= 1.2e-155) {
		tmp = t_1;
	} else if (a <= 7.4e-103) {
		tmp = t_2;
	} else if (a <= 2.1e-36) {
		tmp = (y * -60.0) / (z - t);
	} else {
		tmp = a * 120.0;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = -60.0 / ((z - t) / y)
	t_2 = 60.0 * (x / (z - t))
	tmp = 0
	if a <= -5.8e+31:
		tmp = a * 120.0
	elif a <= -1.15e-294:
		tmp = t_2
	elif a <= 4.4e-268:
		tmp = t_1
	elif a <= 9e-239:
		tmp = t_2
	elif a <= 1.2e-155:
		tmp = t_1
	elif a <= 7.4e-103:
		tmp = t_2
	elif a <= 2.1e-36:
		tmp = (y * -60.0) / (z - t)
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(-60.0 / Float64(Float64(z - t) / y))
	t_2 = Float64(60.0 * Float64(x / Float64(z - t)))
	tmp = 0.0
	if (a <= -5.8e+31)
		tmp = Float64(a * 120.0);
	elseif (a <= -1.15e-294)
		tmp = t_2;
	elseif (a <= 4.4e-268)
		tmp = t_1;
	elseif (a <= 9e-239)
		tmp = t_2;
	elseif (a <= 1.2e-155)
		tmp = t_1;
	elseif (a <= 7.4e-103)
		tmp = t_2;
	elseif (a <= 2.1e-36)
		tmp = Float64(Float64(y * -60.0) / Float64(z - t));
	else
		tmp = Float64(a * 120.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = -60.0 / ((z - t) / y);
	t_2 = 60.0 * (x / (z - t));
	tmp = 0.0;
	if (a <= -5.8e+31)
		tmp = a * 120.0;
	elseif (a <= -1.15e-294)
		tmp = t_2;
	elseif (a <= 4.4e-268)
		tmp = t_1;
	elseif (a <= 9e-239)
		tmp = t_2;
	elseif (a <= 1.2e-155)
		tmp = t_1;
	elseif (a <= 7.4e-103)
		tmp = t_2;
	elseif (a <= 2.1e-36)
		tmp = (y * -60.0) / (z - t);
	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[(z - t), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(60.0 * N[(x / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -5.8e+31], N[(a * 120.0), $MachinePrecision], If[LessEqual[a, -1.15e-294], t$95$2, If[LessEqual[a, 4.4e-268], t$95$1, If[LessEqual[a, 9e-239], t$95$2, If[LessEqual[a, 1.2e-155], t$95$1, If[LessEqual[a, 7.4e-103], t$95$2, If[LessEqual[a, 2.1e-36], N[(N[(y * -60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision], N[(a * 120.0), $MachinePrecision]]]]]]]]]]

Derivation

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

Alternative 5: 83.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{60}{z - t}\\ t_2 := a \cdot 120 + 60 \cdot \frac{x - y}{z}\\ \mathbf{if}\;z \leq -1.05 \cdot 10^{+15}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -1.35 \cdot 10^{-145}:\\ \;\;\;\;t_1 \cdot x + a \cdot 120\\ \mathbf{elif}\;z \leq -1.15 \cdot 10^{-167}:\\ \;\;\;\;t_1 \cdot \left(x - y\right)\\ \mathbf{elif}\;z \leq 580000:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{x - y}{t}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ 60.0 (- z t))) (t_2 (+ (* a 120.0) (* 60.0 (/ (- x y) z)))))
   (if (<= z -1.05e+15)
     t_2
     (if (<= z -1.35e-145)
       (+ (* t_1 x) (* a 120.0))
       (if (<= z -1.15e-167)
         (* t_1 (- x y))
         (if (<= z 580000.0) (+ (* a 120.0) (* -60.0 (/ (- x y) t))) t_2))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = 60.0 / (z - t);
	double t_2 = (a * 120.0) + (60.0 * ((x - y) / z));
	double tmp;
	if (z <= -1.05e+15) {
		tmp = t_2;
	} else if (z <= -1.35e-145) {
		tmp = (t_1 * x) + (a * 120.0);
	} else if (z <= -1.15e-167) {
		tmp = t_1 * (x - y);
	} else if (z <= 580000.0) {
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t));
	} else {
		tmp = t_2;
	}
	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 / (z - t)
    t_2 = (a * 120.0d0) + (60.0d0 * ((x - y) / z))
    if (z <= (-1.05d+15)) then
        tmp = t_2
    else if (z <= (-1.35d-145)) then
        tmp = (t_1 * x) + (a * 120.0d0)
    else if (z <= (-1.15d-167)) then
        tmp = t_1 * (x - y)
    else if (z <= 580000.0d0) then
        tmp = (a * 120.0d0) + ((-60.0d0) * ((x - y) / t))
    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 t_1 = 60.0 / (z - t);
	double t_2 = (a * 120.0) + (60.0 * ((x - y) / z));
	double tmp;
	if (z <= -1.05e+15) {
		tmp = t_2;
	} else if (z <= -1.35e-145) {
		tmp = (t_1 * x) + (a * 120.0);
	} else if (z <= -1.15e-167) {
		tmp = t_1 * (x - y);
	} else if (z <= 580000.0) {
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = 60.0 / (z - t)
	t_2 = (a * 120.0) + (60.0 * ((x - y) / z))
	tmp = 0
	if z <= -1.05e+15:
		tmp = t_2
	elif z <= -1.35e-145:
		tmp = (t_1 * x) + (a * 120.0)
	elif z <= -1.15e-167:
		tmp = t_1 * (x - y)
	elif z <= 580000.0:
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t))
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(60.0 / Float64(z - t))
