Diagrams.Backend.Rasterific:rasterificRadialGradient from diagrams-rasterific-1.3.1.3

Percentage Accurate: 88.0% → 99.9%

Time: 7.4s

Alternatives: 9

Speedup: 1.0×

• 20.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 88.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 2: 59.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \frac{-x}{z}\\ \mathbf{if}\;y \leq -1.25 \cdot 10^{+220}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq -1:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -2.65 \cdot 10^{-52}:\\ \;\;\;\;\frac{x}{z}\\ \mathbf{elif}\;y \leq -4.8 \cdot 10^{-105}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{-17}:\\ \;\;\;\;\frac{x}{z}\\ \mathbf{elif}\;y \leq 6.6 \cdot 10^{+62}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 2.5 \cdot 10^{+304}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* y (/ (- x) z))))
   (if (<= y -1.25e+220)
     y
     (if (<= y -1.0)
       t_0
       (if (<= y -2.65e-52)
         (/ x z)
         (if (<= y -4.8e-105)
           y
           (if (<= y 2.2e-17)
             (/ x z)
             (if (<= y 6.6e+62) y (if (<= y 2.5e+304) t_0 y)))))))))

C

double code(double x, double y, double z) {
	double t_0 = y * (-x / z);
	double tmp;
	if (y <= -1.25e+220) {
		tmp = y;
	} else if (y <= -1.0) {
		tmp = t_0;
	} else if (y <= -2.65e-52) {
		tmp = x / z;
	} else if (y <= -4.8e-105) {
		tmp = y;
	} else if (y <= 2.2e-17) {
		tmp = x / z;
	} else if (y <= 6.6e+62) {
		tmp = y;
	} else if (y <= 2.5e+304) {
		tmp = t_0;
	} else {
		tmp = y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = y * (-x / z)
    if (y <= (-1.25d+220)) then
        tmp = y
    else if (y <= (-1.0d0)) then
        tmp = t_0
    else if (y <= (-2.65d-52)) then
        tmp = x / z
    else if (y <= (-4.8d-105)) then
        tmp = y
    else if (y <= 2.2d-17) then
        tmp = x / z
    else if (y <= 6.6d+62) then
        tmp = y
    else if (y <= 2.5d+304) then
        tmp = t_0
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = y * (-x / z);
	double tmp;
	if (y <= -1.25e+220) {
		tmp = y;
	} else if (y <= -1.0) {
		tmp = t_0;
	} else if (y <= -2.65e-52) {
		tmp = x / z;
	} else if (y <= -4.8e-105) {
		tmp = y;
	} else if (y <= 2.2e-17) {
		tmp = x / z;
	} else if (y <= 6.6e+62) {
		tmp = y;
	} else if (y <= 2.5e+304) {
		tmp = t_0;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = y * (-x / z)
	tmp = 0
	if y <= -1.25e+220:
		tmp = y
	elif y <= -1.0:
		tmp = t_0
	elif y <= -2.65e-52:
		tmp = x / z
	elif y <= -4.8e-105:
		tmp = y
	elif y <= 2.2e-17:
		tmp = x / z
	elif y <= 6.6e+62:
		tmp = y
	elif y <= 2.5e+304:
		tmp = t_0
	else:
		tmp = y
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(y * Float64(Float64(-x) / z))
	tmp = 0.0
	if (y <= -1.25e+220)
		tmp = y;
	elseif (y <= -1.0)
		tmp = t_0;
	elseif (y <= -2.65e-52)
		tmp = Float64(x / z);
	elseif (y <= -4.8e-105)
		tmp = y;
	elseif (y <= 2.2e-17)
		tmp = Float64(x / z);
	elseif (y <= 6.6e+62)
		tmp = y;
	elseif (y <= 2.5e+304)
		tmp = t_0;
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = y * (-x / z);
	tmp = 0.0;
	if (y <= -1.25e+220)
		tmp = y;
	elseif (y <= -1.0)
		tmp = t_0;
	elseif (y <= -2.65e-52)
		tmp = x / z;
	elseif (y <= -4.8e-105)
		tmp = y;
	elseif (y <= 2.2e-17)
		tmp = x / z;
	elseif (y <= 6.6e+62)
		tmp = y;
	elseif (y <= 2.5e+304)
		tmp = t_0;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(y * N[((-x) / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.25e+220], y, If[LessEqual[y, -1.0], t$95$0, If[LessEqual[y, -2.65e-52], N[(x / z), $MachinePrecision], If[LessEqual[y, -4.8e-105], y, If[LessEqual[y, 2.2e-17], N[(x / z), $MachinePrecision], If[LessEqual[y, 6.6e+62], y, If[LessEqual[y, 2.5e+304], t$95$0, y]]]]]]]]

