Data.Number.Erf:$cinvnormcdf from erf-2.0.0.0, B

Percentage Accurate: 99.9% → 99.9%

Time: 7.4s

Alternatives: 5

Speedup: 1.3×

Specification

Math

\[\begin{array}{l} \\ x - \frac{y}{1 + \frac{x \cdot y}{2}} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- x (/ y (+ 1.0 (/ (* x y) 2.0)))))

C

double code(double x, double y) {
	return x - (y / (1.0 + ((x * y) / 2.0)));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x - (y / (1.0d0 + ((x * y) / 2.0d0)))
end function

Java

public static double code(double x, double y) {
	return x - (y / (1.0 + ((x * y) / 2.0)));
}

Python

def code(x, y):
	return x - (y / (1.0 + ((x * y) / 2.0)))

Julia

function code(x, y)
	return Float64(x - Float64(y / Float64(1.0 + Float64(Float64(x * y) / 2.0))))
end

MATLAB

function tmp = code(x, y)
	tmp = x - (y / (1.0 + ((x * y) / 2.0)));
end

Wolfram

code[x_, y_] := N[(x - N[(y / N[(1.0 + N[(N[(x * y), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x - \frac{y}{1 + \frac{x \cdot y}{2}} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- x (/ y (+ 1.0 (/ (* x y) 2.0)))))

C

double code(double x, double y) {
	return x - (y / (1.0 + ((x * y) / 2.0)));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x - (y / (1.0d0 + ((x * y) / 2.0d0)))
end function

Java

public static double code(double x, double y) {
	return x - (y / (1.0 + ((x * y) / 2.0)));
}

Python

def code(x, y):
	return x - (y / (1.0 + ((x * y) / 2.0)))

Julia

function code(x, y)
	return Float64(x - Float64(y / Float64(1.0 + Float64(Float64(x * y) / 2.0))))
end

MATLAB

function tmp = code(x, y)
	tmp = x - (y / (1.0 + ((x * y) / 2.0)));
end

Wolfram

code[x_, y_] := N[(x - N[(y / N[(1.0 + N[(N[(x * y), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ x - \frac{y}{\mathsf{fma}\left(x \cdot y, 0.5, 1\right)} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- x (/ y (fma (* x y) 0.5 1.0))))

C

double code(double x, double y) {
	return x - (y / fma((x * y), 0.5, 1.0));
}

Julia

function code(x, y)
	return Float64(x - Float64(y / fma(Float64(x * y), 0.5, 1.0)))
end

Wolfram

code[x_, y_] := N[(x - N[(y / N[(N[(x * y), $MachinePrecision] * 0.5 + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x - \frac{y}{1 + \frac{x \cdot y}{2}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto x - \frac{y}{1 + \frac{\color{blue}{x \cdot y}}{2}} \]

   2. lift-/.f64 N/A

      \[\leadsto x - \frac{y}{1 + \color{blue}{\frac{x \cdot y}{2}}} \]

   3. +-commutative N/A

      \[\leadsto x - \frac{y}{\color{blue}{\frac{x \cdot y}{2} + 1}} \]

   4. lift-/.f64 N/A

      \[\leadsto x - \frac{y}{\color{blue}{\frac{x \cdot y}{2}} + 1} \]

   5. div-inv N/A

      \[\leadsto x - \frac{y}{\color{blue}{\left(x \cdot y\right) \cdot \frac{1}{2}} + 1} \]

   6. lower-fma.f64 N/A

      \[\leadsto x - \frac{y}{\color{blue}{\mathsf{fma}\left(x \cdot y, \frac{1}{2}, 1\right)}} \]

   7. metadata-eval 100.0

      \[\leadsto x - \frac{y}{\mathsf{fma}\left(x \cdot y, \color{blue}{0.5}, 1\right)} \]

4. Applied rewrites 100.0%

   \[\leadsto x - \frac{y}{\color{blue}{\mathsf{fma}\left(x \cdot y, 0.5, 1\right)}} \]
5. Add Preprocessing

Alternative 2: 89.5% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x - \frac{2}{x}\\ \mathbf{if}\;y \leq -1.45 \cdot 10^{+51}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 7.1 \cdot 10^{+171}:\\ \;\;\;\;x - y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- x (/ 2.0 x))))
   (if (<= y -1.45e+51) t_0 (if (<= y 7.1e+171) (- x y) t_0))))

