Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisLine from plot-0.2.3.4, A

Percentage Accurate: 98.3% → 98.3%

Time: 14.5s

Alternatives: 18

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

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 = x + (y * ((z - t) / (z - a)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 98.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 = x + (y * ((z - t) / (z - a)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.3% accurate, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

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

Alternative 2: 74.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - z \cdot \frac{y}{a}\\ \mathbf{if}\;z \leq -2 \cdot 10^{+134}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;z \leq -5.6 \cdot 10^{-17}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq -6 \cdot 10^{-138}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.6 \cdot 10^{-67}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 0.00079:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{+100}:\\ \;\;\;\;x - t \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (* z (/ y a)))))
   (if (<= z -2e+134)
     (+ y x)
     (if (<= z -5.6e-17)
       (+ x (/ t (/ a y)))
       (if (<= z -6e-138)
         t_1
         (if (<= z 1.6e-67)
           (+ x (/ y (/ a t)))
           (if (<= z 0.00079)
             t_1
             (if (<= z 4.2e+100) (- x (* t (/ y z))) (+ y x)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - (z * (y / a));
	double tmp;
	if (z <= -2e+134) {
		tmp = y + x;
	} else if (z <= -5.6e-17) {
		tmp = x + (t / (a / y));
	} else if (z <= -6e-138) {
		tmp = t_1;
	} else if (z <= 1.6e-67) {
		tmp = x + (y / (a / t));
	} else if (z <= 0.00079) {
		tmp = t_1;
	} else if (z <= 4.2e+100) {
		tmp = x - (t * (y / z));
	} else {
		tmp = y + x;
	}
	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 - (z * (y / a))
    if (z <= (-2d+134)) then
        tmp = y + x
    else if (z <= (-5.6d-17)) then
        tmp = x + (t / (a / y))
    else if (z <= (-6d-138)) then
        tmp = t_1
    else if (z <= 1.6d-67) then
        tmp = x + (y / (a / t))
    else if (z <= 0.00079d0) then
        tmp = t_1
    else if (z <= 4.2d+100) then
        tmp = x - (t * (y / z))
    else
        tmp = y + x
    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 - (z * (y / a));
	double tmp;
	if (z <= -2e+134) {
		tmp = y + x;
	} else if (z <= -5.6e-17) {
		tmp = x + (t / (a / y));
	} else if (z <= -6e-138) {
		tmp = t_1;
	} else if (z <= 1.6e-67) {
		tmp = x + (y / (a / t));
	} else if (z <= 0.00079) {
		tmp = t_1;
	} else if (z <= 4.2e+100) {
		tmp = x - (t * (y / z));
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - (z * (y / a))
	tmp = 0
	if z <= -2e+134:
		tmp = y + x
	elif z <= -5.6e-17:
		tmp = x + (t / (a / y))
	elif z <= -6e-138:
		tmp = t_1
	elif z <= 1.6e-67:
		tmp = x + (y / (a / t))
	elif z <= 0.00079:
		tmp = t_1
	elif z <= 4.2e+100:
		tmp = x - (t * (y / z))
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x - Float64(z * Float64(y / a)))
	tmp = 0.0
	if (z <= -2e+134)
		tmp = Float64(y + x);
	elseif (z <= -5.6e-17)
		tmp = Float64(x + Float64(t / Float64(a / y)));
	elseif (z <= -6e-138)
		tmp = t_1;
	elseif (z <= 1.6e-67)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif (z <= 0.00079)
		tmp = t_1;
	elseif (z <= 4.2e+100)
		tmp = Float64(x - Float64(t * Float64(y / z)));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x - (z * (y / a));
	tmp = 0.0;
	if (z <= -2e+134)
		tmp = y + x;
	elseif (z <= -5.6e-17)
		tmp = x + (t / (a / y));
	elseif (z <= -6e-138)
		tmp = t_1;
	elseif (z <= 1.6e-67)
		tmp = x + (y / (a / t));
	elseif (z <= 0.00079)
		tmp = t_1;
	elseif (z <= 4.2e+100)
		tmp = x - (t * (y / z));
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x - N[(z * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2e+134], N[(y + x), $MachinePrecision], If[LessEqual[z, -5.6e-17], N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -6e-138], t$95$1, If[LessEqual[z, 1.6e-67], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 0.00079], t$95$1, If[LessEqual[z, 4.2e+100], N[(x - N[(t * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]]]]]]

