Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTicks from plot-0.2.3.4, B

Percentage Accurate: 85.8% → 98.2%

Time: 11.5s

Alternatives: 14

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 85.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.2% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 2: 76.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.4:\\ \;\;\;\;x + y\\ \mathbf{elif}\;t \leq -5 \cdot 10^{-30}:\\ \;\;\;\;x - t \cdot \frac{y}{a}\\ \mathbf{elif}\;t \leq -7.5 \cdot 10^{-38}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;t \leq -1.9 \cdot 10^{-47}:\\ \;\;\;\;x - z \cdot \frac{y}{t}\\ \mathbf{elif}\;t \leq -9 \cdot 10^{-78}:\\ \;\;\;\;x + \frac{z}{\frac{a}{y}}\\ \mathbf{elif}\;t \leq -2.8 \cdot 10^{-90}:\\ \;\;\;\;x - y \cdot \frac{z}{t}\\ \mathbf{elif}\;t \leq 2400000000:\\ \;\;\;\;x + \frac{y}{\frac{a}{z}}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -0.4)
   (+ x y)
   (if (<= t -5e-30)
     (- x (* t (/ y a)))
     (if (<= t -7.5e-38)
       (+ x y)
       (if (<= t -1.9e-47)
         (- x (* z (/ y t)))
         (if (<= t -9e-78)
           (+ x (/ z (/ a y)))
           (if (<= t -2.8e-90)
             (- x (* y (/ z t)))
             (if (<= t 2400000000.0) (+ x (/ y (/ a z))) (+ x y)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -0.4) {
		tmp = x + y;
	} else if (t <= -5e-30) {
		tmp = x - (t * (y / a));
	} else if (t <= -7.5e-38) {
		tmp = x + y;
	} else if (t <= -1.9e-47) {
		tmp = x - (z * (y / t));
	} else if (t <= -9e-78) {
		tmp = x + (z / (a / y));
	} else if (t <= -2.8e-90) {
		tmp = x - (y * (z / t));
	} else if (t <= 2400000000.0) {
		tmp = x + (y / (a / z));
	} else {
		tmp = 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 (t <= (-0.4d0)) then
        tmp = x + y
    else if (t <= (-5d-30)) then
        tmp = x - (t * (y / a))
    else if (t <= (-7.5d-38)) then
        tmp = x + y
    else if (t <= (-1.9d-47)) then
        tmp = x - (z * (y / t))
    else if (t <= (-9d-78)) then
        tmp = x + (z / (a / y))
    else if (t <= (-2.8d-90)) then
        tmp = x - (y * (z / t))
    else if (t <= 2400000000.0d0) then
        tmp = x + (y / (a / z))
    else
        tmp = 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 (t <= -0.4) {
		tmp = x + y;
	} else if (t <= -5e-30) {
		tmp = x - (t * (y / a));
	} else if (t <= -7.5e-38) {
		tmp = x + y;
	} else if (t <= -1.9e-47) {
		tmp = x - (z * (y / t));
	} else if (t <= -9e-78) {
		tmp = x + (z / (a / y));
	} else if (t <= -2.8e-90) {
		tmp = x - (y * (z / t));
	} else if (t <= 2400000000.0) {
		tmp = x + (y / (a / z));
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -0.4:
		tmp = x + y
	elif t <= -5e-30:
		tmp = x - (t * (y / a))
	elif t <= -7.5e-38:
		tmp = x + y
	elif t <= -1.9e-47:
		tmp = x - (z * (y / t))
	elif t <= -9e-78:
		tmp = x + (z / (a / y))
	elif t <= -2.8e-90:
		tmp = x - (y * (z / t))
	elif t <= 2400000000.0:
		tmp = x + (y / (a / z))
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -0.4)
		tmp = Float64(x + y);
	elseif (t <= -5e-30)
		tmp = Float64(x - Float64(t * Float64(y / a)));
	elseif (t <= -7.5e-38)
		tmp = Float64(x + y);
	elseif (t <= -1.9e-47)
		tmp = Float64(x - Float64(z * Float64(y / t)));
	elseif (t <= -9e-78)
		tmp = Float64(x + Float64(z / Float64(a / y)));
	elseif (t <= -2.8e-90)
		tmp = Float64(x - Float64(y * Float64(z / t)));
	elseif (t <= 2400000000.0)
		tmp = Float64(x + Float64(y / Float64(a / z)));
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -0.4)
		tmp = x + y;
	elseif (t <= -5e-30)
		tmp = x - (t * (y / a));
	elseif (t <= -7.5e-38)
		tmp = x + y;
	elseif (t <= -1.9e-47)
		tmp = x - (z * (y / t));
	elseif (t <= -9e-78)
		tmp = x + (z / (a / y));
	elseif (t <= -2.8e-90)
		tmp = x - (y * (z / t));
	elseif (t <= 2400000000.0)
		tmp = x + (y / (a / z));
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -0.4], N[(x + y), $MachinePrecision], If[LessEqual[t, -5e-30], N[(x - N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -7.5e-38], N[(x + y), $MachinePrecision], If[LessEqual[t, -1.9e-47], N[(x - N[(z * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -9e-78], N[(x + N[(z / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -2.8e-90], N[(x - N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2400000000.0], N[(x + N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]]]]]]]

