Diagrams.Trail:splitAtParam from diagrams-lib-1.3.0.3, A

Percentage Accurate: 88.9% → 94.7%

Time: 16.4s

Alternatives: 15

Speedup: 0.8×

Specification

Math

\[\begin{array}{l} \\ \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0)))

C

double code(double x, double y, double z, double t) {
	return (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
}

Python

def code(x, y, z, t):
	return (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)

Julia

function code(x, y, z, t)
	return Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 88.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0)))

C

double code(double x, double y, double z, double t) {
	return (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
}

Python

def code(x, y, z, t):
	return (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)

Julia

function code(x, y, z, t)
	return Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 94.7% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.8 \cdot 10^{+102}:\\ \;\;\;\;\frac{x}{x + 1} + \frac{\frac{y}{x + 1} - \frac{x}{z \cdot \left(x + 1\right)}}{t}\\ \mathbf{elif}\;z \leq 1.65 \cdot 10^{+140}:\\ \;\;\;\;\frac{x + \frac{\mathsf{fma}\left(z, y, -x\right)}{z \cdot t - x}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y - \frac{x}{z}}{t}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -2.8e+102)
   (+ (/ x (+ x 1.0)) (/ (- (/ y (+ x 1.0)) (/ x (* z (+ x 1.0)))) t))
   (if (<= z 1.65e+140)
     (/ (+ x (/ (fma z y (- x)) (- (* z t) x))) (+ x 1.0))
     (/ (+ x (/ (- y (/ x z)) t)) (+ x 1.0)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -2.8e+102) {
		tmp = (x / (x + 1.0)) + (((y / (x + 1.0)) - (x / (z * (x + 1.0)))) / t);
	} else if (z <= 1.65e+140) {
		tmp = (x + (fma(z, y, -x) / ((z * t) - x))) / (x + 1.0);
	} else {
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -2.8e+102)
		tmp = Float64(Float64(x / Float64(x + 1.0)) + Float64(Float64(Float64(y / Float64(x + 1.0)) - Float64(x / Float64(z * Float64(x + 1.0)))) / t));
	elseif (z <= 1.65e+140)
		tmp = Float64(Float64(x + Float64(fma(z, y, Float64(-x)) / Float64(Float64(z * t) - x))) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(x + Float64(Float64(y - Float64(x / z)) / t)) / Float64(x + 1.0));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -2.8e+102], N[(N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - N[(x / N[(z * N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.65e+140], N[(N[(x + N[(N[(z * y + (-x)), $MachinePrecision] / N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(N[(y - N[(x / z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 2: 94.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot t - x\\ \mathbf{if}\;z \leq -2 \cdot 10^{+106}:\\ \;\;\;\;\frac{x}{x + 1} + \frac{\frac{y}{x + 1} - \frac{x}{z \cdot \left(x + 1\right)}}{t}\\ \mathbf{elif}\;z \leq 1.7 \cdot 10^{+129}:\\ \;\;\;\;\frac{\frac{z \cdot y}{t_1} + \left(x - \frac{x}{t_1}\right)}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y - \frac{x}{z}}{t}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (* z t) x)))
   (if (<= z -2e+106)
     (+ (/ x (+ x 1.0)) (/ (- (/ y (+ x 1.0)) (/ x (* z (+ x 1.0)))) t))
     (if (<= z 1.7e+129)
       (/ (+ (/ (* z y) t_1) (- x (/ x t_1))) (+ x 1.0))
       (/ (+ x (/ (- y (/ x z)) t)) (+ x 1.0))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (z * t) - x;
	double tmp;
	if (z <= -2e+106) {
		tmp = (x / (x + 1.0)) + (((y / (x + 1.0)) - (x / (z * (x + 1.0)))) / t);
	} else if (z <= 1.7e+129) {
		tmp = (((z * y) / t_1) + (x - (x / t_1))) / (x + 1.0);
	} else {
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z * t) - x
    if (z <= (-2d+106)) then
        tmp = (x / (x + 1.0d0)) + (((y / (x + 1.0d0)) - (x / (z * (x + 1.0d0)))) / t)
    else if (z <= 1.7d+129) then
        tmp = (((z * y) / t_1) + (x - (x / t_1))) / (x + 1.0d0)
    else
        tmp = (x + ((y - (x / z)) / t)) / (x + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (z * t) - x;
	double tmp;
	if (z <= -2e+106) {
		tmp = (x / (x + 1.0)) + (((y / (x + 1.0)) - (x / (z * (x + 1.0)))) / t);
	} else if (z <= 1.7e+129) {
		tmp = (((z * y) / t_1) + (x - (x / t_1))) / (x + 1.0);
	} else {
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (z * t) - x
	tmp = 0
	if z <= -2e+106:
		tmp = (x / (x + 1.0)) + (((y / (x + 1.0)) - (x / (z * (x + 1.0)))) / t)
	elif z <= 1.7e+129:
		tmp = (((z * y) / t_1) + (x - (x / t_1))) / (x + 1.0)
	else:
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(z * t) - x)
	tmp = 0.0
	if (z <= -2e+106)
		tmp = Float64(Float64(x / Float64(x + 1.0)) + Float64(Float64(Float64(y / Float64(x + 1.0)) - Float64(x / Float64(z * Float64(x + 1.0)))) / t));
	elseif (z <= 1.7e+129)
		tmp = Float64(Float64(Float64(Float64(z * y) / t_1) + Float64(x - Float64(x / t_1))) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(x + Float64(Float64(y - Float64(x / z)) / t)) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (z * t) - x;
	tmp = 0.0;
	if (z <= -2e+106)
		tmp = (x / (x + 1.0)) + (((y / (x + 1.0)) - (x / (z * (x + 1.0)))) / t);
	elseif (z <= 1.7e+129)
		tmp = (((z * y) / t_1) + (x - (x / t_1))) / (x + 1.0);
	else
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]}, If[LessEqual[z, -2e+106], N[(N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - N[(x / N[(z * N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.7e+129], N[(N[(N[(N[(z * y), $MachinePrecision] / t$95$1), $MachinePrecision] + N[(x - N[(x / t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(N[(y - N[(x / z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]]

