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

Percentage Accurate: 68.3% → 87.9%

Time: 26.2s

Alternatives: 22

Speedup: 0.8×

Specification

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = x + (((y - x) * (z - t)) / (a - t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 68.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = x + (((y - x) * (z - t)) / (a - t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 87.9% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -6.5 \cdot 10^{+242} \lor \neg \left(t \leq 1.02 \cdot 10^{+124}\right):\\ \;\;\;\;y + \frac{z - a}{{\left(\sqrt[3]{t}\right)}^{2}} \cdot \frac{x - y}{\sqrt[3]{t}}\\ \mathbf{else}:\\ \;\;\;\;x + \left(y - x\right) \cdot \left(\left(t - z\right) \cdot \frac{-1}{a - t}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -6.5e+242) (not (<= t 1.02e+124)))
   (+ y (* (/ (- z a) (pow (cbrt t) 2.0)) (/ (- x y) (cbrt t))))
   (+ x (* (- y x) (* (- t z) (/ -1.0 (- a t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -6.5e+242) || !(t <= 1.02e+124)) {
		tmp = y + (((z - a) / pow(cbrt(t), 2.0)) * ((x - y) / cbrt(t)));
	} else {
		tmp = x + ((y - x) * ((t - z) * (-1.0 / (a - t))));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -6.5e+242) || !(t <= 1.02e+124)) {
		tmp = y + (((z - a) / Math.pow(Math.cbrt(t), 2.0)) * ((x - y) / Math.cbrt(t)));
	} else {
		tmp = x + ((y - x) * ((t - z) * (-1.0 / (a - t))));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -6.5e+242) || !(t <= 1.02e+124))
		tmp = Float64(y + Float64(Float64(Float64(z - a) / (cbrt(t) ^ 2.0)) * Float64(Float64(x - y) / cbrt(t))));
	else
		tmp = Float64(x + Float64(Float64(y - x) * Float64(Float64(t - z) * Float64(-1.0 / Float64(a - t)))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -6.5e+242], N[Not[LessEqual[t, 1.02e+124]], $MachinePrecision]], N[(y + N[(N[(N[(z - a), $MachinePrecision] / N[Power[N[Power[t, 1/3], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] * N[(N[(x - y), $MachinePrecision] / N[Power[t, 1/3], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y - x), $MachinePrecision] * N[(N[(t - z), $MachinePrecision] * N[(-1.0 / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 2: 87.5% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -8.2 \cdot 10^{+242} \lor \neg \left(t \leq 9.2 \cdot 10^{+129}\right):\\ \;\;\;\;{\left(\sqrt[3]{y + \frac{x - y}{\frac{t}{z - a}}}\right)}^{3}\\ \mathbf{else}:\\ \;\;\;\;x + \left(y - x\right) \cdot \left(\left(t - z\right) \cdot \frac{-1}{a - t}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -8.2e+242) (not (<= t 9.2e+129)))
   (pow (cbrt (+ y (/ (- x y) (/ t (- z a))))) 3.0)
   (+ x (* (- y x) (* (- t z) (/ -1.0 (- a t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -8.2e+242) || !(t <= 9.2e+129)) {
		tmp = pow(cbrt((y + ((x - y) / (t / (z - a))))), 3.0);
	} else {
		tmp = x + ((y - x) * ((t - z) * (-1.0 / (a - t))));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -8.2e+242) || !(t <= 9.2e+129)) {
		tmp = Math.pow(Math.cbrt((y + ((x - y) / (t / (z - a))))), 3.0);
	} else {
		tmp = x + ((y - x) * ((t - z) * (-1.0 / (a - t))));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -8.2e+242) || !(t <= 9.2e+129))
		tmp = cbrt(Float64(y + Float64(Float64(x - y) / Float64(t / Float64(z - a))))) ^ 3.0;
	else
		tmp = Float64(x + Float64(Float64(y - x) * Float64(Float64(t - z) * Float64(-1.0 / Float64(a - t)))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -8.2e+242], N[Not[LessEqual[t, 9.2e+129]], $MachinePrecision]], N[Power[N[Power[N[(y + N[(N[(x - y), $MachinePrecision] / N[(t / N[(z - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision], 3.0], $MachinePrecision], N[(x + N[(N[(y - x), $MachinePrecision] * N[(N[(t - z), $MachinePrecision] * N[(-1.0 / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 3: 91.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(t - z\right) \cdot \frac{x - y}{a - t}\\ t_2 := x + \frac{\left(x - y\right) \cdot \left(t - z\right)}{a - t}\\ \mathbf{if}\;t_2 \leq -\infty:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t_2 \leq -2 \cdot 10^{-286}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t_2 \leq 0:\\ \;\;\;\;y + \frac{\left(z - a\right) \cdot x}{t}\\ \mathbf{elif}\;t_2 \leq 10^{+294}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- t z) (/ (- x y) (- a t)))))
        (t_2 (+ x (/ (* (- x y) (- t z)) (- a t)))))
   (if (<= t_2 (- INFINITY))
     t_1
     (if (<= t_2 -2e-286)
       t_2
       (if (<= t_2 0.0)
         (+ y (/ (* (- z a) x) t))
         (if (<= t_2 1e+294) t_2 t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((t - z) * ((x - y) / (a - t)));
	double t_2 = x + (((x - y) * (t - z)) / (a - t));
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = t_1;
	} else if (t_2 <= -2e-286) {
		tmp = t_2;
	} else if (t_2 <= 0.0) {
		tmp = y + (((z - a) * x) / t);
	} else if (t_2 <= 1e+294) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((t - z) * ((x - y) / (a - t)));
	double t_2 = x + (((x - y) * (t - z)) / (a - t));
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else if (t_2 <= -2e-286) {
		tmp = t_2;
	} else if (t_2 <= 0.0) {
		tmp = y + (((z - a) * x) / t);
	} else if (t_2 <= 1e+294) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((t - z) * ((x - y) / (a - t)))
	t_2 = x + (((x - y) * (t - z)) / (a - t))
	tmp = 0
	if t_2 <= -math.inf:
		tmp = t_1
	elif t_2 <= -2e-286:
		tmp = t_2
	elif t_2 <= 0.0:
		tmp = y + (((z - a) * x) / t)
	elif t_2 <= 1e+294:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(t - z) * Float64(Float64(x - y) / Float64(a - t))))
	t_2 = Float64(x + Float64(Float64(Float64(x - y) * Float64(t - z)) / Float64(a - t)))
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = t_1;
	elseif (t_2 <= -2e-286)
		tmp = t_2;
	elseif (t_2 <= 0.0)
		tmp = Float64(y + Float64(Float64(Float64(z - a) * x) / t));
	elseif (t_2 <= 1e+294)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((t - z) * ((x - y) / (a - t)));
	t_2 = x + (((x - y) * (t - z)) / (a - t));
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = t_1;
	elseif (t_2 <= -2e-286)
		tmp = t_2;
	elseif (t_2 <= 0.0)
		tmp = y + (((z - a) * x) / t);
	elseif (t_2 <= 1e+294)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(t - z), $MachinePrecision] * N[(N[(x - y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(N[(x - y), $MachinePrecision] * N[(t - z), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], t$95$1, If[LessEqual[t$95$2, -2e-286], t$95$2, If[LessEqual[t$95$2, 0.0], N[(y + N[(N[(N[(z - a), $MachinePrecision] * x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+294], t$95$2, t$95$1]]]]]]

