Quotient of sum of exps

Average Accuracy: 98.9% → 98.9%

Time: 6.7s

Precision: binary64

Cost: 19520

Math

\[\frac{e^{a}}{e^{a} + e^{b}} \]

↓

\[\frac{e^{a}}{e^{a} + e^{b}} \]

FPCore

(FPCore (a b) :precision binary64 (/ (exp a) (+ (exp a) (exp b))))

↓

(FPCore (a b) :precision binary64 (/ (exp a) (+ (exp a) (exp b))))

C

double code(double a, double b) {
	return exp(a) / (exp(a) + exp(b));
}

↓

double code(double a, double b) {
	return exp(a) / (exp(a) + exp(b));
}

Fortran

real(8) function code(a, b)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = exp(a) / (exp(a) + exp(b))
end function

↓

real(8) function code(a, b)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = exp(a) / (exp(a) + exp(b))
end function

Java

public static double code(double a, double b) {
	return Math.exp(a) / (Math.exp(a) + Math.exp(b));
}

↓

public static double code(double a, double b) {
	return Math.exp(a) / (Math.exp(a) + Math.exp(b));
}

Python

def code(a, b):
	return math.exp(a) / (math.exp(a) + math.exp(b))

↓

def code(a, b):
	return math.exp(a) / (math.exp(a) + math.exp(b))

Julia

function code(a, b)
	return Float64(exp(a) / Float64(exp(a) + exp(b)))
end

↓

function code(a, b)
	return Float64(exp(a) / Float64(exp(a) + exp(b)))
end

MATLAB

function tmp = code(a, b)
	tmp = exp(a) / (exp(a) + exp(b));
end

↓

function tmp = code(a, b)
	tmp = exp(a) / (exp(a) + exp(b));
end

Wolfram

code[a_, b_] := N[(N[Exp[a], $MachinePrecision] / N[(N[Exp[a], $MachinePrecision] + N[Exp[b], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

↓

code[a_, b_] := N[(N[Exp[a], $MachinePrecision] / N[(N[Exp[a], $MachinePrecision] + N[Exp[b], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Target

Original	98.9%
Target	100.0%
Herbie	98.9%

\[\frac{1}{1 + e^{b - a}} \]

Derivation

1. Initial program 98.9%

   \[\frac{e^{a}}{e^{a} + e^{b}} \]

2. Final simplification 98.9%

   \[\leadsto \frac{e^{a}}{e^{a} + e^{b}} \]

Alternatives

Alternative 1
Accuracy	98.4%
Cost	13252

\[\begin{array}{l} \mathbf{if}\;e^{a} \leq 0:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{e^{b} + 1}\\ \end{array} \]

Alternative 2
Accuracy	98.4%
Cost	13252

\[\begin{array}{l} \mathbf{if}\;e^{a} \leq 0:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{2 + \mathsf{expm1}\left(b\right)}\\ \end{array} \]

Alternative 3
Accuracy	79.0%
Cost	6861

\[\begin{array}{l} \mathbf{if}\;a \leq -350:\\ \;\;\;\;0\\ \mathbf{elif}\;a \leq -2.6 \cdot 10^{-90} \lor \neg \left(a \leq -2.15 \cdot 10^{-106}\right):\\ \;\;\;\;\left(1 + \frac{1}{b + 2}\right) + -1\\ \mathbf{else}:\\ \;\;\;\;e^{a}\\ \end{array} \]

Alternative 4
Accuracy	64.4%
Cost	980

\[\begin{array}{l} t_0 := 0.5 + a \cdot 0.25\\ \mathbf{if}\;a \leq -3.4 \cdot 10^{-7}:\\ \;\;\;\;0\\ \mathbf{elif}\;a \leq -1.4 \cdot 10^{-184}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;a \leq -1 \cdot 10^{-202}:\\ \;\;\;\;0\\ \mathbf{elif}\;a \leq 3.8 \cdot 10^{-295}:\\ \;\;\;\;0.5\\ \mathbf{elif}\;a \leq 7.6 \cdot 10^{-263}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 5
Accuracy	64.0%
Cost	724

\[\begin{array}{l} \mathbf{if}\;a \leq -4.3 \cdot 10^{-7}:\\ \;\;\;\;0\\ \mathbf{elif}\;a \leq -8.4 \cdot 10^{-184}:\\ \;\;\;\;0.5\\ \mathbf{elif}\;a \leq -2.4 \cdot 10^{-202}:\\ \;\;\;\;0\\ \mathbf{elif}\;a \leq 1.25 \cdot 10^{-292}:\\ \;\;\;\;0.5\\ \mathbf{elif}\;a \leq 7.2 \cdot 10^{-263}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;0.5\\ \end{array} \]

Alternative 6
Accuracy	79.8%
Cost	708

\[\begin{array}{l} \mathbf{if}\;a \leq -350:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;\left(1 + \frac{1}{b + 2}\right) + -1\\ \end{array} \]

Alternative 7
Accuracy	38.7%
Cost	64

\[0.5 \]

Reproduce

herbie shell --seed 2023151 
(FPCore (a b)
  :name "Quotient of sum of exps"
  :precision binary64

  :herbie-target
  (/ 1.0 (+ 1.0 (exp (- b a))))

  (/ (exp a) (+ (exp a) (exp b))))