Derivation

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

Alternative 3: 74.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.8 \cdot 10^{+132}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;z \leq -5.3 \cdot 10^{-16}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq -3.4 \cdot 10^{-137}:\\ \;\;\;\;x - z \cdot \frac{y}{a}\\ \mathbf{elif}\;z \leq 7 \cdot 10^{-67}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 0.00039:\\ \;\;\;\;x - \frac{y \cdot z}{a}\\ \mathbf{elif}\;z \leq 1.8 \cdot 10^{+106}:\\ \;\;\;\;x - t \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.8e+132)
   (+ y x)
   (if (<= z -5.3e-16)
     (+ x (/ t (/ a y)))
     (if (<= z -3.4e-137)
       (- x (* z (/ y a)))
       (if (<= z 7e-67)
         (+ x (/ y (/ a t)))
         (if (<= z 0.00039)
           (- x (/ (* y z) a))
           (if (<= z 1.8e+106) (- x (* t (/ y z))) (+ y x))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.8e+132) {
		tmp = y + x;
	} else if (z <= -5.3e-16) {
		tmp = x + (t / (a / y));
	} else if (z <= -3.4e-137) {
		tmp = x - (z * (y / a));
	} else if (z <= 7e-67) {
		tmp = x + (y / (a / t));
	} else if (z <= 0.00039) {
		tmp = x - ((y * z) / a);
	} else if (z <= 1.8e+106) {
		tmp = x - (t * (y / z));
	} else {
		tmp = y + x;
	}
	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 <= (-2.8d+132)) then
        tmp = y + x
    else if (z <= (-5.3d-16)) then
        tmp = x + (t / (a / y))
    else if (z <= (-3.4d-137)) then
        tmp = x - (z * (y / a))
    else if (z <= 7d-67) then
        tmp = x + (y / (a / t))
    else if (z <= 0.00039d0) then
        tmp = x - ((y * z) / a)
    else if (z <= 1.8d+106) then
        tmp = x - (t * (y / z))
    else
        tmp = y + x
    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 <= -2.8e+132) {
		tmp = y + x;
	} else if (z <= -5.3e-16) {
		tmp = x + (t / (a / y));
	} else if (z <= -3.4e-137) {
		tmp = x - (z * (y / a));
	} else if (z <= 7e-67) {
		tmp = x + (y / (a / t));
	} else if (z <= 0.00039) {
		tmp = x - ((y * z) / a);
	} else if (z <= 1.8e+106) {
		tmp = x - (t * (y / z));
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.8e+132:
		tmp = y + x
	elif z <= -5.3e-16:
		tmp = x + (t / (a / y))
	elif z <= -3.4e-137:
		tmp = x - (z * (y / a))
	elif z <= 7e-67:
		tmp = x + (y / (a / t))
	elif z <= 0.00039:
		tmp = x - ((y * z) / a)
	elif z <= 1.8e+106:
		tmp = x - (t * (y / z))
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.8e+132)
		tmp = Float64(y + x);
	elseif (z <= -5.3e-16)
		tmp = Float64(x + Float64(t / Float64(a / y)));
	elseif (z <= -3.4e-137)
		tmp = Float64(x - Float64(z * Float64(y / a)));
	elseif (z <= 7e-67)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif (z <= 0.00039)
		tmp = Float64(x - Float64(Float64(y * z) / a));
	elseif (z <= 1.8e+106)
		tmp = Float64(x - Float64(t * Float64(y / z)));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.8e+132)
		tmp = y + x;
	elseif (z <= -5.3e-16)
		tmp = x + (t / (a / y));
	elseif (z <= -3.4e-137)
		tmp = x - (z * (y / a));
	elseif (z <= 7e-67)
		tmp = x + (y / (a / t));
	elseif (z <= 0.00039)
		tmp = x - ((y * z) / a);
	elseif (z <= 1.8e+106)
		tmp = x - (t * (y / z));
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.8e+132], N[(y + x), $MachinePrecision], If[LessEqual[z, -5.3e-16], N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -3.4e-137], N[(x - N[(z * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7e-67], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 0.00039], N[(x - N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.8e+106], N[(x - N[(t * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]]]]]

Derivation

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

Alternative 4: 74.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.8 \cdot 10^{+132}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;z \leq -1.45 \cdot 10^{-17}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq -3.4 \cdot 10^{-137}:\\ \;\;\;\;x - \frac{y}{\frac{a}{z}}\\ \mathbf{elif}\;z \leq 6.5 \cdot 10^{-67}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 0.011:\\ \;\;\;\;x - \frac{y \cdot z}{a}\\ \mathbf{elif}\;z \leq 1.1 \cdot 10^{+100}:\\ \;\;\;\;x - t \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.8e+132)
   (+ y x)
   (if (<= z -1.45e-17)
     (+ x (/ t (/ a y)))
     (if (<= z -3.4e-137)
       (- x (/ y (/ a z)))
       (if (<= z 6.5e-67)
         (+ x (/ y (/ a t)))
         (if (<= z 0.011)
           (- x (/ (* y z) a))
           (if (<= z 1.1e+100) (- x (* t (/ y z))) (+ y x))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.8e+132) {
		tmp = y + x;
	} else if (z <= -1.45e-17) {
		tmp = x + (t / (a / y));
	} else if (z <= -3.4e-137) {
		tmp = x - (y / (a / z));
	} else if (z <= 6.5e-67) {
		tmp = x + (y / (a / t));
	} else if (z <= 0.011) {
		tmp = x - ((y * z) / a);
	} else if (z <= 1.1e+100) {
		tmp = x - (t * (y / z));
	} else {
		tmp = y + x;
	}
	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 <= (-2.8d+132)) then
        tmp = y + x
    else if (z <= (-1.45d-17)) then
        tmp = x + (t / (a / y))
    else if (z <= (-3.4d-137)) then
        tmp = x - (y / (a / z))
    else if (z <= 6.5d-67) then
        tmp = x + (y / (a / t))
    else if (z <= 0.011d0) then
        tmp = x - ((y * z) / a)
    else if (z <= 1.1d+100) then
        tmp = x - (t * (y / z))
    else
        tmp = y + x
    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 <= -2.8e+132) {
		tmp = y + x;
	} else if (z <= -1.45e-17) {
		tmp = x + (t / (a / y));
	} else if (z <= -3.4e-137) {
		tmp = x - (y / (a / z));
	} else if (z <= 6.5e-67) {
		tmp = x + (y / (a / t));
	} else if (z <= 0.011) {
		tmp = x - ((y * z) / a);
	} else if (z <= 1.1e+100) {
		tmp = x - (t * (y / z));
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.8e+132:
		tmp = y + x
	elif z <= -1.45e-17:
		tmp = x + (t / (a / y))
	elif z <= -3.4e-137:
		tmp = x - (y / (a / z))
	elif z <= 6.5e-67:
		tmp = x + (y / (a / t))
	elif z <= 0.011:
		tmp = x - ((y * z) / a)
	elif z <= 1.1e+100:
		tmp = x - (t * (y / z))
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.8e+132)
		tmp = Float64(y + x);
	elseif (z <= -1.45e-17)
		tmp = Float64(x + Float64(t / Float64(a / y)));
	elseif (z <= -3.4e-137)
		tmp = Float64(x - Float64(y / Float64(a / z)));
	elseif (z <= 6.5e-67)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif (z <= 0.011)
		tmp = Float64(x - Float64(Float64(y * z) / a));
	elseif (z <= 1.1e+100)
		tmp = Float64(x - Float64(t * Float64(y / z)));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.8e+132)
		tmp = y + x;
	elseif (z <= -1.45e-17)
		tmp = x + (t / (a / y));
	elseif (z <= -3.4e-137)
		tmp = x - (y / (a / z));
	elseif (z <= 6.5e-67)
		tmp = x + (y / (a / t));
	elseif (z <= 0.011)
		tmp = x - ((y * z) / a);
	elseif (z <= 1.1e+100)
		tmp = x - (t * (y / z));
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.8e+132], N[(y + x), $MachinePrecision], If[LessEqual[z, -1.45e-17], N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -3.4e-137], N[(x - N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 6.5e-67], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 0.011], N[(x - N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.1e+100], N[(x - N[(t * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]]]]]

