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

Percentage Accurate: 89.0% → 94.6%

Time: 14.3s

Alternatives: 16

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: 89.0% 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: 94.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1 \cdot 10^{+134}:\\ \;\;\;\;\left(\frac{y}{t \cdot \left(x + 1\right)} + \frac{x}{x + 1}\right) - \frac{x}{t \cdot \left(z \cdot \left(x + 1\right)\right)}\\ \mathbf{elif}\;z \leq 2.5 \cdot 10^{+82}:\\ \;\;\;\;\frac{x + \frac{z \cdot y - x}{z \cdot t - x}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -1e+134)
   (- (+ (/ y (* t (+ x 1.0))) (/ x (+ x 1.0))) (/ x (* t (* z (+ x 1.0)))))
   (if (<= z 2.5e+82)
     (/ (+ x (/ (- (* z y) x) (- (* z t) x))) (+ x 1.0))
     (/ (+ x (/ y t)) (+ x 1.0)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -1e+134) {
		tmp = ((y / (t * (x + 1.0))) + (x / (x + 1.0))) - (x / (t * (z * (x + 1.0))));
	} else if (z <= 2.5e+82) {
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	} else {
		tmp = (x + (y / 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 <= (-1d+134)) then
        tmp = ((y / (t * (x + 1.0d0))) + (x / (x + 1.0d0))) - (x / (t * (z * (x + 1.0d0))))
    else if (z <= 2.5d+82) then
        tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0d0)
    else
        tmp = (x + (y / 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 <= -1e+134) {
		tmp = ((y / (t * (x + 1.0))) + (x / (x + 1.0))) - (x / (t * (z * (x + 1.0))));
	} else if (z <= 2.5e+82) {
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	} else {
		tmp = (x + (y / t)) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -1e+134:
		tmp = ((y / (t * (x + 1.0))) + (x / (x + 1.0))) - (x / (t * (z * (x + 1.0))))
	elif z <= 2.5e+82:
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0)
	else:
		tmp = (x + (y / t)) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -1e+134)
		tmp = Float64(Float64(Float64(y / Float64(t * Float64(x + 1.0))) + Float64(x / Float64(x + 1.0))) - Float64(x / Float64(t * Float64(z * Float64(x + 1.0)))));
	elseif (z <= 2.5e+82)
		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(y / t)) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -1e+134)
		tmp = ((y / (t * (x + 1.0))) + (x / (x + 1.0))) - (x / (t * (z * (x + 1.0))));
	elseif (z <= 2.5e+82)
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	else
		tmp = (x + (y / t)) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -1e+134], N[(N[(N[(y / N[(t * N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(x / N[(t * N[(z * N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.5e+82], 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[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 2: 79.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x - \frac{x}{z \cdot t - x}}{x + 1}\\ t_2 := \frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{if}\;z \leq -1.22 \cdot 10^{+17}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 4.4 \cdot 10^{-181}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{-135}:\\ \;\;\;\;1 - y \cdot \frac{z}{x}\\ \mathbf{elif}\;z \leq 1.65 \cdot 10^{+54}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (- x (/ x (- (* z t) x))) (+ x 1.0)))
        (t_2 (/ (+ x (/ y t)) (+ x 1.0))))
   (if (<= z -1.22e+17)
     t_2
     (if (<= z 4.4e-181)
       t_1
       (if (<= z 3.2e-135)
         (- 1.0 (* y (/ z x)))
         (if (<= z 1.65e+54) t_1 t_2))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x - (x / ((z * t) - x))) / (x + 1.0);
	double t_2 = (x + (y / t)) / (x + 1.0);
	double tmp;
	if (z <= -1.22e+17) {
		tmp = t_2;
	} else if (z <= 4.4e-181) {
		tmp = t_1;
	} else if (z <= 3.2e-135) {
		tmp = 1.0 - (y * (z / x));
	} else if (z <= 1.65e+54) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	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) :: t_2
    real(8) :: tmp
    t_1 = (x - (x / ((z * t) - x))) / (x + 1.0d0)
    t_2 = (x + (y / t)) / (x + 1.0d0)
    if (z <= (-1.22d+17)) then
        tmp = t_2
    else if (z <= 4.4d-181) then
        tmp = t_1
    else if (z <= 3.2d-135) then
        tmp = 1.0d0 - (y * (z / x))
    else if (z <= 1.65d+54) 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 t_1 = (x - (x / ((z * t) - x))) / (x + 1.0);
	double t_2 = (x + (y / t)) / (x + 1.0);
	double tmp;
	if (z <= -1.22e+17) {
		tmp = t_2;
	} else if (z <= 4.4e-181) {
		tmp = t_1;
	} else if (z <= 3.2e-135) {
		tmp = 1.0 - (y * (z / x));
	} else if (z <= 1.65e+54) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x - (x / ((z * t) - x))) / (x + 1.0)
	t_2 = (x + (y / t)) / (x + 1.0)
	tmp = 0
	if z <= -1.22e+17:
		tmp = t_2
	elif z <= 4.4e-181:
		tmp = t_1
	elif z <= 3.2e-135:
		tmp = 1.0 - (y * (z / x))
	elif z <= 1.65e+54:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x - Float64(x / Float64(Float64(z * t) - x))) / Float64(x + 1.0))
	t_2 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	tmp = 0.0
	if (z <= -1.22e+17)
		tmp = t_2;
	elseif (z <= 4.4e-181)
		tmp = t_1;
	elseif (z <= 3.2e-135)
		tmp = Float64(1.0 - Float64(y * Float64(z / x)));
	elseif (z <= 1.65e+54)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x - (x / ((z * t) - x))) / (x + 1.0);
	t_2 = (x + (y / t)) / (x + 1.0);
	tmp = 0.0;
	if (z <= -1.22e+17)
