Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendOutside from plot-0.2.3.4, A

Percentage Accurate: 100.0% → 100.0%

Time: 2.5s

Alternatives: 3

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ y + x \cdot 2 \end{array} \]

FPCore

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

C

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

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = y + (x * 2.0d0)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\left(x + y\right) + x \]

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing
5. Add Preprocessing

Alternative 2: 73.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{+79}:\\ \;\;\;\;2 \cdot x\\ \mathbf{elif}\;x \leq -2.5 \cdot 10^{+28}:\\ \;\;\;\;y\\ \mathbf{elif}\;x \leq -8.5 \cdot 10^{-47}:\\ \;\;\;\;2 \cdot x\\ \mathbf{elif}\;x \leq 6 \cdot 10^{-35}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;2 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -3.8e+79)
   (* 2.0 x)
   (if (<= x -2.5e+28)
     y
     (if (<= x -8.5e-47) (* 2.0 x) (if (<= x 6e-35) y (* 2.0 x))))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -3.8e+79) {
		tmp = 2.0 * x;
	} else if (x <= -2.5e+28) {
		tmp = y;
	} else if (x <= -8.5e-47) {
		tmp = 2.0 * x;
	} else if (x <= 6e-35) {
		tmp = y;
	} else {
		tmp = 2.0 * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-3.8d+79)) then
        tmp = 2.0d0 * x
    else if (x <= (-2.5d+28)) then
        tmp = y
    else if (x <= (-8.5d-47)) then
        tmp = 2.0d0 * x
    else if (x <= 6d-35) then
        tmp = y
    else
        tmp = 2.0d0 * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -3.8e+79) {
		tmp = 2.0 * x;
	} else if (x <= -2.5e+28) {
		tmp = y;
	} else if (x <= -8.5e-47) {
		tmp = 2.0 * x;
	} else if (x <= 6e-35) {
		tmp = y;
	} else {
		tmp = 2.0 * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -3.8e+79:
		tmp = 2.0 * x
	elif x <= -2.5e+28:
		tmp = y
	elif x <= -8.5e-47:
		tmp = 2.0 * x
	elif x <= 6e-35:
		tmp = y
	else:
		tmp = 2.0 * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -3.8e+79)
		tmp = Float64(2.0 * x);
	elseif (x <= -2.5e+28)
		tmp = y;
	elseif (x <= -8.5e-47)
		tmp = Float64(2.0 * x);
	elseif (x <= 6e-35)
		tmp = y;
	else
		tmp = Float64(2.0 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -3.8e+79)
		tmp = 2.0 * x;
	elseif (x <= -2.5e+28)
		tmp = y;
	elseif (x <= -8.5e-47)
		tmp = 2.0 * x;
	elseif (x <= 6e-35)
		tmp = y;
	else
		tmp = 2.0 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -3.8e+79], N[(2.0 * x), $MachinePrecision], If[LessEqual[x, -2.5e+28], y, If[LessEqual[x, -8.5e-47], N[(2.0 * x), $MachinePrecision], If[LessEqual[x, 6e-35], y, N[(2.0 * x), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if x < -3.8000000000000002e79 or -2.49999999999999979e28 < x < -8.4999999999999999e-47 or 5.99999999999999978e-35 < x

   1. Initial program 100.0%

      \[\left(x + y\right) + x \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -3.8000000000000002e79 < x < -2.49999999999999979e28 or -8.4999999999999999e-47 < x < 5.99999999999999978e-35

   1. Initial program 100.0%

      \[\left(x + y\right) + x \]

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 50.1% accurate, 5.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 y)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return y

Julia

function code(x, y)
	return y
end

MATLAB

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

Wolfram

code[x_, y_] := y

Derivation

1. Initial program 100.0%

   \[\left(x + y\right) + x \]

3. Simplified 0

   \[\leadsto expr\]
4. Add Preprocessing

5. Taylor expanded in y around inf 0

   \[\leadsto expr\]

7. Simplified 0

   \[\leadsto expr\]
8. Add Preprocessing

Developer target: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ y + 2 \cdot x \end{array} \]

FPCore

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

C

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

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = y + (2.0d0 * x)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024110 
(FPCore (x y)
  :name "Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendOutside from plot-0.2.3.4, A"
  :precision binary64

  :alt
  (+ y (* 2.0 x))

  (+ (+ x y) x))