Derivation

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

Alternative 3: 76.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.0085:\\ \;\;\;\;x + y\\ \mathbf{elif}\;t \leq -8.5 \cdot 10^{-34}:\\ \;\;\;\;x - \frac{y \cdot t}{a}\\ \mathbf{elif}\;t \leq -6.8 \cdot 10^{-38}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;t \leq -6.4 \cdot 10^{-47}:\\ \;\;\;\;x - z \cdot \frac{y}{t}\\ \mathbf{elif}\;t \leq -9.6 \cdot 10^{-78}:\\ \;\;\;\;x + \frac{z}{\frac{a}{y}}\\ \mathbf{elif}\;t \leq -4 \cdot 10^{-91}:\\ \;\;\;\;x - y \cdot \frac{z}{t}\\ \mathbf{elif}\;t \leq 1700000000000:\\ \;\;\;\;x + \frac{y}{\frac{a}{z}}\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -0.0085)
   (+ x y)
   (if (<= t -8.5e-34)
     (- x (/ (* y t) a))
     (if (<= t -6.8e-38)
       (+ x y)
       (if (<= t -6.4e-47)
         (- x (* z (/ y t)))
         (if (<= t -9.6e-78)
           (+ x (/ z (/ a y)))
           (if (<= t -4e-91)
             (- x (* y (/ z t)))
             (if (<= t 1700000000000.0) (+ x (/ y (/ a z))) (+ x y)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -0.0085) {
		tmp = x + y;
	} else if (t <= -8.5e-34) {
		tmp = x - ((y * t) / a);
	} else if (t <= -6.8e-38) {
		tmp = x + y;
	} else if (t <= -6.4e-47) {
		tmp = x - (z * (y / t));
	} else if (t <= -9.6e-78) {
		tmp = x + (z / (a / y));
	} else if (t <= -4e-91) {
		tmp = x - (y * (z / t));
	} else if (t <= 1700000000000.0) {
		tmp = x + (y / (a / z));
	} else {
		tmp = 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 (t <= (-0.0085d0)) then
        tmp = x + y
    else if (t <= (-8.5d-34)) then
        tmp = x - ((y * t) / a)
    else if (t <= (-6.8d-38)) then
        tmp = x + y
    else if (t <= (-6.4d-47)) then
        tmp = x - (z * (y / t))
    else if (t <= (-9.6d-78)) then
        tmp = x + (z / (a / y))
    else if (t <= (-4d-91)) then
        tmp = x - (y * (z / t))
    else if (t <= 1700000000000.0d0) then
        tmp = x + (y / (a / z))
    else
        tmp = 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 (t <= -0.0085) {
		tmp = x + y;
	} else if (t <= -8.5e-34) {
		tmp = x - ((y * t) / a);
	} else if (t <= -6.8e-38) {
		tmp = x + y;
	} else if (t <= -6.4e-47) {
		tmp = x - (z * (y / t));
	} else if (t <= -9.6e-78) {
		tmp = x + (z / (a / y));
	} else if (t <= -4e-91) {
		tmp = x - (y * (z / t));
	} else if (t <= 1700000000000.0) {
		tmp = x + (y / (a / z));
	} else {
		tmp = x + y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -0.0085:
		tmp = x + y
	elif t <= -8.5e-34:
		tmp = x - ((y * t) / a)
	elif t <= -6.8e-38:
		tmp = x + y
	elif t <= -6.4e-47:
		tmp = x - (z * (y / t))
	elif t <= -9.6e-78:
		tmp = x + (z / (a / y))
	elif t <= -4e-91:
		tmp = x - (y * (z / t))
	elif t <= 1700000000000.0:
		tmp = x + (y / (a / z))
	else:
		tmp = x + y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -0.0085)
		tmp = Float64(x + y);
	elseif (t <= -8.5e-34)
		tmp = Float64(x - Float64(Float64(y * t) / a));
	elseif (t <= -6.8e-38)
		tmp = Float64(x + y);
	elseif (t <= -6.4e-47)
		tmp = Float64(x - Float64(z * Float64(y / t)));
	elseif (t <= -9.6e-78)
		tmp = Float64(x + Float64(z / Float64(a / y)));
	elseif (t <= -4e-91)
		tmp = Float64(x - Float64(y * Float64(z / t)));
	elseif (t <= 1700000000000.0)
		tmp = Float64(x + Float64(y / Float64(a / z)));
	else
		tmp = Float64(x + y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -0.0085)
		tmp = x + y;
	elseif (t <= -8.5e-34)
		tmp = x - ((y * t) / a);
	elseif (t <= -6.8e-38)
		tmp = x + y;
	elseif (t <= -6.4e-47)
		tmp = x - (z * (y / t));
	elseif (t <= -9.6e-78)
		tmp = x + (z / (a / y));
	elseif (t <= -4e-91)
		tmp = x - (y * (z / t));
	elseif (t <= 1700000000000.0)
		tmp = x + (y / (a / z));
	else
		tmp = x + y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -0.0085], N[(x + y), $MachinePrecision], If[LessEqual[t, -8.5e-34], N[(x - N[(N[(y * t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -6.8e-38], N[(x + y), $MachinePrecision], If[LessEqual[t, -6.4e-47], N[(x - N[(z * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -9.6e-78], N[(x + N[(z / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -4e-91], N[(x - N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1700000000000.0], N[(x + N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]]]]]]]