Derivation

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

Alternative 5: 69.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.1 \cdot 10^{+68}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;a \leq -1.9 \cdot 10^{-258}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{-137}:\\ \;\;\;\;x - t \cdot \frac{y}{z}\\ \mathbf{elif}\;a \leq 10^{-73}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;a \leq 3.7 \cdot 10^{-39}:\\ \;\;\;\;y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.1e+68)
   (+ x (/ t (/ a y)))
   (if (<= a -1.9e-258)
     (+ y x)
     (if (<= a 1.85e-137)
       (- x (* t (/ y z)))
       (if (<= a 1e-73)
         (+ y x)
         (if (<= a 3.7e-39) (* y (- 1.0 (/ t z))) (+ x (/ y (/ a t)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.1e+68) {
		tmp = x + (t / (a / y));
	} else if (a <= -1.9e-258) {
		tmp = y + x;
	} else if (a <= 1.85e-137) {
		tmp = x - (t * (y / z));
	} else if (a <= 1e-73) {
		tmp = y + x;
	} else if (a <= 3.7e-39) {
		tmp = y * (1.0 - (t / z));
	} else {
		tmp = x + (y / (a / 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 <= (-3.1d+68)) then
        tmp = x + (t / (a / y))
    else if (a <= (-1.9d-258)) then
        tmp = y + x
    else if (a <= 1.85d-137) then
        tmp = x - (t * (y / z))
    else if (a <= 1d-73) then
        tmp = y + x
    else if (a <= 3.7d-39) then
        tmp = y * (1.0d0 - (t / z))
    else
        tmp = x + (y / (a / 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 <= -3.1e+68) {
		tmp = x + (t / (a / y));
	} else if (a <= -1.9e-258) {
		tmp = y + x;
	} else if (a <= 1.85e-137) {
		tmp = x - (t * (y / z));
	} else if (a <= 1e-73) {
		tmp = y + x;
	} else if (a <= 3.7e-39) {
		tmp = y * (1.0 - (t / z));
	} else {
		tmp = x + (y / (a / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.1e+68:
		tmp = x + (t / (a / y))
	elif a <= -1.9e-258:
		tmp = y + x
	elif a <= 1.85e-137:
		tmp = x - (t * (y / z))
	elif a <= 1e-73:
		tmp = y + x
	elif a <= 3.7e-39:
		tmp = y * (1.0 - (t / z))
	else:
		tmp = x + (y / (a / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.1e+68)
		tmp = Float64(x + Float64(t / Float64(a / y)));
	elseif (a <= -1.9e-258)
		tmp = Float64(y + x);
	elseif (a <= 1.85e-137)
		tmp = Float64(x - Float64(t * Float64(y / z)));
	elseif (a <= 1e-73)
		tmp = Float64(y + x);
	elseif (a <= 3.7e-39)
		tmp = Float64(y * Float64(1.0 - Float64(t / z)));
	else
		tmp = Float64(x + Float64(y / Float64(a / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.1e+68)
		tmp = x + (t / (a / y));
	elseif (a <= -1.9e-258)
		tmp = y + x;
	elseif (a <= 1.85e-137)
		tmp = x - (t * (y / z));
	elseif (a <= 1e-73)
		tmp = y + x;
	elseif (a <= 3.7e-39)
		tmp = y * (1.0 - (t / z));
	else
		tmp = x + (y / (a / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.1e+68], N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -1.9e-258], N[(y + x), $MachinePrecision], If[LessEqual[a, 1.85e-137], N[(x - N[(t * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1e-73], N[(y + x), $MachinePrecision], If[LessEqual[a, 3.7e-39], N[(y * N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 6: 80.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z}{z - a}\\ \mathbf{if}\;z \leq -1.6 \cdot 10^{-143}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 9.5 \cdot 10^{-68}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 5.5 \cdot 10^{+16} \lor \neg \left(z \leq 2.75 \cdot 10^{+100}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x - t \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ z (- z a))))))
   (if (<= z -1.6e-143)
     t_1
     (if (<= z 9.5e-68)
       (+ x (/ y (/ a t)))
       (if (or (<= z 5.5e+16) (not (<= z 2.75e+100)))
         t_1
         (- x (* t (/ y z))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / (z - a)));
	double tmp;
	if (z <= -1.6e-143) {
		tmp = t_1;
	} else if (z <= 9.5e-68) {
		tmp = x + (y / (a / t));
	} else if ((z <= 5.5e+16) || !(z <= 2.75e+100)) {
		tmp = t_1;
	} else {
		tmp = x - (t * (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) :: t_1
    real(8) :: tmp
    t_1 = x + (y * (z / (z - a)))
    if (z <= (-1.6d-143)) then
        tmp = t_1
    else if (z <= 9.5d-68) then
        tmp = x + (y / (a / t))
    else if ((z <= 5.5d+16) .or. (.not. (z <= 2.75d+100))) then
        tmp = t_1
    else
        tmp = x - (t * (y / z))
    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 + (y * (z / (z - a)));
	double tmp;
	if (z <= -1.6e-143) {
		tmp = t_1;
	} else if (z <= 9.5e-68) {
		tmp = x + (y / (a / t));
	} else if ((z <= 5.5e+16) || !(z <= 2.75e+100)) {
		tmp = t_1;
	} else {
		tmp = x - (t * (y / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * (z / (z - a)))
	tmp = 0
	if z <= -1.6e-143:
		tmp = t_1
	elif z <= 9.5e-68:
		tmp = x + (y / (a / t))
