Graphics.Rendering.Chart.Axis.Types:invLinMap from Chart-1.5.3

Percentage Accurate: 68.3% → 91.0%

Time: 23.5s

Alternatives: 22

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

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 - x)) / (a - z))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 68.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 - x)) / (a - z))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 91.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{\left(y - z\right) \cdot \left(t - x\right)}{a - z}\\ \mathbf{if}\;t_1 \leq -5 \cdot 10^{-281}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y - z}{a - z}, t - x, x\right)\\ \mathbf{elif}\;t_1 \leq 0:\\ \;\;\;\;t + \frac{\left(t - x\right) \cdot \left(a - y\right)}{z}\\ \mathbf{else}:\\ \;\;\;\;x + \left(x - t\right) \cdot \frac{z - y}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (* (- y z) (- t x)) (- a z)))))
   (if (<= t_1 -5e-281)
     (fma (/ (- y z) (- a z)) (- t x) x)
     (if (<= t_1 0.0)
       (+ t (/ (* (- t x) (- a y)) z))
       (+ x (* (- x t) (/ (- z y) (- a z))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (((y - z) * (t - x)) / (a - z));
	double tmp;
	if (t_1 <= -5e-281) {
		tmp = fma(((y - z) / (a - z)), (t - x), x);
	} else if (t_1 <= 0.0) {
		tmp = t + (((t - x) * (a - y)) / z);
	} else {
		tmp = x + ((x - t) * ((z - y) / (a - z)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(Float64(y - z) * Float64(t - x)) / Float64(a - z)))
	tmp = 0.0
	if (t_1 <= -5e-281)
		tmp = fma(Float64(Float64(y - z) / Float64(a - z)), Float64(t - x), x);
	elseif (t_1 <= 0.0)
		tmp = Float64(t + Float64(Float64(Float64(t - x) * Float64(a - y)) / z));
	else
		tmp = Float64(x + Float64(Float64(x - t) * Float64(Float64(z - y) / Float64(a - z))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(N[(y - z), $MachinePrecision] * N[(t - x), $MachinePrecision]), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -5e-281], N[(N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision] * N[(t - x), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t$95$1, 0.0], N[(t + N[(N[(N[(t - x), $MachinePrecision] * N[(a - y), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(x - t), $MachinePrecision] * N[(N[(z - y), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 2: 91.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{\left(y - z\right) \cdot \left(t - x\right)}{a - z}\\ \mathbf{if}\;t_1 \leq -5 \cdot 10^{-281} \lor \neg \left(t_1 \leq 0\right):\\ \;\;\;\;x + \left(x - t\right) \cdot \frac{z - y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t + \frac{\left(t - x\right) \cdot \left(a - y\right)}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (* (- y z) (- t x)) (- a z)))))
   (if (or (<= t_1 -5e-281) (not (<= t_1 0.0)))
     (+ x (* (- x t) (/ (- z y) (- a z))))
     (+ t (/ (* (- t x) (- a y)) z)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (((y - z) * (t - x)) / (a - z));
	double tmp;
	if ((t_1 <= -5e-281) || !(t_1 <= 0.0)) {
		tmp = x + ((x - t) * ((z - y) / (a - z)));
	} else {
		tmp = t + (((t - x) * (a - 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) * (t - x)) / (a - z))
    if ((t_1 <= (-5d-281)) .or. (.not. (t_1 <= 0.0d0))) then
        tmp = x + ((x - t) * ((z - y) / (a - z)))
    else
        tmp = t + (((t - x) * (a - 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) * (t - x)) / (a - z));
	double tmp;
	if ((t_1 <= -5e-281) || !(t_1 <= 0.0)) {
		tmp = x + ((x - t) * ((z - y) / (a - z)));
	} else {
		tmp = t + (((t - x) * (a - y)) / z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (((y - z) * (t - x)) / (a - z))
	tmp = 0
	if (t_1 <= -5e-281) or not (t_1 <= 0.0):
		tmp = x + ((x - t) * ((z - y) / (a - z)))
	else:
		tmp = t + (((t - x) * (a - y)) / z)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(Float64(y - z) * Float64(t - x)) / Float64(a - z)))
	tmp = 0.0
	if ((t_1 <= -5e-281) || !(t_1 <= 0.0))
		tmp = Float64(x + Float64(Float64(x - t) * Float64(Float64(z - y) / Float64(a - z))));
	else
		tmp = Float64(t + Float64(Float64(Float64(t - x) * Float64(a - y)) / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (((y - z) * (t - x)) / (a - z));
	tmp = 0.0;
	if ((t_1 <= -5e-281) || ~((t_1 <= 0.0)))
		tmp = x + ((x - t) * ((z - y) / (a - z)));
	else
		tmp = t + (((t - x) * (a - y)) / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(N[(y - z), $MachinePrecision] * N[(t - x), $MachinePrecision]), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -5e-281], N[Not[LessEqual[t$95$1, 0.0]], $MachinePrecision]], N[(x + N[(N[(x - t), $MachinePrecision] * N[(N[(z - y), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t + N[(N[(N[(t - x), $MachinePrecision] * N[(a - y), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 3: 51.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \frac{-y}{a - z}\\ \mathbf{if}\;a \leq -5.8 \cdot 10^{-11}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 3.75 \cdot 10^{-137}:\\ \;\;\;\;t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{elif}\;a \leq 1.9 \cdot 10^{-90}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 3.5 \cdot 10^{+24}:\\ \;\;\;\;\frac{y \cdot t}{a - z}\\ \mathbf{elif}\;a \leq 1.55 \cdot 10^{+59}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (/ (- y) (- a z)))))
   (if (<= a -5.8e-11)
     (* x (- 1.0 (/ y a)))
     (if (<= a 3.75e-137)
       (* t (- 1.0 (/ y z)))
       (if (<= a 1.9e-90)
         t_1
         (if (<= a 3.5e+24)
           (/ (* y t) (- a z))
           (if (<= a 1.55e+59) t_1 (+ x (/ t (/ a y))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (-y / (a - z));
	double tmp;
	if (a <= -5.8e-11) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 3.75e-137) {
		tmp = t * (1.0 - (y / z));
	} else if (a <= 1.9e-90) {
		tmp = t_1;
	} else if (a <= 3.5e+24) {
		tmp = (y * t) / (a - z);
	} else if (a <= 1.55e+59) {
		tmp = t_1;
	} else {
		tmp = x + (t / (a / 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) :: t_1
    real(8) :: tmp
    t_1 = x * (-y / (a - z))
    if (a <= (-5.8d-11)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 3.75d-137) then
        tmp = t * (1.0d0 - (y / z))
    else if (a <= 1.9d-90) then
        tmp = t_1
    else if (a <= 3.5d+24) then
        tmp = (y * t) / (a - z)
    else if (a <= 1.55d+59) then
        tmp = t_1
    else
        tmp = x + (t / (a / y))
    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 / (a - z));
	double tmp;
	if (a <= -5.8e-11) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 3.75e-137) {
		tmp = t * (1.0 - (y / z));
	} else if (a <= 1.9e-90) {
		tmp = t_1;
	} else if (a <= 3.5e+24) {
		tmp = (y * t) / (a - z);
	} else if (a <= 1.55e+59) {
		tmp = t_1;
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (-y / (a - z))
	tmp = 0
	if a <= -5.8e-11:
		tmp = x * (1.0 - (y / a))
	elif a <= 3.75e-137:
		tmp = t * (1.0 - (y / z))
	elif a <= 1.9e-90:
		tmp = t_1
	elif a <= 3.5e+24:
		tmp = (y * t) / (a - z)
	elif a <= 1.55e+59:
		tmp = t_1
	else:
		tmp = x + (t / (a / y))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(Float64(-y) / Float64(a - z)))
	tmp = 0.0
	if (a <= -5.8e-11)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 3.75e-137)
		tmp = Float64(t * Float64(1.0 - Float64(y / z)));
	elseif (a <= 1.9e-90)
		tmp = t_1;
	elseif (a <= 3.5e+24)
		tmp = Float64(Float64(y * t) / Float64(a - z));
	elseif (a <= 1.55e+59)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(t / Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (-y / (a - z));
	tmp = 0.0;
	if (a <= -5.8e-11)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 3.75e-137)
		tmp = t * (1.0 - (y / z));
	elseif (a <= 1.9e-90)
		tmp = t_1;
	elseif (a <= 3.5e+24)
		tmp = (y * t) / (a - z);
	elseif (a <= 1.55e+59)
		tmp = t_1;
	else
		tmp = x + (t / (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[((-y) / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -5.8e-11], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3.75e-137], N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.9e-90], t$95$1, If[LessEqual[a, 3.5e+24], N[(N[(y * t), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.55e+59], t$95$1, N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 4: 51.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.9 \cdot 10^{-9}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 1.15 \cdot 10^{-137}:\\ \;\;\;\;t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{elif}\;a \leq 5.9 \cdot 10^{-89}:\\ \;\;\;\;\frac{y}{\frac{z - a}{x}}\\ \mathbf{elif}\;a \leq 1.05 \cdot 10^{+24}:\\ \;\;\;\;\frac{y \cdot t}{a - z}\\ \mathbf{elif}\;a \leq 1.8 \cdot 10^{+59}:\\ \;\;\;\;x \cdot \frac{-y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.9e-9)
   (* x (- 1.0 (/ y a)))
   (if (<= a 1.15e-137)
     (* t (- 1.0 (/ y z)))
     (if (<= a 5.9e-89)
       (/ y (/ (- z a) x))
       (if (<= a 1.05e+24)
         (/ (* y t) (- a z))
         (if (<= a 1.8e+59) (* x (/ (- y) (- a z))) (+ x (/ t (/ a y)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.9e-9) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 1.15e-137) {
		tmp = t * (1.0 - (y / z));
	} else if (a <= 5.9e-89) {
		tmp = y / ((z - a) / x);
	} else if (a <= 1.05e+24) {
		tmp = (y * t) / (a - z);
	} else if (a <= 1.8e+59) {
		tmp = x * (-y / (a - z));
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-3.9d-9)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 1.15d-137) then
        tmp = t * (1.0d0 - (y / z))
    else if (a <= 5.9d-89) then
        tmp = y / ((z - a) / x)
    else if (a <= 1.05d+24) then
        tmp = (y * t) / (a - z)
    else if (a <= 1.8d+59) then
        tmp = x * (-y / (a - z))
    else
        tmp = x + (t / (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.9e-9) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 1.15e-137) {
		tmp = t * (1.0 - (y / z));
	} else if (a <= 5.9e-89) {
		tmp = y / ((z - a) / x);
	} else if (a <= 1.05e+24) {
		tmp = (y * t) / (a - z);
	} else if (a <= 1.8e+59) {
		tmp = x * (-y / (a - z));
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.9e-9:
		tmp = x * (1.0 - (y / a))
	elif a <= 1.15e-137:
		tmp = t * (1.0 - (y / z))
	elif a <= 5.9e-89:
		tmp = y / ((z - a) / x)
	elif a <= 1.05e+24:
		tmp = (y * t) / (a - z)
	elif a <= 1.8e+59:
		tmp = x * (-y / (a - z))
	else:
		tmp = x + (t / (a / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.9e-9)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 1.15e-137)
		tmp = Float64(t * Float64(1.0 - Float64(y / z)));
	elseif (a <= 5.9e-89)
		tmp = Float64(y / Float64(Float64(z - a) / x));
	elseif (a <= 1.05e+24)
		tmp = Float64(Float64(y * t) / Float64(a - z));
	elseif (a <= 1.8e+59)
		tmp = Float64(x * Float64(Float64(-y) / Float64(a - z)));
	else
		tmp = Float64(x + Float64(t / Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.9e-9)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 1.15e-137)
		tmp = t * (1.0 - (y / z));
	elseif (a <= 5.9e-89)
		tmp = y / ((z - a) / x);
	elseif (a <= 1.05e+24)
		tmp = (y * t) / (a - z);
	elseif (a <= 1.8e+59)
		tmp = x * (-y / (a - z));
	else
		tmp = x + (t / (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.9e-9], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.15e-137], N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 5.9e-89], N[(y / N[(N[(z - a), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.05e+24], N[(N[(y * t), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.8e+59], N[(x * N[((-y) / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 5: 37.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -5.3 \cdot 10^{+100}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq -8.2 \cdot 10^{+33}:\\ \;\;\;\;\frac{-x}{\frac{a}{y}}\\ \mathbf{elif}\;a \leq -3.7 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 2.8 \cdot 10^{-115}:\\ \;\;\;\;t\\ \mathbf{elif}\;a \leq 6.2 \cdot 10^{+59}:\\ \;\;\;\;\frac{x}{\frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -5.3e+100)
   x
   (if (<= a -8.2e+33)
     (/ (- x) (/ a y))
     (if (<= a -3.7e-12)
       x
       (if (<= a 2.8e-115) t (if (<= a 6.2e+59) (/ x (/ z y)) x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -5.3e+100) {
		tmp = x;
	} else if (a <= -8.2e+33) {
		tmp = -x / (a / y);
	} else if (a <= -3.7e-12) {
		tmp = x;
	} else if (a <= 2.8e-115) {
		tmp = t;
	} else if (a <= 6.2e+59) {
		tmp = x / (z / 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 <= (-5.3d+100)) then
        tmp = x
    else if (a <= (-8.2d+33)) then
        tmp = -x / (a / y)
    else if (a <= (-3.7d-12)) then
        tmp = x
    else if (a <= 2.8d-115) then
        tmp = t
    else if (a <= 6.2d+59) then
        tmp = x / (z / 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 <= -5.3e+100) {
		tmp = x;
	} else if (a <= -8.2e+33) {
		tmp = -x / (a / y);
	} else if (a <= -3.7e-12) {
		tmp = x;
	} else if (a <= 2.8e-115) {
		tmp = t;
	} else if (a <= 6.2e+59) {
		tmp = x / (z / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -5.3e+100:
		tmp = x
	elif a <= -8.2e+33:
		tmp = -x / (a / y)
	elif a <= -3.7e-12:
		tmp = x
	elif a <= 2.8e-115:
		tmp = t
	elif a <= 6.2e+59:
		tmp = x / (z / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -5.3e+100)
		tmp = x;
	elseif (a <= -8.2e+33)
		tmp = Float64(Float64(-x) / Float64(a / y));
	elseif (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 2.8e-115)
		tmp = t;
	elseif (a <= 6.2e+59)
		tmp = Float64(x / Float64(z / y));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -5.3e+100)
		tmp = x;
	elseif (a <= -8.2e+33)
		tmp = -x / (a / y);
	elseif (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 2.8e-115)
		tmp = t;
	elseif (a <= 6.2e+59)
		tmp = x / (z / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -5.3e+100], x, If[LessEqual[a, -8.2e+33], N[((-x) / N[(a / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -3.7e-12], x, If[LessEqual[a, 2.8e-115], t, If[LessEqual[a, 6.2e+59], N[(x / N[(z / y), $MachinePrecision]), $MachinePrecision], x]]]]]