Derivation

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

Alternative 4: 82.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -195:\\ \;\;\;\;x + y\\ \mathbf{elif}\;t \leq -2.2 \cdot 10^{-36}:\\ \;\;\;\;x + y \cdot \frac{z - t}{a}\\ \mathbf{elif}\;t \leq -8 \cdot 10^{-38} \lor \neg \left(t \leq 2300000000\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{a - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -195.0)
   (+ x y)
   (if (<= t -2.2e-36)
     (+ x (* y (/ (- z t) a)))
     (if (or (<= t -8e-38) (not (<= t 2300000000.0)))
       (+ x y)
       (+ x (* z (/ y (- a t))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -195.0) {
		tmp = x + y;
	} else if (t <= -2.2e-36) {
		tmp = x + (y * ((z - t) / a));
	} else if ((t <= -8e-38) || !(t <= 2300000000.0)) {
		tmp = x + y;
	} else {
		tmp = x + (z * (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 (t <= (-195.0d0)) then
        tmp = x + y
    else if (t <= (-2.2d-36)) then
        tmp = x + (y * ((z - t) / a))
    else if ((t <= (-8d-38)) .or. (.not. (t <= 2300000000.0d0))) then
        tmp = x + y
    else
        tmp = x + (z * (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 (t <= -195.0) {
		tmp = x + y;
	} else if (t <= -2.2e-36) {
		tmp = x + (y * ((z - t) / a));
	} else if ((t <= -8e-38) || !(t <= 2300000000.0)) {
		tmp = x + y;
	} else {
		tmp = x + (z * (y / (a - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -195.0:
		tmp = x + y
	elif t <= -2.2e-36:
		tmp = x + (y * ((z - t) / a))
	elif (t <= -8e-38) or not (t <= 2300000000.0):
		tmp = x + y
	else:
		tmp = x + (z * (y / (a - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -195.0)
		tmp = Float64(x + y);
	elseif (t <= -2.2e-36)
		tmp = Float64(x + Float64(y * Float64(Float64(z - t) / a)));
	elseif ((t <= -8e-38) || !(t <= 2300000000.0))
		tmp = Float64(x + y);
	else
		tmp = Float64(x + Float64(z * Float64(y / Float64(a - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -195.0)
		tmp = x + y;
	elseif (t <= -2.2e-36)
		tmp = x + (y * ((z - t) / a));
	elseif ((t <= -8e-38) || ~((t <= 2300000000.0)))
		tmp = x + y;
	else
		tmp = x + (z * (y / (a - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -195.0], N[(x + y), $MachinePrecision], If[LessEqual[t, -2.2e-36], N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[t, -8e-38], N[Not[LessEqual[t, 2300000000.0]], $MachinePrecision]], N[(x + y), $MachinePrecision], N[(x + N[(z * N[(y / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 5: 76.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -0.00108 \lor \neg \left(t \leq -1.95 \cdot 10^{-35} \lor \neg \left(t \leq -1.85 \cdot 10^{-65}\right) \land t \leq 1000000000\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -0.00108)
         (not
          (or (<= t -1.95e-35)
              (and (not (<= t -1.85e-65)) (<= t 1000000000.0)))))
   (+ x y)
   (+ x (/ (* y z) a))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -0.00108) || !((t <= -1.95e-35) || (!(t <= -1.85e-65) && (t <= 1000000000.0)))) {
		tmp = x + y;
	} else {
		tmp = x + ((y * 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 <= (-0.00108d0)) .or. (.not. (t <= (-1.95d-35)) .or. (.not. (t <= (-1.85d-65))) .and. (t <= 1000000000.0d0))) then
        tmp = x + y
    else
        tmp = x + ((y * 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 <= -0.00108) || !((t <= -1.95e-35) || (!(t <= -1.85e-65) && (t <= 1000000000.0)))) {
		tmp = x + y;
	} else {