Derivation

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

Alternative 3: 86.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{z \cdot y}{z \cdot t - x}}{x + 1}\\ \mathbf{if}\;z \leq -1.45 \cdot 10^{+102}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{elif}\;z \leq -1 \cdot 10^{-244}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4 \cdot 10^{-166}:\\ \;\;\;\;\frac{\left(x + 1\right) - \frac{y}{\frac{x}{z}}}{x + 1}\\ \mathbf{elif}\;z \leq 2.35 \cdot 10^{+128}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{x + 1}{x + \frac{y - \frac{x}{z}}{t}}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (* z y) (- (* z t) x))) (+ x 1.0))))
   (if (<= z -1.45e+102)
     (/ (+ x (/ y t)) (+ x 1.0))
     (if (<= z -1e-244)
       t_1
       (if (<= z 4e-166)
         (/ (- (+ x 1.0) (/ y (/ x z))) (+ x 1.0))
         (if (<= z 2.35e+128)
           t_1
           (/ 1.0 (/ (+ x 1.0) (+ x (/ (- y (/ x z)) t))))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + ((z * y) / ((z * t) - x))) / (x + 1.0);
	double tmp;
	if (z <= -1.45e+102) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else if (z <= -1e-244) {
		tmp = t_1;
	} else if (z <= 4e-166) {
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	} else if (z <= 2.35e+128) {
		tmp = t_1;
	} else {
		tmp = 1.0 / ((x + 1.0) / (x + ((y - (x / z)) / t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (x + ((z * y) / ((z * t) - x))) / (x + 1.0d0)
    if (z <= (-1.45d+102)) then
        tmp = (x + (y / t)) / (x + 1.0d0)
    else if (z <= (-1d-244)) then
        tmp = t_1
    else if (z <= 4d-166) then
        tmp = ((x + 1.0d0) - (y / (x / z))) / (x + 1.0d0)
    else if (z <= 2.35d+128) then
        tmp = t_1
    else
        tmp = 1.0d0 / ((x + 1.0d0) / (x + ((y - (x / z)) / t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x + ((z * y) / ((z * t) - x))) / (x + 1.0);
	double tmp;
	if (z <= -1.45e+102) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else if (z <= -1e-244) {
		tmp = t_1;
	} else if (z <= 4e-166) {
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	} else if (z <= 2.35e+128) {
		tmp = t_1;
	} else {
		tmp = 1.0 / ((x + 1.0) / (x + ((y - (x / z)) / t)));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + ((z * y) / ((z * t) - x))) / (x + 1.0)
	tmp = 0
	if z <= -1.45e+102:
		tmp = (x + (y / t)) / (x + 1.0)
	elif z <= -1e-244:
		tmp = t_1
	elif z <= 4e-166:
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0)
	elif z <= 2.35e+128:
		tmp = t_1
	else:
		tmp = 1.0 / ((x + 1.0) / (x + ((y - (x / z)) / t)))
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(Float64(z * y) / Float64(Float64(z * t) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (z <= -1.45e+102)
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	elseif (z <= -1e-244)
		tmp = t_1;
	elseif (z <= 4e-166)
		tmp = Float64(Float64(Float64(x + 1.0) - Float64(y / Float64(x / z))) / Float64(x + 1.0));
	elseif (z <= 2.35e+128)
		tmp = t_1;
	else
		tmp = Float64(1.0 / Float64(Float64(x + 1.0) / Float64(x + Float64(Float64(y - Float64(x / z)) / t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + ((z * y) / ((z * t) - x))) / (x + 1.0);
	tmp = 0.0;
	if (z <= -1.45e+102)
		tmp = (x + (y / t)) / (x + 1.0);
	elseif (z <= -1e-244)
		tmp = t_1;
	elseif (z <= 4e-166)
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	elseif (z <= 2.35e+128)
		tmp = t_1;
	else
		tmp = 1.0 / ((x + 1.0) / (x + ((y - (x / z)) / t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(N[(z * y), $MachinePrecision] / N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.45e+102], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1e-244], t$95$1, If[LessEqual[z, 4e-166], N[(N[(N[(x + 1.0), $MachinePrecision] - N[(y / N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.35e+128], t$95$1, N[(1.0 / N[(N[(x + 1.0), $MachinePrecision] / N[(x + N[(N[(y - N[(x / z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 4: 86.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{z \cdot y}{z \cdot t - x}}{x + 1}\\ \mathbf{if}\;z \leq -1.1 \cdot 10^{+103}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{elif}\;z \leq -5.4 \cdot 10^{-245}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.55 \cdot 10^{-165}:\\ \;\;\;\;\frac{\left(x + 1\right) - \frac{y}{\frac{x}{z}}}{x + 1}\\ \mathbf{elif}\;z \leq 4.4 \cdot 10^{+130}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y - \frac{x}{z}}{t}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (* z y) (- (* z t) x))) (+ x 1.0))))
