Statistics.Sample:$swelfordMean from math-functions-0.1.5.2

Average Error: 0.02% → 0.02%

Time: 8.7s

Precision: binary64

Cost: 448

Math

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

↓

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

FPCore

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

↓

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

C

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

↓

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

Fortran

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

↓

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

Java

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

↓

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

Python

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

↓

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

Julia

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

↓

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

MATLAB

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

↓

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

Wolfram

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

↓

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

Derivation

1. Initial program 0.02

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

2. Final simplification 0.02

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

Alternatives

Alternative 1
Error	36.74%
Cost	1180

\[\begin{array}{l} t_0 := \frac{-x}{z}\\ \mathbf{if}\;z \leq -4.6 \cdot 10^{+14}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -1.18 \cdot 10^{-11}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq 7.4 \cdot 10^{-150}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.7 \cdot 10^{-118}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq 1.85 \cdot 10^{-63}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{-34}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq 0.000232:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 2
Error	18.67%
Cost	1112

\[\begin{array}{l} t_0 := x + \frac{y}{z}\\ t_1 := \frac{-x}{z}\\ \mathbf{if}\;z \leq -7 \cdot 10^{-12}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.42 \cdot 10^{-152}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{-117}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{-62}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 8.2 \cdot 10^{-35}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq 3.6 \cdot 10^{-7}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 3
Error	12.29%
Cost	585

\[\begin{array}{l} \mathbf{if}\;y \leq -1.46 \cdot 10^{-205} \lor \neg \left(y \leq 2.45 \cdot 10^{-60}\right):\\ \;\;\;\;x + \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x}{z}\\ \end{array} \]

Alternative 4
Error	1.4%
Cost	585

\[\begin{array}{l} \mathbf{if}\;z \leq -115 \lor \neg \left(z \leq 0.000232\right):\\ \;\;\;\;x + \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;\frac{y - x}{z}\\ \end{array} \]

Alternative 5
Error	36.46%
Cost	456

\[\begin{array}{l} \mathbf{if}\;z \leq -1.42 \cdot 10^{+14}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 2.65 \cdot 10^{+42}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 6
Error	54.56%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023115 
(FPCore (x y z)
  :name "Statistics.Sample:$swelfordMean from math-functions-0.1.5.2"
  :precision binary64
  (+ x (/ (- y x) z)))