		tmp = x + ((y * z) / a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -0.00108) or not ((t <= -1.95e-35) or (not (t <= -1.85e-65) and (t <= 1000000000.0))):
		tmp = x + y
	else:
		tmp = x + ((y * z) / a)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -0.00108) || !((t <= -1.95e-35) || (!(t <= -1.85e-65) && (t <= 1000000000.0))))
		tmp = Float64(x + y);
	else
		tmp = Float64(x + Float64(Float64(y * z) / a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -0.00108) || ~(((t <= -1.95e-35) || (~((t <= -1.85e-65)) && (t <= 1000000000.0)))))
		tmp = x + y;
	else
		tmp = x + ((y * z) / a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -0.00108], N[Not[Or[LessEqual[t, -1.95e-35], And[N[Not[LessEqual[t, -1.85e-65]], $MachinePrecision], LessEqual[t, 1000000000.0]]]], $MachinePrecision]], N[(x + y), $MachinePrecision], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 6: 77.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -6.2 \cdot 10^{-6}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;t \leq -2.2 \cdot 10^{-36}:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{elif}\;t \leq -5.3 \cdot 10^{-64} \lor \neg \left(t \leq 620000\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -6.2e-6)
   (+ x y)
   (if (<= t -2.2e-36)
     (+ x (/ (* y z) a))
     (if (or (<= t -5.3e-64) (not (<= t 620000.0)))
       (+ x y)
       (+ x (* z (/ y a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -6.2e-6) {
		tmp = x + y;
	} else if (t <= -2.2e-36) {
		tmp = x + ((y * z) / a);
	} else if ((t <= -5.3e-64) || !(t <= 620000.0)) {
		tmp = x + y;
	} else {
		tmp = x + (z * (y / 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 <= (-6.2d-6)) then
        tmp = x + y
    else if (t <= (-2.2d-36)) then
        tmp = x + ((y * z) / a)
    else if ((t <= (-5.3d-64)) .or. (.not. (t <= 620000.0d0))) then
        tmp = x + y
    else
        tmp = x + (z * (y / 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 <= -6.2e-6) {
		tmp = x + y;
	} else if (t <= -2.2e-36) {
		tmp = x + ((y * z) / a);
	} else if ((t <= -5.3e-64) || !(t <= 620000.0)) {
		tmp = x + y;
	} else {
		tmp = x + (z * (y / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -6.2e-6:
		tmp = x + y
	elif t <= -2.2e-36:
		tmp = x + ((y * z) / a)
	elif (t <= -5.3e-64) or not (t <= 620000.0):
		tmp = x + y
	else:
		tmp = x + (z * (y / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -6.2e-6)
		tmp = Float64(x + y);
	elseif (t <= -2.2e-36)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	elseif ((t <= -5.3e-64) || !(t <= 620000.0))
		tmp = Float64(x + y);
	else
		tmp = Float64(x + Float64(z * Float64(y / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -6.2e-6)
		tmp = x + y;
	elseif (t <= -2.2e-36)
		tmp = x + ((y * z) / a);
	elseif ((t <= -5.3e-64) || ~((t <= 620000.0)))
		tmp = x + y;
	else
		tmp = x + (z * (y / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -6.2e-6], N[(x + y), $MachinePrecision], If[LessEqual[t, -2.2e-36], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[t, -5.3e-64], N[Not[LessEqual[t, 620000.0]], $MachinePrecision]], N[(x + y), $MachinePrecision], N[(x + N[(z * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 7: 77.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.7 \cdot 10^{-7}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;t \leq -1.95 \cdot 10^{-35}:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{elif}\;t \leq -5.6 \cdot 10^{-64} \lor \neg \left(t \leq 34000000000000\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -2.7e-7)
   (+ x y)
   (if (<= t -1.95e-35)
     (+ x (/ (* y z) a))
