Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisLine from plot-0.2.3.4, B

Percentage Accurate: 98.1% → 98.1%

Time: 11.0s

Alternatives: 13

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 98.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 2: 78.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z}{a - t}\\ \mathbf{if}\;z \leq -1.2 \cdot 10^{-23}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -4.8 \cdot 10^{-106}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq -4.9 \cdot 10^{-173}:\\ \;\;\;\;x + \frac{y \cdot z}{a}\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{-78}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ z (- a t))))))
   (if (<= z -1.2e-23)
     t_1
     (if (<= z -4.8e-106)
       (+ x y)
       (if (<= z -4.9e-173)
         (+ x (/ (* y z) a))
         (if (<= z 2.2e-78) (+ x y) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / (a - t)));
	double tmp;
	if (z <= -1.2e-23) {
		tmp = t_1;
	} else if (z <= -4.8e-106) {
		tmp = x + y;
	} else if (z <= -4.9e-173) {
		tmp = x + ((y * z) / a);
	} else if (z <= 2.2e-78) {
		tmp = x + y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (y * (z / (a - t)))
    if (z <= (-1.2d-23)) then
        tmp = t_1
    else if (z <= (-4.8d-106)) then
        tmp = x + y
    else if (z <= (-4.9d-173)) then
        tmp = x + ((y * z) / a)
    else if (z <= 2.2d-78) then
        tmp = x + y
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / (a - t)));
	double tmp;
	if (z <= -1.2e-23) {
		tmp = t_1;
	} else if (z <= -4.8e-106) {
		tmp = x + y;
	} else if (z <= -4.9e-173) {
		tmp = x + ((y * z) / a);
	} else if (z <= 2.2e-78) {
		tmp = x + y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * (z / (a - t)))
	tmp = 0
	if z <= -1.2e-23:
		tmp = t_1
	elif z <= -4.8e-106:
		tmp = x + y
	elif z <= -4.9e-173:
		tmp = x + ((y * z) / a)
	elif z <= 2.2e-78:
		tmp = x + y
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(z / Float64(a - t))))
	tmp = 0.0
	if (z <= -1.2e-23)
		tmp = t_1;
	elseif (z <= -4.8e-106)
		tmp = Float64(x + y);
	elseif (z <= -4.9e-173)
		tmp = Float64(x + Float64(Float64(y * z) / a));
	elseif (z <= 2.2e-78)
		tmp = Float64(x + y);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * (z / (a - t)));
	tmp = 0.0;
	if (z <= -1.2e-23)
		tmp = t_1;
	elseif (z <= -4.8e-106)
		tmp = x + y;
	elseif (z <= -4.9e-173)
		tmp = x + ((y * z) / a);
	elseif (z <= 2.2e-78)
		tmp = x + y;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(z / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.2e-23], t$95$1, If[LessEqual[z, -4.8e-106], N[(x + y), $MachinePrecision], If[LessEqual[z, -4.9e-173], N[(x + N[(N[(y * z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.2e-78], N[(x + y), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 3: 77.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z}{a - t}\\ \mathbf{if}\;z \leq -6 \cdot 10^{-21}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -4.8 \cdot 10^{-106}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq -1.85 \cdot 10^{-190}:\\ \;\;\;\;x + y \cdot \frac{z - t}{a}\\ \mathbf{elif}\;z \leq 8 \cdot 10^{-77}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ z (- a t))))))
   (if (<= z -6e-21)
     t_1
     (if (<= z -4.8e-106)
       (+ x y)
       (if (<= z -1.85e-190)
         (+ x (* y (/ (- z t) a)))
         (if (<= z 8e-77) (+ x y) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / (a - t)));
	double tmp;
	if (z <= -6e-21) {
		tmp = t_1;
	} else if (z <= -4.8e-106) {
		tmp = x + y;
	} else if (z <= -1.85e-190) {