	t_2 = Float64(Float64(a * 120.0) + Float64(60.0 * Float64(Float64(x - y) / z)))
	tmp = 0.0
	if (z <= -1.05e+15)
		tmp = t_2;
	elseif (z <= -1.35e-145)
		tmp = Float64(Float64(t_1 * x) + Float64(a * 120.0));
	elseif (z <= -1.15e-167)
		tmp = Float64(t_1 * Float64(x - y));
	elseif (z <= 580000.0)
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(Float64(x - y) / t)));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = 60.0 / (z - t);
	t_2 = (a * 120.0) + (60.0 * ((x - y) / z));
	tmp = 0.0;
	if (z <= -1.05e+15)
		tmp = t_2;
	elseif (z <= -1.35e-145)
		tmp = (t_1 * x) + (a * 120.0);
	elseif (z <= -1.15e-167)
		tmp = t_1 * (x - y);
	elseif (z <= 580000.0)
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.05e+15], t$95$2, If[LessEqual[z, -1.35e-145], N[(N[(t$95$1 * x), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.15e-167], N[(t$95$1 * N[(x - y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 580000.0], N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(N[(x - y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]

Derivation

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

Alternative 6: 83.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot 120 + 60 \cdot \frac{x - y}{z}\\ \mathbf{if}\;z \leq -2.45 \cdot 10^{+17}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -7.4 \cdot 10^{-146}:\\ \;\;\;\;\frac{60}{z - t} \cdot x + a \cdot 120\\ \mathbf{elif}\;z \leq -3.7 \cdot 10^{-162}:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{elif}\;z \leq 75000:\\ \;\;\;\;a \cdot 120 + \left(x - y\right) \cdot \frac{-60}{t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (* a 120.0) (* 60.0 (/ (- x y) z)))))
   (if (<= z -2.45e+17)
     t_1
     (if (<= z -7.4e-146)
       (+ (* (/ 60.0 (- z t)) x) (* a 120.0))
       (if (<= z -3.7e-162)
         (* 60.0 (/ (- x y) (- z t)))
         (if (<= z 75000.0) (+ (* a 120.0) (* (- x y) (/ -60.0 t))) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (a * 120.0) + (60.0 * ((x - y) / z));
	double tmp;
	if (z <= -2.45e+17) {
		tmp = t_1;
	} else if (z <= -7.4e-146) {
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	} else if (z <= -3.7e-162) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else if (z <= 75000.0) {
		tmp = (a * 120.0) + ((x - y) * (-60.0 / t));
	} 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 = (a * 120.0d0) + (60.0d0 * ((x - y) / z))
    if (z <= (-2.45d+17)) then
        tmp = t_1
    else if (z <= (-7.4d-146)) then
        tmp = ((60.0d0 / (z - t)) * x) + (a * 120.0d0)
    else if (z <= (-3.7d-162)) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    else if (z <= 75000.0d0) then
        tmp = (a * 120.0d0) + ((x - y) * ((-60.0d0) / 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 t_1 = (a * 120.0) + (60.0 * ((x - y) / z));
	double tmp;
	if (z <= -2.45e+17) {
		tmp = t_1;
	} else if (z <= -7.4e-146) {
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	} else if (z <= -3.7e-162) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else if (z <= 75000.0) {
		tmp = (a * 120.0) + ((x - y) * (-60.0 / t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (a * 120.0) + (60.0 * ((x - y) / z))
	tmp = 0
	if z <= -2.45e+17:
		tmp = t_1
	elif z <= -7.4e-146:
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0)
	elif z <= -3.7e-162:
		tmp = 60.0 * ((x - y) / (z - t))
	elif z <= 75000.0:
		tmp = (a * 120.0) + ((x - y) * (-60.0 / t))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(a * 120.0) + Float64(60.0 * Float64(Float64(x - y) / z)))
	tmp = 0.0
	if (z <= -2.45e+17)
		tmp = t_1;
	elseif (z <= -7.4e-146)
		tmp = Float64(Float64(Float64(60.0 / Float64(z - t)) * x) + Float64(a * 120.0));
	elseif (z <= -3.7e-162)
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	elseif (z <= 75000.0)
		tmp = Float64(Float64(a * 120.0) + Float64(Float64(x - y) * Float64(-60.0 / t)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (a * 120.0) + (60.0 * ((x - y) / z));
	tmp = 0.0;
	if (z <= -2.45e+17)
		tmp = t_1;
	elseif (z <= -7.4e-146)
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	elseif (z <= -3.7e-162)
		tmp = 60.0 * ((x - y) / (z - t));
	elseif (z <= 75000.0)