Derivation

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

Alternative 4: 90.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{\left(x - y\right) \cdot \left(t - z\right)}{a - t}\\ \mathbf{if}\;t_1 \leq -2 \cdot 10^{-286} \lor \neg \left(t_1 \leq 0\right):\\ \;\;\;\;x + \left(y - x\right) \cdot \left(\left(t - z\right) \cdot \frac{-1}{a - t}\right)\\ \mathbf{else}:\\ \;\;\;\;y + \frac{\left(z - a\right) \cdot x}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (* (- x y) (- t z)) (- a t)))))
   (if (or (<= t_1 -2e-286) (not (<= t_1 0.0)))
     (+ x (* (- y x) (* (- t z) (/ -1.0 (- a t)))))
     (+ y (/ (* (- z a) x) t)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (((x - y) * (t - z)) / (a - t));
	double tmp;
	if ((t_1 <= -2e-286) || !(t_1 <= 0.0)) {
		tmp = x + ((y - x) * ((t - z) * (-1.0 / (a - t))));
	} else {
		tmp = y + (((z - a) * x) / 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 + (((x - y) * (t - z)) / (a - t))
    if ((t_1 <= (-2d-286)) .or. (.not. (t_1 <= 0.0d0))) then
        tmp = x + ((y - x) * ((t - z) * ((-1.0d0) / (a - t))))
    else
        tmp = y + (((z - a) * x) / 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 + (((x - y) * (t - z)) / (a - t));
	double tmp;
	if ((t_1 <= -2e-286) || !(t_1 <= 0.0)) {
		tmp = x + ((y - x) * ((t - z) * (-1.0 / (a - t))));
	} else {
		tmp = y + (((z - a) * x) / t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (((x - y) * (t - z)) / (a - t))
	tmp = 0
	if (t_1 <= -2e-286) or not (t_1 <= 0.0):
		tmp = x + ((y - x) * ((t - z) * (-1.0 / (a - t))))
	else:
		tmp = y + (((z - a) * x) / t)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(Float64(x - y) * Float64(t - z)) / Float64(a - t)))
	tmp = 0.0
	if ((t_1 <= -2e-286) || !(t_1 <= 0.0))
		tmp = Float64(x + Float64(Float64(y - x) * Float64(Float64(t - z) * Float64(-1.0 / Float64(a - t)))));
	else
		tmp = Float64(y + Float64(Float64(Float64(z - a) * x) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (((x - y) * (t - z)) / (a - t));
	tmp = 0.0;
	if ((t_1 <= -2e-286) || ~((t_1 <= 0.0)))
		tmp = x + ((y - x) * ((t - z) * (-1.0 / (a - t))));
	else
		tmp = y + (((z - a) * x) / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(N[(x - y), $MachinePrecision] * N[(t - z), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -2e-286], N[Not[LessEqual[t$95$1, 0.0]], $MachinePrecision]], N[(x + N[(N[(y - x), $MachinePrecision] * N[(N[(t - z), $MachinePrecision] * N[(-1.0 / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + N[(N[(N[(z - a), $MachinePrecision] * x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 5: 64.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y \cdot \left(z - t\right)}{a}\\ t_2 := y \cdot \frac{z - t}{a - t}\\ t_3 := x + \left(y - x\right) \cdot \frac{z}{a}\\ \mathbf{if}\;t \leq -1.06 \cdot 10^{+52}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -3.7 \cdot 10^{-32}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -1.26 \cdot 10^{-65}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -3.3 \cdot 10^{-78}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq -4.6 \cdot 10^{-110}:\\ \;\;\;\;z \cdot \frac{y - x}{a - t}\\ \mathbf{elif}\;t \leq -1.16 \cdot 10^{-141}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 2.15 \cdot 10^{-21}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;t \leq 3 \cdot 10^{+57}:\\ \;\;\;\;y + \frac{z \cdot x}{t}\\ \mathbf{elif}\;t \leq 2.4 \cdot 10^{+94}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (* y (- z t)) a)))
        (t_2 (* y (/ (- z t) (- a t))))
        (t_3 (+ x (* (- y x) (/ z a)))))
   (if (<= t -1.06e+52)
     t_2
     (if (<= t -3.7e-32)
       t_1
       (if (<= t -1.26e-65)
         t_2
         (if (<= t -3.3e-78)
           t_3
           (if (<= t -4.6e-110)
             (* z (/ (- y x) (- a t)))
             (if (<= t -1.16e-141)
               t_1
               (if (<= t 2.15e-21)
                 t_3
                 (if (<= t 3e+57)
                   (+ y (/ (* z x) t))
                   (if (<= t 2.4e+94) t_1 t_2)))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y * (z - t)) / a);
	double t_2 = y * ((z - t) / (a - t));
	double t_3 = x + ((y - x) * (z / a));
	double tmp;
	if (t <= -1.06e+52) {
		tmp = t_2;
	} else if (t <= -3.7e-32) {
		tmp = t_1;
	} else if (t <= -1.26e-65) {
		tmp = t_2;
	} else if (t <= -3.3e-78) {
		tmp = t_3;
	} else if (t <= -4.6e-110) {
		tmp = z * ((y - x) / (a - t));
	} else if (t <= -1.16e-141) {
		tmp = t_1;
	} else if (t <= 2.15e-21) {
		tmp = t_3;
	} else if (t <= 3e+57) {
		tmp = y + ((z * x) / t);
	} else if (t <= 2.4e+94) {
		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) :: t_3
    real(8) :: tmp
    t_1 = x + ((y * (z - t)) / a)
    t_2 = y * ((z - t) / (a - t))
    t_3 = x + ((y - x) * (z / a))
    if (t <= (-1.06d+52)) then
        tmp = t_2
    else if (t <= (-3.7d-32)) then
        tmp = t_1
    else if (t <= (-1.26d-65)) then
        tmp = t_2
    else if (t <= (-3.3d-78)) then
        tmp = t_3
    else if (t <= (-4.6d-110)) then
        tmp = z * ((y - x) / (a - t))
    else if (t <= (-1.16d-141)) then
        tmp = t_1
    else if (t <= 2.15d-21) then
        tmp = t_3
    else if (t <= 3d+57) then
        tmp = y + ((z * x) / t)
    else if (t <= 2.4d+94) 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 + ((y * (z - t)) / a);
	double t_2 = y * ((z - t) / (a - t));
	double t_3 = x + ((y - x) * (z / a));
	double tmp;
	if (t <= -1.06e+52) {
		tmp = t_2;
	} else if (t <= -3.7e-32) {
		tmp = t_1;
	} else if (t <= -1.26e-65) {
		tmp = t_2;
	} else if (t <= -3.3e-78) {
		tmp = t_3;
	} else if (t <= -4.6e-110) {
		tmp = z * ((y - x) / (a - t));
	} else if (t <= -1.16e-141) {
		tmp = t_1;
	} else if (t <= 2.15e-21) {
		tmp = t_3;
	} else if (t <= 3e+57) {
		tmp = y + ((z * x) / t);
	} else if (t <= 2.4e+94) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y * (z - t)) / a)
	t_2 = y * ((z - t) / (a - t))
	t_3 = x + ((y - x) * (z / a))
	tmp = 0
	if t <= -1.06e+52:
		tmp = t_2
	elif t <= -3.7e-32:
		tmp = t_1
	elif t <= -1.26e-65:
		tmp = t_2
	elif t <= -3.3e-78:
		tmp = t_3
	elif t <= -4.6e-110:
		tmp = z * ((y - x) / (a - t))
	elif t <= -1.16e-141:
		tmp = t_1
	elif t <= 2.15e-21:
		tmp = t_3
	elif t <= 3e+57:
		tmp = y + ((z * x) / t)
	elif t <= 2.4e+94:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y * Float64(z - t)) / a))
	t_2 = Float64(y * Float64(Float64(z - t) / Float64(a - t)))
	t_3 = Float64(x + Float64(Float64(y - x) * Float64(z / a)))
	tmp = 0.0
	if (t <= -1.06e+52)
		tmp = t_2;
	elseif (t <= -3.7e-32)
		tmp = t_1;
	elseif (t <= -1.26e-65)
		tmp = t_2;
	elseif (t <= -3.3e-78)
		tmp = t_3;
	elseif (t <= -4.6e-110)
		tmp = Float64(z * Float64(Float64(y - x) / Float64(a - t)));
	elseif (t <= -1.16e-141)
		tmp = t_1;
	elseif (t <= 2.15e-21)
		tmp = t_3;
	elseif (t <= 3e+57)
		tmp = Float64(y + Float64(Float64(z * x) / t));
	elseif (t <= 2.4e+94)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y * (z - t)) / a);
	t_2 = y * ((z - t) / (a - t));
	t_3 = x + ((y - x) * (z / a));
	tmp = 0.0;
	if (t <= -1.06e+52)
		tmp = t_2;
	elseif (t <= -3.7e-32)
		tmp = t_1;
	elseif (t <= -1.26e-65)
		tmp = t_2;
	elseif (t <= -3.3e-78)
		tmp = t_3;
	elseif (t <= -4.6e-110)
		tmp = z * ((y - x) / (a - t));
	elseif (t <= -1.16e-141)
		tmp = t_1;
	elseif (t <= 2.15e-21)
		tmp = t_3;
	elseif (t <= 3e+57)
		tmp = y + ((z * x) / t);
	elseif (t <= 2.4e+94)
		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[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x + N[(N[(y - x), $MachinePrecision] * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.06e+52], t$95$2, If[LessEqual[t, -3.7e-32], t$95$1, If[LessEqual[t, -1.26e-65], t$95$2, If[LessEqual[t, -3.3e-78], t$95$3, If[LessEqual[t, -4.6e-110], N[(z * N[(N[(y - x), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -1.16e-141], t$95$1, If[LessEqual[t, 2.15e-21], t$95$3, If[LessEqual[t, 3e+57], N[(y + N[(N[(z * x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.4e+94], t$95$1, t$95$2]]]]]]]]]]]]

