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

Percentage Accurate: 67.7% → 87.6%

Time: 26.3s

Alternatives: 20

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: 67.7% 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: 87.6% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y - z}{a - z}\\ \mathbf{if}\;a \leq -7.2 \cdot 10^{-152}:\\ \;\;\;\;\mathsf{fma}\left(t_1, t - x, x\right)\\ \mathbf{elif}\;a \leq 2.85 \cdot 10^{-151}:\\ \;\;\;\;t - \frac{t - x}{\frac{z}{y - a}}\\ \mathbf{else}:\\ \;\;\;\;x + t_1 \cdot \left(t - x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- y z) (- a z))))
   (if (<= a -7.2e-152)
     (fma t_1 (- t x) x)
     (if (<= a 2.85e-151)
       (- t (/ (- t x) (/ z (- y a))))
       (+ x (* t_1 (- t x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (y - z) / (a - z);
	double tmp;
	if (a <= -7.2e-152) {
		tmp = fma(t_1, (t - x), x);
	} else if (a <= 2.85e-151) {
		tmp = t - ((t - x) / (z / (y - a)));
	} else {
		tmp = x + (t_1 * (t - x));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(y - z) / Float64(a - z))
	tmp = 0.0
	if (a <= -7.2e-152)
		tmp = fma(t_1, Float64(t - x), x);
	elseif (a <= 2.85e-151)
		tmp = Float64(t - Float64(Float64(t - x) / Float64(z / Float64(y - a))));
	else
		tmp = Float64(x + Float64(t_1 * Float64(t - x)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -7.2e-152], N[(t$95$1 * N[(t - x), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[a, 2.85e-151], N[(t - N[(N[(t - x), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(t$95$1 * N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 2: 75.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t - \frac{t - x}{\frac{z}{y - a}}\\ t_2 := x + \frac{y - z}{\frac{a - z}{t}}\\ t_3 := x + \frac{y}{\frac{a}{t - x}}\\ \mathbf{if}\;a \leq -2.6 \cdot 10^{+52}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq -3.6 \cdot 10^{-6}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -1.76 \cdot 10^{-56}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;a \leq 3.7 \cdot 10^{-23}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 6.2 \cdot 10^{+40}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 1.12 \cdot 10^{+59}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 7.2 \cdot 10^{+138}:\\ \;\;\;\;t_3\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- t (/ (- t x) (/ z (- y a)))))
        (t_2 (+ x (/ (- y z) (/ (- a z) t))))
        (t_3 (+ x (/ y (/ a (- t x))))))
   (if (<= a -2.6e+52)
     t_2
     (if (<= a -3.6e-6)
       t_1
       (if (<= a -1.76e-56)
         t_3
         (if (<= a 3.7e-23)
           t_1
           (if (<= a 6.2e+40)
             t_2
             (if (<= a 1.12e+59) t_1 (if (<= a 7.2e+138) t_3 t_2)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t - ((t - x) / (z / (y - a)));
	double t_2 = x + ((y - z) / ((a - z) / t));
	double t_3 = x + (y / (a / (t - x)));
	double tmp;
	if (a <= -2.6e+52) {
		tmp = t_2;
	} else if (a <= -3.6e-6) {
		tmp = t_1;
	} else if (a <= -1.76e-56) {
		tmp = t_3;
	} else if (a <= 3.7e-23) {
		tmp = t_1;
	} else if (a <= 6.2e+40) {
		tmp = t_2;
	} else if (a <= 1.12e+59) {
		tmp = t_1;
	} else if (a <= 7.2e+138) {
		tmp = t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = t - ((t - x) / (z / (y - a)))
    t_2 = x + ((y - z) / ((a - z) / t))
    t_3 = x + (y / (a / (t - x)))
    if (a <= (-2.6d+52)) then
        tmp = t_2
    else if (a <= (-3.6d-6)) then
        tmp = t_1
    else if (a <= (-1.76d-56)) then
        tmp = t_3
    else if (a <= 3.7d-23) then
        tmp = t_1
    else if (a <= 6.2d+40) then
        tmp = t_2
    else if (a <= 1.12d+59) then
        tmp = t_1
    else if (a <= 7.2d+138) then
        tmp = t_3
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t - ((t - x) / (z / (y - a)));
	double t_2 = x + ((y - z) / ((a - z) / t));
	double t_3 = x + (y / (a / (t - x)));
	double tmp;
	if (a <= -2.6e+52) {
		tmp = t_2;
	} else if (a <= -3.6e-6) {
		tmp = t_1;
	} else if (a <= -1.76e-56) {
		tmp = t_3;
	} else if (a <= 3.7e-23) {
		tmp = t_1;
	} else if (a <= 6.2e+40) {
		tmp = t_2;
	} else if (a <= 1.12e+59) {
		tmp = t_1;
	} else if (a <= 7.2e+138) {
		tmp = t_3;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t - ((t - x) / (z / (y - a)))
	t_2 = x + ((y - z) / ((a - z) / t))
	t_3 = x + (y / (a / (t - x)))
	tmp = 0
	if a <= -2.6e+52:
		tmp = t_2
	elif a <= -3.6e-6:
		tmp = t_1
	elif a <= -1.76e-56:
		tmp = t_3
	elif a <= 3.7e-23:
		tmp = t_1
	elif a <= 6.2e+40:
		tmp = t_2
	elif a <= 1.12e+59:
		tmp = t_1
	elif a <= 7.2e+138:
		tmp = t_3
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t - Float64(Float64(t - x) / Float64(z / Float64(y - a))))
	t_2 = Float64(x + Float64(Float64(y - z) / Float64(Float64(a - z) / t)))
	t_3 = Float64(x + Float64(y / Float64(a / Float64(t - x))))
	tmp = 0.0
	if (a <= -2.6e+52)
		tmp = t_2;
	elseif (a <= -3.6e-6)
		tmp = t_1;
	elseif (a <= -1.76e-56)
		tmp = t_3;
	elseif (a <= 3.7e-23)
		tmp = t_1;
	elseif (a <= 6.2e+40)
		tmp = t_2;
	elseif (a <= 1.12e+59)
		tmp = t_1;
	elseif (a <= 7.2e+138)
		tmp = t_3;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t - ((t - x) / (z / (y - a)));
	t_2 = x + ((y - z) / ((a - z) / t));
	t_3 = x + (y / (a / (t - x)));
	tmp = 0.0;
	if (a <= -2.6e+52)
		tmp = t_2;
	elseif (a <= -3.6e-6)
		tmp = t_1;
	elseif (a <= -1.76e-56)
		tmp = t_3;
	elseif (a <= 3.7e-23)
		tmp = t_1;
	elseif (a <= 6.2e+40)
		tmp = t_2;
	elseif (a <= 1.12e+59)
		tmp = t_1;
	elseif (a <= 7.2e+138)
		tmp = t_3;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t - N[(N[(t - x), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(y - z), $MachinePrecision] / N[(N[(a - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.6e+52], t$95$2, If[LessEqual[a, -3.6e-6], t$95$1, If[LessEqual[a, -1.76e-56], t$95$3, If[LessEqual[a, 3.7e-23], t$95$1, If[LessEqual[a, 6.2e+40], t$95$2, If[LessEqual[a, 1.12e+59], t$95$1, If[LessEqual[a, 7.2e+138], t$95$3, t$95$2]]]]]]]]]]