	elif (z <= 5.5e+16) or not (z <= 2.75e+100):
		tmp = t_1
	else:
		tmp = x - (t * (y / z))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(z / Float64(z - a))))
	tmp = 0.0
	if (z <= -1.6e-143)
		tmp = t_1;
	elseif (z <= 9.5e-68)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif ((z <= 5.5e+16) || !(z <= 2.75e+100))
		tmp = t_1;
	else
		tmp = Float64(x - Float64(t * Float64(y / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * (z / (z - a)));
	tmp = 0.0;
	if (z <= -1.6e-143)
		tmp = t_1;
	elseif (z <= 9.5e-68)
		tmp = x + (y / (a / t));
	elseif ((z <= 5.5e+16) || ~((z <= 2.75e+100)))
		tmp = t_1;
	else
		tmp = x - (t * (y / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.6e-143], t$95$1, If[LessEqual[z, 9.5e-68], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, 5.5e+16], N[Not[LessEqual[z, 2.75e+100]], $MachinePrecision]], t$95$1, N[(x - N[(t * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 7: 81.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z}{z - a}\\ \mathbf{if}\;z \leq -7.6 \cdot 10^{-138}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{-68}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 0.092 \lor \neg \left(z \leq 5 \cdot 10^{+109}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot \left(z - t\right)}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ z (- z a))))))
   (if (<= z -7.6e-138)
     t_1
     (if (<= z 3.2e-68)
       (+ x (/ y (/ a t)))
       (if (or (<= z 0.092) (not (<= z 5e+109)))
         t_1
         (+ x (/ (* y (- z t)) z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / (z - a)));
	double tmp;
	if (z <= -7.6e-138) {
		tmp = t_1;
	} else if (z <= 3.2e-68) {
		tmp = x + (y / (a / t));
	} else if ((z <= 0.092) || !(z <= 5e+109)) {
		tmp = t_1;
	} else {
		tmp = x + ((y * (z - t)) / 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) :: t_1
    real(8) :: tmp
    t_1 = x + (y * (z / (z - a)))
    if (z <= (-7.6d-138)) then
        tmp = t_1
    else if (z <= 3.2d-68) then
        tmp = x + (y / (a / t))
    else if ((z <= 0.092d0) .or. (.not. (z <= 5d+109))) then
        tmp = t_1
    else
        tmp = x + ((y * (z - t)) / z)
    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 + (y * (z / (z - a)));
	double tmp;
	if (z <= -7.6e-138) {
		tmp = t_1;
	} else if (z <= 3.2e-68) {
		tmp = x + (y / (a / t));
	} else if ((z <= 0.092) || !(z <= 5e+109)) {
		tmp = t_1;
	} else {
		tmp = x + ((y * (z - t)) / z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * (z / (z - a)))
	tmp = 0
	if z <= -7.6e-138:
		tmp = t_1
	elif z <= 3.2e-68:
		tmp = x + (y / (a / t))
	elif (z <= 0.092) or not (z <= 5e+109):
		tmp = t_1
	else:
		tmp = x + ((y * (z - t)) / z)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(z / Float64(z - a))))
	tmp = 0.0
	if (z <= -7.6e-138)
		tmp = t_1;
	elseif (z <= 3.2e-68)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif ((z <= 0.092) || !(z <= 5e+109))
		tmp = t_1;
	else
		tmp = Float64(x + Float64(Float64(y * Float64(z - t)) / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * (z / (z - a)));
	tmp = 0.0;
	if (z <= -7.6e-138)
		tmp = t_1;
	elseif (z <= 3.2e-68)
		tmp = x + (y / (a / t));
	elseif ((z <= 0.092) || ~((z <= 5e+109)))
		tmp = t_1;
	else
		tmp = x + ((y * (z - t)) / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7.6e-138], t$95$1, If[LessEqual[z, 3.2e-68], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, 0.092], N[Not[LessEqual[z, 5e+109]], $MachinePrecision]], t$95$1, N[(x + N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 8: 81.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z}{z - a}\\ \mathbf{if}\;z \leq -2 \cdot 10^{-137}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 5.8 \cdot 10^{-67}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 6.2 \cdot 10^{+16} \lor \neg \left(z \leq 2.35 \cdot 10^{+103}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + \left(z - t\right) \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ z (- z a))))))
   (if (<= z -2e-137)
     t_1
     (if (<= z 5.8e-67)
       (+ x (/ y (/ a t)))
       (if (or (<= z 6.2e+16) (not (<= z 2.35e+103)))
         t_1
         (+ x (* (- z t) (/ y z))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / (z - a)));
	double tmp;
	if (z <= -2e-137) {
		tmp = t_1;
	} else if (z <= 5.8e-67) {
		tmp = x + (y / (a / t));
	} else if ((z <= 6.2e+16) || !(z <= 2.35e+103)) {
		tmp = t_1;
	} else {
		tmp = x + ((z - t) * (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) :: t_1
    real(8) :: tmp
    t_1 = x + (y * (z / (z - a)))
    if (z <= (-2d-137)) then
        tmp = t_1
    else if (z <= 5.8d-67) then
        tmp = x + (y / (a / t))
    else if ((z <= 6.2d+16) .or. (.not. (z <= 2.35d+103))) then
        tmp = t_1
    else
        tmp = x + ((z - t) * (y / z))