Derivation

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

Alternative 6: 53.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{if}\;a \leq -1.46 \cdot 10^{-8}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 3.8 \cdot 10^{-37}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.35 \cdot 10^{+51}:\\ \;\;\;\;\left(t - x\right) \cdot \frac{y}{a}\\ \mathbf{elif}\;a \leq 3.1 \cdot 10^{+71}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (- 1.0 (/ y z)))))
   (if (<= a -1.46e-8)
     (* x (- 1.0 (/ y a)))
     (if (<= a 3.8e-37)
       t_1
       (if (<= a 1.35e+51)
         (* (- t x) (/ y a))
         (if (<= a 3.1e+71) t_1 (+ x (/ t (/ a y)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * (1.0 - (y / z));
	double tmp;
	if (a <= -1.46e-8) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 3.8e-37) {
		tmp = t_1;
	} else if (a <= 1.35e+51) {
		tmp = (t - x) * (y / a);
	} else if (a <= 3.1e+71) {
		tmp = t_1;
	} else {
		tmp = x + (t / (a / 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) :: t_1
    real(8) :: tmp
    t_1 = t * (1.0d0 - (y / z))
    if (a <= (-1.46d-8)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 3.8d-37) then
        tmp = t_1
    else if (a <= 1.35d+51) then
        tmp = (t - x) * (y / a)
    else if (a <= 3.1d+71) then
        tmp = t_1
    else
        tmp = x + (t / (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t * (1.0 - (y / z));
	double tmp;
	if (a <= -1.46e-8) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 3.8e-37) {
		tmp = t_1;
	} else if (a <= 1.35e+51) {
		tmp = (t - x) * (y / a);
	} else if (a <= 3.1e+71) {
		tmp = t_1;
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t * (1.0 - (y / z))
	tmp = 0
	if a <= -1.46e-8:
		tmp = x * (1.0 - (y / a))
	elif a <= 3.8e-37:
		tmp = t_1
	elif a <= 1.35e+51:
		tmp = (t - x) * (y / a)
	elif a <= 3.1e+71:
		tmp = t_1
	else:
		tmp = x + (t / (a / y))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(1.0 - Float64(y / z)))
	tmp = 0.0
	if (a <= -1.46e-8)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 3.8e-37)
		tmp = t_1;
	elseif (a <= 1.35e+51)
		tmp = Float64(Float64(t - x) * Float64(y / a));
	elseif (a <= 3.1e+71)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(t / Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t * (1.0 - (y / z));
	tmp = 0.0;
	if (a <= -1.46e-8)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 3.8e-37)
		tmp = t_1;
	elseif (a <= 1.35e+51)
		tmp = (t - x) * (y / a);
	elseif (a <= 3.1e+71)
		tmp = t_1;
	else
		tmp = x + (t / (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.46e-8], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3.8e-37], t$95$1, If[LessEqual[a, 1.35e+51], N[(N[(t - x), $MachinePrecision] * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3.1e+71], t$95$1, N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 7: 53.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{if}\;a \leq -3.3 \cdot 10^{-10}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 1.66 \cdot 10^{-38}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 5.5 \cdot 10^{+50}:\\ \;\;\;\;\frac{y}{\frac{a}{t - x}}\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{+72}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (- 1.0 (/ y z)))))
   (if (<= a -3.3e-10)
     (* x (- 1.0 (/ y a)))
     (if (<= a 1.66e-38)
       t_1
       (if (<= a 5.5e+50)
         (/ y (/ a (- t x)))
         (if (<= a 1.85e+72) t_1 (+ x (/ t (/ a y)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * (1.0 - (y / z));
	double tmp;
	if (a <= -3.3e-10) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 1.66e-38) {
		tmp = t_1;
	} else if (a <= 5.5e+50) {
		tmp = y / (a / (t - x));
	} else if (a <= 1.85e+72) {
		tmp = t_1;
	} else {
		tmp = x + (t / (a / 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) :: t_1
    real(8) :: tmp
    t_1 = t * (1.0d0 - (y / z))
    if (a <= (-3.3d-10)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 1.66d-38) then
        tmp = t_1
    else if (a <= 5.5d+50) then
        tmp = y / (a / (t - x))
    else if (a <= 1.85d+72) then
        tmp = t_1
    else
        tmp = x + (t / (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t * (1.0 - (y / z));
	double tmp;
	if (a <= -3.3e-10) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 1.66e-38) {
		tmp = t_1;
	} else if (a <= 5.5e+50) {
		tmp = y / (a / (t - x));
	} else if (a <= 1.85e+72) {
		tmp = t_1;
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t * (1.0 - (y / z))
	tmp = 0
	if a <= -3.3e-10:
		tmp = x * (1.0 - (y / a))
	elif a <= 1.66e-38:
		tmp = t_1
	elif a <= 5.5e+50:
		tmp = y / (a / (t - x))
	elif a <= 1.85e+72:
		tmp = t_1
	else:
		tmp = x + (t / (a / y))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(1.0 - Float64(y / z)))
	tmp = 0.0
	if (a <= -3.3e-10)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 1.66e-38)
		tmp = t_1;
	elseif (a <= 5.5e+50)
		tmp = Float64(y / Float64(a / Float64(t - x)));
	elseif (a <= 1.85e+72)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(t / Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t * (1.0 - (y / z));
	tmp = 0.0;
	if (a <= -3.3e-10)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 1.66e-38)
		tmp = t_1;
	elseif (a <= 5.5e+50)
		tmp = y / (a / (t - x));
	elseif (a <= 1.85e+72)
		tmp = t_1;
	else
		tmp = x + (t / (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -3.3e-10], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.66e-38], t$95$1, If[LessEqual[a, 5.5e+50], N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.85e+72], t$95$1, N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 8: 57.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.35 \cdot 10^{-21}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 1.75 \cdot 10^{-115}:\\ \;\;\;\;t \cdot \frac{y - z}{a - z}\\ \mathbf{elif}\;a \leq 1.1 \cdot 10^{+75}:\\ \;\;\;\;y \cdot \frac{t - x}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.35e-21)
   (* x (- 1.0 (/ y a)))
   (if (<= a 1.75e-115)
