Diagrams.Backend.Cairo.Internal:setTexture from diagrams-cairo-1.3.0.3

Percentage Accurate: 84.4% → 95.4%

Time: 6.2s

Alternatives: 9

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return (x * (y - z)) / 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 = (x * (y - z)) / y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 84.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return (x * (y - z)) / 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 = (x * (y - z)) / y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 95.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq 1.52 \cdot 10^{+88}:\\ \;\;\;\;x - x \cdot \frac{z}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot \left(y - z\right)}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z 1.52e+88) (- x (* x (/ z y))) (/ (* x (- y z)) y)))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= 1.52e+88) {
		tmp = x - (x * (z / y));
	} else {
		tmp = (x * (y - z)) / 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 <= 1.52d+88) then
        tmp = x - (x * (z / y))
    else
        tmp = (x * (y - z)) / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= 1.52e+88) {
		tmp = x - (x * (z / y));
	} else {
		tmp = (x * (y - z)) / y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= 1.52e+88:
		tmp = x - (x * (z / y))
	else:
		tmp = (x * (y - z)) / y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= 1.52e+88)
		tmp = Float64(x - Float64(x * Float64(z / y)));
	else
		tmp = Float64(Float64(x * Float64(y - z)) / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= 1.52e+88)
		tmp = x - (x * (z / y));
	else
		tmp = (x * (y - z)) / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, 1.52e+88], N[(x - N[(x * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x * N[(y - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]]

Derivation

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

Alternative 2: 69.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3 \cdot 10^{+105} \lor \neg \left(z \leq -1150000000000\right) \land \left(z \leq -3.7 \cdot 10^{-68} \lor \neg \left(z \leq 5.2 \cdot 10^{-27}\right)\right):\\ \;\;\;\;x \cdot \frac{-z}{y}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -3e+105)
         (and (not (<= z -1150000000000.0))
              (or (<= z -3.7e-68) (not (<= z 5.2e-27)))))
   (* x (/ (- z) y))
   x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -3e+105) || (!(z <= -1150000000000.0) && ((z <= -3.7e-68) || !(z <= 5.2e-27)))) {
		tmp = x * (-z / y);
	} else {
		tmp = x;
	}
	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 <= (-3d+105)) .or. (.not. (z <= (-1150000000000.0d0))) .and. (z <= (-3.7d-68)) .or. (.not. (z <= 5.2d-27))) then
        tmp = x * (-z / y)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -3e+105) || (!(z <= -1150000000000.0) && ((z <= -3.7e-68) || !(z <= 5.2e-27)))) {
		tmp = x * (-z / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -3e+105) or (not (z <= -1150000000000.0) and ((z <= -3.7e-68) or not (z <= 5.2e-27))):
		tmp = x * (-z / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -3e+105) || (!(z <= -1150000000000.0) && ((z <= -3.7e-68) || !(z <= 5.2e-27))))
		tmp = Float64(x * Float64(Float64(-z) / y));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -3e+105) || (~((z <= -1150000000000.0)) && ((z <= -3.7e-68) || ~((z <= 5.2e-27)))))
		tmp = x * (-z / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -3e+105], And[N[Not[LessEqual[z, -1150000000000.0]], $MachinePrecision], Or[LessEqual[z, -3.7e-68], N[Not[LessEqual[z, 5.2e-27]], $MachinePrecision]]]], N[(x * N[((-z) / y), $MachinePrecision]), $MachinePrecision], x]