Derivation

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

Alternative 3: 74.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y}{\frac{a}{t - x}}\\ t_2 := x + \frac{y - z}{\frac{a - z}{t}}\\ t_3 := t + \left(y - a\right) \cdot \frac{x - t}{z}\\ \mathbf{if}\;z \leq -7 \cdot 10^{+113}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;z \leq -9 \cdot 10^{-27}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -6.5 \cdot 10^{-36}:\\ \;\;\;\;t - x \cdot \frac{a - y}{z}\\ \mathbf{elif}\;z \leq 3.15 \cdot 10^{-145}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 5.8 \cdot 10^{-5}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 1.1 \cdot 10^{+18}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ y (/ a (- t x)))))
        (t_2 (+ x (/ (- y z) (/ (- a z) t))))
        (t_3 (+ t (* (- y a) (/ (- x t) z)))))
   (if (<= z -7e+113)
     t_3
     (if (<= z -9e-27)
       t_2
       (if (<= z -6.5e-36)
         (- t (* x (/ (- a y) z)))
         (if (<= z 3.15e-145)
           t_1
           (if (<= z 5.8e-5) t_2 (if (<= z 1.1e+18) t_1 t_3))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y / (a / (t - x)));
	double t_2 = x + ((y - z) / ((a - z) / t));
	double t_3 = t + ((y - a) * ((x - t) / z));
	double tmp;
	if (z <= -7e+113) {
		tmp = t_3;
	} else if (z <= -9e-27) {
		tmp = t_2;
	} else if (z <= -6.5e-36) {
		tmp = t - (x * ((a - y) / z));
	} else if (z <= 3.15e-145) {
		tmp = t_1;
	} else if (z <= 5.8e-5) {
		tmp = t_2;
	} else if (z <= 1.1e+18) {
		tmp = t_1;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = x + (y / (a / (t - x)))
    t_2 = x + ((y - z) / ((a - z) / t))
    t_3 = t + ((y - a) * ((x - t) / z))
    if (z <= (-7d+113)) then
        tmp = t_3
    else if (z <= (-9d-27)) then
        tmp = t_2
    else if (z <= (-6.5d-36)) then
        tmp = t - (x * ((a - y) / z))
    else if (z <= 3.15d-145) then
        tmp = t_1
    else if (z <= 5.8d-5) then
        tmp = t_2
    else if (z <= 1.1d+18) then
        tmp = t_1
    else
        tmp = t_3
    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 t_2 = x + ((y - z) / ((a - z) / t));
	double t_3 = t + ((y - a) * ((x - t) / z));
	double tmp;
	if (z <= -7e+113) {
		tmp = t_3;
	} else if (z <= -9e-27) {
		tmp = t_2;
	} else if (z <= -6.5e-36) {
		tmp = t - (x * ((a - y) / z));
	} else if (z <= 3.15e-145) {
		tmp = t_1;
	} else if (z <= 5.8e-5) {
		tmp = t_2;
	} else if (z <= 1.1e+18) {
		tmp = t_1;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y / (a / (t - x)))
	t_2 = x + ((y - z) / ((a - z) / t))
	t_3 = t + ((y - a) * ((x - t) / z))
	tmp = 0
	if z <= -7e+113:
		tmp = t_3
	elif z <= -9e-27:
		tmp = t_2
	elif z <= -6.5e-36:
		tmp = t - (x * ((a - y) / z))
	elif z <= 3.15e-145:
		tmp = t_1
	elif z <= 5.8e-5:
		tmp = t_2
	elif z <= 1.1e+18:
		tmp = t_1
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y / Float64(a / Float64(t - x))))
	t_2 = Float64(x + Float64(Float64(y - z) / Float64(Float64(a - z) / t)))
	t_3 = Float64(t + Float64(Float64(y - a) * Float64(Float64(x - t) / z)))
	tmp = 0.0
	if (z <= -7e+113)
		tmp = t_3;
	elseif (z <= -9e-27)
		tmp = t_2;
	elseif (z <= -6.5e-36)
		tmp = Float64(t - Float64(x * Float64(Float64(a - y) / z)));
	elseif (z <= 3.15e-145)
		tmp = t_1;
	elseif (z <= 5.8e-5)
		tmp = t_2;
	elseif (z <= 1.1e+18)
		tmp = t_1;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y / (a / (t - x)));
	t_2 = x + ((y - z) / ((a - z) / t));
	t_3 = t + ((y - a) * ((x - t) / z));
	tmp = 0.0;
	if (z <= -7e+113)
		tmp = t_3;
	elseif (z <= -9e-27)
		tmp = t_2;
	elseif (z <= -6.5e-36)
		tmp = t - (x * ((a - y) / z));
	elseif (z <= 3.15e-145)
		tmp = t_1;
	elseif (z <= 5.8e-5)
		tmp = t_2;
	elseif (z <= 1.1e+18)
		tmp = t_1;
	else
		tmp = t_3;
	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]}, Block[{t$95$2 = N[(x + N[(N[(y - z), $MachinePrecision] / N[(N[(a - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t + N[(N[(y - a), $MachinePrecision] * N[(N[(x - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7e+113], t$95$3, If[LessEqual[z, -9e-27], t$95$2, If[LessEqual[z, -6.5e-36], N[(t - N[(x * N[(N[(a - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.15e-145], t$95$1, If[LessEqual[z, 5.8e-5], t$95$2, If[LessEqual[z, 1.1e+18], t$95$1, t$95$3]]]]]]]]]