		tmp = x + (y * ((z - t) / a));
	} else if (z <= 8e-77) {
		tmp = x + y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (y * (z / (a - t)))
    if (z <= (-6d-21)) then
        tmp = t_1
    else if (z <= (-4.8d-106)) then
        tmp = x + y
    else if (z <= (-1.85d-190)) then
        tmp = x + (y * ((z - t) / a))
    else if (z <= 8d-77) then
        tmp = x + y
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / (a - t)));
	double tmp;
	if (z <= -6e-21) {
		tmp = t_1;
	} else if (z <= -4.8e-106) {
		tmp = x + y;
	} else if (z <= -1.85e-190) {
		tmp = x + (y * ((z - t) / a));
	} else if (z <= 8e-77) {
		tmp = x + y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * (z / (a - t)))
	tmp = 0
	if z <= -6e-21:
		tmp = t_1
	elif z <= -4.8e-106:
		tmp = x + y
	elif z <= -1.85e-190:
		tmp = x + (y * ((z - t) / a))
	elif z <= 8e-77:
		tmp = x + y
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(z / Float64(a - t))))
	tmp = 0.0
	if (z <= -6e-21)
		tmp = t_1;
	elseif (z <= -4.8e-106)
		tmp = Float64(x + y);
	elseif (z <= -1.85e-190)
		tmp = Float64(x + Float64(y * Float64(Float64(z - t) / a)));
	elseif (z <= 8e-77)
		tmp = Float64(x + y);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * (z / (a - t)));
	tmp = 0.0;
	if (z <= -6e-21)
		tmp = t_1;
	elseif (z <= -4.8e-106)
		tmp = x + y;
	elseif (z <= -1.85e-190)
		tmp = x + (y * ((z - t) / a));
	elseif (z <= 8e-77)
		tmp = x + y;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(z / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -6e-21], t$95$1, If[LessEqual[z, -4.8e-106], N[(x + y), $MachinePrecision], If[LessEqual[z, -1.85e-190], N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 8e-77], N[(x + y), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 4: 77.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.8 \cdot 10^{-23}:\\ \;\;\;\;x + \frac{y}{\frac{a - t}{z}}\\ \mathbf{elif}\;z \leq -4 \cdot 10^{-106}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq -7 \cdot 10^{-193}:\\ \;\;\;\;x + y \cdot \frac{z - t}{a}\\ \mathbf{elif}\;z \leq 1.8 \cdot 10^{-76}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{a - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -8.8e-23)
   (+ x (/ y (/ (- a t) z)))
   (if (<= z -4e-106)
     (+ x y)
     (if (<= z -7e-193)
       (+ x (* y (/ (- z t) a)))
       (if (<= z 1.8e-76) (+ x y) (+ x (* y (/ z (- a t)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -8.8e-23) {
		tmp = x + (y / ((a - t) / z));
	} else if (z <= -4e-106) {
		tmp = x + y;
	} else if (z <= -7e-193) {
		tmp = x + (y * ((z - t) / a));
	} else if (z <= 1.8e-76) {
		tmp = x + y;
	} else {
		tmp = x + (y * (z / (a - t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-8.8d-23)) then
        tmp = x + (y / ((a - t) / z))
    else if (z <= (-4d-106)) then
        tmp = x + y
    else if (z <= (-7d-193)) then
        tmp = x + (y * ((z - t) / a))
    else if (z <= 1.8d-76) then
        tmp = x + y
    else
        tmp = x + (y * (z / (a - t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -8.8e-23) {
		tmp = x + (y / ((a - t) / z));
	} else if (z <= -4e-106) {
		tmp = x + y;
	} else if (z <= -7e-193) {
		tmp = x + (y * ((z - t) / a));
	} else if (z <= 1.8e-76) {
		tmp = x + y;
	} else {
		tmp = x + (y * (z / (a - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -8.8e-23:
		tmp = x + (y / ((a - t) / z))
	elif z <= -4e-106:
		tmp = x + y
	elif z <= -7e-193:
		tmp = x + (y * ((z - t) / a))
	elif z <= 1.8e-76:
		tmp = x + y
	else:
		tmp = x + (y * (z / (a - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -8.8e-23)