Derivation

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

Alternative 3: 85.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.7 \cdot 10^{-40} \lor \neg \left(z \leq 9 \cdot 10^{-62} \lor \neg \left(z \leq 6.5 \cdot 10^{-10}\right) \land z \leq 1.7 \cdot 10^{+54}\right):\\ \;\;\;\;y + \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{z} \cdot \left(1 - y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -2.7e-40)
         (not (or (<= z 9e-62) (and (not (<= z 6.5e-10)) (<= z 1.7e+54)))))
   (+ y (/ x z))
   (* (/ x z) (- 1.0 y))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2.7e-40) || !((z <= 9e-62) || (!(z <= 6.5e-10) && (z <= 1.7e+54)))) {
		tmp = y + (x / z);
	} else {
		tmp = (x / z) * (1.0 - y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z <= (-2.7d-40)) .or. (.not. (z <= 9d-62) .or. (.not. (z <= 6.5d-10)) .and. (z <= 1.7d+54))) then
        tmp = y + (x / z)
    else
        tmp = (x / z) * (1.0d0 - y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2.7e-40) || !((z <= 9e-62) || (!(z <= 6.5e-10) && (z <= 1.7e+54)))) {
		tmp = y + (x / z);
	} else {
		tmp = (x / z) * (1.0 - y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -2.7e-40) or not ((z <= 9e-62) or (not (z <= 6.5e-10) and (z <= 1.7e+54))):
		tmp = y + (x / z)
	else:
		tmp = (x / z) * (1.0 - y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -2.7e-40) || !((z <= 9e-62) || (!(z <= 6.5e-10) && (z <= 1.7e+54))))
		tmp = Float64(y + Float64(x / z));
	else
		tmp = Float64(Float64(x / z) * Float64(1.0 - y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -2.7e-40) || ~(((z <= 9e-62) || (~((z <= 6.5e-10)) && (z <= 1.7e+54)))))
		tmp = y + (x / z);
	else
		tmp = (x / z) * (1.0 - y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -2.7e-40], N[Not[Or[LessEqual[z, 9e-62], And[N[Not[LessEqual[z, 6.5e-10]], $MachinePrecision], LessEqual[z, 1.7e+54]]]], $MachinePrecision]], N[(y + N[(x / z), $MachinePrecision]), $MachinePrecision], N[(N[(x / z), $MachinePrecision] * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 4: 58.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.4 \cdot 10^{+185}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq -1:\\ \;\;\;\;\left(-x\right) \cdot \frac{y}{z}\\ \mathbf{elif}\;y \leq -1.68 \cdot 10^{-53} \lor \neg \left(y \leq -1.45 \cdot 10^{-105}\right) \land y \leq 2.4 \cdot 10^{-17}:\\ \;\;\;\;\frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -5.4e+185)
   y
   (if (<= y -1.0)
     (* (- x) (/ y z))
     (if (or (<= y -1.68e-53) (and (not (<= y -1.45e-105)) (<= y 2.4e-17)))
       (/ x z)
       y))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -5.4e+185) {
		tmp = y;
	} else if (y <= -1.0) {
		tmp = -x * (y / z);
	} else if ((y <= -1.68e-53) || (!(y <= -1.45e-105) && (y <= 2.4e-17))) {
		tmp = x / z;
	} else {
		tmp = y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-5.4d+185)) then
        tmp = y
    else if (y <= (-1.0d0)) then
        tmp = -x * (y / z)
    else if ((y <= (-1.68d-53)) .or. (.not. (y <= (-1.45d-105))) .and. (y <= 2.4d-17)) then
        tmp = x / z
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -5.4e+185) {
		tmp = y;
	} else if (y <= -1.0) {
		tmp = -x * (y / z);
	} else if ((y <= -1.68e-53) || (!(y <= -1.45e-105) && (y <= 2.4e-17))) {
		tmp = x / z;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -5.4e+185:
		tmp = y
	elif y <= -1.0:
		tmp = -x * (y / z)
	elif (y <= -1.68e-53) or (not (y <= -1.45e-105) and (y <= 2.4e-17)):
		tmp = x / z
	else:
		tmp = y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -5.4e+185)
		tmp = y;
	elseif (y <= -1.0)
		tmp = Float64(Float64(-x) * Float64(y / z));
	elseif ((y <= -1.68e-53) || (!(y <= -1.45e-105) && (y <= 2.4e-17)))
		tmp = Float64(x / z);
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -5.4e+185)
		tmp = y;
	elseif (y <= -1.0)
		tmp = -x * (y / z);
	elseif ((y <= -1.68e-53) || (~((y <= -1.45e-105)) && (y <= 2.4e-17)))
		tmp = x / z;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -5.4e+185], y, If[LessEqual[y, -1.0], N[((-x) * N[(y / z), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[y, -1.68e-53], And[N[Not[LessEqual[y, -1.45e-105]], $MachinePrecision], LessEqual[y, 2.4e-17]]], N[(x / z), $MachinePrecision], y]]]