Derivation

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

Alternative 4: 57.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y - z}{a - z} \cdot t\\ t_2 := x + \frac{y}{\frac{a}{t}}\\ \mathbf{if}\;z \leq -1.45 \cdot 10^{+20}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -7 \cdot 10^{-236}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;z \leq 3 \cdot 10^{-291}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 2.25 \cdot 10^{-107}:\\ \;\;\;\;x - \frac{x}{\frac{a}{y}}\\ \mathbf{elif}\;z \leq 9.8 \cdot 10^{+17}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 1.32 \cdot 10^{+28}:\\ \;\;\;\;y \cdot \frac{t - x}{a - z}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* (/ (- y z) (- a z)) t)) (t_2 (+ x (/ y (/ a t)))))
   (if (<= z -1.45e+20)
     t_1
     (if (<= z -7e-236)
       (* x (- 1.0 (/ y a)))
       (if (<= z 3e-291)
         t_2
         (if (<= z 2.25e-107)
           (- x (/ x (/ a y)))
           (if (<= z 9.8e+17)
             t_2
             (if (<= z 1.32e+28) (* y (/ (- t x) (- a z))) t_1))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = ((y - z) / (a - z)) * t;
	double t_2 = x + (y / (a / t));
	double tmp;
	if (z <= -1.45e+20) {
		tmp = t_1;
	} else if (z <= -7e-236) {
		tmp = x * (1.0 - (y / a));
	} else if (z <= 3e-291) {
		tmp = t_2;
	} else if (z <= 2.25e-107) {
		tmp = x - (x / (a / y));
	} else if (z <= 9.8e+17) {
		tmp = t_2;
	} else if (z <= 1.32e+28) {
		tmp = y * ((t - x) / (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) :: t_2
    real(8) :: tmp
    t_1 = ((y - z) / (a - z)) * t
    t_2 = x + (y / (a / t))
    if (z <= (-1.45d+20)) then
        tmp = t_1
    else if (z <= (-7d-236)) then
        tmp = x * (1.0d0 - (y / a))
    else if (z <= 3d-291) then
        tmp = t_2
    else if (z <= 2.25d-107) then
        tmp = x - (x / (a / y))
    else if (z <= 9.8d+17) then
        tmp = t_2
    else if (z <= 1.32d+28) then
        tmp = y * ((t - x) / (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 = ((y - z) / (a - z)) * t;
	double t_2 = x + (y / (a / t));
	double tmp;
	if (z <= -1.45e+20) {
		tmp = t_1;
	} else if (z <= -7e-236) {
		tmp = x * (1.0 - (y / a));
	} else if (z <= 3e-291) {
		tmp = t_2;
	} else if (z <= 2.25e-107) {
		tmp = x - (x / (a / y));
	} else if (z <= 9.8e+17) {
		tmp = t_2;
	} else if (z <= 1.32e+28) {
		tmp = y * ((t - x) / (a - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = ((y - z) / (a - z)) * t
	t_2 = x + (y / (a / t))
	tmp = 0
	if z <= -1.45e+20:
		tmp = t_1
	elif z <= -7e-236:
		tmp = x * (1.0 - (y / a))
	elif z <= 3e-291:
		tmp = t_2
	elif z <= 2.25e-107:
		tmp = x - (x / (a / y))
	elif z <= 9.8e+17:
		tmp = t_2
	elif z <= 1.32e+28:
		tmp = y * ((t - x) / (a - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(Float64(y - z) / Float64(a - z)) * t)
	t_2 = Float64(x + Float64(y / Float64(a / t)))
	tmp = 0.0
	if (z <= -1.45e+20)
		tmp = t_1;
	elseif (z <= -7e-236)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (z <= 3e-291)
		tmp = t_2;
	elseif (z <= 2.25e-107)
		tmp = Float64(x - Float64(x / Float64(a / y)));
	elseif (z <= 9.8e+17)
		tmp = t_2;
	elseif (z <= 1.32e+28)
		tmp = Float64(y * Float64(Float64(t - x) / Float64(a - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = ((y - z) / (a - z)) * t;
	t_2 = x + (y / (a / t));
	tmp = 0.0;
	if (z <= -1.45e+20)
		tmp = t_1;
	elseif (z <= -7e-236)
		tmp = x * (1.0 - (y / a));
	elseif (z <= 3e-291)
		tmp = t_2;
	elseif (z <= 2.25e-107)
		tmp = x - (x / (a / y));
	elseif (z <= 9.8e+17)
		tmp = t_2;
	elseif (z <= 1.32e+28)
		tmp = y * ((t - x) / (a - z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision] * t), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.45e+20], t$95$1, If[LessEqual[z, -7e-236], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3e-291], t$95$2, If[LessEqual[z, 2.25e-107], N[(x - N[(x / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 9.8e+17], t$95$2, If[LessEqual[z, 1.32e+28], N[(y * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]]]

Derivation

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

Alternative 5: 48.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{t}{\frac{a}{y}}\\ t_2 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;z \leq -2.1 \cdot 10^{+20}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -1.45 \cdot 10^{-238}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{-291}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{-106}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 5.1 \cdot 10^{+17}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.85 \cdot 10^{+43}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{+79}:\\ \;\;\;\;\left(-t\right) \cdot \frac{z}{a}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ t (/ a y)))) (t_2 (* x (- 1.0 (/ y a)))))
   (if (<= z -2.1e+20)
     t
     (if (<= z -1.45e-238)
       t_2
       (if (<= z 1.05e-291)
         t_1
         (if (<= z 2.7e-106)
           t_2
           (if (<= z 5.1e+17)
             t_1
             (if (<= z 1.85e+43)
               t
               (if (<= z 1.05e+79) (* (- t) (/ z a)) t)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (t / (a / y));
	double t_2 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -2.1e+20) {
		tmp = t;
	} else if (z <= -1.45e-238) {
		tmp = t_2;
	} else if (z <= 1.05e-291) {
		tmp = t_1;
	} else if (z <= 2.7e-106) {
		tmp = t_2;
	} else if (z <= 5.1e+17) {
		tmp = t_1;
	} else if (z <= 1.85e+43) {
		tmp = t;
	} else if (z <= 1.05e+79) {
		tmp = -t * (z / a);
	} else {
		tmp = 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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = x + (t / (a / y))
    t_2 = x * (1.0d0 - (y / a))
    if (z <= (-2.1d+20)) then
        tmp = t
    else if (z <= (-1.45d-238)) then
        tmp = t_2
    else if (z <= 1.05d-291) then
        tmp = t_1
    else if (z <= 2.7d-106) then
        tmp = t_2
    else if (z <= 5.1d+17) then
        tmp = t_1
    else if (z <= 1.85d+43) then
        tmp = t
    else if (z <= 1.05d+79) then
        tmp = -t * (z / a)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (t / (a / y));
	double t_2 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -2.1e+20) {
		tmp = t;
	} else if (z <= -1.45e-238) {
		tmp = t_2;
	} else if (z <= 1.05e-291) {
		tmp = t_1;
	} else if (z <= 2.7e-106) {
		tmp = t_2;
	} else if (z <= 5.1e+17) {
		tmp = t_1;
	} else if (z <= 1.85e+43) {
		tmp = t;
	} else if (z <= 1.05e+79) {
		tmp = -t * (z / a);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (t / (a / y))
	t_2 = x * (1.0 - (y / a))
	tmp = 0
	if z <= -2.1e+20:
		tmp = t
	elif z <= -1.45e-238:
		tmp = t_2
	elif z <= 1.05e-291:
		tmp = t_1
	elif z <= 2.7e-106:
		tmp = t_2
	elif z <= 5.1e+17:
		tmp = t_1
	elif z <= 1.85e+43:
		tmp = t
	elif z <= 1.05e+79:
		tmp = -t * (z / a)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(t / Float64(a / y)))
	t_2 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (z <= -2.1e+20)
		tmp = t;
	elseif (z <= -1.45e-238)
		tmp = t_2;
	elseif (z <= 1.05e-291)
		tmp = t_1;
	elseif (z <= 2.7e-106)
		tmp = t_2;
	elseif (z <= 5.1e+17)
		tmp = t_1;
	elseif (z <= 1.85e+43)
		tmp = t;
	elseif (z <= 1.05e+79)
		tmp = Float64(Float64(-t) * Float64(z / a));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (t / (a / y));
	t_2 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (z <= -2.1e+20)
		tmp = t;
	elseif (z <= -1.45e-238)
		tmp = t_2;
	elseif (z <= 1.05e-291)
		tmp = t_1;
	elseif (z <= 2.7e-106)
		tmp = t_2;
	elseif (z <= 5.1e+17)
		tmp = t_1;
	elseif (z <= 1.85e+43)
		tmp = t;
	elseif (z <= 1.05e+79)
		tmp = -t * (z / a);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(t / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.1e+20], t, If[LessEqual[z, -1.45e-238], t$95$2, If[LessEqual[z, 1.05e-291], t$95$1, If[LessEqual[z, 2.7e-106], t$95$2, If[LessEqual[z, 5.1e+17], t$95$1, If[LessEqual[z, 1.85e+43], t, If[LessEqual[z, 1.05e+79], N[((-t) * N[(z / a), $MachinePrecision]), $MachinePrecision], t]]]]]]]]]