Derivation

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

Alternative 3: 69.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.6 \cdot 10^{+105}:\\ \;\;\;\;\frac{-x}{\frac{y}{z}}\\ \mathbf{elif}\;z \leq -800000000000 \lor \neg \left(z \leq -2.1 \cdot 10^{-68}\right) \land z \leq 2.6 \cdot 10^{-26}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{-z}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -2.6e+105)
   (/ (- x) (/ y z))
   (if (or (<= z -800000000000.0) (and (not (<= z -2.1e-68)) (<= z 2.6e-26)))
     x
     (* x (/ (- z) y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.6e+105) {
		tmp = -x / (y / z);
	} else if ((z <= -800000000000.0) || (!(z <= -2.1e-68) && (z <= 2.6e-26))) {
		tmp = x;
	} else {
		tmp = x * (-z / 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 <= (-2.6d+105)) then
        tmp = -x / (y / z)
    else if ((z <= (-800000000000.0d0)) .or. (.not. (z <= (-2.1d-68))) .and. (z <= 2.6d-26)) then
        tmp = x
    else
        tmp = x * (-z / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.6e+105) {
		tmp = -x / (y / z);
	} else if ((z <= -800000000000.0) || (!(z <= -2.1e-68) && (z <= 2.6e-26))) {
		tmp = x;
	} else {
		tmp = x * (-z / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -2.6e+105:
		tmp = -x / (y / z)
	elif (z <= -800000000000.0) or (not (z <= -2.1e-68) and (z <= 2.6e-26)):
		tmp = x
	else:
		tmp = x * (-z / y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -2.6e+105)
		tmp = Float64(Float64(-x) / Float64(y / z));
	elseif ((z <= -800000000000.0) || (!(z <= -2.1e-68) && (z <= 2.6e-26)))
		tmp = x;
	else
		tmp = Float64(x * Float64(Float64(-z) / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -2.6e+105)
		tmp = -x / (y / z);
	elseif ((z <= -800000000000.0) || (~((z <= -2.1e-68)) && (z <= 2.6e-26)))
		tmp = x;
	else
		tmp = x * (-z / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -2.6e+105], N[((-x) / N[(y / z), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[z, -800000000000.0], And[N[Not[LessEqual[z, -2.1e-68]], $MachinePrecision], LessEqual[z, 2.6e-26]]], x, N[(x * N[((-z) / y), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 4: 70.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-z}{\frac{y}{x}}\\ \mathbf{if}\;z \leq -2.35 \cdot 10^{+105}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -55000000000:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{-68}:\\ \;\;\;\;x \cdot \frac{-z}{y}\\ \mathbf{elif}\;z \leq 1.45 \cdot 10^{-27}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (- z) (/ y x))))
   (if (<= z -2.35e+105)
     t_0
     (if (<= z -55000000000.0)
       x
       (if (<= z -3.8e-68) (* x (/ (- z) y)) (if (<= z 1.45e-27) x t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = -z / (y / x);
	double tmp;
	if (z <= -2.35e+105) {
		tmp = t_0;
	} else if (z <= -55000000000.0) {
		tmp = x;
	} else if (z <= -3.8e-68) {
		tmp = x * (-z / y);
	} else if (z <= 1.45e-27) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	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 = -z / (y / x)
    if (z <= (-2.35d+105)) then
        tmp = t_0
    else if (z <= (-55000000000.0d0)) then
        tmp = x
    else if (z <= (-3.8d-68)) then
        tmp = x * (-z / y)
    else if (z <= 1.45d-27) then
        tmp = x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = -z / (y / x);
	double tmp;
	if (z <= -2.35e+105) {
		tmp = t_0;
	} else if (z <= -55000000000.0) {
		tmp = x;
	} else if (z <= -3.8e-68) {
		tmp = x * (-z / y);
	} else if (z <= 1.45e-27) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -z / (y / x)
	tmp = 0
	if z <= -2.35e+105:
		tmp = t_0
	elif z <= -55000000000.0:
		tmp = x
	elif z <= -3.8e-68:
		tmp = x * (-z / y)
	elif z <= 1.45e-27:
		tmp = x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(-z) / Float64(y / x))
	tmp = 0.0
	if (z <= -2.35e+105)
		tmp = t_0;
	elseif (z <= -55000000000.0)
		tmp = x;
	elseif (z <= -3.8e-68)
		tmp = Float64(x * Float64(Float64(-z) / y));
	elseif (z <= 1.45e-27)
		tmp = x;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -z / (y / x);
	tmp = 0.0;
	if (z <= -2.35e+105)
		tmp = t_0;
	elseif (z <= -55000000000.0)
		tmp = x;
	elseif (z <= -3.8e-68)
		tmp = x * (-z / y);
	elseif (z <= 1.45e-27)
		tmp = x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[((-z) / N[(y / x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.35e+105], t$95$0, If[LessEqual[z, -55000000000.0], x, If[LessEqual[z, -3.8e-68], N[(x * N[((-z) / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.45e-27], x, t$95$0]]]]]