		tmp = t_2;
	elseif (z <= 4.4e-181)
		tmp = t_1;
	elseif (z <= 3.2e-135)
		tmp = 1.0 - (y * (z / x));
	elseif (z <= 1.65e+54)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x - N[(x / N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.22e+17], t$95$2, If[LessEqual[z, 4.4e-181], t$95$1, If[LessEqual[z, 3.2e-135], N[(1.0 - N[(y * N[(z / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.65e+54], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 3: 79.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x - \frac{x}{z \cdot t - x}}{x + 1}\\ t_2 := \frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{if}\;z \leq -265000000000:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 2.9 \cdot 10^{-182}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 4.5 \cdot 10^{-135}:\\ \;\;\;\;\frac{\left(x + 1\right) - \frac{y}{\frac{x}{z}}}{x + 1}\\ \mathbf{elif}\;z \leq 2.8 \cdot 10^{+54}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (- x (/ x (- (* z t) x))) (+ x 1.0)))
        (t_2 (/ (+ x (/ y t)) (+ x 1.0))))
   (if (<= z -265000000000.0)
     t_2
     (if (<= z 2.9e-182)
       t_1
       (if (<= z 4.5e-135)
         (/ (- (+ x 1.0) (/ y (/ x z))) (+ x 1.0))
         (if (<= z 2.8e+54) t_1 t_2))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x - (x / ((z * t) - x))) / (x + 1.0);
	double t_2 = (x + (y / t)) / (x + 1.0);
	double tmp;
	if (z <= -265000000000.0) {
		tmp = t_2;
	} else if (z <= 2.9e-182) {
		tmp = t_1;
	} else if (z <= 4.5e-135) {
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	} else if (z <= 2.8e+54) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	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) :: t_2
    real(8) :: tmp
    t_1 = (x - (x / ((z * t) - x))) / (x + 1.0d0)
    t_2 = (x + (y / t)) / (x + 1.0d0)
    if (z <= (-265000000000.0d0)) then
        tmp = t_2
    else if (z <= 2.9d-182) then
        tmp = t_1
    else if (z <= 4.5d-135) then
        tmp = ((x + 1.0d0) - (y / (x / z))) / (x + 1.0d0)
    else if (z <= 2.8d+54) 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 t_1 = (x - (x / ((z * t) - x))) / (x + 1.0);
	double t_2 = (x + (y / t)) / (x + 1.0);
	double tmp;
	if (z <= -265000000000.0) {
		tmp = t_2;
	} else if (z <= 2.9e-182) {
		tmp = t_1;
	} else if (z <= 4.5e-135) {
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	} else if (z <= 2.8e+54) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x - (x / ((z * t) - x))) / (x + 1.0)
	t_2 = (x + (y / t)) / (x + 1.0)
	tmp = 0
	if z <= -265000000000.0:
		tmp = t_2
	elif z <= 2.9e-182:
		tmp = t_1
	elif z <= 4.5e-135:
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0)
	elif z <= 2.8e+54:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x - Float64(x / Float64(Float64(z * t) - x))) / Float64(x + 1.0))
	t_2 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	tmp = 0.0
	if (z <= -265000000000.0)
		tmp = t_2;
	elseif (z <= 2.9e-182)
		tmp = t_1;
	elseif (z <= 4.5e-135)
		tmp = Float64(Float64(Float64(x + 1.0) - Float64(y / Float64(x / z))) / Float64(x + 1.0));
	elseif (z <= 2.8e+54)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x - (x / ((z * t) - x))) / (x + 1.0);
	t_2 = (x + (y / t)) / (x + 1.0);
	tmp = 0.0;
	if (z <= -265000000000.0)
		tmp = t_2;
	elseif (z <= 2.9e-182)
		tmp = t_1;
	elseif (z <= 4.5e-135)
		tmp = ((x + 1.0) - (y / (x / z))) / (x + 1.0);
	elseif (z <= 2.8e+54)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x - N[(x / N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -265000000000.0], t$95$2, If[LessEqual[z, 2.9e-182], t$95$1, If[LessEqual[z, 4.5e-135], N[(N[(N[(x + 1.0), $MachinePrecision] - N[(y / N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.8e+54], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 4: 94.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.2 \cdot 10^{+46} \lor \neg \left(z \leq 1.4 \cdot 10^{+83}\right):\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{z \cdot y - x}{z \cdot t - x}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -2.2e+46) (not (<= z 1.4e+83)))
   (/ (+ x (/ y t)) (+ x 1.0))
   (/ (+ x (/ (- (* z y) x) (- (* z t) x))) (+ x 1.0))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -2.2e+46) || !(z <= 1.4e+83)) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = (x + (((z * y) - 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 <= (-2.2d+46)) .or. (.not. (z <= 1.4d+83))) then
        tmp = (x + (y / t)) / (x + 1.0d0)
    else
        tmp = (x + (((z * y) - 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 <= -2.2e+46) || !(z <= 1.4e+83)) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -2.2e+46) or not (z <= 1.4e+83):
		tmp = (x + (y / t)) / (x + 1.0)
	else:
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -2.2e+46) || !(z <= 1.4e+83))
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(x + Float64(Float64(Float64(z * y) - 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 <= -2.2e+46) || ~((z <= 1.4e+83)))
		tmp = (x + (y / t)) / (x + 1.0);
	else
		tmp = (x + (((z * y) - x) / ((z * t) - x))) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -2.2e+46], N[Not[LessEqual[z, 1.4e+83]], $MachinePrecision]], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], 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]]