		tmp = (a * 120.0) + ((x - y) * (-60.0 / t));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.45e+17], t$95$1, If[LessEqual[z, -7.4e-146], N[(N[(N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -3.7e-162], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 75000.0], N[(N[(a * 120.0), $MachinePrecision] + N[(N[(x - y), $MachinePrecision] * N[(-60.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 7: 83.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.1 \cdot 10^{+18}:\\ \;\;\;\;a \cdot 120 + \frac{60}{\frac{z}{x - y}}\\ \mathbf{elif}\;z \leq -3.95 \cdot 10^{-146}:\\ \;\;\;\;\frac{60}{z - t} \cdot x + a \cdot 120\\ \mathbf{elif}\;z \leq -6.1 \cdot 10^{-164}:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{elif}\;z \leq 13000000:\\ \;\;\;\;a \cdot 120 + \left(x - y\right) \cdot \frac{-60}{t}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + 60 \cdot \frac{x - y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -8.1e+18)
   (+ (* a 120.0) (/ 60.0 (/ z (- x y))))
   (if (<= z -3.95e-146)
     (+ (* (/ 60.0 (- z t)) x) (* a 120.0))
     (if (<= z -6.1e-164)
       (* 60.0 (/ (- x y) (- z t)))
       (if (<= z 13000000.0)
         (+ (* a 120.0) (* (- x y) (/ -60.0 t)))
         (+ (* a 120.0) (* 60.0 (/ (- x y) z))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -8.1e+18) {
		tmp = (a * 120.0) + (60.0 / (z / (x - y)));
	} else if (z <= -3.95e-146) {
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	} else if (z <= -6.1e-164) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else if (z <= 13000000.0) {
		tmp = (a * 120.0) + ((x - y) * (-60.0 / t));
	} else {
		tmp = (a * 120.0) + (60.0 * ((x - y) / 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 (z <= (-8.1d+18)) then
        tmp = (a * 120.0d0) + (60.0d0 / (z / (x - y)))
    else if (z <= (-3.95d-146)) then
        tmp = ((60.0d0 / (z - t)) * x) + (a * 120.0d0)
    else if (z <= (-6.1d-164)) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    else if (z <= 13000000.0d0) then
        tmp = (a * 120.0d0) + ((x - y) * ((-60.0d0) / t))
    else
        tmp = (a * 120.0d0) + (60.0d0 * ((x - y) / 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 (z <= -8.1e+18) {
		tmp = (a * 120.0) + (60.0 / (z / (x - y)));
	} else if (z <= -3.95e-146) {
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	} else if (z <= -6.1e-164) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else if (z <= 13000000.0) {
		tmp = (a * 120.0) + ((x - y) * (-60.0 / t));
	} else {
		tmp = (a * 120.0) + (60.0 * ((x - y) / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -8.1e+18:
		tmp = (a * 120.0) + (60.0 / (z / (x - y)))
	elif z <= -3.95e-146:
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0)
	elif z <= -6.1e-164:
		tmp = 60.0 * ((x - y) / (z - t))
	elif z <= 13000000.0:
		tmp = (a * 120.0) + ((x - y) * (-60.0 / t))
	else:
		tmp = (a * 120.0) + (60.0 * ((x - y) / z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -8.1e+18)
		tmp = Float64(Float64(a * 120.0) + Float64(60.0 / Float64(z / Float64(x - y))));
	elseif (z <= -3.95e-146)
		tmp = Float64(Float64(Float64(60.0 / Float64(z - t)) * x) + Float64(a * 120.0));
	elseif (z <= -6.1e-164)
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	elseif (z <= 13000000.0)
		tmp = Float64(Float64(a * 120.0) + Float64(Float64(x - y) * Float64(-60.0 / t)));
	else
		tmp = Float64(Float64(a * 120.0) + Float64(60.0 * Float64(Float64(x - y) / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -8.1e+18)
		tmp = (a * 120.0) + (60.0 / (z / (x - y)));
	elseif (z <= -3.95e-146)
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	elseif (z <= -6.1e-164)
		tmp = 60.0 * ((x - y) / (z - t));
	elseif (z <= 13000000.0)
		tmp = (a * 120.0) + ((x - y) * (-60.0 / t));
	else
		tmp = (a * 120.0) + (60.0 * ((x - y) / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -8.1e+18], N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 / N[(z / N[(x - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -3.95e-146], N[(N[(N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -6.1e-164], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 13000000.0], N[(N[(a * 120.0), $MachinePrecision] + N[(N[(x - y), $MachinePrecision] * N[(-60.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 8: 76.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7 \cdot 10^{+17}:\\ \;\;\;\;a \cdot 120 - \frac{y}{\frac{z}{60}}\\ \mathbf{elif}\;z \leq -1.04 \cdot 10^{-88} \lor \neg \left(z \leq 84000\right):\\ \;\;\;\;a \cdot 120 + x \cdot \frac{60}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{x - y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -7e+17)