		tmp = Float64(x + Float64(y / Float64(Float64(a - t) / z)));
	elseif (z <= -4e-106)
		tmp = Float64(x + y);
	elseif (z <= -7e-193)
		tmp = Float64(x + Float64(y * Float64(Float64(z - t) / a)));
	elseif (z <= 1.8e-76)
		tmp = Float64(x + y);
	else
		tmp = Float64(x + Float64(y * Float64(z / Float64(a - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -8.8e-23)
		tmp = x + (y / ((a - t) / z));
	elseif (z <= -4e-106)
		tmp = x + y;
	elseif (z <= -7e-193)
		tmp = x + (y * ((z - t) / a));
	elseif (z <= 1.8e-76)
		tmp = x + y;
	else
		tmp = x + (y * (z / (a - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -8.8e-23], N[(x + N[(y / N[(N[(a - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -4e-106], N[(x + y), $MachinePrecision], If[LessEqual[z, -7e-193], N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.8e-76], N[(x + y), $MachinePrecision], N[(x + N[(y * N[(z / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 5: 77.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8 \cdot 10^{-21}:\\ \;\;\;\;x + \frac{y}{\frac{a - t}{z}}\\ \mathbf{elif}\;z \leq -7.4 \cdot 10^{-106}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq -6.2 \cdot 10^{-190}:\\ \;\;\;\;x + \frac{y \cdot \left(z - t\right)}{a}\\ \mathbf{elif}\;z \leq 6.5 \cdot 10^{-79}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{a - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -8e-21)
   (+ x (/ y (/ (- a t) z)))
   (if (<= z -7.4e-106)
     (+ x y)
     (if (<= z -6.2e-190)
       (+ x (/ (* y (- z t)) a))
       (if (<= z 6.5e-79) (+ x y) (+ x (* y (/ z (- a t)))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -8e-21) {
		tmp = x + (y / ((a - t) / z));
	} else if (z <= -7.4e-106) {
		tmp = x + y;
	} else if (z <= -6.2e-190) {
		tmp = x + ((y * (z - t)) / a);
	} else if (z <= 6.5e-79) {
		tmp = x + y;
	} else {
		tmp = x + (y * (z / (a - t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-8d-21)) then
        tmp = x + (y / ((a - t) / z))
    else if (z <= (-7.4d-106)) then
        tmp = x + y
    else if (z <= (-6.2d-190)) then
        tmp = x + ((y * (z - t)) / a)
    else if (z <= 6.5d-79) then
        tmp = x + y
    else
        tmp = x + (y * (z / (a - t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -8e-21) {
		tmp = x + (y / ((a - t) / z));
	} else if (z <= -7.4e-106) {
		tmp = x + y;
	} else if (z <= -6.2e-190) {
		tmp = x + ((y * (z - t)) / a);
	} else if (z <= 6.5e-79) {
		tmp = x + y;
	} else {
		tmp = x + (y * (z / (a - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -8e-21:
		tmp = x + (y / ((a - t) / z))
	elif z <= -7.4e-106:
		tmp = x + y
	elif z <= -6.2e-190:
		tmp = x + ((y * (z - t)) / a)
	elif z <= 6.5e-79:
		tmp = x + y
	else:
		tmp = x + (y * (z / (a - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -8e-21)
		tmp = Float64(x + Float64(y / Float64(Float64(a - t) / z)));
	elseif (z <= -7.4e-106)
		tmp = Float64(x + y);
	elseif (z <= -6.2e-190)
		tmp = Float64(x + Float64(Float64(y * Float64(z - t)) / a));
	elseif (z <= 6.5e-79)
		tmp = Float64(x + y);
	else
		tmp = Float64(x + Float64(y * Float64(z / Float64(a - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -8e-21)
		tmp = x + (y / ((a - t) / z));
	elseif (z <= -7.4e-106)
		tmp = x + y;
	elseif (z <= -6.2e-190)
		tmp = x + ((y * (z - t)) / a);
	elseif (z <= 6.5e-79)
		tmp = x + y;
	else