Derivation

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

Alternative 5: 60.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -0.0003:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq -6.8 \cdot 10^{-54} \lor \neg \left(y \leq -4 \cdot 10^{-109}\right) \land y \leq 1.6 \cdot 10^{-17}:\\ \;\;\;\;\frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -0.0003)
   y
   (if (or (<= y -6.8e-54) (and (not (<= y -4e-109)) (<= y 1.6e-17)))
     (/ x z)
     y)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -0.0003) {
		tmp = y;
	} else if ((y <= -6.8e-54) || (!(y <= -4e-109) && (y <= 1.6e-17))) {
		tmp = x / z;
	} else {
		tmp = y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-0.0003d0)) then
        tmp = y
    else if ((y <= (-6.8d-54)) .or. (.not. (y <= (-4d-109))) .and. (y <= 1.6d-17)) then
        tmp = x / z
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -0.0003) {
		tmp = y;
	} else if ((y <= -6.8e-54) || (!(y <= -4e-109) && (y <= 1.6e-17))) {
		tmp = x / z;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -0.0003:
		tmp = y
	elif (y <= -6.8e-54) or (not (y <= -4e-109) and (y <= 1.6e-17)):
		tmp = x / z
	else:
		tmp = y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -0.0003)
		tmp = y;
	elseif ((y <= -6.8e-54) || (!(y <= -4e-109) && (y <= 1.6e-17)))
		tmp = Float64(x / z);
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -0.0003)
		tmp = y;
	elseif ((y <= -6.8e-54) || (~((y <= -4e-109)) && (y <= 1.6e-17)))
		tmp = x / z;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -0.0003], y, If[Or[LessEqual[y, -6.8e-54], And[N[Not[LessEqual[y, -4e-109]], $MachinePrecision], LessEqual[y, 1.6e-17]]], N[(x / z), $MachinePrecision], y]]