Derivation

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

Alternative 5: 80.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.7 \cdot 10^{-84}:\\ \;\;\;\;\frac{x - \frac{x}{z \cdot t - x}}{x + 1}\\ \mathbf{elif}\;x \leq 12.5:\\ \;\;\;\;\frac{\left(x + \frac{y}{t}\right) - \frac{x}{z \cdot t}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{z}{\frac{x \cdot \left(x + 1\right)}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -3.7e-84)
   (/ (- x (/ x (- (* z t) x))) (+ x 1.0))
   (if (<= x 12.5)
     (/ (- (+ x (/ y t)) (/ x (* z t))) (+ x 1.0))
     (- 1.0 (/ z (/ (* x (+ x 1.0)) y))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.7e-84) {
		tmp = (x - (x / ((z * t) - x))) / (x + 1.0);
	} else if (x <= 12.5) {
		tmp = ((x + (y / t)) - (x / (z * t))) / (x + 1.0);
	} else {
		tmp = 1.0 - (z / ((x * (x + 1.0)) / y));
	}
	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 <= (-3.7d-84)) then
        tmp = (x - (x / ((z * t) - x))) / (x + 1.0d0)
    else if (x <= 12.5d0) then
        tmp = ((x + (y / t)) - (x / (z * t))) / (x + 1.0d0)
    else
        tmp = 1.0d0 - (z / ((x * (x + 1.0d0)) / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.7e-84) {
		tmp = (x - (x / ((z * t) - x))) / (x + 1.0);
	} else if (x <= 12.5) {
		tmp = ((x + (y / t)) - (x / (z * t))) / (x + 1.0);
	} else {
		tmp = 1.0 - (z / ((x * (x + 1.0)) / y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -3.7e-84:
		tmp = (x - (x / ((z * t) - x))) / (x + 1.0)
	elif x <= 12.5:
		tmp = ((x + (y / t)) - (x / (z * t))) / (x + 1.0)
	else:
		tmp = 1.0 - (z / ((x * (x + 1.0)) / y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -3.7e-84)
		tmp = Float64(Float64(x - Float64(x / Float64(Float64(z * t) - x))) / Float64(x + 1.0));
	elseif (x <= 12.5)
		tmp = Float64(Float64(Float64(x + Float64(y / t)) - Float64(x / Float64(z * t))) / Float64(x + 1.0));
	else
		tmp = Float64(1.0 - Float64(z / Float64(Float64(x * Float64(x + 1.0)) / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -3.7e-84)
		tmp = (x - (x / ((z * t) - x))) / (x + 1.0);
	elseif (x <= 12.5)
		tmp = ((x + (y / t)) - (x / (z * t))) / (x + 1.0);
	else
		tmp = 1.0 - (z / ((x * (x + 1.0)) / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -3.7e-84], N[(N[(x - N[(x / N[(N[(z * t), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 12.5], N[(N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] - N[(x / N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(1.0 - N[(z / N[(N[(x * N[(x + 1.0), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 6: 67.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x + 1}\\ \mathbf{if}\;x \leq -1.15 \cdot 10^{-52}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.4 \cdot 10^{-192}:\\ \;\;\;\;1 - \frac{z \cdot y}{x}\\ \mathbf{elif}\;x \leq 2.2 \cdot 10^{-83}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{elif}\;x \leq 1.75 \cdot 10^{-10}:\\ \;\;\;\;x - \frac{x}{z \cdot t}\\ \mathbf{elif}\;x \leq 3.3:\\ \;\;\;\;\frac{y}{t \cdot \left(x + 1\right)}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (+ x 1.0))))
   (if (<= x -1.15e-52)
     t_1
     (if (<= x -1.4e-192)
       (- 1.0 (/ (* z y) x))
       (if (<= x 2.2e-83)
         (/ y t)
         (if (<= x 1.75e-10)
           (- x (/ x (* z t)))
           (if (<= x 3.3) (/ y (* t (+ x 1.0))) t_1)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -1.15e-52) {
		tmp = t_1;
	} else if (x <= -1.4e-192) {
		tmp = 1.0 - ((z * y) / x);
	} else if (x <= 2.2e-83) {
		tmp = y / t;
	} else if (x <= 1.75e-10) {
		tmp = x - (x / (z * t));
	} else if (x <= 3.3) {
		tmp = y / (t * (x + 1.0));
	} else {
		tmp = t_1;
	}
	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 / (x + 1.0d0)
    if (x <= (-1.15d-52)) then
        tmp = t_1