   (if (<= z -1.1e+103)
     (/ (+ x (/ y t)) (+ x 1.0))
     (if (<= z -5.4e-245)
       t_1
       (if (<= z 1.55e-165)
         (/ (- (+ x 1.0) (/ y (/ x z))) (+ x 1.0))
         (if (<= z 4.4e+130) t_1 (/ (+ x (/ (- y (/ x z)) t)) (+ x 1.0))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + ((z * y) / ((z * t) - x))) / (x + 1.0);
	double tmp;
	if (z <= -1.1e+103) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else if (z <= -5.4e-245) {
		tmp = t_1;
	} else if (z <= 1.55e-165) {
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	} else if (z <= 4.4e+130) {
		tmp = t_1;
	} else {
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (x + ((z * y) / ((z * t) - x))) / (x + 1.0d0)
    if (z <= (-1.1d+103)) then
        tmp = (x + (y / t)) / (x + 1.0d0)
    else if (z <= (-5.4d-245)) then
        tmp = t_1
    else if (z <= 1.55d-165) then
        tmp = ((x + 1.0d0) - (y / (x / z))) / (x + 1.0d0)
    else if (z <= 4.4d+130) then
        tmp = t_1
    else
        tmp = (x + ((y - (x / z)) / t)) / (x + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x + ((z * y) / ((z * t) - x))) / (x + 1.0);
	double tmp;
	if (z <= -1.1e+103) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else if (z <= -5.4e-245) {
		tmp = t_1;
	} else if (z <= 1.55e-165) {
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	} else if (z <= 4.4e+130) {
		tmp = t_1;
	} else {
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + ((z * y) / ((z * t) - x))) / (x + 1.0)
	tmp = 0
	if z <= -1.1e+103:
		tmp = (x + (y / t)) / (x + 1.0)
	elif z <= -5.4e-245:
		tmp = t_1
	elif z <= 1.55e-165:
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0)
	elif z <= 4.4e+130:
		tmp = t_1
	else:
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(Float64(z * y) / Float64(Float64(z * t) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (z <= -1.1e+103)
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	elseif (z <= -5.4e-245)
		tmp = t_1;
	elseif (z <= 1.55e-165)
		tmp = Float64(Float64(Float64(x + 1.0) - Float64(y / Float64(x / z))) / Float64(x + 1.0));
	elseif (z <= 4.4e+130)
		tmp = t_1;
	else
		tmp = Float64(Float64(x + Float64(Float64(y - Float64(x / z)) / t)) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + ((z * y) / ((z * t) - x))) / (x + 1.0);
	tmp = 0.0;
	if (z <= -1.1e+103)
		tmp = (x + (y / t)) / (x + 1.0);
	elseif (z <= -5.4e-245)
		tmp = t_1;
	elseif (z <= 1.55e-165)
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	elseif (z <= 4.4e+130)
		tmp = t_1;
	else
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(N[(z * y), $MachinePrecision] / N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.1e+103], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -5.4e-245], t$95$1, If[LessEqual[z, 1.55e-165], N[(N[(N[(x + 1.0), $MachinePrecision] - N[(y / N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.4e+130], t$95$1, N[(N[(x + N[(N[(y - N[(x / z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 5: 94.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.35 \cdot 10^{+106}:\\ \;\;\;\;\frac{x}{x + 1} + \frac{\frac{y}{x + 1} - \frac{x}{z \cdot \left(x + 1\right)}}{t}\\ \mathbf{elif}\;z \leq 1.32 \cdot 10^{+132}:\\ \;\;\;\;\frac{x + \frac{z \cdot y - x}{z \cdot t - x}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y - \frac{x}{z}}{t}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -1.35e+106)
   (+ (/ x (+ x 1.0)) (/ (- (/ y (+ x 1.0)) (/ x (* z (+ x 1.0)))) t))
   (if (<= z 1.32e+132)
     (/ (+ x (/ (- (* z y) x) (- (* z t) x))) (+ x 1.0))
     (/ (+ x (/ (- y (/ x z)) t)) (+ x 1.0)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -1.35e+106) {
		tmp = (x / (x + 1.0)) + (((y / (x + 1.0)) - (x / (z * (x + 1.0)))) / t);
	} else if (z <= 1.32e+132) {
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	} else {
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (z <= (-1.35d+106)) then
        tmp = (x / (x + 1.0d0)) + (((y / (x + 1.0d0)) - (x / (z * (x + 1.0d0)))) / t)
    else if (z <= 1.32d+132) then
        tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0d0)
