Numeric.Signal:interpolate from hsignal-0.2.7.1

Percentage Accurate: 80.8% → 91.0%

Time: 22.2s

Alternatives: 20

Speedup: 0.3×

Specification

Math

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

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(y - z) * Float64(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[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 80.8% accurate, 1.0× speedup

Math

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

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(y - z) * Float64(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[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 91.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t - x}{a - z}\\ t_2 := x + \left(y - z\right) \cdot t_1\\ \mathbf{if}\;t_2 \leq -1 \cdot 10^{-241}:\\ \;\;\;\;\mathsf{fma}\left(y - z, t_1, x\right)\\ \mathbf{elif}\;t_2 \leq 10^{-191}:\\ \;\;\;\;t - \frac{t - x}{\frac{z}{y - a}}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- t x) (- a z))) (t_2 (+ x (* (- y z) t_1))))
   (if (<= t_2 -1e-241)
     (fma (- y z) t_1 x)
     (if (<= t_2 1e-191) (- t (/ (- t x) (/ z (- y a)))) t_2))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (t - x) / (a - z);
	double t_2 = x + ((y - z) * t_1);
	double tmp;
	if (t_2 <= -1e-241) {
		tmp = fma((y - z), t_1, x);
	} else if (t_2 <= 1e-191) {
		tmp = t - ((t - x) / (z / (y - a)));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(t - x) / Float64(a - z))
	t_2 = Float64(x + Float64(Float64(y - z) * t_1))
	tmp = 0.0
	if (t_2 <= -1e-241)
		tmp = fma(Float64(y - z), t_1, x);
	elseif (t_2 <= 1e-191)
		tmp = Float64(t - Float64(Float64(t - x) / Float64(z / Float64(y - a))));
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(y - z), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -1e-241], N[(N[(y - z), $MachinePrecision] * t$95$1 + x), $MachinePrecision], If[LessEqual[t$95$2, 1e-191], N[(t - N[(N[(t - x), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]

Derivation

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

Alternative 2: 91.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - z\right) \cdot \frac{t - x}{a - z}\\ \mathbf{if}\;t_1 \leq -1 \cdot 10^{-241} \lor \neg \left(t_1 \leq 10^{-191}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t - \frac{t - x}{\frac{z}{y - a}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- y z) (/ (- t x) (- a z))))))
   (if (or (<= t_1 -1e-241) (not (<= t_1 1e-191)))
     t_1
     (- t (/ (- t x) (/ z (- y a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if ((t_1 <= -1e-241) || !(t_1 <= 1e-191)) {
		tmp = t_1;
	} 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) :: t_1
    real(8) :: tmp
    t_1 = x + ((y - z) * ((t - x) / (a - z)))
    if ((t_1 <= (-1d-241)) .or. (.not. (t_1 <= 1d-191))) then
        tmp = t_1
    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 t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if ((t_1 <= -1e-241) || !(t_1 <= 1e-191)) {
		tmp = t_1;
	} else {
		tmp = t - ((t - x) / (z / (y - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y - z) * ((t - x) / (a - z)))
	tmp = 0
	if (t_1 <= -1e-241) or not (t_1 <= 1e-191):
		tmp = t_1
	else:
		tmp = t - ((t - x) / (z / (y - a)))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - z) * Float64(Float64(t - x) / Float64(a - z))))
	tmp = 0.0
	if ((t_1 <= -1e-241) || !(t_1 <= 1e-191))
		tmp = t_1;
	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)
	t_1 = x + ((y - z) * ((t - x) / (a - z)));
	tmp = 0.0;
	if ((t_1 <= -1e-241) || ~((t_1 <= 1e-191)))
		tmp = t_1;
	else
		tmp = t - ((t - x) / (z / (y - a)));
	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[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -1e-241], N[Not[LessEqual[t$95$1, 1e-191]], $MachinePrecision]], t$95$1, 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 3: 50.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(1 - \frac{y}{z}\right)\\ t_2 := x - x \cdot \frac{y}{a}\\ \mathbf{if}\;a \leq -4.5 \cdot 10^{+94}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq -1.45 \cdot 10^{+41}:\\ \;\;\;\;\frac{t}{1 - \frac{a}{z}}\\ \mathbf{elif}\;a \leq -5.3 \cdot 10^{+30}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq -5.8 \cdot 10^{-81}:\\ \;\;\;\;\left(y - a\right) \cdot \frac{x}{z}\\ \mathbf{elif}\;a \leq -4.1 \cdot 10^{-257}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.65 \cdot 10^{-301}:\\ \;\;\;\;\frac{y}{z} \cdot \left(x - t\right)\\ \mathbf{elif}\;a \leq 3 \cdot 10^{-8}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 2.05 \cdot 10^{+59} \lor \neg \left(a \leq 2.1 \cdot 10^{+93}\right):\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{\frac{a}{y - z}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (- 1.0 (/ y z)))) (t_2 (- x (* x (/ y a)))))
   (if (<= a -4.5e+94)
     t_2
     (if (<= a -1.45e+41)
       (/ t (- 1.0 (/ a z)))
       (if (<= a -5.3e+30)
         t_2
         (if (<= a -5.8e-81)
           (* (- y a) (/ x z))
           (if (<= a -4.1e-257)
             t_1
             (if (<= a 1.65e-301)
               (* (/ y z) (- x t))
               (if (<= a 3e-8)
                 t_1
                 (if (or (<= a 2.05e+59) (not (<= a 2.1e+93)))
                   t_2
                   (/ t (/ a (- y z)))))))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * (1.0 - (y / z));
	double t_2 = x - (x * (y / a));
	double tmp;
	if (a <= -4.5e+94) {
		tmp = t_2;
	} else if (a <= -1.45e+41) {
		tmp = t / (1.0 - (a / z));
	} else if (a <= -5.3e+30) {
		tmp = t_2;
	} else if (a <= -5.8e-81) {
		tmp = (y - a) * (x / z);
	} else if (a <= -4.1e-257) {
		tmp = t_1;
	} else if (a <= 1.65e-301) {
		tmp = (y / z) * (x - t);
	} else if (a <= 3e-8) {
		tmp = t_1;
	} else if ((a <= 2.05e+59) || !(a <= 2.1e+93)) {
		tmp = t_2;
	} else {
		tmp = t / (a / (y - z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = t * (1.0d0 - (y / z))
    t_2 = x - (x * (y / a))
    if (a <= (-4.5d+94)) then
        tmp = t_2
    else if (a <= (-1.45d+41)) then
        tmp = t / (1.0d0 - (a / z))
    else if (a <= (-5.3d+30)) then
        tmp = t_2
    else if (a <= (-5.8d-81)) then
        tmp = (y - a) * (x / z)
    else if (a <= (-4.1d-257)) then
        tmp = t_1
    else if (a <= 1.65d-301) then
        tmp = (y / z) * (x - t)
    else if (a <= 3d-8) then
        tmp = t_1
    else if ((a <= 2.05d+59) .or. (.not. (a <= 2.1d+93))) then
        tmp = t_2
    else
        tmp = t / (a / (y - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t * (1.0 - (y / z));
	double t_2 = x - (x * (y / a));
	double tmp;
	if (a <= -4.5e+94) {
		tmp = t_2;
	} else if (a <= -1.45e+41) {
		tmp = t / (1.0 - (a / z));
	} else if (a <= -5.3e+30) {
		tmp = t_2;
	} else if (a <= -5.8e-81) {
		tmp = (y - a) * (x / z);
	} else if (a <= -4.1e-257) {
		tmp = t_1;
	} else if (a <= 1.65e-301) {
		tmp = (y / z) * (x - t);
	} else if (a <= 3e-8) {
		tmp = t_1;
	} else if ((a <= 2.05e+59) || !(a <= 2.1e+93)) {
		tmp = t_2;
	} else {
		tmp = t / (a / (y - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t * (1.0 - (y / z))
	t_2 = x - (x * (y / a))
	tmp = 0
	if a <= -4.5e+94:
		tmp = t_2
	elif a <= -1.45e+41:
		tmp = t / (1.0 - (a / z))
	elif a <= -5.3e+30:
		tmp = t_2
	elif a <= -5.8e-81:
		tmp = (y - a) * (x / z)
	elif a <= -4.1e-257:
		tmp = t_1
	elif a <= 1.65e-301:
		tmp = (y / z) * (x - t)
	elif a <= 3e-8:
		tmp = t_1
	elif (a <= 2.05e+59) or not (a <= 2.1e+93):
		tmp = t_2
	else:
		tmp = t / (a / (y - z))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(1.0 - Float64(y / z)))
	t_2 = Float64(x - Float64(x * Float64(y / a)))
	tmp = 0.0
	if (a <= -4.5e+94)
		tmp = t_2;
	elseif (a <= -1.45e+41)
		tmp = Float64(t / Float64(1.0 - Float64(a / z)));
	elseif (a <= -5.3e+30)
		tmp = t_2;
	elseif (a <= -5.8e-81)
		tmp = Float64(Float64(y - a) * Float64(x / z));
	elseif (a <= -4.1e-257)
		tmp = t_1;
	elseif (a <= 1.65e-301)
		tmp = Float64(Float64(y / z) * Float64(x - t));
	elseif (a <= 3e-8)
		tmp = t_1;
	elseif ((a <= 2.05e+59) || !(a <= 2.1e+93))
		tmp = t_2;
	else
		tmp = Float64(t / Float64(a / Float64(y - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t * (1.0 - (y / z));
	t_2 = x - (x * (y / a));
	tmp = 0.0;
	if (a <= -4.5e+94)
		tmp = t_2;
	elseif (a <= -1.45e+41)
		tmp = t / (1.0 - (a / z));
	elseif (a <= -5.3e+30)
		tmp = t_2;
	elseif (a <= -5.8e-81)
		tmp = (y - a) * (x / z);
	elseif (a <= -4.1e-257)
		tmp = t_1;
	elseif (a <= 1.65e-301)
		tmp = (y / z) * (x - t);
	elseif (a <= 3e-8)
		tmp = t_1;
	elseif ((a <= 2.05e+59) || ~((a <= 2.1e+93)))
		tmp = t_2;
	else
		tmp = t / (a / (y - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x - N[(x * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -4.5e+94], t$95$2, If[LessEqual[a, -1.45e+41], N[(t / N[(1.0 - N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -5.3e+30], t$95$2, If[LessEqual[a, -5.8e-81], N[(N[(y - a), $MachinePrecision] * N[(x / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -4.1e-257], t$95$1, If[LessEqual[a, 1.65e-301], N[(N[(y / z), $MachinePrecision] * N[(x - t), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3e-8], t$95$1, If[Or[LessEqual[a, 2.05e+59], N[Not[LessEqual[a, 2.1e+93]], $MachinePrecision]], t$95$2, N[(t / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]