    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 + (y * (z / (z - a)));
	double tmp;
	if (z <= -2e-137) {
		tmp = t_1;
	} else if (z <= 5.8e-67) {
		tmp = x + (y / (a / t));
	} else if ((z <= 6.2e+16) || !(z <= 2.35e+103)) {
		tmp = t_1;
	} else {
		tmp = x + ((z - t) * (y / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * (z / (z - a)))
	tmp = 0
	if z <= -2e-137:
		tmp = t_1
	elif z <= 5.8e-67:
		tmp = x + (y / (a / t))
	elif (z <= 6.2e+16) or not (z <= 2.35e+103):
		tmp = t_1
	else:
		tmp = x + ((z - t) * (y / z))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(z / Float64(z - a))))
	tmp = 0.0
	if (z <= -2e-137)
		tmp = t_1;
	elseif (z <= 5.8e-67)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif ((z <= 6.2e+16) || !(z <= 2.35e+103))
		tmp = t_1;
	else
		tmp = Float64(x + Float64(Float64(z - t) * Float64(y / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * (z / (z - a)));
	tmp = 0.0;
	if (z <= -2e-137)
		tmp = t_1;
	elseif (z <= 5.8e-67)
		tmp = x + (y / (a / t));
	elseif ((z <= 6.2e+16) || ~((z <= 2.35e+103)))
		tmp = t_1;
	else
		tmp = x + ((z - t) * (y / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2e-137], t$95$1, If[LessEqual[z, 5.8e-67], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, 6.2e+16], N[Not[LessEqual[z, 2.35e+103]], $MachinePrecision]], t$95$1, N[(x + N[(N[(z - t), $MachinePrecision] * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 9: 69.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + t \cdot \frac{y}{a}\\ \mathbf{if}\;a \leq -2.5 \cdot 10^{+68}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 4.6 \cdot 10^{-178}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;a \leq 9.2 \cdot 10^{-138}:\\ \;\;\;\;y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{elif}\;a \leq 8.4 \cdot 10^{-69}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* t (/ y a)))))
   (if (<= a -2.5e+68)
     t_1
     (if (<= a 4.6e-178)
       (+ y x)
       (if (<= a 9.2e-138)
         (* y (- 1.0 (/ t z)))
         (if (<= a 8.4e-69) (+ y x) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (t * (y / a));
	double tmp;
	if (a <= -2.5e+68) {
		tmp = t_1;
	} else if (a <= 4.6e-178) {
		tmp = y + x;
	} else if (a <= 9.2e-138) {
		tmp = y * (1.0 - (t / z));
	} else if (a <= 8.4e-69) {
		tmp = y + x;
	} 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 + (t * (y / a))
    if (a <= (-2.5d+68)) then
        tmp = t_1
    else if (a <= 4.6d-178) then
        tmp = y + x
    else if (a <= 9.2d-138) then
        tmp = y * (1.0d0 - (t / z))
    else if (a <= 8.4d-69) then
        tmp = y + x
    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 + (t * (y / a));
	double tmp;
	if (a <= -2.5e+68) {
		tmp = t_1;
	} else if (a <= 4.6e-178) {
		tmp = y + x;
	} else if (a <= 9.2e-138) {
		tmp = y * (1.0 - (t / z));
	} else if (a <= 8.4e-69) {
		tmp = y + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (t * (y / a))
	tmp = 0
	if a <= -2.5e+68:
		tmp = t_1
	elif a <= 4.6e-178:
		tmp = y + x
	elif a <= 9.2e-138:
		tmp = y * (1.0 - (t / z))
	elif a <= 8.4e-69:
		tmp = y + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(t * Float64(y / a)))
	tmp = 0.0
	if (a <= -2.5e+68)
		tmp = t_1;
	elseif (a <= 4.6e-178)
		tmp = Float64(y + x);
	elseif (a <= 9.2e-138)
		tmp = Float64(y * Float64(1.0 - Float64(t / z)));
	elseif (a <= 8.4e-69)
		tmp = Float64(y + x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (t * (y / a));
	tmp = 0.0;
	if (a <= -2.5e+68)
		tmp = t_1;
	elseif (a <= 4.6e-178)
		tmp = y + x;
	elseif (a <= 9.2e-138)
		tmp = y * (1.0 - (t / z));
	elseif (a <= 8.4e-69)
		tmp = y + x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.5e+68], t$95$1, If[LessEqual[a, 4.6e-178], N[(y + x), $MachinePrecision], If[LessEqual[a, 9.2e-138], N[(y * N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 8.4e-69], N[(y + x), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 10: 69.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -6.5 \cdot 10^{+69}:\\ \;\;\;\;x + t \cdot \frac{y}{a}\\ \mathbf{elif}\;a \leq 4.8 \cdot 10^{-178}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;a \leq 9.2 \cdot 10^{-138}:\\ \;\;\;\;y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{elif}\;a \leq 1.12 \cdot 10^{-38}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -6.5e+69)
   (+ x (* t (/ y a)))
   (if (<= a 4.8e-178)
     (+ y x)
     (if (<= a 9.2e-138)
       (* y (- 1.0 (/ t z)))
       (if (<= a 1.12e-38) (+ y x) (+ x (* y (/ t a))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -6.5e+69) {
		tmp = x + (t * (y / a));
	} else if (a <= 4.8e-178) {
		tmp = y + x;
	} else if (a <= 9.2e-138) {
		tmp = y * (1.0 - (t / z));
	} else if (a <= 1.12e-38) {
		tmp = y + x;
	} else {
		tmp = x + (y * (t / a));
	}