   (- (* a 120.0) (/ y (/ z 60.0)))
   (if (or (<= z -1.04e-88) (not (<= z 84000.0)))
     (+ (* a 120.0) (* x (/ 60.0 z)))
     (+ (* a 120.0) (* -60.0 (/ (- x y) t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -7e+17) {
		tmp = (a * 120.0) - (y / (z / 60.0));
	} else if ((z <= -1.04e-88) || !(z <= 84000.0)) {
		tmp = (a * 120.0) + (x * (60.0 / z));
	} else {
		tmp = (a * 120.0) + (-60.0 * ((x - y) / 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 (z <= (-7d+17)) then
        tmp = (a * 120.0d0) - (y / (z / 60.0d0))
    else if ((z <= (-1.04d-88)) .or. (.not. (z <= 84000.0d0))) then
        tmp = (a * 120.0d0) + (x * (60.0d0 / z))
    else
        tmp = (a * 120.0d0) + ((-60.0d0) * ((x - y) / 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 (z <= -7e+17) {
		tmp = (a * 120.0) - (y / (z / 60.0));
	} else if ((z <= -1.04e-88) || !(z <= 84000.0)) {
		tmp = (a * 120.0) + (x * (60.0 / z));
	} else {
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -7e+17:
		tmp = (a * 120.0) - (y / (z / 60.0))
	elif (z <= -1.04e-88) or not (z <= 84000.0):
		tmp = (a * 120.0) + (x * (60.0 / z))
	else:
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -7e+17)
		tmp = Float64(Float64(a * 120.0) - Float64(y / Float64(z / 60.0)));
	elseif ((z <= -1.04e-88) || !(z <= 84000.0))
		tmp = Float64(Float64(a * 120.0) + Float64(x * Float64(60.0 / z)));
	else
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(Float64(x - y) / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -7e+17)
		tmp = (a * 120.0) - (y / (z / 60.0));
	elseif ((z <= -1.04e-88) || ~((z <= 84000.0)))
		tmp = (a * 120.0) + (x * (60.0 / z));
	else
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -7e+17], N[(N[(a * 120.0), $MachinePrecision] - N[(y / N[(z / 60.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, -1.04e-88], N[Not[LessEqual[z, 84000.0]], $MachinePrecision]], N[(N[(a * 120.0), $MachinePrecision] + N[(x * N[(60.0 / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(N[(x - y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 9: 87.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -8 \cdot 10^{+132}:\\ \;\;\;\;\frac{60}{z - t} \cdot x + a \cdot 120\\ \mathbf{elif}\;x \leq 3.3 \cdot 10^{+118}:\\ \;\;\;\;\frac{60}{\frac{t - z}{y}} + a \cdot 120\\ \mathbf{elif}\;x \leq 1.9 \cdot 10^{+168}:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x}} + a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -8e+132)
   (+ (* (/ 60.0 (- z t)) x) (* a 120.0))
   (if (<= x 3.3e+118)
     (+ (/ 60.0 (/ (- t z) y)) (* a 120.0))
     (if (<= x 1.9e+168)
       (* 60.0 (/ (- x y) (- z t)))
       (+ (/ 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 <= -8e+132) {
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	} else if (x <= 3.3e+118) {
		tmp = (60.0 / ((t - z) / y)) + (a * 120.0);
	} else if (x <= 1.9e+168) {
		tmp = 60.0 * ((x - y) / (z - t));
	} 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 <= (-8d+132)) then
        tmp = ((60.0d0 / (z - t)) * x) + (a * 120.0d0)
    else if (x <= 3.3d+118) then
        tmp = (60.0d0 / ((t - z) / y)) + (a * 120.0d0)
    else if (x <= 1.9d+168) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    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 <= -8e+132) {
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	} else if (x <= 3.3e+118) {
		tmp = (60.0 / ((t - z) / y)) + (a * 120.0);
	} else if (x <= 1.9e+168) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else {
		tmp = (60.0 / ((z - t) / x)) + (a * 120.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -8e+132:
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0)
	elif x <= 3.3e+118:
		tmp = (60.0 / ((t - z) / y)) + (a * 120.0)
	elif x <= 1.9e+168:
		tmp = 60.0 * ((x - y) / (z - t))
	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 <= -8e+132)
		tmp = Float64(Float64(Float64(60.0 / Float64(z - t)) * x) + Float64(a * 120.0));
	elseif (x <= 3.3e+118)
		tmp = Float64(Float64(60.0 / Float64(Float64(t - z) / y)) + Float64(a * 120.0));
	elseif (x <= 1.9e+168)
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	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 <= -8e+132)
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	elseif (x <= 3.3e+118)
		tmp = (60.0 / ((t - z) / y)) + (a * 120.0);