     (if (or (<= t -5.6e-64) (not (<= t 34000000000000.0)))
       (+ x y)
       (+ x (/ y (/ a z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2.7e-7) {
		tmp = x + y;
	} else if (t <= -1.95e-35) {
		tmp = x + ((y * z) / a);
	} else if ((t <= -5.6e-64) || !(t <= 34000000000000.0)) {
		tmp = x + y;
	} else {
		tmp = x + (y / (a / 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 <= (-2.7d-7)) then
        tmp = x + y
    else if (t <= (-1.95d-35)) then
        tmp = x + ((y * z) / a)
    else if ((t <= (-5.6d-64)) .or. (.not. (t <= 34000000000000.0d0))) then
        tmp = x + y
    else
        tmp = x + (y / (a / 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 <= -2.7e-7) {
		tmp = x + y;
	} else if (t <= -1.95e-35) {
		tmp = x + ((y * z) / a);
	} else if ((t <= -5.6e-64) || !(t <= 34000000000000.0)) {
		tmp = x + y;
	} else {
		tmp = x + (y / (a / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -2.7e-7:
		tmp = x + y
	elif t <= -1.95e-35:
		tmp = x + ((y * z) / a)
	elif (t <= -5.6e-64) or not (t <= 34000000000000.0):
		tmp = x + y
	else:
		tmp = x + (y / (a / z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -2.7e-7)
		tmp = Float64(x + y);
	elseif (t <= -1.95e-35)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	elseif ((t <= -5.6e-64) || !(t <= 34000000000000.0))
		tmp = Float64(x + y);
	else
		tmp = Float64(x + Float64(y / Float64(a / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -2.7e-7)
		tmp = x + y;
	elseif (t <= -1.95e-35)
		tmp = x + ((y * z) / a);
	elseif ((t <= -5.6e-64) || ~((t <= 34000000000000.0)))
		tmp = x + y;
	else
		tmp = x + (y / (a / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -2.7e-7], N[(x + y), $MachinePrecision], If[LessEqual[t, -1.95e-35], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[t, -5.6e-64], N[Not[LessEqual[t, 34000000000000.0]], $MachinePrecision]], N[(x + y), $MachinePrecision], N[(x + N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 8: 76.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -7.5:\\ \;\;\;\;x + y\\ \mathbf{elif}\;t \leq -2.4 \cdot 10^{-31}:\\ \;\;\;\;x - t \cdot \frac{y}{a}\\ \mathbf{elif}\;t \leq -2.8 \cdot 10^{-65} \lor \neg \left(t \leq 1700000000000\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -7.5)
   (+ x y)
   (if (<= t -2.4e-31)
     (- x (* t (/ y a)))
     (if (or (<= t -2.8e-65) (not (<= t 1700000000000.0)))
       (+ x y)
       (+ x (/ y (/ a z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -7.5) {
		tmp = x + y;
	} else if (t <= -2.4e-31) {
		tmp = x - (t * (y / a));
	} else if ((t <= -2.8e-65) || !(t <= 1700000000000.0)) {
		tmp = x + y;
	} else {
		tmp = x + (y / (a / 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 <= (-7.5d0)) then
        tmp = x + y
    else if (t <= (-2.4d-31)) then
        tmp = x - (t * (y / a))
    else if ((t <= (-2.8d-65)) .or. (.not. (t <= 1700000000000.0d0))) then
        tmp = x + y
    else
        tmp = x + (y / (a / 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 <= -7.5) {
		tmp = x + y;
	} else if (t <= -2.4e-31) {
		tmp = x - (t * (y / a));
	} else if ((t <= -2.8e-65) || !(t <= 1700000000000.0)) {
		tmp = x + y;
	} else {
		tmp = x + (y / (a / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -7.5:
		tmp = x + y
	elif t <= -2.4e-31:
		tmp = x - (t * (y / a))
	elif (t <= -2.8e-65) or not (t <= 1700000000000.0):
		tmp = x + y
	else:
		tmp = x + (y / (a / z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -7.5)
		tmp = Float64(x + y);
	elseif (t <= -2.4e-31)