Derivation

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

Alternative 6: 68.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{y - x}\\ t_2 := y - \frac{z}{t_1}\\ t_3 := x + \left(y - x\right) \cdot \frac{z}{a}\\ \mathbf{if}\;a \leq -1.7 \cdot 10^{-57}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;a \leq 8.2 \cdot 10^{-81}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 1.45 \cdot 10^{-36}:\\ \;\;\;\;t_3\\ \mathbf{elif}\;a \leq 2.4 \cdot 10^{+45}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 1.1 \cdot 10^{+57}:\\ \;\;\;\;x + \frac{z}{\frac{a}{y - x}}\\ \mathbf{elif}\;a \leq 2.9 \cdot 10^{+65}:\\ \;\;\;\;y + \frac{a}{t_1}\\ \mathbf{elif}\;a \leq 2.95 \cdot 10^{+65}:\\ \;\;\;\;z \cdot \frac{-y}{t}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot \left(z - t\right)}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ t (- y x)))
        (t_2 (- y (/ z t_1)))
        (t_3 (+ x (* (- y x) (/ z a)))))
   (if (<= a -1.7e-57)
     t_3
     (if (<= a 8.2e-81)
       t_2
       (if (<= a 1.45e-36)
         t_3
         (if (<= a 2.4e+45)
           t_2
           (if (<= a 1.1e+57)
             (+ x (/ z (/ a (- y x))))
             (if (<= a 2.9e+65)
               (+ y (/ a t_1))
               (if (<= a 2.95e+65)
                 (* z (/ (- y) t))
                 (+ x (/ (* y (- z t)) a)))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t / (y - x);
	double t_2 = y - (z / t_1);
	double t_3 = x + ((y - x) * (z / a));
	double tmp;
	if (a <= -1.7e-57) {
		tmp = t_3;
	} else if (a <= 8.2e-81) {
		tmp = t_2;
	} else if (a <= 1.45e-36) {
		tmp = t_3;
	} else if (a <= 2.4e+45) {
		tmp = t_2;
	} else if (a <= 1.1e+57) {
		tmp = x + (z / (a / (y - x)));
	} else if (a <= 2.9e+65) {
		tmp = y + (a / t_1);
	} else if (a <= 2.95e+65) {
		tmp = z * (-y / t);
	} else {
		tmp = x + ((y * (z - t)) / a);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = t / (y - x)
    t_2 = y - (z / t_1)
    t_3 = x + ((y - x) * (z / a))
    if (a <= (-1.7d-57)) then
        tmp = t_3
    else if (a <= 8.2d-81) then
        tmp = t_2
    else if (a <= 1.45d-36) then
        tmp = t_3
    else if (a <= 2.4d+45) then
        tmp = t_2
    else if (a <= 1.1d+57) then
        tmp = x + (z / (a / (y - x)))
    else if (a <= 2.9d+65) then
        tmp = y + (a / t_1)
    else if (a <= 2.95d+65) then
        tmp = z * (-y / t)
    else
        tmp = x + ((y * (z - t)) / a)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t / (y - x);
	double t_2 = y - (z / t_1);
	double t_3 = x + ((y - x) * (z / a));
	double tmp;
	if (a <= -1.7e-57) {
		tmp = t_3;
	} else if (a <= 8.2e-81) {
		tmp = t_2;
	} else if (a <= 1.45e-36) {
		tmp = t_3;
	} else if (a <= 2.4e+45) {
		tmp = t_2;
	} else if (a <= 1.1e+57) {
		tmp = x + (z / (a / (y - x)));
	} else if (a <= 2.9e+65) {
		tmp = y + (a / t_1);
	} else if (a <= 2.95e+65) {
		tmp = z * (-y / t);
	} else {
		tmp = x + ((y * (z - t)) / a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t / (y - x)
	t_2 = y - (z / t_1)
	t_3 = x + ((y - x) * (z / a))
	tmp = 0
	if a <= -1.7e-57:
		tmp = t_3
	elif a <= 8.2e-81:
		tmp = t_2
	elif a <= 1.45e-36:
		tmp = t_3
	elif a <= 2.4e+45:
		tmp = t_2
	elif a <= 1.1e+57:
		tmp = x + (z / (a / (y - x)))
	elif a <= 2.9e+65:
		tmp = y + (a / t_1)
	elif a <= 2.95e+65:
		tmp = z * (-y / t)
	else:
		tmp = x + ((y * (z - t)) / a)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t / Float64(y - x))
	t_2 = Float64(y - Float64(z / t_1))
	t_3 = Float64(x + Float64(Float64(y - x) * Float64(z / a)))
	tmp = 0.0
	if (a <= -1.7e-57)
		tmp = t_3;
	elseif (a <= 8.2e-81)
		tmp = t_2;
	elseif (a <= 1.45e-36)
		tmp = t_3;
	elseif (a <= 2.4e+45)
		tmp = t_2;
	elseif (a <= 1.1e+57)
		tmp = Float64(x + Float64(z / Float64(a / Float64(y - x))));
	elseif (a <= 2.9e+65)
		tmp = Float64(y + Float64(a / t_1));
	elseif (a <= 2.95e+65)
		tmp = Float64(z * Float64(Float64(-y) / t));
	else
		tmp = Float64(x + Float64(Float64(y * Float64(z - t)) / a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t / (y - x);
	t_2 = y - (z / t_1);
	t_3 = x + ((y - x) * (z / a));
	tmp = 0.0;
	if (a <= -1.7e-57)
		tmp = t_3;
	elseif (a <= 8.2e-81)
		tmp = t_2;
	elseif (a <= 1.45e-36)
		tmp = t_3;
	elseif (a <= 2.4e+45)
		tmp = t_2;
	elseif (a <= 1.1e+57)
		tmp = x + (z / (a / (y - x)));
	elseif (a <= 2.9e+65)
		tmp = y + (a / t_1);
	elseif (a <= 2.95e+65)
		tmp = z * (-y / t);
	else
		tmp = x + ((y * (z - t)) / a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t / N[(y - x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y - N[(z / t$95$1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(x + N[(N[(y - x), $MachinePrecision] * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.7e-57], t$95$3, If[LessEqual[a, 8.2e-81], t$95$2, If[LessEqual[a, 1.45e-36], t$95$3, If[LessEqual[a, 2.4e+45], t$95$2, If[LessEqual[a, 1.1e+57], N[(x + N[(z / N[(a / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.9e+65], N[(y + N[(a / t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.95e+65], N[(z * N[((-y) / t), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]