    else
        tmp = (x + ((y - (x / z)) / t)) / (x + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -1.35e+106) {
		tmp = (x / (x + 1.0)) + (((y / (x + 1.0)) - (x / (z * (x + 1.0)))) / t);
	} else if (z <= 1.32e+132) {
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	} else {
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -1.35e+106:
		tmp = (x / (x + 1.0)) + (((y / (x + 1.0)) - (x / (z * (x + 1.0)))) / t)
	elif z <= 1.32e+132:
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0)
	else:
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -1.35e+106)
		tmp = Float64(Float64(x / Float64(x + 1.0)) + Float64(Float64(Float64(y / Float64(x + 1.0)) - Float64(x / Float64(z * Float64(x + 1.0)))) / t));
	elseif (z <= 1.32e+132)
		tmp = Float64(Float64(x + Float64(Float64(Float64(z * y) - x) / Float64(Float64(z * t) - x))) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(x + Float64(Float64(y - Float64(x / z)) / t)) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -1.35e+106)
		tmp = (x / (x + 1.0)) + (((y / (x + 1.0)) - (x / (z * (x + 1.0)))) / t);
	elseif (z <= 1.32e+132)
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	else
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -1.35e+106], N[(N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - N[(x / N[(z * N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.32e+132], N[(N[(x + N[(N[(N[(z * y), $MachinePrecision] - x), $MachinePrecision] / N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(N[(y - N[(x / z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 6: 94.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2 \cdot 10^{+105}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{elif}\;z \leq 5.5 \cdot 10^{+133}:\\ \;\;\;\;\frac{x + \frac{z \cdot y - x}{z \cdot t - x}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y - \frac{x}{z}}{t}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -2e+105)
   (/ (+ x (/ y t)) (+ x 1.0))
   (if (<= z 5.5e+133)
     (/ (+ x (/ (- (* z y) x) (- (* z t) x))) (+ x 1.0))
     (/ (+ x (/ (- y (/ x z)) t)) (+ x 1.0)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -2e+105) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else if (z <= 5.5e+133) {
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	} else {
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (z <= (-2d+105)) then
        tmp = (x + (y / t)) / (x + 1.0d0)
    else if (z <= 5.5d+133) then
        tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0d0)
    else
        tmp = (x + ((y - (x / z)) / t)) / (x + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -2e+105) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else if (z <= 5.5e+133) {
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	} else {
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -2e+105:
		tmp = (x + (y / t)) / (x + 1.0)
	elif z <= 5.5e+133:
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0)
	else:
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -2e+105)
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	elseif (z <= 5.5e+133)
		tmp = Float64(Float64(x + Float64(Float64(Float64(z * y) - x) / Float64(Float64(z * t) - x))) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(x + Float64(Float64(y - Float64(x / z)) / t)) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -2e+105)
		tmp = (x + (y / t)) / (x + 1.0);
	elseif (z <= 5.5e+133)
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	else
		tmp = (x + ((y - (x / z)) / t)) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -2e+105], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 5.5e+133], N[(N[(x + N[(N[(N[(z * y), $MachinePrecision] - x), $MachinePrecision] / N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(N[(y - N[(x / z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 7: 79.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.25 \cdot 10^{+52} \lor \neg \left(z \leq 7 \cdot 10^{-166}\right):\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x - \frac{x}{z \cdot t - x}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.25e+52) (not (<= z 7e-166)))
   (/ (+ x (/ y t)) (+ x 1.0))