		tmp = x + (y * (z / (a - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -8e-21], N[(x + N[(y / N[(N[(a - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -7.4e-106], N[(x + y), $MachinePrecision], If[LessEqual[z, -6.2e-190], N[(x + N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 6.5e-79], N[(x + y), $MachinePrecision], N[(x + N[(y * N[(z / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 6: 76.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.3 \cdot 10^{-8} \lor \neg \left(t \leq -6.2 \cdot 10^{-36}\right) \land \left(t \leq -5.6 \cdot 10^{-64} \lor \neg \left(t \leq 2.65 \cdot 10^{-21}\right)\right):\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (or (<= t -2.3e-8)
         (and (not (<= t -6.2e-36))
              (or (<= t -5.6e-64) (not (<= t 2.65e-21)))))
   (+ x y)
   (+ x (* y (/ z a)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -2.3e-8) || (!(t <= -6.2e-36) && ((t <= -5.6e-64) || !(t <= 2.65e-21)))) {
		tmp = x + y;
	} else {
		tmp = x + (y * (z / a));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if ((t <= (-2.3d-8)) .or. (.not. (t <= (-6.2d-36))) .and. (t <= (-5.6d-64)) .or. (.not. (t <= 2.65d-21))) then
        tmp = x + y
    else
        tmp = x + (y * (z / a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if ((t <= -2.3e-8) || (!(t <= -6.2e-36) && ((t <= -5.6e-64) || !(t <= 2.65e-21)))) {
		tmp = x + y;
	} else {
		tmp = x + (y * (z / a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if (t <= -2.3e-8) or (not (t <= -6.2e-36) and ((t <= -5.6e-64) or not (t <= 2.65e-21))):
		tmp = x + y
	else:
		tmp = x + (y * (z / a))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if ((t <= -2.3e-8) || (!(t <= -6.2e-36) && ((t <= -5.6e-64) || !(t <= 2.65e-21))))
		tmp = Float64(x + y);
	else
		tmp = Float64(x + Float64(y * Float64(z / a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if ((t <= -2.3e-8) || (~((t <= -6.2e-36)) && ((t <= -5.6e-64) || ~((t <= 2.65e-21)))))
		tmp = x + y;
	else
		tmp = x + (y * (z / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Or[LessEqual[t, -2.3e-8], And[N[Not[LessEqual[t, -6.2e-36]], $MachinePrecision], Or[LessEqual[t, -5.6e-64], N[Not[LessEqual[t, 2.65e-21]], $MachinePrecision]]]], N[(x + y), $MachinePrecision], N[(x + N[(y * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 70.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z}{a}\\ \mathbf{if}\;a \leq -2.9 \cdot 10^{-47}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq -8.2 \cdot 10^{-281}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;a \leq 1.26 \cdot 10^{-237}:\\ \;\;\;\;x - z \cdot \frac{y}{t}\\ \mathbf{elif}\;a \leq 4.6 \cdot 10^{-132}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ z a)))))
   (if (<= a -2.9e-47)
     t_1
     (if (<= a -8.2e-281)
       (+ x y)
       (if (<= a 1.26e-237)
         (- x (* z (/ y t)))
         (if (<= a 4.6e-132) (+ x y) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / a));
	double tmp;
	if (a <= -2.9e-47) {
		tmp = t_1;
	} else if (a <= -8.2e-281) {
		tmp = x + y;
	} else if (a <= 1.26e-237) {
		tmp = x - (z * (y / t));
	} else if (a <= 4.6e-132) {
		tmp = x + y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (y * (z / a))
    if (a <= (-2.9d-47)) then
        tmp = t_1
    else if (a <= (-8.2d-281)) then
        tmp = x + y
    else if (a <= 1.26d-237) then
        tmp = x - (z * (y / t))
    else if (a <= 4.6d-132) then
        tmp = x + y
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * (z / a));
	double tmp;
	if (a <= -2.9e-47) {
		tmp = t_1;
	} else if (a <= -8.2e-281) {
		tmp = x + y;
	} else if (a <= 1.26e-237) {
		tmp = x - (z * (y / t));
	} else if (a <= 4.6e-132) {
		tmp = x + y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * (z / a))