Derivation

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

Alternative 7: 73.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y - \frac{z}{\frac{t}{y - x}}\\ t_2 := x + \frac{y - x}{\frac{a}{z - t}}\\ \mathbf{if}\;a \leq -1.35 \cdot 10^{-57}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 1.5 \cdot 10^{-225}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 6.8 \cdot 10^{-146}:\\ \;\;\;\;y + \frac{\left(z - a\right) \cdot \left(x - y\right)}{t}\\ \mathbf{elif}\;a \leq 8.2 \cdot 10^{-81} \lor \neg \left(a \leq 1.28 \cdot 10^{-36}\right) \land a \leq 5.6 \cdot 10^{+44}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- y (/ z (/ t (- y x))))) (t_2 (+ x (/ (- y x) (/ a (- z t))))))
   (if (<= a -1.35e-57)
     t_2
     (if (<= a 1.5e-225)
       t_1
       (if (<= a 6.8e-146)
         (+ y (/ (* (- z a) (- x y)) t))
         (if (or (<= a 8.2e-81) (and (not (<= a 1.28e-36)) (<= a 5.6e+44)))
           t_1
           t_2))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y - (z / (t / (y - x)));
	double t_2 = x + ((y - x) / (a / (z - t)));
	double tmp;
	if (a <= -1.35e-57) {
		tmp = t_2;
	} else if (a <= 1.5e-225) {
		tmp = t_1;
	} else if (a <= 6.8e-146) {
		tmp = y + (((z - a) * (x - y)) / t);
	} else if ((a <= 8.2e-81) || (!(a <= 1.28e-36) && (a <= 5.6e+44))) {
		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 = y - (z / (t / (y - x)))
    t_2 = x + ((y - x) / (a / (z - t)))
    if (a <= (-1.35d-57)) then
        tmp = t_2
    else if (a <= 1.5d-225) then
        tmp = t_1
    else if (a <= 6.8d-146) then
        tmp = y + (((z - a) * (x - y)) / t)
    else if ((a <= 8.2d-81) .or. (.not. (a <= 1.28d-36)) .and. (a <= 5.6d+44)) 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 = y - (z / (t / (y - x)));
	double t_2 = x + ((y - x) / (a / (z - t)));
	double tmp;
	if (a <= -1.35e-57) {
		tmp = t_2;
	} else if (a <= 1.5e-225) {
		tmp = t_1;
	} else if (a <= 6.8e-146) {
		tmp = y + (((z - a) * (x - y)) / t);
	} else if ((a <= 8.2e-81) || (!(a <= 1.28e-36) && (a <= 5.6e+44))) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y - (z / (t / (y - x)))
	t_2 = x + ((y - x) / (a / (z - t)))
	tmp = 0
	if a <= -1.35e-57:
		tmp = t_2
	elif a <= 1.5e-225:
		tmp = t_1
	elif a <= 6.8e-146:
		tmp = y + (((z - a) * (x - y)) / t)
	elif (a <= 8.2e-81) or (not (a <= 1.28e-36) and (a <= 5.6e+44)):
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y - Float64(z / Float64(t / Float64(y - x))))
	t_2 = Float64(x + Float64(Float64(y - x) / Float64(a / Float64(z - t))))
	tmp = 0.0
	if (a <= -1.35e-57)
		tmp = t_2;
	elseif (a <= 1.5e-225)
		tmp = t_1;
	elseif (a <= 6.8e-146)
		tmp = Float64(y + Float64(Float64(Float64(z - a) * Float64(x - y)) / t));
	elseif ((a <= 8.2e-81) || (!(a <= 1.28e-36) && (a <= 5.6e+44)))
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y - (z / (t / (y - x)));
	t_2 = x + ((y - x) / (a / (z - t)));
	tmp = 0.0;
	if (a <= -1.35e-57)
		tmp = t_2;
	elseif (a <= 1.5e-225)
		tmp = t_1;
	elseif (a <= 6.8e-146)
		tmp = y + (((z - a) * (x - y)) / t);
	elseif ((a <= 8.2e-81) || (~((a <= 1.28e-36)) && (a <= 5.6e+44)))
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y - N[(z / N[(t / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(y - x), $MachinePrecision] / N[(a / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.35e-57], t$95$2, If[LessEqual[a, 1.5e-225], t$95$1, If[LessEqual[a, 6.8e-146], N[(y + N[(N[(N[(z - a), $MachinePrecision] * N[(x - y), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[a, 8.2e-81], And[N[Not[LessEqual[a, 1.28e-36]], $MachinePrecision], LessEqual[a, 5.6e+44]]], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 8: 57.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y \cdot z}{a}\\ t_2 := y \cdot \frac{z - t}{a - t}\\ \mathbf{if}\;t \leq -2.8 \cdot 10^{+49}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -4.5 \cdot 10^{-31}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -1.85 \cdot 10^{-66}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq 1.9 \cdot 10^{-21}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 4.3 \cdot 10^{+58}:\\ \;\;\;\;y + \frac{z \cdot x}{t}\\ \mathbf{elif}\;t \leq 2.9 \cdot 10^{+77}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (* y z) a))) (t_2 (* y (/ (- z t) (- a t)))))
   (if (<= t -2.8e+49)
     t_2
     (if (<= t -4.5e-31)
       t_1
       (if (<= t -1.85e-66)
         t_2
         (if (<= t 1.9e-21)
           t_1
           (if (<= t 4.3e+58)
             (+ y (/ (* z x) t))
             (if (<= t 2.9e+77) x t_2))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y * z) / a);
	double t_2 = y * ((z - t) / (a - t));
	double tmp;
	if (t <= -2.8e+49) {
		tmp = t_2;
	} else if (t <= -4.5e-31) {
		tmp = t_1;
	} else if (t <= -1.85e-66) {
		tmp = t_2;
	} else if (t <= 1.9e-21) {
		tmp = t_1;
	} else if (t <= 4.3e+58) {
		tmp = y + ((z * x) / t);
	} else if (t <= 2.9e+77) {
		tmp = x;
	} 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 + ((y * z) / a)
    t_2 = y * ((z - t) / (a - t))
    if (t <= (-2.8d+49)) then
        tmp = t_2
    else if (t <= (-4.5d-31)) then
        tmp = t_1
    else if (t <= (-1.85d-66)) then
        tmp = t_2
    else if (t <= 1.9d-21) then
        tmp = t_1
    else if (t <= 4.3d+58) then
        tmp = y + ((z * x) / t)
    else if (t <= 2.9d+77) then
        tmp = x
    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 + ((y * z) / a);
	double t_2 = y * ((z - t) / (a - t));
	double tmp;
	if (t <= -2.8e+49) {
		tmp = t_2;
	} else if (t <= -4.5e-31) {
		tmp = t_1;
	} else if (t <= -1.85e-66) {
		tmp = t_2;
	} else if (t <= 1.9e-21) {
		tmp = t_1;
	} else if (t <= 4.3e+58) {
		tmp = y + ((z * x) / t);
	} else if (t <= 2.9e+77) {
		tmp = x;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y * z) / a)
	t_2 = y * ((z - t) / (a - t))
	tmp = 0
	if t <= -2.8e+49:
		tmp = t_2
	elif t <= -4.5e-31:
		tmp = t_1
	elif t <= -1.85e-66:
		tmp = t_2
	elif t <= 1.9e-21:
		tmp = t_1
	elif t <= 4.3e+58:
		tmp = y + ((z * x) / t)
	elif t <= 2.9e+77:
		tmp = x
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y * z) / a))
	t_2 = Float64(y * Float64(Float64(z - t) / Float64(a - t)))
	tmp = 0.0
	if (t <= -2.8e+49)
		tmp = t_2;
	elseif (t <= -4.5e-31)
		tmp = t_1;
	elseif (t <= -1.85e-66)
		tmp = t_2;
	elseif (t <= 1.9e-21)
		tmp = t_1;
	elseif (t <= 4.3e+58)
		tmp = Float64(y + Float64(Float64(z * x) / t));
	elseif (t <= 2.9e+77)
		tmp = x;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y * z) / a);
	t_2 = y * ((z - t) / (a - t));
	tmp = 0.0;
	if (t <= -2.8e+49)
		tmp = t_2;
	elseif (t <= -4.5e-31)
		tmp = t_1;
	elseif (t <= -1.85e-66)
		tmp = t_2;
	elseif (t <= 1.9e-21)
		tmp = t_1;
	elseif (t <= 4.3e+58)
		tmp = y + ((z * x) / t);
	elseif (t <= 2.9e+77)
		tmp = x;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2.8e+49], t$95$2, If[LessEqual[t, -4.5e-31], t$95$1, If[LessEqual[t, -1.85e-66], t$95$2, If[LessEqual[t, 1.9e-21], t$95$1, If[LessEqual[t, 4.3e+58], N[(y + N[(N[(z * x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.9e+77], x, t$95$2]]]]]]]]

