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

Percentage Accurate: 100.0% → 100.0%

Time: 3.4s

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

1. Initial program 100.0%

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

2. Final simplification 100.0%

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

Alternative 2: 72.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.2 \cdot 10^{-28}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-149}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-100}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 1.25 \cdot 10^{+61}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -9.2e-28)
   (/ x y)
   (if (<= y 1.35e-149)
     1.0
     (if (<= y 4e-100) (/ x y) (if (<= y 1.25e+61) 1.0 (/ x y))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -9.2e-28) {
		tmp = x / y;
	} else if (y <= 1.35e-149) {
		tmp = 1.0;
	} else if (y <= 4e-100) {
		tmp = x / y;
	} else if (y <= 1.25e+61) {
		tmp = 1.0;
	} else {
		tmp = x / y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-9.2d-28)) then
        tmp = x / y
    else if (y <= 1.35d-149) then
        tmp = 1.0d0
    else if (y <= 4d-100) then
        tmp = x / y
    else if (y <= 1.25d+61) then
        tmp = 1.0d0
    else
        tmp = x / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -9.2e-28) {
		tmp = x / y;
	} else if (y <= 1.35e-149) {
		tmp = 1.0;
	} else if (y <= 4e-100) {
		tmp = x / y;
	} else if (y <= 1.25e+61) {
		tmp = 1.0;
	} else {
		tmp = x / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -9.2e-28:
		tmp = x / y
	elif y <= 1.35e-149:
		tmp = 1.0
	elif y <= 4e-100:
		tmp = x / y
	elif y <= 1.25e+61:
		tmp = 1.0
	else:
		tmp = x / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -9.2e-28)
		tmp = Float64(x / y);
	elseif (y <= 1.35e-149)
		tmp = 1.0;
	elseif (y <= 4e-100)
		tmp = Float64(x / y);
	elseif (y <= 1.25e+61)
		tmp = 1.0;
	else
		tmp = Float64(x / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -9.2e-28)
		tmp = x / y;
	elseif (y <= 1.35e-149)
		tmp = 1.0;
	elseif (y <= 4e-100)
		tmp = x / y;
	elseif (y <= 1.25e+61)
		tmp = 1.0;
	else
		tmp = x / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -9.2e-28], N[(x / y), $MachinePrecision], If[LessEqual[y, 1.35e-149], 1.0, If[LessEqual[y, 4e-100], N[(x / y), $MachinePrecision], If[LessEqual[y, 1.25e+61], 1.0, N[(x / y), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if y < -9.19999999999999942e-28 or 1.35000000000000007e-149 < y < 4.0000000000000001e-100 or 1.25000000000000004e61 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0 77.6%

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

   if -9.19999999999999942e-28 < y < 1.35000000000000007e-149 or 4.0000000000000001e-100 < y < 1.25000000000000004e61

   1. Initial program 100.0%

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

   2. Taylor expanded in x around inf 76.1%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 76.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.2 \cdot 10^{-28}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-149}:\\ \;\;\;\;1\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-100}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 1.25 \cdot 10^{+61}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y}\\ \end{array} \]

Alternative 3: 72.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.9 \cdot 10^{-25}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-149}:\\ \;\;\;\;1 - \frac{y}{x}\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-100}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{+61}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -4.9e-25)
   (/ x y)
   (if (<= y 1.35e-149)
     (- 1.0 (/ y x))
     (if (<= y 4e-100) (/ x y) (if (<= y 2.25e+61) 1.0 (/ x y))))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -4.9e-25) {
		tmp = x / y;
	} else if (y <= 1.35e-149) {
		tmp = 1.0 - (y / x);
	} else if (y <= 4e-100) {
		tmp = x / y;
	} else if (y <= 2.25e+61) {
		tmp = 1.0;
	} else {
		tmp = x / y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-4.9d-25)) then
        tmp = x / y
    else if (y <= 1.35d-149) then
        tmp = 1.0d0 - (y / x)
    else if (y <= 4d-100) then
        tmp = x / y
    else if (y <= 2.25d+61) then
        tmp = 1.0d0
    else
        tmp = x / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -4.9e-25) {
		tmp = x / y;
	} else if (y <= 1.35e-149) {
		tmp = 1.0 - (y / x);
	} else if (y <= 4e-100) {
		tmp = x / y;
	} else if (y <= 2.25e+61) {
		tmp = 1.0;
	} else {
		tmp = x / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -4.9e-25:
		tmp = x / y
	elif y <= 1.35e-149:
		tmp = 1.0 - (y / x)
	elif y <= 4e-100:
		tmp = x / y
	elif y <= 2.25e+61:
		tmp = 1.0
	else:
		tmp = x / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -4.9e-25)
		tmp = Float64(x / y);
	elseif (y <= 1.35e-149)
		tmp = Float64(1.0 - Float64(y / x));
	elseif (y <= 4e-100)
		tmp = Float64(x / y);
	elseif (y <= 2.25e+61)
		tmp = 1.0;
	else
		tmp = Float64(x / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -4.9e-25)
		tmp = x / y;
	elseif (y <= 1.35e-149)
		tmp = 1.0 - (y / x);
	elseif (y <= 4e-100)
		tmp = x / y;
	elseif (y <= 2.25e+61)
		tmp = 1.0;
	else
		tmp = x / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -4.9e-25], N[(x / y), $MachinePrecision], If[LessEqual[y, 1.35e-149], N[(1.0 - N[(y / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 4e-100], N[(x / y), $MachinePrecision], If[LessEqual[y, 2.25e+61], 1.0, N[(x / y), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -4.8999999999999999e-25 or 1.35000000000000007e-149 < y < 4.0000000000000001e-100 or 2.25e61 < y

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0 77.6%

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

   if -4.8999999999999999e-25 < y < 1.35000000000000007e-149

   1. Initial program 100.0%

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

   2. Taylor expanded in x around inf 80.1%

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

      1. mul-1-neg 80.1%

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

      2. unsub-neg 80.1%

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

   4. Simplified 80.1%

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

   if 4.0000000000000001e-100 < y < 2.25e61

   1. Initial program 100.0%

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

   2. Taylor expanded in x around inf 67.7%

      \[\leadsto \color{blue}{1} \]
3. Recombined 3 regimes into one program.

4. Final simplification 77.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.9 \cdot 10^{-25}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-149}:\\ \;\;\;\;1 - \frac{y}{x}\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-100}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{+61}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y}\\ \end{array} \]

Alternative 4: 51.0% 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

1. Initial program 100.0%

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

2. Taylor expanded in x around inf 47.7%

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

3. Final simplification 47.7%

   \[\leadsto 1 \]

Reproduce

herbie shell --seed 2023291 
(FPCore (x y)
  :name "AI.Clustering.Hierarchical.Internal:average from clustering-0.2.1, A"
  :precision binary64
  (/ x (+ x y)))