Derivation

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

Alternative 6: 48.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y}{\frac{a}{t}}\\ t_2 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;z \leq -3.35 \cdot 10^{+19}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -8 \cdot 10^{-242}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 2.1 \cdot 10^{-291}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 2.3 \cdot 10^{-106}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 6.2 \cdot 10^{+16}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.4 \cdot 10^{+45}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{+79}:\\ \;\;\;\;\left(-t\right) \cdot \frac{z}{a}\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ y (/ a t)))) (t_2 (* x (- 1.0 (/ y a)))))
   (if (<= z -3.35e+19)
     t
     (if (<= z -8e-242)
       t_2
       (if (<= z 2.1e-291)
         t_1
         (if (<= z 2.3e-106)
           t_2
           (if (<= z 6.2e+16)
             t_1
             (if (<= z 1.4e+45)
               t
               (if (<= z 1.05e+79) (* (- t) (/ z a)) t)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y / (a / t));
	double t_2 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -3.35e+19) {
		tmp = t;
	} else if (z <= -8e-242) {
		tmp = t_2;
	} else if (z <= 2.1e-291) {
		tmp = t_1;
	} else if (z <= 2.3e-106) {
		tmp = t_2;
	} else if (z <= 6.2e+16) {
		tmp = t_1;
	} else if (z <= 1.4e+45) {
		tmp = t;
	} else if (z <= 1.05e+79) {
		tmp = -t * (z / a);
	} else {
		tmp = 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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = x + (y / (a / t))
    t_2 = x * (1.0d0 - (y / a))
    if (z <= (-3.35d+19)) then
        tmp = t
    else if (z <= (-8d-242)) then
        tmp = t_2
    else if (z <= 2.1d-291) then
        tmp = t_1
    else if (z <= 2.3d-106) then
        tmp = t_2
    else if (z <= 6.2d+16) then
        tmp = t_1
    else if (z <= 1.4d+45) then
        tmp = t
    else if (z <= 1.05d+79) then
        tmp = -t * (z / a)
    else
        tmp = t
    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));
	double t_2 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -3.35e+19) {
		tmp = t;
	} else if (z <= -8e-242) {
		tmp = t_2;
	} else if (z <= 2.1e-291) {
		tmp = t_1;
	} else if (z <= 2.3e-106) {
		tmp = t_2;
	} else if (z <= 6.2e+16) {
		tmp = t_1;
	} else if (z <= 1.4e+45) {
		tmp = t;
	} else if (z <= 1.05e+79) {
		tmp = -t * (z / a);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y / (a / t))
	t_2 = x * (1.0 - (y / a))
	tmp = 0
	if z <= -3.35e+19:
		tmp = t
	elif z <= -8e-242:
		tmp = t_2
	elif z <= 2.1e-291:
		tmp = t_1
	elif z <= 2.3e-106:
		tmp = t_2
	elif z <= 6.2e+16:
		tmp = t_1
	elif z <= 1.4e+45:
		tmp = t
	elif z <= 1.05e+79:
		tmp = -t * (z / a)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y / Float64(a / t)))
	t_2 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (z <= -3.35e+19)
		tmp = t;
	elseif (z <= -8e-242)
		tmp = t_2;
	elseif (z <= 2.1e-291)
		tmp = t_1;
	elseif (z <= 2.3e-106)
		tmp = t_2;
	elseif (z <= 6.2e+16)
		tmp = t_1;
	elseif (z <= 1.4e+45)
		tmp = t;
	elseif (z <= 1.05e+79)
		tmp = Float64(Float64(-t) * Float64(z / a));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y / (a / t));
	t_2 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (z <= -3.35e+19)
		tmp = t;
	elseif (z <= -8e-242)
		tmp = t_2;
	elseif (z <= 2.1e-291)
		tmp = t_1;
	elseif (z <= 2.3e-106)
		tmp = t_2;
	elseif (z <= 6.2e+16)
		tmp = t_1;
	elseif (z <= 1.4e+45)
		tmp = t;
	elseif (z <= 1.05e+79)
		tmp = -t * (z / a);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -3.35e+19], t, If[LessEqual[z, -8e-242], t$95$2, If[LessEqual[z, 2.1e-291], t$95$1, If[LessEqual[z, 2.3e-106], t$95$2, If[LessEqual[z, 6.2e+16], t$95$1, If[LessEqual[z, 1.4e+45], t, If[LessEqual[z, 1.05e+79], N[((-t) * N[(z / a), $MachinePrecision]), $MachinePrecision], t]]]]]]]]]