	elseif (x <= 1.9e+168)
		tmp = 60.0 * ((x - y) / (z - t));
	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, -8e+132], N[(N[(N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.3e+118], N[(N[(60.0 / N[(N[(t - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.9e+168], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $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: 87.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.35 \cdot 10^{+127}:\\ \;\;\;\;\frac{60}{\left(z - t\right) \cdot \frac{1}{x}} + a \cdot 120\\ \mathbf{elif}\;x \leq 5.2 \cdot 10^{+119}:\\ \;\;\;\;\frac{60}{\frac{t - z}{y}} + a \cdot 120\\ \mathbf{elif}\;x \leq 2 \cdot 10^{+168}:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{\frac{z - t}{x}} + a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -1.35e+127)
   (+ (/ 60.0 (* (- z t) (/ 1.0 x))) (* a 120.0))
   (if (<= x 5.2e+119)
     (+ (/ 60.0 (/ (- t z) y)) (* a 120.0))
     (if (<= x 2e+168)
       (* 60.0 (/ (- x y) (- z t)))
       (+ (/ 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 <= -1.35e+127) {
		tmp = (60.0 / ((z - t) * (1.0 / x))) + (a * 120.0);
	} else if (x <= 5.2e+119) {
		tmp = (60.0 / ((t - z) / y)) + (a * 120.0);
	} else if (x <= 2e+168) {
		tmp = 60.0 * ((x - y) / (z - t));
	} 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 <= (-1.35d+127)) then
        tmp = (60.0d0 / ((z - t) * (1.0d0 / x))) + (a * 120.0d0)
    else if (x <= 5.2d+119) then
        tmp = (60.0d0 / ((t - z) / y)) + (a * 120.0d0)
    else if (x <= 2d+168) then
        tmp = 60.0d0 * ((x - y) / (z - t))
    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 <= -1.35e+127) {
		tmp = (60.0 / ((z - t) * (1.0 / x))) + (a * 120.0);
	} else if (x <= 5.2e+119) {
		tmp = (60.0 / ((t - z) / y)) + (a * 120.0);
	} else if (x <= 2e+168) {
		tmp = 60.0 * ((x - y) / (z - t));
	} else {
		tmp = (60.0 / ((z - t) / x)) + (a * 120.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -1.35e+127:
		tmp = (60.0 / ((z - t) * (1.0 / x))) + (a * 120.0)
	elif x <= 5.2e+119:
		tmp = (60.0 / ((t - z) / y)) + (a * 120.0)
	elif x <= 2e+168:
		tmp = 60.0 * ((x - y) / (z - t))
	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 <= -1.35e+127)
		tmp = Float64(Float64(60.0 / Float64(Float64(z - t) * Float64(1.0 / x))) + Float64(a * 120.0));
	elseif (x <= 5.2e+119)
		tmp = Float64(Float64(60.0 / Float64(Float64(t - z) / y)) + Float64(a * 120.0));
	elseif (x <= 2e+168)
		tmp = Float64(60.0 * Float64(Float64(x - y) / Float64(z - t)));
	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 <= -1.35e+127)
		tmp = (60.0 / ((z - t) * (1.0 / x))) + (a * 120.0);
	elseif (x <= 5.2e+119)
		tmp = (60.0 / ((t - z) / y)) + (a * 120.0);
	elseif (x <= 2e+168)
		tmp = 60.0 * ((x - y) / (z - t));
	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, -1.35e+127], N[(N[(60.0 / N[(N[(z - t), $MachinePrecision] * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 5.2e+119], N[(N[(60.0 / N[(N[(t - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2e+168], N[(60.0 * N[(N[(x - y), $MachinePrecision] / N[(z - t), $MachinePrecision]), $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 11: 73.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot 120 \leq -2 \cdot 10^{+70} \lor \neg \left(a \cdot 120 \leq 5 \cdot 10^{+83}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{z - t} \cdot \left(x - y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= (* a 120.0) -2e+70) (not (<= (* a 120.0) 5e+83)))
   (* a 120.0)
   (* (/ 60.0 (- z t)) (- x y))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (((a * 120.0) <= -2e+70) || !((a * 120.0) <= 5e+83)) {
		tmp = a * 120.0;
	} else {
		tmp = (60.0 / (z - t)) * (x - y);
	}
	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) <= (-2d+70)) .or. (.not. ((a * 120.0d0) <= 5d+83))) then
        tmp = a * 120.0d0
    else
        tmp = (60.0d0 / (z - t)) * (x - y)
    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) <= -2e+70) || !((a * 120.0) <= 5e+83)) {
		tmp = a * 120.0;
	} else {
		tmp = (60.0 / (z - t)) * (x - y);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if ((a * 120.0) <= -2e+70) or not ((a * 120.0) <= 5e+83):
		tmp = a * 120.0
	else:
		tmp = (60.0 / (z - t)) * (x - y)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((Float64(a * 120.0) <= -2e+70) || !(Float64(a * 120.0) <= 5e+83))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(Float64(60.0 / Float64(z - t)) * Float64(x - y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (((a * 120.0) <= -2e+70) || ~(((a * 120.0) <= 5e+83)))
