Graphics.Rendering.Chart.Plot.Vectors:renderPlotVectors from Chart-1.5.3

Percentage Accurate: 78.0% → 86.9%

Time: 1.1s

Alternatives: 3

Speedup: 0.6×

Specification

\[\mathsf{TRUE}\left(\right)\]

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 78.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 86.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(1 - x\right) \cdot \left(1 - y\right)\\ \mathbf{if}\;y \leq -9.8 \cdot 10^{-107}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{-46}:\\ \;\;\;\;1 - y\\ \mathbf{else}:\\ \;\;\;\;x + t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (- 1.0 x) (- 1.0 y))))
   (if (<= y -9.8e-107) t_0 (if (<= y 4.8e-46) (- 1.0 y) (+ x t_0)))))

C

double code(double x, double y) {
	double t_0 = (1.0 - x) * (1.0 - y);
	double tmp;
	if (y <= -9.8e-107) {
		tmp = t_0;
	} else if (y <= 4.8e-46) {
		tmp = 1.0 - y;
	} else {
		tmp = x + t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (1.0d0 - x) * (1.0d0 - y)
    if (y <= (-9.8d-107)) then
        tmp = t_0
    else if (y <= 4.8d-46) then
        tmp = 1.0d0 - y
    else
        tmp = x + t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (1.0 - x) * (1.0 - y);
	double tmp;
	if (y <= -9.8e-107) {
		tmp = t_0;
	} else if (y <= 4.8e-46) {
		tmp = 1.0 - y;
	} else {
		tmp = x + t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (1.0 - x) * (1.0 - y)
	tmp = 0
	if y <= -9.8e-107:
		tmp = t_0
	elif y <= 4.8e-46:
		tmp = 1.0 - y
	else:
		tmp = x + t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(1.0 - x) * Float64(1.0 - y))
	tmp = 0.0
	if (y <= -9.8e-107)
		tmp = t_0;
	elseif (y <= 4.8e-46)
		tmp = Float64(1.0 - y);
	else
		tmp = Float64(x + t_0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (1.0 - x) * (1.0 - y);
	tmp = 0.0;
	if (y <= -9.8e-107)
		tmp = t_0;
	elseif (y <= 4.8e-46)
		tmp = 1.0 - y;
	else
		tmp = x + t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(1.0 - x), $MachinePrecision] * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -9.8e-107], t$95$0, If[LessEqual[y, 4.8e-46], N[(1.0 - y), $MachinePrecision], N[(x + t$95$0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -9.79999999999999959e-107

   1. Initial program 90.9%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 91.3%

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

   if -9.79999999999999959e-107 < y < 4.80000000000000027e-46

   1. Initial program 53.0%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 51.2%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 78.9%

      \[\leadsto 1 - \color{blue}{y} \]

   if 4.80000000000000027e-46 < y

   1. Initial program 98.3%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]
   2. Add Preprocessing
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 2: 86.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(1 - x\right) \cdot \left(1 - y\right)\\ \mathbf{if}\;y \leq -9.8 \cdot 10^{-107}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 1:\\ \;\;\;\;1 - y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (- 1.0 x) (- 1.0 y))))
   (if (<= y -9.8e-107) t_0 (if (<= y 1.0) (- 1.0 y) t_0))))

C

double code(double x, double y) {
	double t_0 = (1.0 - x) * (1.0 - y);
	double tmp;
	if (y <= -9.8e-107) {
		tmp = t_0;
	} else if (y <= 1.0) {
		tmp = 1.0 - y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (1.0d0 - x) * (1.0d0 - y)
    if (y <= (-9.8d-107)) then
        tmp = t_0
    else if (y <= 1.0d0) then
        tmp = 1.0d0 - y
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (1.0 - x) * (1.0 - y);
	double tmp;
	if (y <= -9.8e-107) {
		tmp = t_0;
	} else if (y <= 1.0) {
		tmp = 1.0 - y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (1.0 - x) * (1.0 - y)
	tmp = 0
	if y <= -9.8e-107:
		tmp = t_0
	elif y <= 1.0:
		tmp = 1.0 - y
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(1.0 - x) * Float64(1.0 - y))
	tmp = 0.0
	if (y <= -9.8e-107)
		tmp = t_0;
	elseif (y <= 1.0)
		tmp = Float64(1.0 - y);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (1.0 - x) * (1.0 - y);
	tmp = 0.0;
	if (y <= -9.8e-107)
		tmp = t_0;
	elseif (y <= 1.0)
		tmp = 1.0 - y;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(1.0 - x), $MachinePrecision] * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -9.8e-107], t$95$0, If[LessEqual[y, 1.0], N[(1.0 - y), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -9.79999999999999959e-107 or 1 < y

   1. Initial program 95.5%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 95.1%

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

   if -9.79999999999999959e-107 < y < 1

   1. Initial program 55.7%

      \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 52.5%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 77.9%

      \[\leadsto 1 - \color{blue}{y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 62.2% accurate, 3.8× speedup

Math

\[\begin{array}{l} \\ 1 - y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- 1.0 y))

C

double code(double x, double y) {
	return 1.0 - y;
}

Fortran

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

Java

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

Python

def code(x, y):
	return 1.0 - y

Julia

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

MATLAB

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

Wolfram

code[x_, y_] := N[(1.0 - y), $MachinePrecision]

Derivation

1. Initial program 79.3%

   \[x + \left(1 - x\right) \cdot \left(1 - y\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 77.8%

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

6. Taylor expanded in x around inf

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

8. Applied rewrites 61.7%

   \[\leadsto 1 - \color{blue}{y} \]
9. Add Preprocessing

Developer Target 1: 100.0% accurate, 1.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024321 
(FPCore (x y)
  :name "Graphics.Rendering.Chart.Plot.Vectors:renderPlotVectors from Chart-1.5.3"
  :precision binary64
  :pre (TRUE)

  :alt
  (! :herbie-platform default (- (* y x) (- y 1)))

  (+ x (* (- 1.0 x) (- 1.0 y))))