     (* t (/ (- y z) (- a z)))
     (if (<= a 1.1e+75) (* y (/ (- t x) (- a z))) (+ x (/ t (/ a y)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.35e-21) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 1.75e-115) {
		tmp = t * ((y - z) / (a - z));
	} else if (a <= 1.1e+75) {
		tmp = y * ((t - x) / (a - z));
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-1.35d-21)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 1.75d-115) then
        tmp = t * ((y - z) / (a - z))
    else if (a <= 1.1d+75) then
        tmp = y * ((t - x) / (a - z))
    else
        tmp = x + (t / (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.35e-21) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 1.75e-115) {
		tmp = t * ((y - z) / (a - z));
	} else if (a <= 1.1e+75) {
		tmp = y * ((t - x) / (a - z));
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -1.35e-21:
		tmp = x * (1.0 - (y / a))
	elif a <= 1.75e-115:
		tmp = t * ((y - z) / (a - z))
	elif a <= 1.1e+75:
		tmp = y * ((t - x) / (a - z))
	else:
		tmp = x + (t / (a / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.35e-21)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 1.75e-115)
		tmp = Float64(t * Float64(Float64(y - z) / Float64(a - z)));
	elseif (a <= 1.1e+75)
		tmp = Float64(y * Float64(Float64(t - x) / Float64(a - z)));
	else
		tmp = Float64(x + Float64(t / Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -1.35e-21)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 1.75e-115)
		tmp = t * ((y - z) / (a - z));
	elseif (a <= 1.1e+75)
		tmp = y * ((t - x) / (a - z));
	else
		tmp = x + (t / (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.35e-21], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.75e-115], N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.1e+75], N[(y * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 9: 57.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.3 \cdot 10^{-25}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 7.8 \cdot 10^{-116}:\\ \;\;\;\;t \cdot \frac{y - z}{a - z}\\ \mathbf{elif}\;a \leq 1.3 \cdot 10^{+75}:\\ \;\;\;\;\left(t - x\right) \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -2.3e-25)
   (* x (- 1.0 (/ y a)))
   (if (<= a 7.8e-116)
     (* t (/ (- y z) (- a z)))
     (if (<= a 1.3e+75) (* (- t x) (/ y (- a z))) (+ x (/ t (/ a y)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -2.3e-25) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 7.8e-116) {
		tmp = t * ((y - z) / (a - z));
	} else if (a <= 1.3e+75) {
		tmp = (t - x) * (y / (a - z));
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-2.3d-25)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 7.8d-116) then
        tmp = t * ((y - z) / (a - z))
    else if (a <= 1.3d+75) then
        tmp = (t - x) * (y / (a - z))
    else
        tmp = x + (t / (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -2.3e-25) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 7.8e-116) {
		tmp = t * ((y - z) / (a - z));
	} else if (a <= 1.3e+75) {
		tmp = (t - x) * (y / (a - z));
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -2.3e-25:
		tmp = x * (1.0 - (y / a))
	elif a <= 7.8e-116:
		tmp = t * ((y - z) / (a - z))
	elif a <= 1.3e+75:
		tmp = (t - x) * (y / (a - z))
	else:
		tmp = x + (t / (a / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -2.3e-25)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 7.8e-116)
		tmp = Float64(t * Float64(Float64(y - z) / Float64(a - z)));
	elseif (a <= 1.3e+75)
		tmp = Float64(Float64(t - x) * Float64(y / Float64(a - z)));
	else
		tmp = Float64(x + Float64(t / Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -2.3e-25)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 7.8e-116)
		tmp = t * ((y - z) / (a - z));
	elseif (a <= 1.3e+75)
		tmp = (t - x) * (y / (a - z));
	else
		tmp = x + (t / (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -2.3e-25], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 7.8e-116], N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.3e+75], N[(N[(t - x), $MachinePrecision] * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 10: 63.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y}{\frac{a}{t - x}}\\ \mathbf{if}\;a \leq -1.66 \cdot 10^{-12}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.3 \cdot 10^{-115}:\\ \;\;\;\;t \cdot \frac{y - z}{a - z}\\ \mathbf{elif}\;a \leq 10^{+34}:\\ \;\;\;\;\left(t - x\right) \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ y (/ a (- t x))))))
   (if (<= a -1.66e-12)
     t_1
     (if (<= a 1.3e-115)
       (* t (/ (- y z) (- a z)))
       (if (<= a 1e+34) (* (- t x) (/ y (- a z))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y / (a / (t - x)));
	double tmp;
	if (a <= -1.66e-12) {
		tmp = t_1;
	} else if (a <= 1.3e-115) {
		tmp = t * ((y - z) / (a - z));
	} else if (a <= 1e+34) {
		tmp = (t - x) * (y / (a - z));
	} 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 + (y / (a / (t - x)))
    if (a <= (-1.66d-12)) then
        tmp = t_1
    else if (a <= 1.3d-115) then
        tmp = t * ((y - z) / (a - z))
    else if (a <= 1d+34) then
        tmp = (t - x) * (y / (a - z))
    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 + (y / (a / (t - x)));
	double tmp;
	if (a <= -1.66e-12) {
		tmp = t_1;
	} else if (a <= 1.3e-115) {
		tmp = t * ((y - z) / (a - z));
	} else if (a <= 1e+34) {
		tmp = (t - x) * (y / (a - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y / (a / (t - x)))
	tmp = 0
	if a <= -1.66e-12:
		tmp = t_1
	elif a <= 1.3e-115:
		tmp = t * ((y - z) / (a - z))
	elif a <= 1e+34:
		tmp = (t - x) * (y / (a - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y / Float64(a / Float64(t - x))))
	tmp = 0.0
	if (a <= -1.66e-12)
		tmp = t_1;
	elseif (a <= 1.3e-115)
		tmp = Float64(t * Float64(Float64(y - z) / Float64(a - z)));
	elseif (a <= 1e+34)
		tmp = Float64(Float64(t - x) * Float64(y / Float64(a - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y / (a / (t - x)));
	tmp = 0.0;
	if (a <= -1.66e-12)