Derivation

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

Alternative 4: 49.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{if}\;a \leq -2.8 \cdot 10^{+98}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq -3.1 \cdot 10^{-257}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 5.9 \cdot 10^{-302}:\\ \;\;\;\;\frac{y}{z} \cdot \left(x - t\right)\\ \mathbf{elif}\;a \leq 3.6 \cdot 10^{+53}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 1.15 \cdot 10^{+95}:\\ \;\;\;\;\frac{t}{\frac{a}{y - z}}\\ \mathbf{elif}\;a \leq 1.04 \cdot 10^{+104}:\\ \;\;\;\;\frac{t}{1 - \frac{a}{z}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (- 1.0 (/ y z)))))
   (if (<= a -2.8e+98)
     x
     (if (<= a -3.1e-257)
       t_1
       (if (<= a 5.9e-302)
         (* (/ y z) (- x t))
         (if (<= a 3.6e+53)
           t_1
           (if (<= a 1.15e+95)
             (/ t (/ a (- y z)))
             (if (<= a 1.04e+104) (/ t (- 1.0 (/ a z))) x))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * (1.0 - (y / z));
	double tmp;
	if (a <= -2.8e+98) {
		tmp = x;
	} else if (a <= -3.1e-257) {
		tmp = t_1;
	} else if (a <= 5.9e-302) {
		tmp = (y / z) * (x - t);
	} else if (a <= 3.6e+53) {
		tmp = t_1;
	} else if (a <= 1.15e+95) {
		tmp = t / (a / (y - z));
	} else if (a <= 1.04e+104) {
		tmp = t / (1.0 - (a / z));
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = t * (1.0d0 - (y / z))
    if (a <= (-2.8d+98)) then
        tmp = x
    else if (a <= (-3.1d-257)) then
        tmp = t_1
    else if (a <= 5.9d-302) then
        tmp = (y / z) * (x - t)
    else if (a <= 3.6d+53) then
        tmp = t_1
    else if (a <= 1.15d+95) then
        tmp = t / (a / (y - z))
    else if (a <= 1.04d+104) then
        tmp = t / (1.0d0 - (a / z))
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = t * (1.0 - (y / z));
	double tmp;
	if (a <= -2.8e+98) {
		tmp = x;
	} else if (a <= -3.1e-257) {
		tmp = t_1;
	} else if (a <= 5.9e-302) {
		tmp = (y / z) * (x - t);
	} else if (a <= 3.6e+53) {
		tmp = t_1;
	} else if (a <= 1.15e+95) {
		tmp = t / (a / (y - z));
	} else if (a <= 1.04e+104) {
		tmp = t / (1.0 - (a / z));
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t * (1.0 - (y / z))
	tmp = 0
	if a <= -2.8e+98:
		tmp = x
	elif a <= -3.1e-257:
		tmp = t_1
	elif a <= 5.9e-302:
		tmp = (y / z) * (x - t)
	elif a <= 3.6e+53:
		tmp = t_1
	elif a <= 1.15e+95:
		tmp = t / (a / (y - z))
	elif a <= 1.04e+104:
		tmp = t / (1.0 - (a / z))
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(1.0 - Float64(y / z)))
	tmp = 0.0
	if (a <= -2.8e+98)
		tmp = x;
	elseif (a <= -3.1e-257)
		tmp = t_1;
	elseif (a <= 5.9e-302)
		tmp = Float64(Float64(y / z) * Float64(x - t));
	elseif (a <= 3.6e+53)
		tmp = t_1;
	elseif (a <= 1.15e+95)
		tmp = Float64(t / Float64(a / Float64(y - z)));
	elseif (a <= 1.04e+104)
		tmp = Float64(t / Float64(1.0 - Float64(a / z)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t * (1.0 - (y / z));
	tmp = 0.0;
	if (a <= -2.8e+98)
		tmp = x;
	elseif (a <= -3.1e-257)
		tmp = t_1;
	elseif (a <= 5.9e-302)
		tmp = (y / z) * (x - t);
	elseif (a <= 3.6e+53)
		tmp = t_1;
	elseif (a <= 1.15e+95)
		tmp = t / (a / (y - z));
	elseif (a <= 1.04e+104)
		tmp = t / (1.0 - (a / z));
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.8e+98], x, If[LessEqual[a, -3.1e-257], t$95$1, If[LessEqual[a, 5.9e-302], N[(N[(y / z), $MachinePrecision] * N[(x - t), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3.6e+53], t$95$1, If[LessEqual[a, 1.15e+95], N[(t / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.04e+104], N[(t / N[(1.0 - N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], x]]]]]]]

