Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendInside from plot-0.2.3.4

Percentage Accurate: 99.9% → 100.0%

Time: 4.3s

Alternatives: 8

Speedup: 1.2×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ z - \mathsf{fma}\left(x, -3, y \cdot -2\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- z (fma x -3.0 (* y -2.0))))

C

double code(double x, double y, double z) {
	return z - fma(x, -3.0, (y * -2.0));
}

Julia

function code(x, y, z)
	return Float64(z - fma(x, -3.0, Float64(y * -2.0)))
end

Wolfram

code[x_, y_, z_] := N[(z - N[(x * -3.0 + N[(y * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 2: 51.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8 \cdot 10^{+120}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq -1 \cdot 10^{-66}:\\ \;\;\;\;x \cdot 3\\ \mathbf{elif}\;z \leq -1.26 \cdot 10^{-83}:\\ \;\;\;\;y \cdot 2\\ \mathbf{elif}\;z \leq -1.2 \cdot 10^{-236}:\\ \;\;\;\;x \cdot 3\\ \mathbf{elif}\;z \leq -1.55 \cdot 10^{-260}:\\ \;\;\;\;y \cdot 2\\ \mathbf{elif}\;z \leq 7.8 \cdot 10^{-189}:\\ \;\;\;\;x \cdot 3\\ \mathbf{elif}\;z \leq 1.25 \cdot 10^{-94}:\\ \;\;\;\;y \cdot 2\\ \mathbf{elif}\;z \leq 0.00043:\\ \;\;\;\;x \cdot 3\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -8e+120)
   z
   (if (<= z -1e-66)
     (* x 3.0)
     (if (<= z -1.26e-83)
       (* y 2.0)
       (if (<= z -1.2e-236)
         (* x 3.0)
         (if (<= z -1.55e-260)
           (* y 2.0)
           (if (<= z 7.8e-189)
             (* x 3.0)
             (if (<= z 1.25e-94)
               (* y 2.0)
               (if (<= z 0.00043) (* x 3.0) z)))))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -8e+120) {
		tmp = z;
	} else if (z <= -1e-66) {
		tmp = x * 3.0;
	} else if (z <= -1.26e-83) {
		tmp = y * 2.0;
	} else if (z <= -1.2e-236) {
		tmp = x * 3.0;
	} else if (z <= -1.55e-260) {
		tmp = y * 2.0;
	} else if (z <= 7.8e-189) {
		tmp = x * 3.0;
	} else if (z <= 1.25e-94) {
		tmp = y * 2.0;
	} else if (z <= 0.00043) {
		tmp = x * 3.0;
	} else {
		tmp = 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 (z <= (-8d+120)) then
        tmp = z
    else if (z <= (-1d-66)) then
        tmp = x * 3.0d0
    else if (z <= (-1.26d-83)) then
        tmp = y * 2.0d0
    else if (z <= (-1.2d-236)) then
        tmp = x * 3.0d0
    else if (z <= (-1.55d-260)) then
        tmp = y * 2.0d0
    else if (z <= 7.8d-189) then
        tmp = x * 3.0d0
    else if (z <= 1.25d-94) then
        tmp = y * 2.0d0
    else if (z <= 0.00043d0) then
        tmp = x * 3.0d0
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -8e+120) {
		tmp = z;
	} else if (z <= -1e-66) {
		tmp = x * 3.0;
	} else if (z <= -1.26e-83) {
		tmp = y * 2.0;
	} else if (z <= -1.2e-236) {
		tmp = x * 3.0;
	} else if (z <= -1.55e-260) {
		tmp = y * 2.0;
	} else if (z <= 7.8e-189) {
		tmp = x * 3.0;
	} else if (z <= 1.25e-94) {
		tmp = y * 2.0;
	} else if (z <= 0.00043) {
		tmp = x * 3.0;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -8e+120:
		tmp = z
	elif z <= -1e-66:
		tmp = x * 3.0
	elif z <= -1.26e-83:
		tmp = y * 2.0
	elif z <= -1.2e-236:
		tmp = x * 3.0
	elif z <= -1.55e-260:
		tmp = y * 2.0
	elif z <= 7.8e-189:
		tmp = x * 3.0
	elif z <= 1.25e-94:
		tmp = y * 2.0
	elif z <= 0.00043:
		tmp = x * 3.0
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -8e+120)
		tmp = z;
	elseif (z <= -1e-66)
		tmp = Float64(x * 3.0);
	elseif (z <= -1.26e-83)
		tmp = Float64(y * 2.0);
	elseif (z <= -1.2e-236)
		tmp = Float64(x * 3.0);
	elseif (z <= -1.55e-260)
		tmp = Float64(y * 2.0);
	elseif (z <= 7.8e-189)
		tmp = Float64(x * 3.0);
	elseif (z <= 1.25e-94)
		tmp = Float64(y * 2.0);
	elseif (z <= 0.00043)
		tmp = Float64(x * 3.0);
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -8e+120)
		tmp = z;
	elseif (z <= -1e-66)
		tmp = x * 3.0;
	elseif (z <= -1.26e-83)
		tmp = y * 2.0;
	elseif (z <= -1.2e-236)
		tmp = x * 3.0;
	elseif (z <= -1.55e-260)
		tmp = y * 2.0;
	elseif (z <= 7.8e-189)
		tmp = x * 3.0;
	elseif (z <= 1.25e-94)
		tmp = y * 2.0;
	elseif (z <= 0.00043)
		tmp = x * 3.0;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -8e+120], z, If[LessEqual[z, -1e-66], N[(x * 3.0), $MachinePrecision], If[LessEqual[z, -1.26e-83], N[(y * 2.0), $MachinePrecision], If[LessEqual[z, -1.2e-236], N[(x * 3.0), $MachinePrecision], If[LessEqual[z, -1.55e-260], N[(y * 2.0), $MachinePrecision], If[LessEqual[z, 7.8e-189], N[(x * 3.0), $MachinePrecision], If[LessEqual[z, 1.25e-94], N[(y * 2.0), $MachinePrecision], If[LessEqual[z, 0.00043], N[(x * 3.0), $MachinePrecision], z]]]]]]]]