		tmp = Float64(x - Float64(t * Float64(y / a)));
	elseif ((t <= -2.8e-65) || !(t <= 1700000000000.0))
		tmp = Float64(x + y);
	else
		tmp = Float64(x + Float64(y / Float64(a / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -7.5)
		tmp = x + y;
	elseif (t <= -2.4e-31)
		tmp = x - (t * (y / a));
	elseif ((t <= -2.8e-65) || ~((t <= 1700000000000.0)))
		tmp = x + y;
	else
		tmp = x + (y / (a / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -7.5], N[(x + y), $MachinePrecision], If[LessEqual[t, -2.4e-31], N[(x - N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[t, -2.8e-65], N[Not[LessEqual[t, 1700000000000.0]], $MachinePrecision]], N[(x + y), $MachinePrecision], N[(x + N[(y / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 9: 83.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.36 \cdot 10^{+54} \lor \neg \left(t \leq 62000000000000\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{a - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -1.36e+54) (not (<= t 62000000000000.0)))
   (+ x y)
   (+ x (* z (/ y (- a t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -1.36e+54) || !(t <= 62000000000000.0)) {
		tmp = x + y;
	} else {
		tmp = x + (z * (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 ((t <= (-1.36d+54)) .or. (.not. (t <= 62000000000000.0d0))) then
        tmp = x + y
    else
        tmp = x + (z * (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 ((t <= -1.36e+54) || !(t <= 62000000000000.0)) {
		tmp = x + y;
	} else {
		tmp = x + (z * (y / (a - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -1.36e+54) or not (t <= 62000000000000.0):
		tmp = x + y
	else:
		tmp = x + (z * (y / (a - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -1.36e+54) || !(t <= 62000000000000.0))
		tmp = Float64(x + y);
	else
		tmp = Float64(x + Float64(z * Float64(y / Float64(a - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -1.36e+54) || ~((t <= 62000000000000.0)))
		tmp = x + y;
	else
		tmp = x + (z * (y / (a - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -1.36e+54], N[Not[LessEqual[t, 62000000000000.0]], $MachinePrecision]], N[(x + y), $MachinePrecision], N[(x + N[(z * N[(y / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 88.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4 \cdot 10^{+29} \lor \neg \left(z \leq 1.06 \cdot 10^{-21}\right):\\ \;\;\;\;x + z \cdot \frac{y}{a - t}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{y}{\frac{a}{t} + -1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -4e+29) (not (<= z 1.06e-21)))
   (+ x (* z (/ y (- a t))))
   (- x (/ y (+ (/ a t) -1.0)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -4e+29) || !(z <= 1.06e-21)) {
		tmp = x + (z * (y / (a - t)));
	} else {
		tmp = x - (y / ((a / t) + -1.0));
	}
	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 ((z <= (-4d+29)) .or. (.not. (z <= 1.06d-21))) then
        tmp = x + (z * (y / (a - t)))
    else
        tmp = x - (y / ((a / t) + (-1.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -4e+29) || !(z <= 1.06e-21)) {
		tmp = x + (z * (y / (a - t)));
	} else {
		tmp = x - (y / ((a / t) + -1.0));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -4e+29) or not (z <= 1.06e-21):
		tmp = x + (z * (y / (a - t)))
	else:
		tmp = x - (y / ((a / t) + -1.0))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -4e+29) || !(z <= 1.06e-21))
		tmp = Float64(x + Float64(z * Float64(y / Float64(a - t))));
	else
		tmp = Float64(x - Float64(y / Float64(Float64(a / t) + -1.0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -4e+29) || ~((z <= 1.06e-21)))
