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

Average Error: 0.0 → 0.0

Time: 4.4s

Precision: binary64

Cost: 576

Math

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

↓

\[\frac{y}{z} + \left(x - \frac{x}{z}\right) \]

FPCore

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

↓

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

Python

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

↓

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

Julia

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

↓

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

MATLAB

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

↓

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

Wolfram

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

↓

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

Derivation

1. Initial program 0.0

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

2. Applied egg-rr 0.0

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

3. Final simplification 0.0

   \[\leadsto \frac{y}{z} + \left(x - \frac{x}{z}\right) \]

Alternatives

Alternative 1
Error	23.5
Cost	984

\[\begin{array}{l} t_0 := \frac{-x}{z}\\ \mathbf{if}\;z \leq -4.1 \cdot 10^{+29}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -8.8 \cdot 10^{-181}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq 1.1 \cdot 10^{-249}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 6 \cdot 10^{-136}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq 5.7 \cdot 10^{-5}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.6 \cdot 10^{+45}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 2
Error	12.1
Cost	848

\[\begin{array}{l} t_0 := \frac{y}{z} + x\\ t_1 := \frac{-x}{z}\\ \mathbf{if}\;z \leq -1.1 \cdot 10^{-190}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{-249}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 6 \cdot 10^{-136}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{elif}\;z \leq 5.7 \cdot 10^{-5}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 3
Error	6.7
Cost	585

\[\begin{array}{l} \mathbf{if}\;y \leq -3.55 \cdot 10^{-90} \lor \neg \left(y \leq 1.55 \cdot 10^{-157}\right):\\ \;\;\;\;\frac{y}{z} + x\\ \mathbf{else}:\\ \;\;\;\;x - \frac{x}{z}\\ \end{array} \]

Alternative 4
Error	0.9
Cost	585

\[\begin{array}{l} \mathbf{if}\;z \leq -1 \lor \neg \left(z \leq 1\right):\\ \;\;\;\;\frac{y}{z} + x\\ \mathbf{else}:\\ \;\;\;\;\frac{y - x}{z}\\ \end{array} \]

Alternative 5
Error	23.5
Cost	456

\[\begin{array}{l} \mathbf{if}\;z \leq -1.85 \cdot 10^{+29}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 6.2 \cdot 10^{+46}:\\ \;\;\;\;\frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 6
Error	0.0
Cost	448

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

Alternative 7
Error	34.7
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023057 
(FPCore (x y z)
  :name "Statistics.Sample:$swelfordMean from math-functions-0.1.5.2"
  :precision binary64
  (+ x (/ (- y x) z)))