Derivation

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

Alternative 5: 70.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.35 \cdot 10^{+105}:\\ \;\;\;\;\frac{-z}{\frac{y}{x}}\\ \mathbf{elif}\;z \leq -240000000000:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{-68}:\\ \;\;\;\;x \cdot \frac{-z}{y}\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{-27}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;z \cdot \frac{-x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -2.35e+105)
   (/ (- z) (/ y x))
   (if (<= z -240000000000.0)
     x
     (if (<= z -3.8e-68)
       (* x (/ (- z) y))
       (if (<= z 2.7e-27) x (* z (/ (- x) y)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.35e+105) {
		tmp = -z / (y / x);
	} else if (z <= -240000000000.0) {
		tmp = x;
	} else if (z <= -3.8e-68) {
		tmp = x * (-z / y);
	} else if (z <= 2.7e-27) {
		tmp = x;
	} else {
		tmp = z * (-x / 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 <= (-2.35d+105)) then
        tmp = -z / (y / x)
    else if (z <= (-240000000000.0d0)) then
        tmp = x
    else if (z <= (-3.8d-68)) then
        tmp = x * (-z / y)
    else if (z <= 2.7d-27) then
        tmp = x
    else
        tmp = z * (-x / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.35e+105) {
		tmp = -z / (y / x);
	} else if (z <= -240000000000.0) {
		tmp = x;
	} else if (z <= -3.8e-68) {
		tmp = x * (-z / y);
	} else if (z <= 2.7e-27) {
		tmp = x;
	} else {
		tmp = z * (-x / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -2.35e+105:
		tmp = -z / (y / x)
	elif z <= -240000000000.0:
		tmp = x
	elif z <= -3.8e-68:
		tmp = x * (-z / y)
	elif z <= 2.7e-27:
		tmp = x
	else:
		tmp = z * (-x / y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -2.35e+105)
		tmp = Float64(Float64(-z) / Float64(y / x));
	elseif (z <= -240000000000.0)
		tmp = x;
	elseif (z <= -3.8e-68)
		tmp = Float64(x * Float64(Float64(-z) / y));
	elseif (z <= 2.7e-27)
		tmp = x;
	else
		tmp = Float64(z * Float64(Float64(-x) / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -2.35e+105)
		tmp = -z / (y / x);
	elseif (z <= -240000000000.0)
		tmp = x;
	elseif (z <= -3.8e-68)
		tmp = x * (-z / y);
	elseif (z <= 2.7e-27)
		tmp = x;
	else
		tmp = z * (-x / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -2.35e+105], N[((-z) / N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -240000000000.0], x, If[LessEqual[z, -3.8e-68], N[(x * N[((-z) / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.7e-27], x, N[(z * N[((-x) / y), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

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

Alternative 6: 70.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{z \cdot \left(-x\right)}{y}\\ \mathbf{if}\;z \leq -2.35 \cdot 10^{+105}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -9000000000:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -1.65 \cdot 10^{-68}:\\ \;\;\;\;x \cdot \frac{-z}{y}\\ \mathbf{elif}\;z \leq 2.25 \cdot 10^{-26}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* z (- x)) y)))
   (if (<= z -2.35e+105)
     t_0
     (if (<= z -9000000000.0)
       x
       (if (<= z -1.65e-68) (* x (/ (- z) y)) (if (<= z 2.25e-26) x t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = (z * -x) / y;
	double tmp;
	if (z <= -2.35e+105) {
		tmp = t_0;
	} else if (z <= -9000000000.0) {
		tmp = x;
	} else if (z <= -1.65e-68) {
		tmp = x * (-z / y);
	} else if (z <= 2.25e-26) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	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 = (z * -x) / y
    if (z <= (-2.35d+105)) then
        tmp = t_0
    else if (z <= (-9000000000.0d0)) then
        tmp = x
    else if (z <= (-1.65d-68)) then
        tmp = x * (-z / y)
    else if (z <= 2.25d-26) then
        tmp = x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (z * -x) / y;
	double tmp;
	if (z <= -2.35e+105) {
		tmp = t_0;
	} else if (z <= -9000000000.0) {
		tmp = x;
	} else if (z <= -1.65e-68) {
		tmp = x * (-z / y);
	} else if (z <= 2.25e-26) {
		tmp = x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (z * -x) / y
	tmp = 0
	if z <= -2.35e+105:
		tmp = t_0
	elif z <= -9000000000.0:
		tmp = x
	elif z <= -1.65e-68:
		tmp = x * (-z / y)
	elif z <= 2.25e-26:
		tmp = x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(z * Float64(-x)) / y)
	tmp = 0.0
	if (z <= -2.35e+105)
		tmp = t_0;
	elseif (z <= -9000000000.0)
		tmp = x;
	elseif (z <= -1.65e-68)
		tmp = Float64(x * Float64(Float64(-z) / y));
	elseif (z <= 2.25e-26)
		tmp = x;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (z * -x) / y;
	tmp = 0.0;
	if (z <= -2.35e+105)
		tmp = t_0;
	elseif (z <= -9000000000.0)
		tmp = x;
	elseif (z <= -1.65e-68)
		tmp = x * (-z / y);
	elseif (z <= 2.25e-26)
		tmp = x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(z * (-x)), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[z, -2.35e+105], t$95$0, If[LessEqual[z, -9000000000.0], x, If[LessEqual[z, -1.65e-68], N[(x * N[((-z) / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.25e-26], x, t$95$0]]]]]

