AI.Clustering.Hierarchical.Internal:average from clustering-0.2.1, A

Percentage Accurate: 100.0% → 100.0%

Time: 3.0s

Alternatives: 4

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 73.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{-40} \lor \neg \left(y \leq -2.1 \cdot 10^{-88} \lor \neg \left(y \leq -6.6 \cdot 10^{-108}\right) \land \left(y \leq 1.8 \cdot 10^{-75} \lor \neg \left(y \leq 1.5 \cdot 10^{-50}\right) \land y \leq 2.2 \cdot 10^{+25}\right)\right):\\ \;\;\;\;\frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -1.35e-40)
         (not
          (or (<= y -2.1e-88)
              (and (not (<= y -6.6e-108))
                   (or (<= y 1.8e-75)
                       (and (not (<= y 1.5e-50)) (<= y 2.2e+25)))))))
   (/ x y)
   1.0))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -1.35e-40) || !((y <= -2.1e-88) || (!(y <= -6.6e-108) && ((y <= 1.8e-75) || (!(y <= 1.5e-50) && (y <= 2.2e+25)))))) {
		tmp = x / y;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-1.35d-40)) .or. (.not. (y <= (-2.1d-88)) .or. (.not. (y <= (-6.6d-108))) .and. (y <= 1.8d-75) .or. (.not. (y <= 1.5d-50)) .and. (y <= 2.2d+25))) then
        tmp = x / y
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -1.35e-40) || !((y <= -2.1e-88) || (!(y <= -6.6e-108) && ((y <= 1.8e-75) || (!(y <= 1.5e-50) && (y <= 2.2e+25)))))) {
		tmp = x / y;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -1.35e-40) or not ((y <= -2.1e-88) or (not (y <= -6.6e-108) and ((y <= 1.8e-75) or (not (y <= 1.5e-50) and (y <= 2.2e+25))))):
		tmp = x / y
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -1.35e-40) || !((y <= -2.1e-88) || (!(y <= -6.6e-108) && ((y <= 1.8e-75) || (!(y <= 1.5e-50) && (y <= 2.2e+25))))))
		tmp = Float64(x / y);
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -1.35e-40) || ~(((y <= -2.1e-88) || (~((y <= -6.6e-108)) && ((y <= 1.8e-75) || (~((y <= 1.5e-50)) && (y <= 2.2e+25)))))))
		tmp = x / y;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -1.35e-40], N[Not[Or[LessEqual[y, -2.1e-88], And[N[Not[LessEqual[y, -6.6e-108]], $MachinePrecision], Or[LessEqual[y, 1.8e-75], And[N[Not[LessEqual[y, 1.5e-50]], $MachinePrecision], LessEqual[y, 2.2e+25]]]]]], $MachinePrecision]], N[(x / y), $MachinePrecision], 1.0]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 73.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1 \cdot 10^{-40}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq -2.6 \cdot 10^{-88}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq -8.5 \cdot 10^{-108}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 1.26 \cdot 10^{-76}:\\ \;\;\;\;1 - \frac{y}{x}\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{-51} \lor \neg \left(y \leq 3 \cdot 10^{+25}\right):\\ \;\;\;\;\frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -1e-40)
   (/ x y)
   (if (<= y -2.6e-88)
     1.0
     (if (<= y -8.5e-108)
       (/ x y)
       (if (<= y 1.26e-76)
         (- 1.0 (/ y x))
         (if (or (<= y 4.8e-51) (not (<= y 3e+25))) (/ x y) 1.0))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -1e-40) {
		tmp = x / y;
	} else if (y <= -2.6e-88) {
		tmp = 1.0;
	} else if (y <= -8.5e-108) {
		tmp = x / y;
	} else if (y <= 1.26e-76) {
		tmp = 1.0 - (y / x);
	} else if ((y <= 4.8e-51) || !(y <= 3e+25)) {
		tmp = x / y;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-1d-40)) then
        tmp = x / y
    else if (y <= (-2.6d-88)) then
        tmp = 1.0d0
    else if (y <= (-8.5d-108)) then
        tmp = x / y
    else if (y <= 1.26d-76) then
        tmp = 1.0d0 - (y / x)
    else if ((y <= 4.8d-51) .or. (.not. (y <= 3d+25))) then
        tmp = x / y
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -1e-40) {
		tmp = x / y;
	} else if (y <= -2.6e-88) {
		tmp = 1.0;
	} else if (y <= -8.5e-108) {
		tmp = x / y;
	} else if (y <= 1.26e-76) {
		tmp = 1.0 - (y / x);
	} else if ((y <= 4.8e-51) || !(y <= 3e+25)) {
		tmp = x / y;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -1e-40:
		tmp = x / y
	elif y <= -2.6e-88:
		tmp = 1.0
	elif y <= -8.5e-108:
		tmp = x / y
	elif y <= 1.26e-76:
		tmp = 1.0 - (y / x)
	elif (y <= 4.8e-51) or not (y <= 3e+25):
		tmp = x / y
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -1e-40)
		tmp = Float64(x / y);
	elseif (y <= -2.6e-88)
		tmp = 1.0;
	elseif (y <= -8.5e-108)
		tmp = Float64(x / y);
	elseif (y <= 1.26e-76)
		tmp = Float64(1.0 - Float64(y / x));
	elseif ((y <= 4.8e-51) || !(y <= 3e+25))
		tmp = Float64(x / y);
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -1e-40)
		tmp = x / y;
	elseif (y <= -2.6e-88)
		tmp = 1.0;
	elseif (y <= -8.5e-108)
		tmp = x / y;
	elseif (y <= 1.26e-76)
		tmp = 1.0 - (y / x);
	elseif ((y <= 4.8e-51) || ~((y <= 3e+25)))
		tmp = x / y;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -1e-40], N[(x / y), $MachinePrecision], If[LessEqual[y, -2.6e-88], 1.0, If[LessEqual[y, -8.5e-108], N[(x / y), $MachinePrecision], If[LessEqual[y, 1.26e-76], N[(1.0 - N[(y / x), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[y, 4.8e-51], N[Not[LessEqual[y, 3e+25]], $MachinePrecision]], N[(x / y), $MachinePrecision], 1.0]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 51.7% accurate, 5.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 1.0)

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return 1.0

Julia

function code(x, y)
	return 1.0
end

MATLAB

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

Wolfram

code[x_, y_] := 1.0

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Reproduce

herbie shell --seed 2023350 
(FPCore (x y)
  :name "AI.Clustering.Hierarchical.Internal:average from clustering-0.2.1, A"
  :precision binary64
  (/ x (+ x y)))