Derivation

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

Alternative 7: 74.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y - z}{\frac{a - z}{t}}\\ \mathbf{if}\;a \leq -2.6 \cdot 10^{+52}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -1.9 \cdot 10^{-6}:\\ \;\;\;\;t - x \cdot \frac{a - y}{z}\\ \mathbf{elif}\;a \leq -1.05 \cdot 10^{-56}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t - x}}\\ \mathbf{elif}\;a \leq 5.5 \cdot 10^{-25}:\\ \;\;\;\;t - \frac{t - x}{\frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (- y z) (/ (- a z) t)))))
   (if (<= a -2.6e+52)
     t_1
     (if (<= a -1.9e-6)
       (- t (* x (/ (- a y) z)))
       (if (<= a -1.05e-56)
         (+ x (/ y (/ a (- t x))))
         (if (<= a 5.5e-25) (- t (/ (- t x) (/ z y))) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) / ((a - z) / t));
	double tmp;
	if (a <= -2.6e+52) {
		tmp = t_1;
	} else if (a <= -1.9e-6) {
		tmp = t - (x * ((a - y) / z));
	} else if (a <= -1.05e-56) {
		tmp = x + (y / (a / (t - x)));
	} else if (a <= 5.5e-25) {
		tmp = t - ((t - x) / (z / y));
	} 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 - z) / ((a - z) / t))
    if (a <= (-2.6d+52)) then
        tmp = t_1
    else if (a <= (-1.9d-6)) then
        tmp = t - (x * ((a - y) / z))
    else if (a <= (-1.05d-56)) then
        tmp = x + (y / (a / (t - x)))
    else if (a <= 5.5d-25) then
        tmp = t - ((t - x) / (z / y))
    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 - z) / ((a - z) / t));
	double tmp;
	if (a <= -2.6e+52) {
		tmp = t_1;
	} else if (a <= -1.9e-6) {
		tmp = t - (x * ((a - y) / z));
	} else if (a <= -1.05e-56) {
		tmp = x + (y / (a / (t - x)));
	} else if (a <= 5.5e-25) {
		tmp = t - ((t - x) / (z / y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y - z) / ((a - z) / t))
	tmp = 0
	if a <= -2.6e+52:
		tmp = t_1
	elif a <= -1.9e-6:
		tmp = t - (x * ((a - y) / z))
	elif a <= -1.05e-56:
		tmp = x + (y / (a / (t - x)))
	elif a <= 5.5e-25:
		tmp = t - ((t - x) / (z / y))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - z) / Float64(Float64(a - z) / t)))
	tmp = 0.0
	if (a <= -2.6e+52)
		tmp = t_1;
	elseif (a <= -1.9e-6)
		tmp = Float64(t - Float64(x * Float64(Float64(a - y) / z)));
	elseif (a <= -1.05e-56)
		tmp = Float64(x + Float64(y / Float64(a / Float64(t - x))));
	elseif (a <= 5.5e-25)
		tmp = Float64(t - Float64(Float64(t - x) / Float64(z / y)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y - z) / ((a - z) / t));
	tmp = 0.0;
	if (a <= -2.6e+52)
		tmp = t_1;
	elseif (a <= -1.9e-6)
		tmp = t - (x * ((a - y) / z));
	elseif (a <= -1.05e-56)
		tmp = x + (y / (a / (t - x)));
	elseif (a <= 5.5e-25)
		tmp = t - ((t - x) / (z / y));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(y - z), $MachinePrecision] / N[(N[(a - z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.6e+52], t$95$1, If[LessEqual[a, -1.9e-6], N[(t - N[(x * N[(N[(a - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -1.05e-56], N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 5.5e-25], N[(t - N[(N[(t - x), $MachinePrecision] / N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 8: 57.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y - z}{a - z} \cdot t\\ \mathbf{if}\;z \leq -6 \cdot 10^{+17}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{-241}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;z \leq 2.6 \cdot 10^{-287}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 2.55 \cdot 10^{+14}:\\ \;\;\;\;x - \frac{x}{\frac{a}{y}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* (/ (- y z) (- a z)) t)))
   (if (<= z -6e+17)
     t_1
     (if (<= z -4.2e-241)
       (* x (- 1.0 (/ y a)))
       (if (<= z 2.6e-287)
         (+ x (/ y (/ a t)))
         (if (<= z 2.55e+14) (- x (/ x (/ a y))) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = ((y - z) / (a - z)) * t;
	double tmp;
	if (z <= -6e+17) {
		tmp = t_1;
	} else if (z <= -4.2e-241) {
		tmp = x * (1.0 - (y / a));
	} else if (z <= 2.6e-287) {
		tmp = x + (y / (a / t));
	} else if (z <= 2.55e+14) {
		tmp = x - (x / (a / y));
	} 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 = ((y - z) / (a - z)) * t
    if (z <= (-6d+17)) then
        tmp = t_1
    else if (z <= (-4.2d-241)) then
        tmp = x * (1.0d0 - (y / a))
    else if (z <= 2.6d-287) then
        tmp = x + (y / (a / t))
    else if (z <= 2.55d+14) then
        tmp = x - (x / (a / y))
    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 = ((y - z) / (a - z)) * t;
	double tmp;
	if (z <= -6e+17) {
		tmp = t_1;
	} else if (z <= -4.2e-241) {
		tmp = x * (1.0 - (y / a));
	} else if (z <= 2.6e-287) {
		tmp = x + (y / (a / t));
	} else if (z <= 2.55e+14) {
		tmp = x - (x / (a / y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = ((y - z) / (a - z)) * t
	tmp = 0
	if z <= -6e+17:
		tmp = t_1
	elif z <= -4.2e-241:
		tmp = x * (1.0 - (y / a))
	elif z <= 2.6e-287:
		tmp = x + (y / (a / t))
	elif z <= 2.55e+14:
		tmp = x - (x / (a / y))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(Float64(y - z) / Float64(a - z)) * t)
	tmp = 0.0
	if (z <= -6e+17)
		tmp = t_1;
	elseif (z <= -4.2e-241)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (z <= 2.6e-287)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif (z <= 2.55e+14)
		tmp = Float64(x - Float64(x / Float64(a / y)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = ((y - z) / (a - z)) * t;
	tmp = 0.0;
	if (z <= -6e+17)
		tmp = t_1;
	elseif (z <= -4.2e-241)
		tmp = x * (1.0 - (y / a));
	elseif (z <= 2.6e-287)
		tmp = x + (y / (a / t));
	elseif (z <= 2.55e+14)
		tmp = x - (x / (a / y));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision] * t), $MachinePrecision]}, If[LessEqual[z, -6e+17], t$95$1, If[LessEqual[z, -4.2e-241], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.6e-287], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.55e+14], N[(x - N[(x / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 9: 87.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -8.2 \cdot 10^{-153} \lor \neg \left(a \leq 5.5 \cdot 10^{-154}\right):\\ \;\;\;\;x + \frac{y - z}{a - z} \cdot \left(t - x\right)\\ \mathbf{else}:\\ \;\;\;\;t - \frac{t - x}{\frac{z}{y - a}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -8.2e-153) (not (<= a 5.5e-154)))
   (+ x (* (/ (- y z) (- a z)) (- t x)))
   (- t (/ (- t x) (/ z (- y a))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -8.2e-153) || !(a <= 5.5e-154)) {
		tmp = x + (((y - z) / (a - z)) * (t - x));
	} 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 <= (-8.2d-153)) .or. (.not. (a <= 5.5d-154))) then
        tmp = x + (((y - z) / (a - z)) * (t - x))
    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 <= -8.2e-153) || !(a <= 5.5e-154)) {
		tmp = x + (((y - z) / (a - z)) * (t - x));
	} else {
		tmp = t - ((t - x) / (z / (y - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -8.2e-153) or not (a <= 5.5e-154):
		tmp = x + (((y - z) / (a - z)) * (t - x))
	else:
		tmp = t - ((t - x) / (z / (y - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -8.2e-153) || !(a <= 5.5e-154))
		tmp = Float64(x + Float64(Float64(Float64(y - z) / Float64(a - z)) * Float64(t - x)));
	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 <= -8.2e-153) || ~((a <= 5.5e-154)))
		tmp = x + (((y - z) / (a - z)) * (t - x));
	else
		tmp = t - ((t - x) / (z / (y - a)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -8.2e-153], N[Not[LessEqual[a, 5.5e-154]], $MachinePrecision]], N[(x + N[(N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision] * N[(t - x), $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 10: 51.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.4 \cdot 10^{+38}:\\ \;\;\;\;t - x \cdot \frac{a}{z}\\ \mathbf{elif}\;z \leq -1.85 \cdot 10^{-242}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;z \leq 4 \cdot 10^{-289}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 1.35 \cdot 10^{+23}:\\ \;\;\;\;x - \frac{x}{\frac{a}{y}}\\ \mathbf{else}:\\ \;\;\;\;\left(-t\right) \cdot \frac{z}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.4e+38)