Derivation

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

Alternative 7: 96.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return x * (1.0 - (z / 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 = x * (1.0d0 - (z / y))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 8: 96.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return x - (x * (z / 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 = x - (x * (z / y))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 9: 50.1% accurate, 7.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return 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 = x
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

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

Developer target: 96.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z < -2.060202331921739 \cdot 10^{+104}:\\ \;\;\;\;x - \frac{z \cdot x}{y}\\ \mathbf{elif}\;z < 1.6939766013828526 \cdot 10^{+213}:\\ \;\;\;\;\frac{x}{\frac{y}{y - z}}\\ \mathbf{else}:\\ \;\;\;\;\left(y - z\right) \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (< z -2.060202331921739e+104)
   (- x (/ (* z x) y))
   (if (< z 1.6939766013828526e+213) (/ x (/ y (- y z))) (* (- y z) (/ x y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z < -2.060202331921739e+104) {
		tmp = x - ((z * x) / y);
	} else if (z < 1.6939766013828526e+213) {
		tmp = x / (y / (y - z));
	} else {
		tmp = (y - z) * (x / 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 < (-2.060202331921739d+104)) then
        tmp = x - ((z * x) / y)
    else if (z < 1.6939766013828526d+213) then
        tmp = x / (y / (y - z))
    else
        tmp = (y - z) * (x / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z < -2.060202331921739e+104) {
		tmp = x - ((z * x) / y);
	} else if (z < 1.6939766013828526e+213) {
		tmp = x / (y / (y - z));
	} else {
		tmp = (y - z) * (x / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z < -2.060202331921739e+104:
		tmp = x - ((z * x) / y)
	elif z < 1.6939766013828526e+213:
		tmp = x / (y / (y - z))
	else:
		tmp = (y - z) * (x / y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z < -2.060202331921739e+104)
		tmp = Float64(x - Float64(Float64(z * x) / y));
	elseif (z < 1.6939766013828526e+213)
		tmp = Float64(x / Float64(y / Float64(y - z)));
	else
		tmp = Float64(Float64(y - z) * Float64(x / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z < -2.060202331921739e+104)
		tmp = x - ((z * x) / y);
	elseif (z < 1.6939766013828526e+213)
		tmp = x / (y / (y - z));
	else
		tmp = (y - z) * (x / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Less[z, -2.060202331921739e+104], N[(x - N[(N[(z * x), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[Less[z, 1.6939766013828526e+213], N[(x / N[(y / N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y - z), $MachinePrecision] * N[(x / y), $MachinePrecision]), $MachinePrecision]]]

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z)
  :name "Diagrams.Backend.Cairo.Internal:setTexture from diagrams-cairo-1.3.0.3"
  :precision binary64

  :herbie-target
  (if (< z -2.060202331921739e+104) (- x (/ (* z x) y)) (if (< z 1.6939766013828526e+213) (/ x (/ y (- y z))) (* (- y z) (/ x y))))

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