    else if (x <= (-1.4d-192)) then
        tmp = 1.0d0 - ((z * y) / x)
    else if (x <= 2.2d-83) then
        tmp = y / t
    else if (x <= 1.75d-10) then
        tmp = x - (x / (z * t))
    else if (x <= 3.3d0) then
        tmp = y / (t * (x + 1.0d0))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -1.15e-52) {
		tmp = t_1;
	} else if (x <= -1.4e-192) {
		tmp = 1.0 - ((z * y) / x);
	} else if (x <= 2.2e-83) {
		tmp = y / t;
	} else if (x <= 1.75e-10) {
		tmp = x - (x / (z * t));
	} else if (x <= 3.3) {
		tmp = y / (t * (x + 1.0));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x / (x + 1.0)
	tmp = 0
	if x <= -1.15e-52:
		tmp = t_1
	elif x <= -1.4e-192:
		tmp = 1.0 - ((z * y) / x)
	elif x <= 2.2e-83:
		tmp = y / t
	elif x <= 1.75e-10:
		tmp = x - (x / (z * t))
	elif x <= 3.3:
		tmp = y / (t * (x + 1.0))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x / Float64(x + 1.0))
	tmp = 0.0
	if (x <= -1.15e-52)
		tmp = t_1;
	elseif (x <= -1.4e-192)
		tmp = Float64(1.0 - Float64(Float64(z * y) / x));
	elseif (x <= 2.2e-83)
		tmp = Float64(y / t);
	elseif (x <= 1.75e-10)
		tmp = Float64(x - Float64(x / Float64(z * t)));
	elseif (x <= 3.3)
		tmp = Float64(y / Float64(t * Float64(x + 1.0)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x / (x + 1.0);
	tmp = 0.0;
	if (x <= -1.15e-52)
		tmp = t_1;
	elseif (x <= -1.4e-192)
		tmp = 1.0 - ((z * y) / x);
	elseif (x <= 2.2e-83)
		tmp = y / t;
	elseif (x <= 1.75e-10)
		tmp = x - (x / (z * t));
	elseif (x <= 3.3)
		tmp = y / (t * (x + 1.0));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.15e-52], t$95$1, If[LessEqual[x, -1.4e-192], N[(1.0 - N[(N[(z * y), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.2e-83], N[(y / t), $MachinePrecision], If[LessEqual[x, 1.75e-10], N[(x - N[(x / N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.3], N[(y / N[(t * N[(x + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 7: 67.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x + 1}\\ \mathbf{if}\;x \leq -1.9 \cdot 10^{-62}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -5.7 \cdot 10^{-193}:\\ \;\;\;\;1 - \frac{z \cdot y}{x}\\ \mathbf{elif}\;x \leq 7.8 \cdot 10^{-83}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{elif}\;x \leq 3.6 \cdot 10^{-15}:\\ \;\;\;\;x - \frac{x}{z \cdot t}\\ \mathbf{elif}\;x \leq 2.8:\\ \;\;\;\;\frac{y}{t + x \cdot t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (+ x 1.0))))
   (if (<= x -1.9e-62)
     t_1
     (if (<= x -5.7e-193)
       (- 1.0 (/ (* z y) x))
       (if (<= x 7.8e-83)
         (/ y t)
         (if (<= x 3.6e-15)
           (- x (/ x (* z t)))
           (if (<= x 2.8) (/ y (+ t (* x t))) t_1)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -1.9e-62) {
		tmp = t_1;
	} else if (x <= -5.7e-193) {
		tmp = 1.0 - ((z * y) / x);
	} else if (x <= 7.8e-83) {
		tmp = y / t;
	} else if (x <= 3.6e-15) {
		tmp = x - (x / (z * t));
	} else if (x <= 2.8) {
		tmp = y / (t + (x * t));
	} else {
		tmp = t_1;
	}
	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 / (x + 1.0d0)
    if (x <= (-1.9d-62)) then
        tmp = t_1
    else if (x <= (-5.7d-193)) then
        tmp = 1.0d0 - ((z * y) / x)
    else if (x <= 7.8d-83) then
        tmp = y / t
    else if (x <= 3.6d-15) then
        tmp = x - (x / (z * t))
    else if (x <= 2.8d0) then
        tmp = y / (t + (x * t))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -1.9e-62) {
		tmp = t_1;
	} else if (x <= -5.7e-193) {
		tmp = 1.0 - ((z * y) / x);
	} else if (x <= 7.8e-83) {
		tmp = y / t;
	} else if (x <= 3.6e-15) {
		tmp = x - (x / (z * t));
	} else if (x <= 2.8) {
		tmp = y / (t + (x * t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x / (x + 1.0)
	tmp = 0
	if x <= -1.9e-62:
		tmp = t_1
	elif x <= -5.7e-193:
		tmp = 1.0 - ((z * y) / x)