		tmp = t_1;
	elseif (a <= 1.3e-115)
		tmp = t * ((y - z) / (a - z));
	elseif (a <= 1e+34)
		tmp = (t - x) * (y / (a - z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.66e-12], t$95$1, If[LessEqual[a, 1.3e-115], N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1e+34], N[(N[(t - x), $MachinePrecision] * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 11: 69.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y}{\frac{a}{t - x}}\\ \mathbf{if}\;a \leq -8.8 \cdot 10^{-10}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 2.6 \cdot 10^{-137}:\\ \;\;\;\;t + \frac{y}{z} \cdot \left(x - t\right)\\ \mathbf{elif}\;a \leq 7.5 \cdot 10^{+33}:\\ \;\;\;\;\left(t - x\right) \cdot \frac{y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ y (/ a (- t x))))))
   (if (<= a -8.8e-10)
     t_1
     (if (<= a 2.6e-137)
       (+ t (* (/ y z) (- x t)))
       (if (<= a 7.5e+33) (* (- t x) (/ y (- a z))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y / (a / (t - x)));
	double tmp;
	if (a <= -8.8e-10) {
		tmp = t_1;
	} else if (a <= 2.6e-137) {
		tmp = t + ((y / z) * (x - t));
	} else if (a <= 7.5e+33) {
		tmp = (t - x) * (y / (a - z));
	} 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 + (y / (a / (t - x)))
    if (a <= (-8.8d-10)) then
        tmp = t_1
    else if (a <= 2.6d-137) then
        tmp = t + ((y / z) * (x - t))
    else if (a <= 7.5d+33) then
        tmp = (t - x) * (y / (a - z))
    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 + (y / (a / (t - x)));
	double tmp;
	if (a <= -8.8e-10) {
		tmp = t_1;
	} else if (a <= 2.6e-137) {
		tmp = t + ((y / z) * (x - t));
	} else if (a <= 7.5e+33) {
		tmp = (t - x) * (y / (a - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y / (a / (t - x)))
	tmp = 0
	if a <= -8.8e-10:
		tmp = t_1
	elif a <= 2.6e-137:
		tmp = t + ((y / z) * (x - t))
	elif a <= 7.5e+33:
		tmp = (t - x) * (y / (a - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y / Float64(a / Float64(t - x))))
	tmp = 0.0
	if (a <= -8.8e-10)
		tmp = t_1;
	elseif (a <= 2.6e-137)
		tmp = Float64(t + Float64(Float64(y / z) * Float64(x - t)));
	elseif (a <= 7.5e+33)
		tmp = Float64(Float64(t - x) * Float64(y / Float64(a - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y / (a / (t - x)));
	tmp = 0.0;
	if (a <= -8.8e-10)
		tmp = t_1;
	elseif (a <= 2.6e-137)
		tmp = t + ((y / z) * (x - t));
	elseif (a <= 7.5e+33)
		tmp = (t - x) * (y / (a - z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -8.8e-10], t$95$1, If[LessEqual[a, 2.6e-137], N[(t + N[(N[(y / z), $MachinePrecision] * N[(x - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 7.5e+33], N[(N[(t - x), $MachinePrecision] * N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 12: 74.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.8 \cdot 10^{-8} \lor \neg \left(a \leq 1.3 \cdot 10^{-39}\right):\\ \;\;\;\;x + \frac{t - x}{\frac{a}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;t - \frac{y}{\frac{z}{t - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -3.8e-8) (not (<= a 1.3e-39)))
   (+ x (/ (- t x) (/ a (- y z))))
   (- t (/ y (/ z (- t x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -3.8e-8) || !(a <= 1.3e-39)) {
		tmp = x + ((t - x) / (a / (y - z)));
	} else {
		tmp = t - (y / (z / (t - 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 <= (-3.8d-8)) .or. (.not. (a <= 1.3d-39))) then
        tmp = x + ((t - x) / (a / (y - z)))
    else
        tmp = t - (y / (z / (t - 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 <= -3.8e-8) || !(a <= 1.3e-39)) {
		tmp = x + ((t - x) / (a / (y - z)));
	} else {
		tmp = t - (y / (z / (t - x)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -3.8e-8) or not (a <= 1.3e-39):
		tmp = x + ((t - x) / (a / (y - z)))
	else:
		tmp = t - (y / (z / (t - x)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -3.8e-8) || !(a <= 1.3e-39))
		tmp = Float64(x + Float64(Float64(t - x) / Float64(a / Float64(y - z))));
	else
		tmp = Float64(t - Float64(y / Float64(z / Float64(t - x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -3.8e-8) || ~((a <= 1.3e-39)))
		tmp = x + ((t - x) / (a / (y - z)));
	else
		tmp = t - (y / (z / (t - x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -3.8e-8], N[Not[LessEqual[a, 1.3e-39]], $MachinePrecision]], N[(x + N[(N[(t - x), $MachinePrecision] / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t - N[(y / N[(z / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 77.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -5.6 \cdot 10^{-11} \lor \neg \left(a \leq 4.1 \cdot 10^{-21}\right):\\ \;\;\;\;x + \frac{t - x}{\frac{a}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;t - \frac{t - x}{\frac{z}{y - a}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -5.6e-11) (not (<= a 4.1e-21)))
   (+ x (/ (- t x) (/ a (- y z))))
   (- t (/ (- t x) (/ z (- y a))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -5.6e-11) || !(a <= 4.1e-21)) {
		tmp = x + ((t - x) / (a / (y - z)));
	} else {
		tmp = t - ((t - 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 ((a <= (-5.6d-11)) .or. (.not. (a <= 4.1d-21))) then
        tmp = x + ((t - x) / (a / (y - z)))
    else
        tmp = t - ((t - 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 ((a <= -5.6e-11) || !(a <= 4.1e-21)) {
		tmp = x + ((t - x) / (a / (y - z)));
	} else {
		tmp = t - ((t - x) / (z / (y - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -5.6e-11) or not (a <= 4.1e-21):
		tmp = x + ((t - x) / (a / (y - z)))
	else:
		tmp = t - ((t - x) / (z / (y - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -5.6e-11) || !(a <= 4.1e-21))
		tmp = Float64(x + Float64(Float64(t - x) / Float64(a / Float64(y - z))));
	else
		tmp = Float64(t - Float64(Float64(t - x) / Float64(z / Float64(y - a))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -5.6e-11) || ~((a <= 4.1e-21)))
		tmp = x + ((t - x) / (a / (y - z)));
	else