   (- t (* x (/ a z)))
   (if (<= z -1.85e-242)
     (* x (- 1.0 (/ y a)))
     (if (<= z 4e-289)
       (+ x (/ y (/ a t)))
       (if (<= z 1.35e+23) (- x (/ x (/ a y))) (* (- t) (/ z (- a z))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.4e+38) {
		tmp = t - (x * (a / z));
	} else if (z <= -1.85e-242) {
		tmp = x * (1.0 - (y / a));
	} else if (z <= 4e-289) {
		tmp = x + (y / (a / t));
	} else if (z <= 1.35e+23) {
		tmp = x - (x / (a / y));
	} else {
		tmp = -t * (z / (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 (z <= (-2.4d+38)) then
        tmp = t - (x * (a / z))
    else if (z <= (-1.85d-242)) then
        tmp = x * (1.0d0 - (y / a))
    else if (z <= 4d-289) then
        tmp = x + (y / (a / t))
    else if (z <= 1.35d+23) then
        tmp = x - (x / (a / y))
    else
        tmp = -t * (z / (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 (z <= -2.4e+38) {
		tmp = t - (x * (a / z));
	} else if (z <= -1.85e-242) {
		tmp = x * (1.0 - (y / a));
	} else if (z <= 4e-289) {
		tmp = x + (y / (a / t));
	} else if (z <= 1.35e+23) {
		tmp = x - (x / (a / y));
	} else {
		tmp = -t * (z / (a - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.4e+38:
		tmp = t - (x * (a / z))
	elif z <= -1.85e-242:
		tmp = x * (1.0 - (y / a))
	elif z <= 4e-289:
		tmp = x + (y / (a / t))
	elif z <= 1.35e+23:
		tmp = x - (x / (a / y))
	else:
		tmp = -t * (z / (a - z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.4e+38)
		tmp = Float64(t - Float64(x * Float64(a / z)));
	elseif (z <= -1.85e-242)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (z <= 4e-289)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif (z <= 1.35e+23)
		tmp = Float64(x - Float64(x / Float64(a / y)));
	else
		tmp = Float64(Float64(-t) * Float64(z / Float64(a - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.4e+38)
		tmp = t - (x * (a / z));
	elseif (z <= -1.85e-242)
		tmp = x * (1.0 - (y / a));
	elseif (z <= 4e-289)
		tmp = x + (y / (a / t));
	elseif (z <= 1.35e+23)
		tmp = x - (x / (a / y));
	else
		tmp = -t * (z / (a - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.4e+38], N[(t - N[(x * N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.85e-242], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4e-289], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.35e+23], N[(x - N[(x / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[((-t) * N[(z / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 11: 51.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.7 \cdot 10^{+38}:\\ \;\;\;\;t - x \cdot \frac{a}{z}\\ \mathbf{elif}\;z \leq -2.7 \cdot 10^{-242}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;z \leq 7 \cdot 10^{-290}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 3.6 \cdot 10^{+23}:\\ \;\;\;\;x - \frac{x}{\frac{a}{y}}\\ \mathbf{else}:\\ \;\;\;\;\frac{-t}{\frac{a}{z} + -1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -1.7e+38)
   (- t (* x (/ a z)))
   (if (<= z -2.7e-242)
     (* x (- 1.0 (/ y a)))
     (if (<= z 7e-290)
       (+ x (/ y (/ a t)))
       (if (<= z 3.6e+23) (- x (/ x (/ a y))) (/ (- t) (+ (/ a z) -1.0)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -1.7e+38) {
		tmp = t - (x * (a / z));
	} else if (z <= -2.7e-242) {
		tmp = x * (1.0 - (y / a));
	} else if (z <= 7e-290) {
		tmp = x + (y / (a / t));
	} else if (z <= 3.6e+23) {
		tmp = x - (x / (a / y));
	} else {
		tmp = -t / ((a / z) + -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 <= (-1.7d+38)) then
        tmp = t - (x * (a / z))
    else if (z <= (-2.7d-242)) then
        tmp = x * (1.0d0 - (y / a))
    else if (z <= 7d-290) then
        tmp = x + (y / (a / t))
    else if (z <= 3.6d+23) then
        tmp = x - (x / (a / y))
    else
        tmp = -t / ((a / z) + (-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 <= -1.7e+38) {
		tmp = t - (x * (a / z));
	} else if (z <= -2.7e-242) {
		tmp = x * (1.0 - (y / a));
	} else if (z <= 7e-290) {
		tmp = x + (y / (a / t));
	} else if (z <= 3.6e+23) {
		tmp = x - (x / (a / y));
	} else {
		tmp = -t / ((a / z) + -1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -1.7e+38:
		tmp = t - (x * (a / z))
	elif z <= -2.7e-242:
		tmp = x * (1.0 - (y / a))
	elif z <= 7e-290:
		tmp = x + (y / (a / t))
	elif z <= 3.6e+23:
		tmp = x - (x / (a / y))
	else:
		tmp = -t / ((a / z) + -1.0)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -1.7e+38)
		tmp = Float64(t - Float64(x * Float64(a / z)));
	elseif (z <= -2.7e-242)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (z <= 7e-290)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif (z <= 3.6e+23)
		tmp = Float64(x - Float64(x / Float64(a / y)));
	else
		tmp = Float64(Float64(-t) / Float64(Float64(a / z) + -1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -1.7e+38)
		tmp = t - (x * (a / z));
	elseif (z <= -2.7e-242)
		tmp = x * (1.0 - (y / a));
	elseif (z <= 7e-290)
		tmp = x + (y / (a / t));
	elseif (z <= 3.6e+23)
		tmp = x - (x / (a / y));
	else
		tmp = -t / ((a / z) + -1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -1.7e+38], N[(t - N[(x * N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -2.7e-242], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7e-290], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.6e+23], N[(x - N[(x / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[((-t) / N[(N[(a / z), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 12: 48.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{if}\;z \leq -3.6 \cdot 10^{+19}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -3.2 \cdot 10^{-277}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.92 \cdot 10^{-302}:\\ \;\;\;\;y \cdot \frac{t - x}{a}\\ \mathbf{elif}\;z \leq 3.7 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))))
   (if (<= z -3.6e+19)
     t
     (if (<= z -3.2e-277)
       t_1
       (if (<= z 1.92e-302) (* y (/ (- t x) a)) (if (<= z 3.7e+23) t_1 t))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double tmp;
	if (z <= -3.6e+19) {
		tmp = t;
	} else if (z <= -3.2e-277) {
		tmp = t_1;
	} else if (z <= 1.92e-302) {
		tmp = y * ((t - x) / a);
	} else if (z <= 3.7e+23) {
		tmp = t_1;
	} else {
		tmp = 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) :: t_1
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    if (z <= (-3.6d+19)) then
        tmp = t
    else if (z <= (-3.2d-277)) then
        tmp = t_1
    else if (z <= 1.92d-302) then
        tmp = y * ((t - x) / a)
    else if (z <= 3.7d+23) then
        tmp = t_1
    else
        tmp = t
    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 * (1.0 - (y / a));
	double tmp;
	if (z <= -3.6e+19) {
		tmp = t;
	} else if (z <= -3.2e-277) {
		tmp = t_1;
	} else if (z <= 1.92e-302) {
		tmp = y * ((t - x) / a);
	} else if (z <= 3.7e+23) {
		tmp = t_1;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	tmp = 0
	if z <= -3.6e+19:
		tmp = t
	elif z <= -3.2e-277:
		tmp = t_1
	elif z <= 1.92e-302:
		tmp = y * ((t - x) / a)
	elif z <= 3.7e+23:
		tmp = t_1
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	tmp = 0.0
	if (z <= -3.6e+19)
		tmp = t;
	elseif (z <= -3.2e-277)
		tmp = t_1;
	elseif (z <= 1.92e-302)
		tmp = Float64(y * Float64(Float64(t - x) / a));
	elseif (z <= 3.7e+23)
		tmp = t_1;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	tmp = 0.0;
	if (z <= -3.6e+19)
		tmp = t;
	elseif (z <= -3.2e-277)
		tmp = t_1;
	elseif (z <= 1.92e-302)
		tmp = y * ((t - x) / a);
	elseif (z <= 3.7e+23)
		tmp = t_1;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -3.6e+19], t, If[LessEqual[z, -3.2e-277], t$95$1, If[LessEqual[z, 1.92e-302], N[(y * N[(N[(t - x), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.7e+23], t$95$1, t]]]]]