Derivation

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

Alternative 9: 65.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z - t}{a - t}\\ \mathbf{if}\;t \leq -1.32 \cdot 10^{+53}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -1.5 \cdot 10^{-34}:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{elif}\;t \leq -4.8 \cdot 10^{-64}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 2.6 \cdot 10^{-21}:\\ \;\;\;\;x + \left(y - x\right) \cdot \frac{z}{a}\\ \mathbf{elif}\;t \leq 8.6 \cdot 10^{+57}:\\ \;\;\;\;y + \frac{z \cdot x}{t}\\ \mathbf{elif}\;t \leq 2.9 \cdot 10^{+77}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ (- z t) (- a t)))))
   (if (<= t -1.32e+53)
     t_1
     (if (<= t -1.5e-34)
       (+ x (/ (* y z) a))
       (if (<= t -4.8e-64)
         t_1
         (if (<= t 2.6e-21)
           (+ x (* (- y x) (/ z a)))
           (if (<= t 8.6e+57)
             (+ y (/ (* z x) t))
             (if (<= t 2.9e+77) x t_1))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * ((z - t) / (a - t));
	double tmp;
	if (t <= -1.32e+53) {
		tmp = t_1;
	} else if (t <= -1.5e-34) {
		tmp = x + ((y * z) / a);
	} else if (t <= -4.8e-64) {
		tmp = t_1;
	} else if (t <= 2.6e-21) {
		tmp = x + ((y - x) * (z / a));
	} else if (t <= 8.6e+57) {
		tmp = y + ((z * x) / t);
	} else if (t <= 2.9e+77) {
		tmp = 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 = y * ((z - t) / (a - t))
    if (t <= (-1.32d+53)) then
        tmp = t_1
    else if (t <= (-1.5d-34)) then
        tmp = x + ((y * z) / a)
    else if (t <= (-4.8d-64)) then
        tmp = t_1
    else if (t <= 2.6d-21) then
        tmp = x + ((y - x) * (z / a))
    else if (t <= 8.6d+57) then
        tmp = y + ((z * x) / t)
    else if (t <= 2.9d+77) then
        tmp = 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 = y * ((z - t) / (a - t));
	double tmp;
	if (t <= -1.32e+53) {
		tmp = t_1;
	} else if (t <= -1.5e-34) {
		tmp = x + ((y * z) / a);
	} else if (t <= -4.8e-64) {
		tmp = t_1;
	} else if (t <= 2.6e-21) {
		tmp = x + ((y - x) * (z / a));
	} else if (t <= 8.6e+57) {
		tmp = y + ((z * x) / t);
	} else if (t <= 2.9e+77) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * ((z - t) / (a - t))
	tmp = 0
	if t <= -1.32e+53:
		tmp = t_1
	elif t <= -1.5e-34:
		tmp = x + ((y * z) / a)
	elif t <= -4.8e-64:
		tmp = t_1
	elif t <= 2.6e-21:
		tmp = x + ((y - x) * (z / a))
	elif t <= 8.6e+57:
		tmp = y + ((z * x) / t)
	elif t <= 2.9e+77:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(Float64(z - t) / Float64(a - t)))
	tmp = 0.0
	if (t <= -1.32e+53)
		tmp = t_1;
	elseif (t <= -1.5e-34)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	elseif (t <= -4.8e-64)
		tmp = t_1;
	elseif (t <= 2.6e-21)
		tmp = Float64(x + Float64(Float64(y - x) * Float64(z / a)));
	elseif (t <= 8.6e+57)
		tmp = Float64(y + Float64(Float64(z * x) / t));
	elseif (t <= 2.9e+77)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * ((z - t) / (a - t));
	tmp = 0.0;
	if (t <= -1.32e+53)
		tmp = t_1;
	elseif (t <= -1.5e-34)
		tmp = x + ((y * z) / a);
	elseif (t <= -4.8e-64)
		tmp = t_1;
	elseif (t <= 2.6e-21)
		tmp = x + ((y - x) * (z / a));
	elseif (t <= 8.6e+57)
		tmp = y + ((z * x) / t);
	elseif (t <= 2.9e+77)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -1.32e+53], t$95$1, If[LessEqual[t, -1.5e-34], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -4.8e-64], t$95$1, If[LessEqual[t, 2.6e-21], N[(x + N[(N[(y - x), $MachinePrecision] * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 8.6e+57], N[(y + N[(N[(z * x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 2.9e+77], x, t$95$1]]]]]]]

Derivation

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

Alternative 10: 50.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y \cdot z}{a}\\ \mathbf{if}\;a \leq -1.7 \cdot 10^{-57}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -3.2 \cdot 10^{-271}:\\ \;\;\;\;y + \frac{z \cdot x}{t}\\ \mathbf{elif}\;a \leq 6.1 \cdot 10^{-207}:\\ \;\;\;\;y - y \cdot \frac{z}{t}\\ \mathbf{elif}\;a \leq 3.7 \cdot 10^{-126}:\\ \;\;\;\;\left(z - a\right) \cdot \frac{x}{t}\\ \mathbf{elif}\;a \leq 1.25 \cdot 10^{-36} \lor \neg \left(a \leq 3.1 \cdot 10^{+68}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{z - a}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ (* y z) a))))
   (if (<= a -1.7e-57)
     t_1
     (if (<= a -3.2e-271)
       (+ y (/ (* z x) t))
       (if (<= a 6.1e-207)
         (- y (* y (/ z t)))
         (if (<= a 3.7e-126)
           (* (- z a) (/ x t))
           (if (or (<= a 1.25e-36) (not (<= a 3.1e+68)))
             t_1
             (* x (/ (- z a) t)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y * z) / a);
	double tmp;
	if (a <= -1.7e-57) {
		tmp = t_1;
	} else if (a <= -3.2e-271) {
		tmp = y + ((z * x) / t);
	} else if (a <= 6.1e-207) {
		tmp = y - (y * (z / t));
	} else if (a <= 3.7e-126) {
		tmp = (z - a) * (x / t);
	} else if ((a <= 1.25e-36) || !(a <= 3.1e+68)) {
		tmp = t_1;
	} else {
		tmp = x * ((z - a) / t);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + ((y * z) / a)
    if (a <= (-1.7d-57)) then
        tmp = t_1
    else if (a <= (-3.2d-271)) then
        tmp = y + ((z * x) / t)
    else if (a <= 6.1d-207) then
        tmp = y - (y * (z / t))
    else if (a <= 3.7d-126) then
        tmp = (z - a) * (x / t)
    else if ((a <= 1.25d-36) .or. (.not. (a <= 3.1d+68))) then
        tmp = t_1
    else
        tmp = x * ((z - a) / 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 * z) / a);
	double tmp;
	if (a <= -1.7e-57) {
		tmp = t_1;
	} else if (a <= -3.2e-271) {
		tmp = y + ((z * x) / t);
	} else if (a <= 6.1e-207) {
		tmp = y - (y * (z / t));
	} else if (a <= 3.7e-126) {
		tmp = (z - a) * (x / t);
	} else if ((a <= 1.25e-36) || !(a <= 3.1e+68)) {
		tmp = t_1;
	} else {
		tmp = x * ((z - a) / t);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y * z) / a)
	tmp = 0
	if a <= -1.7e-57:
		tmp = t_1
	elif a <= -3.2e-271:
		tmp = y + ((z * x) / t)
	elif a <= 6.1e-207:
		tmp = y - (y * (z / t))
	elif a <= 3.7e-126:
		tmp = (z - a) * (x / t)
	elif (a <= 1.25e-36) or not (a <= 3.1e+68):
		tmp = t_1
	else:
		tmp = x * ((z - a) / t)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y * z) / a))
	tmp = 0.0
	if (a <= -1.7e-57)
		tmp = t_1;
	elseif (a <= -3.2e-271)
		tmp = Float64(y + Float64(Float64(z * x) / t));
	elseif (a <= 6.1e-207)
		tmp = Float64(y - Float64(y * Float64(z / t)));
	elseif (a <= 3.7e-126)
		tmp = Float64(Float64(z - a) * Float64(x / t));
	elseif ((a <= 1.25e-36) || !(a <= 3.1e+68))
		tmp = t_1;
	else
		tmp = Float64(x * Float64(Float64(z - a) / t));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y * z) / a);
	tmp = 0.0;
	if (a <= -1.7e-57)
		tmp = t_1;
	elseif (a <= -3.2e-271)
		tmp = y + ((z * x) / t);
	elseif (a <= 6.1e-207)
		tmp = y - (y * (z / t));
	elseif (a <= 3.7e-126)
		tmp = (z - a) * (x / t);
	elseif ((a <= 1.25e-36) || ~((a <= 3.1e+68)))
		tmp = t_1;
	else
		tmp = x * ((z - a) / t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.7e-57], t$95$1, If[LessEqual[a, -3.2e-271], N[(y + N[(N[(z * x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 6.1e-207], N[(y - N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3.7e-126], N[(N[(z - a), $MachinePrecision] * N[(x / t), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[a, 1.25e-36], N[Not[LessEqual[a, 3.1e+68]], $MachinePrecision]], t$95$1, N[(x * N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