	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 <= (-6.5d+69)) then
        tmp = x + (t * (y / a))
    else if (a <= 4.8d-178) then
        tmp = y + x
    else if (a <= 9.2d-138) then
        tmp = y * (1.0d0 - (t / z))
    else if (a <= 1.12d-38) then
        tmp = y + x
    else
        tmp = x + (y * (t / a))
    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 <= -6.5e+69) {
		tmp = x + (t * (y / a));
	} else if (a <= 4.8e-178) {
		tmp = y + x;
	} else if (a <= 9.2e-138) {
		tmp = y * (1.0 - (t / z));
	} else if (a <= 1.12e-38) {
		tmp = y + x;
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -6.5e+69:
		tmp = x + (t * (y / a))
	elif a <= 4.8e-178:
		tmp = y + x
	elif a <= 9.2e-138:
		tmp = y * (1.0 - (t / z))
	elif a <= 1.12e-38:
		tmp = y + x
	else:
		tmp = x + (y * (t / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -6.5e+69)
		tmp = Float64(x + Float64(t * Float64(y / a)));
	elseif (a <= 4.8e-178)
		tmp = Float64(y + x);
	elseif (a <= 9.2e-138)
		tmp = Float64(y * Float64(1.0 - Float64(t / z)));
	elseif (a <= 1.12e-38)
		tmp = Float64(y + x);
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -6.5e+69)
		tmp = x + (t * (y / a));
	elseif (a <= 4.8e-178)
		tmp = y + x;
	elseif (a <= 9.2e-138)
		tmp = y * (1.0 - (t / z));
	elseif (a <= 1.12e-38)
		tmp = y + x;
	else
		tmp = x + (y * (t / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -6.5e+69], N[(x + N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.8e-178], N[(y + x), $MachinePrecision], If[LessEqual[a, 9.2e-138], N[(y * N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.12e-38], N[(y + x), $MachinePrecision], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 11: 69.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.6 \cdot 10^{+68}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;a \leq 2.65 \cdot 10^{-179}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;a \leq 9.2 \cdot 10^{-138}:\\ \;\;\;\;y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{elif}\;a \leq 2.1 \cdot 10^{-37}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -2.6e+68)
   (+ x (/ t (/ a y)))
   (if (<= a 2.65e-179)
     (+ y x)
     (if (<= a 9.2e-138)
       (* y (- 1.0 (/ t z)))
       (if (<= a 2.1e-37) (+ y x) (+ x (* y (/ t a))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -2.6e+68) {
		tmp = x + (t / (a / y));
	} else if (a <= 2.65e-179) {
		tmp = y + x;
	} else if (a <= 9.2e-138) {
		tmp = y * (1.0 - (t / z));
	} else if (a <= 2.1e-37) {
		tmp = y + x;
	} else {
		tmp = x + (y * (t / a));
	}
	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 <= (-2.6d+68)) then
        tmp = x + (t / (a / y))
    else if (a <= 2.65d-179) then
        tmp = y + x
    else if (a <= 9.2d-138) then
        tmp = y * (1.0d0 - (t / z))
    else if (a <= 2.1d-37) then
        tmp = y + x
    else
        tmp = x + (y * (t / a))
    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 <= -2.6e+68) {
		tmp = x + (t / (a / y));
	} else if (a <= 2.65e-179) {
		tmp = y + x;
	} else if (a <= 9.2e-138) {
		tmp = y * (1.0 - (t / z));
	} else if (a <= 2.1e-37) {
		tmp = y + x;
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -2.6e+68:
		tmp = x + (t / (a / y))
	elif a <= 2.65e-179:
		tmp = y + x
	elif a <= 9.2e-138:
		tmp = y * (1.0 - (t / z))
	elif a <= 2.1e-37:
		tmp = y + x
	else:
		tmp = x + (y * (t / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -2.6e+68)
		tmp = Float64(x + Float64(t / Float64(a / y)));
	elseif (a <= 2.65e-179)
		tmp = Float64(y + x);
	elseif (a <= 9.2e-138)
		tmp = Float64(y * Float64(1.0 - Float64(t / z)));
	elseif (a <= 2.1e-37)
		tmp = Float64(y + x);
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -2.6e+68)
		tmp = x + (t / (a / y));
	elseif (a <= 2.65e-179)
		tmp = y + x;
	elseif (a <= 9.2e-138)
		tmp = y * (1.0 - (t / z));
	elseif (a <= 2.1e-37)
		tmp = y + x;
	else
		tmp = x + (y * (t / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -2.6e+68], N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.65e-179], N[(y + x), $MachinePrecision], If[LessEqual[a, 9.2e-138], N[(y * N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.1e-37], N[(y + x), $MachinePrecision], N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 12: 69.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.7 \cdot 10^{+68}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \mathbf{elif}\;a \leq 2.5 \cdot 10^{-179}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{-137}:\\ \;\;\;\;y \cdot \left(1 - \frac{t}{z}\right)\\ \mathbf{elif}\;a \leq 4.8 \cdot 10^{-39}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -2.7e+68)
   (+ x (/ t (/ a y)))
   (if (<= a 2.5e-179)
     (+ y x)
     (if (<= a 1.85e-137)
       (* y (- 1.0 (/ t z)))
       (if (<= a 4.8e-39) (+ y x) (+ x (/ y (/ a t))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -2.7e+68) {