	tmp = 0
	if a <= -2.9e-47:
		tmp = t_1
	elif a <= -8.2e-281:
		tmp = x + y
	elif a <= 1.26e-237:
		tmp = x - (z * (y / t))
	elif a <= 4.6e-132:
		tmp = x + y
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(z / a)))
	tmp = 0.0
	if (a <= -2.9e-47)
		tmp = t_1;
	elseif (a <= -8.2e-281)
		tmp = Float64(x + y);
	elseif (a <= 1.26e-237)
		tmp = Float64(x - Float64(z * Float64(y / t)));
	elseif (a <= 4.6e-132)
		tmp = Float64(x + y);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * (z / a));
	tmp = 0.0;
	if (a <= -2.9e-47)
		tmp = t_1;
	elseif (a <= -8.2e-281)
		tmp = x + y;
	elseif (a <= 1.26e-237)
		tmp = x - (z * (y / t));
	elseif (a <= 4.6e-132)
		tmp = x + y;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(z / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.9e-47], t$95$1, If[LessEqual[a, -8.2e-281], N[(x + y), $MachinePrecision], If[LessEqual[a, 1.26e-237], N[(x - N[(z * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.6e-132], N[(x + y), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 8: 86.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - y \cdot \left(\frac{z}{t} + -1\right)\\ \mathbf{if}\;t \leq -4.3 \cdot 10^{+98}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq -3.8 \cdot 10^{-38}:\\ \;\;\;\;x - \frac{y \cdot t}{a - t}\\ \mathbf{elif}\;t \leq 15000000000000:\\ \;\;\;\;x + \frac{y}{\frac{a - t}{z}}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- x (* y (+ (/ z t) -1.0)))))
   (if (<= t -4.3e+98)
     t_1
     (if (<= t -3.8e-38)
       (- x (/ (* y t) (- a t)))
       (if (<= t 15000000000000.0) (+ x (/ y (/ (- a t) z))) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x - (y * ((z / t) + -1.0));
	double tmp;
	if (t <= -4.3e+98) {
		tmp = t_1;
	} else if (t <= -3.8e-38) {
		tmp = x - ((y * t) / (a - t));
	} else if (t <= 15000000000000.0) {
		tmp = x + (y / ((a - t) / z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x - (y * ((z / t) + (-1.0d0)))
    if (t <= (-4.3d+98)) then
        tmp = t_1
    else if (t <= (-3.8d-38)) then
        tmp = x - ((y * t) / (a - t))
    else if (t <= 15000000000000.0d0) then
        tmp = x + (y / ((a - t) / z))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x - (y * ((z / t) + -1.0));
	double tmp;
	if (t <= -4.3e+98) {
		tmp = t_1;
	} else if (t <= -3.8e-38) {
		tmp = x - ((y * t) / (a - t));
	} else if (t <= 15000000000000.0) {
		tmp = x + (y / ((a - t) / z));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x - (y * ((z / t) + -1.0))
	tmp = 0
	if t <= -4.3e+98:
		tmp = t_1
	elif t <= -3.8e-38:
		tmp = x - ((y * t) / (a - t))
	elif t <= 15000000000000.0:
		tmp = x + (y / ((a - t) / z))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x - Float64(y * Float64(Float64(z / t) + -1.0)))
	tmp = 0.0
	if (t <= -4.3e+98)
		tmp = t_1;
	elseif (t <= -3.8e-38)
		tmp = Float64(x - Float64(Float64(y * t) / Float64(a - t)));
	elseif (t <= 15000000000000.0)
		tmp = Float64(x + Float64(y / Float64(Float64(a - t) / z)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x - (y * ((z / t) + -1.0));
	tmp = 0.0;
	if (t <= -4.3e+98)
		tmp = t_1;
	elseif (t <= -3.8e-38)
		tmp = x - ((y * t) / (a - t));
	elseif (t <= 15000000000000.0)
		tmp = x + (y / ((a - t) / z));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x - N[(y * N[(N[(z / t), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -4.3e+98], t$95$1, If[LessEqual[t, -3.8e-38], N[(x - N[(N[(y * t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 15000000000000.0], N[(x + N[(y / N[(N[(a - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