		tmp = t - ((t - x) / (z / (y - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -5.6e-11], N[Not[LessEqual[a, 4.1e-21]], $MachinePrecision]], N[(x + N[(N[(t - x), $MachinePrecision] / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t - N[(N[(t - x), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 14: 58.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.35 \cdot 10^{-21}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;a \leq 1.5 \cdot 10^{+27}:\\ \;\;\;\;t \cdot \frac{y - z}{a - z}\\ \mathbf{elif}\;a \leq 1.05 \cdot 10^{+59}:\\ \;\;\;\;x \cdot \frac{-y}{a - z}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{t}{\frac{a}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.35e-21)
   (* x (- 1.0 (/ y a)))
   (if (<= a 1.5e+27)
     (* t (/ (- y z) (- a z)))
     (if (<= a 1.05e+59) (* x (/ (- y) (- a z))) (+ x (/ t (/ a y)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.35e-21) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 1.5e+27) {
		tmp = t * ((y - z) / (a - z));
	} else if (a <= 1.05e+59) {
		tmp = x * (-y / (a - z));
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-1.35d-21)) then
        tmp = x * (1.0d0 - (y / a))
    else if (a <= 1.5d+27) then
        tmp = t * ((y - z) / (a - z))
    else if (a <= 1.05d+59) then
        tmp = x * (-y / (a - z))
    else
        tmp = x + (t / (a / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.35e-21) {
		tmp = x * (1.0 - (y / a));
	} else if (a <= 1.5e+27) {
		tmp = t * ((y - z) / (a - z));
	} else if (a <= 1.05e+59) {
		tmp = x * (-y / (a - z));
	} else {
		tmp = x + (t / (a / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -1.35e-21:
		tmp = x * (1.0 - (y / a))
	elif a <= 1.5e+27:
		tmp = t * ((y - z) / (a - z))
	elif a <= 1.05e+59:
		tmp = x * (-y / (a - z))
	else:
		tmp = x + (t / (a / y))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.35e-21)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (a <= 1.5e+27)
		tmp = Float64(t * Float64(Float64(y - z) / Float64(a - z)));
	elseif (a <= 1.05e+59)
		tmp = Float64(x * Float64(Float64(-y) / Float64(a - z)));
	else
		tmp = Float64(x + Float64(t / Float64(a / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -1.35e-21)
		tmp = x * (1.0 - (y / a));
	elseif (a <= 1.5e+27)
		tmp = t * ((y - z) / (a - z));
	elseif (a <= 1.05e+59)
		tmp = x * (-y / (a - z));
	else
		tmp = x + (t / (a / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.35e-21], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.5e+27], N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.05e+59], N[(x * N[((-y) / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 15: 69.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -4.1 \cdot 10^{-8} \lor \neg \left(a \leq 3 \cdot 10^{-19}\right):\\ \;\;\;\;x + \frac{y}{\frac{a}{t - x}}\\ \mathbf{else}:\\ \;\;\;\;t - \frac{y}{\frac{z}{t - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -4.1e-8) (not (<= a 3e-19)))
   (+ x (/ y (/ a (- t x))))
   (- t (/ y (/ z (- t x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -4.1e-8) || !(a <= 3e-19)) {
		tmp = x + (y / (a / (t - x)));
	} else {
		tmp = t - (y / (z / (t - 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 <= (-4.1d-8)) .or. (.not. (a <= 3d-19))) then
        tmp = x + (y / (a / (t - x)))
    else
        tmp = t - (y / (z / (t - 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 <= -4.1e-8) || !(a <= 3e-19)) {
		tmp = x + (y / (a / (t - x)));
	} else {
		tmp = t - (y / (z / (t - x)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -4.1e-8) or not (a <= 3e-19):
		tmp = x + (y / (a / (t - x)))
	else:
		tmp = t - (y / (z / (t - x)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -4.1e-8) || !(a <= 3e-19))
		tmp = Float64(x + Float64(y / Float64(a / Float64(t - x))));
	else
		tmp = Float64(t - Float64(y / Float64(z / Float64(t - x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -4.1e-8) || ~((a <= 3e-19)))
		tmp = x + (y / (a / (t - x)));
	else
		tmp = t - (y / (z / (t - x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -4.1e-8], N[Not[LessEqual[a, 3e-19]], $MachinePrecision]], N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t - N[(y / N[(z / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 16: 37.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.1 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 3.1 \cdot 10^{-115}:\\ \;\;\;\;t\\ \mathbf{elif}\;a \leq 1.45 \cdot 10^{+15}:\\ \;\;\;\;t \cdot \frac{y}{a}\\ \mathbf{elif}\;a \leq 4.2 \cdot 10^{+69}:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -2.1e-12)
   x
   (if (<= a 3.1e-115)
     t
     (if (<= a 1.45e+15) (* t (/ y a)) (if (<= a 4.2e+69) t x)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -2.1e-12) {
		tmp = x;
	} else if (a <= 3.1e-115) {
		tmp = t;
	} else if (a <= 1.45e+15) {
		tmp = t * (y / a);
	} else if (a <= 4.2e+69) {
		tmp = t;
	} 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 <= (-2.1d-12)) then
        tmp = x
    else if (a <= 3.1d-115) then
        tmp = t
    else if (a <= 1.45d+15) then
        tmp = t * (y / a)
    else if (a <= 4.2d+69) then
        tmp = t
    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 <= -2.1e-12) {
		tmp = x;
	} else if (a <= 3.1e-115) {
		tmp = t;
	} else if (a <= 1.45e+15) {
		tmp = t * (y / a);
	} else if (a <= 4.2e+69) {
		tmp = t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -2.1e-12:
		tmp = x
	elif a <= 3.1e-115:
		tmp = t
	elif a <= 1.45e+15:
		tmp = t * (y / a)
	elif a <= 4.2e+69:
		tmp = t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -2.1e-12)
		tmp = x;
	elseif (a <= 3.1e-115)
		tmp = t;
	elseif (a <= 1.45e+15)
		tmp = Float64(t * Float64(y / a));
	elseif (a <= 4.2e+69)
		tmp = t;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -2.1e-12)
		tmp = x;
	elseif (a <= 3.1e-115)
		tmp = t;
	elseif (a <= 1.45e+15)
		tmp = t * (y / a);
	elseif (a <= 4.2e+69)
		tmp = t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -2.1e-12], x, If[LessEqual[a, 3.1e-115], t, If[LessEqual[a, 1.45e+15], N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.2e+69], t, x]]]]