	elif x <= 7.8e-83:
		tmp = y / t
	elif x <= 3.6e-15:
		tmp = x - (x / (z * t))
	elif x <= 2.8:
		tmp = y / (t + (x * t))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x / Float64(x + 1.0))
	tmp = 0.0
	if (x <= -1.9e-62)
		tmp = t_1;
	elseif (x <= -5.7e-193)
		tmp = Float64(1.0 - Float64(Float64(z * y) / x));
	elseif (x <= 7.8e-83)
		tmp = Float64(y / t);
	elseif (x <= 3.6e-15)
		tmp = Float64(x - Float64(x / Float64(z * t)));
	elseif (x <= 2.8)
		tmp = Float64(y / Float64(t + Float64(x * t)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x / (x + 1.0);
	tmp = 0.0;
	if (x <= -1.9e-62)
		tmp = t_1;
	elseif (x <= -5.7e-193)
		tmp = 1.0 - ((z * y) / x);
	elseif (x <= 7.8e-83)
		tmp = y / t;
	elseif (x <= 3.6e-15)
		tmp = x - (x / (z * t));
	elseif (x <= 2.8)
		tmp = y / (t + (x * t));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.9e-62], t$95$1, If[LessEqual[x, -5.7e-193], N[(1.0 - N[(N[(z * y), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 7.8e-83], N[(y / t), $MachinePrecision], If[LessEqual[x, 3.6e-15], N[(x - N[(x / N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.8], N[(y / N[(t + N[(x * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 8: 67.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x + 1}\\ \mathbf{if}\;x \leq -2.35 \cdot 10^{-47}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.12 \cdot 10^{-192}:\\ \;\;\;\;\frac{z}{x} \cdot \left(t - y\right) + 1\\ \mathbf{elif}\;x \leq 1.62 \cdot 10^{-84}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{elif}\;x \leq 8.6 \cdot 10^{-14}:\\ \;\;\;\;x - \frac{x}{z \cdot t}\\ \mathbf{elif}\;x \leq 2.8:\\ \;\;\;\;\frac{y}{t + x \cdot t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (+ x 1.0))))
   (if (<= x -2.35e-47)
     t_1
     (if (<= x -1.12e-192)
       (+ (* (/ z x) (- t y)) 1.0)
       (if (<= x 1.62e-84)
         (/ y t)
         (if (<= x 8.6e-14)
           (- x (/ x (* z t)))
           (if (<= x 2.8) (/ y (+ t (* x t))) t_1)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -2.35e-47) {
		tmp = t_1;
	} else if (x <= -1.12e-192) {
		tmp = ((z / x) * (t - y)) + 1.0;
	} else if (x <= 1.62e-84) {
		tmp = y / t;
	} else if (x <= 8.6e-14) {
		tmp = x - (x / (z * t));
	} else if (x <= 2.8) {
		tmp = y / (t + (x * t));
	} else {
		tmp = t_1;
	}
	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 / (x + 1.0d0)
    if (x <= (-2.35d-47)) then
        tmp = t_1
    else if (x <= (-1.12d-192)) then
        tmp = ((z / x) * (t - y)) + 1.0d0
    else if (x <= 1.62d-84) then
        tmp = y / t
    else if (x <= 8.6d-14) then
        tmp = x - (x / (z * t))
    else if (x <= 2.8d0) then
        tmp = y / (t + (x * t))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -2.35e-47) {
		tmp = t_1;
	} else if (x <= -1.12e-192) {
		tmp = ((z / x) * (t - y)) + 1.0;
	} else if (x <= 1.62e-84) {
		tmp = y / t;
	} else if (x <= 8.6e-14) {
		tmp = x - (x / (z * t));
	} else if (x <= 2.8) {
		tmp = y / (t + (x * t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x / (x + 1.0)
	tmp = 0
	if x <= -2.35e-47:
		tmp = t_1
	elif x <= -1.12e-192:
		tmp = ((z / x) * (t - y)) + 1.0
	elif x <= 1.62e-84:
		tmp = y / t
	elif x <= 8.6e-14:
		tmp = x - (x / (z * t))
	elif x <= 2.8:
		tmp = y / (t + (x * t))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x / Float64(x + 1.0))
	tmp = 0.0
	if (x <= -2.35e-47)
		tmp = t_1;
	elseif (x <= -1.12e-192)
		tmp = Float64(Float64(Float64(z / x) * Float64(t - y)) + 1.0);
	elseif (x <= 1.62e-84)
		tmp = Float64(y / t);
	elseif (x <= 8.6e-14)
		tmp = Float64(x - Float64(x / Float64(z * t)));
	elseif (x <= 2.8)
		tmp = Float64(y / Float64(t + Float64(x * t)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x / (x + 1.0);
	tmp = 0.0;
	if (x <= -2.35e-47)
		tmp = t_1;