C

double code(double x, double y) {
	double t_0 = x - (2.0 / x);
	double tmp;
	if (y <= -1.45e+51) {
		tmp = t_0;
	} else if (y <= 7.1e+171) {
		tmp = x - 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 = x - (2.0d0 / x)
    if (y <= (-1.45d+51)) then
        tmp = t_0
    else if (y <= 7.1d+171) then
        tmp = x - y
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x - (2.0 / x);
	double tmp;
	if (y <= -1.45e+51) {
		tmp = t_0;
	} else if (y <= 7.1e+171) {
		tmp = x - y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x - (2.0 / x)
	tmp = 0
	if y <= -1.45e+51:
		tmp = t_0
	elif y <= 7.1e+171:
		tmp = x - y
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x - Float64(2.0 / x))
	tmp = 0.0
	if (y <= -1.45e+51)
		tmp = t_0;
	elseif (y <= 7.1e+171)
		tmp = Float64(x - y);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x - (2.0 / x);
	tmp = 0.0;
	if (y <= -1.45e+51)
		tmp = t_0;
	elseif (y <= 7.1e+171)
		tmp = x - y;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x - N[(2.0 / x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.45e+51], t$95$0, If[LessEqual[y, 7.1e+171], N[(x - y), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.4499999999999999e51 or 7.09999999999999969e171 < y

   1. Initial program 99.9%

      \[x - \frac{y}{1 + \frac{x \cdot y}{2}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto x - \color{blue}{\frac{2}{x}} \]
   4. Step-by-step derivation

      1. lower-/.f64 84.1

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

   5. Applied rewrites 84.1%

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

   if -1.4499999999999999e51 < y < 7.09999999999999969e171

   1. Initial program 100.0%

      \[x - \frac{y}{1 + \frac{x \cdot y}{2}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x + -1 \cdot y} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto x + \color{blue}{\left(\mathsf{neg}\left(y\right)\right)} \]

      2. unsub-neg N/A

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

      3. lower--.f64 97.5

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

   5. Applied rewrites 97.5%

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

Alternative 3: 78.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.2 \cdot 10^{+229}:\\ \;\;\;\;\frac{-2}{x}\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{+238}:\\ \;\;\;\;x - y\\ \mathbf{else}:\\ \;\;\;\;\frac{-2}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -7.2e+229) (/ -2.0 x) (if (<= y 1.35e+238) (- x y) (/ -2.0 x))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -7.2e+229) {
		tmp = -2.0 / x;
	} else if (y <= 1.35e+238) {
		tmp = x - 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 (y <= (-7.2d+229)) then
        tmp = (-2.0d0) / x
    else if (y <= 1.35d+238) then
        tmp = x - y
    else
        tmp = (-2.0d0) / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -7.2e+229) {
		tmp = -2.0 / x;
	} else if (y <= 1.35e+238) {
		tmp = x - y;
	} else {
		tmp = -2.0 / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -7.2e+229:
		tmp = -2.0 / x
	elif y <= 1.35e+238:
		tmp = x - y
	else:
		tmp = -2.0 / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -7.2e+229)
		tmp = Float64(-2.0 / x);
	elseif (y <= 1.35e+238)
		tmp = Float64(x - y);
	else
		tmp = Float64(-2.0 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -7.2e+229)
		tmp = -2.0 / x;
	elseif (y <= 1.35e+238)
		tmp = x - y;
	else
		tmp = -2.0 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -7.2e+229], N[(-2.0 / x), $MachinePrecision], If[LessEqual[y, 1.35e+238], N[(x - y), $MachinePrecision], N[(-2.0 / x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -7.19999999999999973e229 or 1.35000000000000011e238 < y

   1. Initial program 99.7%

      \[x - \frac{y}{1 + \frac{x \cdot y}{2}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto x - \color{blue}{\frac{2}{x}} \]
   4. Step-by-step derivation

      1. lower-/.f64 87.8

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

   5. Applied rewrites 87.8%

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

   6. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{-2}{x}} \]
   7. Step-by-step derivation

      1. lower-/.f64 49.5

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

   8. Applied rewrites 49.5%

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

   if -7.19999999999999973e229 < y < 1.35000000000000011e238

   1. Initial program 100.0%

      \[x - \frac{y}{1 + \frac{x \cdot y}{2}} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x + -1 \cdot y} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto x + \color{blue}{\left(\mathsf{neg}\left(y\right)\right)} \]

      2. unsub-neg N/A

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

      3. lower--.f64 87.7

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

   5. Applied rewrites 87.7%

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

Alternative 4: 74.9% accurate, 8.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

   \[x - \frac{y}{1 + \frac{x \cdot y}{2}} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{x + -1 \cdot y} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto x + \color{blue}{\left(\mathsf{neg}\left(y\right)\right)} \]

   2. unsub-neg N/A

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

   3. lower--.f64 77.3

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

5. Applied rewrites 77.3%

   \[\leadsto \color{blue}{x - y} \]
6. Add Preprocessing

Alternative 5: 25.7% accurate, 11.3× 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 Float64(-y)
end

MATLAB

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

Wolfram

code[x_, y_] := (-y)

Derivation

1. Initial program 100.0%

   \[x - \frac{y}{1 + \frac{x \cdot y}{2}} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(y\right)} \]

   2. lower-neg.f64 29.4

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

5. Applied rewrites 29.4%

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

Reproduce

herbie shell --seed 2024216 
(FPCore (x y)
  :name "Data.Number.Erf:$cinvnormcdf from erf-2.0.0.0, B"
  :precision binary64
  (- x (/ y (+ 1.0 (/ (* x y) 2.0)))))