Derivation

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

Alternative 5: 58.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - x \cdot \frac{y}{a}\\ t_2 := \left(y - z\right) \cdot \frac{t}{a - z}\\ \mathbf{if}\;t \leq -6.5 \cdot 10^{-75}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;t \leq -6 \cdot 10^{-240}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -7.6 \cdot 10^{-280}:\\ \;\;\;\;\frac{x}{\frac{z}{y - a}}\\ \mathbf{elif}\;t \leq 3.4 \cdot 10^{-173}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 1.7 \cdot 10^{-55}:\\ \;\;\;\;t + x \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (* x (/ y a)))) (t_2 (* (- y z) (/ t (- a z)))))
   (if (<= t -6.5e-75)
     t_2
     (if (<= t -6e-240)
       t_1
       (if (<= t -7.6e-280)
         (/ x (/ z (- y a)))
         (if (<= t 3.4e-173)
           t_1
           (if (<= t 1.7e-55) (+ t (* x (/ y z))) t_2)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - (x * (y / a));
	double t_2 = (y - z) * (t / (a - z));
	double tmp;
	if (t <= -6.5e-75) {
		tmp = t_2;
	} else if (t <= -6e-240) {
		tmp = t_1;
	} else if (t <= -7.6e-280) {
		tmp = x / (z / (y - a));
	} else if (t <= 3.4e-173) {
		tmp = t_1;
	} else if (t <= 1.7e-55) {
		tmp = t + (x * (y / z));
	} 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 - (x * (y / a))
    t_2 = (y - z) * (t / (a - z))
    if (t <= (-6.5d-75)) then
        tmp = t_2
    else if (t <= (-6d-240)) then
        tmp = t_1
    else if (t <= (-7.6d-280)) then
        tmp = x / (z / (y - a))
    else if (t <= 3.4d-173) then
        tmp = t_1
    else if (t <= 1.7d-55) then
        tmp = t + (x * (y / z))
    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 - (x * (y / a));
	double t_2 = (y - z) * (t / (a - z));
	double tmp;
	if (t <= -6.5e-75) {
		tmp = t_2;
	} else if (t <= -6e-240) {
		tmp = t_1;
	} else if (t <= -7.6e-280) {
		tmp = x / (z / (y - a));
	} else if (t <= 3.4e-173) {
		tmp = t_1;
	} else if (t <= 1.7e-55) {
		tmp = t + (x * (y / z));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - (x * (y / a))
	t_2 = (y - z) * (t / (a - z))
	tmp = 0
	if t <= -6.5e-75:
		tmp = t_2
	elif t <= -6e-240:
		tmp = t_1
	elif t <= -7.6e-280:
		tmp = x / (z / (y - a))
	elif t <= 3.4e-173:
		tmp = t_1
	elif t <= 1.7e-55:
		tmp = t + (x * (y / z))
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x - Float64(x * Float64(y / a)))
	t_2 = Float64(Float64(y - z) * Float64(t / Float64(a - z)))
	tmp = 0.0
	if (t <= -6.5e-75)
		tmp = t_2;
	elseif (t <= -6e-240)
		tmp = t_1;
	elseif (t <= -7.6e-280)
		tmp = Float64(x / Float64(z / Float64(y - a)));
	elseif (t <= 3.4e-173)
		tmp = t_1;
	elseif (t <= 1.7e-55)
		tmp = Float64(t + Float64(x * Float64(y / z)));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x - (x * (y / a));
	t_2 = (y - z) * (t / (a - z));
	tmp = 0.0;
	if (t <= -6.5e-75)
		tmp = t_2;
	elseif (t <= -6e-240)
		tmp = t_1;
	elseif (t <= -7.6e-280)
		tmp = x / (z / (y - a));
	elseif (t <= 3.4e-173)
		tmp = t_1;
	elseif (t <= 1.7e-55)
		tmp = t + (x * (y / z));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x - N[(x * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(y - z), $MachinePrecision] * N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -6.5e-75], t$95$2, If[LessEqual[t, -6e-240], t$95$1, If[LessEqual[t, -7.6e-280], N[(x / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3.4e-173], t$95$1, If[LessEqual[t, 1.7e-55], N[(t + N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]

Derivation

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

Alternative 6: 71.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y}{\frac{a}{t - x}}\\ t_2 := x - \left(t - x\right) \cdot \frac{z}{a - z}\\ \mathbf{if}\;a \leq -8 \cdot 10^{+176}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq -3.7 \cdot 10^{+95}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 6.6 \cdot 10^{-17}:\\ \;\;\;\;t - \frac{t - x}{\frac{z}{y - a}}\\ \mathbf{elif}\;a \leq 5.2 \cdot 10^{+185}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ y (/ a (- t x))))) (t_2 (- x (* (- t x) (/ z (- a z))))))
   (if (<= a -8e+176)
     t_2
     (if (<= a -3.7e+95)
       t_1
       (if (<= a 6.6e-17)
         (- t (/ (- t x) (/ z (- y a))))
         (if (<= a 5.2e+185) t_1 t_2))))))