	elseif (x <= -1.12e-192)
		tmp = ((z / x) * (t - y)) + 1.0;
	elseif (x <= 1.62e-84)
		tmp = y / t;
	elseif (x <= 8.6e-14)
		tmp = x - (x / (z * t));
	elseif (x <= 2.8)
		tmp = y / (t + (x * t));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.35e-47], t$95$1, If[LessEqual[x, -1.12e-192], N[(N[(N[(z / x), $MachinePrecision] * N[(t - y), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision], If[LessEqual[x, 1.62e-84], N[(y / t), $MachinePrecision], If[LessEqual[x, 8.6e-14], N[(x - N[(x / N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.8], N[(y / N[(t + N[(x * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 9: 67.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x + 1}\\ \mathbf{if}\;x \leq -1.3 \cdot 10^{-52}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.4 \cdot 10^{-192}:\\ \;\;\;\;1 - \frac{z \cdot \left(y - t\right)}{x}\\ \mathbf{elif}\;x \leq 4.2 \cdot 10^{-85}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{elif}\;x \leq 1.7 \cdot 10^{-11}:\\ \;\;\;\;x - \frac{x}{z \cdot t}\\ \mathbf{elif}\;x \leq 2.8:\\ \;\;\;\;\frac{y}{t + x \cdot t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (+ x 1.0))))
   (if (<= x -1.3e-52)
     t_1
     (if (<= x -1.4e-192)
       (- 1.0 (/ (* z (- y t)) x))
       (if (<= x 4.2e-85)
         (/ y t)
         (if (<= x 1.7e-11)
           (- x (/ x (* z t)))
           (if (<= x 2.8) (/ y (+ t (* x t))) t_1)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -1.3e-52) {
		tmp = t_1;
	} else if (x <= -1.4e-192) {
		tmp = 1.0 - ((z * (y - t)) / x);
	} else if (x <= 4.2e-85) {
		tmp = y / t;
	} else if (x <= 1.7e-11) {
		tmp = x - (x / (z * t));
	} else if (x <= 2.8) {
		tmp = y / (t + (x * t));
	} else {
		tmp = t_1;
	}
	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 / (x + 1.0d0)
    if (x <= (-1.3d-52)) then
        tmp = t_1
    else if (x <= (-1.4d-192)) then
        tmp = 1.0d0 - ((z * (y - t)) / x)
    else if (x <= 4.2d-85) then
        tmp = y / t
    else if (x <= 1.7d-11) then
        tmp = x - (x / (z * t))
    else if (x <= 2.8d0) then
        tmp = y / (t + (x * t))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -1.3e-52) {
		tmp = t_1;
	} else if (x <= -1.4e-192) {
		tmp = 1.0 - ((z * (y - t)) / x);
	} else if (x <= 4.2e-85) {
		tmp = y / t;
	} else if (x <= 1.7e-11) {
		tmp = x - (x / (z * t));
	} else if (x <= 2.8) {
		tmp = y / (t + (x * t));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x / (x + 1.0)
	tmp = 0
	if x <= -1.3e-52:
		tmp = t_1
	elif x <= -1.4e-192:
		tmp = 1.0 - ((z * (y - t)) / x)
	elif x <= 4.2e-85:
		tmp = y / t
	elif x <= 1.7e-11:
		tmp = x - (x / (z * t))
	elif x <= 2.8:
		tmp = y / (t + (x * t))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x / Float64(x + 1.0))
	tmp = 0.0
	if (x <= -1.3e-52)
		tmp = t_1;
	elseif (x <= -1.4e-192)
		tmp = Float64(1.0 - Float64(Float64(z * Float64(y - t)) / x));
	elseif (x <= 4.2e-85)
		tmp = Float64(y / t);
	elseif (x <= 1.7e-11)
		tmp = Float64(x - Float64(x / Float64(z * t)));
	elseif (x <= 2.8)
		tmp = Float64(y / Float64(t + Float64(x * t)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x / (x + 1.0);
	tmp = 0.0;
	if (x <= -1.3e-52)
		tmp = t_1;
	elseif (x <= -1.4e-192)
		tmp = 1.0 - ((z * (y - t)) / x);
	elseif (x <= 4.2e-85)
		tmp = y / t;
	elseif (x <= 1.7e-11)
		tmp = x - (x / (z * t));
	elseif (x <= 2.8)
		tmp = y / (t + (x * t));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.3e-52], t$95$1, If[LessEqual[x, -1.4e-192], N[(1.0 - N[(N[(z * N[(y - t), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.2e-85], N[(y / t), $MachinePrecision], If[LessEqual[x, 1.7e-11], N[(x - N[(x / N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.8], N[(y / N[(t + N[(x * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