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

Alternative 11: 73.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -7 \cdot 10^{-58} \lor \neg \left(a \leq 7.5 \cdot 10^{-81}\right) \land \left(a \leq 1.1 \cdot 10^{-36} \lor \neg \left(a \leq 5.6 \cdot 10^{+48}\right)\right):\\ \;\;\;\;x + \frac{y - x}{\frac{a}{z - t}}\\ \mathbf{else}:\\ \;\;\;\;y - \frac{z}{\frac{t}{y - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -7e-58)
         (and (not (<= a 7.5e-81)) (or (<= a 1.1e-36) (not (<= a 5.6e+48)))))
   (+ x (/ (- y x) (/ a (- z t))))
   (- y (/ z (/ t (- y x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -7e-58) || (!(a <= 7.5e-81) && ((a <= 1.1e-36) || !(a <= 5.6e+48)))) {
		tmp = x + ((y - x) / (a / (z - t)));
	} else {
		tmp = y - (z / (t / (y - x)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((a <= (-7d-58)) .or. (.not. (a <= 7.5d-81)) .and. (a <= 1.1d-36) .or. (.not. (a <= 5.6d+48))) then
        tmp = x + ((y - x) / (a / (z - t)))
    else
        tmp = y - (z / (t / (y - x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((a <= -7e-58) || (!(a <= 7.5e-81) && ((a <= 1.1e-36) || !(a <= 5.6e+48)))) {
		tmp = x + ((y - x) / (a / (z - t)));
	} else {
		tmp = y - (z / (t / (y - x)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -7e-58) or (not (a <= 7.5e-81) and ((a <= 1.1e-36) or not (a <= 5.6e+48))):
		tmp = x + ((y - x) / (a / (z - t)))
	else:
		tmp = y - (z / (t / (y - x)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((a <= -7e-58) || (!(a <= 7.5e-81) && ((a <= 1.1e-36) || !(a <= 5.6e+48))))
		tmp = Float64(x + Float64(Float64(y - x) / Float64(a / Float64(z - t))));
	else
		tmp = Float64(y - Float64(z / Float64(t / Float64(y - x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((a <= -7e-58) || (~((a <= 7.5e-81)) && ((a <= 1.1e-36) || ~((a <= 5.6e+48)))))
		tmp = x + ((y - x) / (a / (z - t)));
	else
		tmp = y - (z / (t / (y - x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[a, -7e-58], And[N[Not[LessEqual[a, 7.5e-81]], $MachinePrecision], Or[LessEqual[a, 1.1e-36], N[Not[LessEqual[a, 5.6e+48]], $MachinePrecision]]]], N[(x + N[(N[(y - x), $MachinePrecision] / N[(a / N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y - N[(z / N[(t / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 12: 36.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.8 \cdot 10^{-53}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq -6 \cdot 10^{-191}:\\ \;\;\;\;y\\ \mathbf{elif}\;a \leq -1.66 \cdot 10^{-286}:\\ \;\;\;\;\frac{x}{\frac{t}{z}}\\ \mathbf{elif}\;a \leq 3.7 \cdot 10^{-239}:\\ \;\;\;\;z \cdot \frac{-y}{t}\\ \mathbf{elif}\;a \leq 1.4 \cdot 10^{+66}:\\ \;\;\;\;x \cdot \frac{z - a}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.8e-53)
   x
   (if (<= a -6e-191)
     y
     (if (<= a -1.66e-286)
       (/ x (/ t z))
       (if (<= a 3.7e-239)
         (* z (/ (- y) t))
         (if (<= a 1.4e+66) (* x (/ (- z a) t)) x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.8e-53) {
		tmp = x;
	} else if (a <= -6e-191) {
		tmp = y;
	} else if (a <= -1.66e-286) {
		tmp = x / (t / z);
	} else if (a <= 3.7e-239) {
		tmp = z * (-y / t);
	} else if (a <= 1.4e+66) {
		tmp = x * ((z - a) / 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 <= (-1.8d-53)) then
        tmp = x
    else if (a <= (-6d-191)) then
        tmp = y
    else if (a <= (-1.66d-286)) then
        tmp = x / (t / z)
    else if (a <= 3.7d-239) then
        tmp = z * (-y / t)
    else if (a <= 1.4d+66) then
        tmp = x * ((z - a) / 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 <= -1.8e-53) {
		tmp = x;
	} else if (a <= -6e-191) {
		tmp = y;
	} else if (a <= -1.66e-286) {
		tmp = x / (t / z);
	} else if (a <= 3.7e-239) {
		tmp = z * (-y / t);
	} else if (a <= 1.4e+66) {
		tmp = x * ((z - a) / t);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -1.8e-53:
		tmp = x
	elif a <= -6e-191:
		tmp = y
	elif a <= -1.66e-286:
		tmp = x / (t / z)
	elif a <= 3.7e-239:
		tmp = z * (-y / t)
	elif a <= 1.4e+66:
		tmp = x * ((z - a) / t)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.8e-53)
		tmp = x;
	elseif (a <= -6e-191)
		tmp = y;
	elseif (a <= -1.66e-286)
		tmp = Float64(x / Float64(t / z));
	elseif (a <= 3.7e-239)
		tmp = Float64(z * Float64(Float64(-y) / t));
	elseif (a <= 1.4e+66)
		tmp = Float64(x * Float64(Float64(z - a) / t));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -1.8e-53)
		tmp = x;
	elseif (a <= -6e-191)
		tmp = y;
	elseif (a <= -1.66e-286)
		tmp = x / (t / z);
	elseif (a <= 3.7e-239)
		tmp = z * (-y / t);
	elseif (a <= 1.4e+66)
		tmp = x * ((z - a) / t);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.8e-53], x, If[LessEqual[a, -6e-191], y, If[LessEqual[a, -1.66e-286], N[(x / N[(t / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3.7e-239], N[(z * N[((-y) / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.4e+66], N[(x * N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], x]]]]]