Derivation

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

Alternative 17: 37.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.7 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 1.3 \cdot 10^{-115}:\\ \;\;\;\;t\\ \mathbf{elif}\;a \leq 1.95 \cdot 10^{+59}:\\ \;\;\;\;x \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.7e-12)
   x
   (if (<= a 1.3e-115) t (if (<= a 1.95e+59) (* x (/ y z)) x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.7e-12) {
		tmp = x;
	} else if (a <= 1.3e-115) {
		tmp = t;
	} else if (a <= 1.95e+59) {
		tmp = x * (y / z);
	} 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 <= (-3.7d-12)) then
        tmp = x
    else if (a <= 1.3d-115) then
        tmp = t
    else if (a <= 1.95d+59) then
        tmp = x * (y / z)
    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 <= -3.7e-12) {
		tmp = x;
	} else if (a <= 1.3e-115) {
		tmp = t;
	} else if (a <= 1.95e+59) {
		tmp = x * (y / z);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.7e-12:
		tmp = x
	elif a <= 1.3e-115:
		tmp = t
	elif a <= 1.95e+59:
		tmp = x * (y / z)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 1.3e-115)
		tmp = t;
	elseif (a <= 1.95e+59)
		tmp = Float64(x * Float64(y / z));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 1.3e-115)
		tmp = t;
	elseif (a <= 1.95e+59)
		tmp = x * (y / z);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.7e-12], x, If[LessEqual[a, 1.3e-115], t, If[LessEqual[a, 1.95e+59], N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision], x]]]

Derivation

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

Alternative 18: 37.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.7 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 3 \cdot 10^{-115}:\\ \;\;\;\;t\\ \mathbf{elif}\;a \leq 9.6 \cdot 10^{+58}:\\ \;\;\;\;\frac{x}{\frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.7e-12)
   x
   (if (<= a 3e-115) t (if (<= a 9.6e+58) (/ x (/ z y)) x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.7e-12) {
		tmp = x;
	} else if (a <= 3e-115) {
		tmp = t;
	} else if (a <= 9.6e+58) {
		tmp = x / (z / 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 <= (-3.7d-12)) then
        tmp = x
    else if (a <= 3d-115) then
        tmp = t
    else if (a <= 9.6d+58) then
        tmp = x / (z / 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 <= -3.7e-12) {
		tmp = x;
	} else if (a <= 3e-115) {
		tmp = t;
	} else if (a <= 9.6e+58) {
		tmp = x / (z / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.7e-12:
		tmp = x
	elif a <= 3e-115:
		tmp = t
	elif a <= 9.6e+58:
		tmp = x / (z / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 3e-115)
		tmp = t;
	elseif (a <= 9.6e+58)
		tmp = Float64(x / Float64(z / y));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 3e-115)
		tmp = t;
	elseif (a <= 9.6e+58)
		tmp = x / (z / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.7e-12], x, If[LessEqual[a, 3e-115], t, If[LessEqual[a, 9.6e+58], N[(x / N[(z / y), $MachinePrecision]), $MachinePrecision], x]]]