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 - ((t - x) * (z / (a - z)));
	double tmp;
	if (a <= -8e+176) {
		tmp = t_2;
	} else if (a <= -3.7e+95) {
		tmp = t_1;
	} else if (a <= 6.6e-17) {
		tmp = t - ((t - x) / (z / (y - a)));
	} else if (a <= 5.2e+185) {
		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 + (y / (a / (t - x)))
    t_2 = x - ((t - x) * (z / (a - z)))
    if (a <= (-8d+176)) then
        tmp = t_2
    else if (a <= (-3.7d+95)) then
        tmp = t_1
    else if (a <= 6.6d-17) then
        tmp = t - ((t - x) / (z / (y - a)))
    else if (a <= 5.2d+185) 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 / (a / (t - x)));
	double t_2 = x - ((t - x) * (z / (a - z)));
	double tmp;
	if (a <= -8e+176) {
		tmp = t_2;
	} else if (a <= -3.7e+95) {
		tmp = t_1;
	} else if (a <= 6.6e-17) {
		tmp = t - ((t - x) / (z / (y - a)));
	} else if (a <= 5.2e+185) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y / (a / (t - x)))
	t_2 = x - ((t - x) * (z / (a - z)))
	tmp = 0
	if a <= -8e+176:
		tmp = t_2
	elif a <= -3.7e+95:
		tmp = t_1
	elif a <= 6.6e-17:
		tmp = t - ((t - x) / (z / (y - a)))
	elif a <= 5.2e+185:
		tmp = t_1
	else:
		tmp = t_2
	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(t - x) * Float64(z / Float64(a - z))))
	tmp = 0.0
	if (a <= -8e+176)
		tmp = t_2;
	elseif (a <= -3.7e+95)
		tmp = t_1;
	elseif (a <= 6.6e-17)
		tmp = Float64(t - Float64(Float64(t - x) / Float64(z / Float64(y - a))));
	elseif (a <= 5.2e+185)
		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 / (a / (t - x)));
	t_2 = x - ((t - x) * (z / (a - z)));
	tmp = 0.0;
	if (a <= -8e+176)
		tmp = t_2;
	elseif (a <= -3.7e+95)
		tmp = t_1;
	elseif (a <= 6.6e-17)
		tmp = t - ((t - x) / (z / (y - a)));
	elseif (a <= 5.2e+185)
		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[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x - N[(N[(t - x), $MachinePrecision] * N[(z / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -8e+176], t$95$2, If[LessEqual[a, -3.7e+95], t$95$1, If[LessEqual[a, 6.6e-17], N[(t - N[(N[(t - x), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 5.2e+185], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 7: 67.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3 \cdot 10^{-69} \lor \neg \left(z \leq 6.5 \cdot 10^{-65} \lor \neg \left(z \leq 1.1 \cdot 10^{+34}\right) \land z \leq 3.8 \cdot 10^{+62}\right):\\ \;\;\;\;t - \frac{y}{\frac{z}{t - x}}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{y}{\frac{a}{t - x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= z -3e-69)
         (not (or (<= z 6.5e-65) (and (not (<= z 1.1e+34)) (<= z 3.8e+62)))))
   (- t (/ y (/ z (- t x))))
   (+ x (/ y (/ a (- t x))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((z <= -3e-69) || !((z <= 6.5e-65) || (!(z <= 1.1e+34) && (z <= 3.8e+62)))) {
		tmp = t - (y / (z / (t - x)));
	} 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 <= (-3d-69)) .or. (.not. (z <= 6.5d-65) .or. (.not. (z <= 1.1d+34)) .and. (z <= 3.8d+62))) then
        tmp = t - (y / (z / (t - x)))
    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 <= -3e-69) || !((z <= 6.5e-65) || (!(z <= 1.1e+34) && (z <= 3.8e+62)))) {
		tmp = t - (y / (z / (t - x)));
	} else {
		tmp = x + (y / (a / (t - x)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (z <= -3e-69) or not ((z <= 6.5e-65) or (not (z <= 1.1e+34) and (z <= 3.8e+62))):
		tmp = t - (y / (z / (t - x)))
	else:
		tmp = x + (y / (a / (t - x)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((z <= -3e-69) || !((z <= 6.5e-65) || (!(z <= 1.1e+34) && (z <= 3.8e+62))))
		tmp = Float64(t - Float64(y / Float64(z / Float64(t - x))));
	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 <= -3e-69) || ~(((z <= 6.5e-65) || (~((z <= 1.1e+34)) && (z <= 3.8e+62)))))
		tmp = t - (y / (z / (t - x)));
	else
		tmp = x + (y / (a / (t - x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[z, -3e-69], N[Not[Or[LessEqual[z, 6.5e-65], And[N[Not[LessEqual[z, 1.1e+34]], $MachinePrecision], LessEqual[z, 3.8e+62]]]], $MachinePrecision]], N[(t - N[(y / N[(z / N[(t - x), $MachinePrecision]), $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 8: 64.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y}{\frac{a}{t - x}}\\ \mathbf{if}\;z \leq -9 \cdot 10^{+98}:\\ \;\;\;\;t + x \cdot \frac{y}{z}\\ \mathbf{elif}\;z \leq 5.5 \cdot 10^{-65}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 3.1 \cdot 10^{+34}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{t}{a - z}\\ \mathbf{elif}\;z \leq 7.8 \cdot 10^{+61}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t + y \cdot \frac{x}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ y (/ a (- t x))))))
   (if (<= z -9e+98)
     (+ t (* x (/ y z)))
     (if (<= z 5.5e-65)
       t_1
       (if (<= z 3.1e+34)
         (* (- y z) (/ t (- a z)))
         (if (<= z 7.8e+61) t_1 (+ t (* y (/ x z)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y / (a / (t - x)));
	double tmp;
	if (z <= -9e+98) {
		tmp = t + (x * (y / z));
	} else if (z <= 5.5e-65) {
		tmp = t_1;
	} else if (z <= 3.1e+34) {
		tmp = (y - z) * (t / (a - z));
	} else if (z <= 7.8e+61) {