   (/ (- x (/ x (- (* z t) x))) (+ x 1.0))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.25e+52) || !(z <= 7e-166)) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = (x - (x / ((z * t) - x))) / (x + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-1.25d+52)) .or. (.not. (z <= 7d-166))) then
        tmp = (x + (y / t)) / (x + 1.0d0)
    else
        tmp = (x - (x / ((z * t) - x))) / (x + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.25e+52) || !(z <= 7e-166)) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = (x - (x / ((z * t) - x))) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.25e+52) or not (z <= 7e-166):
		tmp = (x + (y / t)) / (x + 1.0)
	else:
		tmp = (x - (x / ((z * t) - x))) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.25e+52) || !(z <= 7e-166))
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(x - Float64(x / Float64(Float64(z * t) - x))) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -1.25e+52) || ~((z <= 7e-166)))
		tmp = (x + (y / t)) / (x + 1.0);
	else
		tmp = (x - (x / ((z * t) - x))) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.25e+52], N[Not[LessEqual[z, 7e-166]], $MachinePrecision]], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(x - N[(x / N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 8: 79.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.8 \cdot 10^{-149} \lor \neg \left(z \leq 3.2 \cdot 10^{-145}\right):\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(x + 1\right) - \frac{y}{\frac{x}{z}}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -5.8e-149) (not (<= z 3.2e-145)))
   (/ (+ x (/ y t)) (+ x 1.0))
   (/ (- (+ x 1.0) (/ y (/ x z))) (+ x 1.0))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -5.8e-149) || !(z <= 3.2e-145)) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-5.8d-149)) .or. (.not. (z <= 3.2d-145))) then
        tmp = (x + (y / t)) / (x + 1.0d0)
    else
        tmp = ((x + 1.0d0) - (y / (x / z))) / (x + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -5.8e-149) || !(z <= 3.2e-145)) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -5.8e-149) or not (z <= 3.2e-145):
		tmp = (x + (y / t)) / (x + 1.0)
	else:
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -5.8e-149) || !(z <= 3.2e-145))
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(Float64(x + 1.0) - Float64(y / Float64(x / z))) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -5.8e-149) || ~((z <= 3.2e-145)))
		tmp = (x + (y / t)) / (x + 1.0);
	else
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -5.8e-149], N[Not[LessEqual[z, 3.2e-145]], $MachinePrecision]], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(x + 1.0), $MachinePrecision] - N[(y / N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 9: 78.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.1 \cdot 10^{-181} \lor \neg \left(z \leq 7.2 \cdot 10^{-164}\right):\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{x} \cdot \left(t - y\right) + 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.1e-181) (not (<= z 7.2e-164)))
   (/ (+ x (/ y t)) (+ x 1.0))
   (+ (* (/ z x) (- t y)) 1.0)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.1e-181) || !(z <= 7.2e-164)) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = ((z / x) * (t - y)) + 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-1.1d-181)) .or. (.not. (z <= 7.2d-164))) then
        tmp = (x + (y / t)) / (x + 1.0d0)
    else
        tmp = ((z / x) * (t - y)) + 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.1e-181) || !(z <= 7.2e-164)) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = ((z / x) * (t - y)) + 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.1e-181) or not (z <= 7.2e-164):
		tmp = (x + (y / t)) / (x + 1.0)
	else:
		tmp = ((z / x) * (t - y)) + 1.0
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.1e-181) || !(z <= 7.2e-164))
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(Float64(z / x) * Float64(t - y)) + 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -1.1e-181) || ~((z <= 7.2e-164)))
		tmp = (x + (y / t)) / (x + 1.0);
	else
		tmp = ((z / x) * (t - y)) + 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.1e-181], N[Not[LessEqual[z, 7.2e-164]], $MachinePrecision]], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(z / x), $MachinePrecision] * N[(t - y), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]]