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

Alternative 10: 77.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -200000000 \lor \neg \left(z \leq 1.55 \cdot 10^{+54}\right):\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{z}{\frac{x \cdot \left(x + 1\right)}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -200000000.0) (not (<= z 1.55e+54)))
   (/ (+ x (/ y t)) (+ x 1.0))
   (- 1.0 (/ z (/ (* x (+ x 1.0)) y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -200000000.0) || !(z <= 1.55e+54)) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = 1.0 - (z / ((x * (x + 1.0)) / y));
	}
	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 <= (-200000000.0d0)) .or. (.not. (z <= 1.55d+54))) then
        tmp = (x + (y / t)) / (x + 1.0d0)
    else
        tmp = 1.0d0 - (z / ((x * (x + 1.0d0)) / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -200000000.0) || !(z <= 1.55e+54)) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = 1.0 - (z / ((x * (x + 1.0)) / y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -200000000.0) or not (z <= 1.55e+54):
		tmp = (x + (y / t)) / (x + 1.0)
	else:
		tmp = 1.0 - (z / ((x * (x + 1.0)) / y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -200000000.0) || !(z <= 1.55e+54))
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	else
		tmp = Float64(1.0 - Float64(z / Float64(Float64(x * Float64(x + 1.0)) / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -200000000.0) || ~((z <= 1.55e+54)))
		tmp = (x + (y / t)) / (x + 1.0);
	else
		tmp = 1.0 - (z / ((x * (x + 1.0)) / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -200000000.0], N[Not[LessEqual[z, 1.55e+54]], $MachinePrecision]], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(1.0 - N[(z / N[(N[(x * N[(x + 1.0), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 78.3% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.6 \cdot 10^{+26}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 62:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -1.6e+26) 1.0 (if (<= x 62.0) (/ (+ x (/ y t)) (+ x 1.0)) 1.0)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -1.6e+26) {
		tmp = 1.0;
	} else if (x <= 62.0) {
		tmp = (x + (y / t)) / (x + 1.0);
	} 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 <= (-1.6d+26)) then
        tmp = 1.0d0
    else if (x <= 62.0d0) then
        tmp = (x + (y / t)) / (x + 1.0d0)
    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 <= -1.6e+26) {
		tmp = 1.0;
	} else if (x <= 62.0) {
		tmp = (x + (y / t)) / (x + 1.0);
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -1.6e+26:
		tmp = 1.0
	elif x <= 62.0:
		tmp = (x + (y / t)) / (x + 1.0)
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -1.6e+26)
		tmp = 1.0;
	elseif (x <= 62.0)
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -1.6e+26)
		tmp = 1.0;
	elseif (x <= 62.0)
		tmp = (x + (y / t)) / (x + 1.0);
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -1.6e+26], 1.0, If[LessEqual[x, 62.0], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], 1.0]]