		tmp = t_1;
	} else {
		tmp = t + (y * (x / z));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (y / (a / (t - x)))
    if (z <= (-9d+98)) then
        tmp = t + (x * (y / z))
    else if (z <= 5.5d-65) then
        tmp = t_1
    else if (z <= 3.1d+34) then
        tmp = (y - z) * (t / (a - z))
    else if (z <= 7.8d+61) then
        tmp = t_1
    else
        tmp = t + (y * (x / z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y / (a / (t - x)));
	double tmp;
	if (z <= -9e+98) {
		tmp = t + (x * (y / z));
	} else if (z <= 5.5e-65) {
		tmp = t_1;
	} else if (z <= 3.1e+34) {
		tmp = (y - z) * (t / (a - z));
	} else if (z <= 7.8e+61) {
		tmp = t_1;
	} else {
		tmp = t + (y * (x / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y / (a / (t - x)))
	tmp = 0
	if z <= -9e+98:
		tmp = t + (x * (y / z))
	elif z <= 5.5e-65:
		tmp = t_1
	elif z <= 3.1e+34:
		tmp = (y - z) * (t / (a - z))
	elif z <= 7.8e+61:
		tmp = t_1
	else:
		tmp = t + (y * (x / z))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y / Float64(a / Float64(t - x))))
	tmp = 0.0
	if (z <= -9e+98)
		tmp = Float64(t + Float64(x * Float64(y / z)));
	elseif (z <= 5.5e-65)
		tmp = t_1;
	elseif (z <= 3.1e+34)
		tmp = Float64(Float64(y - z) * Float64(t / Float64(a - z)));
	elseif (z <= 7.8e+61)
		tmp = t_1;
	else
		tmp = Float64(t + Float64(y * Float64(x / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y / (a / (t - x)));
	tmp = 0.0;
	if (z <= -9e+98)
		tmp = t + (x * (y / z));
	elseif (z <= 5.5e-65)
		tmp = t_1;
	elseif (z <= 3.1e+34)
		tmp = (y - z) * (t / (a - z));
	elseif (z <= 7.8e+61)
		tmp = t_1;
	else
		tmp = t + (y * (x / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -9e+98], N[(t + N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 5.5e-65], t$95$1, If[LessEqual[z, 3.1e+34], N[(N[(y - z), $MachinePrecision] * N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.8e+61], t$95$1, N[(t + N[(y * N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 9: 67.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y}{\frac{a}{t - x}}\\ t_2 := t - \frac{y}{\frac{z}{t - x}}\\ \mathbf{if}\;z \leq -3 \cdot 10^{-69}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 6.6 \cdot 10^{-65}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 2.5 \cdot 10^{+28}:\\ \;\;\;\;t + \frac{y \cdot \left(x - t\right)}{z}\\ \mathbf{elif}\;z \leq 1.5 \cdot 10^{+63}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ y (/ a (- t x))))) (t_2 (- t (/ y (/ z (- t x))))))
   (if (<= z -3e-69)
     t_2
     (if (<= z 6.6e-65)
       t_1
       (if (<= z 2.5e+28)
         (+ t (/ (* y (- x t)) z))
         (if (<= z 1.5e+63) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y / (a / (t - x)));
	double t_2 = t - (y / (z / (t - x)));
	double tmp;
	if (z <= -3e-69) {
		tmp = t_2;
	} else if (z <= 6.6e-65) {
		tmp = t_1;
	} else if (z <= 2.5e+28) {
		tmp = t + ((y * (x - t)) / z);
	} else if (z <= 1.5e+63) {
		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 + (y / (a / (t - x)))
    t_2 = t - (y / (z / (t - x)))
    if (z <= (-3d-69)) then
        tmp = t_2
    else if (z <= 6.6d-65) then
        tmp = t_1
    else if (z <= 2.5d+28) then
        tmp = t + ((y * (x - t)) / z)
    else if (z <= 1.5d+63) 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 / (a / (t - x)));
	double t_2 = t - (y / (z / (t - x)));
	double tmp;
	if (z <= -3e-69) {
		tmp = t_2;
	} else if (z <= 6.6e-65) {
		tmp = t_1;
	} else if (z <= 2.5e+28) {
		tmp = t + ((y * (x - t)) / z);
	} else if (z <= 1.5e+63) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y / (a / (t - x)))
	t_2 = t - (y / (z / (t - x)))
	tmp = 0
	if z <= -3e-69:
		tmp = t_2
	elif z <= 6.6e-65:
		tmp = t_1
	elif z <= 2.5e+28:
		tmp = t + ((y * (x - t)) / z)
	elif z <= 1.5e+63:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y / Float64(a / Float64(t - x))))
	t_2 = Float64(t - Float64(y / Float64(z / Float64(t - x))))
	tmp = 0.0
	if (z <= -3e-69)
		tmp = t_2;
	elseif (z <= 6.6e-65)
		tmp = t_1;
	elseif (z <= 2.5e+28)
		tmp = Float64(t + Float64(Float64(y * Float64(x - t)) / z));
	elseif (z <= 1.5e+63)
		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 / (a / (t - x)));
	t_2 = t - (y / (z / (t - x)));
	tmp = 0.0;
	if (z <= -3e-69)
		tmp = t_2;
	elseif (z <= 6.6e-65)
		tmp = t_1;
	elseif (z <= 2.5e+28)
		tmp = t + ((y * (x - t)) / z);
	elseif (z <= 1.5e+63)
		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[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t - N[(y / N[(z / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -3e-69], t$95$2, If[LessEqual[z, 6.6e-65], t$95$1, If[LessEqual[z, 2.5e+28], N[(t + N[(N[(y * N[(x - t), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.5e+63], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 10: 67.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y}{\frac{a}{t - x}}\\ \mathbf{if}\;z \leq -1.3 \cdot 10^{-69}:\\ \;\;\;\;t - \frac{y}{\frac{z}{t - x}}\\ \mathbf{elif}\;z \leq 6.4 \cdot 10^{-65}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.22 \cdot 10^{+31}:\\ \;\;\;\;t + \frac{y \cdot \left(x - t\right)}{z}\\ \mathbf{elif}\;z \leq 8.5 \cdot 10^{+62}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t - \frac{t - x}{\frac{z}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (/ y (/ a (- t x))))))
