Graphics.Rasterific.Shading:$sradialGradientWithFocusShader from Rasterific-0.6.1

Percentage Accurate: 100.0% → 100.0%

Time: 2.2s

Alternatives: 3

Speedup: 1.0×

• 24.7% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[x - y \cdot y \]

2. Final simplification 100.0%

   \[\leadsto x - y \cdot y \]

Alternative 2: 68.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 6.2 \cdot 10^{-83}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 7 \cdot 10^{-58} \lor \neg \left(y \leq 1.3 \cdot 10^{+23}\right):\\ \;\;\;\;y \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y 6.2e-83)
   x
   (if (or (<= y 7e-58) (not (<= y 1.3e+23))) (* y (- y)) x)))

C

double code(double x, double y) {
	double tmp;
	if (y <= 6.2e-83) {
		tmp = x;
	} else if ((y <= 7e-58) || !(y <= 1.3e+23)) {
		tmp = y * -y;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 6.2d-83) then
        tmp = x
    else if ((y <= 7d-58) .or. (.not. (y <= 1.3d+23))) then
        tmp = y * -y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 6.2e-83) {
		tmp = x;
	} else if ((y <= 7e-58) || !(y <= 1.3e+23)) {
		tmp = y * -y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 6.2e-83:
		tmp = x
	elif (y <= 7e-58) or not (y <= 1.3e+23):
		tmp = y * -y
	else:
		tmp = x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 6.2e-83)
		tmp = x;
	elseif ((y <= 7e-58) || !(y <= 1.3e+23))
		tmp = Float64(y * Float64(-y));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 6.2e-83)
		tmp = x;
	elseif ((y <= 7e-58) || ~((y <= 1.3e+23)))
		tmp = y * -y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 6.2e-83], x, If[Or[LessEqual[y, 7e-58], N[Not[LessEqual[y, 1.3e+23]], $MachinePrecision]], N[(y * (-y)), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if y < 6.19999999999999985e-83 or 6.9999999999999998e-58 < y < 1.29999999999999996e23

   1. Initial program 100.0%

      \[x - y \cdot y \]

   2. Taylor expanded in x around inf 59.5%

      \[\leadsto \color{blue}{x} \]

   if 6.19999999999999985e-83 < y < 6.9999999999999998e-58 or 1.29999999999999996e23 < y

   1. Initial program 100.0%

      \[x - y \cdot y \]

   2. Taylor expanded in x around 0 87.4%

      \[\leadsto \color{blue}{-1 \cdot {y}^{2}} \]
   3. Step-by-step derivation

      1. unpow2 87.4%

         \[\leadsto -1 \cdot \color{blue}{\left(y \cdot y\right)} \]

      2. neg-mul-1 87.4%

         \[\leadsto \color{blue}{-y \cdot y} \]

      3. distribute-rgt-neg-in 87.4%

         \[\leadsto \color{blue}{y \cdot \left(-y\right)} \]

   4. Simplified 87.4%

      \[\leadsto \color{blue}{y \cdot \left(-y\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 66.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 6.2 \cdot 10^{-83}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 7 \cdot 10^{-58} \lor \neg \left(y \leq 1.3 \cdot 10^{+23}\right):\\ \;\;\;\;y \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 3: 51.3% accurate, 5.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 x)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return x

Julia

function code(x, y)
	return x
end

MATLAB

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

Wolfram

code[x_, y_] := x

Derivation

1. Initial program 100.0%

   \[x - y \cdot y \]

2. Taylor expanded in x around inf 47.6%

   \[\leadsto \color{blue}{x} \]

3. Final simplification 47.6%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023257 
(FPCore (x y)
  :name "Graphics.Rasterific.Shading:$sradialGradientWithFocusShader from Rasterific-0.6.1"
  :precision binary64
  (- x (* y y)))