Derivation

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

Alternative 19: 52.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -4.5 \cdot 10^{-8} \lor \neg \left(a \leq 2.6 \cdot 10^{-38}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(1 - \frac{y}{z}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -4.5e-8) (not (<= a 2.6e-38)))
   (* x (- 1.0 (/ y a)))
   (* t (- 1.0 (/ y z)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -4.5e-8) || !(a <= 2.6e-38)) {
		tmp = x * (1.0 - (y / a));
	} else {
		tmp = t * (1.0 - (y / z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((a <= (-4.5d-8)) .or. (.not. (a <= 2.6d-38))) then
        tmp = x * (1.0d0 - (y / a))
    else
        tmp = t * (1.0d0 - (y / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -4.5e-8) || !(a <= 2.6e-38)) {
		tmp = x * (1.0 - (y / a));
	} else {
		tmp = t * (1.0 - (y / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -4.5e-8) or not (a <= 2.6e-38):
		tmp = x * (1.0 - (y / a))
	else:
		tmp = t * (1.0 - (y / z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -4.5e-8) || !(a <= 2.6e-38))
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	else
		tmp = Float64(t * Float64(1.0 - Float64(y / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -4.5e-8) || ~((a <= 2.6e-38)))
		tmp = x * (1.0 - (y / a));
	else
		tmp = t * (1.0 - (y / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -4.5e-8], N[Not[LessEqual[a, 2.6e-38]], $MachinePrecision]], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 20: 49.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.55 \cdot 10^{+100}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 7.8 \cdot 10^{+74}:\\ \;\;\;\;t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.55e+100) x (if (<= a 7.8e+74) (* t (- 1.0 (/ y z))) x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.55e+100) {
		tmp = x;
	} else if (a <= 7.8e+74) {
		tmp = t * (1.0 - (y / z));
	} 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 <= (-1.55d+100)) then
        tmp = x
    else if (a <= 7.8d+74) then
        tmp = t * (1.0d0 - (y / z))
    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 <= -1.55e+100) {
		tmp = x;
	} else if (a <= 7.8e+74) {
		tmp = t * (1.0 - (y / z));
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -1.55e+100:
		tmp = x
	elif a <= 7.8e+74:
		tmp = t * (1.0 - (y / z))
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.55e+100)
		tmp = x;
	elseif (a <= 7.8e+74)
		tmp = Float64(t * Float64(1.0 - Float64(y / z)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -1.55e+100)
		tmp = x;
	elseif (a <= 7.8e+74)
		tmp = t * (1.0 - (y / z));
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.55e+100], x, If[LessEqual[a, 7.8e+74], N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], x]]

Derivation

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

Alternative 21: 39.0% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.7 \cdot 10^{-12}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 2.3 \cdot 10^{+69}:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.7e-12) x (if (<= a 2.3e+69) t x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.7e-12) {
		tmp = x;
	} else if (a <= 2.3e+69) {
		tmp = t;
	} 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 <= (-3.7d-12)) then
        tmp = x
    else if (a <= 2.3d+69) then
        tmp = t
    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 <= -3.7e-12) {
		tmp = x;
	} else if (a <= 2.3e+69) {
		tmp = t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.7e-12:
		tmp = x
	elif a <= 2.3e+69:
		tmp = t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 2.3e+69)
		tmp = t;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.7e-12)
		tmp = x;
	elseif (a <= 2.3e+69)
		tmp = t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.7e-12], x, If[LessEqual[a, 2.3e+69], t, x]]

Derivation

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

Alternative 22: 25.4% accurate, 13.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 84.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t - \frac{y}{z} \cdot \left(t - x\right)\\ \mathbf{if}\;z < -1.2536131056095036 \cdot 10^{+188}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z < 4.446702369113811 \cdot 10^{+64}:\\ \;\;\;\;x + \frac{y - z}{\frac{a - z}{t - x}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- t (* (/ y z) (- t x)))))
   (if (< z -1.2536131056095036e+188)
     t_1
     (if (< z 4.446702369113811e+64)
       (+ x (/ (- y z) (/ (- a z) (- t x))))
       t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t - ((y / z) * (t - x));
	double tmp;
	if (z < -1.2536131056095036e+188) {
		tmp = t_1;
	} else if (z < 4.446702369113811e+64) {
		tmp = x + ((y - z) / ((a - z) / (t - 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 = t - ((y / z) * (t - x))
    if (z < (-1.2536131056095036d+188)) then
        tmp = t_1
    else if (z < 4.446702369113811d+64) then
        tmp = x + ((y - z) / ((a - z) / (t - 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 = t - ((y / z) * (t - x));
	double tmp;
	if (z < -1.2536131056095036e+188) {
		tmp = t_1;
	} else if (z < 4.446702369113811e+64) {
		tmp = x + ((y - z) / ((a - z) / (t - x)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t - ((y / z) * (t - x))
	tmp = 0
	if z < -1.2536131056095036e+188:
		tmp = t_1
	elif z < 4.446702369113811e+64:
		tmp = x + ((y - z) / ((a - z) / (t - x)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t - Float64(Float64(y / z) * Float64(t - x)))
	tmp = 0.0
	if (z < -1.2536131056095036e+188)
		tmp = t_1;
	elseif (z < 4.446702369113811e+64)
		tmp = Float64(x + Float64(Float64(y - z) / Float64(Float64(a - z) / Float64(t - x))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t - ((y / z) * (t - x));
	tmp = 0.0;
	if (z < -1.2536131056095036e+188)
		tmp = t_1;
	elseif (z < 4.446702369113811e+64)
		tmp = x + ((y - z) / ((a - z) / (t - x)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t - N[(N[(y / z), $MachinePrecision] * N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[z, -1.2536131056095036e+188], t$95$1, If[Less[z, 4.446702369113811e+64], N[(x + N[(N[(y - z), $MachinePrecision] / N[(N[(a - z), $MachinePrecision] / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Chart.Axis.Types:invLinMap from Chart-1.5.3"
  :precision binary64

  :herbie-target
  (if (< z -1.2536131056095036e+188) (- t (* (/ y z) (- t x))) (if (< z 4.446702369113811e+64) (+ x (/ (- y z) (/ (- a z) (- t x)))) (- t (* (/ y z) (- t x)))))

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