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

Average Accuracy: 100.0% → 100.0%

Time: 4.8s

Precision: binary64

Cost: 320

Math

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

↓

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

FPCore

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

↓

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

C

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

↓

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

↓

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);
}

↓

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

Python

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

↓

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

Julia

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

↓

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

MATLAB

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

↓

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

Wolfram

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

↓

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%

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

Alternatives

Alternative 1
Accuracy	71.9%
Cost	720

\[\begin{array}{l} \mathbf{if}\;x \leq -1.3 \cdot 10^{+133}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 230:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{elif}\;x \leq 5.5 \cdot 10^{+66}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 1.26 \cdot 10^{+118}:\\ \;\;\;\;\frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]

Alternative 2
Accuracy	50.6%
Cost	64

\[1 \]

Reproduce

herbie shell --seed 2023122 
(FPCore (x y)
  :name "AI.Clustering.Hierarchical.Internal:average from clustering-0.2.1, A"
  :precision binary64
  (/ x (+ x y)))