Derivation

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

Alternative 13: 52.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{a}\right)\\ t_2 := t - x \cdot \frac{a}{z}\\ \mathbf{if}\;z \leq -1.15 \cdot 10^{+38}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -3.3 \cdot 10^{-242}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.5 \cdot 10^{-287}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 7.3 \cdot 10^{+22}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y a)))) (t_2 (- t (* x (/ a z)))))
   (if (<= z -1.15e+38)
     t_2
     (if (<= z -3.3e-242)
       t_1
       (if (<= z 4.5e-287) (+ x (/ y (/ a t))) (if (<= z 7.3e+22) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double t_2 = t - (x * (a / z));
	double tmp;
	if (z <= -1.15e+38) {
		tmp = t_2;
	} else if (z <= -3.3e-242) {
		tmp = t_1;
	} else if (z <= 4.5e-287) {
		tmp = x + (y / (a / t));
	} else if (z <= 7.3e+22) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = x * (1.0d0 - (y / a))
    t_2 = t - (x * (a / z))
    if (z <= (-1.15d+38)) then
        tmp = t_2
    else if (z <= (-3.3d-242)) then
        tmp = t_1
    else if (z <= 4.5d-287) then
        tmp = x + (y / (a / t))
    else if (z <= 7.3d+22) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (1.0 - (y / a));
	double t_2 = t - (x * (a / z));
	double tmp;
	if (z <= -1.15e+38) {
		tmp = t_2;
	} else if (z <= -3.3e-242) {
		tmp = t_1;
	} else if (z <= 4.5e-287) {
		tmp = x + (y / (a / t));
	} else if (z <= 7.3e+22) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (1.0 - (y / a))
	t_2 = t - (x * (a / z))
	tmp = 0
	if z <= -1.15e+38:
		tmp = t_2
	elif z <= -3.3e-242:
		tmp = t_1
	elif z <= 4.5e-287:
		tmp = x + (y / (a / t))
	elif z <= 7.3e+22:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(1.0 - Float64(y / a)))
	t_2 = Float64(t - Float64(x * Float64(a / z)))
	tmp = 0.0
	if (z <= -1.15e+38)
		tmp = t_2;
	elseif (z <= -3.3e-242)
		tmp = t_1;
	elseif (z <= 4.5e-287)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif (z <= 7.3e+22)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (1.0 - (y / a));
	t_2 = t - (x * (a / z));
	tmp = 0.0;
	if (z <= -1.15e+38)
		tmp = t_2;
	elseif (z <= -3.3e-242)
		tmp = t_1;
	elseif (z <= 4.5e-287)
		tmp = x + (y / (a / t));
	elseif (z <= 7.3e+22)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t - N[(x * N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.15e+38], t$95$2, If[LessEqual[z, -3.3e-242], t$95$1, If[LessEqual[z, 4.5e-287], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.3e+22], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 14: 52.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t - x \cdot \frac{a}{z}\\ \mathbf{if}\;z \leq -5.8 \cdot 10^{+36}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -6.2 \cdot 10^{-237}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{elif}\;z \leq 6.2 \cdot 10^{-290}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t}}\\ \mathbf{elif}\;z \leq 4 \cdot 10^{+22}:\\ \;\;\;\;x - \frac{x}{\frac{a}{y}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- t (* x (/ a z)))))
   (if (<= z -5.8e+36)
     t_1
     (if (<= z -6.2e-237)
       (* x (- 1.0 (/ y a)))
       (if (<= z 6.2e-290)
         (+ x (/ y (/ a t)))
         (if (<= z 4e+22) (- x (/ x (/ a y))) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t - (x * (a / z));
	double tmp;
	if (z <= -5.8e+36) {
		tmp = t_1;
	} else if (z <= -6.2e-237) {
		tmp = x * (1.0 - (y / a));
	} else if (z <= 6.2e-290) {
		tmp = x + (y / (a / t));
	} else if (z <= 4e+22) {
		tmp = x - (x / (a / y));
	} 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 - (x * (a / z))
    if (z <= (-5.8d+36)) then
        tmp = t_1
    else if (z <= (-6.2d-237)) then
        tmp = x * (1.0d0 - (y / a))
    else if (z <= 6.2d-290) then
        tmp = x + (y / (a / t))
    else if (z <= 4d+22) then
        tmp = x - (x / (a / y))
    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 - (x * (a / z));
	double tmp;
	if (z <= -5.8e+36) {
		tmp = t_1;
	} else if (z <= -6.2e-237) {
		tmp = x * (1.0 - (y / a));
	} else if (z <= 6.2e-290) {
		tmp = x + (y / (a / t));
	} else if (z <= 4e+22) {
		tmp = x - (x / (a / y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t - (x * (a / z))
	tmp = 0
	if z <= -5.8e+36:
		tmp = t_1
	elif z <= -6.2e-237:
		tmp = x * (1.0 - (y / a))
	elif z <= 6.2e-290:
		tmp = x + (y / (a / t))
	elif z <= 4e+22:
		tmp = x - (x / (a / y))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t - Float64(x * Float64(a / z)))
	tmp = 0.0
	if (z <= -5.8e+36)
		tmp = t_1;
	elseif (z <= -6.2e-237)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	elseif (z <= 6.2e-290)
		tmp = Float64(x + Float64(y / Float64(a / t)));
	elseif (z <= 4e+22)
		tmp = Float64(x - Float64(x / Float64(a / y)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t - (x * (a / z));
	tmp = 0.0;
	if (z <= -5.8e+36)
		tmp = t_1;
	elseif (z <= -6.2e-237)
		tmp = x * (1.0 - (y / a));
	elseif (z <= 6.2e-290)
		tmp = x + (y / (a / t));
	elseif (z <= 4e+22)
		tmp = x - (x / (a / y));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t - N[(x * N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -5.8e+36], t$95$1, If[LessEqual[z, -6.2e-237], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 6.2e-290], N[(x + N[(y / N[(a / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4e+22], N[(x - N[(x / N[(a / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 15: 69.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t - x \cdot \frac{a - y}{z}\\ \mathbf{if}\;z \leq -4.8 \cdot 10^{+44}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4 \cdot 10^{+22}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t - x}}\\ \mathbf{elif}\;z \leq 2.15 \cdot 10^{+136}:\\ \;\;\;\;\frac{t}{\frac{a - z}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- t (* x (/ (- a y) z)))))
   (if (<= z -4.8e+44)
     t_1
     (if (<= z 4e+22)
       (+ x (/ y (/ a (- t x))))
       (if (<= z 2.15e+136) (/ t (/ (- a z) (- y z))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t - (x * ((a - y) / z));
	double tmp;
	if (z <= -4.8e+44) {
		tmp = t_1;
	} else if (z <= 4e+22) {
		tmp = x + (y / (a / (t - x)));
	} else if (z <= 2.15e+136) {
		tmp = t / ((a - z) / (y - 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 = t - (x * ((a - y) / z))
    if (z <= (-4.8d+44)) then
        tmp = t_1
    else if (z <= 4d+22) then
        tmp = x + (y / (a / (t - x)))
    else if (z <= 2.15d+136) then
        tmp = t / ((a - z) / (y - 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 = t - (x * ((a - y) / z));
	double tmp;
	if (z <= -4.8e+44) {
		tmp = t_1;
	} else if (z <= 4e+22) {
		tmp = x + (y / (a / (t - x)));
	} else if (z <= 2.15e+136) {
		tmp = t / ((a - z) / (y - z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t - (x * ((a - y) / z))