   (if (<= z -1.3e-69)
     (- t (/ y (/ z (- t x))))
     (if (<= z 6.4e-65)
       t_1
       (if (<= z 1.22e+31)
         (+ t (/ (* y (- x t)) z))
         (if (<= z 8.5e+62) t_1 (- t (/ (- t x) (/ z y)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y / (a / (t - x)));
	double tmp;
	if (z <= -1.3e-69) {
		tmp = t - (y / (z / (t - x)));
	} else if (z <= 6.4e-65) {
		tmp = t_1;
	} else if (z <= 1.22e+31) {
		tmp = t + ((y * (x - t)) / z);
	} else if (z <= 8.5e+62) {
		tmp = t_1;
	} else {
		tmp = t - ((t - x) / (z / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (y / (a / (t - x)))
    if (z <= (-1.3d-69)) then
        tmp = t - (y / (z / (t - x)))
    else if (z <= 6.4d-65) then
        tmp = t_1
    else if (z <= 1.22d+31) then
        tmp = t + ((y * (x - t)) / z)
    else if (z <= 8.5d+62) then
        tmp = t_1
    else
        tmp = t - ((t - x) / (z / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y / (a / (t - x)));
	double tmp;
	if (z <= -1.3e-69) {
		tmp = t - (y / (z / (t - x)));
	} else if (z <= 6.4e-65) {
		tmp = t_1;
	} else if (z <= 1.22e+31) {
		tmp = t + ((y * (x - t)) / z);
	} else if (z <= 8.5e+62) {
		tmp = t_1;
	} else {
		tmp = t - ((t - x) / (z / y));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y / (a / (t - x)))
	tmp = 0
	if z <= -1.3e-69:
		tmp = t - (y / (z / (t - x)))
	elif z <= 6.4e-65:
		tmp = t_1
	elif z <= 1.22e+31:
		tmp = t + ((y * (x - t)) / z)
	elif z <= 8.5e+62:
		tmp = t_1
	else:
		tmp = t - ((t - x) / (z / y))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y / Float64(a / Float64(t - x))))
	tmp = 0.0
	if (z <= -1.3e-69)
		tmp = Float64(t - Float64(y / Float64(z / Float64(t - x))));
	elseif (z <= 6.4e-65)
		tmp = t_1;
	elseif (z <= 1.22e+31)
		tmp = Float64(t + Float64(Float64(y * Float64(x - t)) / z));
	elseif (z <= 8.5e+62)
		tmp = t_1;
	else
		tmp = Float64(t - Float64(Float64(t - x) / Float64(z / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y / (a / (t - x)));
	tmp = 0.0;
	if (z <= -1.3e-69)
		tmp = t - (y / (z / (t - x)));
	elseif (z <= 6.4e-65)
		tmp = t_1;
	elseif (z <= 1.22e+31)
		tmp = t + ((y * (x - t)) / z);
	elseif (z <= 8.5e+62)
		tmp = t_1;
	else
		tmp = t - ((t - x) / (z / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.3e-69], N[(t - N[(y / N[(z / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 6.4e-65], t$95$1, If[LessEqual[z, 1.22e+31], N[(t + N[(N[(y * N[(x - t), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 8.5e+62], t$95$1, N[(t - N[(N[(t - x), $MachinePrecision] / N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 11: 49.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.4 \cdot 10^{+95}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 1.65 \cdot 10^{+54}:\\ \;\;\;\;t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{elif}\;a \leq 1.7 \cdot 10^{+95}:\\ \;\;\;\;\frac{t}{\frac{a}{y - z}}\\ \mathbf{elif}\;a \leq 6.4 \cdot 10^{+103}:\\ \;\;\;\;\frac{t}{1 - \frac{a}{z}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -2.4e+95)
   x
   (if (<= a 1.65e+54)
     (* t (- 1.0 (/ y z)))
     (if (<= a 1.7e+95)
       (/ t (/ a (- y z)))
       (if (<= a 6.4e+103) (/ t (- 1.0 (/ a z))) x)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -2.4e+95) {
		tmp = x;
	} else if (a <= 1.65e+54) {
		tmp = t * (1.0 - (y / z));
	} else if (a <= 1.7e+95) {
		tmp = t / (a / (y - z));
	} else if (a <= 6.4e+103) {
		tmp = t / (1.0 - (a / z));
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-2.4d+95)) then
        tmp = x
    else if (a <= 1.65d+54) then
        tmp = t * (1.0d0 - (y / z))
    else if (a <= 1.7d+95) then
        tmp = t / (a / (y - z))
    else if (a <= 6.4d+103) then
        tmp = t / (1.0d0 - (a / z))
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -2.4e+95) {
		tmp = x;
	} else if (a <= 1.65e+54) {
		tmp = t * (1.0 - (y / z));
	} else if (a <= 1.7e+95) {
		tmp = t / (a / (y - z));
	} else if (a <= 6.4e+103) {
		tmp = t / (1.0 - (a / z));
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -2.4e+95:
		tmp = x
	elif a <= 1.65e+54:
		tmp = t * (1.0 - (y / z))
	elif a <= 1.7e+95:
		tmp = t / (a / (y - z))
	elif a <= 6.4e+103:
		tmp = t / (1.0 - (a / z))
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -2.4e+95)
		tmp = x;
	elseif (a <= 1.65e+54)
		tmp = Float64(t * Float64(1.0 - Float64(y / z)));
	elseif (a <= 1.7e+95)
		tmp = Float64(t / Float64(a / Float64(y - z)));
	elseif (a <= 6.4e+103)
		tmp = Float64(t / Float64(1.0 - Float64(a / z)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -2.4e+95)
		tmp = x;
	elseif (a <= 1.65e+54)
		tmp = t * (1.0 - (y / z));
	elseif (a <= 1.7e+95)
		tmp = t / (a / (y - z));
	elseif (a <= 6.4e+103)
		tmp = t / (1.0 - (a / z));
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -2.4e+95], x, If[LessEqual[a, 1.65e+54], N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.7e+95], N[(t / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 6.4e+103], N[(t / N[(1.0 - N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], x]]]]