Derivation

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

Alternative 10: 68.5% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.5 \cdot 10^{-78}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 4.7 \cdot 10^{-78}:\\ \;\;\;\;\frac{z \cdot y}{z \cdot t - x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{x + 1}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -3.5e-78)
   1.0
   (if (<= x 4.7e-78) (/ (* z y) (- (* z t) x)) (/ 1.0 (/ (+ x 1.0) x)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.5e-78) {
		tmp = 1.0;
	} else if (x <= 4.7e-78) {
		tmp = (z * y) / ((z * t) - x);
	} else {
		tmp = 1.0 / ((x + 1.0) / x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (x <= (-3.5d-78)) then
        tmp = 1.0d0
    else if (x <= 4.7d-78) then
        tmp = (z * y) / ((z * t) - x)
    else
        tmp = 1.0d0 / ((x + 1.0d0) / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.5e-78) {
		tmp = 1.0;
	} else if (x <= 4.7e-78) {
		tmp = (z * y) / ((z * t) - x);
	} else {
		tmp = 1.0 / ((x + 1.0) / x);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -3.5e-78:
		tmp = 1.0
	elif x <= 4.7e-78:
		tmp = (z * y) / ((z * t) - x)
	else:
		tmp = 1.0 / ((x + 1.0) / x)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -3.5e-78)
		tmp = 1.0;
	elseif (x <= 4.7e-78)
		tmp = Float64(Float64(z * y) / Float64(Float64(z * t) - x));
	else
		tmp = Float64(1.0 / Float64(Float64(x + 1.0) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -3.5e-78)
		tmp = 1.0;
	elseif (x <= 4.7e-78)
		tmp = (z * y) / ((z * t) - x);
	else
		tmp = 1.0 / ((x + 1.0) / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -3.5e-78], 1.0, If[LessEqual[x, 4.7e-78], N[(N[(z * y), $MachinePrecision] / N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(x + 1.0), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 11: 68.4% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.5 \cdot 10^{-61}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 1.95 \cdot 10^{-76}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{x + 1}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -3.5e-61) 1.0 (if (<= x 1.95e-76) (/ y t) (/ 1.0 (/ (+ x 1.0) x)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.5e-61) {
		tmp = 1.0;
	} else if (x <= 1.95e-76) {
		tmp = y / t;
	} else {
		tmp = 1.0 / ((x + 1.0) / x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (x <= (-3.5d-61)) then
        tmp = 1.0d0
    else if (x <= 1.95d-76) then
        tmp = y / t
    else
        tmp = 1.0d0 / ((x + 1.0d0) / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.5e-61) {
		tmp = 1.0;
	} else if (x <= 1.95e-76) {
		tmp = y / t;
	} else {
		tmp = 1.0 / ((x + 1.0) / x);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -3.5e-61:
		tmp = 1.0
	elif x <= 1.95e-76:
		tmp = y / t
	else:
		tmp = 1.0 / ((x + 1.0) / x)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -3.5e-61)
		tmp = 1.0;
	elseif (x <= 1.95e-76)
		tmp = Float64(y / t);
	else
		tmp = Float64(1.0 / Float64(Float64(x + 1.0) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -3.5e-61)
		tmp = 1.0;
	elseif (x <= 1.95e-76)
		tmp = y / t;
	else
		tmp = 1.0 / ((x + 1.0) / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -3.5e-61], 1.0, If[LessEqual[x, 1.95e-76], N[(y / t), $MachinePrecision], N[(1.0 / N[(N[(x + 1.0), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 12: 68.4% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{-59}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2.85 \cdot 10^{-80}:\\ \;\;\;\;\frac{y}{\left(x + 1\right) \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{x + 1}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -2e-59)
   1.0
   (if (<= x 2.85e-80) (/ y (* (+ x 1.0) t)) (/ 1.0 (/ (+ x 1.0) x)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -2e-59) {