Derivation

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

Alternative 6: 95.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1 \lor \neg \left(y \leq 0.0152\right):\\ \;\;\;\;\frac{y}{z} \cdot \left(z - x\right)\\ \mathbf{else}:\\ \;\;\;\;y + \frac{x}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -1.0) (not (<= y 0.0152))) (* (/ y z) (- z x)) (+ y (/ x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.0) || !(y <= 0.0152)) {
		tmp = (y / z) * (z - x);
	} else {
		tmp = y + (x / z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((y <= (-1.0d0)) .or. (.not. (y <= 0.0152d0))) then
        tmp = (y / z) * (z - x)
    else
        tmp = y + (x / z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.0) || !(y <= 0.0152)) {
		tmp = (y / z) * (z - x);
	} else {
		tmp = y + (x / z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -1.0) or not (y <= 0.0152):
		tmp = (y / z) * (z - x)
	else:
		tmp = y + (x / z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -1.0) || !(y <= 0.0152))
		tmp = Float64(Float64(y / z) * Float64(z - x));
	else
		tmp = Float64(y + Float64(x / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -1.0) || ~((y <= 0.0152)))
		tmp = (y / z) * (z - x);
	else
		tmp = y + (x / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -1.0], N[Not[LessEqual[y, 0.0152]], $MachinePrecision]], N[(N[(y / z), $MachinePrecision] * N[(z - x), $MachinePrecision]), $MachinePrecision], N[(y + N[(x / z), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 99.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1 \lor \neg \left(y \leq 0.00012\right):\\ \;\;\;\;y - \frac{y}{\frac{z}{x}}\\ \mathbf{else}:\\ \;\;\;\;y + \frac{x}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -1.0) (not (<= y 0.00012))) (- y (/ y (/ z x))) (+ y (/ x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.0) || !(y <= 0.00012)) {
		tmp = y - (y / (z / x));
	} else {
		tmp = y + (x / z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((y <= (-1.0d0)) .or. (.not. (y <= 0.00012d0))) then
        tmp = y - (y / (z / x))
    else
        tmp = y + (x / z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.0) || !(y <= 0.00012)) {
		tmp = y - (y / (z / x));
	} else {
		tmp = y + (x / z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -1.0) or not (y <= 0.00012):
		tmp = y - (y / (z / x))
	else:
		tmp = y + (x / z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -1.0) || !(y <= 0.00012))
		tmp = Float64(y - Float64(y / Float64(z / x)));
	else
		tmp = Float64(y + Float64(x / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -1.0) || ~((y <= 0.00012)))
		tmp = y - (y / (z / x));
	else
		tmp = y + (x / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -1.0], N[Not[LessEqual[y, 0.00012]], $MachinePrecision]], N[(y - N[(y / N[(z / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + N[(x / z), $MachinePrecision]), $MachinePrecision]]

Derivation

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1. Add Preprocessing

Alternative 8: 78.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.08 \cdot 10^{+66}:\\ \;\;\;\;y + \frac{x}{z}\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{-x}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 1.08e+66) (+ y (/ x z)) (* y (/ (- x) z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.08e+66) {
		tmp = y + (x / z);
	} else {
		tmp = y * (-x / z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= 1.08d+66) then
        tmp = y + (x / z)
    else
        tmp = y * (-x / z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.08e+66) {
		tmp = y + (x / z);
	} else {
		tmp = y * (-x / z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= 1.08e+66:
		tmp = y + (x / z)
	else:
		tmp = y * (-x / z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 1.08e+66)
		tmp = Float64(y + Float64(x / z));
	else
		tmp = Float64(y * Float64(Float64(-x) / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= 1.08e+66)
		tmp = y + (x / z);
	else
		tmp = y * (-x / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 1.08e+66], N[(y + N[(x / z), $MachinePrecision]), $MachinePrecision], N[(y * N[((-x) / z), $MachinePrecision]), $MachinePrecision]]

Derivation

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1. Add Preprocessing

Alternative 9: 40.9% accurate, 9.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := y

Derivation

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1. Add Preprocessing

Developer target: 93.7% accurate, 0.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024010 
(FPCore (x y z)
  :name "Diagrams.Backend.Rasterific:rasterificRadialGradient from diagrams-rasterific-1.3.1.3"
  :precision binary64

  :herbie-target
  (- (+ y (/ x z)) (/ y (/ z x)))

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