Derivation

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

Alternative 13: 67.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - x\right) \cdot \frac{z}{a}\\ \mathbf{if}\;a \leq -1.5 \cdot 10^{-57}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.12 \cdot 10^{-82}:\\ \;\;\;\;y - \frac{z}{\frac{t}{y - x}}\\ \mathbf{elif}\;a \leq 1.35 \cdot 10^{-36}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.25 \cdot 10^{+66}:\\ \;\;\;\;y - \frac{x \cdot \left(a - z\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot \left(z - t\right)}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- y x) (/ z a)))))
   (if (<= a -1.5e-57)
     t_1
     (if (<= a 1.12e-82)
       (- y (/ z (/ t (- y x))))
       (if (<= a 1.35e-36)
         t_1
         (if (<= a 1.25e+66)
           (- y (/ (* x (- a z)) t))
           (+ x (/ (* y (- z t)) a))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - x) * (z / a));
	double tmp;
	if (a <= -1.5e-57) {
		tmp = t_1;
	} else if (a <= 1.12e-82) {
		tmp = y - (z / (t / (y - x)));
	} else if (a <= 1.35e-36) {
		tmp = t_1;
	} else if (a <= 1.25e+66) {
		tmp = y - ((x * (a - z)) / t);
	} else {
		tmp = x + ((y * (z - t)) / a);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + ((y - x) * (z / a))
    if (a <= (-1.5d-57)) then
        tmp = t_1
    else if (a <= 1.12d-82) then
        tmp = y - (z / (t / (y - x)))
    else if (a <= 1.35d-36) then
        tmp = t_1
    else if (a <= 1.25d+66) then
        tmp = y - ((x * (a - z)) / t)
    else
        tmp = x + ((y * (z - t)) / a)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - x) * (z / a));
	double tmp;
	if (a <= -1.5e-57) {
		tmp = t_1;
	} else if (a <= 1.12e-82) {
		tmp = y - (z / (t / (y - x)));
	} else if (a <= 1.35e-36) {
		tmp = t_1;
	} else if (a <= 1.25e+66) {
		tmp = y - ((x * (a - z)) / t);
	} else {
		tmp = x + ((y * (z - t)) / a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y - x) * (z / a))
	tmp = 0
	if a <= -1.5e-57:
		tmp = t_1
	elif a <= 1.12e-82:
		tmp = y - (z / (t / (y - x)))
	elif a <= 1.35e-36:
		tmp = t_1
	elif a <= 1.25e+66:
		tmp = y - ((x * (a - z)) / t)
	else:
		tmp = x + ((y * (z - t)) / a)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - x) * Float64(z / a)))
	tmp = 0.0
	if (a <= -1.5e-57)
		tmp = t_1;
	elseif (a <= 1.12e-82)
		tmp = Float64(y - Float64(z / Float64(t / Float64(y - x))));
	elseif (a <= 1.35e-36)
		tmp = t_1;
	elseif (a <= 1.25e+66)
		tmp = Float64(y - Float64(Float64(x * Float64(a - z)) / t));
	else
		tmp = Float64(x + Float64(Float64(y * Float64(z - t)) / a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y - x) * (z / a));
	tmp = 0.0;
	if (a <= -1.5e-57)
		tmp = t_1;
	elseif (a <= 1.12e-82)
		tmp = y - (z / (t / (y - x)));
	elseif (a <= 1.35e-36)
		tmp = t_1;
	elseif (a <= 1.25e+66)
		tmp = y - ((x * (a - z)) / t);
	else
		tmp = x + ((y * (z - t)) / a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(y - x), $MachinePrecision] * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.5e-57], t$95$1, If[LessEqual[a, 1.12e-82], N[(y - N[(z / N[(t / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.35e-36], t$95$1, If[LessEqual[a, 1.25e+66], N[(y - N[(N[(x * N[(a - z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 14: 84.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -8.5 \cdot 10^{+184} \lor \neg \left(t \leq 8 \cdot 10^{+122}\right):\\ \;\;\;\;y - \frac{z}{\frac{t}{y - x}}\\ \mathbf{else}:\\ \;\;\;\;x + \left(t - z\right) \cdot \frac{x - y}{a - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -8.5e+184) (not (<= t 8e+122)))
   (- y (/ z (/ t (- y x))))
   (+ x (* (- t z) (/ (- x y) (- a t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -8.5e+184) || !(t <= 8e+122)) {
		tmp = y - (z / (t / (y - x)));
	} else {
		tmp = x + ((t - z) * ((x - y) / (a - t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((t <= (-8.5d+184)) .or. (.not. (t <= 8d+122))) then
        tmp = y - (z / (t / (y - x)))
    else
        tmp = x + ((t - z) * ((x - y) / (a - t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -8.5e+184) || !(t <= 8e+122)) {
		tmp = y - (z / (t / (y - x)));
	} else {
		tmp = x + ((t - z) * ((x - y) / (a - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -8.5e+184) or not (t <= 8e+122):
		tmp = y - (z / (t / (y - x)))
	else:
		tmp = x + ((t - z) * ((x - y) / (a - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -8.5e+184) || !(t <= 8e+122))
		tmp = Float64(y - Float64(z / Float64(t / Float64(y - x))));
	else
		tmp = Float64(x + Float64(Float64(t - z) * Float64(Float64(x - y) / Float64(a - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -8.5e+184) || ~((t <= 8e+122)))
		tmp = y - (z / (t / (y - x)));
	else
		tmp = x + ((t - z) * ((x - y) / (a - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -8.5e+184], N[Not[LessEqual[t, 8e+122]], $MachinePrecision]], N[(y - N[(z / N[(t / N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(t - z), $MachinePrecision] * N[(N[(x - y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 15: 35.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{\frac{t}{z}}\\ \mathbf{if}\;a \leq -5.5 \cdot 10^{-54}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq -3.7 \cdot 10^{-193}:\\ \;\;\;\;y\\ \mathbf{elif}\;a \leq -1.45 \cdot 10^{-283}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.35 \cdot 10^{-239}:\\ \;\;\;\;z \cdot \frac{-y}{t}\\ \mathbf{elif}\;a \leq 3 \cdot 10^{+62}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ x (/ t z))))
   (if (<= a -5.5e-54)
     x
     (if (<= a -3.7e-193)
       y
       (if (<= a -1.45e-283)
         t_1
         (if (<= a 1.35e-239) (* z (/ (- y) t)) (if (<= a 3e+62) t_1 x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x / (t / z);
	double tmp;
	if (a <= -5.5e-54) {
		tmp = x;
	} else if (a <= -3.7e-193) {
		tmp = y;
	} else if (a <= -1.45e-283) {
		tmp = t_1;
	} else if (a <= 1.35e-239) {
		tmp = z * (-y / t);
	} else if (a <= 3e+62) {
		tmp = t_1;
	} 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) :: t_1
    real(8) :: tmp
    t_1 = x / (t / z)
    if (a <= (-5.5d-54)) then
        tmp = x
    else if (a <= (-3.7d-193)) then
        tmp = y
    else if (a <= (-1.45d-283)) then
        tmp = t_1
    else if (a <= 1.35d-239) then
        tmp = z * (-y / t)
    else if (a <= 3d+62) then
        tmp = t_1
    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 t_1 = x / (t / z);
	double tmp;
	if (a <= -5.5e-54) {
		tmp = x;
	} else if (a <= -3.7e-193) {
		tmp = y;
	} else if (a <= -1.45e-283) {
		tmp = t_1;
	} else if (a <= 1.35e-239) {
		tmp = z * (-y / t);
	} else if (a <= 3e+62) {
		tmp = t_1;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x / (t / z)
	tmp = 0
	if a <= -5.5e-54:
		tmp = x
	elif a <= -3.7e-193:
		tmp = y
	elif a <= -1.45e-283:
		tmp = t_1
	elif a <= 1.35e-239:
		tmp = z * (-y / t)
	elif a <= 3e+62:
		tmp = t_1
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x / Float64(t / z))
	tmp = 0.0
	if (a <= -5.5e-54)
		tmp = x;
	elseif (a <= -3.7e-193)
		tmp = y;
	elseif (a <= -1.45e-283)
		tmp = t_1;
	elseif (a <= 1.35e-239)
		tmp = Float64(z * Float64(Float64(-y) / t));
	elseif (a <= 3e+62)
		tmp = t_1;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x / (t / z);
	tmp = 0.0;
	if (a <= -5.5e-54)
		tmp = x;
	elseif (a <= -3.7e-193)
		tmp = y;
	elseif (a <= -1.45e-283)
		tmp = t_1;
	elseif (a <= 1.35e-239)
		tmp = z * (-y / t);
	elseif (a <= 3e+62)
		tmp = t_1;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x / N[(t / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -5.5e-54], x, If[LessEqual[a, -3.7e-193], y, If[LessEqual[a, -1.45e-283], t$95$1, If[LessEqual[a, 1.35e-239], N[(z * N[((-y) / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3e+62], t$95$1, x]]]]]]

Derivation

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

Alternative 16: 53.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -3.25 \cdot 10^{+50} \lor \neg \left(t \leq -3.2 \cdot 10^{-36} \lor \neg \left(t \leq -3.35 \cdot 10^{-65}\right) \land t \leq 1.05 \cdot 10^{+95}\right):\\ \;\;\;\;y - y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -3.25e+50)
         (not
          (or (<= t -3.2e-36) (and (not (<= t -3.35e-65)) (<= t 1.05e+95)))))
   (- y (* y (/ z t)))
   (+ x (/ (* y z) a))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -3.25e+50) || !((t <= -3.2e-36) || (!(t <= -3.35e-65) && (t <= 1.05e+95)))) {
		tmp = y - (y * (z / t));
	} else {
		tmp = x + ((y * z) / a);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((t <= (-3.25d+50)) .or. (.not. (t <= (-3.2d-36)) .or. (.not. (t <= (-3.35d-65))) .and. (t <= 1.05d+95))) then
        tmp = y - (y * (z / t))
    else
        tmp = x + ((y * z) / a)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -3.25e+50) || !((t <= -3.2e-36) || (!(t <= -3.35e-65) && (t <= 1.05e+95)))) {
		tmp = y - (y * (z / t));
	} else {
		tmp = x + ((y * z) / a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -3.25e+50) or not ((t <= -3.2e-36) or (not (t <= -3.35e-65) and (t <= 1.05e+95))):
		tmp = y - (y * (z / t))
	else:
		tmp = x + ((y * z) / a)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -3.25e+50) || !((t <= -3.2e-36) || (!(t <= -3.35e-65) && (t <= 1.05e+95))))
		tmp = Float64(y - Float64(y * Float64(z / t)));
	else
		tmp = Float64(x + Float64(Float64(y * z) / a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -3.25e+50) || ~(((t <= -3.2e-36) || (~((t <= -3.35e-65)) && (t <= 1.05e+95)))))
		tmp = y - (y * (z / t));
	else
		tmp = x + ((y * z) / a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -3.25e+50], N[Not[Or[LessEqual[t, -3.2e-36], And[N[Not[LessEqual[t, -3.35e-65]], $MachinePrecision], LessEqual[t, 1.05e+95]]]], $MachinePrecision]], N[(y - N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 17: 32.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{a}\\ \mathbf{if}\;z \leq -5.2 \cdot 10^{+35}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2.2 \cdot 10^{-118}:\\ \;\;\;\;y\\ \mathbf{elif}\;z \leq 2.1 \cdot 10^{+61}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ z a))))
   (if (<= z -5.2e+35) t_1 (if (<= z -2.2e-118) y (if (<= z 2.1e+61) x t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / a);
	double tmp;
	if (z <= -5.2e+35) {
		tmp = t_1;
	} else if (z <= -2.2e-118) {
		tmp = y;
	} else if (z <= 2.1e+61) {
		tmp = 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 = y * (z / a)
    if (z <= (-5.2d+35)) then
        tmp = t_1
    else if (z <= (-2.2d-118)) then
        tmp = y
    else if (z <= 2.1d+61) then
        tmp = 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 = y * (z / a);
	double tmp;
	if (z <= -5.2e+35) {
		tmp = t_1;
	} else if (z <= -2.2e-118) {
		tmp = y;
	} else if (z <= 2.1e+61) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * (z / a)
	tmp = 0
	if z <= -5.2e+35:
		tmp = t_1
	elif z <= -2.2e-118:
		tmp = y
	elif z <= 2.1e+61:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(z / a))
	tmp = 0.0
	if (z <= -5.2e+35)
		tmp = t_1;
	elseif (z <= -2.2e-118)
		tmp = y;
	elseif (z <= 2.1e+61)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * (z / a);
	tmp = 0.0;
	if (z <= -5.2e+35)
		tmp = t_1;
	elseif (z <= -2.2e-118)
		tmp = y;
	elseif (z <= 2.1e+61)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(z / a), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -5.2e+35], t$95$1, If[LessEqual[z, -2.2e-118], y, If[LessEqual[z, 2.1e+61], x, t$95$1]]]]