		tmp = 1.0;
	} else if (x <= 2.85e-80) {
		tmp = y / ((x + 1.0) * t);
	} else {
		tmp = 1.0 / ((x + 1.0) / x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (x <= (-2d-59)) then
        tmp = 1.0d0
    else if (x <= 2.85d-80) then
        tmp = y / ((x + 1.0d0) * t)
    else
        tmp = 1.0d0 / ((x + 1.0d0) / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -2e-59) {
		tmp = 1.0;
	} else if (x <= 2.85e-80) {
		tmp = y / ((x + 1.0) * t);
	} else {
		tmp = 1.0 / ((x + 1.0) / x);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -2e-59:
		tmp = 1.0
	elif x <= 2.85e-80:
		tmp = y / ((x + 1.0) * t)
	else:
		tmp = 1.0 / ((x + 1.0) / x)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -2e-59)
		tmp = 1.0;
	elseif (x <= 2.85e-80)
		tmp = Float64(y / Float64(Float64(x + 1.0) * t));
	else
		tmp = Float64(1.0 / Float64(Float64(x + 1.0) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -2e-59)
		tmp = 1.0;
	elseif (x <= 2.85e-80)
		tmp = y / ((x + 1.0) * t);
	else
		tmp = 1.0 / ((x + 1.0) / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -2e-59], 1.0, If[LessEqual[x, 2.85e-80], N[(y / N[(N[(x + 1.0), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(x + 1.0), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 13: 68.4% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.4 \cdot 10^{-57}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 6 \cdot 10^{-82}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -4.4e-57) 1.0 (if (<= x 6e-82) (/ y t) (/ x (+ x 1.0)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -4.4e-57) {
		tmp = 1.0;
	} else if (x <= 6e-82) {
		tmp = y / t;
	} else {
		tmp = x / (x + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (x <= (-4.4d-57)) then
        tmp = 1.0d0
    else if (x <= 6d-82) then
        tmp = y / t
    else
        tmp = x / (x + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -4.4e-57) {
		tmp = 1.0;
	} else if (x <= 6e-82) {
		tmp = y / t;
	} else {
		tmp = x / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -4.4e-57:
		tmp = 1.0
	elif x <= 6e-82:
		tmp = y / t
	else:
		tmp = x / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -4.4e-57)
		tmp = 1.0;
	elseif (x <= 6e-82)
		tmp = Float64(y / t);
	else
		tmp = Float64(x / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -4.4e-57)
		tmp = 1.0;
	elseif (x <= 6e-82)
		tmp = y / t;
	else
		tmp = x / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -4.4e-57], 1.0, If[LessEqual[x, 6e-82], N[(y / t), $MachinePrecision], N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 14: 68.3% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.5 \cdot 10^{-59}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 3.5 \cdot 10^{-20}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -1.5e-59) 1.0 (if (<= x 3.5e-20) (/ y t) 1.0)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -1.5e-59) {
		tmp = 1.0;
	} else if (x <= 3.5e-20) {
		tmp = y / t;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (x <= (-1.5d-59)) then
        tmp = 1.0d0
    else if (x <= 3.5d-20) then
        tmp = y / t
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -1.5e-59) {
		tmp = 1.0;
	} else if (x <= 3.5e-20) {
		tmp = y / t;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -1.5e-59:
		tmp = 1.0
	elif x <= 3.5e-20:
		tmp = y / t
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -1.5e-59)
		tmp = 1.0;
	elseif (x <= 3.5e-20)
		tmp = Float64(y / t);
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -1.5e-59)
		tmp = 1.0;
	elseif (x <= 3.5e-20)
		tmp = y / t;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -1.5e-59], 1.0, If[LessEqual[x, 3.5e-20], N[(y / t), $MachinePrecision], 1.0]]