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

Alternative 9: 82.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.5 \cdot 10^{+28}:\\ \;\;\;\;x + \frac{y}{\frac{a - t}{z}}\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{-85}:\\ \;\;\;\;x - \frac{y \cdot t}{a - t}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{a - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -4.5e+28)
   (+ x (/ y (/ (- a t) z)))
   (if (<= z 4.2e-85) (- x (/ (* y t) (- a t))) (+ x (* y (/ z (- a t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -4.5e+28) {
		tmp = x + (y / ((a - t) / z));
	} else if (z <= 4.2e-85) {
		tmp = x - ((y * t) / (a - t));
	} else {
		tmp = x + (y * (z / (a - t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-4.5d+28)) then
        tmp = x + (y / ((a - t) / z))
    else if (z <= 4.2d-85) then
        tmp = x - ((y * t) / (a - t))
    else
        tmp = x + (y * (z / (a - t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -4.5e+28) {
		tmp = x + (y / ((a - t) / z));
	} else if (z <= 4.2e-85) {
		tmp = x - ((y * t) / (a - t));
	} else {
		tmp = x + (y * (z / (a - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -4.5e+28:
		tmp = x + (y / ((a - t) / z))
	elif z <= 4.2e-85:
		tmp = x - ((y * t) / (a - t))
	else:
		tmp = x + (y * (z / (a - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -4.5e+28)
		tmp = Float64(x + Float64(y / Float64(Float64(a - t) / z)));
	elseif (z <= 4.2e-85)
		tmp = Float64(x - Float64(Float64(y * t) / Float64(a - t)));
	else
		tmp = Float64(x + Float64(y * Float64(z / Float64(a - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -4.5e+28)
		tmp = x + (y / ((a - t) / z));
	elseif (z <= 4.2e-85)
		tmp = x - ((y * t) / (a - t));
	else
		tmp = x + (y * (z / (a - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -4.5e+28], N[(x + N[(y / N[(N[(a - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.2e-85], N[(x - N[(N[(y * t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(z / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 10: 87.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4 \cdot 10^{+29}:\\ \;\;\;\;x + \frac{y}{\frac{a - t}{z}}\\ \mathbf{elif}\;z \leq 1.06 \cdot 10^{-21}:\\ \;\;\;\;x - y \cdot \frac{t}{a - t}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{a - t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -4e+29)
   (+ x (/ y (/ (- a t) z)))
   (if (<= z 1.06e-21) (- x (* y (/ t (- a t)))) (+ x (* y (/ z (- a t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -4e+29) {
		tmp = x + (y / ((a - t) / z));
	} else if (z <= 1.06e-21) {
		tmp = x - (y * (t / (a - t)));
	} else {
		tmp = x + (y * (z / (a - t)));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (z <= (-4d+29)) then
        tmp = x + (y / ((a - t) / z))
    else if (z <= 1.06d-21) then
        tmp = x - (y * (t / (a - t)))
    else
        tmp = x + (y * (z / (a - t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -4e+29) {
		tmp = x + (y / ((a - t) / z));
	} else if (z <= 1.06e-21) {
		tmp = x - (y * (t / (a - t)));
	} else {
		tmp = x + (y * (z / (a - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -4e+29:
		tmp = x + (y / ((a - t) / z))
	elif z <= 1.06e-21:
		tmp = x - (y * (t / (a - t)))
	else:
		tmp = x + (y * (z / (a - t)))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -4e+29)
		tmp = Float64(x + Float64(y / Float64(Float64(a - t) / z)));
	elseif (z <= 1.06e-21)
		tmp = Float64(x - Float64(y * Float64(t / Float64(a - t))));
	else
		tmp = Float64(x + Float64(y * Float64(z / Float64(a - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -4e+29)
		tmp = x + (y / ((a - t) / z));
	elseif (z <= 1.06e-21)
		tmp = x - (y * (t / (a - t)));
	else
		tmp = x + (y * (z / (a - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -4e+29], N[(x + N[(y / N[(N[(a - t), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.06e-21], N[(x - N[(y * N[(t / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * N[(z / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 11: 52.6% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{-37}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 1.25 \cdot 10^{-151}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -3.8e-37) x (if (<= x 1.25e-151) y x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -3.8e-37) {
		tmp = x;
	} else if (x <= 1.25e-151) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (x <= (-3.8d-37)) then
        tmp = x
    else if (x <= 1.25d-151) then
        tmp = y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -3.8e-37) {
		tmp = x;
	} else if (x <= 1.25e-151) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -3.8e-37:
		tmp = x
	elif x <= 1.25e-151:
		tmp = y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (x <= -3.8e-37)
		tmp = x;
	elseif (x <= 1.25e-151)
		tmp = y;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (x <= -3.8e-37)
		tmp = x;
	elseif (x <= 1.25e-151)
		tmp = y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[x, -3.8e-37], x, If[LessEqual[x, 1.25e-151], y, x]]