		tmp = a * 120.0;
	else
		tmp = (60.0 / (z - t)) * (x - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[N[(a * 120.0), $MachinePrecision], -2e+70], N[Not[LessEqual[N[(a * 120.0), $MachinePrecision], 5e+83]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision] * N[(x - y), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 57.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -60 \cdot \frac{y}{z - t}\\ \mathbf{if}\;a \leq -3400000000:\\ \;\;\;\;a \cdot 120\\ \mathbf{elif}\;a \leq -2.8 \cdot 10^{-147}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -4.8 \cdot 10^{-167}:\\ \;\;\;\;\frac{x}{t \cdot -0.016666666666666666}\\ \mathbf{elif}\;a \leq 2.25 \cdot 10^{-35}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* -60.0 (/ y (- z t)))))
   (if (<= a -3400000000.0)
     (* a 120.0)
     (if (<= a -2.8e-147)
       t_1
       (if (<= a -4.8e-167)
         (/ x (* t -0.016666666666666666))
         (if (<= a 2.25e-35) 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 <= -3400000000.0) {
		tmp = a * 120.0;
	} else if (a <= -2.8e-147) {
		tmp = t_1;
	} else if (a <= -4.8e-167) {
		tmp = x / (t * -0.016666666666666666);
	} else if (a <= 2.25e-35) {
		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 <= (-3400000000.0d0)) then
        tmp = a * 120.0d0
    else if (a <= (-2.8d-147)) then
        tmp = t_1
    else if (a <= (-4.8d-167)) then
        tmp = x / (t * (-0.016666666666666666d0))
    else if (a <= 2.25d-35) 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 <= -3400000000.0) {
		tmp = a * 120.0;
	} else if (a <= -2.8e-147) {
		tmp = t_1;
	} else if (a <= -4.8e-167) {
		tmp = x / (t * -0.016666666666666666);
	} else if (a <= 2.25e-35) {
		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 <= -3400000000.0:
		tmp = a * 120.0
	elif a <= -2.8e-147:
		tmp = t_1
	elif a <= -4.8e-167:
		tmp = x / (t * -0.016666666666666666)
	elif a <= 2.25e-35:
		tmp = t_1
	else:
		tmp = a * 120.0
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(-60.0 * Float64(y / Float64(z - t)))
	tmp = 0.0
	if (a <= -3400000000.0)
		tmp = Float64(a * 120.0);
	elseif (a <= -2.8e-147)
		tmp = t_1;
	elseif (a <= -4.8e-167)
		tmp = Float64(x / Float64(t * -0.016666666666666666));
	elseif (a <= 2.25e-35)
		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 <= -3400000000.0)
		tmp = a * 120.0;
	elseif (a <= -2.8e-147)
		tmp = t_1;
	elseif (a <= -4.8e-167)
		tmp = x / (t * -0.016666666666666666);
	elseif (a <= 2.25e-35)
		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, -3400000000.0], N[(a * 120.0), $MachinePrecision], If[LessEqual[a, -2.8e-147], t$95$1, If[LessEqual[a, -4.8e-167], N[(x / N[(t * -0.016666666666666666), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.25e-35], t$95$1, N[(a * 120.0), $MachinePrecision]]]]]]

Derivation

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

Alternative 13: 81.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.6 \cdot 10^{-162} \lor \neg \left(z \leq 4400000\right):\\ \;\;\;\;a \cdot 120 + 60 \cdot \frac{x - y}{z}\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 + -60 \cdot \frac{x - y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -3.6e-162) (not (<= z 4400000.0)))
   (+ (* a 120.0) (* 60.0 (/ (- x y) z)))
   (+ (* a 120.0) (* -60.0 (/ (- x y) t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -3.6e-162) || !(z <= 4400000.0)) {
		tmp = (a * 120.0) + (60.0 * ((x - y) / z));
	} else {
		tmp = (a * 120.0) + (-60.0 * ((x - y) / 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 ((z <= (-3.6d-162)) .or. (.not. (z <= 4400000.0d0))) then
        tmp = (a * 120.0d0) + (60.0d0 * ((x - y) / z))
    else
        tmp = (a * 120.0d0) + ((-60.0d0) * ((x - y) / 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 ((z <= -3.6e-162) || !(z <= 4400000.0)) {
		tmp = (a * 120.0) + (60.0 * ((x - y) / z));
	} else {
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -3.6e-162) or not (z <= 4400000.0):
		tmp = (a * 120.0) + (60.0 * ((x - y) / z))
	else:
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -3.6e-162) || !(z <= 4400000.0))
		tmp = Float64(Float64(a * 120.0) + Float64(60.0 * Float64(Float64(x - y) / z)));
	else
