Data.Random.Distribution.T:$ccdf from random-fu-0.2.6.2

Percentage Accurate: 99.9% → 99.9%

Time: 2.7s

Alternatives: 6

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{y + y} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 74.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.9 \cdot 10^{-11}:\\ \;\;\;\;0.5\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{-71}:\\ \;\;\;\;\frac{x}{y + y}\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -2.9e-11) 0.5 (if (<= y 1.15e-71) (/ x (+ y y)) 0.5)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -2.9e-11) {
		tmp = 0.5;
	} else if (y <= 1.15e-71) {
		tmp = x / (y + y);
	} else {
		tmp = 0.5;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-2.9d-11)) then
        tmp = 0.5d0
    else if (y <= 1.15d-71) then
        tmp = x / (y + y)
    else
        tmp = 0.5d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -2.9e-11) {
		tmp = 0.5;
	} else if (y <= 1.15e-71) {
		tmp = x / (y + y);
	} else {
		tmp = 0.5;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -2.9e-11:
		tmp = 0.5
	elif y <= 1.15e-71:
		tmp = x / (y + y)
	else:
		tmp = 0.5
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -2.9e-11)
		tmp = 0.5;
	elseif (y <= 1.15e-71)
		tmp = Float64(x / Float64(y + y));
	else
		tmp = 0.5;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -2.9e-11)
		tmp = 0.5;
	elseif (y <= 1.15e-71)
		tmp = x / (y + y);
	else
		tmp = 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -2.9e-11], 0.5, If[LessEqual[y, 1.15e-71], N[(x / N[(y + y), $MachinePrecision]), $MachinePrecision], 0.5]]

Derivation

1. Split input into 2 regimes

2. if y < -2.9e-11 or 1.1499999999999999e-71 < y

   1. Initial program 100.0%

      \[\frac{x + y}{y + y} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x \cdot \frac{0.5}{y} - -0.5} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 78.0%

      \[\leadsto \color{blue}{0.5} \]

   if -2.9e-11 < y < 1.1499999999999999e-71

   1. Initial program 100.0%

      \[\frac{x + y}{y + y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 84.2%

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

Alternative 3: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \frac{0.5}{y} - -0.5 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- (* x (/ 0.5 y)) -0.5))

C

double code(double x, double y) {
	return (x * (0.5 / y)) - -0.5;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x * (0.5d0 / y)) - (-0.5d0)
end function

Java

public static double code(double x, double y) {
	return (x * (0.5 / y)) - -0.5;
}

Python

def code(x, y):
	return (x * (0.5 / y)) - -0.5

Julia

function code(x, y)
	return Float64(Float64(x * Float64(0.5 / y)) - -0.5)
end

MATLAB

function tmp = code(x, y)
	tmp = (x * (0.5 / y)) - -0.5;
end

Wolfram

code[x_, y_] := N[(N[(x * N[(0.5 / y), $MachinePrecision]), $MachinePrecision] - -0.5), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{y + y} \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{x \cdot \frac{0.5}{y} - -0.5} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 4: 50.0% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ 0.5 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 0.5)

C

double code(double x, double y) {
	return 0.5;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 0.5d0
end function

Java

public static double code(double x, double y) {
	return 0.5;
}

Python

def code(x, y):
	return 0.5

Julia

function code(x, y)
	return 0.5
end

MATLAB

function tmp = code(x, y)
	tmp = 0.5;
end

Wolfram

code[x_, y_] := 0.5

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{y + y} \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{x \cdot \frac{0.5}{y} - -0.5} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 49.8%

   \[\leadsto \color{blue}{0.5} \]
6. Add Preprocessing

Alternative 5: 11.3% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ 0.3333333333333333 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 0.3333333333333333)

C

double code(double x, double y) {
	return 0.3333333333333333;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 0.3333333333333333d0
end function

Java

public static double code(double x, double y) {
	return 0.3333333333333333;
}

Python

def code(x, y):
	return 0.3333333333333333

Julia

function code(x, y)
	return 0.3333333333333333
end

MATLAB

function tmp = code(x, y)
	tmp = 0.3333333333333333;
end

Wolfram

code[x_, y_] := 0.3333333333333333

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{y + y} \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{x \cdot \frac{0.5}{y} - -0.5} \]
4. Add Preprocessing

5. Taylor expanded in y around 0 100.0%

   \[\leadsto \color{blue}{\frac{0.5 \cdot x + 0.5 \cdot y}{y}} \]

7. Simplified 3.7%

   \[\leadsto \color{blue}{\frac{-2}{y}} \]

9. Applied egg-rr 11.3%

   \[\leadsto \color{blue}{0.3333333333333333} \]
10. Add Preprocessing

Alternative 6: 2.3% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ -8 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 -8.0)

C

double code(double x, double y) {
	return -8.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = -8.0d0
end function

Java

public static double code(double x, double y) {
	return -8.0;
}

Python

def code(x, y):
	return -8.0

Julia

function code(x, y)
	return -8.0
end

MATLAB

function tmp = code(x, y)
	tmp = -8.0;
end

Wolfram

code[x_, y_] := -8.0

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{y + y} \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{x \cdot \frac{0.5}{y} - -0.5} \]
4. Add Preprocessing

5. Taylor expanded in y around 0 100.0%

   \[\leadsto \color{blue}{\frac{0.5 \cdot x + 0.5 \cdot y}{y}} \]

7. Simplified 3.7%

   \[\leadsto \color{blue}{\frac{-2}{y}} \]

9. Applied egg-rr 2.1%

   \[\leadsto \color{blue}{-8} \]
10. Add Preprocessing

Developer Target 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \frac{x}{y} + 0.5 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ (* 0.5 (/ x y)) 0.5))

C

double code(double x, double y) {
	return (0.5 * (x / y)) + 0.5;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (0.5d0 * (x / y)) + 0.5d0
end function

Java

public static double code(double x, double y) {
	return (0.5 * (x / y)) + 0.5;
}

Python

def code(x, y):
	return (0.5 * (x / y)) + 0.5

Julia

function code(x, y)
	return Float64(Float64(0.5 * Float64(x / y)) + 0.5)
end

MATLAB

function tmp = code(x, y)
	tmp = (0.5 * (x / y)) + 0.5;
end

Wolfram

code[x_, y_] := N[(N[(0.5 * N[(x / y), $MachinePrecision]), $MachinePrecision] + 0.5), $MachinePrecision]

Reproduce

herbie shell --seed 2024180 
(FPCore (x y)
  :name "Data.Random.Distribution.T:$ccdf from random-fu-0.2.6.2"
  :precision binary64

  :alt
  (! :herbie-platform default (+ (* 1/2 (/ x y)) 1/2))

  (/ (+ x y) (+ y y)))