Derivation

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

Alternative 15: 53.0% accurate, 17.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 1.0)

C

double code(double x, double y, double z, double t) {
	return 1.0;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = 1.0d0
end function

Java

public static double code(double x, double y, double z, double t) {
	return 1.0;
}

Python

def code(x, y, z, t):
	return 1.0

Julia

function code(x, y, z, t)
	return 1.0
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = 1.0;
end

Wolfram

code[x_, y_, z_, t_] := 1.0

Derivation

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

Developer target: 99.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \frac{x + \left(\frac{y}{t - \frac{x}{z}} - \frac{x}{t \cdot z - x}\right)}{x + 1} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (/ (+ x (- (/ y (- t (/ x z))) (/ x (- (* t z) x)))) (+ x 1.0)))

C

double code(double x, double y, double z, double t) {
	return (x + ((y / (t - (x / z))) - (x / ((t * z) - x)))) / (x + 1.0);
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (x + ((y / (t - (x / z))) - (x / ((t * z) - x)))) / (x + 1.0d0)
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x + ((y / (t - (x / z))) - (x / ((t * z) - x)))) / (x + 1.0);
}

Python

def code(x, y, z, t):
	return (x + ((y / (t - (x / z))) - (x / ((t * z) - x)))) / (x + 1.0)

Julia

function code(x, y, z, t)
	return Float64(Float64(x + Float64(Float64(y / Float64(t - Float64(x / z))) - Float64(x / Float64(Float64(t * z) - x)))) / Float64(x + 1.0))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x + ((y / (t - (x / z))) - (x / ((t * z) - x)))) / (x + 1.0);
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x + N[(N[(y / N[(t - N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(x / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024008 
(FPCore (x y z t)
  :name "Diagrams.Trail:splitAtParam  from diagrams-lib-1.3.0.3, A"
  :precision binary64

  :herbie-target
  (/ (+ x (- (/ y (- t (/ x z))) (/ x (- (* t z) x)))) (+ x 1.0))

  (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0)))