		tmp = x + (t / (a / y));
	} else if (a <= 2.5e-179) {
		tmp = y + x;
	} else if (a <= 1.85e-137) {
		tmp = y * (1.0 - (t / z));
	} else if (a <= 4.8e-39) {
		tmp = y + x;
	} else {
		tmp = x + (y / (a / 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+68)) then
        tmp = x + (t / (a / y))
    else if (a <= 2.5d-179) then
        tmp = y + x
    else if (a <= 1.85d-137) then
        tmp = y * (1.0d0 - (t / z))
    else if (a <= 4.8d-39) then
        tmp = y + x
    else
        tmp = x + (y / (a / 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+68) {
		tmp = x + (t / (a / y));
	} else if (a <= 2.5e-179) {
		tmp = y + x;
	} else if (a <= 1.85e-137) {
		tmp = y * (1.0 - (t / z));
	} else if (a <= 4.8e-39) {
		tmp = y + x;
	} else {
		tmp = x + (y / (a / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -2.7e+68:
		tmp = x + (t / (a / y))
	elif a <= 2.5e-179:
		tmp = y + x
	elif a <= 1.85e-137:
		tmp = y * (1.0 - (t / z))
	elif a <= 4.8e-39:
		tmp = y + x
	else:
		tmp = x + (y / (a / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -2.7e+68)
		tmp = Float64(x + Float64(t / Float64(a / y)));
	elseif (a <= 2.5e-179)
		tmp = Float64(y + x);
	elseif (a <= 1.85e-137)
		tmp = Float64(y * Float64(1.0 - Float64(t / z)));
	elseif (a <= 4.8e-39)
		tmp = Float64(y + x);
	else
		tmp = Float64(x + Float64(y / Float64(a / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -2.7e+68)
		tmp = x + (t / (a / y));
	elseif (a <= 2.5e-179)
		tmp = y + x;
	elseif (a <= 1.85e-137)
		tmp = y * (1.0 - (t / z));
	elseif (a <= 4.8e-39)
		tmp = y + x;
	else
		tmp = x + (y / (a / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -2.7e+68], N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.5e-179], N[(y + x), $MachinePrecision], If[LessEqual[a, 1.85e-137], N[(y * N[(1.0 - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.8e-39], N[(y + x), $MachinePrecision], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 13: 81.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z}{z - a}\\ \mathbf{if}\;a \leq -9 \cdot 10^{-74}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 9.5 \cdot 10^{-37}:\\ \;\;\;\;x + \frac{y}{\frac{z}{z - t}}\\ \mathbf{elif}\;a \leq 1.75 \cdot 10^{+75}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{t}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ z (- z a))))))
   (if (<= a -9e-74)
     t_1
     (if (<= a 9.5e-37)
       (+ x (/ y (/ z (- z t))))
       (if (<= a 1.75e+75) t_1 (+ x (* y (/ t a))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / (z - a)));
	double tmp;
	if (a <= -9e-74) {
		tmp = t_1;
	} else if (a <= 9.5e-37) {
		tmp = x + (y / (z / (z - t)));
	} else if (a <= 1.75e+75) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / a));
	}
	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 + (y * (z / (z - a)))
    if (a <= (-9d-74)) then
        tmp = t_1
    else if (a <= 9.5d-37) then
        tmp = x + (y / (z / (z - t)))
    else if (a <= 1.75d+75) then
        tmp = t_1
    else
        tmp = x + (y * (t / a))
    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 + (y * (z / (z - a)));
	double tmp;
	if (a <= -9e-74) {
		tmp = t_1;
	} else if (a <= 9.5e-37) {
		tmp = x + (y / (z / (z - t)));
	} else if (a <= 1.75e+75) {
		tmp = t_1;
	} else {
		tmp = x + (y * (t / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * (z / (z - a)))
	tmp = 0
	if a <= -9e-74:
		tmp = t_1
	elif a <= 9.5e-37:
		tmp = x + (y / (z / (z - t)))
	elif a <= 1.75e+75:
		tmp = t_1
	else:
		tmp = x + (y * (t / a))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(z / Float64(z - a))))
	tmp = 0.0
	if (a <= -9e-74)
		tmp = t_1;
	elseif (a <= 9.5e-37)
		tmp = Float64(x + Float64(y / Float64(z / Float64(z - t))));
	elseif (a <= 1.75e+75)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(y * Float64(t / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * (z / (z - a)));
	tmp = 0.0;
	if (a <= -9e-74)
		tmp = t_1;
	elseif (a <= 9.5e-37)
		tmp = x + (y / (z / (z - t)));
	elseif (a <= 1.75e+75)
		tmp = t_1;
	else
		tmp = x + (y * (t / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -9e-74], t$95$1, If[LessEqual[a, 9.5e-37], N[(x + N[(y / N[(z / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.75e+75], t$95$1, N[(x + N[(y * N[(t / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 14: 87.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -7.6 \cdot 10^{-32} \lor \neg \left(t \leq 0.096\right):\\ \;\;\;\;x - y \cdot \frac{t}{z - a}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{z - a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -7.6e-32) (not (<= t 0.096)))
   (- x (* y (/ t (- z a))))
   (+ x (* y (/ z (- z a))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -7.6e-32) || !(t <= 0.096)) {