Derivation

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

Alternative 3: 79.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.4 \cdot 10^{+184} \lor \neg \left(y \leq 1.05 \cdot 10^{+155}\right):\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;z - x \cdot -3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -5.4e+184) (not (<= y 1.05e+155))) (* y 2.0) (- z (* x -3.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -5.4e+184) || !(y <= 1.05e+155)) {
		tmp = y * 2.0;
	} else {
		tmp = z - (x * -3.0);
	}
	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) :: tmp
    if ((y <= (-5.4d+184)) .or. (.not. (y <= 1.05d+155))) then
        tmp = y * 2.0d0
    else
        tmp = z - (x * (-3.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -5.4e+184) || !(y <= 1.05e+155)) {
		tmp = y * 2.0;
	} else {
		tmp = z - (x * -3.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -5.4e+184) or not (y <= 1.05e+155):
		tmp = y * 2.0
	else:
		tmp = z - (x * -3.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -5.4e+184) || !(y <= 1.05e+155))
		tmp = Float64(y * 2.0);
	else
		tmp = Float64(z - Float64(x * -3.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -5.4e+184) || ~((y <= 1.05e+155)))
		tmp = y * 2.0;
	else
		tmp = z - (x * -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -5.4e+184], N[Not[LessEqual[y, 1.05e+155]], $MachinePrecision]], N[(y * 2.0), $MachinePrecision], N[(z - N[(x * -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 4: 83.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8.3 \cdot 10^{+183} \lor \neg \left(y \leq 6 \cdot 10^{+54}\right):\\ \;\;\;\;z - y \cdot -2\\ \mathbf{else}:\\ \;\;\;\;z - x \cdot -3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -8.3e+183) (not (<= y 6e+54)))
   (- z (* y -2.0))
   (- z (* x -3.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -8.3e+183) || !(y <= 6e+54)) {
		tmp = z - (y * -2.0);
	} else {
		tmp = z - (x * -3.0);
	}
	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) :: tmp
    if ((y <= (-8.3d+183)) .or. (.not. (y <= 6d+54))) then
        tmp = z - (y * (-2.0d0))
    else
        tmp = z - (x * (-3.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -8.3e+183) || !(y <= 6e+54)) {
		tmp = z - (y * -2.0);
	} else {
		tmp = z - (x * -3.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -8.3e+183) or not (y <= 6e+54):
		tmp = z - (y * -2.0)
	else:
		tmp = z - (x * -3.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -8.3e+183) || !(y <= 6e+54))
		tmp = Float64(z - Float64(y * -2.0));
	else
		tmp = Float64(z - Float64(x * -3.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -8.3e+183) || ~((y <= 6e+54)))
		tmp = z - (y * -2.0);
	else
		tmp = z - (x * -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -8.3e+183], N[Not[LessEqual[y, 6e+54]], $MachinePrecision]], N[(z - N[(y * -2.0), $MachinePrecision]), $MachinePrecision], N[(z - N[(x * -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 5: 83.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.45 \cdot 10^{+175}:\\ \;\;\;\;x + 2 \cdot \left(x + y\right)\\ \mathbf{elif}\;y \leq 3.6 \cdot 10^{+54}:\\ \;\;\;\;z - x \cdot -3\\ \mathbf{else}:\\ \;\;\;\;z - y \cdot -2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.45e+175)
   (+ x (* 2.0 (+ x y)))
   (if (<= y 3.6e+54) (- z (* x -3.0)) (- z (* y -2.0)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.45e+175) {
		tmp = x + (2.0 * (x + y));
	} else if (y <= 3.6e+54) {
		tmp = z - (x * -3.0);
	} else {
		tmp = z - (y * -2.0);
	}
	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) :: tmp
    if (y <= (-1.45d+175)) then
        tmp = x + (2.0d0 * (x + y))
    else if (y <= 3.6d+54) then
        tmp = z - (x * (-3.0d0))
    else
        tmp = z - (y * (-2.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.45e+175) {
		tmp = x + (2.0 * (x + y));
	} else if (y <= 3.6e+54) {
		tmp = z - (x * -3.0);
	} else {
		tmp = z - (y * -2.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.45e+175:
		tmp = x + (2.0 * (x + y))
	elif y <= 3.6e+54:
		tmp = z - (x * -3.0)
	else:
		tmp = z - (y * -2.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.45e+175)
		tmp = Float64(x + Float64(2.0 * Float64(x + y)));
	elseif (y <= 3.6e+54)
		tmp = Float64(z - Float64(x * -3.0));
	else
		tmp = Float64(z - Float64(y * -2.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.45e+175)
		tmp = x + (2.0 * (x + y));
	elseif (y <= 3.6e+54)
		tmp = z - (x * -3.0);
	else
		tmp = z - (y * -2.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.45e+175], N[(x + N[(2.0 * N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 3.6e+54], N[(z - N[(x * -3.0), $MachinePrecision]), $MachinePrecision], N[(z - N[(y * -2.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 6: 99.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(x + y\right) + \left(z + x\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* 2.0 (+ x y)) (+ z x)))