		tmp = Float64(Float64(a * 120.0) + Float64(-60.0 * Float64(Float64(x - y) / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -3.6e-162) || ~((z <= 4400000.0)))
		tmp = (a * 120.0) + (60.0 * ((x - y) / z));
	else
		tmp = (a * 120.0) + (-60.0 * ((x - y) / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -3.6e-162], N[Not[LessEqual[z, 4400000.0]], $MachinePrecision]], N[(N[(a * 120.0), $MachinePrecision] + N[(60.0 * N[(N[(x - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] + N[(-60.0 * N[(N[(x - y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 14: 88.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.55 \cdot 10^{+100} \lor \neg \left(y \leq 2.4 \cdot 10^{-18}\right):\\ \;\;\;\;\frac{y \cdot -60}{z - t} + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{60}{z - t} \cdot x + a \cdot 120\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= y -1.55e+100) (not (<= y 2.4e-18)))
   (+ (/ (* y -60.0) (- z t)) (* 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 ((y <= -1.55e+100) || !(y <= 2.4e-18)) {
		tmp = ((y * -60.0) / (z - t)) + (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 ((y <= (-1.55d+100)) .or. (.not. (y <= 2.4d-18))) then
        tmp = ((y * (-60.0d0)) / (z - t)) + (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 ((y <= -1.55e+100) || !(y <= 2.4e-18)) {
		tmp = ((y * -60.0) / (z - t)) + (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 (y <= -1.55e+100) or not (y <= 2.4e-18):
		tmp = ((y * -60.0) / (z - t)) + (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 ((y <= -1.55e+100) || !(y <= 2.4e-18))
		tmp = Float64(Float64(Float64(y * -60.0) / Float64(z - t)) + Float64(a * 120.0));
	else
		tmp = Float64(Float64(Float64(60.0 / 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 ((y <= -1.55e+100) || ~((y <= 2.4e-18)))
		tmp = ((y * -60.0) / (z - t)) + (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[Or[LessEqual[y, -1.55e+100], N[Not[LessEqual[y, 2.4e-18]], $MachinePrecision]], N[(N[(N[(y * -60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 15: 88.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{+96}:\\ \;\;\;\;\frac{y \cdot -60}{z - t} + a \cdot 120\\ \mathbf{elif}\;y \leq 2.9 \cdot 10^{-18}:\\ \;\;\;\;\frac{60}{z - t} \cdot x + a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;a \cdot 120 - 60 \cdot \frac{y}{z - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= y -9.5e+96)
   (+ (/ (* y -60.0) (- z t)) (* a 120.0))
   (if (<= y 2.9e-18)
     (+ (* (/ 60.0 (- z t)) x) (* a 120.0))
     (- (* a 120.0) (* 60.0 (/ y (- z t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -9.5e+96) {
		tmp = ((y * -60.0) / (z - t)) + (a * 120.0);
	} else if (y <= 2.9e-18) {
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	} else {
		tmp = (a * 120.0) - (60.0 * (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 (y <= (-9.5d+96)) then
        tmp = ((y * (-60.0d0)) / (z - t)) + (a * 120.0d0)
    else if (y <= 2.9d-18) then
        tmp = ((60.0d0 / (z - t)) * x) + (a * 120.0d0)
    else
        tmp = (a * 120.0d0) - (60.0d0 * (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 (y <= -9.5e+96) {
		tmp = ((y * -60.0) / (z - t)) + (a * 120.0);
	} else if (y <= 2.9e-18) {
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	} else {
		tmp = (a * 120.0) - (60.0 * (y / (z - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if y <= -9.5e+96:
		tmp = ((y * -60.0) / (z - t)) + (a * 120.0)
	elif y <= 2.9e-18:
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0)
	else:
		tmp = (a * 120.0) - (60.0 * (y / (z - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (y <= -9.5e+96)
		tmp = Float64(Float64(Float64(y * -60.0) / Float64(z - t)) + Float64(a * 120.0));
	elseif (y <= 2.9e-18)
		tmp = Float64(Float64(Float64(60.0 / Float64(z - t)) * x) + Float64(a * 120.0));
	else
		tmp = Float64(Float64(a * 120.0) - Float64(60.0 * Float64(y / Float64(z - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (y <= -9.5e+96)
		tmp = ((y * -60.0) / (z - t)) + (a * 120.0);
	elseif (y <= 2.9e-18)
		tmp = ((60.0 / (z - t)) * x) + (a * 120.0);
	else