Derivation

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

Alternative 12: 67.8% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x + 1}\\ \mathbf{if}\;x \leq -8.6 \cdot 10^{-57}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.4 \cdot 10^{-192}:\\ \;\;\;\;1 - y \cdot \frac{z}{x}\\ \mathbf{elif}\;x \leq 5.4 \cdot 10^{-88}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (+ x 1.0))))
   (if (<= x -8.6e-57)
     t_1
     (if (<= x -1.4e-192)
       (- 1.0 (* y (/ z x)))
       (if (<= x 5.4e-88) (/ y t) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -8.6e-57) {
		tmp = t_1;
	} else if (x <= -1.4e-192) {
		tmp = 1.0 - (y * (z / x));
	} else if (x <= 5.4e-88) {
		tmp = y / t;
	} else {
		tmp = t_1;
	}
	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 / (x + 1.0d0)
    if (x <= (-8.6d-57)) then
        tmp = t_1
    else if (x <= (-1.4d-192)) then
        tmp = 1.0d0 - (y * (z / x))
    else if (x <= 5.4d-88) then
        tmp = y / t
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -8.6e-57) {
		tmp = t_1;
	} else if (x <= -1.4e-192) {
		tmp = 1.0 - (y * (z / x));
	} else if (x <= 5.4e-88) {
		tmp = y / t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x / (x + 1.0)
	tmp = 0
	if x <= -8.6e-57:
		tmp = t_1
	elif x <= -1.4e-192:
		tmp = 1.0 - (y * (z / x))
	elif x <= 5.4e-88:
		tmp = y / t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x / Float64(x + 1.0))
	tmp = 0.0
	if (x <= -8.6e-57)
		tmp = t_1;
	elseif (x <= -1.4e-192)
		tmp = Float64(1.0 - Float64(y * Float64(z / x)));
	elseif (x <= 5.4e-88)
		tmp = Float64(y / t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x / (x + 1.0);
	tmp = 0.0;
	if (x <= -8.6e-57)
		tmp = t_1;
	elseif (x <= -1.4e-192)
		tmp = 1.0 - (y * (z / x));
	elseif (x <= 5.4e-88)
		tmp = y / t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -8.6e-57], t$95$1, If[LessEqual[x, -1.4e-192], N[(1.0 - N[(y * N[(z / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 5.4e-88], N[(y / t), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 13: 67.8% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x + 1}\\ \mathbf{if}\;x \leq -2.6 \cdot 10^{-54}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.35 \cdot 10^{-192}:\\ \;\;\;\;1 - \frac{z \cdot y}{x}\\ \mathbf{elif}\;x \leq 6.4 \cdot 10^{-90}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (+ x 1.0))))
   (if (<= x -2.6e-54)
     t_1
     (if (<= x -1.35e-192)
       (- 1.0 (/ (* z y) x))
       (if (<= x 6.4e-90) (/ y t) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -2.6e-54) {
		tmp = t_1;
	} else if (x <= -1.35e-192) {
		tmp = 1.0 - ((z * y) / x);
	} else if (x <= 6.4e-90) {
		tmp = y / t;
	} else {
		tmp = t_1;
	}
	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 / (x + 1.0d0)
    if (x <= (-2.6d-54)) then
        tmp = t_1
    else if (x <= (-1.35d-192)) then
        tmp = 1.0d0 - ((z * y) / x)
    else if (x <= 6.4d-90) then
        tmp = y / t
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x / (x + 1.0);
	double tmp;
	if (x <= -2.6e-54) {
		tmp = t_1;
	} else if (x <= -1.35e-192) {
		tmp = 1.0 - ((z * y) / x);
	} else if (x <= 6.4e-90) {
		tmp = y / t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x / (x + 1.0)
	tmp = 0
	if x <= -2.6e-54:
		tmp = t_1
	elif x <= -1.35e-192:
		tmp = 1.0 - ((z * y) / x)
	elif x <= 6.4e-90:
		tmp = y / t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x / Float64(x + 1.0))
	tmp = 0.0
	if (x <= -2.6e-54)
		tmp = t_1;
	elseif (x <= -1.35e-192)
		tmp = Float64(1.0 - Float64(Float64(z * y) / x));
	elseif (x <= 6.4e-90)
		tmp = Float64(y / t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x / (x + 1.0);
	tmp = 0.0;
	if (x <= -2.6e-54)
		tmp = t_1;
	elseif (x <= -1.35e-192)
		tmp = 1.0 - ((z * y) / x);
	elseif (x <= 6.4e-90)
		tmp = y / t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.6e-54], t$95$1, If[LessEqual[x, -1.35e-192], N[(1.0 - N[(N[(z * y), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 6.4e-90], N[(y / t), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 14: 68.6% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.2 \cdot 10^{-56} \lor \neg \left(x \leq 2.75 \cdot 10^{-89}\right):\\ \;\;\;\;\frac{x}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= x -6.2e-56) (not (<= x 2.75e-89))) (/ x (+ x 1.0)) (/ y t)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -6.2e-56) || !(x <= 2.75e-89)) {
		tmp = x / (x + 1.0);
	} else {
		tmp = y / 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) :: tmp
    if ((x <= (-6.2d-56)) .or. (.not. (x <= 2.75d-89))) then
        tmp = x / (x + 1.0d0)
    else
        tmp = y / t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x <= -6.2e-56) || !(x <= 2.75e-89)) {
		tmp = x / (x + 1.0);
	} else {
		tmp = y / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x <= -6.2e-56) or not (x <= 2.75e-89):
		tmp = x / (x + 1.0)
	else:
		tmp = y / t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((x <= -6.2e-56) || !(x <= 2.75e-89))
		tmp = Float64(x / Float64(x + 1.0));
	else
		tmp = Float64(y / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x <= -6.2e-56) || ~((x <= 2.75e-89)))
		tmp = x / (x + 1.0);
	else
		tmp = y / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[x, -6.2e-56], N[Not[LessEqual[x, 2.75e-89]], $MachinePrecision]], N[(x / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(y / t), $MachinePrecision]]

Derivation

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

Alternative 15: 67.9% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.6 \cdot 10^{-84}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2.8:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -3.6e-84) 1.0 (if (<= x 2.8) (/ y t) 1.0)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -3.6e-84) {
		tmp = 1.0;
	} else if (x <= 2.8) {
		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 <= (-3.6d-84)) then
        tmp = 1.0d0
    else if (x <= 2.8d0) 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 <= -3.6e-84) {
		tmp = 1.0;
	} else if (x <= 2.8) {
		tmp = y / t;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -3.6e-84:
		tmp = 1.0
	elif x <= 2.8:
		tmp = y / t
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -3.6e-84)
		tmp = 1.0;
	elseif (x <= 2.8)
		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 <= -3.6e-84)
		tmp = 1.0;
	elseif (x <= 2.8)
		tmp = y / t;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -3.6e-84], 1.0, If[LessEqual[x, 2.8], N[(y / t), $MachinePrecision], 1.0]]

Derivation

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

Alternative 16: 54.1% 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 2023340 
(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)))