Derivation

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

Alternative 12: 61.8% accurate, 2.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq 3.8 \cdot 10^{+72}:\\ \;\;\;\;x + y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a) :precision binary64 (if (<= a 3.8e+72) (+ x y) x))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= 3.8e+72) {
		tmp = x + y;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= 3.8d+72) then
        tmp = x + y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= 3.8e+72) {
		tmp = x + y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= 3.8e+72:
		tmp = x + y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= 3.8e+72)
		tmp = Float64(x + y);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= 3.8e+72)
		tmp = x + y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, 3.8e+72], N[(x + y), $MachinePrecision], x]

Derivation

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

Alternative 13: 51.1% accurate, 11.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Developer target: 99.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + y \cdot \frac{z - t}{a - t}\\ \mathbf{if}\;y < -8.508084860551241 \cdot 10^{-17}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y < 2.894426862792089 \cdot 10^{-49}:\\ \;\;\;\;x + \left(y \cdot \left(z - t\right)\right) \cdot \frac{1}{a - t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* y (/ (- z t) (- a t))))))
   (if (< y -8.508084860551241e-17)
     t_1
     (if (< y 2.894426862792089e-49)
       (+ x (* (* y (- z t)) (/ 1.0 (- a t))))
       t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + (y * ((z - t) / (a - t)));
	double tmp;
	if (y < -8.508084860551241e-17) {
		tmp = t_1;
	} else if (y < 2.894426862792089e-49) {
		tmp = x + ((y * (z - t)) * (1.0 / (a - t)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (y * ((z - t) / (a - t)))
    if (y < (-8.508084860551241d-17)) then
        tmp = t_1
    else if (y < 2.894426862792089d-49) then
        tmp = x + ((y * (z - t)) * (1.0d0 / (a - 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 a) {
	double t_1 = x + (y * ((z - t) / (a - t)));
	double tmp;
	if (y < -8.508084860551241e-17) {
		tmp = t_1;
	} else if (y < 2.894426862792089e-49) {
		tmp = x + ((y * (z - t)) * (1.0 / (a - t)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + (y * ((z - t) / (a - t)))
	tmp = 0
	if y < -8.508084860551241e-17:
		tmp = t_1
	elif y < 2.894426862792089e-49:
		tmp = x + ((y * (z - t)) * (1.0 / (a - t)))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(y * Float64(Float64(z - t) / Float64(a - t))))
	tmp = 0.0
	if (y < -8.508084860551241e-17)
		tmp = t_1;
	elseif (y < 2.894426862792089e-49)
		tmp = Float64(x + Float64(Float64(y * Float64(z - t)) * Float64(1.0 / Float64(a - t))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + (y * ((z - t) / (a - t)));
	tmp = 0.0;
	if (y < -8.508084860551241e-17)
		tmp = t_1;
	elseif (y < 2.894426862792089e-49)
		tmp = x + ((y * (z - t)) * (1.0 / (a - t)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(y * N[(N[(z - t), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[y, -8.508084860551241e-17], t$95$1, If[Less[y, 2.894426862792089e-49], N[(x + N[(N[(y * N[(z - t), $MachinePrecision]), $MachinePrecision] * N[(1.0 / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2024008 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisLine from plot-0.2.3.4, B"
  :precision binary64

  :herbie-target
  (if (< y -8.508084860551241e-17) (+ x (* y (/ (- z t) (- a t)))) (if (< y 2.894426862792089e-49) (+ x (* (* y (- z t)) (/ 1.0 (- a t)))) (+ x (* y (/ (- z t) (- a t))))))

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