Derivation

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

Alternative 12: 71.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.2 \cdot 10^{+95} \lor \neg \left(a \leq 6.6 \cdot 10^{-17}\right):\\ \;\;\;\;x + \frac{y}{\frac{a}{t - x}}\\ \mathbf{else}:\\ \;\;\;\;t - \frac{t - x}{\frac{z}{y - a}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= a -2.2e+95) (not (<= a 6.6e-17)))
   (+ x (/ y (/ a (- 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 <= -2.2e+95) || !(a <= 6.6e-17)) {
		tmp = x + (y / (a / (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 <= (-2.2d+95)) .or. (.not. (a <= 6.6d-17))) then
        tmp = x + (y / (a / (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 <= -2.2e+95) || !(a <= 6.6e-17)) {
		tmp = x + (y / (a / (t - x)));
	} else {
		tmp = t - ((t - x) / (z / (y - a)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (a <= -2.2e+95) or not (a <= 6.6e-17):
		tmp = x + (y / (a / (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 <= -2.2e+95) || !(a <= 6.6e-17))
		tmp = Float64(x + Float64(y / Float64(a / 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 <= -2.2e+95) || ~((a <= 6.6e-17)))
		tmp = x + (y / (a / (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, -2.2e+95], N[Not[LessEqual[a, 6.6e-17]], $MachinePrecision]], N[(x + N[(y / N[(a / N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t - N[(N[(t - x), $MachinePrecision] / N[(z / N[(y - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 13: 53.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t + y \cdot \frac{x}{z}\\ \mathbf{if}\;z \leq -1.1 \cdot 10^{-48}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -6.2 \cdot 10^{-153}:\\ \;\;\;\;\frac{t}{\frac{a}{y - z}}\\ \mathbf{elif}\;z \leq 6.5 \cdot 10^{-65}:\\ \;\;\;\;x - x \cdot \frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ t (* y (/ x z)))))
   (if (<= z -1.1e-48)
     t_1
     (if (<= z -6.2e-153)
       (/ t (/ a (- y z)))
       (if (<= z 6.5e-65) (- x (* x (/ y a))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t + (y * (x / z));
	double tmp;
	if (z <= -1.1e-48) {
		tmp = t_1;
	} else if (z <= -6.2e-153) {
		tmp = t / (a / (y - z));
	} else if (z <= 6.5e-65) {
		tmp = x - (x * (y / a));
	} 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 * (x / z))
    if (z <= (-1.1d-48)) then
        tmp = t_1
    else if (z <= (-6.2d-153)) then
        tmp = t / (a / (y - z))
    else if (z <= 6.5d-65) then
        tmp = x - (x * (y / a))
    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 * (x / z));
	double tmp;
	if (z <= -1.1e-48) {
		tmp = t_1;
	} else if (z <= -6.2e-153) {
		tmp = t / (a / (y - z));
	} else if (z <= 6.5e-65) {
		tmp = x - (x * (y / a));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t + (y * (x / z))
	tmp = 0
	if z <= -1.1e-48:
		tmp = t_1
	elif z <= -6.2e-153:
		tmp = t / (a / (y - z))
	elif z <= 6.5e-65:
		tmp = x - (x * (y / a))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t + Float64(y * Float64(x / z)))
	tmp = 0.0
	if (z <= -1.1e-48)
		tmp = t_1;
	elseif (z <= -6.2e-153)
		tmp = Float64(t / Float64(a / Float64(y - z)));
	elseif (z <= 6.5e-65)
		tmp = Float64(x - Float64(x * Float64(y / a)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t + (y * (x / z));
	tmp = 0.0;
	if (z <= -1.1e-48)
		tmp = t_1;
	elseif (z <= -6.2e-153)
		tmp = t / (a / (y - z));
	elseif (z <= 6.5e-65)
		tmp = x - (x * (y / a));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t + N[(y * N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.1e-48], t$95$1, If[LessEqual[z, -6.2e-153], N[(t / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 6.5e-65], N[(x - N[(x * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 14: 53.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.5 \cdot 10^{-48}:\\ \;\;\;\;t + x \cdot \frac{y}{z}\\ \mathbf{elif}\;z \leq -1.3 \cdot 10^{-151}:\\ \;\;\;\;\frac{t}{\frac{a}{y - z}}\\ \mathbf{elif}\;z \leq 6.6 \cdot 10^{-65}:\\ \;\;\;\;x - x \cdot \frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;t + y \cdot \frac{x}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -5.5e-48)
   (+ t (* x (/ y z)))
   (if (<= z -1.3e-151)
     (/ t (/ a (- y z)))
     (if (<= z 6.6e-65) (- x (* x (/ y a))) (+ t (* y (/ x z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -5.5e-48) {
		tmp = t + (x * (y / z));
	} else if (z <= -1.3e-151) {
		tmp = t / (a / (y - z));
	} else if (z <= 6.6e-65) {
		tmp = x - (x * (y / a));
	} else {
		tmp = t + (y * (x / 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 <= (-5.5d-48)) then
        tmp = t + (x * (y / z))
    else if (z <= (-1.3d-151)) then
        tmp = t / (a / (y - z))
    else if (z <= 6.6d-65) then
        tmp = x - (x * (y / a))
    else
        tmp = t + (y * (x / 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 <= -5.5e-48) {
		tmp = t + (x * (y / z));
	} else if (z <= -1.3e-151) {
		tmp = t / (a / (y - z));
	} else if (z <= 6.6e-65) {
		tmp = x - (x * (y / a));
	} else {
		tmp = t + (y * (x / z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -5.5e-48:
		tmp = t + (x * (y / z))
	elif z <= -1.3e-151:
		tmp = t / (a / (y - z))
	elif z <= 6.6e-65:
		tmp = x - (x * (y / a))
	else:
		tmp = t + (y * (x / z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -5.5e-48)
		tmp = Float64(t + Float64(x * Float64(y / z)));
	elseif (z <= -1.3e-151)
		tmp = Float64(t / Float64(a / Float64(y - z)));
	elseif (z <= 6.6e-65)
		tmp = Float64(x - Float64(x * Float64(y / a)));
	else
		tmp = Float64(t + Float64(y * Float64(x / z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -5.5e-48)
		tmp = t + (x * (y / z));
	elseif (z <= -1.3e-151)
		tmp = t / (a / (y - z));
	elseif (z <= 6.6e-65)
		tmp = x - (x * (y / a));
	else
		tmp = t + (y * (x / z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -5.5e-48], N[(t + N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.3e-151], N[(t / N[(a / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 6.6e-65], N[(x - N[(x * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t + N[(y * N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 15: 38.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -7.5 \cdot 10^{+95}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 1.65 \cdot 10^{-261}:\\ \;\;\;\;t\\ \mathbf{elif}\;a \leq 1.22 \cdot 10^{-230}:\\ \;\;\;\;y \cdot \frac{x}{z}\\ \mathbf{elif}\;a \leq 5.3 \cdot 10^{-21}:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -7.5e+95)
   x
   (if (<= a 1.65e-261)
     t
     (if (<= a 1.22e-230) (* y (/ x z)) (if (<= a 5.3e-21) t x)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -7.5e+95) {
		tmp = x;
	} else if (a <= 1.65e-261) {
		tmp = t;
	} else if (a <= 1.22e-230) {
		tmp = y * (x / z);
	} else if (a <= 5.3e-21) {
		tmp = t;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-7.5d+95)) then
        tmp = x
    else if (a <= 1.65d-261) then
        tmp = t
    else if (a <= 1.22d-230) then
        tmp = y * (x / z)
    else if (a <= 5.3d-21) then
        tmp = t
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -7.5e+95) {
		tmp = x;
	} else if (a <= 1.65e-261) {
		tmp = t;
	} else if (a <= 1.22e-230) {
		tmp = y * (x / z);
	} else if (a <= 5.3e-21) {
		tmp = t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -7.5e+95:
		tmp = x
	elif a <= 1.65e-261:
		tmp = t
	elif a <= 1.22e-230:
		tmp = y * (x / z)
	elif a <= 5.3e-21:
		tmp = t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -7.5e+95)
		tmp = x;
	elseif (a <= 1.65e-261)
		tmp = t;
	elseif (a <= 1.22e-230)
		tmp = Float64(y * Float64(x / z));
	elseif (a <= 5.3e-21)
		tmp = t;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -7.5e+95)
		tmp = x;
	elseif (a <= 1.65e-261)
		tmp = t;
	elseif (a <= 1.22e-230)
		tmp = y * (x / z);
	elseif (a <= 5.3e-21)
		tmp = t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -7.5e+95], x, If[LessEqual[a, 1.65e-261], t, If[LessEqual[a, 1.22e-230], N[(y * N[(x / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 5.3e-21], t, x]]]]