C

double code(double x, double y, double z) {
	return (2.0 * (x + 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 = (2.0d0 * (x + y)) + (z + x)
end function

Java

public static double code(double x, double y, double z) {
	return (2.0 * (x + y)) + (z + x);
}

Python

def code(x, y, z):
	return (2.0 * (x + y)) + (z + x)

Julia

function code(x, y, z)
	return Float64(Float64(2.0 * Float64(x + y)) + Float64(z + x))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (2.0 * (x + y)) + (z + x);
end

Wolfram

code[x_, y_, z_] := N[(N[(2.0 * N[(x + y), $MachinePrecision]), $MachinePrecision] + N[(z + x), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 7: 52.9% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.4 \cdot 10^{+85}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq 1.5:\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -2.4e+85) z (if (<= z 1.5) (* y 2.0) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.4e+85) {
		tmp = z;
	} else if (z <= 1.5) {
		tmp = y * 2.0;
	} else {
		tmp = 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 (z <= (-2.4d+85)) then
        tmp = z
    else if (z <= 1.5d0) then
        tmp = y * 2.0d0
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.4e+85) {
		tmp = z;
	} else if (z <= 1.5) {
		tmp = y * 2.0;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -2.4e+85:
		tmp = z
	elif z <= 1.5:
		tmp = y * 2.0
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -2.4e+85)
		tmp = z;
	elseif (z <= 1.5)
		tmp = Float64(y * 2.0);
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -2.4e+85)
		tmp = z;
	elseif (z <= 1.5)
		tmp = y * 2.0;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -2.4e+85], z, If[LessEqual[z, 1.5], N[(y * 2.0), $MachinePrecision], z]]

Derivation

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

Alternative 8: 34.8% accurate, 11.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := z

Derivation

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

Reproduce

herbie shell --seed 2024008 
(FPCore (x y z)
  :name "Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendInside from plot-0.2.3.4"
  :precision binary64
  (+ (+ (+ (+ (+ x y) y) x) z) x))