	tmp = 0
	if z <= -4.8e+44:
		tmp = t_1
	elif z <= 4e+22:
		tmp = x + (y / (a / (t - x)))
	elif z <= 2.15e+136:
		tmp = t / ((a - z) / (y - z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t - Float64(x * Float64(Float64(a - y) / z)))
	tmp = 0.0
	if (z <= -4.8e+44)
		tmp = t_1;
	elseif (z <= 4e+22)
		tmp = Float64(x + Float64(y / Float64(a / Float64(t - x))));
	elseif (z <= 2.15e+136)
		tmp = Float64(t / Float64(Float64(a - z) / Float64(y - z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t - (x * ((a - y) / z));
	tmp = 0.0;
	if (z <= -4.8e+44)
		tmp = t_1;
	elseif (z <= 4e+22)
		tmp = x + (y / (a / (t - x)));
	elseif (z <= 2.15e+136)
		tmp = t / ((a - z) / (y - z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t - N[(x * N[(N[(a - y), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -4.8e+44], t$95$1, If[LessEqual[z, 4e+22], N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.15e+136], N[(t / N[(N[(a - z), $MachinePrecision] / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 16: 66.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.4 \cdot 10^{+18} \lor \neg \left(z \leq 6.2 \cdot 10^{+22}\right):\\ \;\;\;\;\frac{y - z}{a - z} \cdot t\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -4.4e+18) (not (<= z 6.2e+22)))
   (* (/ (- y z) (- a z)) t)
   (+ x (/ y (/ a (- t x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -4.4e+18) || !(z <= 6.2e+22)) {
		tmp = ((y - z) / (a - z)) * t;
	} else {
		tmp = x + (y / (a / (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 ((z <= (-4.4d+18)) .or. (.not. (z <= 6.2d+22))) then
        tmp = ((y - z) / (a - z)) * t
    else
        tmp = x + (y / (a / (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 ((z <= -4.4e+18) || !(z <= 6.2e+22)) {
		tmp = ((y - z) / (a - z)) * t;
	} else {
		tmp = x + (y / (a / (t - x)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -4.4e+18) or not (z <= 6.2e+22):
		tmp = ((y - z) / (a - z)) * t
	else:
		tmp = x + (y / (a / (t - x)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -4.4e+18) || !(z <= 6.2e+22))
		tmp = Float64(Float64(Float64(y - z) / Float64(a - z)) * t);
	else
		tmp = Float64(x + Float64(y / Float64(a / Float64(t - x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -4.4e+18) || ~((z <= 6.2e+22)))
		tmp = ((y - z) / (a - z)) * t;
	else
		tmp = x + (y / (a / (t - x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -4.4e+18], N[Not[LessEqual[z, 6.2e+22]], $MachinePrecision]], N[(N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision] * t), $MachinePrecision], N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 17: 70.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.22 \cdot 10^{+20} \lor \neg \left(z \leq 3.7 \cdot 10^{+20}\right):\\ \;\;\;\;t - \frac{t - x}{\frac{z}{y}}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -1.22e+20) (not (<= z 3.7e+20)))
   (- t (/ (- t x) (/ z y)))
   (+ x (/ y (/ a (- t x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -1.22e+20) || !(z <= 3.7e+20)) {
		tmp = t - ((t - x) / (z / y));
	} else {
		tmp = x + (y / (a / (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 ((z <= (-1.22d+20)) .or. (.not. (z <= 3.7d+20))) then
        tmp = t - ((t - x) / (z / y))
    else
        tmp = x + (y / (a / (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 ((z <= -1.22e+20) || !(z <= 3.7e+20)) {
		tmp = t - ((t - x) / (z / y));
	} else {
		tmp = x + (y / (a / (t - x)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -1.22e+20) or not (z <= 3.7e+20):
		tmp = t - ((t - x) / (z / y))
	else:
		tmp = x + (y / (a / (t - x)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -1.22e+20) || !(z <= 3.7e+20))
		tmp = Float64(t - Float64(Float64(t - x) / Float64(z / y)));
	else
		tmp = Float64(x + Float64(y / Float64(a / Float64(t - x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((z <= -1.22e+20) || ~((z <= 3.7e+20)))
		tmp = t - ((t - x) / (z / y));
	else
		tmp = x + (y / (a / (t - x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -1.22e+20], N[Not[LessEqual[z, 3.7e+20]], $MachinePrecision]], N[(t - N[(N[(t - x), $MachinePrecision] / N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 18: 47.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+20}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq 3.7 \cdot 10^{+23}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{a}\right)\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.1e+20) t (if (<= z 3.7e+23) (* x (- 1.0 (/ y a))) t)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.1e+20) {
		tmp = t;
	} else if (z <= 3.7e+23) {
		tmp = x * (1.0 - (y / a));
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-2.1d+20)) then
        tmp = t
    else if (z <= 3.7d+23) then
        tmp = x * (1.0d0 - (y / a))
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.1e+20) {
		tmp = t;
	} else if (z <= 3.7e+23) {
		tmp = x * (1.0 - (y / a));
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.1e+20:
		tmp = t
	elif z <= 3.7e+23:
		tmp = x * (1.0 - (y / a))
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.1e+20)
		tmp = t;
	elseif (z <= 3.7e+23)
		tmp = Float64(x * Float64(1.0 - Float64(y / a)));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.1e+20)
		tmp = t;
	elseif (z <= 3.7e+23)
		tmp = x * (1.0 - (y / a));
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.1e+20], t, If[LessEqual[z, 3.7e+23], N[(x * N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t]]

Derivation

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

Alternative 19: 37.9% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7 \cdot 10^{+18}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{+22}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -7e+18) t (if (<= z 4.7e+22) x t)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -7e+18) {
		tmp = t;
	} else if (z <= 4.7e+22) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-7d+18)) then
        tmp = t
    else if (z <= 4.7d+22) then
        tmp = x
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -7e+18) {
		tmp = t;
	} else if (z <= 4.7e+22) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -7e+18:
		tmp = t
	elif z <= 4.7e+22:
		tmp = x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -7e+18)
		tmp = t;
	elseif (z <= 4.7e+22)
		tmp = x;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -7e+18)
		tmp = t;
	elseif (z <= 4.7e+22)
		tmp = x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -7e+18], t, If[LessEqual[z, 4.7e+22], x, t]]

Derivation

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

Alternative 20: 24.7% 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: 83.9% 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 2023350 
(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))))