		tmp = x - (y * (t / (z - a)));
	} else {
		tmp = x + (y * (z / (z - a)));
	}
	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 <= (-7.6d-32)) .or. (.not. (t <= 0.096d0))) then
        tmp = x - (y * (t / (z - a)))
    else
        tmp = x + (y * (z / (z - a)))
    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 <= -7.6e-32) || !(t <= 0.096)) {
		tmp = x - (y * (t / (z - a)));
	} else {
		tmp = x + (y * (z / (z - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -7.6e-32) or not (t <= 0.096):
		tmp = x - (y * (t / (z - a)))
	else:
		tmp = x + (y * (z / (z - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -7.6e-32) || !(t <= 0.096))
		tmp = Float64(x - Float64(y * Float64(t / Float64(z - a))));
	else
		tmp = Float64(x + Float64(y * Float64(z / Float64(z - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -7.6e-32) || ~((t <= 0.096)))
		tmp = x - (y * (t / (z - a)));
	else
		tmp = x + (y * (z / (z - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -7.6e-32], N[Not[LessEqual[t, 0.096]], $MachinePrecision]], N[(x - N[(y * N[(t / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 15: 87.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -4.4 \cdot 10^{-32} \lor \neg \left(t \leq 1.58\right):\\ \;\;\;\;x - \frac{y}{\frac{z - a}{t}}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{z - a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -4.4e-32) (not (<= t 1.58)))
   (- x (/ y (/ (- z a) t)))
   (+ x (* y (/ z (- z a))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -4.4e-32) || !(t <= 1.58)) {
		tmp = x - (y / ((z - a) / t));
	} else {
		tmp = x + (y * (z / (z - a)));
	}
	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 <= (-4.4d-32)) .or. (.not. (t <= 1.58d0))) then
        tmp = x - (y / ((z - a) / t))
    else
        tmp = x + (y * (z / (z - a)))
    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 <= -4.4e-32) || !(t <= 1.58)) {
		tmp = x - (y / ((z - a) / t));
	} else {
		tmp = x + (y * (z / (z - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -4.4e-32) or not (t <= 1.58):
		tmp = x - (y / ((z - a) / t))
	else:
		tmp = x + (y * (z / (z - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -4.4e-32) || !(t <= 1.58))
		tmp = Float64(x - Float64(y / Float64(Float64(z - a) / t)));
	else
		tmp = Float64(x + Float64(y * Float64(z / Float64(z - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -4.4e-32) || ~((t <= 1.58)))
		tmp = x - (y / ((z - a) / t));
	else
		tmp = x + (y * (z / (z - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -4.4e-32], N[Not[LessEqual[t, 1.58]], $MachinePrecision]], N[(x - N[(y / N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(z / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 16: 98.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 = x + (y * ((z - t) / (z - a)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 17: 62.1% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.8 \cdot 10^{+126} \lor \neg \left(z \leq 2060000000000\right):\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -3.8e+126) (not (<= z 2060000000000.0))) (+ y x) x))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -3.8e+126) || !(z <= 2060000000000.0)) {
		tmp = y + x;
	} else {
		tmp = x;
	}
	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 <= (-3.8d+126)) .or. (.not. (z <= 2060000000000.0d0))) then
        tmp = y + x
    else
        tmp = x
    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 <= -3.8e+126) || !(z <= 2060000000000.0)) {
		tmp = y + x;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -3.8e+126) or not (z <= 2060000000000.0):
		tmp = y + x
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -3.8e+126) || !(z <= 2060000000000.0))
		tmp = Float64(y + x);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -3.8e+126) || ~((z <= 2060000000000.0)))
		tmp = y + x;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -3.8e+126], N[Not[LessEqual[z, 2060000000000.0]], $MachinePrecision]], N[(y + x), $MachinePrecision], x]

Derivation

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

Alternative 18: 50.6% accurate, 11.0× speedup

Math

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

FPCore

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

C

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

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 = x
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := x

Derivation

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

Developer target: 98.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 = x + (y / ((z - a) / (z - t)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisLine from plot-0.2.3.4, A"
  :precision binary64

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

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