		tmp = x + (z * (y / (a - t)));
	else
		tmp = x - (y / ((a / t) + -1.0));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -4e+29], N[Not[LessEqual[z, 1.06e-21]], $MachinePrecision]], N[(x + N[(z * N[(y / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(y / N[(N[(a / t), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 81.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.9 \cdot 10^{-43}:\\ \;\;\;\;x + y \cdot \frac{z - t}{a}\\ \mathbf{elif}\;a \leq 1.6 \cdot 10^{-82}:\\ \;\;\;\;x + \left(y - \frac{y}{\frac{t}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{a - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.9e-43)
   (+ x (* y (/ (- z t) a)))
   (if (<= a 1.6e-82) (+ x (- y (/ y (/ t z)))) (+ x (* z (/ y (- a t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.9e-43) {
		tmp = x + (y * ((z - t) / a));
	} else if (a <= 1.6e-82) {
		tmp = x + (y - (y / (t / z)));
	} else {
		tmp = x + (z * (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 <= (-1.9d-43)) then
        tmp = x + (y * ((z - t) / a))
    else if (a <= 1.6d-82) then
        tmp = x + (y - (y / (t / z)))
    else
        tmp = x + (z * (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 <= -1.9e-43) {
		tmp = x + (y * ((z - t) / a));
	} else if (a <= 1.6e-82) {
		tmp = x + (y - (y / (t / z)));
	} else {
		tmp = x + (z * (y / (a - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -1.9e-43:
		tmp = x + (y * ((z - t) / a))
	elif a <= 1.6e-82:
		tmp = x + (y - (y / (t / z)))
	else:
		tmp = x + (z * (y / (a - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.9e-43)
		tmp = Float64(x + Float64(y * Float64(Float64(z - t) / a)));
	elseif (a <= 1.6e-82)
		tmp = Float64(x + Float64(y - Float64(y / Float64(t / z))));
	else
		tmp = Float64(x + Float64(z * Float64(y / Float64(a - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -1.9e-43)
		tmp = x + (y * ((z - t) / a));
	elseif (a <= 1.6e-82)
		tmp = x + (y - (y / (t / z)));
	else
		tmp = x + (z * (y / (a - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.9e-43], N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.6e-82], N[(x + N[(y - N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z * N[(y / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 12: 98.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 13: 61.8% accurate, 2.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq 5.5 \cdot 10^{+73}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (if (<= a 5.5e+73) (+ x y) x))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= 5.5e+73) {
		tmp = x + y;
	} 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 (a <= 5.5d+73) then
        tmp = x + y
    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 (a <= 5.5e+73) {
		tmp = x + y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= 5.5e+73:
		tmp = x + y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= 5.5e+73)
		tmp = Float64(x + y);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= 5.5e+73)
		tmp = x + y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, 5.5e+73], N[(x + y), $MachinePrecision], x]

Derivation

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

Alternative 14: 51.1% 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: 98.2% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024008 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTicks from plot-0.2.3.4, B"
  :precision binary64

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

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