Derivation

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

Alternative 16: 37.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.7 \cdot 10^{+158}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -2.8 \cdot 10^{-41}:\\ \;\;\;\;y \cdot \frac{x}{z}\\ \mathbf{elif}\;z \leq -2.35 \cdot 10^{-154}:\\ \;\;\;\;t \cdot \frac{y}{a}\\ \mathbf{elif}\;z \leq 6 \cdot 10^{+59}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -3.7e+158)
   t
   (if (<= z -2.8e-41)
     (* y (/ x z))
     (if (<= z -2.35e-154) (* t (/ y a)) (if (<= z 6e+59) x t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -3.7e+158) {
		tmp = t;
	} else if (z <= -2.8e-41) {
		tmp = y * (x / z);
	} else if (z <= -2.35e-154) {
		tmp = t * (y / a);
	} else if (z <= 6e+59) {
		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 <= (-3.7d+158)) then
        tmp = t
    else if (z <= (-2.8d-41)) then
        tmp = y * (x / z)
    else if (z <= (-2.35d-154)) then
        tmp = t * (y / a)
    else if (z <= 6d+59) 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 <= -3.7e+158) {
		tmp = t;
	} else if (z <= -2.8e-41) {
		tmp = y * (x / z);
	} else if (z <= -2.35e-154) {
		tmp = t * (y / a);
	} else if (z <= 6e+59) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -3.7e+158:
		tmp = t
	elif z <= -2.8e-41:
		tmp = y * (x / z)
	elif z <= -2.35e-154:
		tmp = t * (y / a)
	elif z <= 6e+59:
		tmp = x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -3.7e+158)
		tmp = t;
	elseif (z <= -2.8e-41)
		tmp = Float64(y * Float64(x / z));
	elseif (z <= -2.35e-154)
		tmp = Float64(t * Float64(y / a));
	elseif (z <= 6e+59)
		tmp = x;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -3.7e+158)
		tmp = t;
	elseif (z <= -2.8e-41)
		tmp = y * (x / z);
	elseif (z <= -2.35e-154)
		tmp = t * (y / a);
	elseif (z <= 6e+59)
		tmp = x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -3.7e+158], t, If[LessEqual[z, -2.8e-41], N[(y * N[(x / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -2.35e-154], N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 6e+59], x, t]]]]

Derivation

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

Alternative 17: 37.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.15 \cdot 10^{+158}:\\ \;\;\;\;t\\ \mathbf{elif}\;z \leq -6 \cdot 10^{-41}:\\ \;\;\;\;\frac{y}{\frac{z}{x}}\\ \mathbf{elif}\;z \leq -8.8 \cdot 10^{-156}:\\ \;\;\;\;t \cdot \frac{y}{a}\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{+60}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.15e+158)
   t
   (if (<= z -6e-41)
     (/ y (/ z x))
     (if (<= z -8.8e-156) (* t (/ y a)) (if (<= z 4.7e+60) x t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.15e+158) {
		tmp = t;
	} else if (z <= -6e-41) {
		tmp = y / (z / x);
	} else if (z <= -8.8e-156) {
		tmp = t * (y / a);
	} else if (z <= 4.7e+60) {
		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 <= (-2.15d+158)) then
        tmp = t
    else if (z <= (-6d-41)) then
        tmp = y / (z / x)
    else if (z <= (-8.8d-156)) then
        tmp = t * (y / a)
    else if (z <= 4.7d+60) 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 <= -2.15e+158) {
		tmp = t;
	} else if (z <= -6e-41) {
		tmp = y / (z / x);
	} else if (z <= -8.8e-156) {
		tmp = t * (y / a);
	} else if (z <= 4.7e+60) {
		tmp = x;
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.15e+158:
		tmp = t
	elif z <= -6e-41:
		tmp = y / (z / x)
	elif z <= -8.8e-156:
		tmp = t * (y / a)
	elif z <= 4.7e+60:
		tmp = x
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.15e+158)
		tmp = t;
	elseif (z <= -6e-41)
		tmp = Float64(y / Float64(z / x));
	elseif (z <= -8.8e-156)
		tmp = Float64(t * Float64(y / a));
	elseif (z <= 4.7e+60)
		tmp = x;
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.15e+158)
		tmp = t;
	elseif (z <= -6e-41)
		tmp = y / (z / x);
	elseif (z <= -8.8e-156)
		tmp = t * (y / a);
	elseif (z <= 4.7e+60)
		tmp = x;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.15e+158], t, If[LessEqual[z, -6e-41], N[(y / N[(z / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -8.8e-156], N[(t * N[(y / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.7e+60], x, t]]]]

Derivation

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

Alternative 18: 48.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -4.2 \cdot 10^{+94}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 5.1 \cdot 10^{-21}:\\ \;\;\;\;t \cdot \left(1 - \frac{y}{z}\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -4.2e+94) x (if (<= a 5.1e-21) (* t (- 1.0 (/ y z))) x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -4.2e+94) {
		tmp = x;
	} else if (a <= 5.1e-21) {
		tmp = t * (1.0 - (y / z));
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-4.2d+94)) then
        tmp = x
    else if (a <= 5.1d-21) then
        tmp = t * (1.0d0 - (y / z))
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -4.2e+94) {
		tmp = x;
	} else if (a <= 5.1e-21) {
		tmp = t * (1.0 - (y / z));
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -4.2e+94:
		tmp = x
	elif a <= 5.1e-21:
		tmp = t * (1.0 - (y / z))
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -4.2e+94)
		tmp = x;
	elseif (a <= 5.1e-21)
		tmp = Float64(t * Float64(1.0 - Float64(y / z)));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -4.2e+94)
		tmp = x;
	elseif (a <= 5.1e-21)
		tmp = t * (1.0 - (y / z));
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -4.2e+94], x, If[LessEqual[a, 5.1e-21], N[(t * N[(1.0 - N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], x]]

Derivation

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

Alternative 19: 38.6% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -3.4 \cdot 10^{+94}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 2.7 \cdot 10^{-21}:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -3.4e+94) x (if (<= a 2.7e-21) t x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.4e+94) {
		tmp = x;
	} else if (a <= 2.7e-21) {
		tmp = t;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-3.4d+94)) then
        tmp = x
    else if (a <= 2.7d-21) then
        tmp = t
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -3.4e+94) {
		tmp = x;
	} else if (a <= 2.7e-21) {
		tmp = t;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -3.4e+94:
		tmp = x
	elif a <= 2.7e-21:
		tmp = t
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -3.4e+94)
		tmp = x;
	elseif (a <= 2.7e-21)
		tmp = t;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -3.4e+94)
		tmp = x;
	elseif (a <= 2.7e-21)
		tmp = t;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -3.4e+94], x, If[LessEqual[a, 2.7e-21], t, x]]

Derivation

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

Alternative 20: 25.2% 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

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1. Add Preprocessing

Reproduce

herbie shell --seed 2023347 
(FPCore (x y z t a)
  :name "Numeric.Signal:interpolate   from hsignal-0.2.7.1"
  :precision binary64
  (+ x (* (- y z) (/ (- t x) (- a z)))))