		tmp = (a * 120.0) - (60.0 * (y / (z - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[y, -9.5e+96], N[(N[(N[(y * -60.0), $MachinePrecision] / N[(z - t), $MachinePrecision]), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.9e-18], N[(N[(N[(60.0 / N[(z - t), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision] + N[(a * 120.0), $MachinePrecision]), $MachinePrecision], N[(N[(a * 120.0), $MachinePrecision] - N[(60.0 * N[(y / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 16: 73.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.2 \cdot 10^{+67} \lor \neg \left(a \leq 3.7 \cdot 10^{+81}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;60 \cdot \frac{x - y}{z - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -1.2e+67) (not (<= a 3.7e+81)))
   (* 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 <= -1.2e+67) || !(a <= 3.7e+81)) {
		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 <= (-1.2d+67)) .or. (.not. (a <= 3.7d+81))) 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 <= -1.2e+67) || !(a <= 3.7e+81)) {
		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 <= -1.2e+67) or not (a <= 3.7e+81):
		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 <= -1.2e+67) || !(a <= 3.7e+81))
		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 <= -1.2e+67) || ~((a <= 3.7e+81)))
		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, -1.2e+67], N[Not[LessEqual[a, 3.7e+81]], $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 17: 99.8% 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 18: 50.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -9.2 \cdot 10^{-217} \lor \neg \left(t \leq 2.8 \cdot 10^{-216}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;-60 \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -9.2e-217) (not (<= t 2.8e-216)))
   (* a 120.0)
   (* -60.0 (/ y z))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -9.2e-217) || !(t <= 2.8e-216)) {
		tmp = a * 120.0;
	} else {
		tmp = -60.0 * (y / 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 ((t <= (-9.2d-217)) .or. (.not. (t <= 2.8d-216))) then
        tmp = a * 120.0d0
    else
        tmp = (-60.0d0) * (y / 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 ((t <= -9.2e-217) || !(t <= 2.8e-216)) {
		tmp = a * 120.0;
	} else {
		tmp = -60.0 * (y / z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -9.2e-217) or not (t <= 2.8e-216):
		tmp = a * 120.0
	else:
		tmp = -60.0 * (y / z)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -9.2e-217) || !(t <= 2.8e-216))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(-60.0 * Float64(y / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -9.2e-217) || ~((t <= 2.8e-216)))
		tmp = a * 120.0;
	else
		tmp = -60.0 * (y / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -9.2e-217], N[Not[LessEqual[t, 2.8e-216]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(-60.0 * N[(y / z), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 19: 50.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.42 \cdot 10^{-216} \lor \neg \left(t \leq 10^{-218}\right):\\ \;\;\;\;a \cdot 120\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot -60}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -1.42e-216) (not (<= t 1e-218))) (* a 120.0) (/ (* y -60.0) z)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -1.42e-216) || !(t <= 1e-218)) {
		tmp = a * 120.0;
	} else {
		tmp = (y * -60.0) / 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 ((t <= (-1.42d-216)) .or. (.not. (t <= 1d-218))) then
        tmp = a * 120.0d0
    else
        tmp = (y * (-60.0d0)) / 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 ((t <= -1.42e-216) || !(t <= 1e-218)) {
		tmp = a * 120.0;
	} else {
		tmp = (y * -60.0) / z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -1.42e-216) or not (t <= 1e-218):
		tmp = a * 120.0
	else:
		tmp = (y * -60.0) / z
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -1.42e-216) || !(t <= 1e-218))
		tmp = Float64(a * 120.0);
	else
		tmp = Float64(Float64(y * -60.0) / z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -1.42e-216) || ~((t <= 1e-218)))
		tmp = a * 120.0;
	else
		tmp = (y * -60.0) / z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -1.42e-216], N[Not[LessEqual[t, 1e-218]], $MachinePrecision]], N[(a * 120.0), $MachinePrecision], N[(N[(y * -60.0), $MachinePrecision] / z), $MachinePrecision]]

Derivation

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

Alternative 20: 50.5% 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: 99.8% 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 2024003 
(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)))