Derivation

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

Alternative 18: 32.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.2 \cdot 10^{+38}:\\ \;\;\;\;z \cdot \frac{y}{a}\\ \mathbf{elif}\;z \leq -6 \cdot 10^{-117}:\\ \;\;\;\;y\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{+62}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{z}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -8.2e+38)
   (* z (/ y a))
   (if (<= z -6e-117) y (if (<= z 2.2e+62) x (* y (/ z a))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -8.2e+38) {
		tmp = z * (y / a);
	} else if (z <= -6e-117) {
		tmp = y;
	} else if (z <= 2.2e+62) {
		tmp = x;
	} else {
		tmp = y * (z / a);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-8.2d+38)) then
        tmp = z * (y / a)
    else if (z <= (-6d-117)) then
        tmp = y
    else if (z <= 2.2d+62) then
        tmp = x
    else
        tmp = y * (z / a)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -8.2e+38) {
		tmp = z * (y / a);
	} else if (z <= -6e-117) {
		tmp = y;
	} else if (z <= 2.2e+62) {
		tmp = x;
	} else {
		tmp = y * (z / a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -8.2e+38:
		tmp = z * (y / a)
	elif z <= -6e-117:
		tmp = y
	elif z <= 2.2e+62:
		tmp = x
	else:
		tmp = y * (z / a)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -8.2e+38)
		tmp = Float64(z * Float64(y / a));
	elseif (z <= -6e-117)
		tmp = y;
	elseif (z <= 2.2e+62)
		tmp = x;
	else
		tmp = Float64(y * Float64(z / a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -8.2e+38)
		tmp = z * (y / a);
	elseif (z <= -6e-117)
		tmp = y;
	elseif (z <= 2.2e+62)
		tmp = x;
	else
		tmp = y * (z / a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -8.2e+38], N[(z * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -6e-117], y, If[LessEqual[z, 2.2e+62], x, N[(y * N[(z / a), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 19: 31.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.7 \cdot 10^{+38}:\\ \;\;\;\;z \cdot \frac{y}{a}\\ \mathbf{elif}\;z \leq -1.2 \cdot 10^{-118}:\\ \;\;\;\;y\\ \mathbf{elif}\;z \leq 2.85 \cdot 10^{+103}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{t}{z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -3.7e+38)
   (* z (/ y a))
   (if (<= z -1.2e-118) y (if (<= z 2.85e+103) x (/ x (/ t z))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -3.7e+38) {
		tmp = z * (y / a);
	} else if (z <= -1.2e-118) {
		tmp = y;
	} else if (z <= 2.85e+103) {
		tmp = x;
	} else {
		tmp = x / (t / z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-3.7d+38)) then
        tmp = z * (y / a)
    else if (z <= (-1.2d-118)) then
        tmp = y
    else if (z <= 2.85d+103) then
        tmp = x
    else
        tmp = x / (t / 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 <= -3.7e+38) {
		tmp = z * (y / a);
	} else if (z <= -1.2e-118) {
		tmp = y;
	} else if (z <= 2.85e+103) {
		tmp = x;
	} else {
		tmp = x / (t / z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -3.7e+38:
		tmp = z * (y / a)
	elif z <= -1.2e-118:
		tmp = y
	elif z <= 2.85e+103:
		tmp = x
	else:
		tmp = x / (t / z)
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -3.7e+38)
		tmp = Float64(z * Float64(y / a));
	elseif (z <= -1.2e-118)
		tmp = y;
	elseif (z <= 2.85e+103)
		tmp = x;
	else
		tmp = Float64(x / Float64(t / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -3.7e+38)
		tmp = z * (y / a);
	elseif (z <= -1.2e-118)
		tmp = y;
	elseif (z <= 2.85e+103)
		tmp = x;
	else
		tmp = x / (t / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -3.7e+38], N[(z * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.2e-118], y, If[LessEqual[z, 2.85e+103], x, N[(x / N[(t / z), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 20: 50.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -6.9 \cdot 10^{+53}:\\ \;\;\;\;y\\ \mathbf{elif}\;t \leq 1.5 \cdot 10^{+95}:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -6.9e+53) y (if (<= t 1.5e+95) (+ x (/ (* y z) a)) y)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -6.9e+53) {
		tmp = y;
	} else if (t <= 1.5e+95) {
		tmp = x + ((y * z) / a);
	} else {
		tmp = y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (t <= (-6.9d+53)) then
        tmp = y
    else if (t <= 1.5d+95) then
        tmp = x + ((y * z) / a)
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -6.9e+53) {
		tmp = y;
	} else if (t <= 1.5e+95) {
		tmp = x + ((y * z) / a);
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -6.9e+53:
		tmp = y
	elif t <= 1.5e+95:
		tmp = x + ((y * z) / a)
	else:
		tmp = y
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -6.9e+53)
		tmp = y;
	elseif (t <= 1.5e+95)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -6.9e+53)
		tmp = y;
	elseif (t <= 1.5e+95)
		tmp = x + ((y * z) / a);
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -6.9e+53], y, If[LessEqual[t, 1.5e+95], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], y]]

Derivation

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

Alternative 21: 38.9% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.8 \cdot 10^{-53}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 2.5 \cdot 10^{+85}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.8e-53) x (if (<= a 2.5e+85) y x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.8e-53) {
		tmp = x;
	} else if (a <= 2.5e+85) {
		tmp = 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 <= (-1.8d-53)) then
        tmp = x
    else if (a <= 2.5d+85) then
        tmp = 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 <= -1.8e-53) {
		tmp = x;
	} else if (a <= 2.5e+85) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -1.8e-53:
		tmp = x
	elif a <= 2.5e+85:
		tmp = y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.8e-53)
		tmp = x;
	elseif (a <= 2.5e+85)
		tmp = y;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -1.8e-53)
		tmp = x;
	elseif (a <= 2.5e+85)
		tmp = y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.8e-53], x, If[LessEqual[a, 2.5e+85], y, x]]

Derivation

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

Alternative 22: 25.3% accurate, 13.0× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = x
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 87.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y - x}{1} \cdot \frac{z - t}{a - t}\\ \mathbf{if}\;a < -1.6153062845442575 \cdot 10^{-142}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a < 3.774403170083174 \cdot 10^{-182}:\\ \;\;\;\;y - \frac{z}{t} \cdot \left(y - x\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (/ (- y x) 1.0) (/ (- z t) (- a t))))))
   (if (< a -1.6153062845442575e-142)
     t_1
     (if (< a 3.774403170083174e-182) (- y (* (/ z t) (- y x))) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (((y - x) / 1.0) * ((z - t) / (a - t)));
	double tmp;
	if (a < -1.6153062845442575e-142) {
		tmp = t_1;
	} else if (a < 3.774403170083174e-182) {
		tmp = y - ((z / t) * (y - x));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (((y - x) / 1.0d0) * ((z - t) / (a - t)))
    if (a < (-1.6153062845442575d-142)) then
        tmp = t_1
    else if (a < 3.774403170083174d-182) then
        tmp = y - ((z / t) * (y - x))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (((y - x) / 1.0) * ((z - t) / (a - t)));
	double tmp;
	if (a < -1.6153062845442575e-142) {
		tmp = t_1;
	} else if (a < 3.774403170083174e-182) {
		tmp = y - ((z / t) * (y - x));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (((y - x) / 1.0) * ((z - t) / (a - t)))
	tmp = 0
	if a < -1.6153062845442575e-142:
		tmp = t_1
	elif a < 3.774403170083174e-182:
		tmp = y - ((z / t) * (y - x))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(Float64(y - x) / 1.0) * Float64(Float64(z - t) / Float64(a - t))))
	tmp = 0.0
	if (a < -1.6153062845442575e-142)
		tmp = t_1;
	elseif (a < 3.774403170083174e-182)
		tmp = Float64(y - Float64(Float64(z / t) * Float64(y - x)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (((y - x) / 1.0) * ((z - t) / (a - t)));
	tmp = 0.0;
	if (a < -1.6153062845442575e-142)
		tmp = t_1;
	elseif (a < 3.774403170083174e-182)
		tmp = y - ((z / t) * (y - x));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(N[(y - x), $MachinePrecision] / 1.0), $MachinePrecision] * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[a, -1.6153062845442575e-142], t$95$1, If[Less[a, 3.774403170083174e-182], N[(y - N[(N[(z / t), $MachinePrecision] * N[(y - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2024008 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Chart.Axis.Types:linMap from Chart-1.5.3"
  :precision binary64

  :herbie-target
  (if (< a -1.6153062845442575e-142) (+ x (* (/ (- y x) 1.0) (/ (- z t) (- a t)))) (if (< a 3.774403170083174e-182) (- y (* (/ z t) (- y x))) (+ x (* (/ (- y x) 1.0) (/ (- z t) (- a t))))))

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